esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
By combining global structural consistency with local detail differences, we create highly discriminative representations of pedestrians. These fused feature vectors are used for cross-camera person re-identification, which effectively improves the accuracy and robustness of the system. The main contributions of this paper are as follows:  
* We propose an innovative person re-identification network that uniquely combines the richness of global features with the specificity of edge-enhanced details and the relationship among different body parts. By doing so, it not only captures the holistic appearance of individuals but also emphasizes fine-grained details and the structural relationships between various body segments. This comprehensive feature extraction strategy leads to a more robust and discriminative representation for pedestrian re-identification tasks. * We propose an Edge-enhanced Feature Extraction Module (EFEM) that leverages dual deep learning network branches to independently capture the contour details and the comprehensive attributes of pedestrians. Facilitating information exchange between these distinct feature sets, significantly amplifies the system's ability to extract and understand the global contextual features of pedestrians, leading to an enriched and more robust global representation. * We propose an Inter-part Relationship Extraction Module (IREM) to extract the inter-dependence between pedestrian local features. This module aims at the main area of pedestrians, where we use a semantic-based soft segmentation strategy to subdivide the area into several different parts, and then carefully extract and analyze the associated features between these separate segments. The module significantly enhances the ability of the model to understand and interpret local features with higher accuracy, so that it can more effectively deal with the complex challenges related to cross-perspective changes and partial occlusion. * We verified the performance of the network on three mainstream datasets, including Market-1501, DukeMTMC-reID, and MSMT17. The experimental results show that our method achieves optimal or near-optimal performance on multiple data sets, which proves the effectiveness of our proposed method. The organization of the paper is as follows: Section 2 presents related work, Section 3 states the problem, Section 4 provides a detailed description of the proposed method, Section 5 presents the experimental setup and results, and finally, Section 6 concludes the article and offers prospects for future research.
## 3 The Proposed SSM-NTF
### Learning Model of Dictionary Pair  
Assuming \(\mathbf{X}\in\mathbb{R}^{N\times L}\) is the training data with signal vectors \(\mathbf{x}_{i}\in\mathbb{R}^{N},i=1,2,\ldots,L\), as its columns. The dictionary pair learning model can be written as  
\[\begin{split}\min_{\mathbf{\Phi},\mathbf{\Upsilon},\mathbf{A},\mathbf{\Upsilon }}\|\mathbf{X}-\mathbf{\Phi}\mathbf{Y}\|_{F}^{2}+\gamma_{1}\|\mathbf{Y}-\mathcal{S}_{\mathbf{ \lambda}}(\mathbf{\Psi}^{T}\mathbf{X})\|_{F}^{2}+\gamma_{2}\|\mathbf{Y}\|_{0}+\gamma_{3}\| \mathbf{\Psi}-\mathbf{F}^{-1}\mathbf{\Phi}\|_{F}^{2}\\ \text{s.t.}\ \sqrt{A}\leq\eta_{\mathbf{\Phi}}\leq\sqrt{B}\end{split} \tag{20}\]  
However, the Problem (20) is difficult to solve. First, the inverse of the frame operator \(\mathbf{F}\) has no closed-form explicit expression. Secondly, the thresholding operator is a highly nonlinear operator which makes the optimization with respect to \(\mathbf{\lambda}\) hard to optimize.  
Apparently, the Problem (20) is difficult to solve as the existence of the inverse of \(\mathbf{F}\). Fortunately, the matrix \(\mathbf{F}^{-1}\) can be expressed as a convergent series [36] which is formulated as  
\[\mathbf{F}^{-1}=\frac{2}{A+B}\sum_{k=0}^{\infty}(\mathbf{I}-\frac{2\mathbf{F}} {A+B})^{k} \tag{21}\]  
Here, we truncated the series at \(k=1\) to make a tradeoff between computational complexity and approximation accuracy. It is formulated as  
\[\mathbf{F}^{-1}\approx\frac{2}{A+B}+\frac{2}{A+B}(\mathbf{I}-\frac{2\mathbf{F} }{A+B})=\frac{2}{A+B}(2\mathbf{I}-\frac{2\mathbf{F}}{A+B}) \tag{22}\]  
In this way, once the frame bounds are given, the inverse of \(\mathbf{F}\) can be calculated easily. Then the optimization problem for training RIP-dictionary pair is formulated as  
\[\begin{split}\min_{\mathbf{\Phi},\mathbf{\Upsilon},\mathbf{\lambda},\mathbf{ \Upsilon}}\|\mathbf{X}-\mathbf{\Phi}\mathbf{Y}\|_{F}^{2}+\gamma_{1}\|\mathbf{Y}-\mathcal{S}_{ \mathbf{\lambda}}(\mathbf{\Psi}^{T}\mathbf{X})\|_{F}^{2}+\gamma_{2}\|\mathbf{Y}\|_{0}+\gamma_{ 3}\|\mathbf{\Psi}-\frac{2}{A+B}(2\mathbf{I}-\frac{2\mathbf{F}}{A+B})\mathbf{\Phi}\|_{F }^{2}\\ \text{s.t.}\ \sqrt{A}\leq\sigma_{\mathbf{\Phi}}\leq\sqrt{B}\end{split} \tag{23}\]  
where \(\mathcal{S}_{\mathbf{\lambda}}(\cdot)\) is the elementwise thresholding operator. There are two basic thresholding methods: The hard thresholding method whose thresholding operator defines as \(\mathcal{S}_{\mathbf{\lambda}}(\cdot)\to max(|\cdot|-\mathbf{\lambda},0)\) and the soft thresholding whose operator is defined as \(\mathcal{S}_{\mathbf{\lambda}}(\cdot)\to sgn(\cdot)max(|\cdot|-\mathbf{\lambda},0)\). Both of the two operator are are non-convex and highly discontinuous which lead to big challenges to solve Problem (23). The mean reason is the fact that the update of the thresholding values \(\mathbf{\lambda}\) causing non-smooth changes to the cost function. To solve this difficulty, we design an alternative direction method via global search and least square that will be introduce in Section 4.1.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 2 Materials and Models  
### Materials  
The Continental Tectonic Environment Monitoring Network of China and the Tianjin Crustal Movement Observation Network have constructed 18 GPS continuous observation stations in and around Tianjin [21, 22], as shown in Figure 1, accumulating continuous and stable observation data for many years.  
The 8 stations in the north are located in the non-subsidence area, and the vertical motions of the stations are stable, as shown in Figure 2a,b. The 10 stations in the south are located in the land subsidence area, and their vertical motions are controlled by land subsidence. From 2014 to 2019, the rates of vertical decline of the stations in the land subsidence area reached tens of millimeters per year. After 2019, with the continuous advancement of land subsidence control and water supplementation from the South-to-North Water Diversion Project, the area and rate of land subsidence in southern Tianjin decreased dramatically [8; 23]. The vertical decline rates of all stations slowed down simultaneously, as shown in Figure 2d,f, and some stations rebounded, as shown in Figure 2c,e.  
By calculating the horizontal velocity field in the Eurasian reference frame from 2014 to 2023 in the study area (Figure 3), it can be seen that the non-consistency between the horizontal velocities of the stations in the land subsidence area in southern Tianjin are more significant compared with the consistent overall trendiness of the stations in the non-subsidence area. The rates and directions of horizontal motions at each station demonstrate a state of disorder, exhibiting characteristics that are discrete and similar to those observed at the GPS stations in the Beijing land subsidence area [15; 16]. The findings indicate that the horizontal motion of GPS stations in the Tianjin land subsidence area is markedly influenced by land subsidence. This phenomenon is deemed to be incapable of accurately representing the true state of crustal horizontal motion. Consequently, it is not employed in the calculation of products such as regional velocity and strain fields. This lack of data impedes the provision of evidence-based insights for regional seismic hazard analysis and crustal motion studies.  
Figure 1: Distribution of GPS continuous observation stations in Tianjin and its surrounding areas.  
Figure 3: Horizontal velocity field in Eurasian reference frame of study area (2014–2023).  
Figure 2: Time series of vertical motion of some stations in the study area. (**a–f**) Time series of vertical component and rate of motion from 2014 to 2023 for stations JJXN, BYQ, WQCG, QING, NIHE, and HECX, respectively.
## 5 Regression Estimation Results
### All Sample Countries (90)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model (1)** & **Model (2)** & **Model (3)** & **Model (4)** & **Model (5)** & **Model (6)** \\ \hline  & **Coefficient** & **Coefficient** & **Coefficient** & **Coefficient** & **Coefficient** & **Coefficient** \\  & **(Prob.)** & **(Prob.)** & **(RSE)** & **(RSE)** & **(RSE)** & **(RSE)** \\ \hline  & \(-\)0.003 & \(-\)0.033 *** & \(-\)0.033 *** & \(-\)0.029 ** & \(-\)0.034 *** & \(-\)0.030 ** \\  & (0.53) & (0.00) & (0.01) & (0.01) & (0.01) & (0.01) \\  & \(-\)0.028 *** & \(-\)0.031 *** & \(-\)0.031 *** & \(-\)0.026 *** & \(-\)0.027 *** & \(-\)0.025 *** \\  & (0.00) & (0.00) & (0.01) & (0.01) & (0.01) & (0.01) \\ SER & \(-\)0.014 *** & \(-\)0.018 *** & \(-\)0.018 *** & \(-\)0.018 *** & \(-\)0.019 *** & \(-\)0.019 *** \\  & (0.00) & (0.00) & (0.00) & (0.01) & (0.01) & (0.01) \\ INT & \(-\)0.012 *** & \(-\)0.005 *** & \(-\)0.005 * & \(-\)0.005 * & \(-\)0.002 & \(-\)0.004 * \\  & (0.00) & (0.00) & (0.00) & (0.00) & (0.00) & (0.00) \\ MYS & 0.075 *** & 0.013 & 0.013 & 0.015 & 0.001 & 0.010 \\  & (0.00) & (0.58) & (0.05) & (0.05) & (0.05) & (0.05) \\ Ln(GNIPC) & \(-\)2.958 *** & \(-\)0.956 *** & \(-\)0.956 & \(-\)0.078 & 0.592 *** & 0.591 *** \\  & (0.00) & (0.01) & (0.80) & (0.89) & (0.18) & (0.20) \\ Ln(GNIPC)2 & 0.215 *** & 0.093 *** & 0.093 * & 0.040 & & \\  & (0.00) & (0.00) & (0.05) & (0.06) & & \\ Constant & 13.858 *** & 8.228 *** & 8.228 *** & 4.353 & 2.031 & 1.673 \\  & (0.00) & (0.00) & (3.16) & (3.48) & (1.29) & (1.40) \\ Prob \(>\) F-statistic & & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Prob \(>\) chi2 & 0.00 & & & & & \\ _R_-squared & 0.68 & 0.18 & 0.18 & 0.15 & 0.17 & 0.15 \\ Obs & 1728 & 1728 & 1728 & 1729 & 1728 & 1729 \\ Groups & 90 & 90 & 90 & 90 & 90 & 90 \\ \hline \hline \end{tabular}  
* Notes: *, *, and *** denote significance at the 10%, 5%, and 1% levels, respectively; for Models (1) and (2), probability values (Prob.) are reported in parentheses; for Models (3–6), robust standard errors (RSE) are reported in parentheses.  
\end{table} 
Table 8: Estimations of impacts of influencing factors and control variables for all sample countries. only had lagged negative impacts on ecological consumption; education had no significant impacts on ecological consumption; ecological consumption and income were positively related. The relationship was linear rather than inversely U-shaped, and the EKC hypothesis was not supported.
## 4 Simulation Studies and Performance Assessment
### Voltage Waveform  
In addition to the system frequency and power distribution curves, a comprehensive analysis of the voltage operation on the DG's output ports was conducted. The results of the harmonic component analysis are shown in Figure 13. The study focuses on the voltage waveform of phase a of DG1, conducting a one-second test (50 cycles in total) starting from 5 s. This test duration helps avoid inaccurate voltage waveform assessment caused by random factors (general 2 cycles). The results reveal that power values derived from SNN fitting show a significant reduction in voltage waveform harmonic content, while dynamic timing regularization further improves and stabilizes voltage supply quality.  
Figure 14 shows the real-time voltage waveforms and zoomed-in views under three scenarios during the one-second period from 5 s to 6 s. The results demonstrate that the proposed method achieves high-quality and stable voltage supply, which differs from using droop control alone. While using droop control, changes in common load power significantly affect the voltage phase waveform and frequency.  
Figure 11: System performance using SNN and dynamic timing regularization. (**a**) frequency, (**b**) power sharing.  
Figure 12: Comparison of DG1’s performance effects. (**a**) frequency, (**b**) power sharing.  
Figure 13: Quality analysis of DG1 a-phase voltage waveform starting at 5 s for 50 cycles lasting 1 s.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 2 LDPC Codes Constructed from BIBDs and Their Tanner Graphs
### LDPC Codes Constructed Based on BIBDs
Therefore, the code rate (\(R\)) of a BIBD-LDPC code constructed from BIBD\((\kappa,1;v)\) is at least  
\[R\geq 1-\frac{\kappa(\kappa-1)}{v(v-1)}.\]
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 2 Materials and Methods
### Methods  
#### 2.2.1 Evaluation of Overall Image Quality  
The overall image quality was evaluated with five test participants. These were medical engineers aged between 25 and 35 years with no visual deficits. The TG18-OIQ test pattern (Figure 2) was evaluated in both the diagnostic room and the office environment.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Material** \\ \hline Test device & HoloLens 2 \\ Display software & VSI HoloMedicine (apoQlar GmbH, Hamburg, Germany) \\ Test patterns (AAPM): & TG18-OIQ \\  & TG18-LN12-03 to TG18-LN12-18 \\  & Red (RGB: 255, 0, 0) \\ Other test patterns & Green (RGB: 0, 255, 0) \\  & Blue (RGB: 0, 0, 255) \\  & Luminance: MAVO SPOT 2 with contact measurement \\  & probe (Gossen Foto- und Lichtmesstechnik GmbH, \\  & Nürnberg, Germany) \\ Measuring devices & Illuminance: MAVOLUX (Gossen Foto- und \\  & Lichtmesstechnik GmbH, Nürnberg, Germany) \\  & Color coordinates: MAVOSPEC BASE (Gossen Foto- und \\  & Lichtmesstechnik GmbH, Nürnberg, Germany) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the materials used to examine HoloLens 2.  
The participants viewed the test pattern displayed by HoloLens 2 from a distance of 30 cm. An examiner asked the control questions listed in Table 2.  
For primary displays, all image elements must be discernible. In contrast, for secondary display devices, image elements 2 and 4 may be unclear, and the text \"QUALITY CONTROL\" is only legible in the white and gray field (image element 3) [6]. An image element is considered discernible or legible if the majority of participants confirm this in both settings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Element** & \multicolumn{2}{c}{**Control Questions**} & \
\begin{tabular}{c} **Primary Display** \\ **Devices** \\ \end{tabular} &
\begin{tabular}{c} **Secondary Display** \\ **Devices** \\ \end{tabular} \\ \hline \hline \multirow{3}{*}{1} & - & Are the line pairs visible? & Yes & Yes \\  & - & Are the wide, low-contrast line pairs visible? & Yes & Yes \\ \hline \hline
2 & - & Are the 5\% and 95\% patches visible? & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Questionnaire for observing participants.  
Figure 2: TG18-OIQ test pattern for evaluating overall image quality with labeling of the image elements contained and the control questions and requirements for the test.
# Spontaneous Brillouin Scattering Spectrum and Coherent Brillouin Gain in Optical Fibers
## 2 Results
### Spatial Noise Filtering
#### 2.4.2 Continuum of Elastic Waves  
The case of a continuum of elastic waves is quite complicated in the general vector anisotropic case. We will only outline how the generating noise is filtered spatially thanks to phase matching in the case of scalar waves in a homogeneous and isotropic medium. For this case, the Langevin noise term in Equation (20) slightly simplifies to  
\[\langle f(t,\mathbf{r})\rangle=0\,\ \ \langle f(t,\mathbf{r})f^{*}(t^{ \prime},\mathbf{r^{\prime}})\rangle=Q^{\prime}\delta(t-t^{\prime})\delta( \mathbf{r}-\mathbf{r^{\prime}}). \tag{24}\]  
Taking the Fourier transform in both time and space of Equation (7), without the electrostriction term, we have  
\[(v^{2}+i\omega\Gamma^{\prime})(k_{x}^{2}+k_{y}^{2}+k_{z}^{2})\bar{\rho}-\omega ^{2}\bar{\rho}=\tilde{f}_{a}. \tag{25}\]  
It follows at once using the Wiener-Khinchin theorem that  
\[S_{\rho}(\omega;k_{x},k_{y},k_{z})=S_{f_{a}}|\omega^{2}-(v^{2}+i\omega\Gamma^ {\prime})(k_{x}^{2}+k_{y}^{2}+k_{z}^{2})|^{-2}. \tag{26}\]  
The meaning of this equation is that the acoustic phonons are continuously distributed along the dispersion relation. They can propagate to any direction of space with equal probability. In an actual experiment, however, both the incident optical wave and the backscattered optical wave are strongly conditioned by the propagation structure. In a single mode optical fiber, they are efficiently projected on the mode after propagation over a few thousands of optical wavelengths. Since the backscattered optical wave eventually provides the measurement of the spontaneous Brillouin spectrum, its spatial frequency spectrum determines the acoustic phonons that are picked up in the measurement. As the forward and the backward propagating modes are phase-conjugate, \(k_{x}^{2}+k_{y}^{2}\approx 0\) and \(k_{z}\approx q\) are effectively imposed. As a result, the power spectral density of the stochastic response is filtered to pick only \(S_{\rho}(\omega\approx\omega_{B};0,0,q)\). Note that this is a heuristic argument rather than a proper demonstration.
## 2 Circuit Description
### On-Chip APD  
Optical receivers with off-chip photodetectors face significant challenges due to the signal distortions that occur from the variable bond-wire inductance. Also, the inevitable on-chip electrostatic discharge (ESD) protection diodes add to the parasitic capacitance, thereby shrinking the bandwidth considerably. Therefore, the on-chip integration of photodetectors helps to mitigate these issues, despite their deficient sensitivity in CMOS technologies, and also offers several advantages, including their compact size and low cost. Particularly, on-chip APDs are preferred to p-i-n photodiodes, even with their noisier characteristics, due to their ability to generate substantial photocurrents, which is suitable for short-range LiDAR sensor applications. Also, a P\({}^{+}\)/N-well/Deep N-well APD (P\({}^{+}\)/NW/DNW APD) architecture is incorporated into this work since it can be facilely optimized for better responsivity and a wider bandwidth than other topologies (typ. P\({}^{+}\)/NW APD). Figure 4a presents a cross-sectional view of the P\({}^{+}\)/NW/DNW APD, where avalanche multiplication is initiated by holes at the P\({}^{+}\)/NW junction, with shallow trench insulators (STIs) preventing premature edge breakdown. The incorporation of the DNW layer enhances the near-infrared sensitivity by reducing the number of holes penetrating into the p-substrate. In addition, the built-in potential barrier between the DNW and the p-substrate blocks the photocurrents generated in the p-substrate effectively, thus further improving the APD performance.  
Figure 4b depicts an APD layout that adopts an octagonal shape to minimize the risk of edge breakdown. The P\({}^{+}\) source and the drain regions are covered with a salicide blocking layer to form an optical window, hence ensuring that the high resistivity contacts remain exposed to reduce the contact resistivity [13]. The optical window, with a diagonal length of 40 \(\upmu\)m, results in a total depletion capacitance (C\({}_{\text{PD}}\)) of 470 fF and a photodetection bandwidth of 1.7 GHz at a reverse bias of 10.25 V.  
Figure 3: Schematic diagrams of (**a**) the m-TIA and (**b**) the replica TIA.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### TiO\({}_{2}\)-Based NPs
#### 4.5.1 Impact on Germination, Plant Growth and Assimilation Pigments
Treatment with TiO\({}_{2}\) NPs at 50\(-\)200 mg/L (83.7 nm) moderately increased the root and shoot fresh biomass of _Coriandrum sativum_ L. plants cultivated in hydroponic solution. At application of 100-400 mg/L TiO\({}_{2}\) NPs, enhanced activities of SOD, CAT, and APX mitigated the oxidative stress; pronouncedly reduced root fresh biomass and water content was observed using a dose of 400 mg/L. At exposure to 50 mg/L TiO\({}_{2}\) NPs stimulated accumulation of Ca, Mg, Fe, Mn, Zn, and B in shoots, resulting in improved nutrient quality of edible plan parts was observed; internalization or translocation of TiO\({}_{2}\) NPs to shoots was not detected [254]. At studying of the transgenerational effects TiO\({}_{2}\) NPs in _Ocinum basilicum_ plants, it was found that hydrophobic or hydrophilic TiO\({}_{2}\) NPs exhibited beneficial impact on plants growth, but more negative effects on photosynthesis compared to pristine TiO\({}_{2}\) NPs. Treatments with hydrophobic and hydrophilic TiO\({}_{2}\) NPs resulted in the reduction of Chll\(b\) and total Chl by 52% and 30%, respectively, while total sugar and reducing sugar contents achieved 186% and 145% of contents observed in unexposed plants in both cycles [255]. Spraying of _Veiteria zizanioides_ plants with 90 mg/L TiO\({}_{2}\) NPs, which was performed 300 d after transplantation, increased the Chl content and maximum photochemical efficiency of PS II and yield of EO by 27.2%, 23.5%, and 55.1%, respectively, compared to the control [145].
## 4 Simulation Results and Discussion
### Analysis of the Performance of the Reverse Combination (RC) Configuration
#### 4.1.3 Fill Factor (FF)  
The FF is a crucial indicator used to measure the efficiency of solar cell outputs. It is determined by the ratio between the product of the open-circuit voltage and short-circuit current of the array and the maximum power of the array. A higher fill factor indicates the superior quality of the solar cell array. The calculation formula is as follows [44]:  
\[FF=\frac{V_{mp}\times I_{mp}}{V_{\infty}\times I_{sc}} \tag{35}\]  
Figure 11 illustrates a comparison of the FF for 4 configurations: SP, VTCT, RC, and HTCT. The results indicate that the proposed RC configuration significantly increases the  
Figure 10: Power loss rates for conditions 1 to 4.  
Figure 9: Mismatch loss power for conditions 1 to 4.  
FF value. Furthermore, under various operating conditions, the PV array's FF remains stable at approximately 0.74, which is close to the theoretical optimum value.