esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results
### Sensor Comparison
#### 7.3.3 Star Detectability  
To assess the camera sensors' capacity to distinguish stars from the background sky, contrast maps are used in starfield imaging. A sliding window approach was employed to perform local contrast measurement within these images. This method calculates contrast over small, localized areas of the image and then moves this window across the entire image. Figure 14 displays the contrast maps of images from subpayload 1 and subpayload 2.  
In starfield imaging, a histogram can visualize intensity distribution, with a characteristic peak at the lower intensities and some spikes at the high intensity end, as shown in Figure 15 using the minimum and maximum pixel values within the images.  
Figure 14: Contrast maps for (**a**) subpayload 1 and (**b**) subpayload 2 capturing the same starfield, which reveal the variations in local contrast across each sensor’s image. Brighter squares indicate areas of higher contrast, likely corresponding to celestial bodies, against the darker background of space.  
Figure 13: (**a**) Subpayload 1 and (**b**) subpayload 2 display parts of the Pisces constellation captured by subpayloads 1 and 2, respectively, towards the end of their operational period during flight.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
### Contributions and Novelty  
To overcome these challenges, this study proposes a novel integrated safety monitoring and early warning system specifically designed for fishing vessels. The integrated system adopts a modular and cost-effective hardware architecture, supporting flexible deployment. Real-time monitoring is achieved through 4G/5G wireless communication, enabling low-latency data transmission without the need for satellite services. The system incorporates multiple sensors--including attitude, temperature, and positioning modules--as well as surveillance video. A fuzzy logic-based decision-making algorithm is applied to intelligently evaluate risk and generate timely alerts in uncertain conditions. This integrated approach enhances operational safety while maintaining affordability and scalability, making it suitable for widespread adoption in the fishing industry.  
The main contributions of this paper are summarized as follows:  
* A real-time data acquisition and processing system has been developed through the integration of a nine-axis sensor, temperature and humidity sensors, a GPS module, and a surveillance camera. The integrated system is specifically designed to monitor vessel attitude, environmental conditions, and the vessel's location. The use of 4G/5G wireless communication ensures low-latency data transmission without relying on satellite networks.
* The system is characterized by cost-effective and modular architecture, allowing for flexible deployment and scalability across fishing vessels of different types and sizes, including those operating in small-scale or resource-limited settings.
* A fuzzy logic-based decision support algorithm is designed to compute a \"danger score\" for evaluating vessel safety status, effectively handling uncertainties in maritime environments and providing timely alerts to crew members.
* Field experiments conducted in the Zhoushan Sea area validate the effectiveness of the proposed system, demonstrating its ability to improve safety.  
Aside from the Introduction, this paper is divided into four sections, with the second part concentrating on the Literature Review. The hardware, software, and algorithm components are presented in Section 3, which is dedicated to the integrated system's components. The field tests and related discussion are covered in the Section 4. Lastly, Section 5 provides a summary of the main findings.
## 4 Discussion  
As seen from Tables 1 and 2, winter SH was closely related to arctic autumn eastern Siberian Sea ice extent and the winter BKS precipitation anomaly. This phenomenon may be caused by water vapor transport. When the autumn eastern Siberian Sea ice extent decreased, especially in September and October, the winter (December-February) water vapor transport increased in BKS, resulting in winter BKS temperature rise and ice melt. The reduction of BKS sea ice in winter will lead to SH strengthening. As reported in [36; 37], the decrease of BKS sea ice extent led to the excitation of the Rossby wave, disturbed the polar vortex, and produced a positive geopotential height anomaly extending to northern Europe, which enhanced the high-pressure ridge anomaly near the Ural Mountains. Ridge/trough pattern anomalies and associated near-surface winds would enhance the advection of arctic cold air in the mid-latitudes of Europe and Asia, resulting in the strengthening of Siberian high pressure. In autumn and winter Siberian high Asia, BKS sea ice is closely related to China's air temperature [26; 37]. When the eastern Siberia sea ice extent in September-October decreased, cold temperatures would occur in northern China (see Figure 11). This phenomenon may also relate to Siberian high pressure. Before 2000, when the Siberian high weakened, winter BKS precipitation showed a declining trend, but the correlation between winter SH and BKS precipitation was very weak (r = \(-\)0.064). After 2001, the Siberian high was strengthened, and the precipitation of BKS in winter showed an increasing trend, and the correlation between BKS precipitation and SH in winter was strengthened (r = 0.56 **). Future work will still need to further refine the analysis of the relationship between autumn eastern Siberian Sea ice extent and BKS winter precipitation, addressing their impact on northern China's temperature.  
Figure 10: (**a**) Outgoing long-wave radiation (OLR) anomaly in low sea ice index years (2007, 2008, 2012, 2016); (**b**) OLR anomaly in high sea ice index years, unit: W/m\({}^{2}\). The black part passed the t test at 90% confidence (1988, 1992, 1994, 1996, 1997, 1998, and 2001).
## 3 Experiment and Analyses
### Dynamic Experiment
#### 3.2.3 The Horizontal Protection Level  
In Experiment 2, HPL is calculated according to position estimate uncertainty, which is obtained by the first two elements of the covariance matrix \(P\). In practical applications, HPL being less than HAL is a prerequisite for RAIM. When the system is operating, the circle centered on the motion position with the HPL as the radius will contain the true position in this epoch. Figure 17 shows the HPL calculated in Experiment 2. The calculation of HPL is obtained by (19). The color bar represents the epochs.  
Figure 17a shows the motion trajectory of the first dataset in the second period and the corresponding HPL. In this dataset, all ambiguities are fixed, and the constellation is stable without the replacement of the reference pseudolite. Figure 17b shows the HPL of the full dataset in Experiment 2. The HPL changes within 5 cm and is relatively stable throughout the movement except for the first epoch. The reason is that the ambiguity has not been fixed and the variance is relatively large, which is why the peak appears in Figure 17b. In addition, according to the definition of HPL and the theory of RAIM, it can be considered that HPE is smaller than HPL, that is, the positioning precision of the dynamic experiment is below 5 cm. Since there is no true value in positioning, this method also provides a reference index for the accuracy assessment of positioning. Therefore, according to Experiments 1 and 2, it can be considered that the proposed indoor PLRAIM algorithm has the abilitytoensuring the accuracy and integrity of pseudolite system. All test statistics and HPL in Figures 15 and 17 are relatively stable, which also proves the reliability of AR.
# Will the Use of Pharmacogenetics Improve Treatment Efficiency in COVID-19?
## 11 Molinupiravir  
Molinupiravir is another new oral drug that received emergency use authorization (EUA) for the treatment of COVID-19 [154]. This drug can be used for the therapy of adults with mild to moderate symptoms of COVID-19 that appeared no more than 5 days earlier, in the case of patients who are at high risk of progressing to severe disease, and for whom alternative antiviral therapies cannot be used for any reason. Molinupiravir is administered in the form of a prodrug of N-hydroxycydidine (NHC). This small-molecule ribonucleoside displays the activity against SARS-CoV-2 and other RNA viruses [155; 156]. The results of studies have demonstrated a high genetic barrier to NHC resistance [155]. Following the administration, circulating NHC is phosphorylated intracellularly to NHC triphosphate which becomes incorporated into viral RNA by viral RNA polymerase. Such incorporation leads to the accumulation of viral mutations and lethal mutagenesis. The occurrence of deleterious errors is associated with the incorporation of viral polymerase either guanosine or adenosine during viral replication which eventually render the virus noninfectious and unable to replicate [157; 158; 159].  
The results of phase 1 and 2 clinical trials were promising therefore, this drug was included in the evaluation in the phase 3 trial [160; 161]. The recommended oral dose of molnupiravir is 800 mg twice daily for 5 days, and only in the case where other therapies (remdesivir, ritonavir-boosted nirmatrelvir (Paxlovid), and sorrovimab) cannot be used.  
The Phase 3 MOVe-OUT trial which enrolled non-hospitalized high-risk patients revealed that early initiation of treatment (within 5 days from the onset of symptoms) decreased the risk of hospitalization or any cause of death by 30% [154]. The efficacy of the therapy was found to be comparable in participants infected with the delta, gamma, and mu variants of SARS-CoV-2. However, in some groups of patients, including those with low baseline viral load, patients with evidence of previous SARS-CoV-2 infection, and those with diabetes mellitus, the outcomes were not significantly better compared to placebo [154]. The use of molnupiravir was found to be associated with mild adverse effects, including dizziness, nausea, and diarrhea. The results of in vitro studies have demonstrated that molnupiravir and its active metabolite NHC do not inhibit or stimulate any vital drug-metabolizing enzymes or inhibit drug transporters. Moreover, no significant drug-drug interactions have been observed yet.
## 2 Inductive-Based Digital Isolation System
### Common Mode Noise Analysis  
One important characteristic of digital isolators is the system's ability to reject CMT pulses while maintaining error-free transmission at the desired data rate. The common mode voltage refers to the voltage difference between the Tx ground and the Rx ground. The common mode voltage is related to the rapid switching of the integrated gate bipolar transistor (IGBT) at the high voltage side, which propagates through the overlapping capacitance of transformer coils (\(C_{\_}{op}\)) and affects the Rx-detected signal level. Consequently,  
Figure 6: Schematic of the differential amplifier.  
Figure 7: Schematic of the hysteresis comparator.  
the Rx signal V_pulse will follow similar rise/fall transitions of the common mode pulse in the range of hundreds of kV/us range. The unwanted slew rate added to V_pulse can lead to false detection at the Rx side and hence miss-determining the correct logic level at the final system output. Since the common mode component of V_pulse is with a frequency less than the actual modulation frequency (\(f_{cm}<f_{mod}\)), the amount of this noise can be further reduced by the HPF, the first element of the Rx circuit. The differential architecture of the Rx helps further improve the common mode rejection of the overall implemented system. Figure 8 illustrates the block diagram of the digital isolator under CMTI simulation. A high voltage pulse V_cmit = 550 V with a rising/falling time of 2.5 ns which corresponds to a CMTI of 220 kV/us is being injected between the Tx ground (GND_Tx) and the Rx ground (GND_Rx).  
The system's response to the CMT pulse is demonstrated in Figure 9. The input digital signal at the Tx with a frequency of 10 MHz is modulated and transmitted to the Rx input through the isolation transformer. The second waveform is the 42 MHz high-voltage pulse between the Tx and Rx grounds (V_cmit = 220 kV/us). In typical scenarios, the high-voltage pulse combinations are applied at multiple IN rising/falling edges, i.e., during different surge events: rising, high amplitude, falling, and low amplitude. Although the applied surge added additional top/bottom peaks on the comparator input V_com, the final Rx output OUT is successfully recovered. This is because the unwanted peaks fall above/below the threshold level of the comparator within a small duration. The total system propagation delay from the Tx input to the Rx output during the surge event is 28 ns.  
Figure 8: Block diagram of the digital isolator under common mode transient immunity (CMTI) simulation.  
Figure 9: System functionality simulation showing the digital isolator’s signal flow in the presence of a common mode transients (CMT) pulse.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Multi-Robot Formation Control Simulation Experiment
#### 4.1.6 Simulation and Results Analysis of Dynamic Formation in Narrow Passage  
Assuming that the obstacles in the scene are large and sparsely distributed, as shown in Figure 10a, we realize obstacle avoidance by setting the density of the obstacle area to 0 and then dynamically zooming the boundaries of the Voronoi division area to drive the overall formation to shrink to pass through the narrow-passage terrain. Figure 10f,g shows the position error and formation maintenance of the system, respectively. When the sum of the distances between the robots and the center of mass is equal to \(\sqrt{a^{2}+b^{2}}\), where a and b are the length and width of the region \(Q\), respectively, the formation remains stable. When  
Figure 9: Dynamic formation simulation under random obstacle scenario: (**a**) distribution of density function under random obstacle scenario; (**b**) \(k=2\); (**c**) \(k=57\); (**d**) \(k=31\); (**e**) \(k=182\); (**f**) position error; (**g**) formation preservation function.  
the number of iterations reaches \(k=\) about 60, the side length of the area \(Q\) is contracted, and the robot can track the center of mass more easily. When the four robots maintain a linear formation up to \(k=350\), all the robots pass through the narrow passage. Due to the change of the density function of the position of the robot at this time, to form the optimal configuration, the position error of the robot fluctuates slightly. At \(k=350\)-400, to form a square formation from a linear formation, there is an adjustment process before the regional boundary is adjusted to a square, as shown in Figure 10g. When the number of iterations \(k=580\), the robots formed the expected square formation, and the position error of the robots also tended to zero.  
Figure 10: Dynamic formation simulation in a narrow channel: (**a**) density function distribution in a narrow channel; (**b**) \(k=10\); (**c**) \(k=60\); (**d**) \(k=321\); (**e**) \(k=610\); (**f**) position error; (**g**) formation preservation function.
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 4 Discussion
### Model-Wise Performance Comparison  
The NeQuick G and Klobuchar models are compared in their ability to mitigate ionospheric effects on GNSS signals for SF users. NeQuick G uses a physical-based approach and is more accurate than Klobuchar, especially at high latitudes and during intense solar activity, while the Klobuchar model uses an empirical approach. NeQuick G can model smaller ionospheric plasma structures and the 3D distribution of plasma density better than Klobuchar. The results, therefore, show better performances of NeQuick G compared to Klobuchar. TEC availability is more stable for NeQuick G at high middle north and middle south latitudes, and the effect on TEC availability is more evident at low-latitude regions with the best performances achieved by NeQuick G. Although, in terms of horizontal positioning, both models provide similar results at all analysed periods and stations, NeQuick G performs better than Klobuchar for vertical positioning.
# Bayesian Binary Search
## 2 Methodology
### Search Space Probability Density Function Estimation
#### 2.7.2 Unsupervised Learning Approaches  
* **Gaussian Mixture Models (GMMs):** GMMs (typically fit using the Expectation-Maximization (EM) algorithm) can model complex, multimodal distributions by representing the PDF as a weighted sum of Gaussian components.
* **Kernel Density Estimation (KDE):** KDE is a non-parametric method for estimating PDFs, which can capture complex shapes without assuming a specific functional form.
* **Maximum Likelihood Estimation (MLE):** MLE is a method of estimating the parameters of a probability distribution by maximizing a likelihood function. It can be used to fit various parametric distributions (e.g., normal, exponential, Poisson) to data, providing a PDF that best explains the observed samples according to the chosen distribution family.
# Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content
## 3 Test Program
### Sample Preparation  
The samples used in this test are residual soil, which contains block stones, some of which have huge particle sizes. The distribution of these block stones is not uniform at the top and foot of the slope. In this test, particles with a size greater than 5 mm are classified as stones, while particles smaller than 5 mm are considered soil. The stone content is calculated as the ratio of the mass of particles larger than 5 mm to the total specimen mass. The stone content in the collected specimens ranged from 45% to 75%. In order to investigate the effect of stone content on the creep of residual soil more scientifically, four groups of different stone contents of 10%, 30%, 50%, and 70% were configured. The water content for all specimens was set at 15%.  
To prepare the sample, first dry the natural residual soil. Then, separate the block stones and soil by rolling. Use a 5 mm sieve to divide the accumulated material into >5 mm block stones and <5 mm soil. Mix the soil and stone in the desired proportion for large-scale direct shear tests, such as 10% block stones and 90% soil, to obtain a shear specimen with a stone content of 10%. Divide each group of samples into three equal parts and mix with water at 15% moisture content. The sample ingredients and mixing process are shown in Figure 3. Load the soil sample into the shear box in three stages, compacting it to a fixed height each time. Ensure that the second soil layer is higher than the shear surface to avoid compaction on the shear surface. The gradation of the specimens with different stone contents is presented in Table 2 and Figure 4.  
Figure 3: Mixing of sheared soil samples. **(a)** Mixing process and **(b)** sample after mixing with water.  
Figure 2: ZJ50-2G-Large direct shear test machine.
###### Abstract  
The moisture condition of the active layer in Arctic regions can induce severe problems, such as ground subsidence and frost heave. Thus, the water content in the active layer needs to be estimated using a light and portable in-situ testing device. In this study, a penetration-type time domain reflectometry (PTDR) device is developed for the estimation of volumetric water content in the active layer. The developed PTDR is applied at a site for an electrical resistivity survey to characterize the water distribution along a measurement line. A PTDR consists of a PTDR module, connecting rods, and a guide with a hammer. The PTDR module can determine the dielectric constant of a material from the measurement of the travel time of electromagnetic waves. Using remolded soil samples, the dielectric constants measured from the PTDR are calibrated with the volumetric water content. The PTDR calibration demonstrates that the dielectric constant increases with the water content. For the temperature of 0.1 to 15.2 \({}^{\circ}\)C, the travel time only slightly depends on the temperature variance. For field application, a PTDR is pressed into the ground and measures the electromagnetic waves and temperature with depth. The results of the field tests show that the volumetric water content measured by the PTDR increases with depth due to the impermeable layer located underneath the active layer. The electrical resistivity survey conducted at the same site provides the electrical resistivity profile for a long distance and shallow depth soils. Furthermore, the electrical resistivity survey and PTDR establish a significant correlation between electrical resistivity and water content. The PTDR developed in this study can be effectively used as an advanced in-situ testing method to estimate the water distribution in the active layer.  
active layer; electrical resistivity; time domain reflectometry (TDR); water content +
Footnote †: journal: Applied Physics Letters  
**Keywords:** active layer; electrical resistivity; time domain reflectometry (TDR); water content
## 3 Research Results and Discussion
### Measurement of Surface Hardness  
Figure 7 presents a change in the surface hardness as a function of the number of fatigue cycles. Hardness is an important parameter in the evaluation of the functional strength of steel components.  
The initial hardness of both the sleeve and shaft was 187 HV. As the number of fatigue cycles increases, the decrease in surface hardness in the area affected by wear is noted. When the fatigue tests were completed, shaft hardness equal to 184 HV and sleeve hardness equal to 183 HV was measured. Thus, hardness change by 3-4 units in relation to the initial parameters was recorded. Lower hardness values may be due to the abrasion of the top layer of the elements. That phenomenon occurs when wear products travel, first during the joint assembly process and, second, during wear tests, as a result of microvibrations appearing in the tribological kinematic pair. Wear products from the fatigue tests considerably influence the \"abrasion\" of the surface because, due to those  
Figure 6: Change of the coefficient of friction depending on the number of fatigue cycles.  
Figure 7: Change of shaft and sleeve hardness depending on the number of fatigue cycles.  
products' oxidation, they are harder than the remaining wear products and even harder than the surface on which they occur.  
In relation to the above, the steel hardness decrease may be the cause of the reduced resistance of an element to friction wear and plastic deformations, which are conducive to adhesion processes. Those factors create a suitable environment for the development of fretting wear, whose consequence is the development of fatigue wear and the cracking of the element.