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The Gravitational Wave highly energetic Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to detecting gravitational wave gamma-ray bursts. It is capable of all-sky monitoring over and discovering gamma-ray bursts and new radiation phenomena. GECAM consists of two microsatellites, each equipped with 8 charged particle detectors (CPDs) and 25 gamma-ray detectors (GRDs). The CPD is used to measure charged particles in the space environment, monitor energy and flow intensity changes, and identify between gamma-ray bursts and space charged particle events in conjunction with GRD. CPD uses plastic scintillator as the sensitive material for detection, silicon photomultiplier (SiPM) array as the optically readable device, and the inlaid Am-241 radioactive source as the onboard calibration means. In this paper, we will present the working principle, physical design, functional implementation and preliminary performance test results of the CPD., Comment: accepted to RDTM

The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) satellite consists of two small satellites. Each GECAM payload contains 25 gamma ray detectors (GRD) and 8 charged particle detectors (CPD). GRD is the main detector which can detect gamma-rays and particles and localize the Gamma-Ray Bursts (GRB),while CPD is used to help GRD to discriminate gamma-ray bursts and charged particle bursts. The GRD makes use of lanthanum bromide (LaBr3) crystal readout by SiPM. As the all available SiPM devices belong to commercial grade, quality assurance tests need to be performed in accordance with the aerospace specifications. In this paper, we present the results of quality assurance tests, especially a detailed mechanism analysis of failed devices during the development of GECAM. This paper also summarizes the application experience of commercial-grade SiPM devices in aerospace payloads, and provides suggestions for forthcoming SiPM space applications., Comment: 13 pages, 23 figures

Summary We can obtain elastic parameters through prestack amplitude versus offset (AVO) inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction result. In this paper, we develop a AVO inversion method based on Bayesian theory in ray parameter domain, whose output is density, P-wave impedance and Vp/Vs. These elastic parameters have high precision, which are valuable input for reservoir characterization because they are related to lithology and fluid content of the reservoir. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation, thus extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable.

Background: Each GECAM satellite payload contains 25 gamma-ray detectors (GRDs), which can detect gamma-rays and particles and can roughly localize the Gamma-Ray Bursts (GRBs). GRD was designed using lanthanum bromide (LaBr3) crystal as the sensitive material with the rear end coupled with silicon photomultiplier (SiPM) array for readout. Purpose: In aerospace engineering design of GRD, there are many key points to be studied. In this paper, we present the specific design scheme of GRD, the assembly and the performance test results of detectors. Methods: Based on Monte Carlo simulation and experimental test results, the specific schematic design and assembling process ofGRDwere optimized. After being fully assembled, theGRDswere conducted performance tests by using radioactive source and also conducted random vibration tests. Result and conclusion: The test results show that all satellite-borne GRDs have energy resolution, 10 pages, 15 figures

Prestack AVO inversion is multidimensional and ill posed, and is often strongly affected by noise and measurement uncertainty, so it is necessary to perform constraints in order to obtain steady and rational inversion results. In this paper, we develop a prestack AVO joint inversion method, whose output is P-wave modulus contrast, S-wave modulus contrast, and density contrast. These parameters derived from seismic data are valuable input for reservoir characterization because they are related to lithology and fluid content of the reservoir through empirical relationships. Combining PP and PS seismic data, we introduce a more accurate and stable system of equations than for PP and PS seismic data separately. The joint inversion is based on linearized approximation of the Zoeppritz equation and Bayesian parameter estimation theory. Tests on synthetic and real data show that all inverted parameters are almost perfectly retrieved even the SNR is not high. The result of joint inversion with a Bayesian scheme is more accurate, stable and reliable.

In traumatic brain injury (TBI), diffuse axonal injury (DAI) accounts for a significant portion of intra-axial injury. Past studies have shown diffusion-weighted magnetic resonance imaging (DWI) to be sensitive in detecting visible DAI lesions; however, diffuse lesions are frequently not seen on imaging. In this study, we focused on detecting nonvisible, quantifiable diffusion changes using an apparent diffusion coefficient (ADC) map. Findings were correlated to injury using admission Glasgow Coma Scale (GCS) and to long-term outcome using Glasgow Outcome Score (GOS) at 6-12 months after injury. Thirty-seven adults with TBI (mean age 32 yrs, range 18-70 yrs) were studied in addition to 35 age-matched controls. DWI was performed using single-shot echo planar technique (TR/TE = 4,000/110 milliseconds; 5 mm thick). Twenty-eight regions of interest (ROI) were manually drawn on the corresponding ADC maps in predetermined locations and ADC values were recorded. All ROIs excluded visibly abnormal areas on the T2-weighted images. Brain regions were categorized into 5 zones: peripheral gray matter, peripheral white matter, deep gray matter, deep white matter, and posterior fossa. Admission GCS score was dichotomized into “severe” (3-8) or “mild/moderate” (9-15) injury. The GOS was dichotomized into “poor” (1-3) or “good” outcome (4-5). Statistical analysis was performed using the ANOVA method with Scheffe and Tukey HSD post hoc comparisons. In the ROIs, DWI was able to detect greater abnormalities than conventional MRI. Mean ADC values in all brain zones were significantly different between subjects and controls ( p ≤ .05). When comparing zones, the “severe” injury GCS category had significantly higher mean ADC values than the controls ( p ≤ .05). In the posterior fossa, the “severe” injury group had significantly different mean ADC values than the “mild/moderate” injury group ( p = .034). When focusing on the GOS, the “good” outcome group had significantly higher mean ADC values when compared to controls ( p ≤ .05). In the deep gray matter, the “good” and “poor” outcome groups showed a significant mean ADC difference ( p = .0001). This study concludes that ADC maps can be used to detect nonvisible DAI, which is frequently not seen, and that injuries in the deep gray matter can be used to predict outcome in adult TBI patients.