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This paper describes the finding of an experimental study to measure the detector paralysis factor and the use of this parameter in conjunction with detector dead time to better model detector dead-time response. The idealized one-parameter models, the paralyzable and nonparalyzable models, are inadequate to properly model the dead-time response of any real detector system. To address this deficiency, a more realistic two-parameter model is proposed that incorporates the paralysis factor of the detector in addition to the dead time. The revised two-parameter-based model is an extension of Lee and Gardner's two-dead-time model. A simple scheme is proposed to deduce these parameters from the recorded data based on the rise and drop of count rates from a decaying source. Measurements were made using 56Mn and 52V. The data collected in this study show that a high-purity germanium (HPGe) detector has a paralysis factor of ~50 to 77% and a dead time of 6 to 10 µS. Using the data collected by Lee and Gardner, the paralysis factor for a Geiger-Mueller (GM) counter is estimated to be ~5%. These results are consistent with the approximating assumption that GM counters are nonparalyzing and HPGe detectors are paralyzing. [ABSTRACT FROM AUTHOR]

Abstract: The decay of 124Sb (60d half-life) to levels of 124Te has been studied using two HPGe detectors in singles and coincidence mode. We identify 75 transitions in this decay, including 14 gamma rays, the emission of which are being reported here for the first time. In confirming the existence of 26 well-established levels below 2900keV excitation, we also introduce 5 other levels at 2490.5, 2618.4, 2747.05, 2834.99, and 2859.4keV. The data on energies of gamma transitions and the levels are reported at high precision. [Copyright &y& Elsevier]

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km 2 : the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: 10.23728/b2share.282675586fb94f44ab2fd09da0856883 and 10.23728/b2share.bd89f066c26a4507ad654e994153358b. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while well established sensors typically suffer from a small spatial support. With a sensor footprint up to several hectares, Cosmic-Ray Neutron Sensing (CRNS) is an emerging technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km²: the pre-alpine Rott headwater catchment in Southern Germany which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, that network was designed to study root zone soil moisture dynamics at the catchment-scale. The observations of the dense CRNS network were complemented by extensive measurements that allow to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from Unmanned Areal Systems (UAS), permanent and temporary wireless sensor networks, profile probes as well as comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive dataset to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatio-temporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land - atmosphere exchange processes. The data set is available through EUDAT, splitted into the two subsets https://doi.org/10.23728/b2share.85fe0f9dac0f48df9215c17e65d1f1e1 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.93ed99e486904d48a8a6a68083066198 (Fersch et al., 2020b). [ABSTRACT FROM AUTHOR]