esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### THz Wave Time-Domain Spectroscopy in the Transmission/Reflection Directions  
THz wave emission in the reflection and the transmission directions, which are the front side and the back side of the flow, respectively, was recorded simultaneously by EO sampling with a \(\langle 110\rangle\)-oriented ZnTe crystal (1-mm thick, Nippon Mining & Metals Co., Ltd. Tokyo, Japan). This setup is an update from the previous one [32], which has shortened the experimental time much. Briefly, the emission was collected by an OAPM (\(f=101.6\) mm, MPD249-M01, ThorLabs, Newton, NJ, USA) in reflection and by another OAPM (\(f=152.4\) mm, MPD269-M01, ThorLabs, Newton, NJ, USA) in transmission. The probe for TDS was split into two beams spatially with a d-shaped mirror (PFD05-03-M01, ThorLabs, Newton, NJ, USA) which was set on a manual stage (ODL2) in order to match the time zero between the reflection and the transmission directions. The difference of the detection efficiencies in the reflection and the transmission directions was calibrated as follows; THz emission from a 1-mm thick \(\langle 110\rangle\)-oriented ZnTe crystal set before the OAPM1 was split into the transmission and the reflection components by an undoped silicon thin film as a THz beam splitter or bent by a conventional gold mirror placed at the water flow position. By measuring the TDS signal of THz wave in each direction, the difference of the detection efficiencies  
Figure 1: (**a**) The experimental setup for THz wave with X-ray detections in the transmission and the reflection directions with time-domain spectroscopy (TDS). ODL1, optical delay for TDS. ODL2, optical delay for time matching between the transmission and the reflection directions. L, plano-convex lens (\(f=50\) cm). The thickness of \(\langle 110\rangle\)-oriented ZnTe crystal for TDS was 1 mm. The parent focal lengths for the off-axis parabolic mirrors (OAPMs) were \(f=50.8\) mm (OAPM1, 1-inch diameter), 101.6 mm (OAPM2, 2-inch diameter), 152.4 mm (OAPM3, 2-inch diameter), and two 101.6 mm (2-inch diameter with a hole in its center for the probe) respectively. GC, Geiger counter. The inset shows the geometrical configuration of the flow, the laser incidence, and ablation phenomena in the nanosecond time delay. (**b**) TDS signals from a 1-mm thick \(\langle 110\rangle\)-oriented ZnTe crystal though air and the water flow at 60 degrees to the laser incident angle. The inset represents the TDS signals near the zero delay. (**c**) Emission spectra in the THz frequency calculated from the TDS signals (**the top**) and calculated absorption spectrum of the water flow as \(\log\) (\(\left.\left|\,\boldsymbol{E}\,\right|_{dir}\,^{2}/\left.\left|\,\boldsymbol{E} \,\right|_{water}\,^{2}\right)\right)\) (**the bottom**). The arrow indicates the absorption peak at 0.56 THz [45].  
in the two directions was calibrated. With this system setup using the 1-mm thick \(\langle 110\rangle\)-oriented ZnTe crystals as detectors for EO sampling, the detectable frequency range extends to \(\sim\)3 THz at the highest [14].
## 4 Numeric Analysis  
To explore the influence of beam structure parameters and seawater parameters on BER and the channel capacity of OAM links in isotropic turbulent as well as absorbent seawater, in this section, we numerically study the BER and channel capacity of the link with a RCDPPGV carrier. In the following analysis, we set the calculation parameters as: \(|\,\alpha\,|=1,\,w_{0}=0.05\,\mathrm{m},\,D=0.1\,\mathrm{m},\,z=100\,\, \mathrm{m},\,\lambda=561\,\,\mathrm{nm},\,\chi_{l}=10^{-8}\,\,\mathrm{K}^{2}/ \mathrm{s},\,\epsilon=10^{-4}\,\,\mathrm{m}^{2}/\mathrm{s}^{3},\)\(\eta=1\,\mathrm{mm},\,l_{0}=1,\,L_{0}=10\,\mathrm{m},\,\omega=-4,\,M=5,\,n_{r}=1.34,\) and \(n_{i}=0.6741\times 10^{-10}\), unless other variable parameters are specified in calculation. Moreover, in the following numerical analysis, we first analyze the influence of system parameters and turbulent seawater on the BER of OAM channels and, then, analyze the influence of system parameters and turbulent seawater on channel capacity.
## 2 Principle of Noise Reduction Based on Singular Value Decomposition (SVD)
### Singular Value Decomposition (SVD) Algorithm  
The noisy signal sequence is as follows:  
\[x\ =\ \{x_{1},\,x_{2},\,x_{3},\,\ldots,\,x_{n}\} \tag{1}\]  
where \(n\) is the length of the sequence \(x\). Assuming that the mixed signal is a linear superposition of signal and noise, then the trajectory matrix A can be reconstructed by identifying the refactoring dimension. The rules for dimension \(p\) are defined as follows [18]:  
\[p\ =\ \left\{\begin{array}{cc}\dfrac{n}{2}&n\ =\ 2k\\ \dfrac{n-1}{2}&n\ =\ 2k+1\end{array}\right. \tag{2}\]  
\(k\in\) N*, formula (2) shows that the value of dimension can be defined according to \(n\), \(k\) is a non-negative integer. When \(n\) is an even, the dimension \(p\) equals to half of \(n\), otherwise, the dimension \(p\) equals to half of \(n-1\). The Hankel matrices \(H\) of the original signal with dimension \(p\) are defined as follows:  
\[H=\left[\begin{array}{cccc}x_{1}&x_{2}&x_{3}&\cdots&x_{\rm q}\\ x_{2}&x_{3}&x_{4}&\cdots&x_{\rm q+1}\\ &\cdots&&&\\ x_{\rm p}&x_{\rm p+1}&x_{\rm p+2}&\cdots&x_{\rm p+q}\end{array}\right] \tag{3}\]  
where \(1<q<n\), \(p=N+1-q\); \(H\in R_{p\times q}\). The existing orthogonal matrices \(u=(u_{1},\,u_{2},\,u_{3},\,\ldots,\,u_{\rm p})\), \(v=(v_{1},\,v_{2},\,v_{3},\,\ldots,\,v_{\rm p})\) can be expressed as shown in Equation (4):  
\[H\ =\ U\times S\times V \tag{4}\]  
It can be seen from the data structure of the Hankel matrix in Equation (3) that there is a unit difference of phase between two adjacent row vectors. For the pure signal, any two adjacent row vectors are highly correlated in the Hankel matrix. \(S=\) diag(\(\alpha_{1},\,\alpha_{2},\,\alpha_{3},\,\ldots,\,\alpha_{\rm p}\)) is the sequence of the singular values whose elements are sorted in descending order. Among them, the front large singular values are part of the useful signal, and several smaller singular values belong to the noise. The modified singular matrix \(S^{\prime}\) contains singular values of a useful signal obtained by setting the lowest singular values to zero and reconstructing the trajectory matrix, \(H^{\prime}\), on the basis of Equations (5) and (6):  
\[H^{\prime}\ =\ U\times
## 4 Initial Analyses and Findings
### The Factor Analysis
#### 4.3.1 Factor Extraction
The first one includes the consistency limits, montmorillonite, and clay content. The second one represents the calcium carbonate content. The first factor explains 69.61% of the data and the second one explains 14.53%. This concludes that 84.14% of the total variance can be explained by considering only two factors which are reduced from five variables. Additionally, as mentioned before, the factors are divided and labelled into two that explain the soil plasticity characteristics both positively and negatively (Table 4).
# Respiration Symptoms Monitoring in Body Area Networks
###### Abstract  
This work presents a framework that monitors particular symptoms such as respiratory conditions (abnormal breathing pattern) experienced by hyperhyreosis, sleep apnea, and sudden infant death syndrome (SIDS) patients. The proposed framework detects and monitors respiratory condition using S-Band sensing technique that leverages the wireless devices such as antenna, card, omni-directional antenna operating in 2 GHz to 4 GHz frequency range, and wireless channel information extraction tool. The rhythmic patterns extracted using S-Band sensing present the periodic and non-periodic waveforms that correspond to normal and abnormal respiratory conditions, respectively. The fine-grained amplitude information obtained using aforementioned devices is used to examine the breathing pattern over a period of time and accurately identifies the particular condition.  
S-Band sensing; wireless channel information; hyperhyreosis/sleep apnea disease monitoring +
Footnote †: journal: Applied Sciences
## 2 Models and Methods
### Solar Irradiation Model and Mission Conditions
#### 2.4.2 Calculation Model of Solar Radiation Intensity in the Body Coordinate System  
This paper defines the aircraft body-fixed frame as \(O_{b}X_{b}Y_{b}Z_{b}\), where \(O_{b}X_{b}\) is located in the symmetry of the aircraft and points forward parallel to the fuselage axis, \(O_{b}Y_{b}\) points to the left perpendicular to the symmetry plane, \(O_{b}Z_{b}\) is expressed out to point upwards and perpendicular to \(O_{b}X_{b}\) and \(O_{b}Y_{b}\), as shown in Figure 3. Therefore, the solar radiation intensities \(I_{Xb}\), \(I_{Yb}\), and \(I_{Zb}\) received in aircraft body-fixed frame can be obtained by the attitude angle (yaw angle \(\varphi\), pitch angle \(\gamma\), roll angle \(\psi\) ) through coordinate transformation, as shown in Equation (12):  
\[\left[\begin{array}{c}I_{Xb}\\ I_{Yb}\\ I_{Zb}\end{array}\right]=\left[\begin{array}{ccc}\cos\gamma\cos\varphi&\cos \gamma\sin\varphi&-\sin\gamma&I_{Xg}\\ \sin\gamma\sin\psi\cos\varphi-\cos\psi\sin\varphi&\sin\gamma\sin\psi\sin\varphi+ \cos\psi\cos\varphi&\sin\psi\cos\gamma\\ \sin\gamma\cos\psi\cos\varphi+\sin\psi\sin\varphi-\sin\psi\cos\varphi&\sin \gamma\cos\psi\sin\varphi&\cos\psi\cos\gamma\end{array}\right] \tag{13}\]  
Consequently, a solar irradiation model is established in a MATLAB file for later model integration, which can calculate solar radiation intensity with the altitude, date, longitude, latitude, moment, roll angle, pitch angle and yaw angle.
###### Abstract  
On average, one hundred people die each year under avalanche snow. Despite extensive global research on gas exchange in buried avalanche victims, it remains unclear how the diffusion of respiratory gases affects survival under avalanche snow. This study aims to determine how oxygen and carbon dioxide diffuse through snow, as well as through wet and dry perlite, which may serve as a surrogate for avalanche snow. A custom-made apparatus to study the diffusion of respiratory gases consisted of a plastic cylinder (1200 mm long, ID 300 mm) with 13 gas sampling needles evenly spaced along the axis of the cylinder filled with the tested material. Following 60 min of free diffusion, gas samples were analyzed using a vital signs monitor with a module for respiratory gas analysis (E-CAiOVX, Datex-Ohmeda, GE Healthcare, Chicago, IL, USA). A combination of 16% oxygen, 5% carbon dioxide, and 79% nitrogen was used. The rates of diffusion for both respiratory gases were comparable in snow and both forms of perlite. Oxygen propagated faster than carbon dioxide. Due to similar diffusion characteristics to snow, perlite possesses the potential to stand in as an effective substitute for soft snow for the study of respiratory dynamics, for conducting breathing experiments, and for testing avalanche safety equipment.  
diffusion; diffusion coefficient; oxygen; carbon dioxide; snow; avalanche; safety equipment; perlite +
Footnote †: journal: Applied sciences
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 5 Experimental Setup
### Modified Gravitational Search Algorithm with Two Fitness Functions
Routing complexity is reduced by using MGSA with two FF, as well as data aggregation using MGSA-with two elected relay nodes. These metric results are shown in Figure 17, where the selection of the CH is determined by sink distance parameters, and then reducing the communication overhead between the sink and CH. The inputs of data deliveries on over time due to packet failures and delays, inappropriate relay node election which are frequently affecting packet delivery ratio by electing the relay nodes through the inputs of transmit capability and simulation outcome is shown in Table 9.  
Figure 16: Flow of proposed factors by Throughput. Figure 17: Overhead. \begin{table}
\begin{tabular}{c c c c c} \hline
**TIME** & **FEARM** & **EN—LEACH** & **PROPOSED—ONE FF** & **PROPOSED—TWO FFs** \\ \hline
300 & 2.900 & 2.500 & 2.0300 & 1.5600 \\ \hline
600 & 2.900 & 2.300 & 1.9300 & 1.5600 \\ \hline
900 & 2.700 & 2.300 & 1.9100 & 1.5200 \\ \hline
1200 & 2.600 & 2.200 & 1.8600 & 1.5300 \\ \hline
1500 & 2.600 & 2.200 & 1.8600 & 1.5200 \\ \hline \end{tabular}
\end{table} 
Table 8: Information regarding overhead proposal. The PDR is expressed as the ratio of fruitful data deliveries over time. Due to packet failures and delays, inappropriate relay node election frequently affects PDR. The MGSA-ORS with the FF method solves this problem by selecting relay nodes with sufficient data transmit capability. Simulation outcomes are shown in Figure 18, and they are related to the PDR; the percentage of PDR is greater than the already recommended techniques.
# A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
## 4 Experiments and Discussion
### SOTA Methods
#### 4.1.1 Reverse Distillation from One-Class Embedding for Anomaly Detection (RD4 AD)  
The Reverse Distillation from One-Class Embedding for Anomaly Detection (RD4 AD) [24] method comprises a pre-trained teacher encoder, a trainable one-class bottleneck embedding module, and a student decoder. This methodology leverages a multi-scale feature fusion block to amalgamate low- and high-level features extracted by the encoder and subsequently map them onto a compact code using the one-class embedding block.  
During the training phase, the RD4 AD method employs reverse distillation, wherein the student decoder endeavours to replicate the teacher encoder's behaviour by minimizing the similarity loss function. In the inference stage, the reverse distillation encoder extracts features, while the decoder generates anomaly-free representations. The detection of anomalies is predicated upon discerning low similarity between feature vectors from the encoder and decoder. The RD4 AD method computes the final prediction by aggregating multi-scale similarity maps.
## 1 Introduction
The method effectively improves resource utilization and reduces task latency, ultimately achieving stable resource scheduling and task offloading in dynamic environments by employing multi-objective optimization and gradient correction techniques, combined with gradient descent and distributed optimization. In this paper, our main contributions are as follows:  
1. To address the dynamic resource scheduling and task offloading optimization problem in satellite networks, we propose a dynamic resource scheduling framework based on distributed optimization, incorporating delay optimization and resource efficiency models. This method achieves the goal of effectively optimizing resource allocation and task scheduling in dynamic environments. 2. To address the task offloading and resource management problem in satellite time-space information networks, we propose a task offloading and resource allocation optimization method using a distributed optimization framework and Lagrange multipliers. By combining gradient descent and gradient correction techniques, the framework effectively solves the resource conflict and delay optimization issues. 3. To validate the effectiveness of the proposed method, we conducted experiments on multiple satellite network simulation datasets using a CSTK simulation platform and selected five baseline algorithms for comparison across four datasets. The experimental results demonstrate that our method outperforms existing methods in resource utilization and running time.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 5 Experimental Verification
### Identification of Cable Damage
#### 5.2.1 Location Identification  
The cable force of each cable was adjusted to 400 N, and the state can be considered as the completed state of the bridge. As shown in Figure 21 vehicles are added to the cable-stayed bridge to simulate concentrated loads. The front wheel corresponds to the cable (W-5). The mass of the vehicle and counterweight was summed to 47.17 kg, and the line of the main girder at this time was measured as the line under concentrated load in a healthy state. Then, the line of the main girder was simulated when the damage listed in Table 4 (Case1-Case3) occurred in cable W-4. Figure 22 shows the deflection values of the main beam under concentrated load, in the healthy state and after the W-4 damage, of 22.4%, 43.8% and 69.9% (Case1-Case3).  
Following the location identification method described in Section 3, the damage location identification results were obtained, as shown in Figure 23. From Figure 23a, the wavelet coefficient distribution curve of the first derivative of the deflection difference intersects the null point at the anchorage of cable W-4 and the main beam, the same as the preset damage location. From Figure 23b, the damage cable was W-3 and W-6, the same as the preset damage location.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 2 Abnormal Behaviors of Pedestrians in Public Places
### Definition and Classification of Abnormal Behaviors  
#### 2.2.1 Definition of Abnormal Behaviors  
Abnormal behaviors refer to the behaviors that individuals or groups exhibit that are significantly different from the normal social behavior patterns in crowded scenes. These behaviors not only include direct physical conflicts and dangerous actions, but also involve any atypical behaviors that may lead to public disorder, the spread of panic emotions, and damage to the safety of individuals or collectives. In a high-density environment, the combined effects of the density, mobility, emotional state of the crowd, and environmental factors make the identification and management of abnormal behaviors extremely complex and urgent. The ubiquitous characteristics of abnormal behaviors [11] include 1 suddenness, abnormal behaviors of pedestrians often occur suddenly without warning; 2 harmfulness, causing harm to the pedestrians themselves and triggering abnormal reactions of the surrounding crowd; 3 abnormal emotions [12], when the crowd faces a certain emergency, panic emotions arise; and 4 spatial abnormality: irregular flow and aggregation, which may cause safety accidents such as stampede.
## 4 Three-Dimensional Magnetic Field Modeling
### Sea of Galilee (Northern Israel)  
[PERSON] et al. [48] have demonstrated that the bottom relief morphology in the southern part of the sea testifies to a significant contrast from other zones of the basin. The magnetic field pattern in the sea's southern part also differs from the magnetic field behavior in the northern and central parts of the basin. The magnetic field 3D modeling along the interpreting profile (see also Section 5.1.1) indicates that a thick hidden basaltic plate causes this anomaly [49]. This inclined plate is characterized by the following parameters: magnetization of 3000 mA/m, vector inclination of 135\({}^{\circ}\), the dip of about 12\({}^{\circ}\) from NE (depth of upper edge = 1140 m) to SW (depth of upper edge = 760 m), length of 4000 m, and averaged vertical thickness of 480 m.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Method and Model
#### 2.2.2 Swarm-C Satellite Cone Shadow Modeling
Therefore, when the satellite enters the fully irradiated area, the value of the light radiation acceleration fluctuates little. However, it is still not a completely fixed value because, as the satellite's flight attitude changes, the effective area exposed to light will also have small changes, so the light radiation acceleration of the satellite will also have small fluctuations. Since the Antumbra area is very far from the Earth's center, the low-orbit satellites studied in this paper will not reach this area. The shadow can be divided into Umbra and Penumbra areas to simplify the original cone shadow model. Figure 6: Schematic diagram of conical shadow model. Figure 7: Light radiation acceleration (LRA) when Swarm-C after full-shadowed. (a) LRA along the orbit; (b) LRA in orthogonal directions; (c) LRA in the radial direction.
# Solving Multi-Document Summarization as an Orienteering Problem
## 3 Orienteering Problem  
The orienteering problem (OP) is an NP-hard problem which was introduced in 1987 by [PERSON] et al. [62]. Its name came from the orienteering sport [63], which is the game where the competitors must find a path by visiting some of the control points within a limited amount of time. Each control point has a score or profit. Each competitor should start at a certain control point and return to another one. The competitors try to maximize the total collected profit gained from the visited control points without exceeding the time budget constraint. OP belongs to the family of problems called traveling salesman problems (TSPs) with profits [64]. These problems are variants of the TSP where each vertex has a profit and the solution can include a subset of the existing vertices. The objective function of OP is to maximize the collected scores while the travel cost (e.g., time) is a constraint to satisfy (e.g., time to not exceed) [3]. In other words, the OP asks to find a path starting from the first vertex and ending at the last one that maximizes the total collected scores while the total traveling time does not exceed a predefined time budget.  
More formally, OP can be described as follows [3]. Given a graph \(G(V,A)\) where \(A\) is the set of the graph arcs, and \(V=\{v_{1},\ldots,v_{N}\}\) is the set of the \(N\) graph vertices (i.e., the set of all control points in the problem). Each vertex \(v_{i}\) has a profit \(p_{i}\). Each arc \(a_{ij}\) has a traveled time \(t_{ij}\). A binary variable \(x_{ij}\) represents a traversed arc \(a_{ij}\) from vertex \(v_{i}\) to vertex \(v_{j}\). OP asks to maximize the objective:  
\[Max(\sum_{i=2}^{N-1}\sum_{j=2}^{N}p_{i}x_{ij}) \tag{1}\]  
subject to the following constraints:  
\[\sum_{i=1}^{N-1}\sum_{j=2}^{N}t_{ij}x_{ij}\leqslant T_{max} \tag{2}\]  
\[\sum_{i=2}^{N}x_{1j}=\sum_{j=1}^{N-1}x_{iN}=1 \tag{3}\]\[\sum_{i=1}^{N-1}x_{ik}=\sum_{j=2}^{N}x_{kj}\leqslant 1;\forall k=2,\ldots,N-1 \tag{4}\]  
\[2\leqslant u_{i}\leqslant N;\forall i=2,\ldots,N \tag{5}\]  
\[u_{i}-u_{j}+1\leqslant(N-1)(1-x_{ij});\forall i,j=2,\ldots,N. \tag{6}\]  
The aforementioned objective function maximizes the total profit of the selected vertices. Equation (2) represents the time constraint by ensuring that the total traveled time does not exceed a pre-defined time budget \(T_{max}\). Equation (3) guarantees that the vertices \(v_{1}\) and \(v_{N}\) are selected as the first and the last vertices of the solution path, respectively. Equation (4) ensures the connectivity of the solution path and the uniqueness of its vertices. Equations (5) and (6) guarantee that the solution path does not contain sub-tours where \(u_{i}\) stands for the position of the vertex \(v_{i}\) in the path.