Search

This article revisits Austria's migration history from the end of World War II until the end of the Cold War. Recent research has challenged the persistently commemorated welcoming Austrian attitude toward refugees who had been living under communism. The initial humanitarian efforts in 1956 and 1968, respectively, were remarkable. However, an analysis beyond the first weeks of both events reveals that (though to different degrees) public and political attitudes toward refugees took a negative turn. Throughout the 1970s, asylum for dissidents was portrayed as a continuation of the country's humanitarian tradition. However, in 1981, refugees from Poland were immediately perceived as unwanted labor migrants. In 1989/90, the scenario was similar: while the transiting East German refugees were welcomed, migrants from other countries (like Romania) were not. In the early 1990s, Austria decided on a reform of its asylum and foreigner policies. But when and why did the (supposedly welcome) refugees from countries under communist rule turn into unwelcome labor migrants? The analysis in this article explores the potential impact of the age of détente and the repercussions of the 1970s economic crises and the resulting end to active recruitment of foreign workers. [ABSTRACT FROM AUTHOR]

Two simple feedforward neural networks (MLPs) are trained to classify wet and dry periods using signal attenuation from commercial microwave links (CMLs) as predictors and high temporal resolution reference data as target. MLP GA is trained against nearby rain gauges and MLP RA is trained against gauge-adjusted weather radar. Both MLPs perform better than existing methods, showcasing their effectiveness in capturing the intermittent behaviour of rainfall. This study is the first using both radar and rain gauges for training and testing for CML wet-dry classification. Where previous studies has mainly focused on hourly reference data, our findings show that it is possible to predict wet and dry periods with a higher temporal precision. [ABSTRACT FROM AUTHOR]

The most reliable areal precipitation estimation is usually generated via combinations of different measurements. Path-averaged rainfall rates can be derived from commercial microwave links (CMLs), where attenuation of the emitted radiation is strongly related to rainfall rate. CMLs can be combined with data from other rainfall measurements or can be used individually. They are available almost worldwide and often represent the only opportunity for ground-based measurement in data-scarce regions. However, deriving rainfall estimates from CML data requires extensive data processing. The separation of the attenuation time series into rainy and dry periods (rain event detection) is the most important step in this processing and has a high impact on the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. We used data from 3748 CMLs in Germany for 4 months in the summer of 2021 and data from the two SEVIRI-derived products PC and PC-Ph. We analyzed all rain event detection methods for different rainfall intensities, differences between day and night, and their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW was used for validation. The results showed that both SEVIRI products are promising candidates for ADB rainfall detection, yielding only slightly worse results than the TSB methods, with the main advantage that the ADB method does not rely on extensive validation for different CML datasets. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night due to the reduced availability of SEVIRI channels at night. In general, the ADB methods led to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations improved the Matthews correlation coefficient of the rain event detection from 0.49 (or 0.51) to 0.59 during the day and from 0.41 (or 0.50) to 0.55 during the night. Additionally, these combinations increased the number of true-positive classifications, especially for light rainfall compared to the TSB methods, and reduced the number of false negatives while only leading to a slight increase in false-positive classifications. Our results show that utilizing MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. While the improvement is useful even for applications in Germany, we see the main potential of using ADB methods in data-scarce regions like West Africa where extensive validation is not possible. [ABSTRACT FROM AUTHOR]

The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. [ABSTRACT FROM AUTHOR]

We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities. Significance Statement: In this study, we investigate the possibility of deriving accurate high-resolution rainfall maps from commercial microwave link (CML) data in West Africa. The main challenges are the lack of reference data in this area and the adoption of existing processing tools without reference data. We show CML rainfall maps for Ouagadougou, Burkina Faso, with a resolution of 5 min and 0.55 km, which is unprecedented in this region. The comparison with the only available rain gauge, which provides data only at a daily resolution, yields a Pearson correlation of >0.95. An analysis of synthetically thinned-out networks shows that this accuracy is valid for the whole domain. Comparing reanalysis and satellite data with the rain gauge and CML data showed a poor performance of these gridded reference datasets. Also, a high coincidence of temporal dynamics between CML rainfall maps and satellite products was observed. Overall, these findings support the potential of CMLs for future hydrometeorological applications in West Africa. [ABSTRACT FROM AUTHOR]

Purpose: The new Medical Licensing Regulations 2025 (Ärztliche Approbationsordnung, ÄApprO) require the development of competence-oriented teaching formats. In addition, there is a great need for high-quality teaching in the field of radiation oncology, which manifests itself already during medical school. For this reason, we developed a simulation-based, hands-on medical education format to teach competency in performing accelerated partial breast irradiation (APBI) with interstitial multicatheter brachytherapy for early breast cancer. In addition, we designed realistic breast models suitable for teaching both palpation of the female breast and implantation of brachytherapy catheters. Methods: From June 2021 to July 2022, 70 medical students took part in the hands-on brachytherapy workshop. After a propaedeutic introduction, the participants simulated the implantation of single-lead catheters under supervision using the silicone-based breast models. Correct catheter placement was subsequently assessed by CT scans. Participants rated their skills before and after the workshop on a six-point Likert scale in a standardized questionnaire. Results: Participants significantly improved their knowledge-based and practical skills on APBI in all items as assessed by a standardized questionnaire (mean sum score 42.4 before and 16.0 after the course, p < 0.001). The majority of respondents fully agreed that the workshop increased their interest in brachytherapy (mean 1.15, standard deviation [SD] 0.40 on the six-point Likert scale). The silicone-based breast model was found to be suitable for achieving the previously defined learning objectives (1.19, SD 0.47). The learning atmosphere and didactic quality were rated particularly well (mean 1.07, SD 0.26 and 1.13, SD 0.3 on the six-point Likert scale). Conclusion: The simulation-based medical education course for multicatheter brachytherapy can improve self-assessed technical competence. Residency programs should provide resources for this essential component of radiation oncology. This course is exemplary for the development of innovative practical and competence-based teaching formats to meet the current reforms in medical education. [ABSTRACT FROM AUTHOR]

An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremesMagnitude of observed blackouts exceeds climatologically expected valuesUnexpectedly, longer CMLs experience more blackouts [ABSTRACT FROM AUTHOR]

This state-of-the-field article examines the historiography of Austria as a Cold War refuge. It starts by identifying research trends in Austrian migration history and relating these to general developments in research on contemporary Austrian history. The state of the field reflects general temporal foci of historians and has surged at moments when migration became more prominent in public debates as was the case in the early 1990s and again since 2015. Against this backdrop, both generally and regarding migration history, the postwar decade is the most thoroughly researched period of the Cold War. Still, a closer look at the history of DPs and expellees in postwar Austria reveals a fragmented scholarship; the recent renewed interest in the topic has the potential to broaden and deepen our knowledge. During the Cold War, Austria successfully protected its image as a haven for refugees. Since the 1990s, however, historians have begun revisiting and de-mythologizing this master narrative of Austria’s Cold War history. Their findings clearly demonstrate that Austria always aimed to be a country of transit only and that public discourse about refugees repeatedly turned negative over time-not only in the later stages of the Cold War but already in regard to the Hungarian refugees of 1956. Despite substantial progress in the past decade, studies addressing the country’s history as a refuge from the 1960s until the end of the Cold War are still scarce. While publications on major crisis-related refugee movements to Austria continue to grow in number, there is a lack of analysis addressing long-term developments and integrating their findings into the broader history of migration in Austria (especially labor migration since the 1960s). Furthermore, studies aiming to overcome the East-West focus in Austria’s migration history by applying a global perspective are only in their infancy. [ABSTRACT FROM AUTHOR]

The Institute of Measurement, Control, and Microtechnology at Ulm University has investigated advanced driver assistance systems for decades and concentrated, in large part, on autonomous driving. It is well known that motion planning is a key technology for autonomous driving. It is, first and foremost, responsible for the safety of vehicle passengers as well as all surrounding traffic participants. However, a further task consists of providing smooth and comfortable driving behavior. In Ulm, we are grateful to have the opportunity to test our algorithms under real conditions in public traffic and diversified scenarios. In this article, we give readers an insight into our work, including the vehicle and test track, as well as related problems, challenges, and solutions. We describe the motion planning system and explain the implemented functionalities. Furthermore, we show how our vehicle moves through public road traffic and deals with challenging scenarios, e.g., driving through roundabouts and intersections. [ABSTRACT FROM AUTHOR]

The interaction between the land surface and the atmosphere is a crucial driver of atmospheric processes. Soil moisture and precipitation are key components in this feedback. Both variables are intertwined in a cycle, that is, the soil moisture – precipitation feedback for which involved processes and interactions are still discussed. In this study the soil moisture – precipitation feedback is compared for the sempiternal humid Ammer catchment in Southern Germany and for the semiarid to subhumid Sissili catchment in West Africa during the warm season, using precipitation datasets from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), from the German Weather Service (REGNIE) and simulation datasets from the Weather Research and Forecasting (WRF) model and the hydrologically enhanced WRF‐Hydro model. WRF and WRF‐Hydro differ by their representation of terrestrial water flow. With this setup we want to investigate the strength, sign and variables involved in the soil moisture – precipitation feedback for these two regions. The normalized model spread between the two simulation results shows linkages between precipitation variability and diagnostic variables surface fluxes, moisture flux convergence above the surface and convective available potential energy in both study regions. The soil moisture – precipitation feedback is evaluated with a classification of soil moisture spatial heterogeneity based on the strength of the soil moisture gradients. This allows us to assess the impact of soil moisture anomalies on surface fluxes, moisture flux convergence, convective available potential energy and precipitation. In both regions the amount of precipitation generally increases with soil moisture spatial heterogeneity. For the Ammer region the soil moisture – precipitation feedback has a weak negative sign with more rain near drier patches while it has a positive signal for the Sissili region with more rain over wetter patches. At least for the observed moderate soil moisture values and the spatial scale of the Ammer region, the spatial variability of soil moisture is more important for surface‐atmosphere interactions than the actual soil moisture content. Overall, we found that soil moisture heterogeneity can greatly affect the soil moisture – precipitation feedback. [ABSTRACT FROM AUTHOR]

It is well accepted that summer precipitation can be altered by soil moisture condition. Coupled land surface – atmospheric models have been routinely used to quantify soil moisture – precipitation feedback processes. However, most of the land surface models (LSMs) assume a vertical soil water transport and neglect lateral terrestrial water flow at the surface and in the subsurface, which potentially reduces the realism of the simulated soil moisture – precipitation feedback. In this study, the contribution of lateral terrestrial water flow to summer precipitation is assessed in two different climatic regions, Europe and West Africa, for the period June–September 2008. A version of the coupled atmospheric‐hydrological model WRF‐Hydro with an option to tag and trace land surface evaporation in the modelled atmosphere, named WRF‐Hydro‐tag, is employed. An ensemble of 30 simulations with terrestrial routing and 30 simulations without terrestrial routing is generated with random realizations of turbulent energy with the stochastic kinetic energy backscatter scheme, for both Europe and West Africa. The ensemble size allows to extract random noise from continental‐scale averaged modelled precipitation. It is found that lateral terrestrial water flow increases the relative contribution of land surface evaporation to precipitation by 3.6% in Europe and 5.6% in West Africa, which enhances a positive soil moisture – precipitation feedback and generates more uncertainty in modelled precipitation, as diagnosed by a slight increase in normalized ensemble spread. This study demonstrates the small but non‐negligible contribution of lateral terrestrial water flow to precipitation at continental scale. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h -1 , more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany. [ABSTRACT FROM AUTHOR]

Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade, commercial microwave links (CMLs), operated by mobile network providers, have proven to be an additional source of rainfall information to complement traditional rainfall measurements. In this study, we present the processing and evaluation of a German-wide data set of CMLs. This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of 1 min. The analysis period of 1 year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of misclassification. This was achieved by using a new approach to determine the threshold, which separates a rolling window standard deviation of the CMLs' signal into wet and dry periods. For the compensation for wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent model performs better for our data set. We use RADOLAN-RW, a gridded gauge-adjusted hourly radar product from the German Meteorological Service (DWD) as a precipitation reference, from which we derive the path-averaged rain rates along each CML path. Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly, and seasonal rainfall sums, we found good agreement between CML-derived rainfall and the reference, except for the winter season due to non-liquid precipitation. We discuss performance measures for different subset criteria, and we show that CML-derived rainfall maps are comparable to the reference. This analysis shows that opportunistic sensing with CMLs yields rainfall information with good agreement with gauge-adjusted radar data during periods without non-liquid precipitation. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4 x 105 trainable parameters. With a structure, inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time-series. We test the CNNs ability to generalize to CMLs and time periods outside the training data. Our CNN is trained on four months of data from 400 randomly selected CMLs and validated on two different months of data, once for all CMLs and once for the 3504 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set we use the gauge adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 87 % of all rainy and 91 % of all non-rainy periods. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall induced attenuation patterns in CML signal levels from a large CML data set covering entire Germany. [ABSTRACT FROM AUTHOR]

Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade commercial microwave links (CMLs) operated by mobile network providers have proven to be an additional source of rainfall information to complement traditional rainfall measurements. In this study we present the processing and evaluation of a German-wide data set of CMLs. This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of one minute. The analyzed period of one year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of miss-classification. This was achieved by a new approach to determine the threshold which separates a rolling window standard deviation of the CMLs signal into wet and dry periods. For the compensation of wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent method performs better for our data. As precipitation reference, we use RADOLAN-RW, a gridded gauge-adjusted hourly radar product of the German Meteorological Service (DWD), from which we derive the path-averaged rain rates along each CML path. Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly and seasonal rainfall sums we found high agreement between CML-derived rainfall and the reference, except for the cold season with non-liquid precipitation. This analysis shows that opportunistic sensing with CMLs yields rainfall information with a quality similar to gauge-adjusted radar data during periods without non-liquid precipitation. [ABSTRACT FROM AUTHOR]

Austrian-Czechoslovakian economic relations constitute a special case auf Austria's Osthandel (Eastern Trade) in the Cold War era. The reasons for this can be traced back to the historical development of economic exchange as well as the complicated political relations between Vienna and Prague. Against this historical background and the context of Austria's Ostpolitik and Osthandel after 1945, the study delivers a first analysis of economic interactions in the Kreisky era (1970-1983). The post-1945 development of bilateral relations between the two Central European neighbors also hindered improvement on the economic level. In comparison to other Socialist states, Austria's nationalized industries played a rather small roll in trade with Czechoslovakia. Despite good will on both sides, negotiations on the ministerial level remained complicated. While most COMECON members (except the Soviet Union) continuously faced severe trade balance deficits with Austria, Czechoslovakia's balance was usually active. By the end of the 1970s, Austria's deficit grew massively. Additionally, this article presents case studies on trilateral cooperation in the fields of economic and energy supplies. The difficulties in realizing those projects with Poland or Hungary strengthen the image of a highly heterogeneous "Eastern bloc". The analysis is primarily based on Austrian and East German archival sources. [ABSTRACT FROM AUTHOR]

Copyright of Römische Historische Mitteilungen is the property of Oesterreichische Akademie der Wissenschaften Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Author-supplied Abstracts.)