esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Optimization Model
### Conjugate Gradient Method  
In comparison to SA method, which is not constrained with the shape of constrains and cost functions, the conjugate gradient (CG) method works with continuous differential functions. CG methods enable a local search, and because of this, continual cost functions with one optimum are recommended. It is based on finding a close local minimum of the function with n variables, with an assumption that the gradient of that function can be calculated [30].  
Successive approximations for the cost function f(x) minimum in the CG method [31] are generated using the iterative equation:  
\[\mathbf{x}^{(k+1)}=\mathbf{x}^{(k)}+\alpha_{k}\;\mathbf{d}^{(k)} \tag{11}\]  
where \(\alpha_{k}\) is optimal length of the step in direction \(\mathbf{d}^{(k)}\). The solution of the 1D optimization problem is:  
\[\min_{\alpha>0}\mathrm{f}\left(\mathbf{x}^{(k)}+\alpha_{k}\;\mathbf{d}^{(k)}\right) \tag{12}\]  
Gradient of cost function f (x) = f (x\({}_{1}\), x\({}_{2}\),..., x\({}_{n}\)) represents a vector:  
\[\mathbf{c}=\
abla\mathrm{f}(\mathbf{x})=\left[\frac{\partial\mathrm{f}}{ \partial\mathrm{x}_{1}}\frac{\partial\mathrm{f}}{\partial\mathrm{x}_{2}} \ldots\frac{\partial\mathrm{f}}{\partial\mathrm{x}_{n}}\right]^{\mathrm{T}} \tag{13}\]  
Important properties of the gradient vector are in the fact that the gradient in point x is directed in the direction of the maximal increasing of cost function. The maximal decreasing is in the opposite direction. It represents a negative gradient vector. A small step in the direction of negative gradient vector will result in maximal local decreasing of cost function. Negative gradient vector represents a direction of the maximal decreasing of cost function. It can be described as:  
\[\mathbf{d}=-\mathbf{c}\quad\mathrm{i}\mathrm{i}\mathrm{i}\mathrm{d}_{\mathrm{ i}}=-\mathrm{c}_{\mathrm{i}}=-\frac{\partial\mathrm{f}}{\partial\mathrm{x}_{i}} \;\mathrm{i}=1,\;2,\;\ldots,\;\mathrm{n} \tag{14}\]
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 3 Octonion Wigner Distribution
### Uncertainty Principle  
The general (Heisenberg) uncertainty principle has been formulated for the octonion Fourier transform in [44] (see Section 2). Since OWD is the OFT of \(h(\cdot;\mathbf{x})\), we can write that if \(h(\cdot;\mathbf{x})\in L^{2}(\mathbb{R}^{3};\mathbb{O})\) and \(s,\beta>0\), then there exists the constant \(C_{s,\beta}>0\) such that  
\[\left(\int_{\mathbb{R}^{3}}\lvert\mathbf{y}\rvert^{2s}\lvert h(\mathbf{y}; \mathbf{x})\rvert^{2}\,\mathrm{d}\mathbf{y}\right)^{\frac{\beta}{s+\beta}} \cdot\left(\int_{\mathbb{R}^{3}}\lvert\mathbf{\omega}\rvert^{2\beta}\Bigl{|}W^{R}_{ f,g}(\mathbf{x};\mathbf{\omega})\Bigr{|}^{2}\,\mathrm{d}\mathbf{\omega}\right)^{ \frac{s}{s+\beta}}\geq C_{s,\beta}\int_{\mathbb{R}^{3}}\lvert h(\mathbf{y}; \mathbf{x})\rvert^{2}\,\mathrm{d}\mathbf{y}.\]Integrating both sides with respect to \(\mathbf{x}\in\mathbb{R}^{3}\) and using the Holder inequality with \(p=\frac{s+\beta}{\beta}\) and \(q=\frac{s+\beta}{s}\), we conclude immediately with the following theorem.  
Theorem 4.2: _Let \(f,g\colon\mathbb{R}^{3}\to\mathbb{O}\) be such that for every \(\mathbf{x}\in\mathbb{R}^{3}\) we have \(h(\cdot;\mathbf{x})\in L^{2}(\mathbb{R}^{3};\mathbb{O})\). If \(s,\beta>0\), then there exists constant \(C_{s,\beta}>0\) such that_  
\[\left\|\mathbf{y}|^{s}h(\mathbf{y};\mathbf{x})\right\|_{L^{2}(\mathbb{R}^{3} \times\mathbb{R}^{3};\mathbb{O})}^{\frac{2\beta}{s+\beta}}\left\|\left| \boldsymbol{\omega}\right|^{\beta}W_{f,g}^{R}(\mathbf{x},\boldsymbol{\omega}) \right\|_{L^{2}(\mathbb{R}^{3}\times\mathbb{R}^{3};\mathbb{O})}^{\frac{2\beta }{s+\beta}}\geq C_{s,\beta}\|f\|_{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}\| \delta\|_{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}.\]  
The exact values of the \(C_{s,\beta}\) constants can be found in the literature (see [44]). In the above theorem, they remain the same.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 5 Cryptanalysis of [PERSON]'s Scheme
### Session Key Disclosure Attack  
An attacker must compute \(D_{M}\) and \(C_{M}\) to create the session key.
**Step 1:**: An attacker can eavesdrop on public-channel messages \(\{I_{M},PK_{M},A_{D},C_{M},H_{DM},T_{3}\}\) to obtain these values.
**Step 2:**: An attacker can compute \(D_{M}\) by performing an XOR operation on \(H_{DM}\) and the drone's public key, \(PK_{D}\).
**Step 3:**: Using the drone's identity, \(D_{M}\), and \(C_{M}\), the attacker can calculate \(SK_{D}\).  
Therefore, [PERSON]'s protocol cannot defend against session key disclosure attacks.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 3 The S-LMB-GOM Filter
### A Method to Overcome the Cardinality Estimate Bias  
#### 3.3.1 The Reason of The Bias  
New birth hypothesized tracks often violate the separable likelihood assumption [20] which leads to a cardinality estimate bias for the S-LMB-GOM filter.  
Each hypothesized track is updated independently and the probability density of each track is approximated by a group of particles in the state space. As shown in the Figure 1, when a new target \(\mathbf{x}\) is born, the particle group of the hypothesized track \(\ell_{1}\) surrounds the target \(\mathbf{x}\) and then \(\mathbf{x}\) is subsequently tracked by track \(\ell_{1}\). The particle group of \(\ell_{1}\) always surrounds \(\mathbf{x}\) until the target dies. If the particle group of another birth hypothesized track \(\ell_{2}\) also surrounds the target before the target died, the target \(\mathbf{x}\) will be detected again and an increase of cardinality estimate will occur.  
The reason is that the target \(\mathbf{x}\) is surrounded by multiple particle groups of different tracks which makes the measurements affected by the target \(\mathbf{x}\) associate with multiple tracks, then the target will be detected repeatedly. Eventually, a cardinality estimate bias occurs.
# PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
## 2 Related Work
### Self-Attention Mechanism  
The Self-Attention mechanism originally originated in the field of NLP [10], and the emergence of the Transformer [9] model led to a new trend in the academic community. The two-dimensional image field was the first to innovate, and many methods began to use the Self-Attention method to replace the previous mainstream CNN method [27; 28; 29; 30]. Then, the field of target detection also began to use the Self-Attention mechanism as the core feature extractor [15; 31; 32]. Afterward, researchers began to apply the Self-Attention mechanism to major processing tasks of point cloud data [10].  
PointCloudTransformer (PCT) [33] completes the task of point cloud classification by obtaining the global context of the input and abandoning the downsampling operation. PATs [34] propose the Group Shuffle Attention (GSA) mechanism to replace the expensive overhead MultiHead Attention (MSA), which enhances the flow of information between multiple Heads by adding channel shuffling in MSA.  
The models in the form of Local Transformer [35; 36; 37; 38; 39] strive to limit Self-Attention computation to local regions. LFT-Net [36] propose a trans-pooling model with four stacked transformer modules to strengthen the learning ability of fine-grained features. [PERSON] et al. [37] adopt the Transformer blocks to extract global and local features and combine them for better 3D detection results. [PERSON] et al. [39] convert the point cloud data into a voxelized grid style and perform local Self-Attention calculations in non-overlapping windows.  
The models in the form of Point-Wise Transformer [35; 40; 41] aim to discover natural spatial relationships of point clouds. PointTransformer [35] combines the multi-scale idea with the Self-Attention method in the field of point cloud processing. It reduces the parameters in the Transformer hierarchy by downsampling the input step by step. [PERSON] et al. [41] constructed a hierarchical point-wise structure for classification tasks and applied a local feature extraction module to complete the feature extraction work.  
The models in the form of Channel-Wise Transformer [42; 43; 44] are dedicated to mining the similarities and associations between different dimensions. DT-Net [43] uses both Point-Wise Transformer and Channel-Wise Transformer for feature extraction and then splices the two for subsequent calculations. TCE [44] has combined both the coordinate channel and feature channel to generate a channel-wise transformer.  
By using directed attention to learn points to achieve the effect of feature enhancement, the performance of point cloud downstream tasks can be improved [45; 46].  
The above works apply the Self-Attention mechanism to the processing tasks of point cloud data from the global or local perspective. Our work focuses on improving the perceptual ability of the model by applying the Self-Attention mechanism with the Shifted-Window.
## 4 Anti-Cancer Activities of Tanshinones
### Synergy with Chemotherapy and Radiotherapy  
Tanshinones have also been reported as a sensitizing agent for chemotherapy and radiotherapy. Combination of chemotherapeutic drugs with TIIA achieved significant higher inhibition rate on various cancer cells than the drugs alone. For instance, compared to cisplatin, 5-fluorouracil (5-FU) and oxaliplatin alone, combination of these drugs with TIIA significantly enhanced apoptosis and growth inhibition in HeLa human cervical cancer cells and SMMC-7721 liver cancer cells [271, 272, 273, 274]. In an _in vivo_ study, [PERSON] _et al._[275] reported that combination of cyclophosphamide (CTX, 20 mg/kg) and TIIA (15 mg/kg) achieved significantly better efficacy than CTX alone in a multi-drug resistance (MDR) lung cancer model. The enhanced efficacy was associated with induction of apoptosis and decreases of drug-resistant proteins including p-glycoprotein (P-gp), lung cancer resistance protein (LRP) and topoisomerase II (TOPOII) [275]. Similarly, CT at low uM levels enhanced the potencies of TNF-a, cisplatin, etoposide and 5-FU against cancer cells, in association with induction of endoplasmic reticulum (ER) stress and reactive oxygen species (ROS) production, as well as activation of ERK, JNK and p38 MAPK kinases [276,277]. DH-TI at low micromolar level (3.4 uM) could sensitize the anti-proliferative and pro-apoptotic effects of radiotherapy (2-8 Gy) on Hela cells [278]. Molecular changes associated with the sensitization included decreased expression of HPV E6, cyclin B\({}_{1}\) and cdc2, and increased expression of P21 and activation of caspase-3. _In vivo_ administration of DH-TI (10 mg/kg of DH-TI, i.p. injection for 22 days) significantly reduced tumor volume and weight of irradiated Hela xenografts in mice [278].
# Design and Analysis of a Floor Radiant Heating System Based on Energy Substitution Technology
## 6 Actual System Energy Savings Calculation and Analysis  
### Calculation  
The total heat of the proposed floor radiant heating system can be calculated based on this section. The total heat of the proposed floor radiant heating system can be provided by solar energy except in extreme cases. The heat supplied by solar energy is the energy saving relative to the traditional floor radiant heating system. The heat calculation of the proposed floor radiant heating system is shown below:  
1. For the normal operation of the proposed floor radiant heating system, the following conditions need to be satisfied for calculation purposes; these conditions exist in the actual system: 1. The heat transfer process is stable during the normal operation of the proposed floor radiant heating system. All heat transfer analyses occur under steady state conditions. 2. The room temperature is constant during the normal operation of the proposed floor radiant heating system. 3. The insulation material, with a certain thickness, is laid in the floor. The heat transferred to the underside through the insulation material and the heat lost from the floor are negligible. 4. The thermal contact resistances are negligible. The material quality of all layers is uniform, and the physical properties are the same and constant. 5. Hot water flow is stable in the photothermal floor radiant heating system; the heating cable current is stable in the photovoltaic floor radiant heating system. 6. The surface temperature of hot water pipes and heating cables is uniform. 7. The normal operation of the proposed floor radiant heating system does not involve starting, stopping or other non-normal process. 8. Calculation of heat calculation provided by floor radiant heating system According to heat transfer theory, instantaneous heat \(q(i)\) provided by the floor radiant heating system is as follows (Equation (3)): \[q(i)=q_{\mathrm{r}}(i)+q_{\mathrm{c}}(i)\] (3) where: \(q(i)\) is the instantaneous heat provided by the floor radiant heating system, \(\mathrm{W/m^{2}}\); \(q_{\mathrm{r}}(i)\) is the instantaneous radiation heat provided by the floor radiant heating system, \(\mathrm{W/m^{2}}\); \(q_{\mathrm{c}}(i)\) is the instantaneous convection heat provided by the floor radiant heating system, \(\mathrm{W/m^{2}}\). 9. The heat provided by the photothermal floor radiant heating system of the proposed floor radiant heating system is calculated. The heat transfer process of the photothermal floor radiant heating system of the proposed floor radiant heating system involves the following: hot water in the pipes transmits heat to the pipe wall first; then, the floor is heated and the floor transfers heat to the walls and indoor air through convection and radiation. The calculation of the heat transfer process, in the floor, follows classical heat transfer theory, which is not repeated here.
## 3 Results and Discussion
### Application to Wine Samples  
The selected nine analytes in four wine samples were identified and quantified to verify the applicability of our proposed method. As described in Section 2.4, analytes in four wine samples (i.e., white wine A and B, beer; and rice wine, respectively) were analyzed by CE-UV, as shown in the electropherograms in Figure 4A-D, respectively. All unidentified peaks were corresponding to compounds with absorbance at 200 nm. Table 3 summarizes the qualitative and quantitative analyses achieved by spiking with nine standard analyte mixtures in the rice wine sample. Two BAs (i.e., TA and Trp, respectively) were detected in white wine samples, and Trp and Tyr were detected in beer, but the concentrations of some analytes were extremely low for quantification (Table 3). The recoveries for the beer sample were 98.8-115.6%.  
Figure 4: Electropherograms of (**A**) white wine A, (**B**) white wine B, (**C**) beer, and (**D**) rice wine. Other conditions were the same as shown in Figure 2C. Peak identities: (**1**) TA, (**2**) Trp, and (**3**) Tyr.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**White Wine A**} & \multicolumn{3}{c}{**White Wine B**} & \multicolumn{3}{c}{**Beer**} & \multicolumn{3}{c}{**Rice Wine**} \\ \cline{2-13}
**Analyte** & \begin{tabular}{c} **Original** \\ **(uM)** \\ \end{tabular} & \begin{tabular}{c} **Recovery \({}^{\bf a}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **RSD** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **(uM)** \\ \end{tabular} & \begin{tabular}{c} **Recovery (\%)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **RSD** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **(uM)** \\ \end{tabular} & \begin{tabular}{c} **Recovery (\%)** \\ \end{tabular} & \begin{tabular}{c} **RSD** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **(uM)** \\ \end{tabular} & \begin{tabular}{c} **Recovery (\%)** \\ \end{tabular} & \begin{tabular}{c} **RSD** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **(uM)** \\ \end{tabular} & \
\begin{tabular}{c} **Recovery (\%)** \\ \end{tabular} &
\begin{tabular}{c} **RSD** \\ **(\%)** \\ \end{tabular} \\ \hline T & N.D. & & & N.D. & & N.D. & & & N.D. & & 111.6 & 2.89 \\
5-HT & N.D. & & N.D. & & N.D. & & & N.D. & & 101.5 & 2.32 \\ TA & 3.52 & 105.7 & 4.66 & 3.57 & 96.8 & 4.60 & N.D. & & & N.D. & 115.6 & 5.54 \\ DA & N.D. & & N.D. & & N.D. & & & N.D. & & 111.1 & 3.17 \\ Trp & N.D. & & N.D. & & 1.52 & 96.5 & 7.01 & N.D. & 98.8 & 1.25 \\ E & N.D. & & N.D. & & N.D. & & & N.D. & & 107.4 & 2.65 \\
5-HT & N.D. & & N.D. & & N.D. & & & N.D. & & 104.2 & 3.67 \\ NE & N.D. & & N.D. & & N.D. & & & N.D. & & 104.2 & 3.67 \\ Tyr & 2.23 & 99.6 & 2.65 & 3.59 & 104.0 & 5.02 & 3.95 & 104.8 & 8.29 & N.D. & 104.4 & 4.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analytical results in wine samples (\(n=3\)).
# Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
## 3 Satellite Network Capacity Calculation Method  
Assume that the number of orbits and the number of satellites in each orbit in \(\Psi_{A}\) are \(N\) and \(M\), respectively. The ISLs are connected in the \"+Grid\" mode, as shown in Figure 3. In \(\Psi_{A}\), the link rates of \(A(i,j)\) are \(\left(R^{\times^{\prime}}_{i,j,t},R^{\times^{\prime}}_{i,j,t},R^{\times}_{i,j, t},R^{\times}_{i,j,t}\right)\), respectively. \(A(i,j)\) sends data equally to all other satellites, i.e., the traffic rates sent by \(A(i,j)\) along the four directions are \(\frac{R^{\times^{\prime}}_{i,j,t}}{NM-1}\), \(\frac{R^{\times^{\prime}}_{i,j,t}}{NM-1}\), \(\frac{R^{\times^{\prime}}_{i,j,t}}{NM-1}\), \(\frac{R^{\times^{\prime}}_{i,j,t}}{NM-1}\), which can be denoted as \(\bar{R}^{\times^{\prime}}_{i,j,t}\), \(\bar{R}^{\times^{\prime}}_{i,j,t}\), \(\bar{R}^{\times}_{i,j,t}\), \(\bar{R}^{\times}_{i,j,t}\), respectively. The network capacity \(C_{A}\) of \(\Psi_{A}\) equals the total transmission rate \(R\) divided by the average hop count \(E(H)\).
## 1 Introduction
This study also builds on prior research examining the determinants of border crossings between Mexico and the U.S. This study makes three significant contributions to the literature. First, data on border crossings were retrieved from all the U.S. Ports of Entry (PoEs) along the US-Mexico border and grouped into three regions. Second, it used sentiment variables built from the volume of internet search queries as a proxy to assess decision-makers' sentiments. Lastly, a rolling regression analysis was performed to examine the relationship between the sentiment variables and border crossings over time. The rest of this paper proceeds as follows. The literature review is examined in the next section. Then, in the third section, the variables and data are described. Section 4 presents the method used in the study, and the results are presented in Section 5. The discussion and trends for future research are proposed in Section 6, and the conclusion is presented in the last section. Figure 1: Annual pedestrian and personal vehicle crossings from 2004 to 2019 (millions). Note: Annual data for the 2004–2019 period. Data in millions. Pedestrian crossings are on the right vertical axis. Source: Own estimations using data from the Bureau of Transportation Statistics.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 4 Results
### Effect of Surrounding Environment on Handover  
To provide a detailed analysis of the handover procedure at different heights, the effect of the surrounding environment on handover needs to be investigated. To do so, first, the impact of the eNodeB tower's height on the handover decision making is considered. Table 3 presents the height of considered eNodeB towers. As shown in Figure 20, by changing the flight height, the drone is served by a different set of cells. When the drone flies at altitudes lower than eNodeB height, it is usually served by the nearby eNodeBs. This is because the eNodeB antennas are downtilted to optimally serve terrestrial UEs. Therefore, drone UEs will be served by the main lobes of eNodeB antennas. While for cases in which the drone flies beyond the height of eNodeB towers, the drone is served by sidelobes of the antennas. As can be seen from the results of the 170 m height route, the drone UE was served by Cell 12, which is located farther than other cells in this area. That means the signal strength of Cell 12 sidelobes is stronger than sidelobes of adjacent cells, or the route is located at the null of adjacent cells sidelobes.  
To investigate the effect of geographic terrain and the surrounding environment, the research area was narrowed into two locations, Location A and Location B, as shown in Figure 20. By comparing the results of heights 0 m and 40 m, it can be revealed that when the drone flies above the height of buildings and trees, where the probability of LoS is higher than ground UEs, the drone is almost served by nearby eNodeBs, while for  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cell ID** & **Height (m)** \\ \hline
1 & 105 \\
2 & 94 \\
11 & 137 \\
12 & 198 \\
14 & 56 \\
16 & 105 \\
42 & 105 \\
41 & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cell ID and its height.  
Figure 20: Location of user equipment (UE) and serving cell.  
terrestrial UEs scenario of serving cell selection impacts by factors such as signal blocking and shadowing effects from the trees and buildings.
## 3 Methods
### Passive Seismic Methods  
The microseismic sounding method (MSM) is based on the proposal that the vertical component of ambient seismic noise is presented predominantly by the fundamental mode of Rayleigh waves. This assumption is true for natural microseismic oscillation [40]. According to [16; 17; 36; 41; 42], the fundamental mode of Rayleigh wave increases its amplitude above low-velocity inhomogeneity and decreases its amplitude above high-velocity inhomogeneity. The variations in the intensity of microseisms were recorded on the surface, while inhomogeneity was located under the surface at some depth. Also, analysis of amplitude information allows for improving the resolution of the method in the horizontal [16; 41]. The MSM does not require the medium to be layered. As a consequence, the MSM can be used in complex geological environments near kimberlite pipes.  
MSM is a differential amplitude technique where measurements are conducted successively at the points of a profile. Simultaneously, the microseismic signal should be recorded at the reference point located around the studied area to apply a correction to eliminate the non-stationary sounding of the microseismic signal. The result of the processing is the geophysical cross-section, a distribution of relative intensity of microseisms along the profile and in-depth (I). Zones with a higher relative microseism intensity represent an area with relatively reduced velocity properties and vice versa. A more detailed description of the method is presented in [17; 19].  
Passive seismic interferometry is based on the estimation of an empirical Green's function by cross-correlation or convolution of ambient noise recorded simultaneously in different locations [43; 44; 45]. This method is widely used in surface wave tomography [46; 47; 48] and body wave imaging [49] for the study of fault structure and shallow subsurface structure [50; 51; 52]. In the present study, an enhanced passive seismic interferometry methodology was employed to reduce the duration of the measurement and enhance the precision of the resulting dispersion curve [37]. The field measurements require simultaneous records of ambient seismic noise at least three points. It is necessary to remove the effect of the azimuthal distribution of ambient noise sources.