esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 2 Background
### Universal Software Radio Peripheral  
USRP is an off-the-shelf communication device with configurable operating parameters, such as operating frequency bands and types of modulations. Our testbed consists of USRP N200 series units (see Figure 2) with high processing and computation capabilities. Each USRP unit of our testbed consists of two omni-directional VERT900 antennas that enable simultaneous transmission and reception, respectively, in different frequency bands within 850-960 MHz VHF bands. Digital signals are transferred from the RP3 processing unit to the front-end antenna for transmission in a _transmit path_, and analogue signals are transferred from the front-end antenna to the RP3 processing unit for reception in a _receive path_.  
In this testbed, the USRP unit consists of three main sections as below:  
Figure 1: A 5G network consists of control and data planes. The source, intermediate, and destination nodes in the data plane enables traffic offload from the control plane to the data plane. This network scenario is implemented in our testbed shown in Section 4.1.  
Figure 2: Transmit and receive paths between an antenna and a RP3 via a USRP/GNU radio unit. Solid line is part of a receive path, and dotted line is part of a transmit path.  
Field-programmable gate array (FPGA), which is the Xilinx Spartan 3A-DSP 1800 board, consists of: (a) interpolation filter that constructs data within a predefined range in the transmit path, and decimation filter that ensures signal achieves the required interface bandwidth in the receive path; (b) USRP hardware driver (UHD) block is a software interface that enables communication with GNU radio in RP3; (c) a processor that processes signals required for software defined radio (SDR), including encoding/decoding, modulation/demodulation, and time synchronization operations [23]. FPGA communicates with RP3 using power over Ethernet (POE), which has a maximum data rate of 1000 megabits per second (Mbps).
2. Converter consists of; (a) analogue-to-digital converter in the receive path; (b) digital down converter that selects desired signals from an array of signals captured by the analogue-to-digital converter in the receive path; (c) digital-to-analogue converter in the transmit path; and (d) digital-up converter that increases the bandwidth of the baseband signal so that it is compatible for digital-to-analogue conversion in the transmit path.
3. Wide bandwidth transceiver (WBX) is the RF front-end that provides access to different frequency bands, particularly 16 bit-sample (or 40 MHz RF bandwidth) and 8 bit-sample (or 25 MHz bandwidth). The maximum transmission power is 20 dBm (or 100 mW) with a noise figure of 5 dB.
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 2 Materials and Methods
### Model Architecture
#### 2.1.2 Text Detector  
Text detection has been implemented using the East model [24] with the pretrained weights available at [25]. East's peculiarity is its ability to perform accurate detection on images that are not perfectly centered and rotated. The model is composed by three parts: the feature extractor, the feature merger and the predictor. The detected geometry is represented as a rotated box (R-BOX), consisting of four distances from the top, right, bottom, left boundaries of the rectangle and a rotation angle. The final step is the Non-Maximum Suppression algorithm, which avoids multi-detections of the same object.
## 5 Results
### Evaluation of the Bit Error Rate (BER)  
Figure 4 shows the simulation results of the BER. This figure plots the BER as a function of the transmission distance. The transmission distances are from 100 m to 1000 m, and the transmitter powers are set as 1 W, 2 W, 10 W, 20 W, 30 W, and 40 W.  
When the transmission distance increases, the BER also increases. This is because when the distance increases, the SNR decreases (as shown in Figure 3). For wireless communications, the lower SNR would lead to the higher BER. To sum up, when the distance increases, the SNR decreases, and thereby the BER increases. As shown in Figure 4, the same phenomenon occurs in all the cases of different transmitter powers. Because the phenomenon remains consistent, it confirms that when calculating the BER, our channel models are reasonable and applicable to different transmitter powers.  
In addition, when the transmitter power is higher, the BER is lower. Specifically, when the transmission distance is the same, the transmitter powers from high BERs to low BERs are: 1 W, 2 W, 10 W, 20 W, 30 W, and then 40 W. We can explain the phenomenon by using the relationship between the SNR and BER. When the transmitter power is higher, the SNR is higher (as shown in Figure 3); when the SNR is higher, the BER should be lower (as mentioned in the above paragraph). As a result, the BER is inversely proportional to the transmitter power (i.e., when the transmitter power is higher, the BER is lower). Because the results are consistent in all the cases of different transmission distances (from 100 m to 1000 m), the simulation results confirm that when calculating the BER, our channel models are also applicable to different distances.  
Note that although the 10 W, 20 W, 30 W and 40 W transmitters outperform the 1 W and 2 W transmitters in terms of the BER, the 10 W, 20 W, 30 W, and 40 W devices are potentially heavier and more expensive [52]. Our simulation results indicate the trade-off between performance and cost. Specifically, the 1 W and 2 W transmitters may cause higher BER (lower performance) for underwater communications, whereas the 1 W and 2 W transmitters are lighter and less expensive.  
Figure 4: Simulation results of the Bit Error Rate (BER) when the channel models are applied to different transmitter powers (1–40 W) and distances (100–1000 m).
## 5 Conclusions  
In this paper, we propose a dynamic BWP allocation scheme that switches between two multiplexing methods, dynamic multiplexing (DM) and orthogonal slicing (OS). It aims at minimizing an impact of URLLC traffic on eMBB traffic for a UAV network in which diverse QoS requirements are imposed to receive highly reliable autonomous flight messages with low latency for command and control while receiving video streaming services over downlink broadband traffic. To implement efficient BWP allocation, this paper presents a comprehensive performance analysis to identify capacity region by considering the effect of physical layer parameters, such as modulation and coding scheme (MCS) level and code block group size on DM and OS. It was shown that OS is effective for improving the eMBB throughput subject to the URLLC latency constraint when URLLC traffic is deterministic and predictable, as opposed to DM, which has a limited error-correcting  
Figure 5: Capacity region with varying the eMBB MCS index: DM vs. OS.  
Figure 6: eMBB throughput comparison with different numbers of CBG \(N_{\text{CBG}}\).  
capability against the puncturing effect of URLLC. Furthermore, the relative MCS level of eMBB and URLLC is a critical factor in determining the durability of eMBB traffic against the puncturing effect. In fact, it has been quantified by a switching threshold of the URLLC load expressed in an approximate closed form, which can serve as a reference for dynamic BWP allocation. As future work, it would be interesting to generalize a proposed scheme considering implementation issues, such as BWP switching delay and the effect of model inaccuracy.  
Conceptualization, M.H., J.L. and M.R.; methodology, M.H.; software, M.H.; validation, M.H., J.L., M.R. and C.G.K.; formal analysis, M.H.; investigation, M.H.; resources, C.G.K.; data curation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, M.H. and C.G.K.; visualization, M.H.; supervision, C.G.K.; project administration, C.G.K.; funding acquisition, C.G.K. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
# Research on Data Transmission of Laser Sensors for Reading Ruler
## 4 Sensor Technology and Code Reading Error Analysis
### Opposed Laser Sensor  
A laser sensor is mainly composed of a laser transmitter, receiver, and signal processor. Its working principle is based on the photoelectric effect and the transmission of light beams. When the laser transmitter emits a laser beam, the light beam travels along a specific path. If there is no object blocking the beam path, the receiver receives the complete laser beam and generates the corresponding electrical signal.  
Once an object enters the beam path and blocks the beam, the laser intensity received by the receiver is attenuated or completely lost depending on the material that blocks the object, causing the output electrical signal to change.  
The existence, location, distance, and other information of an object can be calculated by analyzing the changes in electrical signals through a signal processor [16].  
Laser sensors are less sensitive to lighting and color changes in the working environment and can operate normally under various lighting conditions.  
Paired laser sensors have strong anti-interference ability, can perform sub-centimeter level transmission within a few milliseconds, and have high measurement accuracy. Therefore, paired laser sensors are widely used in the fields of object detection, positioning, and counting on automated production lines.
## 2 Materials and Methods
### Evaluation Metrics  
Nash efficiency coefficient (NSE, Equation (6)), root mean square error (RMSE, Equation (7)), bias (Equation (8)), coefficient of variation (CV, Equation (9)), unbiased RMSE (URMSE, Equation (10)), and the slope of the fitting line between ground-measured and estimated GPP were used to evaluate different models' performance. The closer NSE and slope are to one, and the closer RMSE, Bias, CV, and URMSE are to zero, the better the performance of the model.  
\[NSE=1-\frac{\sum_{i=1}^{n}\left(y_{m,i}-y_{g,i}\right)^{2}}{\sum\left(y_{m,i}- \overline{y_{g,i}}\right)^{2}} \tag{6}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{m,i}-y_{g,i}\right)^{2}}{n-1}} \tag{7}\]  
\[Bias=\frac{\sum_{i=1}^{n}\left(y_{m,i}-y_{g,i}\right)}{n} \tag{8}\]  
\[CV=\frac{n\times RMSE}{\sum_{i=1}^{n}y_{g,i}} \tag{9}\]  
\[URMSE=\sqrt{RMSE^{2}-Bias^{2}} \tag{10}\]  
where \(y_{m,i}\) and \(y_{g,i}\) are, respectively, the estimated and ground-measured GPP values of \(i\) model, and \(n\) is data size.  
A list of acronyms and the corresponding explanations for input variables, ML methods, and evaluation metrics are presented in Appendix A.
## 3 Results and Discussion
### In-Plane Electrical Conductivity Results
The increase in the conductivity was less pronounced for the epoxy/G150/G44 composite (from \(\sim\)27 S/cm to 34 S/cm). [PERSON] et al. [34] and [PERSON] et al. [53] found that the processing temperature affected the viscosity and electrical conductivity of the polymer matrix in epoxy/CNTs composites. The electrical conductivity properties were affected by the ability of the polymer matrix to wet the conductive filler. The higher the temperature of the forming process, the lower the bonding of the polymer composite material mixture. This condition increases the movement of the conductive filler in the polymer matrix progressively with increasing processing temperature. The same trend was also observed in Figure 5b when increasing the curing time from 60 to 120 min at a specified curing temperature of 150 \({}^{\circ}\)C, and a further increase in the conductivity was also achieved, which reached up to 50 S/cm for the epoxy/G150/G75 composite and up to 42 S/cm for the epoxy/G150/G44 composite. This was because the formation of conductive tissue in the matrix is strongly influenced by the curing time used [16; 34]. Figure 3: SEM surface and fracture surface images of epoxy/G150 at 65 and 80 wt.% filling loads of G prepared at 110 \({}^{\circ}\)C for 60 min.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods
### Candidate Boxes Enhanced Filtering (CEF)
#### 2.1.3 Initializing and Executing  
When processing the first frame of the video, since the predicted bounding boxes from the tracking algorithm cannot be obtained, the algorithm needs to be initialized. We decided to set the score \(S\) directly as the updated score \(S_{new}\) and initialize the threshold vector _threshold_ to a fixed value of 0.7. Then, the CET algorithm was executed for initialization. Next, the filtered detection boxes were obtained through this process and input into the tracking algorithm to generate the initial tracking boxes. The initial tracking boxes are  
Figure 3: Selection of the fixed threshold. When \(threshold=0.7\), the HOTA results reach their peak, indicating that this is the optimal parameter. The best results are highlighted with a red circle.  
defined as those detection boxes in the first frame of the video with a confidence score greater than 0.6.  
After processing the first frame, the tracking algorithm can output the prediction boxes. Based on the prediction boxes, we obtained the updated score vector \(S_{new}\) and threshold vector \(threshold\). At this point, the suppression threshold for each candidate detection was no longer fixed, so the NMS algorithm needed to be improved to accommodate dynamically adjusted thresholds. We enhanced the suppression step in the traditional NMS algorithm, allowing the suppression threshold to be dynamically adjusted based on the \(threshold\). By dynamically adjusting the suppression thresholds for the neighborhoods of the detection candidates, this approach reduces missed detections caused by overlaps or occlusions. This method effectively utilizes predictive information and better preserves overlapping object detection boxes. The workflow of our CEF algorithm is shown in Figure 4 and Algorithm 1.  
```
Input:\(B=\{b_{1},b_{2},\ldots,b_{m}\}\) is the list of initial detection boxes; \(S=\{s_{1},s_{2},\ldots,s_{m}\}\) is the corresponding detection score after the update; \(T=\{t_{1},t_{2},\ldots,t_{m}\}\) is the corresponding NMS threshold after the update. Output:\(D=\{d_{1},d_{2},\ldots,d_{n}\}\) is the detection box after CEF is executed.
1. while\(B\
eq\varnothing\)do
2.\(m=\operatorname*{argmax}S\)
3.\(M=b_{m}\)
4.\(D=D\cup\{M\}\)
5.\(B=B-\{M\}\)
6.for\(b_{i}\in B\)do
7.if\(\operatorname*{IoU}(M,b_{i})\geq t_{m}\)then
8.\(B=B-\{b_{i}\}\)
9.\(S=S-\{s_{i}\}\)
10. end
11. end
12. end return\(D\)
```  
**Algorithm 1**CEF
## 4 Results and Analysis
### Analysis of the Impact of Various Factors on WUE  
#### 4.3.1 Primary Influencing Factor Analysis  
The correlation coefficients of WUE with GPP and ET and their spatial distribution characteristics were obtained using partial correlation analysis, as shown in Figure 5a,b. The correlation coefficients of GPP and WUE were positively correlated with those of GPP, ranging from 0.8125 to 0.9996; the correlation coefficients of ET and WUE were the opposite  
Figure 4: Temporal variation in mean WUE values for different land cover types in Li River Basin from 2001 to 2020.  
of that of GPP, which were negatively correlated, with correlation coefficients ranging from \(-0.91014\) to \(-0.9997\).  
Based on the definition of WUE, the contributions of GPP and ET to the change in WUE were explored to determine which one is the primary influencing factor. The partial correlation coefficients of GPP, ET, and WUE can indicate the relative importance of the two in influencing the change in WUE, and the partial correlation coefficients were further utilized, and the ratios were calculated [30; 31] using the following equation:  
\[\mathbf{X}=\left|\frac{\mathbf{GPP}_{c}}{\mathbf{ET}_{\mathbf{c}}}\right| \tag{10}\]  
where GPP and ETc are the partial correlation coefficients of GPP and ET, respectively, and X is the ratio of the partial correlation coefficients, and it is the absolute value because the partial correlation coefficients are negative in the case of negative correlation. If X \(>\) 1, GPP is the primary influencing factor with a larger contribution to WUE; if X \(<\) 1, ET is the primary influencing factor influencing the change in WUE.  
Based on the spatial distribution of the correlation coefficients of GPP, ET, and WUE in Figure 5a,b, the raster calculator in ArcGIS 10.8 was used, raster calculations were performed on the data results from Figure 5a,b, the absolute value was taken, and the ratio of partial correlation coefficients was obtained, as shown in Figure 5c. In this case, the percentage of the area with the absolute value of the ratio of GPP to ET correlation coefficients higher than 1 was 18.83%, and the percentage of the area lower than 1 was 81.17%. From this, it can be judged that the primary influencing factor of WUE change in the basin was ET, which is consistent with the findings of some scholars [32].
# Surface Subsidence Modelling Induced by Formation of Cavities in Underground Coal Gasification
## 2 Model Configuration
### Model Setup  
Due to the stress redistribution around a cavity, in the numerical models, the distance between model boundaries and cavities should be large enough to reduce the boundary effects. A sensitivity analysis was conducted in this study and this distance was found to be at least ten times the size of the disturbed area for the geological conditions of this project. All numerical models set up in this study had the same dimensions of \(x\times y\times z=3465\times 1665\times 418\) m\({}^{3}\), which ensured the distance (1650 m) between the front, back, left, and right boundaries of the cavities was ten times the size of the final disturbed area (4 \(\times\) 30 + 3 \(\times\) 15 = 165 m), as shown in Figure 1. Note, due to symmetry, only 1/2 of the full model was used. For boundary conditions, no \(x\) displacements were assumed at the left and right boundaries (perpendicular to the \(x\) axis) and no \(y\) displacements were assumed at the front and back boundaries (perpendicular to the \(y\) axis). This was to simulate the actual condition that the horizontal disturbance caused by the cavity only had a limited zone of influence. No vertical (\(z\)) displacements were assumed at the bottom boundary of the model, and the top boundary was a free boundary. The geomechanical properties of the rocks considered in the model are listed in Table 1, which are sourced from Lucas and TriLab [31,32]. The commercial code ANSYS was used to calculate stress field and deformation field based on this model setup. ANSYS code is characterized by strong multi-physical modeling, solution, and nonlinear analysis.  
To investigate the effect of the gasification sequence, two scenarios were simulated, as listed in Table 2. In Scenario I, the gasification of cavities 1 and 2 happened first (stage 1), followed by the gasification of cavities 3 and 4 (stage 2). In Scenario II, the gasification of cavities 1 and 3 was completed first (stage 1) before that of cavities 2 and 4 (stage 2).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Property** & **Quaternary** & \
\begin{tabular}{c} **Main Series** \\ **(Mudstone)** \\ \end{tabular} & **Coal** &
\begin{tabular}{c} **Lower Series** \\ **Overburden** \\ \end{tabular} \\ \hline Density (kg/m\({}^{3}\)) & 2040 & 2040 & 1040 & 2040 \\ [PERSON]’s modulus \(E\) (GPa) & 3.4 & 3.4 & 0.785 & 3.4 \\ [PERSON]’s ratio \(\
u\) & 0.22 & 0.22 & 0.25 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geomechanical properties of rocks.
## 5 RIS Implementation Structures  
Reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks as a novel paradigm for regulating wireless channels. In this section, first, various implementations of reconfigurable intelligent surfaces and their differences are discussed. The literature has studied two major implementations based on standards, i.e., reflectarrays or metasurfaces [88]. Following this, a novel in-depth review of passive and phase shift-based active RIS and liquid crystals (LCs) RIS structures is carried out.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Muon Tomography Using a Liquid Scintillator Detector
#### 2.2.1 Detector and Dataset  
A spherical liquid scintillator detector [25] with a target mass of one ton was built and operated in the JNE, and its structure has been adopted by the simulations of this study. Figure 3a shows its external structure. The outermost structure is a white cylindrical steel tank of 2 m in height and 2 m in diameter. The internal structure is shown in Figure 3b. Thirty inward-facing 8-inch photomultiplier tubes (PMTs) mounted on the blacklight shading sphere detect the emitted scintillation light to measure the particle energy deposit. Inside the light shading sphere is a transparent acrylic sphere with a diameter of 1.3 m that contains one-ton liquid scintillator. The remaining space between the acrylic sphere and the steel tank is filled with water as a buffer to block the radioactive background. The electronics system uses one logic trigger module of CAEN V1495 and four FlashADC boards of CAEN V1751. The FlashADC boards can sample the electrical signals of the PMTs as waveforms for subsequent offline analysis at a sampling rate of 1 GHz. The time information extracted from the waveform is used for the subsequent muon direction reconstruction.  
The detector has been placed in the CJPL with a 2400 m rock overburden. Using the detector, the JNE's collaboration [26; 27] detected 343 cosmic-ray muon events over 820.3 days and measured their direction distribution based on a direction reconstruction method. This method and the measured distribution will be introduced in Sections 2.3 and 3.1, respectively. The background of the muon signal is dominantly natural radioactivity with an energy deposit of less than 10 MeV and is much lower than the minimum 100 MeV required by the cosmic-ray muon event selection criteria. Consequently, this background can be supp  
Figure 2: Distribution of opacity of the matter with which sea-level muons interact until stopping or decaying.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 4 System Architecture
### Advanced Software Architecture and Optimization
#### 4.2.2 Performance Optimization Techniques  
Five novel optimization methods implemented:  
1. DMA Optimization: A 35% reduction in memory transfer overhead through 32-bit aligned circular buffers with predictive prefetching.
2. ISR Minimization: A <2 \(\upmu\)s execution time through register-level optimization, preventing audio dropout during motor events.
3. Dynamic Clock Management: A 25% power reduction during low-activity periods while maintaining real-time guarantees.
4. Cache-Optimized Data Structures: Cache hit rates >95% through spatial and temporal locality optimization.
5. Predictive Buffer Management: Machine-learning-based prediction preventing overflow/underflow with 99.8% accuracy.