esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### K-M Model Analysis of Lane Departure Duration  
#### 3.2.1 Modelling the Driving Style  
In order to investigate the effect of driving style on the utility of LDW modes, the LDD survival probabilities were computed with the K-M model for three departure warning timings, as shown in Figure 6. The areas around the lines represent the confidence interval. A \(p\)-value of less than 0.05 indicates that the LDD has a statistically significant relationship with the driving style. For the no warning condition (\(p\) = 0.020), as presented in Figure 6a, the time (LDD) for median survival were 3.001 s, 3.362 s, and 4.002 s, respectively, for the cautious, moderate, and aggressive styles. The LDD increased with the aggressive level of driving styles, and cautious drivers tended to adjust the vehicle back to the original lane in shortest time. For the early warning condition (\(p\) = 0.016), as shown in Figure 6b, the time (LDD) for median survival were 1.830 s, 2.245 s, and 3.296 s, respectively, for the cautious, moderate, and aggressive styles. Compared with the no warning condition, early warning shortened the LDD for all driving styles, and the effect was the most significant for cautious drivers and the weakest for aggressive drivers. Thus, early warning was more suitable for the cautious drivers. For the late warning condition (\(p\) = 0.010), as depicted in Figure 6c, the time (LDD) for median survival were 2.796 s, 3.012 s, and 4.774 s, respectively, for the three driving styles. Compared with the no warning condition, there was a small increase in the median departure durations of aggressive drivers for late warning timing. Further analysis using a COX model will be required to investigate better the impact of the late warning timing on the driving styles.  
Figure 5: Distribution diagrams for LDD: (**a**) no warning; (**b**) early warning; (**c**) late warning.
## 3 Experimental Setup and Measurements
[24], which can further be compared with the attenuation as recovered by the SLaTCoW method (for further discussions, see Section 4). By scanning the aluminum plate over 30 mm in front of the fixed laser beams (Configuration (1), Figure 2b), a zone of constant thickness of 4 mm-long in aluminum is found. At this location, the thickness is estimated to be \(2h=71.5\) um, which is obtained thanks to the measure of the ZGV resonance frequency and assuming the elastic properties of our aluminum sample to be those reported in Table 1. Note that, starting from a nominal thickness of 75 um, the polishing leads to a varying thickness over the complete 30 mm long scanned line in the range [70.7 um, 74.5 um], which was estimated with the variation of the ZGV frequency. Spatiotemporal measurements were then carried out using the scanning configuration shown in Figure 2c. We note here that Figure 3c shows the normalized signal amplitude as a function of \(x\) (distance between the generation and the detection areas) on the horizontal axis and of time on the vertical axis. Furthermore, in Figure 3c, the \(A_{0}\) Lamb mode is mainly observed, i.e., the low frequency content. From these measurements, a double Fourier transform,  
\[\vec{u}(k_{r},\omega)=\int_{0}^{T}\int_{0}^{L}u(x,t)e^{-k_{r}x}e^{\mu\omega t} \mathrm{d}x\mathrm{d}t, \tag{2}\]  
is applied in order to see the wavenumber-frequency diagram in the real plane (\(k_{r},f\)) shown in Figure 3d, with \(T\) the maximum time of acquisition and \(L\) the length of the scanned line. In the zoomed part around the ZGV frequency (Figure 3e), three branches are present (from left to right): mode \(S_{1b}\), mode \(S_{2}\), and mode \(A_{1}\) (second anti-symmetric mode). Note that, since the measurements are made in a single direction, i.e., in the direction of the positive group velocities, the branch of the \(S_{1b}\) mode appears where the real part of the wavenumber is negative and that of the \(S_{-1b}\) mode is not present since its group velocity is negative. Following this first analysis of the experimental results in the real plane, it is now proposed to use the SLaTCoW method [27], based on the spatial Laplace transform for the recovery of the experimental dispersion curves in the complex wavenumber-frequency space. Figure 2: **(a)** Experimental setup. **(b,c)** Illustration of the two scanning configurations used: **(b)** the rotational mirror is fixed with \(d=20\) μm, and then the plate is moved along \(x\)-axis; **(c)** the plate is fixed, and the mirror rotates.
###### Abstract  
Real-time smart applications are now possible because to developments in communication and sensor technology. Wireless sensor networks (WSNs) are used to collect data from specific disaster sites, such as fire events, gas leaks, land mines, earthquake, landslides, etc., where it is necessary to know the exact location of the detected information to safely rescue the people. For instance, the detection and disposal of explosive materials is a difficult task because land mines consistently threaten human life. Here, the T-based Routing Topology (TRT) is suggested to gather data from sensors (metal detectors, Ground Penetrating Radars (GPR), Infra-Red sensors, etc.), Global Positioning System (GPS), and cameras in land mine-affected areas. Buried explosive materials can be found and located with high accuracy. Additionally, it will be simpler to eliminate bombs and reduce threats to humans. The efficiency of the suggested data collection method is evaluated using Network Simulator-2 (NS-2). Also, the proposed T-based routing topology requires a minimal number of nodes to cover the entire searching area and establish effective communication. In contrast, the number of nodes participating in the sensing area grows, as the depth of the tree increases in the existing tree topology-based data gathering. And for cluster topology, the number of nodes deployment depends on the transmission range of the sensor nodes.  
c 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
class:energy efficiency; land mine; T-based routing topology; target tracking; wireless sensor networks 202323 2162 2023 2023
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives
### Technology Development Perspective
#### 5.1.2 UAV-Based Survey  
Unmanned Aerial vehicles (UAVs) have become more sophisticated during the last few decades [119]. The use of UAVs in the world's major military services for international security has been growing enormously. UAVs are being used to survey the battlefield remotely with a bundle of sensors mounted onto the aerial vehicle, giving adequate image resolution. An ensemble of tiny UAVs working with each other in collaboration is another technological advancement that can be used for remote sensing-based surveys for international peace and security applications. UAVs can be used to improve border control by monitoring large areas of border quickly and more efficiently than conventional means of border patrolling [120; 121].
## 1 Introduction  
Tibet, a land shrouded in mystery atop the world, boasts breathtaking natural landscapes and is a repository of rich cultural and ecological diversity. Table 1, for example, shows the basic information of the four highways into Tibet. A surge in tourism increases traffic volumes in Tibet. Based on the statistical data of each monitoring station, the annual average daily traffic (AADT) of the four highways into Tibet was calculated. Figure 1 shows that the traffic volume of Qinghai-Tibet Highway is the highest, more than twice that of the Sichuan-Tibet Highway, while the traffic volumes of the Yunnan-Tibet Highway and Xinjiang-Tibet Highway are significantly lower than those of the former, posing unprecedented challenges to road management and safety.  
In recent years, the highways to Tibet have suffered significant losses due to natural calamities and traffic accidents exacerbated by extreme weather and geological hazards. In 2022, the losses from geological disasters were heavier than usual, mainly concentrated in the western regions such as Qinghai, Sichuan, and Xinjiang. These disasters affected a total of 940,000 people, resulting in 122 deaths and missing persons, and causing direct economic losses of 22.45 billion yuan. The four highways of Qinghai-Tibet, Sichuan-Tibet, Xinjiang-Tibet, and Yunnan-Tibet have an average of 1870 disasters per year, with traffic interruption amounting to 50-150 times per year, making these highways passable for an average of 320 days per year. Wide-scale heavy snow, dense fog, and torrential rain increasingly disrupt the maintenance and safe functioning of the highways [1,2]. Therefore, there is an urgent need to implement a grading method for traffic interruptions. This method can provide guidelines for relevant departments to quickly identify the severity of incidents and take corresponding emergency measures. With limited human and material resources, it will enable the rational allocation of funds and technical support needed for different levels of disasters, thereby creating favorable conditions for achieving year-round, all-weather accessibility.  
In this study, we aim to analyze traffic disruptions on all four major highways leading to Tibet. By considering the entire network, we seek to provide a comprehensive understanding of the factors contributing to traffic disruptions, including mudslides, snow accumulation, landslides, traffic accidents, traffic control, and infrastructure damage. This holistic approach enables us to identify common and unique characteristics across the different highways, thereby offering valuable insights for future planning and management.
# Salient Object Detection via Fusion of Multi-Visual Perception
## 5 Experiments
### Time Complexity Analysis  
The input image has dimensions of \(M\times N\) pixels, and the program executes \(k\times M\times N\) times, where \(k\) is a constant. In the feature extraction phase, each of the five features requires a constant number of operations on each pixel. The time complexity of the feature extraction phase is \(O(M\times N)\). The feature fusion phase involves adding the feature maps obtained from feature extraction and performing binary thresholding. This requires processing each pixel in each of the five feature maps, resulting in a time complexity of \(O(M\times N)\) for the feature fusion module. The superpixel segmentation utilizes the SLIC algorithm with a time complexity of \(O(M\times N)\). Therefore, the overall time complexity of the proposed algorithm is \(O(M\times N)\).  
The proposed method processes images of 300 \(\times\) 400 pixels in an average time of 2.1 s on an AMD Ryzen 7 5800X 8-Core CPU with 32 GB RAM using MATLAB. This speed is due to MATLAB's serial processing, where the single-core processing capability of the CPU affects the algorithm's runtime. Theoretically, using more advanced CPU processors, our algorithm will achieve a higher processing speed.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**ECSSD**} & \multicolumn{2}{c}{**MSRA10K**} \\ \cline{2-5}
**Methods** & **MAE** & **F-Measure** & **MAE** & **F-Measure** \\ \hline MSS [40] & 0.1972 & 0.9337 & 0.1488 & 0.9543 \\ COV [41] & 0.1941 & 0.9356 & 0.1765 & 0.9471 \\ GR [42] & 0.1522 & 0.9247 & 0.0814 & 0.9624 \\ PCA [43] & 0.1474 & 0.9311 & 0.0835 & **0.9668** \\ LDS [44] & 0.1824 & 0.9365 & 0.1537 & 0.9523 \\ FCB [45] & 0.1611 & 0.9369 & 0.1116 & 0.9611 \\ CNS [26] & 0.1469 & **0.9418** & 0.0947 & **0.9666** \\ MDF [46] & 0.1680 & **0.9388** & 0.1177 & 0.9620 \\ PPMRF [47] & 0.1466 & 0.9357 & 0.1264 & 0.9484 \\ Saliency bagging [2] & 0.1386 & 0.7698 & 0.0805 & 0.8838 \\ MTL-ITSOD [48] & **0.0047** & 0.9101 & - & - \\ PDLSO [7] & 0.1210 & 0.8240 & - & - \\ redLC-SA [6] & - & - & 0.1073 & 0.8198 \\
**Proposed** & **0.1302** & **0.9382** & **0.0783** & **0.9635** \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAE and F-measure on two benchmark datasets.  
Figure 8: Visual comparison of all evaluated methods. (**a**) Img. (**b**) GT. (**c**) Ours. (**d**) MSS. (**e**) COV. (**f**) GR. (**g**) PCA. (**h**) LDS. (**i**) FCB. (**j**) CNS. (**k**) MDF. (**l**) PPMRF.  
We compared the COV algorithm [41], also implemented in MATLAB, with an average runtime of approximately 3.6 s. However, compared to the GR algorithm [42], the runtime is approximately 0.8 s, owing to the higher efficiency of GR using C++ code. Additionally, our method extracts more low-level image features, which consequently increases the computational workload.  
Time complexity analysis suggests the method's computational demands correlate linearly with the product of the image dimensions. The method's efficiency, demonstrated by execution time on specific hardware, supports viability for real-time or near-real-time image processing applications.
## 2 Proportional Short-Circuit Blowing Model Test Bench and Orthogonal Experiment Design
### Detailed Setup of Model Test Bench  
The test bench comprised a gas cylinder group, an air compressor, a high-pressure gas cylinder group, and a gas distribution mechanism, along with both the sea tank and ballast tank. Notably, the ballast water tank and sea tank were positioned between sea valves of varying diameters. The air compressor inflated the gas cylinder group while simultaneously pressurizing the sea tank's top to create back pressure. Upon the initiation of blowing, gas from the cylinder group flew into the ballast tank via the gas supply pipeline, displacing water into the sea tank through the sea valve. A diagram of the proportional short-circuit blowing test bench for submersibles is presented in Figure 1, with its main parameters detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Apparatus** & **Main Parameters** & **Remark** \\ \hline
1 & Air compressor & Maximum air inflation pressure: & \\  & & 20.0 (MPa) & \\  & & Maximum air working pressure: & \\
2 & Gas cylinder group & 35 (MPa) & Gas source for blowing and back \\  & & Volume: 750 (L) & pressure in the sea tank \\
3 & Ballast tank & Volume: 1.2 (m\({}^{3}\)) & \\  & & Pressure handling capacity: 7 (MPa) & \\
4 & Sea tank & Volume: 13 (m\({}^{3}\)) & The back pressure exceeds the maximum \\  & & Pressure handling capacity: 5 (MPa) & working depth of the actual ship. \\
5 & Gas supply pipeline & Three length specifications available: & The inlet is equipped with three \\  & & 10 (m), 20 (m), and 30 (m) & replaceable pipe sections with internal \\  & & 10 (m), 8 (mm), and 10 (mm), each measuring 0.3 (m) in length. \\
6 & Sea pipeline & Internal diameters available: & Connection between the ballast tank \\  & & 125 (mm), 150 (mm), and 200 (mm) & outlet and seawater tank inlet. \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters of the experimental bench.  
Figure 1: Proportional short-circuit blowing test bench of submersible. 1—high-pressure (Hp) air cylinder, 2—Hp air compressor, 3—control air cylinder, 4—stop valve, 5—safety valve, 6—muffler, 7—pressure gauge, 8—relief valve, 9—electric control valve, 10—thermometer, 11—air flow meter, 12—sea valve, 13—content gauge, 14—ballast tank, 15—sea tank. (**a**) Principle of the test bench. (**b**) Overall layout of test bench. (**c**) Valve mechanism of test bench.
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 2 Related Work
### Obstacle-Dependent Gaussian Potential Field Algorithm  
This section describes a risk-based algorithm, ODG PF. [PERSON] et al. [33] found that the laser range finder (LRF) sensor data measured in the LRF sensor are used to determine obstacles and measure the risk using the Gaussian function at the measured value. Using the measured risk, move the yaw angle of the vehicle through the inertial measurement unit sensor to a place with low risk.  
The received data are measured in Figure 2, and the measured data are listed in Figure 3. As a result, the measured data determine the obstacle [33]. Figure 4 stands for the enlargement of the angle occupied by the obstacle after considering the vehicle width. The ODG PF algorithm measures the width for the determined obstacle, as depicted in Figure 4, and calculates the repulsive field for the measured obstacle. Thus, for \(\theta\) measured for each obstacle in the sensor, as presented in (1), the Gaussian function is calculated to produce a reactive field:  
\[f_{rep}(\theta_{i})=\sum_{k=1}^{n}k_{rep}\exp(-\frac{(\theta_{k}-\theta_{i})^{ 2}}{2\sigma_{k}^{2}}). \tag{1}\]  
\[k_{rep}=(d_{\max}-d_{k})\exp(\frac{1}{2}), \tag{2}\]  
where \(\theta_{k}\) is a center angle of each obstacle, \(d_{max}\) is the maximum detection range, \(\sigma_{k}\) denotes half of the angle occupied by the \(k\)th obstacle, and \(n\) is the number of obstacles. In \(\theta_{i}\), \(i\)th indicates the data contract of the sensor data (\(i=1,...,361\)), and it means the sequence number of the approach angle. The coefficient \(k_{rep}\) is set up for each obstacle to embrace the Gaussian likelihood for each obstacle fully.  
\[f_{att}(\theta_{i})=k_{att}\Big{|}\theta_{goal}-\theta_{i}\Big{|}, \tag{3}\]  
\[f_{total}(\theta_{i})=f_{rep}(\theta_{i})+f_{att}(\theta_{i}), \tag{4}\]  
where \(k_{att}\) is the weight parameter for an attractive field. The attractive field is calculated to follow the direction of the goal point (\(\theta_{goal}\)), as shown in (3). Thus, the total fields are available, as presented in (4). When the total field is obtained, the vehicle is moved by the \(\theta\) of the minimum point, as depicted in Figure 5.  
Figure 4: Enlarging the angle occupied by obstacles.  
Figure 3: Sensor data and threshold distance.  
Figure 2: Illustration with vehicle and obstacles of the laser range finder (LRF) sensor.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Classification-Based Techniques  
Classification-based techniques are important precise methods in data mining and machine learning. Classification techniques aim to identify a classification model (named as a classifier) using a collection of designated sensor data points (training points) and then classify obscure data instances into one of the learned (normal/outlier) group. This type of technique requires constant updating to accommodate the new sensor data that belongs to a normal class. In the case of IoTs, this classification technique is adequately suited for the faults and outlier detection as this technique tends to operate under the common assumption that a classifier could be learned from a provided space feature to identify normal and outlier classes [37]. In order to make this technique, it needs to be devised into two stages which are training and experimenting [38]. In the training phase, the technique aims at learning a classifier using the available labelled training data followed by an experimenting phase that classifies a test instance as regular or an outlier or a sensor fault [39; 40].
## 2 Materials and Methods
### Performance Evaluation Metrics  
The performance evaluation metrics used in this study include balanced accuracy, a type of accuracy that account for the class imbalance. Accuracy, the most popular performance evaluation metric, shows the fraction of predictions the classifier got correctly [68]. Accuracy focuses on the number of correctly classified test samples with respect to the total number of test instances. In medical diagnosis, where the cost of misclassifying a sick patient as healthy is more than that of classifying a healthy patient as sick, using the accuracy metric would not be sufficient [69]. Furthermore, when reporting the performance of classifiers trained using imbalanced data, the accuracy metric can be misleading, and balanced accuracy is a better metric in such instances. Balanced accuracy does not mislead when the dataset is imbalanced, as it considers both the negative and positive classes. It can be defined as the arithmetic mean of specificity and sensitivity. A model with a balanced accuracy closer to 1 implies the model is capable of correctly predicting the test instances. Hence, we employed balanced accuracy, sensitivity, and specificity. Sensitivity, also called true positive rate (TPR), measures the model's ability to detect patients with the disease. Meanwhile, specificity, also called true negative rate (TNR), measures the model's ability to detect people who do not have the disease [70]. The mathematical representation of the performance evaluation metrics is shown below:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{15}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{16}\]  
\[BalancedAccuracy=\frac{Sensitivity+Specificity}{2} \tag{17}\]where TP, TN, FP, FN represent true positive, true negative, false positive, and false negative, respectively. True positive is when a positive test sample is classified as positive, while false positive is when a negative test sample is classified as positive. True negative is when a negative test sample is classified as negative, while false negative is when a positive test sample is classified as negative [71]. Furthermore, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are significant metrics used to show the performance of ML classifiers. Hence, we utilized both metrics in this study to further demonstrate the ML models' performance. The ROC curve is a graph of TPR versus FPR at various threshold values, while the AUC is a summary of the ROC curve, indicating the classifier's ability to distinguish between healthy (negative) and sick (positive) patients. The higher the AUC value, the better the classifier distinguishes between both classes [72].
## 3 Comparison to Previous Work and Additional Theoretical Outcomes
### Special Case for 3-Class Classification Tasks and Detection of FS-Ordinal Structures  
For the special case of \(c=3\), i.e., for 3-class classification tasks, we obtain the following theorem (Theorem 1), which will be later extended as an additional theoretical outcome, at the end of this section, in Section 3.2.  
**Theorem 1** (FS-ordinal class structures in 3-class classification tasks).: _Let \(X_{\Omega}\subset\mathbb{R}^{d}\times\{\omega_{1},\omega_{2},\omega_{3}\},d\in \mathbb{N}\), be a \(d\)-dimensional labelled data set, which constitutes a \(3\)-class classification task. Moreover, let the corresponding PSM, \(M\), be defined as follows, for some LSM, \(\mu\in\mathcal{M}^{d}\),_  
\[M^{(id)}=\begin{pmatrix}0&e&f\\ e&0&g\\ f&g&0\end{pmatrix},\quad e,f,g>0.\]  
_If \(e,f,g\) are pairwise distinct, i.e., \(e\
eq f,e\
eq g\), and \(f\
eq g\), then there exists a permutation \(\tau\in\mathcal{T}^{3}\), such that \(X_{\Omega}\) constitutes an FS-ordinal classification task specific to \((\mu,\pm\tau)\)._The proof of Theorem 1 is provided in the appendix of our previous study [21] for \(e,f,g\in(0,1]\), but works analogously for \(e,f,g>0\), as it is the case in the current work.  
Based on our proposed working definition which is introduced in Definition 3, there exists a simple way to check for FS-ordinal class structures. A corresponding pseudo code is presented in Figure 3. Note that a numerical example for the detection algorithm depicted in Figure 3 is also provided in our previous work [21].