esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Prospects  
### Conclusions  
1. Aiming at the traditional Bayes discriminant, which needs to consider whether all the variables in each formation satisfy the multivariate normal distribution, the kernel density estimation method is proposed for factors that do not conform to normality, leading to the development of a KDE-Bayes discriminant model;
2. The KDE-Bayes discriminant analysis model was constructed through existing hydrological borehole pumping test data. Comparing the naive Bayes discriminant model and the KDE-Bayes discriminant model, the KDE-Bayes discriminant model offers higher accuracy and reliability. The obtained water-bearing properties zoning reveals that the high water-bearing properties zone is distributed mainly around working face S1210 and is concentrated in the southern part of the minefield; the moderate water-bearing properties zone is widely distributed in the central part of the study area; the low and the extremely low water-bearing properties zone are mainly found in the northern part of the study area, with scattered occurrences in the central and southern parts of the study areas; and the water-bearing properties of the weathered bedrock aquifer is unevenly distributed;
3. The water-bearing prediction results were validated based on the water evacuation data and water inrush point information from the S1210 working face. The predicted results are in good agreement with the actual outcomes. This indicates that the model has high accuracy and provides a valuable reference for the prediction of the water-bearing properties of the weathered bedrock in the southern wing of the Ningtiaota Coal Mine and surrounding mines.
## Results  
### Data Collections at High Spatial or Stand Level Resolution  
#### 3.1.1 The NC2 Flux Tower  
For this study, daytime average incident PARi and GEP were calculated for the time period between 09:30 and 15:30, to represent the fluxes during the flights, for all days over the entire 2013 year (Figure 3A,C). In these data series there is significant variability from day to day, with a summer maxima for daily averaged GEP of 31.2 \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) on DOY 188 (7 July); and a PARi maximum of 1981 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) three weeks earlier, on DOY 170 (19 June). The daily averaged fluxes at the time of the FLEX-US campaign flights (26-27 October) were approximately 50% of the summertime maximum GEP (16.8 and 16.6 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), respectively) and approximately 60% of the maximum PARi (1231 and 1162 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), respectively).  
We examined the diurnal change in GEP and PARi within the days of the flights, as well as for two days before and after the campaign (Figure 3B,D), at half hourly intervals. Photosynthetic light use efficiency (LUE) calculated from tower fluxes as GEP/PARi shows a strong diurnal variation with noontime LUE values of 0.013 \(\upmu\)mol CO\({}_{2}\)\(\upmu\)mol\({}^{-1}\) photons on both of the flight days, with LUE increasing into the afternoon to achieve much higher values, 0.036 \(\upmu\)mol CO\({}_{2}\)\(\upmu\)mol\({}^{-1}\) photons (Figure 3F). For this coniferous evergreen site the daytime average LUE varies widely from day to day, mirroring irradiance, but the daytime averages for LUE during the October flight days were not significantly different from midsummer values (Figure 3E).
## Appendix A Information on Geometry Dimensions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Geometry** & **Parameter** & **Value** & **Geometry** & **Parameter** & **Value** \\ \hline Rectangular prism & \(h_{\text{rp}}\) & 7.00 & & \(r_{\text{cyc}}\) & 8.60 \\ \(L_{\text{rp}}\) & 29.00 & & \(L_{\text{type}}\) & 30.00 \\ \(W_{\text{rp}}\) & 15.00 & & \(W_{\text{cyc}}\) & 19.86 \\ \hline Hollow rectangular prism & \(h_{\text{hrp}}\) & 17.80 & & \(r^{\prime}_{\text{cy}}\) & 19.00 \\ \(L_{\text{hrp}}\) & 32.00 & & \(W_{\text{all-cylinder}}\) & \(L^{\prime}_{\text{cyf}}\) & 28.80 \\ \(L_{\text{prism}}\) & \(W_{\text{hrp}}\) & 18.00 & & \(W_{\text{cyf}}\) & 18.00 \\ \hline \multirow{4}{*}{Cylindrical hemisphere} & \(h_{\text{hy}}\) & 1.44 & & \(r^{\prime}_{\text{cyh}}\) & 3.00 \\ \(L_{\text{rp}}\) & 8.40 & & \(W_{\text{all-cylinder}}\) & \(L^{\prime}_{\text{cyh}}\) & 28.77 \\ \cline{1-1}  & & & \(h^{\prime}_{\text{cyh}}\) & 15.00 \\ \cline{1-1} \cline{2-5}  & \(r_{\text{sp}}\) & 22.70 & & \(r^{\prime}_{\text{cyc}}\) & 6.90 \\ \cline{1-1} \cline{2-5}  & & & \(h^{\prime}_{\text{self-cylinder}}\) & \(L^{\prime}_{\text{cyc}}\) & 28.80 \\ \cline{1-1} \cline{2-5}  & & & \(W_{\text{all-cylinder}}\) & \(W_{\text{cyc}}\) & 18.00 \\ \hline \multirow{4}{*}{Hollow concave hemisphere} & \(r_{\text{sph}}\) & 16.00 & & \(h_{\text{p}}\) & 26.50 \\ \cline{1-1}  & & & LPA inverted & \(L_{\text{bs}}\) & 28.20 \\ \cline{1-1}  & & & trapezoidal prism & \(W_{\text{bs}}\) & 28.28 \\ \cline{1-1}  & & & prism & \(L_{\text{bip}}\) & 14.00 \\ \cline{1-1} \cline{2-5}  & & & & \(W_{\text{bip}}\) & 15.21 \\ \hline \multirow{4}{*}{Hemisphere} & \(r_{\text{spc}}\) & 22.97 & & \(h_{\text{ph}}\) & 21.30 \\ \cline{1-1}  & & & LPA inverted & \(L_{\text{bsh}}\) & 28.00 \\ \cline{1-1}  & & & hollow & \(W_{\text{bsh}}\) & 28.28 \\ \cline{1-1}  & & & trapezoidal prism & \(L_{\text{bih}}\) & 14.00 \\ \cline{1-1}  & & & prism & \(W_{\text{bih}}\) & 15.21 \\ \cline{1-1}  & & & & \(W_{\text{bip}}\) & 15.21 \\ \hline \multirow{4}{*}{Inverted cone} & \(h_{\text{cone}}\) & 27.25 & & \(h_{\text{pc}}\) & 25.00 \\ \cline{1-1}  & \(r_{\text{b}}\) & 7.75 & & \(L_{\text{bsc}}\) & 35.00 \\ \cline{1-1}  & \(r_{\text{t}}\) & 14.25 & & \(W_{\text{bsc}}\) & 15.21 \\ \cline{1-1} \cline{2-5}  & & & LPA trapezoidal prism & \(L_{\text{bipc}}\) & 28.00 \\ \cline{1-1} \cline{2-5}  & & & & \(W_{\text{bipc}}\) & 28.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The DW-made resonator: The dimensions (in mm) offering the best MLM in the numerical simulations.
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 2 Materials and Methods
### Training Load Measures  
#### 2.4.1 External Training Load  
The external training loads were obtained with time-motion data: total distance (TD) covered (m), average speed (AvS), maximal running speed (MRS) (m\(\cdot\)s\({}^{-1}\)), relative high-speed running (rHSR) distance (m), high metabolic load distance (HMLD) (m), sprinting (SPR) distance (m), dynamic stress load (DSL) (a.u.), number of accelerations (ACC) and number of decelerations (DEC). The GPS software provided information only about the locomotor categories above 19.8 km\(\cdot\)h\({}^{-1}\): rHSR (19.8-25.1 km\(\cdot\)h\({}^{-1}\)) and SPR (\(>\)25.1 km\(\cdot\)h\({}^{-1}\)). The sprints were measured by number and average sprint distance (m). The HMLD is a metabolic variable defined as the distance, expressed in meters, covered by a player when the metabolic power exceeds 25.5 W\(\cdot\)kg\({}^{-1}\). HMLD variables include all high-speed running, accelerations and decelerations above 3 m\(\cdot\)s\({}^{-2}\)[39]. Both acceleration variables (ACC/DEC) considered the movements made in the maximum intensity zone (\(>\)3 m\(\cdot\)s\({}^{-2}\) and \(<\)3 m\(\cdot\)s\({}^{-2}\), respectively). DSL variables were evaluated by a 100 Hz triaxial accelerometer integrated into the GPS devices. The sum of the accelerations in the three orthogonal axes of movement (X, Y and Z planes) comprise the following magnitude vector (expressed as a G force):  
\[\left(\left(a_{y1}-a_{y-1}\right)^{2}+a_{x1}-a_{x-1}\right)^{2}+\left(a_{z1}-a_ {z-1}\right)^{2}\]  
where \(a_{x}\) = medio-lateral acceleration, \(a_{y}\) = anteroposterior acceleration and \(a_{z}\) = vertical acceleration. The DSL was expressed in arbitrary units (a.u.) [40].  
The high-intensity activity thresholds were adapted from previous studies [41; 42]. The GPS variables were recorded for each individual player. Training data were excluded from the analysis in the case of data collection errors, injury events, missing training or early withdrawal. The exclusion criteria resulted in the elimination of 36 observation cases.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 2 Taxonomy of Malware in the Smart Home Network
### Attacks Based on the Smart Home Architecture
#### 2.1.4 Perception Layer  
This is considered one of the closest levels for the environment, wherein it is responsible for collecting packets and converting this information into digital signals and identifying objects [31]. The communication between the nodes in this layer and the attacker can directly access the devices-related attributes through physical attacks such as tag cloning and forgery attacks wherein hackers can easily spy on them. This layer has one of the main technologies consisting of various sensor nodes, cameras, and actuators, RFID readers, mobile phones, tablets, GPS, and others to communicate in the smart home. The technologies of the perception layer are usually affected by the energy and the computer [32]. This sensor device can operate in a hostile environment, and it can also be destroyed easily. The malicious attack on the sensor is considered one of the main challenges for the perception layer and the identification technology, which interferes with the data collection [33]. This attack on the perception layer generally aims at destroying communication and data collection. The entire system has a direct effect on efficiency.  
* Spoofing attack: The attacker uses many different fake source addresses by sending packets on the network across. By amplifying the attack, the attacker disguises a tag as a valid tag, which gains the same permission and service as the valid tag [34]. Spoofing attacks may lead to packet loss in the transmission process [35].
* Sleep deprivation attack: The perception layer is limited by the battery power in the node. To prolong the life of the battery, it is necessary for the device to sleep when not in operation. This type of attack attempts to subvert this process by constantly controlling and sending information to the network devices [36].
* Radio frequency jamming attack: This attack targets one of the key technologies of this layer, which consists of sensor nodes, cameras, actuators, tags / RFID readers, cell phones, tablets, GPS, and others to communicate in the smart home. The attacker can destroy the data collection process at the perception layer.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 3 Equipment Debugging and Simulation Analysis
### Simulation Analysis of the Robotic Arm
#### 3.2.3 Workspace Analysis of the Robotic Arm
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Working Position** & **Position and Attitude Information (World Coordinates)** \\ \hline
1 & Robotic arm base position & \((540,-425,675)\) \\ \hline
2 & Initial working position & \(J1\sim J6;(0^{\circ},-120^{\circ},120^{\circ},-180^{\circ},90^{\circ},0^{ \circ})\begin{pmatrix}-712.22,-81.86,370.22,\\ -123.03,112.8,29.8,U(-93.69)\end{pmatrix}\) \\ \hline
3 &
\begin{tabular}{c} Initial posture: J1 \(\sim J6;\begin{pmatrix}0^{\circ},-121.1^{\circ},&113.5^{\circ},\\ -180^{\circ},-70.7^{\circ},-300.2^{\circ}\end{pmatrix}\end{pmatrix}\) \\ \hline
3 &
\begin{tabular}{c} Treatment coordinate: \(\begin{pmatrix}-185,&45.05,538.1,&85.18\\ 80.95,-84.47,U(314.69)\end{pmatrix}\) \\ Discontinuance posture: J1 \(\sim J6;\begin{pmatrix}20^{\circ},-126^{\circ},114^{\circ},\\ -161.5^{\circ},-71.15^{\circ},-295.85^{\circ}\end{pmatrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental parameters of the confined robotic arm welding attitude. Through the analysis of experimental results, it can be seen that this scheme can completely cover the welding parts required for this working condition.
###### Abstract  
Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional-Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller's behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method's effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system's robustness and potential for real-time applications in power electronics.  
DC-DC converters; finite control set; boost converter; non-minimum phase; hardware-in-the-loop +
Footnote †: journal: applied sciences  
Article  
Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional-Integral Controllers
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation
### Resistivity to DoS and DDoS Attacks  
To show that our 5 GSBA can fight against DoS and DDoS attacks, we further design two simulations to compare all related protocols. All simulation results show that the 5 GSBA can achieve a high level of security functionality with reasonable delays.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 1 Introduction
By leveraging the finite element simulation to stimulate the actual engineering boundary conditions to assess the designed mirror component, and to further enhance the design during iteration, an optimal solution is finally obtained. A series of environmental tests, including small-scale sinusoidal scanning, sinusoidal vibration, and random vibration, have been carried out on the mirror assembly. The test results revealed that the design of the mirror component in this paper satisfied the requirements for the remote sensing payload in space.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Recovery Mechanisms
#### 4.2.2 Role of Potential Determining Ions (PDIs)  
The PDIs in the EW polymer solution affect the polymer adsorption and incremental recovery differently. The presence of SO42- ions in seawater or EW is the key factor for wettability modification, as it reduces the carbonate rock surface potential, promoting oil detachment by Ca2+ or Mg2+ [171; 172; 173; 174]. [PERSON] et al. [135] performed a detailed study in order to analyze the effects of the PDIs and the pH on oil recovery using EWPF in a carbonate reservoir. Coreflooding experiments showed that use of the neutral HPAM polymer solution containing SO42- ions resulted in a considerable decrease in S\({}_{\text{or}}\) and less polymer adsorption compared to Ca2+ ions (Figure 5a). The incremental oil recovery was 12.3% for this case, the highest value among all cases (Figure 8a). Contact angle measurements using the captive droplet method also confirmed the wettability change towards more water-wet conditions in the case of the neutral polymer solution with a higher SO42- concentration (Figure 8b). The reason for the lower oil recovery in the case of the polymer solution containing Ca2+ ions was the higher polymer adsorption and permeability reduction due to the precipitation of Ca2+ ions.  
Figure 7: Illustration of governing mechanisms for incremental oil recovery using hybrid LSPF: (**a–d**) the functions of LSW [58; 169] (**e**) the polymer stability in LSW; (**f**) the formation of the oil bank [170]; (**g**) the final outcomes of the process, i.e., detachment, mobilization, and displacement of the residual oil bank by combined LSPF.
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 4 Experiment and Result  
The network proposed undergoes training and testing on a personal computer with NVIDIA GTX 1080 Ti dual GPUs. This network includes a point cloud image feature fusion network based on PRF and an IF-Fusion image multi-scale feature fusion network. Figure 6 shows the final output effect. This study divides the 7481 training frames from the KITTI dataset [4] into a training set and a validation set at approximately a 1:1 ratio. For assessment, this study follows the simple, moderate, and hard difficulty classifications proposed by KITTI. The network is optimized using the Adaptive Moment Estimation (Adam) algorithm. The initial learning rate is set to 0.002, with a weight decay of 0.001 and a momentum factor of 0.9. The model is trained end-to-end for approximately 50 epochs, with a batch size of 4. The balancing weights \(\lambda\) in the loss function are configured to 5.  
The experiment consists of three parts. Initially, the study conducts trials on the challenging KITTI dataset. It validates quantitative results by projecting 3D bounding boxes onto 2D images and comparing them with the most advanced networks. Subsequently, it selects different parameters, specifically k values, within PRF to find the most effective outcome. Following this, the study confirms the performance of IF-Fusion independently, demonstrating its effectiveness in detecting small objects. Finally, the study presents ablation research to evaluate the contribution of each method.
## 3 Efficient Hierarchical Co-Occurrence Graph Convolution Networks  
### Model Architecture  
Figure 2 illustrates the structure of the EHC-GCN. Firstly, after data preprocessing, two input branches of identical size are obtained: motion velocity and bone features. Each input sequence can be represented by a tensor of dimensions \(C_{in}\times T_{in}\times V_{in}\), where \(C_{in}\), \(T_{in}\), and \(V_{in}\) represent the channels, frames, and joints, respectively. These inputs are processed as streams into the network, with each branch sharing the same network architecture. Each input branch includes a BatchNorm layer, an initialization ST-GCN module [7], and the efficient hierarchical co-occurrence Graph Convolution Network (EHC-GCN) module proposed in this paper. The details of the module will be explained in the following subsections. Following the input branches, we integrate the feature maps from the two branches with concatenation operation. The main branch also employs two EHC-GCN modules. Finally, the output feature maps undergo global averaging to create a feature vector, followed by the application of a fully connected layer to classify the action category.  
Figure 2: The overview of the proposed EHC-GCN model.
## 3 Results and Discussion  
### Density of Fillers  
The microparticles will not occupy the entire volume of the cavity due to the shape of the particles, so there will be some empty space occupied by air. Therefore, the effective density of the filled cavity will not be the same as the density of the individual particles. The effective density and the average fill factor were needed to accurately determine the average mass added to each cavity. The fill factor represents the volume of particles as a function of cavity volume. The fill factor was determined by first measuring the mass before and after adding the filler to the cavity, which can be used to determine the experimental density based on the volume of the cavity. Then the fill factor was determined by dividing the experimental density by the manufacturer's measured density of the particles. If there were no air gaps, then the density values would be equal, and the fill factor would be 100%. The effective densities of the three powders used are illustrated in Table 1, where the average density is based on the volume and the average mass used to fill an individual cavity. The individual particles still have the same density as determined by their specifications, but due to gaps between particles, the effective density was decreased. The average fill factor was dependent on size of the particles, as smaller particles resulted in a higher fill factor. However, since all the filler particles had a similar size, the fill factors were similar, resulting in W having the highest effective density, followed by Mo, and then Silica, which was expected. The fill factor could be improved by using a compression method or smaller particles, but for this study we wanted to determine the effects of fillers with different densities.
## 5 Research Results and Discussion
### Model Testing
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**MODEL**} & \multicolumn{8}{c}{**FACILITY**} \\ \cline{2-10}  & **Loc.** & **Loc.** & **Loc.** & **Loc.** & **Loc.** & **Loc.** & **Loc.** & **Loc.** & **Loc.** \\ \hline \multirow{4}{*}{UBO} & Model 1 & 6636 & 6027 & 1259 & 5019 & 2569. & 3416 & 3417 & 2833 \\  & Model 2 & 6580 & 6168 & 1810 & 4939 & 2587 & 3349 & 3240 & 3063 \\  & Model 3 & 6832 & 6106 & 2691 & 4759 & 3049 & 3327 & 3115 & 2979 \\  & Model 4 & 6356 & 5896 & 3578 & 5431 & 3506 & 2814 & 3065 & 2975 \\  & Model 5 & 6705 & 5182 & 3746. & 5418 & 3345 & 2592 & 3282 & 3612 \\  & Model 1 & 4115 & 3806 & 696 & 3159 & 1516 & 2199 & 2198 & 1925 \\  & Model 2 & 4199 & 3985 & 988 & 3226 & 1587 & 2215 & 2166 & 2052 \\ UBV & Model 3 & 4421 & 3935 & 1759 & 3068 & 1993 & 2199 & 2058 & 1978 \\  & Model 4 & 4042 & 3724 & 2316 & 3458 & 2261 & 1831 & 2000 & 1957 \\  & Model 5 & 4238 & 3297 & 2410 & 3443 & 2162 & 1697 & 2124 & 2327 \\  & Measured UBO & 6639 & 6283 & 3857 & 4799 & 2637 & 3049 & 3858 & 2831 \\  & Measured UBV & 4159 & 4030 & 2473 & 3053 & 1589. & 1972 & 2513 & 1936 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparative overview of measured and UBO and UBV values obtained through all five analyzed models. Model 4 is not accepted because the constant (Sig. = 0.112) and coefficients with the variable UPP (Sig. = 0.158) and with the variable BD (Sig. = 0.210) at UBO, as well as the constant (Sig. = 0.109) and coefficients with the variable UPP (Sig. = 0.185) and with the variable BD (Sig. = 0.277) at UBV, come out of the given accuracy threshold (Sig. < 0.05)  
In practical application, Model 4's values could be taken into account and viewed as an indicative estimate of the expected error for other objects with similar characteristics, which can be seen in Tables 16 and 17. In this case, the limitations for the above model would be that it is possible to apply it in cases where the total sales area of the shopping centre (UPP) has a value between 4000 and 30,200 m\({}^{2}\) and for towns of a size between 18,862 and 77,717 households (BD).