esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Encoding to Reject Background Signals
###### Abstract  
Communicating information at the few photon level typically requires some complexity in the transmitter or receiver in order to operate in the presence of noise. This in turn incurs expense in the necessary spatial volume and power consumption of the system. In this work, we present a self-synchronised free-space optical communications system based on simple, compact and low power consumption semiconductor devices. A temporal encoding method, implemented using a gallium nitride micro-LED source and a silicon single photon avalanche photo-detector (SPAD), demonstrates data transmission at rates up to 100 kb/s for 8.25 pW received power, corresponding to 27 photons per bit. Furthermore, the signals can be decoded in the presence of both constant and modulated background noise at levels significantly exceeding the signal power. The system's low power consumption and modest electronics requirements are demonstrated by employing it as a communications channel between two nano-satellite simulator systems.  
LED; GaN; single-photon avalanche diode; optical communications; CubeSats Article
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Calibration
#### 2.2.3 Determining Calibration Parameters  
Prior to performing calibration, a calibration image series must be captured of the calibration plate. The calibration plate is a planar object containing distinct, point-like features, called calibration targets (CTs), at known 3D locations which define the position and orientation of the world CS. A corresponding set of 2D coordinates in the distorted sensor CS are obtained by locating the CTs in the calibration images. These sets of 3D and 2D coordinates are used to solve for the parameters of the camera model, which describe the relationship between the two. Using at least 50 calibration images is recommended since this inverse problem is sensitive to noise in the 3D and 2D coordinates [41; 62].  
Calibration is performed using two steps. First, initial estimates for \(\mathbf{K}_{j}\) and \(\mathbf{V}_{j}\) are obtained using the method proposed by [PERSON] [61] while \(\kappa_{j}^{1}\) and \(\kappa_{j}^{2}\) are set to zero. Secondly, non-linear, least-squares optimization is used to optimize all the parameters by minimizing the total projection error, \(E_{proj}\), given as  
\[E_{proj}=\sum_{l=1}^{L}\bigg{(}\bigg{(}x_{l}^{calc}-x_{l}^{true}\bigg{)}^{2}+ \bigg{(}y_{l}^{calc}-y_{l}^{true}\bigg{)}^{2}\bigg{)}. \tag{5}\]  
Here \(\mathbf{x}_{l}^{true}=\left[\begin{array}{cc}x_{l}^{true}&y_{l}^{true}\end{array} \right]^{T}\) and \(\mathbf{x}_{l}^{calc}=\left[\begin{array}{cc}x_{l}^{calc}&y_{l}^{calc}\end{array} \right]^{T}\) are the locations of the \(l^{\text{th}}\) CT in the distorted sensor CS determined from the calibration image series and predicted by the camera model respectively. There is a total of \(L\) many CTs visible within the calibration image series.
# Machine Learning Reveals Migmatic Fertility of Skarn-Type Tungsten Deposits
District-level ensemble models--random forests and genetic-algorithm-optimized LightGBM--return F-scores of 0.96 when ranking cryptic Cup- polymetallic drill targets [27; 28; 29], whereas CNN, SVM, and LightGBM meta-analyses outperform logistic regression in tungsten-skarn prospectivity mapping across South China's Nanling Belt [30]. Mineral-scale studies mirror these gains: partial least squares and deep classifiers trained on ~4000 magnetite LA-ICP-MS analyses now separate Fe-Sn, Fe-Zn, and W-Mo skarms with >90% accuracy [31; 32], and random forest trace element fingerprints of grandite garnet reliably distinguish Au, Cu, and Zn skarn sub-types even within detrital sediments [33]. Beyond mineralogy, deposit-scale geochemical networks built on sphalerite trace element vectors achieve 94% precision when classifying skarn versus VMS, MVT, and SEDEX environments, underscoring the transferability of ML discriminants across ore systems [34]. Random forest (RF) ensembles excel on geochemical datasets because their bootstrap sampling and feature randomness mitigate over-fitting, handle high-dimensional, noisy or multicollinear element suites, and capture non-linear interactions among proxies such as trace element ratios and isotopes [35]. Because each tree votes independently, RF maintains robust accuracy even when classes are imbalanced or rare anomalies dominate, a common situation in regional mineral-prospectivity studies [36]. Support vector machine (SVM) perform strongly when sample sizes are limited yet predictor space is large, owing to their structural-risk minimization and kernel trick, which project complex geochemical boundaries into linearly separable hyperspaces [37]. SVM offers slightly sharper margins, whereas RF delivers greater interpretability, rendering the two algorithms complementary in exploration workflows [38]. In this study, we employed two widely used supervised learning algorithms--SVM and RF--to train models on global apatite trace element datasets to evaluate apatite-based magmatic fertility for skarn-type tungsten deposits (Figure 1). Cross-validation results demonstrated the efficiency of the trained models in accurately classifying apatite derived from fertile and barren magmas. After verifying the efficacy of the models, we applied them to the Nanling tungsten-tin skarn district in southern China, an extensively studied region hosting numerous known skarn-type deposits with well-constrained geological backgrounds, such as Weijia, Tongshanling, Xihuashan, Shuikoushan, Zhuxi, Dahutang, and Xintianling [39; 40; 41]. Through this investigation, we aimed to uncover fundamental relationships between apatite geochemistry and tungsten mineralization, providing robust scientific frameworks and efficient exploration methods for skarn-type tungsten deposits.
## 6 Protocol Comparison and Security Analysis
### Computational Overhead  
The comparison results are shown in Table 1. In the _X/Y_s in the table, \(X\) represents the calculation times of user operations, and \(Y\) represents the calculation times of satellite operations. In _X/Y/Z_, \(X\) remains the same, \(Y\) represents the calculation times of satellite operations before the switchover, and \(Z\) is the times after the switchover. In communication times, users send messages to satellites, satellites to users or satellites to satellites as communication. We compare three protocols: Fast-access, Handover and our protocol BAPC. The Fast-access and the Handover algorithms are the protocols in [26], and we adopt similar settings in our simulation. Compared with other related protocols, BAPC is better in computing overhead. According to the analysis, two comparison protocols require hash operation, but BAPC does not require hash operation. In addition, the operation of asymmetric encryption, signature and verification operation in BAPC is one times on both the client side and satellite side, which is at the average computation level compared with other protocols. In terms of communication times, the comparison protocols are 2 and 4 times, and BAPC is 2 times, which is less.
## 4 Discussion
### Air Velocity above Water in the Tunnel  
Several cross-sections along the flow direction in the tunnel were chosen to analyze the air flow characteristics in the tunnel. The region denoting the area residual of the tunnel was extracted from these cross-sections according to the volume fraction \(\alpha_{\rm a}>0.5\). This allowed for the further analysis of air velocity, pressure, and other quantities that we care about in the tunnel. As can be seen in Figure 11, the contours of air velocity on this section showed the relative larger gradient on the direction perpendicular to the water surface than the direction perpendicular to the axial plane of the spillway tunnel. The maximum air velocity can be up to 50 m/s, while the minimum value can be close to zero.  
As for the sectional average air velocity along flow direction in the tunnel shown in Figure 12a, due to the mass and momentum input of the air ducts, the air velocity increased suddenly at every intersection of the air ducts and the spillway tunnel. However, as the water flowed downstream, the water depth decreased. The increase of area residual resulted in the continuous decrease of average air velocity. Nevertheless, for the locations near the downstream of the gate where the water surface was disturbed intensively, the air entrainment of the water body led to the loss of air volume. The average air velocity at these sections decreased slightly.  
Figure 11: The regions of residual space along the tunnel and the contours of air velocity on it.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Gate Opening** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Flow Rate** \\ **(m\({}^{3}\)/s)** \\ \end{tabular} } & \multicolumn{3}{c}{**Aerator No. 2**} & \multicolumn{3}{c}{**Aerator No. 4**} \\ \cline{3-7}  & & **Calculated** & **Prototype** & **Error (\%)** & **Calculated** & **Prototype** & **Error (\%)** \\ \hline
25\% & 793 & 31.68\% & 27.79\% & 13.99 & 36.29\% & 32.19\% & 12.74 \\
50\% & 1460 & 20.13\% & 20.72\% & \(-\)2.83 & 24.49\% & 27.00\% & \(-\)9.29 \\
75\% & 2140 & 14.08\% & 17.35\% & \(-\)18.82 & 17.63\% & 22.11\% & \(-\)20.25 \\
100\% & 3220 & 9.33\% & 12.57\% & \(-\)25.80 & 11.54\% & 16.10\% & \(-\)28.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average aeration concentration of cross-sections behind the aerators.  
As for the sectional average air pressure along flow direction shown in Figure 12b, due to the sudden increases of the air velocities at every intersection, the air pressure decreased suddenly according to Bernoulli's law. Generally, the air pressure increased in the flow direction and eventually became atmospheric at the outlet of the tunnel. Furthermore, the air pressure under the 25% gate opening ratio was the largest, while the air pressure under the 75% gate opening ration rather than 100% was generally the smallest.
# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 3 Improved CCDBSCAN Algorithm
### Improved CCDBSCAN Diagnosis Method  
As the original DBSCAN determines that the standard of each cluster is to calculate the Euclidean distance between each point, the data density represented by the Euclidean distance is used for clustering. However, due to the high correlation of the same fault type data, rather than Euclidean distance, the clustering based on correlation density is more reasonable and accurate. The specific diagnosis methods are as follows:  
Definition 1 (_Eps_ neighborhood) the _Eps_ neighborhood of an object \(p\) refers to the region with the object \(p\) as the center and the correlation coefficient greater than _Eps_,  
\[N_{Eps}(p)=\{q\in D\}[r(p,q)\geq Eps] \tag{14}\]  
where \(D\) is the dataset, \(r\) (\(p\), \(q\)) is the correlation coefficient between object \(p\) and \(q\), which means all objects in dataset \(D\) whose correlation coefficient with object \(p\) is not less than _Eps_.  
Other definitions remain unchanged, and the flow chart of CCDBSCAN clustering algorithm is shown in Figure 3.  
Using DBSCAN to deal with DGA data unsupervised, analyzing the correlation and dispersion between the sample vectors of DGA data, classifying according to the correlation of the samples, so that the DGA data samples of the same fault set are as similar as possible, and the data samples of different fault sets are as different as possible. This method is called CCDBSCAN.
# Numerical Solution for the Single-Impulse Flyby Co-Orbital Spacecraft Problem
## 1 Introduction
For geostationary orbits, the study of the co-orbital multi-target flyby/intercept problem is an important basis for traversing all satellites. Previous research mainly addresses the problem of multiple-target flyby/interception on non-coplanar surfaces, and the majority of them use being non-coplanar as a necessary condition. Nevertheless, a singular value phenomenon occurs when the orbits are coplanar or co-orbital, and the calculation is unattainable. Therefore, this study provided a basic definition of the single-impulse flyby two co-orbital target problem, analyzed the constraint relations in the problem to simplify it, and proposed a numerical solution to solve it. The structure of the remainder of this article is organized as follows. In Section 2, a basic description of the problem is given, upon which a mathematical abstraction is constructed initially. In Section 3, the constraint relations in the problem are constructed, reducing the six-dimensional problem to a one-dimensional problem, and the problem's mathematical expression is stated. In Section 4, the relationship between the vertical component of the eccentricity vector and the countdown of transfer time is presented, and a quadratic interpolation secant method without derivatives is proposed for solving the coplanar Lambert problem. In addition, a multi-solution secant method is proposed to solve the co-orbital target flyby problem. In Section 5, several numerical examples demonstrate the method's effectiveness. Finally, the conclusions are provided in Section 6.
## 2 Materials and Methodology
### Effect of Temperature (23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C)
#### 3.1.1 Mixed Recycled Aggregates (MRAs)  
Figure 3 shows the weight loss due to decarbonation (400-500 \({}^{\circ}\)C) before and after forced carbonation, considering the time at which maximum CO\({}_{2}\) uptake was achieved. In general, it was concluded that the increase in temperature from 23 \({}^{\circ}\)C to 40 \({}^{\circ}\)C resulted in lower CO\({}_{2}\) absorption by the mixed recycled aggregates. However, at 60 \({}^{\circ}\)C, two of the MRAs (MRA-RP2 and MRA-RP3) absorbed a quantity of CO\({}_{2}\) higher than that recorded at 23 \({}^{\circ}\)C. For MRA-RP1, the maximum CO2 capture was 12.6%, 3.6%, and 10.8%, at 23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, respectively. For MRA-RP2, the maximum was 22.5%, 10.0%, and 28.7%, respectively. Finally, for MRA-RP3, the maximum values of CO\({}_{2}\) captured were 12.3%, 10.3%, and 14.4% at 23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, respectively. For industrial implementation, it is important to check that CO\({}_{2}\) capture remains viable at a temperature as high as 60 \({}^{\circ}\)C.  
Furthermore, for the tested MRAs, it was found that the maximum CO\({}_{2}\) uptake occurred faster as the temperature increased. For MRA-RP1, the maximum CO\({}_{2}\) capture at 23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C occurred after 5 h, 3 h, and 2 h, respectively. For MRA-RP2, the respective maximum CO\({}_{2}\) uptake was achieved at 5 h, 3 h, and 1 h, respectively. Finally, for MRA-RP3, the maximum CO\({}_{2}\) capture occurred at 12 h, 8 h, and 2 h, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**CO\({}_{2}\) Maximum Uptake (\%)/Carbonation Period (h)**} \\ \cline{3-10}  & & **MRA-RP1** & **MRA-RP2** & **MRA-RP3** & **RCA-RP1** & **RCA-RP2** & **RCA-IW1** & **CA-L** \\ \hline \multirow{3}{*}{Temperature} & 23 \({}^{\circ}\)C & 12.6\%/5 h & 22.5\%/5 h & 12.3\%/12 h & 13.3\%/12 h & 5.2\%/5 h & 34.8\%/12 h & 16.9\%/5 h \\ \cline{2-10}  & 40 \({}^{\circ}\)C & 3.6\%/3 h & 10.0\%/3 h & 10.3\%/8 h & 8.0\%/8 h & 15.5\%/3 h & 20.4\%/5 h & 16.1\%/3 h \\ \cline{1-1} \cline{2-10}  & 60 \({}^{\circ}\)C & 10.8\%/2 h & 28.7\%/1 h & 14.4\%/2 h & 11.9\%/3 h & 25.0\%/1 h & 22.1\%/3 h & 9.3\%/3 h \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum CO\({}_{2}\) capture by weight of cement paste in RAs and carbonation time at varying temperatures.  
Figure 2: Maximum CO\({}_{2}\) uptake content captured by each RA at 23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, in relation to the respective cement paste content (relative humidity of 60% and 25% of CO\({}_{2}\)).
# Feed Rate Variation Strategy for Semi-Conical Shell Workpiece in Ball Head End Milling Process
## 5 Discussion  
In order to improve the machining efficiency and reduce the machining quality losses in the end milling process of a semi-conical workpiece, a feed rate variation strategy is proposed considering the special stiffness distribution, geometry parameters, boundary conditions and practical machining condition of the workpiece. For better understanding, a specific flow diagram of the proposed feed rate variation strategy is given in Figure 18. Before the feed rate planning procedure, the geometry parameters of the cutter and workpiece and the machining parameters of the cutting process should be definite. The cutter-workpiece contact area obtained by the Z-map method and machining parameters is used to simulate the cutting force at each cut segment. Considering the geometry and boundary conditions of the workpiece, the dynamic performance of the workpiece is calculated, and the stability analysis of the machining system is further conducted. The stability of the machining system must be guaranteed; otherwise, the spindle speed needs to be replaced. For each tooth pass segment, the feed rate is set to maintain the vibration response at a user-defined value, and its upper limit is subjected to a user-defined cutting force threshold. Considering the performance of the servo motor and practical condition, an improved discrete feed rate variation is used, and the numerical control (NC) codes are programmed and applied in the practical milling experiment. By analyzing the surface roughness from milling experiments with and without IDFV, the surface roughness variation is so small that it can be ignored.  
Figure 17: Surface roughness for each contour tool path with and without feed rate variation.  
As for the obtained improved discrete feed rate variation, the low feed rate area at the start and the end of each contour path can be selected empirically according to the stiffness distribution of the workpiece. However, the low feed rate area in the middle of each contour path can hardly be noticed until the cutting force simulation. Hence, both cutting force and vibration are needed to be considered in the feed rate planning with virtual machining technology.  
It can also be observed that only 25% of the consuming time is saved, which is not impressive. This is because the machining of the workpiece in the aerospace or mold industry needs to be extremely precise, and time-saving is not the main goal. However, considering the almost same surface roughness, and there is a turning process behind, it can save almost 3750 s, which is still meaningful.
## 3 Validation of the Numerical Model
### Validation of Temperature Field  
Temperature field validation was carried out using a pool fire experiment [41], where temperature measurements were taken at different heights above the pool fire. The temperature-versus-height curves obtained are shown as black lines in Figure 3, with the simulated data represented by red lines. The basic trend and values of the two sets of data are consistent, with differences appearing only near the flame core. This discrepancy is attributed to the fact that the FDS software does not account for the lower temperature in the flame core. However, this error does not affect the calculation of the temperature field outside the flame and can be treated as an outlier. In summary, the model's temperature field prediction can be considered accurate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Size** & **Experimental Value [kg/(m\({}^{2}\cdot\)s)]** & **Analog Value [kg/(m\({}^{2}\cdot\)s)]** & **Inaccuracies** \\ \hline
0.20 m & 0.067 & 0.088 & 31.0\% \\
0.10 m & 0.067 & 0.074 & 10.0\% \\
0.05 m & 0.067 & 0.068 & 1.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental comparison results.  
Figure 2: Grid independence verification.
## 2 Mathematical Formulation
The constraints in Equations (4) and (5) ensure that exactly \(K\) arcs leave and return to the depot. The constraints in Equation (6) establish that a customer node can be visited for exactly one vehicle coming from the depot. Additionally, the constraints in Equations (7) and (8) impose that exactly one arc enters and leaves for each node associated with a customer. The constraints in Equations (10)-(13) help to avoid sub-tours and allow to calculate the position of the customer \(j\) on its respective route. The constraints in Equations (12) and (13) force variables \(y_{0j}\) and \(y_{ij}\) to be zero when \(w^{k}_{0j}=0\) and \(x_{ij}=0\), respectively. Regarding the capacity of the vehicles, the constraints in Equations (14) and (15) allow establishing lower and upper bounds for the cumulative demand of any route. These constraints are derived from a generalization of restrictions in Equations (10) and (11). The constraints in Equations (16) and (17) force variables \(v^{k}_{0j}\) and \(v_{ij}\) to be zero when variables \(w^{k}_{0j}=0\) and \(x_{ij}=0\), respectively. The constraints in Equation (18) ensure that the demand at each node \(i\) is fulfilled and in conjunction with Equations (16) and (17) estimate the load per vehicle. The constraints in Equations (19)-(23) control the arrival time to the nodes (customers). The constraints in Equation (24) estimate the tardiness (in case of violating the priority constraints). Finally, the constraints in Equations (25)-(32) establish the nature of the variables.
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 3 Background
### METIS Algorithm  
Graph partitioning [47] is the process of evenly dividing a large graph into a series of subgraphs to facilitate distributed applications. Each subgraph is stored on a separate machine, allowing for parallel execution between subgraphs. If a current subgraph requires information from another subgraph, a communication overhead is incurred. The quality of graph partitioning directly impacts the storage cost on each machine and the communication cost between machines.  
METIS [17] is a method for vertex partitioning, which is a type of graph partitioning. It assigns graph vertices to different subgraphs while maintaining the integrity of the connections between vertices in each subgraph. METIS is commonly used to achieve efficient and balanced graph partitioning in distributed systems. There are mature software packages available that can be directly used for this type of graph partitioning. In Cluster-GCN, we use this method for graph segmentation to lay the foundation for distributed botnet detection.