esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Facilities and Procedures  
### Facilities  
Figure 7 is a schematic of a SPWJ erosion test device. A SPWJ generation system was used in this experiment, which was independently developed and processed by our research team. A SPWJ is generated by the system and acts on the rock mass specimen. Specifically, the hydraulic pump provides the hydraulic power source, which passes through the reversing valve. Power hydraulic oil is supplied to the SPWJ generating device, which drives the extrusion plunger in the generating device to move forward after entering the generating device. The hydraulic oil drives the reversing slide valve in the cavity to move and controls the reciprocating movement of the extrusion plunger back and forth through the slide valve. Water is supplied to the front of the generating device through a water pump and a one-way valve, and this action occurs in the backward movement gap  
Figure 6: Schematic of the erosion mechanism of a pulsed jet on a rock surface.  
of the squeezing plunger, supplying water to the front part of the generating device. The reciprocating plunger periodically squeezes the water in the front part and forms a water jet through the nozzle, eroding and destroying the rock sample fixed on the clamping device. Rock fragments falling from the test pieces are splashed onto the platform for recycling. In addition, the adjustable range of the hydraulic pump is 0-15 MPa, the adjustable range of the water pump is 0-56 MPa, the pressure increase ratio can reach 5.8, and the highest output pressure of the PWJ generator can reach 80 MPa. As shown in Figure 7, three pressure sensors (model: SDA1000), calibrated by the manufacturer with an accuracy of \(\pm\)0.1% FS, are installed on the hydraulic oil inlet, hydraulic oil outlet, and pressurization chamber, and the changes in hydraulic oil inlet pressure, hydraulic oil outlet pressure, and pressure in the pressurization chamber are obtained, respectively. During each test, the pressure changes at three positions in the entire experimental process can be obtained directly from the data acquisition system. In addition, the nozzle is installed at the center of the outlet on the right side of the pressurization chamber, and the rock sample is fixed on the clamping device. The clamping device can move axially in the range of 0-3000 mm, and thus the target distance from the nozzle outlet to the specimen surface can be continuously adjusted by moving the clamping device. After the nozzle diameter is normalized, this distance is defined as the dimensionless standoff distance S.
# Genesis of the Baiyangping Cu-Co and Pb-Zn Mineralizations in Lamping Basin, SW China
## 5 Discussion  
### Ore-Forming Conditions  
Fluid inclusions from the Cu\(-\)Co ores in quartz and carbonates of the Baiyangping deposit show homogenization temperatures of 146\(-\)233 \({}^{\circ}\)C, with the majority between 160 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, and salinities of 2.7\(-\)24.8 wt % NaCl equiv (Figure 7). Fluid inclusions from the Pb-Zn ore indicate that the homogenization temperatures are between 137 \({}^{\circ}\)C and 175 \({}^{\circ}\)C, with a predominant temperature range between 120 \({}^{\circ}\)C and 160 \({}^{\circ}\)C. The salinities range from 5.9 to 22.8 wt % NaCl equiv. These data indicate that the mineralizing fluids are characterized by medium-low temperatures and relatively variable salinities [31; 32]. However, the ore-forming fluid of the Cu-Co ore may have had a slightly higher temperature and similar salinities relative to the Pb-Zn ore (Figure 7). Previous research has shown that the deposition of Cu-Co-Pb-Zn minerals is affected by factors such as mineral liquefaction composition and co-existing minerals as well as by physical and chemical conditions [32; 33; 34].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Stage** & **Mineral** & **\(\mathbf{\xi^{34}}\)S (V-CDT)\%** \\ \hline BYP15D03B10 & Cu–Co stage & tetrahedrite & +7.9 \\ BYP15D03B11 & Cu–Co stage & tetrahedrite & +6.6 \\ BYP15D03B12 & Cu–Co stage & tetrahedrite & +7.0 \\ BYP15D03B2 & Cu–Co stage & tetrahedrite & +8.2 \\ LZP15D02B13 & Cu–Co stage & tetrahedrite & +5.2 \\ LZP15D03B1 & Cu–Co stage & tetrahedrite & +9.0 \\ LZP15D03B6 & Cu–Co stage & tetrahedrite & +6.9 \\ FLC15D01B1-2 & Pb–Zn stage & sphalerite & +7.2 \\ FLC15D01B11 & Pb–Zn stage & sphalerite & +9.0 \\ FLC15D01B7 & Pb–Zn stage & sphalerite & +8.2 \\ FLC15D01B1-1 & Pb–Zn stage & sphalerite & +7.5 \\ FLC15D01B4 & Pb–Zn stage & sphalerite & +7.8 \\ FLC15D01B14 & Pb–Zn stage & sphalerite & +8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Histogram of the sulfur isotope composition of each stage from the Baiyangping Cu–Co–Pb–Zn deposit.
## 4 Key Design Requirements and System Components
### Layer 3: NW Providers or MNOs  
Layer 3 is the NW providers or MNOs, which are pivotal entities tasked with managing NW resources and ensuring comprehensive wireless communication coverage. NW providers work collaboratively under a decentralized agreement to allocate and optimize the use of NW resources, such as bandwidth and infrastructure, to maximize efficiency and service quality. SSFXG enables a decentralized approach for flexible and responsive management of NW resources, adapting to subscribers' needs and evolving wireless technology. It promotes a cooperative environment among NW providers, sharing resources and coverage responsibilities collectively. This ensures seamless wireless coverage and uninterrupted, high-quality communication services across different locations.
## 2 Related Work
### Traditional Ad Hoc Network Route Selection  
In ad hoc network application, the packet routing is critical for optimizing transmission delay, throughput, and packet loss. There are two types of routing methods commonly used in ad hoc networks: proactive routing and reactive routing. In proactive routing, the OLSR protocol [12] is to store the information of each relay node into the routing table by sending HELLO packets in advance, and then select the shortest path from routing table. The DSDV protocol [13] uses the Bellman-Ford algorithm to select relay nodes in the routing table. Although these approaches allow the optimal route to be selected, they require a large amount of information to be exchanged among all nodes. Especially when the nodes are dynamic, this leads to a rapid increase in the amount of information exchanged. Nevertheless, in the CBTC system, the trains move rapidly, and transmission latency is strict; thus, it is more suitable to use reactive routing.  
In reactive routing, nodes cannot know global information of the whole network and can only make decisions for selecting the next-hop node. In [22], the GPSR protocol was derived, and the node which is closest to the destination within the communication range was selected as next-hop node. This minimizes the number of hops in the entire link, thus reducing latency. In order to solve the high outage probability caused by the high speed movement of vehicles, the authors of [23] proposed a hybrid relay node selection strategy. The relay with the best channel quality and the relay closest to the destination were simultaneously selected as the next-hop node. Although this method improved the transmission successful rate, the overhead of transmitting both packets simultaneously was huge.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 3 Traffic-Assisted BOF Vulnerability Mining and Exploitation Method in ADASs
### Firmware Extraction  
Firmware is a type of software that is written into hardware devices to control their application and system functions. We can obtain the corresponding binary files for the business program by extracting and analyzing the firmware. Thus, we proceed with the subsequent BOF vulnerability discovery.  
There is no universal method for extracting firmware from devices. We list four practical firmware extraction methods from ADAS devices: update package interception, console command extracting, debug interface extracting, and flash dumping.  
1. Update package interception. We can capture the device-to-cloud communication traffic through the automatic update function of the corresponding PC software for the device. If the update package that we obtain is complete, we extract the complete firmware from the update package by combining the reverse engineering results of the client program.
2. Console command extracting. We use a dictionary to brute-force Telnet/SSH login passwords for devices that can be accessed through the console. If we crack the password, we can log in to the console and use console commands to extract the corresponding binary file (such as dd) and transfer it back to the local machine (such as scp).  
Figure 4: Traffic-assisted BOF vulnerability mining process.  
3. Debug interface extracting. If a debug interface (such as SWD/TTAG) exists on the ADAS device, we use the testing interface to extract the firmware.
4. Flash dumping. However, if none of the above methods can be used, we only remove the flash storage chip from the device board, connect the programmer to read the chip content, and write a program according to the file system to restore the binary file.
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 3 Calibration of SMAP-R Signals
### SMAP-R Calibration Method
In the examples shown in Figure 12, in the case of 40\({}^{\circ}\) incidence angle, most of the delay-Doppler bins near the specular point are not affected by the SMAP antenna footprint and, as the delay increases, the filtering effect starts to be noticeable. In the case of 42.5\({}^{\circ}\), the effect is noticeable for all DDM bins, including the bin corresponding to the DDM peak. Therefore, in order to make consecutive measurements that are comparable with information from all incidence angles there is a need to compensate for the loss of power caused by a filtered scattering surface. Also, note that the mapping of the surface area contributing to each delay-Doppler map bin has the same shape at that observed in the scattering of the measurements in Figures 11b and 11a, for 40\({}^{\circ}\)and 42.5\({}^{\circ}\), respectively. \(\overline{B}(\tau,\ f_{d})\) has been precomputed for multiple geometries, from 37.3\({}^{\circ}\) to 42.7\({}^{\circ}\) incidence angles in steps of 0.1\({}^{\circ}\). The resulting calibration equation is shown in Equation (4) and Equation (5), i.e., the radar cross section \(\sigma_{0}\), which is the same equation used in CYGNSS calibrations but slightly modified to the SMAP-R characteristics, for the incoherent and coherent assumptions. \[\overrightarrow{\rho}\]0  
\[\overrightarrow{\rho}\]1  
\[\overrightarrow{\rho}\]2 Spatially filtered effective surface scattering area used for (**a**) 40\({}^{\circ}\) and (**b**) 42.5\({}^{\circ}\) incidence angle.
## 2 Experimental Method and Results
### Experimental Results of Determining the AE Source Location on the Steel Plate  
In Figure 2, the results of 1D linear location technique implementation with the UT-1000 transmitting transducer connected to the INTERUNIS-IT acoustic simulator were presented. The histogram of the AE source location pattern was figured with the coordinate axis discretization step of 10 mm.  
As can be seen from Figure 2, the transmitting transducer positions on the steel plate were highlighted by red color, while the fixed locations of the receiving transducers were marked as the green one. The dark blue columns displayed the distribution of AE events N\({}_{\rm I}\) recorded at each position of the AE simulator (UT-1000) on the distance varied from 100 to 700 mm from the AE sensor #1. It follows from the column graph that the highest reduced error was noted when the acoustic source was located at the \(X\) = 100 mm coordinate.  
Figure 2: Experimental result of AE source linear location recorded on a steel plate. \(\blacksquare\)—AE source location; \(\blacksquare\)—AE sensors location, \(X\)—coordinate, \(N_{l}\)—number of recorded AE events (responses from the AE source).  
When the AE simulator was mounted on the plate at a distance of less than 200 mm from AE sensor #1, it was impossible to clearly determine its position due to the scattering of AE signals. Since the distance between the AE source and the receiving transducers increased, the range of the calculated coordinates has tended to a noticeable decrease. Thus, the positioning error \(\gamma\) of the linear location technique did not exceed 2.5% in the coordinates \(X=400\) and 500 mm. The results of experimentally determined \(\gamma\) which is basically dependent on the acoustic source location and the amplitude of AE signals were given in Table 1.  
From Table 1 it can be derived that the highest average error of the AE source location was registered when the acoustic simulator of low-amplitude signals located in the coordinate \(X=100\) mm. In case the acoustic source was mounted at the center between two receiving transducers, the location error equaled to \(\gamma_{av}\approx 2\%\).
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 3 Method
### Pseudo-Image-Based Feature Extraction Branch  
This branch projects the point cloud onto the BEV to create a pseudo image and then uses the 2D feature-extraction network to extract features.  
We refer to the practice of PointPillars [9] to convert point clouds into pseudo images. First, the point cloud space is discretized into cylinders. Second, according to the description in PointPillars, the original four-dimensional feature of the point cloud is expanded to a nine-dimensional feature. Third, for the points in each pillar, a small PointNet [23] network is employed for feature coding. When the coding features output by PointNet are scattered to the position of each pillar, a pseudo image with a size of \(C\times H\times W\) is created, where C, H, and W represent the channel, height, and width, respectively, of the feature map.  
We input the created pseudo image into a 2D feature-extraction network for feature extraction. The 2D backbone network is shown in Figure 3. First, we use the convolution layer and maximum pooling layer to compress the size of the pseudo image, that is, we input the pseudo image of \(C_{in}\times H\times W\) to obtain the original feature map of \(C_{out}\times\frac{H}{4}\times\frac{W}{4}\). Second, the original feature map is multilayer convolved while the size of the feature map is kept unchanged to obtain the feature map \(F_{1}\) after the first feature extraction. Third, we use multilayer convolution to further compress the size of feature map \(F_{1}\), extract more abstract features, and obtain a new feature map \(F_{2}\). We input feature maps \(F_{1}\) and \(F_{2}\) into a deconvolution layer to adjust the number of channels of \(F_{1}\) and restore \(F_{2}\) to the width and height dimensions of the original feature map to obtain feature maps \(F_{3}\) and \(F_{4}\). Last, we concatenate \(F_{3}\) and \(F_{4}\) in series as the output features of this branch and input them into the M-BEV FF module (Section 3.3).
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 5 Results
### Dataset 3: Suction Cups  
The goal of this case study is to detect the occurrence of a failure as soon as possible. Unlike the case packer case study, in which the available fault conditions could not occur in sequence, in this case, the two available fault conditions correspond to two different severities of the same fault mode. Hence, the AAD + ADP algorithm is applied from scratch to signals corresponding to all conditions. The peak, the peak-to-peak, the mean, and the RMS are selected through the Pearson correlation analysis used as the input of the streaming algorithm. Results are shown in Figure 10 and summarized in Table 6. The first behavior change is detected after five cycles, equal to 0.67 s, while the second change is detected as it occurs, with zero latency. For each condition, a cluster is created and points are correctly assigned to the clusters. In addition, 55 false alarms have been avoided.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault**} & **Cycle of Detection** & **Latency (s)** & **N Created Clusters** & **N Points** & **False Alarms** \\ \hline Fault s1 & 124 & 0.67 & 1 & 88 & \\ Fault s2 & 212 & 0 & 1 & 21 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances of the clustering-based from scratch approach (suction cups).  
Figure 9: Results of the AAD + ADP algorithm applied to (a) features selected through the Pearson correlation analysis and (b) features extracted through the PCA (case packer).  
Note that, in both cases, there are several data points considered anomalous. However, none of them creates a new cluster. These points correspond to true anomalies in the data, such as measurement errors or anomalous peaks, which are evident in the raw signals.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 6 Results and Discussion
### Detection Complexity Results  
In order to obtain fair comparisons, all systems are using optimal ML detection criterion. Table 3 shows a comparison of the detection complexity for all systems considering the two configurations used as study cases. The results show that considering the \((4\times 2)\times 8\times 8\) configuration with an SE of 8 bpcu, the RIS-MU-DQSM-I, and the RIS-MU-DQSM-II systems have 25% and 16% lower detection complexity, respectively, when compared with the conventional MU-MIMO-SMux scheme. Considering the \((8\times 4)\times 32\times 32\) configuration with an SE of 12 bpcu, the proposed RIS-MU-DQSM-I and the RIS-MU-DQSM-II systems have 56% and 51% lower detection complexity, respectively, when compared with the conventional MU-MIMO-SMux system used as reference. The AF-MU-DQSM system used as a second reference has the same detection complexity as the RIS-MU-DQSM-II system since they are using the same modulation, precoding, and detection strategies.  
\begin{table}
\begin{tabular}{c c} \hline
**Operation** & **Complexity** \\ \hline \(\mathbf{Q}_{k}=\mathbf{H}_{k}\mathbf{W}_{k}\) & \(8N_{r}^{2}N_{s}\) \\ \(\mathbf{Q}_{k}\mathbf{D}\) & \(8N_{r}^{2}2^{m}\xi\) \\ Subtractions & \(2N_{r}2^{m}\) \\ \(\|\cdot\|_{F}^{2}\) & \(3N_{r}2^{m}\) \\ MRC & \(2N_{r}2^{m}\) \\ Ordering & \(2(2^{m})\) \\ \(\mathbf{Q}=\mathbf{H}_{k}\mathbf{G}\) & \(8N_{r}N_{s}N_{t}\) \\ \(\mathbf{P}=\mathbf{Q}\mathbf{Z}_{k}^{Eq}\) & \(8N_{r}^{2}N_{t}\) \\
**PD** & \(8N_{r}^{2}2^{m}\xi\) \\ \hline \end{tabular}
\end{table}
Table 2: Detection complexity.
## 5 Discussion
### Maximum Seam Opening Displacement of the Lining  
The maximum allowable seam opening displacement is a critical factor in the design of lining structures. An excessively large limit may cause sealing difficulties, while a very small limit can lead to significant increases in economic costs. Figure 16b shows the range of the elastic modulus and gas pressures for which the maximum seam opening displacement satisfies the limits of \(D_{\max}\leq 1\) cm, \(D_{\max}\leq 2\) cm, and \(D_{\max}\leq 3\) cm. It is clear that, when the maximum seam opening displacement is used as the control standard, there is a nonlinear relationship between the internal pressure and the surrounding rock modulus. Higher internal pressure results in much stricter rock conditions. Under constant seam opening displacement limits, the required elastic modulus of the surrounding rock increases sharply as the internal pressure gradually rises. For medium-quality rock mass, to control the maximum seam opening displacement to no more than 2 cm, the maximum internal pressure should not exceed 12 MPa. For poor- to extremely poor-quality rock mass, pressures below 8 MPa would also cause the maximum seam opening displacement to exceed 3 cm. In the rock mass of poor quality or worse, the maximum internal pressure should not exceed 6 MPa. For extremely hard rock mass, to meet the requirement of a maximum seam opening displacement of 2 cm, the internal pressure can reach up to 21 MPa.  
Smaller limits for seam displacement result in more stringent rock conditions for high-pressure workings. Even with larger seam opening displacement limits, high-pressure workings cannot be constructed in rock masses of fair to extremely poor quality. The maximum allowable seam displacement should be reasonably chosen based on the specific engineering conditions, as larger allowable displacements present sealing challenges.
## 3 Results and Discussion
### Scenario 1
#### 3.1.2 Throughput Performance  
This part shows the throughput performance of the high-gain SU in Figure 5a, before and after pairing it with the interfering MU, using different modulation schemes. The results are derived using the BER performance of SU, presented above. These simulations are also provided in comparison of conventional NOMA with WHT-NOMA given in Figures 10-13.  
In Figure 10, the results are derived using BPSK modulation. The observation in this figure yields that using NOMA, the throughput performance of SU degrades with pairing, even after performing SIC, compared to its unpaired state. As aforementioned, in hybrid-access small-cells, throughput degradation, because of the nonsubscriber presence, is unacceptable as SUs are the rightful owners or subscribers of the small-cell. Thus, to deal with the situation, it can be seen that using WHT-NOMA, the throughput performance increases in both perfect and imperfect SIC conditions compared to the conventional NOMA. The paired SUs' performance is even better than its unpaired state. At 10 dB SNR, using WHT-NOMA, compared to conventional NOMA, it can be observed that for paired SU, there is an increase of approximately 0.3 Mbps using the ideal SIC and 0.34 Mbps using nonideal SIC. Furthermore, it can be seen that the paired SUs' throughput performance compared to its unpaired state also increases using WHT-NOMA, i.e., 0.13 Mbps increase in throughput using ideal SIC and 0.11 Mbps increase using nonideal SIC conditions. A similar trend can be seen in Figure 11, where QPSK modulation is used with the WHT matrix of order 4 (\(H_{4}\) from (16)).  
The results in Figures 12 and 13 are derived using the higher-order modulation schemes, i.e., QAM and 8-QAM, respectively, and are based on the BER performance shown in Figures 8 and 9, respectively. It can be observed that the trend in throughput performance using WHT-NOMA is similar to the trend followed in Figures 10 and 11. For example, in Figure 13, at 10 dB SNR using WHT-NOMA, compared to conventional NOMA, the throughput of paired SU increases approximately 2.25 Mbps with the ideal SIC and 2.22 Mbps with nonideal SIC. It even exceeds the throughput of its unpaired state, i.e., approximately 1.30 Mbps.