esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Network Architecture
### Twin Delayed Deep Deterministic Policy Gradient
In Equation (9), the smaller value between two outputs (\(Q_{\phi^{\prime}_{1}},\,Q_{\theta^{\prime}_{2}}\)) of target critic networks is fed into the Bellman equation to avoid overestimating the \(Q\)-value: \[y\big{(}r,s^{\prime}\big{)}=r+\gamma\underset{i=1,2}{min}Q_{\phi^{\prime}_{i}} \big{(}s^{\prime},\pi_{\phi}\big{(}s^{\prime}\big{)}+\epsilon\big{)};\] (9)
* The parameter sets (\(\theta_{1}\) and \(\theta_{2}\)) are updated by minimizing the loss values \(L_{\theta_{1},\,B}\) (10) and \(L_{\theta_{2},B}\) (11) which are the expected value of the difference between the target value \(y(r,s^{\prime})\) and the two \(Q\)-value, where (\(s,\,a,\,r,s^{\prime}\)) are retrieved from replay buffer \(B\): \[L_{\theta_{1},B}=\underset{(s,\,a,\,r,s^{\prime})\sim B}{\mathbb{E}}\Big{[} \big{(}Q_{\theta_{1}}(s,a)-y\big{(}r,s^{\prime}\big{)}\big{)}^{2}\Big{]}\] (10) \[L_{\theta_{2},B}=\underset{(s,\,a,\,r,s^{\prime})\sim B}{\mathbb{E}}\Big{[} \big{(}Q_{\theta_{2}}(s,a)-y\big{(}r,s^{\prime}\big{)}\big{)}^{2}\Big{]}\] (11)  
The parameter set \(\phi\) of the actor network is updated by maximizing \(Q_{\theta_{1}}\) value \(\underset{\phi\sim B}{\mathbb{E}}\big{[}Q_{\theta_{1}}\big{(}s,\pi_{\phi}(s) \big{)}\big{]}\);  
The parameters of the target networks are not updated consecutively like those of the actor and critic networks in order to avoid overestimation during the training process. These parameters are updated after a certain number of timesteps (in this research, the delay timestep is 2), in addition, a hyperparameter \(\tau\) (\(0<\tau<1\)) is used to make updating these weights slower. These parameters of the target networks are updated as the Equations (12) and (13):  
\[\theta^{\prime}_{i} \leftarrow\tau\theta_{i}+(1-\tau)\theta^{\prime}_{i} \tag{12}\] \[\phi^{\prime} \leftarrow\tau\phi+(1-\tau)\phi^{\prime} \tag{13}\]
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 2 Theoretical Design of CLSDAT
### Calculating the Optimal Solution for the Coordinate of the Circle Center
#### 2.3.1 Mean Shift Vector and Its Clustering Algorithm  
Given that the sample data points \(P_{i}(i=1,2,\cdots,n)\) exist in a D-dimensional space, Equation (2) represents the mean shift vector expression at point \(P\), which can be any point in the sample data points \(P_{i}\). The mean shift vector at point \(P\) represents the vector sum of the total \(k\) data points within the high-dimensional sphere \(S_{r}\) centered at \(P\) with a radius of \(r\).  
\[M_{b}(p)=\frac{1}{k}\sum_{p_{i}\in S_{r}}(p_{i}-p) \tag{2}\]  
In the sample data set \(P_{i}\), an initial point \(P\) is randomly selected. After calculating the mean shift vector for the first time, the center of the high-dimensional sphere \(S_{r}\) is moved to the endpoint of this vector. Then, this endpoint becomes the new \(P\) point, and the above operation is repeated to calculate the current mean shift vector. This process is repeated until the moving distance reaches a pre-defined threshold, at which point the iteration is terminated.  
Usually, the direction of the mean shift vector points towards the densest direction of data points. However, there can be exceptions. For example, if the \(S_{r}\) region is chosen to be large, and if a vector in this region has a relatively large magnitude and points in the opposite direction of the dense direction of the sample data, it can cause interference to the next mean shift vector movement. This issue can be addressed by introducing a kernel function and weight coefficients.
# Open-Pit Map: An HD Map Data Model for Open-Pit Mines
## 7 Conclusions  
Currently, open-pit mines are continuously advancing toward intelligent and unmanned construction. Many business scenarios such as unmanned transportation, shoveltruck coordination, production scheduling, and safety control all require the support of a mining map. This article, based on the business requirements of open-pit mines, describes the application scenarios of an HD map, and designs a universal high-definition map data model called Open-Pit Map, abbreviated as OP map, for data creation and exchange. It helps meet the demands of various business scenarios with a single HD map, significantly enhancing the comprehensive control capabilities of mining areas. Eight definitions of basic elements and attributes in OP map were defined, including geometric points (PointENU), road network maps (Map), blast areas (Blast), output elements (ElementDef), output materials (MaterialDef), benches (MineBench), safety warning zones (WarningLine), and location areas (Location). Eleven detailed location elements in open-pit mines were proposed, including crushing stations, stockyards, dumps, intersection control areas, maintenance areas, fueling stations, rest areas, loading points, shift change areas, area entrances, and area exits. Eight common traffic elements in open-pit mines were proposed, including roads (Road), lanes (Lane), location areas (Location), blast areas (Blast), marking polygons (MarkPolygon), marking lines (MarkLine), traffic signals (Signal), and road intersections (Junction). The practicality and effectiveness of the data model were verified in the intelligent shipping system of the specified mine.  
**Author Contributions:** Conceptualization, X.X. and L.B.; Methodology, X.X., Z.W. and L.B.; Validation, Z.W., P.H., S.T. and L.B.; Formal analysis, Z.W.; Data curation, Z.W. and P.H.; Writing--original draft, X.X. and Z.W.; Writing--review & editing, X.X., P.H., S.T. and L.B.; Visualization, S.T.; Supervision, L.B.; Funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China University of Mining and Technology, grant number SKLCRSM21X003.  
**Data Availability Statement:** This data model can be found here: [[https://gitee.com/wang-zhuo2734/openpit_map](https://gitee.com/wang-zhuo2734/openpit_map)]([https://gitee.com/wang-zhuo2734/openpit_map](https://gitee.com/wang-zhuo2734/openpit_map)), accessed on 29 October 2023.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Analysis on the Performance of a Radiant Cooling Panel with Serpentine-Based Design
## 3 Results and Discussions
### CFD Simulation--ANSYS Fluent
#### 3.3.2 Temperature Distribution
The plate is divided into the right-hand side, middle, and left-hand side of the plate in the graphical analysis shown in Figures 11-13. Figure 11: Temperature distribution of the cooling panel for Design 1. Figure 12: Temperature distribution of the cooling panel for Design 2. By comparing the three temperature distribution graphs, it can be seen that Design 1 has the least temperature distribution uniformity where the plotted temperature for each segment is not concentrated in the same location, unlike in Design 2. Although the minimum, maximum, and mean plate temperatures in Design 3 do not differ that much from those of Design 2, the temperature distribution graph shows that the temperature is not distributed uniformly throughout the plate. In terms of calculation, the temperature difference of Design 2 is the lowest compared to those of Design 1 and Design 3 at 4.505 \({}^{\circ}\)C, whereas Design 1 and Design 3 have a temperature difference of 5.478 \({}^{\circ}\)C and 5.175 \({}^{\circ}\)C, respectively. A smaller temperature difference indicates that the temperature distribution of a particular radiant cooling panel has a higher uniformity because its temperature spread is limited to that specific range. From this finding, Design 2 shows the most promising result for temperature difference calculation, with the most concentrated temperature distribution graph plotted. Therefore, Design 2, with a two-inlet-two-outlet counterflow serpentine configuration, has the highest uniformity in terms of temperature distribution.
# Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory
## 3 Results
### Analog Resistance Switching Optimization
Noticeably, the considered device exhibits an extremely linear behavior during both the set and reset operations, especially if compared with the characteristics of the one-layer device (Figures 7 and 8). However, the pulse voltage required for the switching to occur is considerably larger, starting at 2.5 V  
Figure 8: (**a**) Experimental (symbols) and simulated (lines) conductance evolution of TiN/6 nm HfO\({}_{x}\)/TiO\({}_{y}\)/TiN RRAM stacks under the application of set pulse trains with a 0.9 V amplitude and different widths: (red) 10 \(\upmu\)S; (green) 100 \(\upmu\)S; (blue) 1 ms. Initial forming was performed with a 100 \(\upmu\)A compliance current. (**b**) Simulated conductance modulation obtained using set pulse trains with an increasing width: (black) 1 \(\upmu\)S; (red) 10 \(\upmu\)S; (green) 100 \(\upmu\)S; (blue) 1 ms; (magenta) 10 ms.  
Figure 9: Experimental (symbols) and simulated (lines) conductance evolution of TiN/2 nm Ta\({}_{2}\)O\({}_{x}\)/35-nm TiO\({}_{y}\)/TiN RRAM stacks under the application of set pulse trains with a 100 \(\upmu\)S width and variable amplitude. (**a**) Set operation: (red) 2 V; (green) 2.5 V; (blue) \(-\)3.5 V. (**b**) Reset operation: (red) \(-\)3 V; (green) \(-\)4 V; (blue) \(-\)5 V.  
for the set process and requiring up to \(-4\) V for the reset process. All these trends are accurately reproduced by the simulations. While the two-layer stack structure allows for the fabrication of RRAM devices with an intrinsically linear characteristic, the lack of symmetry still prevents their use for ANN and SNN applications and could be detrimental in NVMs. The symmetry of the electrical characteristic can be pursued by further investigating the novel two-layer stack, using the presented modeling platform to optimize the materials and geometries combination, or by using specifically designed pulse sequences. Interestingly, the latter solution is relatively easy to implement: thanks to the intrinsic linearity of the set and reset characteristics, symmetry can be obtained by using different pulses (i.e., with different amplitudes) in the two operations.
## 4 Discussion
### Potentially Toxic Metals
Another explanation for the presence of toxic metals in oils may be contamination during the refining process or contact with storage materials [60]. Some metals are considered to be essential if they occur in low concentrations because they are involved in metabolic functions (Co, Cr, Cu, and Mn), while others (Cd, Ni, and Pb) are considered carcinogenic. They can cause cancers of the bladder, liver, and kidneys, and peripheral and central nervous system damage [65]. Based on research on chronic exposure to Pb, it has been observed that it can cause anemia, hypertension, nervous system disorders, infertility, and brain damage [66]  
Chronic diseases driven by excess Cr include nasal, pulmonary, and gastrointestinal irritations; gastric ulcers; and dermatitis. Cr, Pb, and Cd are toxic metals that catalyze the decomposition of aldehydes, acids, ketones, and epoxides in the body; affect the digestive, endocrine, and immune systems; and increase the carcinogenic effect due to their reaction with proteins or pigments [60; 67]. The Cd concentration found in this study (0.001-0.010 mg/kg) is lower than the metal values reported by other researchers in all of the studied oils. For example, [PERSON] et al. [61] found a range of 0.005-0.01 mg/kg Cd in rice oil, and [PERSON] et al. [12] reported a Cd range in extra virgin olive oil of 2.39-2.76 mg/kg, a range of 6.11-6.45 mg/kg in rapeseed oil, and a range of 3.34-3.75 mg/kg in sunflower oil. The Pb concentration in the rice oil in the current study (0.217 mg/kg) was within the limits reported by [PERSON], 2019 (0.066-0.30 mg/kg) [61]. [PERSON] et al. [68] reported a value of Pb in rice oil from Iran of 0.15 mg/kg. A higher amount of Pb was also found in the studied rapeseed oil, at 0.061 mg/kg, compared to the range reported by [PERSON] et al. (0.007-0.016 mg/kg) [12]. A similar behavior was observed for sunflower oil. Thus, in the present study, the Pb concentration was 0.075 mg/kg, while such values reported in other studies are in the range 0.006-0.039 mg/kg [12; 24; 63]. For extra virgin olive oil, the Pb concentration in the current study (0.026 mg/kg) was slightly higher than that reported by [PERSON] et al. [12] (0.013 mg/kg), and those reported by [PERSON] et al. [24] (0.009-0.016 mg/kg). The concentrations of Cu, Mn, Co, and Ni in the studied oils vary within wide limits depending on the oil analyzed. The concentrations of these metals are lower in the oils used in the current study (0.010 mg/kg in extra virgin olive oil for Cu) than in the oils analyzed by other researchers (range 0.248-0.276 mg/kg Cu for the same type of oil) [12; 63; 65; 24].
# In Vitro Synovial Membrane 3D Model Developed by Volumetric Extrusion Bioprinting
## 2 Materials and Methods  
### Bioink Synthesis  
GelMA was synthesized following the general protocol first reported by [PERSON] et al. [14]. Briefly, 10% weight/volume (\(w\)/\(v\)) Type B gelatin from bovine skin (Sigma Aldrich, St. Louis, MO, USA, gel strength 50-120 bloom) was dissolved into Dulbecco's Phosphate Buffered Saline (DPBS, Sigma) at 50 \({}^{\circ}\)C for 1 h. Then, 4 mL every 100 mL of phosphate buffered saline (PBS) of Methacrylic Anhydride (MA, Sigma) was slowly added to the solution, with a 2 h reaction in dark at 40 \({}^{\circ}\)C, to introduce the methacryloyl groups to gelatin's reactive amine and hydroxyl groups. The reaction was stopped by diluting the reaction mixture with DPBS. The resulting solution was dialyzed against deionized water with a cellulose membrane (12-14 kDa molecular weight cutoff, Sigma) for 1 week at 40 \({}^{\circ}\)C to remove low molecular weight impurities that could be cytotoxic. Finally, GelMA was freeze-dried. GelMA Degree of functionalization (DoF) was determined by using fluoraldehyde o-phthalaldehyde (OPA) reagent, which reacts with primary amines [15]. In particular, GelMA was dissolved in PBS at the concentration of 2 mg/mL. Then, 300 \(\mu\)L of GelMA solution was mixed with 600 \(\mu\)L of fluoraldehyde o-phthalaldehyde reagent solution. Gelatin was used as a positive control. After 1 minute, fluorescence intensity was read by using Synergy(tm) HTX Multi-Mode Microplate Reader (BioTek, Winoosky, VT, USA) at 450 nm (Eexc = 360 nm). The DoF is then calculated as DoF = [1 - (Isample-Icontrol)]. The DoF value found was 70%.  
Three different bioink formulations were evaluated as ink candidates, with a GelMA content of, respectively, 7.5, 10, and 12.5% \(w\)/\(v\) in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS). The Lithium phenyl-2,4,6-trimethylbenzoyl phosphinate (LAP) photoinitiator concentration was kept constant at 0.25 \(w\)/\(v\)%.  
To prepare the bioinks, 10 mg of LAP were first dissolved in 4 mL of culture medium. Then, the respective amount of lyophilized GelMA was gradually added to the solution until complete solubilization was observed. The process was performed in a heated bath at 37 \({}^{\circ}\)C to increase GelMA solubility and in a UV-protected container to avoid premature material crosslinking.  
To sterilize the prepared ink formulations, the UV-protected containers were first placed in a heated bath at 60 \({}^{\circ}\)C. Subsequently, the different GelMAinks were filtered in a two-step approach using 0.45 and 0.22 micron polyethersulfone (PES) filters under laminar flow.
# Policy Similarity Measure for Two-Player Zero-Sum Games
## 4 Methodology  
### Design Motivation: Limitations of UDM  
To illustrate the issue of redundant policies and their impact on the UDM, consider the example of the rock-paper-scissors game. The payoff matrices for two different versions of this game are shown below:  
\[G_{1}=\begin{bmatrix}0&-1&1\\ 1&0&-1\\ -1&1&0\end{bmatrix}\quad\text{and}\quad G_{2}=\begin{bmatrix}0&-1&1&1\\ 1&0&-1&-1\\ -1&1&0&1\\ -1&1&0&1\end{bmatrix} \tag{8}\]  
The first matrix represents the traditional rock-paper-scissors interactions, while the second matrix is identical except for the inclusion of two additional scissors strategies. In both cases, the diversity kernels, which quantify the diversity of strategies based on the corresponding payoff tables, are computed using the feature vector \(\phi^{i}_{t}=\frac{G_{[i,]}}{\|G_{[i,]}\|}\).  
The diversity kernels for each payoff matrix are as follows:  
\[L_{1}=\begin{bmatrix}1&-1/2&-1/2\\ -1/2&1&-1/2\\ -1/2&-1/2&1\end{bmatrix}\quad\text{and}\quad L_{2}=\begin{bmatrix}1&-2/3&-\sqrt{6}/ 6&-\sqrt{6}/6\\ -2/3&1&-\sqrt{6}/6&-\sqrt{6}/6\\ -\sqrt{6}/6&-\sqrt{6}/6&1&1\\ \end{bmatrix} \tag{9}\]  
The eigenvalues of \(L_{1}\) and \(L_{2}\) are \(\lambda(L_{1})=\{\frac{3}{2},\frac{3}{2},0\}\) and \(\lambda(L_{2})=\{\frac{7}{3},\frac{5}{5},0,0\}\), respectively. Here, the eigenvalues reflect the RD of the populations. Using a linear kernel function \(f(x)=\frac{1}{1+\exp(-x)}-\frac{1}{2}\), the UDM values are computed as follows:  
\[\text{UDM}(\{R,P,S\})=f\bigg{(}\frac{3}{2}\bigg{)}+f\bigg{(}\frac{3}{2}\bigg{)} +f(0)\approx 0.6375,\]  
\[\text{UDM}(\{R,P,S,S\})=f\bigg{(}\frac{7}{3}\bigg{)}+f\bigg{(}\frac{5}{3}\bigg{)} +f(0)+f(0)\approx 0.7527.\]  
These results indicate that the emergence of redundant strategies contributes to an increase in population diversity, which may, in turn, lead the agent to converge toward suboptimal strategies, thereby limiting the overall effectiveness of the learning process. To address this issue, an improved approach is necessary to measure strategy redundancy and facilitate policy diversification.
## 4 Measurement Setup and Results
### Measurement Results
The FFT can be efficiently implemented using vendor-provided IP cores with support for streaming interfaces and pipelined operation. CA-CFAR requires only basic arithmetic and memory for sliding window averaging, making it ideal for low-latency designs. In contrast, SVD-based and LTSP techniques exhibit cubic complexity due to matrix decomposition, requiring significantly more DSP slices, LUTs, and on-chip memory. These methods may be infeasible for full hardware implementation on resource-constrained devices and are better suited for deployment on embedded processing systems (e.g., ARM Cortex-A cores) or via hardware/software co-design using Vitis HLS 2024.2 or high-level synthesis tools. Overall, the table highlights the practical trade-offs between algorithmic accuracy and hardware efficiency. In summary, CA-CFAR and LTSP are the most reliable methods for real-time implementation, balancing performance and robustness. The hardware feasibility of each technique, including resource estimates on a Xilinx FPGA platform, is discussed in Table 3.  
The FPGA-based implementation discussed in this paper was conducted offline using VHDL and simulated on a Xilinx SoC development platform to evaluate the feasibility of deploying the proposed algorithms in real-time systems. However, the actual radar measurements shown in Section 4 were acquired using a separate hardware receiver without direct FPGA integration. Therefore, the reported FPGA synthesis results in Table 3 reflect the estimated hardware cost for future deployment, not a real-time in-loop implementation during the measurement campaign.
# Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
## 2 Experimental Procedure
### Adsorption Experiments  
#### 2.4.1 Adsorption Kinetic Experiments  
Anionic dye adsorption kinetic experiments were conducted using 100 ppm initial MB and MO solutions. Before the adsorption experiments, powdered ACOP was dispersed in DI water at the designed dosages (0.5, 1.0, and 2.0 g/L), and the dispersion was ultrasonicated for 5 min to obtain a homogeneous ACOP suspension. Then, 100 mL of this ACOP suspension and 100 mL of dye solution were mixed in a 250 mL glass beaker under magnetic stirring to obtain a homogenous solution. The supernatant was collected at 1, 5, 15, 30, 60, 120, and 180 min and filtered with a syringe filter (polyethersulfone (PES), 0.45 \(\upmu\)m). The concentration of each dye in the supernatant was quantified using a UV-Vis spectrometer (Genesys50, ThermoFisher Scientific, MA, USA) at the maximum wavelengths (\(\uplambda_{\text{max}}\)) of the dyes, which are 463 nm and 481 nm for MO and OG, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Chemical Name**} & \multicolumn{1}{c}{**Chemical**} & \multicolumn{1}{c}{**Molecular**} & \multicolumn{1}{c}{**pK\({}_{\text{a}}\)**} & \multicolumn{1}{c}{**log K\({}_{\text{ow}}\)**} & \multicolumn{1}{c}{**Chemical Structure**} \\ \hline \multicolumn{1}{c}{Methyl Orange} & C\({}_{14}\)H\({}_{14}\)N\({}_{3}\)NaO\({}_{3}\)S & 327.33 & 4.3 & \(-\)0.66 & \\ \cline{2-6} \multicolumn{1}{c}{Anionic dye} & & & & & \\ \cline{2-6} \multicolumn{1}{c}{Orange G} & C\({}_{16}\)H\({}_{10}\)N\({}_{2}\)Na\({}_{2}\)O\({}_{3}\)S\({}_{2}\) & 452.38 & 11.5 (\(-\)OH) & \(-\)4.56 & \\ \cline{2-6} \multicolumn{1}{c}{Cationic dye} & Methylene Blue & C\({}_{16}\)H\({}_{18}\)CIN\({}_{3}\)S & 319.85 & 3.8 & 0.75 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical properties of dye molecules.  
The intraparticle diffusion kinetic model (Equation (1)) was applied to analyze the kinetic data and underlying adsorption mechanisms [42].  
\[q_{t}=K_{id}t^{\frac{1}{2}}+c \tag{1}\]  
where \(K_{id}\) (mg/g\(\cdot\)min\({}^{1/2}\)) is the intraparticle rate constant, and c (mg/g) is the thickness of the boundary layer formed in the first interval.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 3 Materials and Methods  
This study used InSAR, boreholes, GNSS, GRACE and Global Land Data Assimilation System (GLDAS) data obtained from various sources. The specifications of data used is summarised in Table 2 and the overall methodology used in this study is shown in Figure 2.  
The methodology mainly involved processing of InSAR data to calculate land subsidence/uplift using PSInSAR principle and validating the InSAR results using GNSS data. It also involved calculating change in groundwater level using GRACE anomalies and GLDAS models. Finally, land subsidence/uplift and groundwater variation were correlated and studied in conjunction. The study was carried out for the time period between December 2002 to December 2010 in London.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **InSAR Data** & **Borehole and GNSS Data** & **GRACE Data** & **GLDAS \({}^{*}\) Data** \\ \hline
1. & 47 Envisat-Asar Images & & & & \\
2. & VV polarization, Track 51, & & & & \\  & Desending, Left looking, & & & & \\  & Swath Id: IS2, Incidence & & & & \\  & Angle: 22.78\({}^{\circ}\) & & & & \\  & Resolution: Azimuth: 4.05 & & & & \\  & m by Range: 7.80 m & 1. & & & \\  & Repeat Cycle: 35 days & & & & \\  & Wavelength: 0.056 & & & & \\  & m, C-band & & & & \\  & Master Image: 20 July 2007 & & & & \\  & Time period: & & & & \\  & December-2002 & & & & \\  & to December-2010 & & & & \\  & 8. & Digital Elevation Model: & & & \\  & SRTM V4 & & & & \\  & 9. & Software Used: ENVI & & & \\  & SARscape, ArcGIS & & & & \\  & (ArcMap 10.2.2) & & & & \\ \hline \hline \end{tabular} \({}^{*}\) GLDAS—Global Land Data Assimilation System.  
\end{table}
Table 2: Data and software used.