esa-sceva/satcom-chunk-collection huggingface dataset:
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 2 Generalized Hilbert Transform (GHT)
### Synthetic 1D Channel Data Testing  
Here, we used one set of synthetic 1D channel data (as shown in Figure 1a) to analyze the GHT, with comparison of HT. The time-sample interval of the synthetic 1D channel data is one millisecond, thus, the Nyquist frequency \(f_{N}\) is 500 Hz.  
Figure 1b shows the instantaneous amplitudes obtained by HT, which indicate the edges of the synthetic channel; however, the HT result shows lower resolution than we desired. Figure 1c shows the instantaneous amplitudes obtained by GHT with \(p=1.0\). Figure 1d shows the instantaneous amplitudes obtained by GHT with \(p=0.8\) (ratio of an even number 4 and an odd number 5). Figure 1e shows the instantaneous amplitudes obtained by GHT \(p=0.6\) (ratio of two odd numbers 3 and 5).  
We can see that when \(p=1.0\) and 0.6, GHT results effectively detected the edges of the synthetic channel and have a higher resolution than the HT result; whereas, GHT obtains a lower resolution or stability result with \(p=0.8\), due to the corresponding sign changing. Comparison of Figure 1c-e shows that GHT results are unstable with changing order parameter \(p\).
# Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm +
## 4 Simulation results
### Extensible Hook System Deployment  
To demonstrate the required forces to move the EHS, the CubeSat was controlled to keep the initial pose, while the EHS was rotated 135\({}^{\circ}\) and deployed 4.5 m. Figure 6 shows the resulting force and torque to keep the CubeSat in a fixed pose. As can be seen, the maximum required forces were 5.527 mN for the X and Y axes and 2.942 mN for the Z axis. The system required a total energy of \((40\text{ mNs},6.8\text{ mNs},39.\text{ }9\text{mNs})\) for the X, Y, and Z axes, respectively. The maximum required torque was \((-7.553\text{ mNm},5.806\text{ mNm},0.1389\text{ mNm})\) for the X, Y, and Z axes. On the other hand, the actuator of joint 1 from the EHS was limited to 40 N, and joint 2 required a maximum torque of 194.1 mNm, as shown in Figure 7.  
Figure 5: Required torques and forces to move the CubeSat 36.7523 m in 500 s following a trapezoidal profile.  
Figure 6: Required torques and forces to keep the CubeSat in a fixed pose during the motion of the EHS.  
Figure 7: Required force and torque to rotate and deploy the EHS.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 3 System Model  
As shown in Figure 1, the components of the system network model are as follows:  
The trust authority (TA) has resourceful system resources and is trustable. As an authorization center, it is responsible for issuing public and private key certificates for each mobile node. In addition, it is also responsible for generating nodes' IDs and calculating routing utility value.  
The fixed network, including the internet and AP (denoted by the triangle symbol in Figure 1), is responsible for connecting mobile nodes and the TA. DTN mobile nodes can obtain public and  
Figure 1: System model.  
private keys from the TA via the AP and internet. When encountering the AP, a node will send the encounter record information to the TA through the AP.  
Mobile nodes \(N_{1}\)-\(N_{8}\) denote pedestrians carrying equipment with short-distance wireless functions, such as Bluetooth and WiFi. A mobile node has one real and many pseudo-IDs. Mobile nodes acquire keys and IDs from the TA through the fixed network. They send node encounter information to the TA and obtain the routing utility value calculated by the TA.  
Assuming that a utility-based routing protocol is adopted when forwarding messages, that is, when node \(N_{i}\) encounters another node \(N_{j}\), if \(N_{j}\) has a higher probability to be the destination node, the message is sent to node \(N_{j}\).  
It is also assumed that DTN mobile nodes may launch some active attacks to modify or damage the received messages. They may also launch some passive attacks, such as eavesdropping on other nodes' IDs, routing utility, encounter time, location, and other privacy information.
## 2 Materials and Methods  
### Procurement of Materials and Reagents  
Fetal bovine serum (FBS), Dulbecco's modified Eagle's medium (DMEM), and penicillin/streptomycin solution were purchased from Thermo Fisher Scientific Inc. (Logan, UT, USA). LPS from _Escherichia coli_ O127:B8 and 2\({}^{\prime}\),7\({}^{\prime}\)-dichlor fluorescein diacetate (DCFH-DA) were purchased from Sigma-Aldrich Co. (St. Louis, MO, USA). Antibodies against iNOS, COX-2, p65, phosphorylated p65 (Ser536), lxB\(\alpha\), phosphorylated lkR\(\alpha\) (Ser32), IKK\(\alpha\), phosphorylated IKK\(\alpha\)/\(\beta\) (Ser176/180), extracellular signal-regulated kinase (ERK1/2, p38, c-JUN N-terminal kinase (JNK), phosphorylated ERK1/2, phosphorylated p38, phosphorylated JNK, Heme oxygenase (HO)-1, \(\alpha\)-tubulin, and Kelch-like ECH-associated protein 1 (KEAP1) were acquired from Cell Signaling Technologies (Danvers, MA, USA). The nuclear factor erythroid 2-related factor 2 (NRF2) antibody was purchased from Novus Biological (Centennial, CO, USA). The Lamin B1 antibody was obtained from Abcam (Cambridge, UK). The primary antibody against \(\beta\)-actin was obtained from Santa Cruz Biotechnology, Inc. (Santa Cruz, CA, USA).
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
###### Abstract  
A power transformer (PT) in power generation or transmission is critical to maintaining electrical continuity. Fault detection on a PT is needed, especially of incipient faults, which are often caused by a turn-to-turn fault (TTF) before it develops into a more severe fault. We use a hybrid algorithm between conventional and modern techniques to detect a developing fault in a PT. The current response signals from a negative sequence current directional algorithm, extended park vector algorithm (EPVA), differential negative sequence current, and EPVA-fuzzy system are combined to distinguish the possibility of a TTF. The subalgorithms are combined using a hybrid detection algorithm to distinguish the faults. The model is a 10 MVA, three-phase PT with \(\Delta\)-Y configuration 150/300 kV, simulated using MATLAB Simulink software. The results show that by combining the subalgorithms, several limitations are distinguished within the TTF with a slight increase in accuracy.  
power transformer protection; turn-to-turn fault; negative sequence current +
Footnote †: 10.000
## 5 Discussion
### Occurrence Modes for Arsenic in Coal and Its Influence on the Environment
#### 5.4.2 The Impact of Arsenic in Coal on the Environment  
Understanding the occurrence modes of arsenic in coal is of great significance because it has significant impacts on coal mining, preparation, combustion, and utilization, and has adverse effects on the environment and human health.  
Figure 6: Distribution of enriched elements and sources of sediments in the Santanghu Basin (modified from [PERSON] et al., 2018) [25].  
During the mining process, the dust produced by coal mining may contain arsenic. Long-term inhalation of arsenic-containing dust by people engaged in mining operations may lead to chronic arsenic poisoning [8; 9]; in addition, untreated mine water containing arsenic may also affect groundwater and even surface water.  
During the preparation stage, if the coal-washing wastewater generated from coal washing and selection is not properly treated, it will also cause water pollution. Arsenic that cannot be completely removed by washing (such as in organic form or in the form of fine mineral particles) will affect the subsequent combustion process [41].  
During the coal combustion process, arsenic has a relatively high volatility. Most of it enters the atmosphere along with the flue gas, while a small portion remains in the ash and slag. The release form of arsenic (for example, gaseous As\({}_{2}\)O\({}_{3}\), As\({}_{2}\)O\({}_{5}\), or a granular adsorbed state) directly affects its environmental migration ability [8; 9; 41].  
During the utilization stage, the ash produced by coal combustion is widely used in the construction industry, but the arsenic in coal limits its safe and clean utilization. In the industry of coal chemical utilization, arsenic in coal can inhibit the activity of catalysts, thereby affecting production efficiency.  
In summary, arsenic in coal has the following impacts on the environment. (1) Air pollution: Coal-fired power plants, industrial boilers, and the burning of loose coal by residents are the main sources of arsenic emissions. Arsenic is oxidized to gaseous oxides at high temperatures and then adsorbed on fine particulate matter (PM\({}_{2.5}\)) through condensation, forming pollutants that can be transported over long distances. In addition, arsenic enters the surface through dry and wet deposition. Especially in areas with dense coal burning (China, India), atmospheric arsenic deposition can significantly increase the arsenic load of the surrounding soil and water bodies. (2) Water and soil pollution: When coal ash is stored in the open air or landfilled, rainwater leaching leads to arsenic leaching, polluting groundwater and surface water. (3) Human health risks: Residents in coal-burning areas may suffer from chronic arsenic poisoning by breathing arsenic-containing particulate matter, drinking contaminated groundwater, or consuming high-arsenic crops [5].
## 3 Methodology
### Determination of the Properties, Parameters, Input Variables and Initial Conditions for the Model
#### 3.3.2 Determination of the Density and Heat Capacity of the Reaction Mixture  
The prediction method used to estimate the thermo-physical properties of the reaction mixture components is essential to obtaining accurate estimations. [PERSON] and [PERSON] evaluated the methods for predicting ten essential properties of TG, DG, and MG from feed oils for biodiesel manufacturing, recommending the most appropriate methods for predicting each property based on accuracy, consistency, and generality [47]. The present study used these recommendations to estimate the densities and heat capacities of the glycerides (see Table 1). Moreover, the liquid heat capacity of fatty acid methyl esters was estimated by means of a group contribution method developed forfatty compounds presented in the biofuel industries [49]. A general-purpose group contribution method was used to determine the liquid density of the esters and glycerol [50], while the specific heat capacity for glycerol was computed using an algebraic equation adjusted using glycerol heat capacity measurements of 298 to 383 K [51]. The liquid density of the methanol was calculated using a modified Rackett equation [52], with the specific heat estimated using an algebraic equation [53]. Table 1 summarizes the methods used to both characterize the glyceride and ester molecules and estimate the thermo-physical properties of each component of the reaction mixture. The density and specific heat to every reaction component were estimated at 60 \({}^{\circ}\)C.  
The properties of the mixture were calculated from the properties of each reaction component using the ideal mixing rule based on the mass fractions according to Equations (38) and (39):  
\[\rho = \sum X_{n,i}\rho_{n,i}, \tag{38}\]  
\[c_{p} = \sum X_{n,i}c_{p,n,i}, \tag{39}\]  
where \(X_{n,i}\) is the mass fraction of \(n\)-th component in the mixture, \(\rho_{n,i}\) and \(c_{p,n,i}\) are, respectively, the density and heat capacity of each reaction component (including all components formed in the cross-linking reactions).
## 2 Materials and Methods
### GEDI Level 4A Data
#### 2.5.1 Aboveground Biomass Density and Canopy Height Analysis  
The correlation between AGBD and CHM data was assessed using all GEDI pulses acquired in Italy over areas where ALS data were available, resulting in 3,107,244 pulses. When the datasets are very large raw data, interpretation may be difficult, and data aggregation is needed [72; 73]. For this reason, firstly, CHM and AGBD were ordered by CHM values and then aggregated by averaging into groups of size 25. Then, the coefficient of determination (r\({}^{2}\)) between aggregated AGBD and CHM was calculated [72; 73]. The analysis was also repeated separately for each Italian region (NUTS2) to identify relevant differences in CHM-AGBD correlations among regions.
###### Abstract  
This study aimed to analyze the structural requirements for di- and tripeptides exhibiting a DPP IV-inhibitory effect. The sequences of 46 di- and 33 tripeptides, including their bioactivity (IC\({}_{50}\); uM), were implemented from the BIOPEP-UWM database, whereas modeling was performed using SCIGRESS Explorer: Version FJ 3.5.1 software. Models included 336 (dipeptide dataset) and 184 descriptors (tripeptide dataset). The values of the determination coefficient (R\({}^{2}\)) defining model reliability were 0.782 and 0.829 for di- and tripeptides, respectively. Based on the implemented descriptors, it was concluded that increased numbers of nitrogen atoms, as well as the methyl groups, are required for dipeptides to enhance the DPP IV-inhibitory effect. This was indicated by the presence of amino acids with an aliphatic side chain (e.g., Leu, Val, Ile) and an aromatic ring (Trp). In the case of tripeptides, a correlation was found between their molecular weight (MW) and studied bioactivity. A tripeptide with a molecular weight of up to 500 Da was found suitable for the sequence to act as the DPP IV inhibitor. Although there is still a gap in explaining the relations between the structural nature and the DPP IV-inhibitory activity of peptides, and certain issues related to this topic still remain unknown, the results are in line with those reported by other authors. Additionally, the suitability of the SCIGRESS tool in the QSAR analysis of peptides derived from foods can be confirmed. Interpretable descriptors enabled the achievement of more unequivocal results concerning the main structural factors affecting the DPP IV inhibition of di- and tripeptides.  
DDP IV inhibitors; BIOPEP-UWM database; structure; QSAR Article
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 3 Inter-Satellite Links Pointing and Tracking in-Orbit Test Method
### Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking
The angular velocity can be obtained as \(2\pi/T_{o}\)  
Figure 2: Indirect test process for single satellite’s inter-satellite links pointing and tracking. While tracking the virtual target satellite, when the beam points to the virtual target satellite and ground station simultaneously, the single local satellite transmits signals to ground stations A and B. In this situation, a high-confidence test for a single satellite's inter-satellite link pointing and tracking can be executed. Note that in practical missions, for inter-satellite links with larger beam angles, the time needed for constructing a link to the ground station is longer; for laser inter-satellite links, the link-construction time is shorter. The proposed method can test both kinds of link pointing.
# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 3 Materials and Methods
### Methods
#### 3.2.3 Training Steps
Two of the paths involve passing \(f_{t}\) through the fully connected layers \(FC_{1}\) and \(FC_{2}\) trained from two different source centers (for simplicity, the multiple fully connected layers within each center are described here as a whole; in the actual training, they are still two layers of fully connected layers), whose output features are then passed to two expert classifiers \(C_{1}\) and \(C_{2}\). These classifiers are trained to make highly accurate classification predictions for their respective source center data. The third path includes a well-trained domain discrimination expert \(D\), which assesses the probability \(p_{1}\) and \(p_{2}\) that each sample belongs to source center 1 or source center 2. When making predictions, the model does not simply choose the source center with the highest probability or average them, but weights the outputs of the two expert classifiers according to the probability assessment of the domain discrimination expert. This means that the output of each classifier is weighted by the probability of its corresponding source center being chosen as the correct source, and the weighted sum is then taken to obtain the final prediction \(y\). ```
Input: Target center data \(\{x_{i}\}\); feature extractor \(G\); expert classifiers \(\{C_{1},C_{2}\}\); multicenter domain adaptation layers \(\{FC_{1},FC_{2}\}\); domain discriminator expert \(D\). 1: Extracting target domain image features \(f_{t}=G(x_{t})\) through G.
2: The features are obtained through the fully connected layers trained by two source centers \(f_{t1}=FC_{\text{s1}}(f_{t})\), \(f_{t2}=FC_{\text{s2}}(f_{t})\). 3: The outputs of the two classifiers are obtained as \(o_{1}=C_{1}(f_{t1}),o_{2}=C_{2}(f_{t2})\). 4: The prediction outputs (probabilities) \(p_{1}\) and \(p_{2}\) are obtained from the domain expert discriminator \(D\) as \(p_{1},p_{2}=D(f_{t})\). 5: The prediction output is calculated as the weighted sum of the two classification results \(y=p_{1}\cdot o_{1}+p_{2}\cdot o_{2}\). ```  
**Output:** The label prediction \(y\)  
**Algorithm 2** Testing the WUCF
## 4 Simulation and Experimental Results  
Simulations and experiments were tested to validate the performance of the proposed method. The parameters listed in Table 1 were used. The control gains were obtained using \(Q_{c}=diag(0.5,500,000,5000,100,100)\), \(R_{c}=diag(1,1)\), \(Q_{o}=diag(50,10,10)\), and \(R_{o}=diag(1,1)\) The desired position \(\theta_{r}(t)=sin(\pi t)(1+e^{-10t})\) used in the simulations is shown in Figure 3.  
Figure 3: Desired position.  
Figure 2: Block diagram of the proposed method.
## 1 Introduction
DRAINMOD is the most commonly used process-based model to simulate tile drainage systems, subsurface hydrology, and nitrogen and phosphorus dynamics in agricultural fields [2, 13, 14, 15, 16]. A few studies were conducted on the uncertainty analysis of hydrological and water quality parameters using DRAINMOD. For instance, [PERSON] [17] made a relative sensitivity analysis of the DRAINMOD hydrological parameters in the study on tile drain spacing optimization in four fields in the Little Vermilion watershed, Illinois. [PERSON] [9] performed a two-step sensitivity analysis using global variables to assess the sensitivity of nitrate losses on drainage using model prediction. Similarly, recently, [PERSON] [15] made a sensitivity analysis for DRAINMOD-H and DRAINMOD-N modules using the Morris screening. The degree of the sensitivity of these parameters, however, differed. For example, [PERSON] [17] considered maximum surface storage an insensitive parameter, but [PERSON] [15] considered it a sensitive parameter for drainage flow. The possible reason for the differing results may be the variation in field conditions and soil types which the authors still need to explain in their study. The researchers suggested that model calibration and uncertainty analysis based purely on one variable does not ensure the reliability of hydrological models since water balance elements might need to be more accurately represented [18]. The multi-objective calibration and sensitivity analysis of the hydrological models may reduce the uncertainty and help with the issue of equifinality in the model calibration process [19]. However, a few studies were performed regarding the multi-objective sensitivity analysis of DRAINMOD. So, this study aimed to enhance the model calibration and reduce the equifinality and parameter uncertainty of DRAINMOD using multi-objective sensitivity analysis of 17 hydrological parameters. The authors analyzed the local sensitivity of the DRAINMOD hydrological parameters for multiple objective functions representing drainage flow, water balance, and relative yield. Moreover, a comparative sensitivity evaluation was performed for two fields with distinct drainage designs, soil types, and other field conditions.
###### Abstract  
The Collective Detection (CD) technique is a promising approach to meet the requirements for signal acquisition in GNSS-harsh environments. The CD approach has been proposed because of its potential to operate as both a direct positioning method and a high-sensitivity acquisition method. This paper is dedicated to the development of a new CD architecture for processing satellite signals in challenging environments. It proposes the best signal acquisition method used according to the reception conditions of the different receivers that can assist the user in difficulty. Knowing that the CD approach is beneficial in the case where the maximum of satellite signals can be combined, the proposed approach consists in choosing the best receiver(s) from several connected receivers to serve as a reference station, as smart cooperative navigation concept. New metrics of the CD with optimal weighting of visible satellites are exploited. Analysis of optimization method in order to use better satellites according to some defined parameters (elevation, C/N\({}_{0}\), and GDOP) were carried out. Real GPS L1 C/A signals are exploited to analyze the efficiency of the proposed approach. A comparison of the results through the accumulation of some good satellites among all visible satellites have shown the effectiveness of this method.  
collective detection; high sensitivity; cooperative positioning; cognitive GNSS receivers; weak signals; challenging environments +
Footnote †: journal: Sensors
# Comparative Vulnerability Analysis of Thai and Non-Thai Mobile Banking Applications
## 4 Results and Discussion
### Overall Vulnerability Assessment  
Table 3 provides an overall assessment of the vulnerabilities found in each of the mobile banking applications using the three analysis tools, namely AndroBugs, MobSF, and QARK.  
Our analysis of mobile banking applications from Thai and non-Thai banks revealed a concerning occurrence of security vulnerabilities across all examined applications. Using three different vulnerability assessment tools (AndroBugs, MobSF, and QARK), we evaluated the presence of the OWASP Mobile Top 10 vulnerabilities (M1-M10), which can be described as follows.  
The most frequently found vulnerabilities across all examined applications were M9 (reverse engineering), detected in 85.2% of cases, followed by M8 (code tampering) at 77.8%, and M5 (insufficient cryptography) at 74.1%. Conversely, M2 (insecure data storage) was the least common, present in only 14.8% of cases. This suggests that while banks have made efforts to ensure that third-party libraries are secure, they may be neglecting other critical areas of mobile application security. To quantify the occurrence of vulnerabilities,  
Figure 1: Research Methodology.  
we conducted a statistical analysis across all nine applications. The analysis is shown in Table 4 in the order of occurrence, together with the precision of our estimate based on the 95% confidence interval.