esa-sceva/satcom-chunk-collection huggingface dataset:
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 2 System Model  
### Network Model  
The layered LSN network architecture is shown in Figure 1. The control satellite layer is composed of high-orbit satellites denoted as \(S_{c}\), responsible for making aggregation decisions for the service satellites. The business satellite layer consists of \(M\) LEO satellites in low Earth orbit, responsible for handling specific businesses. We define the set of business satellites as \(\mathbb{S}=\{S_{1},\ldots,S_{i},\ldots,S_{k},\ldots,S_{M}\}\), where \(S_{i}\) and \(S_{k}\) represent any arbitrary business satellites. The user layer consists of various types of terminals located on the ground, which generate businesses and transmit them to the business satellites covering their region.  
The set of businesses received by the business satellite layer is defined as \(\mathcal{B}=\{B_{1},...,B_{i},...,B_{k},...,B_{M}\}\), where \(B_{i}\) and \(B_{k}\) are the businesses received by satellites \(S_{i}\) and \(S_{k}\), respectively. The set of business counts received by the business satellites is \(\mathcal{N}=\{N_{1},\ldots,N_{i},\ldots,N_{k},\ldots,N_{M}\}\), where \(N_{i}\) and \(N_{k}\) represent the number of businesses received by satellites \(S_{i}\) and \(S_{k}\), respectively. We define the sequence of businesses received by \(S_{i}\) as \(B_{i}=\{\mathbb{B}_{i1},...,\mathbb{B}_{ij},...,\mathbb{B}_{iN_{i}}\}\), where \(\mathbb{B}_{ij}\) is the \(j\)-th business received by \(S_{i}\). The attribute list for business \(\mathbb{B}_{ij}\) is defined as \(\mathbb{B}_{ij}=\{B_{ij},*\mathbb{r}_{ij}^{\text{max}}\}\), where \(B_{ij}\) is the data size of business \(\mathbb{B}_{ij}\), in bits, and \(\mathbb{r}_{ij}^{\text{max}}\) is its maximum tolerable delay, in seconds.  
The LEO satellite orbit model is given as follows:  
\[r(t)=(R+h)[1-e\cos\alpha(t)] \tag{1}\]  
where \(R\) is the Earth's radius, \(h\) is the altitude of the satellite, \(e\) is the orbital eccentricity, and \(\alpha(t)\) is the true anomaly angle.  
The distance between two satellites \(S_{i}\) and \(S_{k}\) is calculated as follows:  
\[\mathcal{D}_{ik}=\sqrt{d_{ik}^{2}+(h_{i}-h_{k})^{2}} \tag{2}\]  
where \(d_{ik}\) is the spherical distance between \(S_{i}\) and \(S_{k}\), and \(h_{i}\), \(h_{k}\) are their respective orbital heights.
# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 4 Practical Implications
### Injuries and Training Effects  
The studies by [PERSON] et al. [50], [PERSON] et al. [47], and [PERSON] and [PERSON] [42] each offer significant advancements with practical implications for coaches in tennis.  
[PERSON] et al. [50] introduced an approach to quantify hitting load using an automated stroke classification system. By leveraging inertial measurement unit (IMU) technology and machine learning algorithms, coaches can now accurately monitor players' hitting load, crucial for injury prevention and training optimization. This system enables coaches to tailor training programs, prescribe adequate rest periods, and mitigate the risk of overuse injuries, particularly in areas prone to strain, such as the lower back and shoulders.  
[PERSON] et al. [47] analyzed the challenge of diagnosing ACL injuries during gameplay by developing an AI-based video analysis system. By extracting biomechanical features from video footage and training neural network algorithms, the study demonstrated promising results in automatically detecting ACL injuries. This technology empowers coaches and medical staff to identify potentially serious injuries promptly, facilitating timely treatment and reducing the risk of further complications. Integration of this system into injury prevention protocols allows coaches to proactively safeguard players' long-term health and performance.  
[PERSON] and [PERSON] [42] proposed a predictive method for assessing athletes' training effectiveness using neural network algorithms. By analyzing various physiological and performance-related factors, such as heart rate and body composition, the study developed a model capable of accurately predicting training outcomes. This approach equips coaches with the tools to optimize training regimens tailored to individual athletes' needs, maximizing performance gains while minimizing the risk of overtraining or undertraining. Leveraging predictive analytics empowers coaches to make informed decisions that enhance players' development and performance potential on the court.  
Collectively, these studies offer coaches in tennis valuable insights and technological innovations to revolutionize injury prevention strategies and training optimization. With access to advanced tools and methodologies, coaches can safeguard players' well-being, optimize performance outcomes, and elevate their overall success in the sport.
# Dynamic Analysis of Underwater Torpedo during Straight-Line Navigation
## 3 Validation Cases  
In order to validate the correctness of the above derivation and OrcaFlex software, we carried out three case studies. The first case is the calculation of the Munk moment of a semi-ellipsoid. The dimensions of the semi-ellipsoid are as follows: the length of the long half-axis is 1 m, the length of the short transverse half-axis is 0.1 m, and the length of the short vertical half-axis is 0.1 m. The added mass of the semi-ellipsoid is \(m_{11}\) = 0.445 kg and \(m_{22}\) = 20.906 kg. According to the results of Reference [27], using a lift-free model, the Munk moment of the semi-ellipsoid with a sideslip angle of \(5^{\circ}\) was calculated under inviscid conditions, and the result was 1.687 N\(\cdot\)m. The Munk moment, calculated using the above-derived formula, is 1.794 N\(\cdot\)m. Both results are very close, with an error of 6.34%.  
The second case is the calculation of the Munk moment of a Wigley ship. The Wigley ship is a mathematical model commonly used in ship research internationally. The mathematical definition of the standard Wigley ship is as follows:  
\[y=2B\left[\frac{1}{4}-\left(\frac{x}{L_{pp}}\right)^{2}\right]\left[1-\left( \frac{z}{D}\right)^{2}\right] \tag{25}\]  
where \(L_{pp}\) is the length between the perpendiculars, \(B\) is the beam of the ship, \(D\) is the draft of the ship, and \(x\), \(y\), and \(z\) represent the three-dimensional point coordinates of the ship-type value. In this paper, \(L_{pp}\) = 2 m, \(B\) = 0.2 m, and \(D\) = 0.1 m.  
The added mass of the Wigley ship is \(m_{11}\) = 0.335 kg and \(m_{22}\) = 24.767 kg. According to the results of Reference [27], using a lift-free model, the Munk moment of the Wigley ship with a sideslip angle of \(5^{\circ}\) was calculated under inviscid conditions, and the result was 2.035 N\(\cdot\)m. The Munk moment, calculated using the above-derived formula, is 2.198 N\(\cdot\)m. Both results are very close, with an error of 8.01%.  
The third case is the calculation of the Munk moment of a trimaran with a square stern. The length, beam, and draft of the main hull of the trimaran are 7 m, 0.56 m, and 0.28 m, respectively. The length, beam, and draft of the side hull of the trimaran are 2.5 m, 0.12 m, and 0.1 m, respectively. The distance from the side hull to the main hull is 0.673 m, and the longitudinal distance from the side hull is 2.25 m. The added mass of the trimaran is \(m_{11}\) = 10.855 kg and \(m_{22}\) = 713.369 kg. According to the results of Reference [27], using a lift-free model, the Munk moment of the [PERSON] ship with a sideslip angle of \(5^{\circ}\) was calculated under inviscid conditions, and the result was 52.896\(\cdot\)m. The Munk moment, calculated using the above-derived formula, is 56.389 N\(\cdot\)m. Both results are very close, with an error of 6.61%.  
The above three cases have proved the reliability of the Munk moment equation derived in this paper.
# Numerical Simulation of Seepage Surface and Analysis of
## 3 Results  
### Three-Dimensional Seepage Back Analysis of the Penetration Coefficient  
To more accurately analyze the three-dimensional seepage field distribution law of the high fine-grained tailings dam under complex topographic conditions, the measured data of the phreatic line from ZK1 to ZK9 (the boreholes are distributed at different heights of the tailings dams) were used for carrying out the back analysis of the penetration coefficient and the infiltration characteristics of the current tailings dam (Table 3).  
The best solutions of the permeability coefficient for the tailings dam were obtained by back analysis using the finally developed model (Table 4). During the analysis of the current dam height, the seepage calculation was carried out using the best solutions of the permeability coefficient for seepage calculation, and the resulting infiltration line depth was compared with the measured depth in the field; we considered that the results of the inversion calculation were good when the absolute error and the relative error between the calculated infiltration line depth and the measured depth were limited to a certain range. Table 4 shows that the permeability coefficients of the tailings obtained from inversion analysis were smaller than those obtained from indoor tests, and the permeability coefficients of the tailings were also smaller than the reference values in \"Code for Design of Tailings Facilities\" (GB50863-2013). The main reason for this situation was that the role of tailings' impoundment drainage measures has been equivalently reflected in the best-fit model permeability coefficients. At the same time, because the tailings belong to the fine-grained tailings, and tailings less than 0.019 mm accounted for 38% of the total tailings mass and tailings less than 0.005 mm accounted for 15.5% of the total tailings mass, tailings with heavy clay content could led to the small permeability coefficient of the tailings.
## 3 System Overview  
### Total System Architecture  
The system architecture of the SOMS consists of four main sessions of mental monitoring: (1) Baseline settlement session; (2) Assessment session; (3) Sensing & monitoring session; and (4) Analysis session. Figure 1 shows the total system architecture.  
In the baseline settlement session, we obtain the psychological information of all patients using surveys and an offline medical test. The psychological information is obtained to assess the socio-demographical status, Internet usage status, smartphone usage status, Smartphone Addiction Proneness Scale (SAPS), depression status, anxiety status, impulsivity status, and self-control status of each patient. In the assessment phase, the patient information is processed using the Chi-square Automatic Interaction Detector (CHAID) algorithm [26]. Six important indicators--gaming costs, average weekday game usage, offline community, average weekend and holiday game usage, marital status, and perceived addiction--are assessed using the CHAID algorithm.  
After the assessment phase, the mobile device usage behavior of the patient is sensed and monitored via the mobile application (app), which is developed as a part of the SOMS. The general device usage, game usage periodical pattern, social network service (SNS) and Internet usage are monitored to obtain usage behavior evidence.  
The analysis session includes the total Internet dependency analysis of the patient. This session provides a conclusion for the overdependence usage status. Personalized feedback and treatment programs are developed.  
Considering the diagnosis based on scientific indicators, the system provides a feedback service for patients when intervention is necessary. The system is implemented to randomly selected adults nationwide and willingly participating middle school and high school adolescents.  
Figure 1: Total System Architecture of the Smartphone Overdependence Management System (SOMS).
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
## 2 Developed Simulative Model and Steps to Its Implementation
### Verification Measurements  
After the preparation measurements, the ring was measured in further positions to create the verifying data set. The 84 additional positions were selected with B changing at 20\({}^{\circ}\) between (\(-160^{\circ}\), 180\({}^{\circ}\)), and \(A\) changing at 15\({}^{\circ}\) between (0\({}^{\circ}\), 90\({}^{\circ}\)). The same measuring procedure was used. The results obtained during these measurements are used to check the difference between the simulated values and the empirically gathered data. The authors assumed that the PE value may be considered as simulated correctly if it differs from the corresponding value obtained experimentally not more than \(\pm\)3 * standard deviation assigned to the considered point.
## 3 Results and Discussion
### RUSLE Model
#### 3.1.1 Rainfall Erosivity (R) Factor
The R-factor values derived from GPM data ranged between 108.41 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) to 1286.11 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\); however, the R-factor values derived from rain gauge data had larger minimum and smaller maximum values, ranging from 240.248 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) to 545.536 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\). The variation between the R-factor map derived from GPM data and in situ data can be explained by the difference in the data used to derive R-factor values (satellite-based data vs. ground-based data). Additionally, the GPM rainfall product has a tendency to overestimate low precipitation and underestimate intense precipitation [49]. Nevertheless, GPM proved to be a better alternative in estimating rainfall compared to rain gauge interpolation in areas where gauges are sparse. Figure 5: Shaded relief map of Charia (western part of Crete Island) showing the locations and spatial distribution of the meteorological (rain gauges) stations used in the current research. \begin{table}
\begin{tabular}{c c} \hline \hline
**Station Name** & **R-Factor (MJ mm/ha h yr)** \\ \hline EloS & 288.429 \\ Paleocharo & 545.625 \\ Kandanos & 240.214 \\ Sebronas & 131.25 \\ Samaria & 958.875 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: R-factor values for the precipitation stations.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Estimation of the Number of Rounds Required  
In this section, we provide the number \(r\) of rounds that was used in the chaos-based cryptosystem implementation. For this, we determined the average Hamming distance (HD) between two ciphered images \(C_{1}\), using 100 secret keys, and \(C_{2}\) using the same secret keys, each with a different LSB-bit. This test was applied to 10 different plain images. The \(HD(C_{1},\ C_{2})\) was defined by the following equation:  
\[HD(C_{1},\ C_{2})=\frac{1}{Nb}\sum_{i=1}^{Nb}\left(C_{1}(i)\oplus\ C_{2}(i)\right) \tag{27}\]  
where \(Nb\) was the number of bits in an ciphered image. In Table 3, a list of the Hamming distance outcomes for \(r=1,\ 2,\ \text{and}\ 3\) is provided.  
It is noteworthy from Table 3 that for \(r\geq 1\), the HDs for the various plain images encrypted by the proposed encryption algorithm were close to the optimal value of 50%. Therefore, for all subsequent tests, we used \(r\ =\ 1\).  
This result was also confirmed by the plaintext sensitivity test carried out in Section 3.3.3.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 2 Methodology
### The Structure of Memory Bank-Based Methods (MBBM)
#### 2.2.2 Feature Fusion  
In pure image anomaly detection (AD) tasks, the issue of feature fusion is not inherently involved. However, DN2 is explored as a GAD problem: as shown in Figure 2, DN2 compares three methods of feature fusion, concatenate, max, and mean, and found that using the mean for feature fusion combined with the \(k\)NN anomaly score calculation algorithm resulted in the best ROCAUC performance. In contrast, the performance of the concatenate method in terms of ROCAUC first increased and then decreased with the enlargement of the group size. This experiment offers some inspiration for feature fusion methods in subsequent work.  
Figure 2: Performance comparison of anomaly detection methods using feature concatenation, max pooling, and mean pooling with varying numbers of images per group [34]. (Reprinted with permission from Ref. [34]. Copyright 2020 Copyright [PERSON]).  
In the anomaly score (AS) of methods derived from PatchCore, two variants of feature fusion appear: (1) on the MVTec-3D AD dataset, the PatchCore code upsamples the high-level feature map to the same resolution as the low-level features using bilinear interpolation, then maps different levels to the same dimension (e.g., hyperparameter 1024), concatenates them together, and finally maps them to a feature vector of a specific dimension. (2) On 3D datasets, BTF and M3 DM first use the mean method to perform domain feature fusion for different layer features (including RBB multi-scale features and geometric features), then the high-level feature map is upsampled to the same resolution as the low-level features using bilinear interpolation, and finally, the features from all layers are concatenated together to generate a feature vector.  
Overall, the fusion of features from the same modality, such as the fusion of features from the same spatial domain, can be approached as a GAD problem, where using the mean method can yield better performance, especially when many features are involved. On the other hand, for the fusion of features from different modalities, including different scales and different characteristics, concatenation is a more common choice. More sophisticated and complex feature fusion techniques, such as those used by M3 DM, are beyond the scope of this discussion.  
Referring to DNZ's work on GAD, in BTM we make the mean convolution kernel of size 3 and step 1 perform neighbor feature fusion, which is the same as in BTF. The difference is, given the feature alignment, we add padding of size 1 to the convolution kernel. For features other than nearest neighbor features, we simply use concatenate as a feature fusion method.
## 2 Materials and Methods
### ABTS\({}^{\bullet+}\) Radical Scavenging Capacity of Spice Extracts  
The antioxidant activity of spice extracts could be determined by methods, which reflect the various mechanisms of antioxidant action including the scavenging of free radicals (DPPH\({}^{\bullet}\) and ABTS\({}^{\bullet+}\)) or chelation of transition metal ions (FRAP method). The results on the DPPH\({}^{\bullet}\) radical scavenging activity and FRAP were presented previously, thus this paper includes the results of TEAC assay.  
The radical scavenging activity of extracts were assessed by the TEAC (Trolox Equivalent Antioxidant Capacity) assay [17]. The ABTS\({}^{\bullet+}\) radical cation was generated with potassium disulphate according to the modifications of [18]. Briefly, 7 mM ABTS in water was mixed with 2.45 mM potassium disulphate at a ratio of 2:1. To produce radicals the mixture was incubated in dark at room temperature for 12-16 h. Then, the ABTS\({}^{\bullet+}\) solution was diluted with PBS (phosphate buffer saline), pH 7.4 to an absorbance of 0.7 (\(\pm\)0.01). 990 \(\mu\)L of ABTS\({}^{\bullet+}\) solution was mixed with 10 \(\mu\)L of spice extract and, after 6 min of incubation in dark, the decrease in the absorbance of the mixture was read at 734 nm against blank sample (ABTS\({}^{\bullet+}\) solution with ethanol). The results were expressed as TEAC values in \(\mu\)M Trolox/g dry weight.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.4 PAS Domain  
PAS (Per-Arnt-Sim) domain-containing proteins are widely distributed among all domains of life. PAS domain acts as a sensor, generally found at the N terminus of sensory and signaling transduction related proteins, and detect a variety of stimuli and regulating the functions of a diverse array of effector domains [88; 89]. Members of the PAS domain family can bind a diverse range of small-molecule metabolites [22], which could either directly act as a signal and be involved in initiating a cellular signaling response [90], or which could serve as a cofactor and respond to subsequent messages like gas molecules, redox potential, or photons [39]. Although PAS domain-containing proteins are chemically and functionally diverse, almost all PAS domains have a conserved core comprised of a five-stranded antiparallel \(\beta\)-sheet and several \(\alpha\)-helices, which are responsible for the generation and propagation of a signal to the adjoining effector domain. In the present study, we have selected four proteins, three of them from different _Legionella_ strains and one from _Allochromatium vinosum_ DSM 180, respectively, having combinations of the BLUF and PAS sensor domain (Table 1). We performed the alignment of sequences representing the PAS domain in four different proteins against a well-characterized photoactivated yellow protein (PYP; 1 NW_Z) from _Halorhodospira halophila_[91] (Figure S3). As discussed earlier, PAS domains are structurally diverse; the same is revealed from the multiple sequence alignment analysis. Although, the PAS core (5' NAAEGDIT 3') in PYP is not conserved among other PAS domain-containing proteins, interestingly, in three of the selected PAS domains containing proteins for _Legionella_ genus, the sequences representing the PAS core are conserved (Figure S3). From the above observation, we could suggest that, although they are diverse in different organisms, PAS cores might be conserved in plants belonging to the same genus. We also performed a protein-protein interaction analysis, which revealed the possible interacting partners for PAS domain-containing proteins, which range from multisensor histidine kinase, CheA signal transduction histidine kinase, CheW protein, CheB methylesterase, 4-coumarate-CoA ligase, phenylalanine/histidine ammonia- lyase and Hpt sensor hybrid histidine kinase (Figure S7c and Table S1).
# Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics
## 4 Testing and Verification  
To improve the carrier phase accuracy under high dynamics, we improved INS-aided PLLs and analyzed the tracking error by simulation. To evaluate INS-aided PLL performance, our deeply-coupled system was tested under the field condition. The tracking and navigation results of stand-alone PLLs and MEMS INS-aided PLLs were compared.