esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 2 Related Works
[26] studied a periodic timetabling problem in public transportation planning, and developed an exact preprocessing method for reducing the problem size and a heuristic reduction approach in which only a subset of the passengers is considered. It provides upper and lower bounds on the objective value, such that it can be adjusted with respect to quality and computation time. Because railways provide both passenger and freight services, there are naturally passenger train and freight train timetabling problems, e.g., [30; 31; 32; 33; 34; 35; 36]. [PERSON] and [PERSON] [33] introduced two mathematical formulations to cope with the rapidly increasing freight demand for railway transportation, and presented several heuristics that can significantly reduce the solution time of the exact method carried out by CPLEX, yet produce a satisfactory solution quality. [PERSON] et al. [31] introduced the integrated passenger and freight train timetable adjustment problem which handles both passenger as well as freight trains and developed a mixed integer linear programming model to simultaneously retime, reroute, and cancel trains in the network. [PERSON] et al. [35] studied freight train locomotive rescheduling when dealing with a disrupted situation in the daily operations in Japan, and solved the problem by changing the assignment of the locomotives to all the trains and considering their periodic inspections. [PERSON] et al. [34] considered a new concept for freight transport within the borders of a city with an urban rail transit system, and presented a decision support framework for the problem of urban freight movement by rail together with mathematical methods for the optimal distribution of goods. Given a transportation network with fixed routes for passenger trains and a set of freight trains (requests), [PERSON] et al. [30] addressed a freight train routing problem, and calculated a feasible route for each freight train, such that the sum of all expected delays and all running times is minimal. [PERSON] and [PERSON] [36] studied decision-making problems that railway infrastructure managers face in a rail network with dedicated tracks and shared-use corridors, and analyzed the consolidation strategy for shared-use corridors, where the track serves passenger and freight. These research lines are viewed from different perspectives but are not truly independent of each other. For example, passenger train scheduling is usually a periodic timetabling problem. [PERSON] et al. [13] studied a nonperiodic freight train scheduling problem, in which a schedule is planned for freight trains and can be different during different periods of the day. The proposed method considers car flow transfer between consecutive trains and the shipment delivery time requirements. A tabu search algorithm is developed that extends the applicability of the proposed optimisation method for large-scale problems. Experiments on real-world instances of the Menghua railway indicate the effectiveness of the proposed algorithm.
## 2 Methodology
### Classification Performance Evaluation  
In this work, we were concerned with developing an accurate detector of pavement distress based on image data supplied to it. There were four possible outcomes concerning the judgments of road segments given by the classification system:  
* true negative (\(TN\)): there is no defect, and the system does not detect a defect;
* true positive (\(TP\)): there is a defect, and the system correctly detects it;
* false negative (\(FN\)): there is a defect, but the system does not detect it;
* false positive (\(FP\)): there is no defect, but the system detects a defect.  
Based on this, it is possible to impose accuracy criteria for the system, where \(TN\), \(TP\), \(FN\), and \(FP\) denote the total counts of the corresponding detection outcomes. First, we argue that the bare accuracy measure given as:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
is not as meaningful as the recall and precision measures since it is critical to identify actual defects properly. The recall measure shows the percentage of how many actual defects were detected by the system and is defined as:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{2}\]  
and precision shows the percentage of how many of the detected defects were actual defects and is defined as:  
\[\text{Precision}=\frac{TP}{TP+FP}. \tag{3}\]  
We also used the so-called Matthews correlation coefficient (MCC) metric, which is defined as:  
\[\text{MCC}=\frac{TP\cdot TN-FP\cdot FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{4}\]  
because it provides a more reliable measure in the case of imbalanced data.  
Finally, since ConvNets returns probabilities and not discrete values, one must use two threshold values: detection threshold \(P_{det}\) and suspicion threshold \(P_{sus}\), such that:  
\[P(\text{defect})\geqslant P_{det}\Rightarrow\text{defect is detected}\]  
and  
\[P(\text{defect})\geqslant P_{sus}\Rightarrow\text{defect is suspected}.\]
# Spectral Correction of CPV Modules Equipped with GaInP/GaInAs/Ge Solar Cells and Fresnel Lenses
## 2 Materials and Methods
### Proposed Analytical Equations  
The detailed procedure used to extract the analytical equations evaluated in this work can be found in [10]. The Simple Model of the Atmosphere Radiative Transfer of Sunshine version 2 (SMARTS2) [12] was initially used to simulate the following spectra:  
* \(1\leq AM\leq 5\), step 0.25
* \(0.05\leq AOD\leq 0.6\), step 0.05
* \(0.25\leq PW\leq 4\) cm, step 0.25 cm  
Hence, a total of 3264 spectra were generated, covering a wide range of spectral conditions. The \(SF\) of each subcell and for the whole multi-junction cell was calculated for every simulated spectrum using the equations discussed in the previous sub-section. The analysis of the impact of these spectra on each subcell of a lattice-matched GaInP/GaInAs/Ge MJ solar cell leads to the following analytical equations:  
\[f_{s,i}=SF_{i}=f(AM, AOD,PW)_{i} \tag{5}\]  
\[f(AM, AOD,PW)_{i}=f(AM)_{i}+f( AOD)_{i}+f(PW)_{i} \tag{6}\]  
\[f( AOD)_{i}=f( AOD\ coeff.)_{i}\cdot\left( AOD- AOD_{ref}\right) \tag{7}\]  
\[f(PW)_{i}=f(PW\ coeff.)_{i}\cdot\left(PW-PW_{ref}\right) \tag{8}\]  
where \(f(AM),f( AOD\ coeff.)\) and \(f(PW\ coeff.)\) are the corresponding functions of each subcell and the subscript \"coeff.\" refers to the coefficients. The \( AOD_{ref}\) and \(PW_{ref}\) reference values are 0.084 and 1.42 cm respectively. The proposed spectral corrections for each subcell are given in Table 1.
## 4 Results and Discussion  
### Comparison of Simulation and Experimental Results  
Figure 19 shows the results of the experiment conducted under the same conditions as the simulation (Section 2.3). All flaws were presented as sector-scan (S-scan) images, and no particular skill was applied for flaw characterization. Figure 19a,c shows the results of the upper notch experiment, and Figure 19b shows the results of the improved surface LoF experiment. Generally, a weld overlay is inserted on the resulting image from a PAUT inspection to simplify the assessment of the flaw position. Meanwhile, as shown by the solid line in Figure 19, the restructured weld overlay that considered the actual curvatures in the weld zone could accurately detect the flaw position. Thus, the results were in agreement with the simulation results, and the FPAPr contact at the specimens with curvatures was valid.
## 4 Results and Analysis
### Tracking Result Analysis
#### 4.3.2 Qualitative Evaluation  
This section chooses three representative trackers for qualitative evaluations against our proposed method, as presented in Figure 14, where the tracked objects are planes and vehicles. Figure 14 indicates that the selected four trackers show different tracking results on the Plane1, Plane2, Car1, and Car2 sequences, where all objects are unoccluded. On the Plane1 and Plane2 sequences, our method and the SiamRPN tracker achieve good tracking results. In frame 34 (Plane1) and frame 53 (Plane2), we observe that the selected four trackers can accurately track the object in Figure 14, while in frame 89 (Plane1) and frame 82 (Plane2), the KCF and SiamFC tracker present some tracking drift but can still track the object. As the object continues to move, our method and the SiamRPN tracker continue to track the object for the rest of the moving process, while the other two trackers present a significant drift and eventually lose the object. In frame 213 (Plane1) and frame 251 (Plane2), the object in the Plane2 sequences has a complex background with some similar interferences in the surroundings, increasing the tracking difficulties.  
Our method has similar interferences in the surroundings, increasing the tracking difficulties. Our method can track the object because we extract multiple objects' features and then compensate and correct the object's motion trajectory. The objects in the Car1 and Car2 sequences are both moving vehicles. Regarding the Car2 sequences, due to the change of the object motion direction, all trackers except for SiamRPN suffer from bounding box drift, and thus SiamRPN can continue to track the object accurately, probably because SiamRPN employs the multi anchor points method, which can resist the object from short rotation within a short period.  
Considering the Car3, Car4, Car5, and Car6 sequences, all objects are occluded by the bridge, and our tracker is the only one that continues tracking the object without losing it. When the object is occluded, the adaptive Kalman filter in our method predicts the object position in the next frame by automatically increasing the covariance noise R and decreasing the covariance Q. When the target occlusion ends, our method can immediately re-track the target. Opposing, the other trackers cannot handle the occlusion problem and are unable to predict the object's position, so when the target is occluded, the target is lost, resulting in tracking failure.  
Figure 14: Screenshots of some tracking results without occlusion. At each frame, the bounding boxes with different colors are the tracking results of the different trackers and the red number in the top-left corner is the frame number of the current frame in the satellite videos.  
In conclusion, the visual tracking results show that our method can solve the problem of short-time target occlusion and is suitable for satellite video targets with complex backgrounds and fast movements, improving the object's tracking accuracy.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 3 Proposed Speech Separation Algorithm
### Speech Separation According to Different Mixing Condition
#### 3.2.2 Case II: \(M^{\prime}=\text{N}\)  
It is the most common situation that the number of speech components is equal to the number of sound sources. ICA can be used normally, and the processing matrix is expressed as  
\[W_{[3]}(f)=W_{\text{ICA}}(f)\cdot V(f) \tag{23}\]  
where \(W_{[3]}(f)\) contains \(N\) rows.
## 5 Results and Analysis
### Model Validation and Comparision
Using this method, the performances of the models were presented in a graph with 100-specificity on the x-axis and sensitivity on the y-axis that showed that the BLR model with AUC = 0.888 had a better training performance, followed by the CGLR (AUC = 0.885), LR (AUC = 0.884), RSSLR (AUC = 0.869), and DLR (AUC = 0.856) models, respectively. For the validation performance, the DLR model was better with AUC = 0.769, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and LR (AUC = 0.710) models, respectively.
## 4 Results and Discussion
### Impact of \(\lambda\)  
This subsection checks the effectiveness of the proposed joint loss for different values of the hyperparameter \(\lambda\) which weights the combined conventional cross-entropy loss (Equation (1)) and the intra-loss (Equation (2)). These experiments were performed on ResNet50 with mix-up-based data augmentation. In the proposed method, \(\lambda\) is always greater than 0.00. Recall that when \(\lambda\) = 0.00, the method reduces to the existing method with conventional cross-entropy loss. The classification accuracies, defined as the ratios of the correctly classified samples over all classified samples, are presented for different \(\lambda\) in Table 4. The proposed joint loss (\(\lambda\) > 0.00) consistently outperformed the conventional loss (\(\lambda\) = 0.00) and was optimized at \(\lambda\) = 0.0025. Therefore, we set \(\lambda\) = 0.0025 in further detailed comparisons.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{\lambda}\) & 0 & 0.001 & 0.0025 & 0.005 & 0.01 & 0.1 \\ \hline
**Accuracy** & 88.90\% & 89.53\% & 91.03\% & 90.12\% & 89.32\% & 89.53\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracies of the proposed joint loss with different \(\lambda\) (CNN: ResNet50) (\(\lambda\) = 0.00 corresponds to the conventional cross-entropy loss).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Train** & **Test** \\ \hline BAPL1 & 165 subject (1,815\(\rightarrow\)5,445 slices) & 23 subjects (253 slices) \\ \hline BAPL2 & 42 subjects (462\(\rightarrow\)4620 slices) & 6 subjects (66 slices) \\ \hline BAPL3 & 126 subjects (1386\(\rightarrow\)4,620 slices) & 18 subjects (198 slices) \\ \hline Total & 15,609 slices & 517 slices \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets after data augmentation for eight-fold cross-validation.
## 2 Materials and Methods  
In the present work, BaTiO\({}_{3}\) thin film evolution was studied at P03 at PETRA III [11], two 2D detectors were used simultaneously. One 2D detector (Pilatus 1M, 981 \(\times\) 1043 pixels, pixel size of 172 \(\upmu\)m \(\times\) 172 \(\upmu\)m) was used for GISAXS at a Sample-to-Detector Distance of SDD = 4 m. The X-ray beam had an energy of 13 keV and a beam size of 40 \(\upmu\)m (horizontal) \(\times\) 20 \(\upmu\)m (vertical) FWHM, and the angle of incidence (\(\alpha_{i}\)) was set to 0.5\({}^{\circ}\). This angle is above the critical angle of the materials involved. A second single-photon counting pixel detector with 487 \(\times\) 619 pixel of 172 \(\upmu\)m each (Pilatus 300k, Dectris, Baden, Switzerland) was mounted on the detector arm of the diffractometer with SDD = 0.28 m covering the Q-range from 2 A\({}^{-1}\)-4.5 A\({}^{-1}\). With this second 2D detector, we were following the evolution by GIXRPD simultaneously to the GISAXS measurement. Both detectors were in air. The measurements were performed with the following parameters. The plasma process and substrate heating was started 10 min. before opening the shutter in front of the sputtering target. The sputtering power on the BaTiO\({}_{3}\) target was 14 W/cm\({}^{2}\) and the gas mixture was 80% Argon (Ar) and 20% Oxygen (O\({}_{2}\)) with a total flow of 1 standard cubic centimetre per minute (SCCM), and a substrate temperature of 600 \({}^{\circ}\)C. The distance between the sputtering target and substrate was fixed at a distance of 4 cm. The films were deposited on pre-cleaned SiO\({}_{2}\) wafers with a size of 10 \(\times\) 10 mm\({}^{2}\). A more detailed description of the measurement can be found here [8; 10]. SiO\({}_{2}\) wafers are not the industrial standard for BaTiO\({}_{3}\) thin film deposition, but we have chosen it to avoid surface related influence on the crystallization process. A more detailed study of the influence of various parameters, such as surface structure and deposition temperature, on the crystallization behavior will be published elsewhere. Figure 1 illustrates the principal scheme of the experiment. The X-rays arrived from the left hand side and hit the wafer inside the sputtering unit under gracing incidence and get detected with the 2D detector behind the chamber.  
Figure 1: Schematic setup for in situ sputter deposition measurements, with the X-ray beam (yellow), the chamber (silver) including the sample holder in grazing incidence geometry and a 2D detector (blue). Shown is a GISAXS pattern after 40 s of deposition of BaTiO\({}_{3}\) at 600 \({}^{\circ}\)C. Denoted is the specular beam \(\alpha_{s}\) = 2 \(\times\)\(\alpha_{i}\), the Yoneda peak \(\alpha_{y}\) = \(\alpha_{c}\) + \(\alpha_{i}\) with the \(\alpha_{c}\) the critical angle, sample horizon \(\alpha_{h}\) = \(\alpha_{i}\) and the direct beam \(\alpha_{d}\). Not shown is the second GIXRPD Detector.
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 5 Experimental Analysis and Result
### Dt
#### 5.2.2 Implementation and Analysis of DT Hardware  
Based on the DT architectures in Appendix A, Table A4, we use the appropriate EDA (with the Balanced Optimization Mode in Synthesis Settings) to implement DT on 10 different FPGA boards. In terms of computing performance, Intel MAX10M50 DAF outperforms the competition in all six datasets. While in terms of comprehensiveness, Lattice's ICE40 UP5 came to first place again in most application scenarios for its extremely low power consumption and cost-effectiveness. The FPGA DT deployment results with the best comprehensive performance under each task are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & **Device** & **Score** & **LUTs** & **DSPs** & **Latency/us** & **Power/mW** & **PLP** & **CPLP** \\ \hline Binary classification & Gutter Oil & ICE40 UP5 & 97.14\% & 1898 & 8 & 11.12 & 140 & 1556.10 & 59,131.80 \\ Multiclass & Smart Grid & ICE40 UP5 & 98.73\% & 1855 & 8 & 11.12 & 140 & 1556.10 & 59,131.80 \\ Multiclass & Gutter Oil & ICE40 UP5 & 97.09\% & 3887 & 8 & 11 & 140 & 1540 & 58,520 \\ classification & Wine Quality & MachXO2 & 73.01\% & 6114 & / & 12.73 & 192 & 2444.54 & 73,091.87 \\ Regression & Rain & ICE40 UP5 & 0.8583 (R2) & 3553 & 8 & 8.73 & 135 & 1178.69 & 44,790.03 \\  & Power & & & & & & & \\  & Consumption & ICE40 UP5 & 0.9979 (R2) & 3653 & 8 & 11.98 & 140 & 1676.50 & 63,707 \\ \hline \hline \end{tabular}
\end{table}
Table 8: ANN deployment results on the best CPLP-performing FPGAs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset** & **Score** & **Max Depth** & **Max Leaf Nodes** \\ \hline Binary classification & Gutter Oil & 96.18\% & 5 & 16 \\ classification & Smart Grid & 97.98\% & 4 & 8 \\ Multiclass & Gutter Oil & 96.85\% & 6 & 32 \\ classification & Wine Quality & 83.69\% & 4 & 16 \\ Regression & Rain & 0.8481 (R2) & 4 & 8 \\  & Power Consumption & 0.9976 (R2) & 6 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The highest-scoring parameters of DT obtained in different datasets.
# Investigation of Spatial Chirp Induced by Misalignments in a Parallel Grating Pair Pulse Stretcher
## 3 Spatial Chirp Induced by the Misalignments and its Adjustment  
### Misalignments of the Gratings and Mirrors in the Stretcher  
Without losing generality, we only take the parallel grating pair structure shown in Figure 1a as an example to show the effects of misalignments in our simulation. Here, we define the direction parallel to the grating grooves as the vertical direction, while the direction perpendicular to the grooves as the horizontal direction. Thus, the adjustment errors of the grating pair and mirror consist of five components, such as the horizontal tilt of the gratings \(\theta_{\mathrm{h}}\), the vertical tilt of the gratingsthe unparallel groves between the two gratings \(\theta_{\rm r}\), the horizontal tilt of the mirror \(\varphi_{\rm h}\), and the vertical tilt of the mirror \(\varphi_{\rm v}\). The schematic diagram of the five misalignments is shown in Figure 2. Since the first grating G1 has been selected as the baseline and is fixed, only the second grating G2 and the mirror M are shown in Figure 2 for brevity.
# Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm
## 4 Experimental Simulation and Analysis
### Network Attack Detection and Analysis  
Network attack prediction is realized by combining network traffic prediction with threshold setting. The current network traffic is input into IGWO-BiLSTM to get the future network traffic and the predicted value is compared with the threshold value to judge whether the network attack occurs. In normal cases, the IPDCF value of network traffic is small, but in attack time, the IPDCF value of network traffic is large. By counting the interval between normal flow and predicted characteristic values, the threshold \(U\) is calculated to be 7184. When the predicted value exceeds 7184, a network attack can be identified. Due to the large types of attacks and inconsistent attack judgment methods, this paper can predict and identify the network traffic under attack by setting thresholds. Specific categories of attacks will be realized in the next experiment.  
As can be seen from Figure 12, in the 40 min, the predicted value exceeds the threshold, which determines that a network attack occurs at this time. In the 80 th to 90 th minute, although the deviation between the real value and the predicted value is large, it does not exceed the threshold. Therefore, it is judged not as a network attack, but as the actual network demand of users increases at this time to avoid false positives.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & \(RMSE\) & \(R^{2}\) & \(MAE\) \\ \hline IGWO-RNN & 0.0483 & 0.6688 & 0.0443 \\ IGWO-LSTM & 0.0529 & 0.4263 & 0.0326 \\ IGWO-GRU & 0.0352 & 0.9313 & 0.0259 \\ IGWO-BiLSTM & 0.0094 & 0.9905 & 0.0200 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of improved algorithms to optimize network performance.  
Figure 11: DARPA99 real value and predicted value comparison chart.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 2 Zero-Copy DMA Technology Overview
### PF_RING Zero-Copy  
PF_RING ZC [10] implements the Direct NIC Access DNA technique, which maps the NIC memory and registers it to the userspace. The flow of its zero-copy implementation is as follows: (1) The DMA controller copies the packet from the NIC to the host memory (RX buffer). (2) Call the Library functions mmap() to map the packet of the RX buffer to the userspace for application access directly. PF RING ZC's API emphasizes convenient multi-core support. It does not use standard system calls, provides custom functions (polling), and realizes its driver based on the Linux driver. When PF_RING ZC is active, applications cannot send or receive data using their respective default system interfaces. Since PF_RING ZC's packet processing goes through fewer processes, it can achieve almost the same data processing efficiency as DPDK.  
Ntop [15] provides a large number of applications running on top of PF_RING ZC, such as n2 disk [16] (packet capture tool) and nProbe [17] (traffic monitoring tool). However, when applying PF_RING ZC to multi-queue NICs, applications must negotiate in advance to decide on the queue to distribute packets because applications can directly manipulate the multiple queues simultaneously. Otherwise, it is easy to cause conflicts in the use of queues.
## 4 Experimental Evaluations  
This section presents experimental evaluations of the actuators, and it compares their performance in terms of resonant vibration intensities. Static and dynamic experiments were first conducted on both ERAs to identify the actuator parameters, such as the stiffness, resonant frequency, damping ratio, and mass. The vibration performance of the dual-electrode ERA was measured and compared under different electrode configurations to study the effect that electrode activations had on performance. The performance of the single-electrode ERA was also compared to the dual-electrode ERA when both electrodes in the module were activated.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 5 V2V Aids toward Sustainable Transportation Systems  
The performance evaluations of V2V communication architectures provide valuable insights into their effectiveness in supporting reliable, efficient, sustainable, and intelligent transportation systems. Here, we focused on sustainable transportation and smart cities to evaluate the benefits of V2V communication architectures [67]. The details of the benefits are given below.