esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Restoration Using a Fixed-Point Method for a TVL2 Regularization Problem
## 8 Conclusions  
In this paper, we first proposed a new TVL2 regularization model (4) for image restoration, and then we proposed the fixed-point method (Algorithm 2) and the fixed-point-like method (Algorithm 3) for solving TVL2 problem (4). According to numerical experiments, the fixed-point-like method for the new TVL2 problem (4) restores true image better than the fixed-point method for TVL2 problem (4). The reason for this is that the linear system in line 7 of Algorithm 3 is well-conditioned and \(a^{k+1}\) is computed after the solution step of finding \(u^{k+1}\), i.e., \(a^{k+1}\) is computed using the new value of \(u^{k+1}\) instead of using the old value of \(u^{k}\).  
Both of the split Bregman methods for TVL2 problems (3) and (4) perform the same for almost all cases, while the fixed-point-like method for TVL2 problem (4) performs better than the fixed-point method for TVL2 problem (3). It can be also seen that the execution time of the fixed-point-like method for TVL2 problem (4) is less than that of the fixed-point method for TVL2 problem (3). Hence, it can be concluded that the new proposed TVL2 model (4) for image restoration is preferred over the TVL2 model (3), and the proposed fixed-point-like method (Algorithm 3) is well suited for the new TVL2 model (4).  
The fixed-point-like method and TVL2 model (4) proposed in this paper can be applied to the image inpainting problem or image restoration problem with Poisson noise. Future work will study these kinds of problems.  
Conceptualization, J.H.Y.; Formal analysis, J.H.Y.; Methodology, K.S.K. and J.H.Y.; Software, K.S.K. and J.H.Y.; Writing--original draft, K.S.K. and J.H.Y.; Writing--review and editing, K.S.K. and J.H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A6A3A01012976), and the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No. 2019R1F1A1060718).  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions which greatly improved the quality of the paper.  
The authors declare no conflicts of interest.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**Part A**} & \multicolumn{2}{c}{**mIOU (\%)**} & \multirow{2}{*}{**Improvement (\%)**} \\
**Structure** & & & & **Part B** & & **Average** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Simple \\ network \\ \end{tabular} } & Single & Raw & 70.48 & 69.84 & 70.16 & - \\ \cline{2-6}  & \multirow{2}{*}{Dataset} & Enhanced & 73.69 & 72.29 & 72.99 & +2.83 \\ \cline{3-6}  & & Consensus & **74.37** & **72.88** & **73.62** & **+3.46** \\ \cline{3-6}  & \multirow{2}{*}{Multi-Dataset} & Raw & 74.30 & 73.10 & 73.70 & +3.54 \\ \cline{3-6}  & & Enhanced & **76.51** & **75.27** & **75.89** & **+5.73** \\ \cline{3-6}  & & Consensus & 74.89 & 74.15 & 74.52 & +4.36 \\ \hline \multirow{4}{*}{U-Net} & Single & Raw & 72.14 & 71.28 & 71.71 & - \\ \cline{3-6}  & Dataset & Enhanced & **74.45** & 73.38 & **73.92** & **+2.21** \\ \cline{3-6}  & & Consensus & 74.13 & **73.40** & 73.76 & +2.05 \\ \cline{2-6}  & \multirow{2}{*}{Multi-Dataset} & Raw & 73.96 & 72.68 & 73.32 & +1.61 \\ \cline{3-6}  & & Enhanced & **75.97** & **74.62** & **75.29** & **+3.58** \\ \cline{3-6}  & & Consensus & 74.70 & 73.61 & 74.16 & +2.45 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} DeepLabV3 \\ \end{tabular} } & Single & Raw & 72.09 & 70.86 & 71.48 & - \\ \cline{3-6}  & Dataset & Enhanced & **74.51** & 73.30 & **73.91** & **+2.43** \\ \cline{3-6}  & & Consensus & 73.79 & **73.70** & 73.75 & +2.27 \\ \cline{3-6}  & & Raw & 71.05 & 70.88 & 70.96 & \(-0.51\) \\ \cline{3-6}  & & Enhanced & 72.89 & 71.64 & 72.26 & +0.79 \\ \cline{3-6}  & & Consensus & **74.31** & **73.15** & **73.73** & **+2.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the estimated performance of six models across three network structures. The highest scores for each network and dataset configuration are emphasized in bold.
# Development of Rock Embedded Drilled Shaft Resistance Factors in Korea based on Field Tests
## 2 Evaluation of Resistance for the Drilled Shaft
### Procedures for Determination of Measured Resistance  
Measured resistances were determined using the ultimate bearing capacities obtained from the load tests. In general cases, the measured resistance indicates the total measured resistance from the load-displacement curve of a static load test or the equivalent load-displacement curve of a bi-directional load test. However, load-displacement curves are generally drawn using the applied load and measured displacement of the shaft head, so they cannot include detailed ground properties or axial load transfer characteristics of a drilled shaft in the loading stage. To overcome these limitations and check the shaft and base resistances according to depth, a load transfer analysis using the measured strain data was performed.  
The procedure for the calculation of measured resistance can be summarized as follows:  
From the applied load and displacement measured during the load test, draw the load-displacement curve and determine the total resistance using this curve. If the load test was a bi-directional load test, draw the equivalent load-displacement curve  
If it is necessary to calculate the shaft and base resistances, a load transfer analysis is conducted using data from strain gauges installed along the pile shaft that report the strain at each evaluated depth.  
Calculate the axial load for each depth using a load transfer analysis as follows:  
\[\mathrm{P}=\varepsilon\times\left(\mathrm{E_{pile}}\times\mathrm{A_{pile}}\right) \tag{1}\]  
where P is the calculated axial load at each depth, \(\varepsilon\) is measured strain at each depth, \(\mathrm{E_{pile}}\) is the elastic modulus of pile, and \(\mathrm{A_{pile}}\) is the area of the test shaft.  
From the calculated axial load and displacement for each depth, draw the shaft resistance-displacement curve (f-w curve) and base resistance-displacement curve (q-w curve) and determine the shaft and base resistances using these curves.
## 3 Results
### Evaluation of Tracking Accuracy  
For two datasets encompassing around 3000 s of recording each, we have available ground truth positional data for two points along the track. The recording was done on an open track with the cable installed in a cable trench. The available ground truth is accurate to one second in time, which leads to an accuracy of around 30 to 40 m considering that this is the speed the objects move on the test infrastructure. The tracking errors in meters can be found in Table 2. The tracking error is in the range of 40 m, which is in the range of the available ground truth considering that both, the ground truth and the tracking algorithm, work on a one second basis that leads to a maximally two-second difference between the two systems, and the trains move at a speed of 30-40 m/s.  
Figure 4: Train tracking result for an example dataset (colored lines), detection result in black and white; note that the depicted signal is the same as in Figure 2.  
Figure 3: Leading and trailing edge of the object in blue and orange, respectively, plotted over the vibration detection results in black and white; note that the depicted signal consists of the first 35 s of the signal shown in Figure 2.
# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 2 Materials and Methods
### Evaluation Criteria  
This study evaluates the anomaly detection performance of the proposed algorithm from both qualitative and quantitative aspects. The qualitative evaluation is mainly based on the visual effect of the detection result graph, while the quantitative evaluation is carried out by drawing the receiver operating characteristic curve (ROC) [47], calculating the area under the ROC curve (AUC) value [48] and comparing the calculation time. The ROCcurve originating from the communication field is widely used to describe detection performance, showing the relationship between the true positive rate (TPR) and the false positive rate (FPR) by varying the threshold between 0 and 1. The TPR represents the detection probability, and the FPR is also known as the false alarm rate, which can be defined as follows:  
\[\begin{array}{l}TPR=\frac{TP}{TP_{+}^{2}FN}\\ FPR=\frac{TP}{TN+FP}\end{array} \tag{12}\]  
where _TP_, _TN_, _FP_, and _FN_ stand for true positive, true negative, false positive, and false negative, respectively. In general, a higher TPR value means that the algorithm detection performance is better when the FPR value is the same. Moreover, the corresponding ROC curve will be closer to the upper left corner of the coordinate plane, and the area under the curve will be larger. However, observing the ROC curve alone cannot directly and intuitively judge and compare the effectiveness of the anomaly detectors. Thus, AUC is taken into consideration, which is the value of the area under the ROC curve. The closer the AUC value is to unity, the better the detection performance of the algorithm is.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 1 Introduction
The results indicate that the proposed algorithm is adaptive to the structure of the image and performs better in adhering to boundaries and irregular objects. The main contributions of this paper are as follows:  
1. First, an adaptive seed sampling strategy of contour encoding is proposed to solve the problem of the insensitivity of regular grid seed sampling to the image content;
2. Second, a novel balanced weighted similarity measurement enables accurate similarity evaluation between superpixels with different sizes, and a contour penalty term increases the boundary adherence capability and adaptation to irregular objects;
3. Finally, a constrained recombination method is applied to the initial superpixels, which significantly reduces over-segmentation and optimizes the labeling of irregular objects. The rest of the paper is organized as follows. In Section 2, we briefly review both of the image segmentation categories by discussing some representative methods. Section 3 describes the proposed method in detail. Section 4 presents the experimental results of our algorithm and several existing methods. Finally, a summary is presented in Section 5.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Parameters Used in Ensemble Model for Attack Detection and Noise Reduction
#### 5.3.3 Svm  
We assume that there is noise in the dataset in our experiments, which we mean here includes data that has a low impact on constructing the decision boundary of the overall model. Therefore, when the hyperplane of the SVM is determined, we set the regularization parameter to 0.8, lower than the default value of 1.0, so that the entire model can form a flexible decision boundary by forming a sufficient margin for the noise data. Also, due to the complexity of the dataset, we use a linear kernel for the availability of classification.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Capacitance Sensors  
A capacitor allows electric potential to be stored between plate electrodes. The material separating the plates, distance between the plates and area of the plates determines the quantity of potential energy storage [70]. The dielectric constant describes the permittivity of the material between the plates. For moisture sensing, the material between the plates is tested for permittivity, which changes with water content because the dielectric constant of water is much larger than hay [68].  
The depth of the reading is determined by the field distribution. Though this is not well modelled to define the exact depth, it can be optimized by relating the distance between the plates [70]. The depth of reading can also be increased via the addition of a copper back plate to direct the field away from the sensor [71]. Though these relationships have been noted, there are no final relations for the field distribution and the depth could only be optimized but not directly known from design parameters. This method has been tested to produce accurate results, though the variance of precision is widely based on the implementation environment. There is a high possibility for the recorded signal to be influenced by the incoming current frequency or possible material interference with the field. This requires significant signal filtering to be implemented to produce reliable results [72].  
Capacitive sensors are not sensitive to the bale density or volume being tested [73]. The two main configurations for capacitive sensing are parallel and planar based on fringe effect theory [74]. Parallel is widely used in lab applications as the standard capacitive configuration. The planar configuration produces a fringe field that is less restrictive to configuration, though this has not yet been used in any industrial agricultural applications.
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### RTD Classification Summary
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Assumption-Driven RTD** & **Defining Condition** & **Distinguishing Feature** & **Example from Case Study** \\ \hline Unvalidated Assumption Debt & The assumption is stated, but not verified or tested under real-world or expected conditions. & The assumption is explicit, but its correctness is uncertain. & Assuming GPS accuracy is always within 2 m without field validation. \\ \hline Incomplete Assumption Debt & Key environmental factors are omitted entirely from the requirement specification. & The assumption is absent or only partially captured. & Not specifying wind variability in the separation distance requirements. \\ \hline Outdated Assumption Debt & The assumption was once valid but has changed due to external/system evolution. & Reflects temporal decay or shifts in operational conditions. & Assuming reliable cloud connectivity that degrades as deployment context changes. \\ \hline Incorrect Assumption Debt & The assumption is explicitly stated but factually wrong. & The assumption is invalid in context or leads to faulty behavior. & Assuming fixed communication latency when network delays fluctuate. \\ \hline Overconstrained Assumption Debt & The system lacks flexibility to adapt when assumptions break or vary. & No alternative or adaptive mechanism is provided. & Assuming static geofence boundaries, with no support for dynamic airspace constraints. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of Assumption-Driven RTD Categories. classification to quantitative metrics in RTDQM, enabling more nuanced measurement and prioritization strategies for managing RTD in safety-critical systems.
### Wavelet Transformation
#### 1.1.5 Multilevel Decomposition  
Multilevel decomposition is a technique for filtering the high- and low-frequency components of a signal at multiple levels. At each level, the signal is split into detail coefficients (high-frequency) and approximation coefficients (low-frequency). The approximation coefficients capture the low-frequency components, while the detail coefficients capture the high-frequency components [30].  
This is achieved by applying low-pass and high-pass filters (LPF and HPF) at each level. LPF captures lower frequencies while rejecting higher ones, and HPF captures higher frequencies while rejecting lower ones [31]. In Figure 2b, approximation and detail coefficients are represented by \"A\" and \"D\", respectively. The approximation coefficients are iteratively filtered at each level in a dyadic process.  
Figure 2: _Cont._
## 3 Experiments and Results  
Multiple subjects wore the system, and the data was collected data for each sport mode. Before the experiment, the skin that needed to be in contact with the electrode pads was treated carefully and cleaned with medical alcohol, and the surface EMG data was checked to see if the position of the electrode pads was correct. The acquisition time of each movement mode was kept to 3 min and was ensured to be continuous. After completion of an exercise mode, rest was taken for 15 min. Since the EMG acquisition system we designed is wearable, it was not restricted by the sports environment. Data collection was not limited to being carried out in a laboratory environment, so the subjects wore the EMG collection equipment and moved freely outdoors. The duration of each exercise mode was random and appropriate. Figure 6 shows a case of a subject wearing an EMG acquisition device while exercising outdoors.
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 3 System Model  
### Network Model  
We assume that \(N\) sensor nodes are randomly and uniformly placed in a target water area and sink nodes are randomly distributed on the surface of the water. Once a node is in the water, it will anchor itself. Each node is capable of communication, perception, and mobility. The symbol \(s_{i}\) is used to denote the \(i\)-th node position, and the corresponding node set is \(S=\{s_{1},s_{2},\ldots,s_{n}\}\). The following assumptions are established.  
1. In addition to the sink nodes, all nodes in the network have the same communication and sensing radii.
2. When the network is initialized, the nodes are distributed randomly in the 3D monitoring space, and the nodes can sense their own location information and their neighbors.
3. The obstacles (irregular and polyhedral) are distributed randomly in the underwater 3D monitoring space. If a node hits an underwater obstacle in the process of moving, node communication will break down.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 2 Materials and Methods  
### Dynamic Models of EHPS System  
The EHPS system cannot only provide optimal assistance torque for the driver but also provide reliable steering angle and torque for the intelligent driving of commercial vehicles. The components of the EHPS system can be divided into a mechanic system, a hydraulic system, and a steering resistance system. The mechanical system is equipped with TAS, which allows us to obtain the steering wheel angle and torque signals through the sensor and send them to the ECU controller to control the power assist motor to provide appropriate torque. The scheme of the EHPS system in this paper is shown in Figure 1, which mainly includes a PMSM, a reducing mechanism, a recirculating ball steering gear, and a torque and angle sensor (TAS).
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Remote Monitoring Applications in VANETs
#### 2.1.2 Commercial Driver Safety  
In [19], it was reported that commercial drivers often involve in more road accidents as compared to those who drive their personal vehicles. According to the studies by the social security organization (SOCSO), there were at least 2 work-related driving deaths in 2011. Also, there is a rise of almost 49% in work-related driving accidents from 17,682 accidents in 2007 to 26,262 in 2012 [20]. In [21],as part of Shell exploration and production hearts and minds research programme, four operating companies across Holland, Nigeria, Thailand and Sultanate of Oman took part in a study analyzing the commercial drivers' attitudes in oil and gas industry that lead to risk-taking behaviour. Also, it was further reported that 50% of all fatalities in Shell's exploration and production were related to driving.  
Commercial drivers often involve in risk-taking behaviours as they are not monitored [21; 22]. Additionally, since the drivers do not own the vehicle, they do not bear any financial burden in case of vehicle crash, which results in less responsible and more risky driving [23; 24; 25]. According to [26], half of the workers involved in vehicle crashes in the United States of America were either not using safety belts or were in ejected state from the vehicle. Fatigue could be defined as a risk factor between sleepiness and reduced vigilance among drivers, and it has been reported in [27] that commercial drivers are more involved in sleepiness as compared to drivers using their personal vehicles. This is because most of the commercial drivers tend to perform odd jobs at night while also driving at daytime and, hence, do not have enough sleep. In Malaysia, for instance, most of the fatigue-related fatal crashes were caused by heavy-vehicle drivers [28].  
Video monitoring of commercial drivers while driving can contribute to improving road safety and can be used to caution the drivers and other vehicles on the road should there be any occurrence of unusual behaviour. According to the study carried out in [22], the use of driver monitoring resulted in an estimated average of 20% accident reduction. Moreover, drivers when aware of being monitored tend to change their attitude. Video monitoring can also assist in fatigue detection [29; 30; 31] by observing drivers' various physiological features such as eye blinking, yawning and other movements.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 4 Results
### Ablation Experiment
#### 4.2.2 Influence of Hyper-Parameter \(K\)  
To alleviate the problem caused by imbalance between road and background pixels as described in Section 3.5, the loss function used to optimize our model contains a manually selected hyper-parameter \(K\), which is used to balance the loss contribution of BCE and dice loss function. Therefore, choosing an appropriate hyper-parameter \(K\) may be beneficial to reach the local optimum quickly and improve performance. We train our proposed model with different values of hyper-parameter \(K\) in ascending orders, while other conditions are maintained the same to select an optimal \(K\). Considering representativeness and experiment quantity, \(K\) is set to 0, 0.2, 0.4, 0.6, 0.8 and 1, respectively, to observe the performances in this paper.  
The experiment results with different configurations of hyper-parameter \(K\) are shown in Figure 6. It can be seen that the F1-score and MIoU are fluctuating as \(K\) increases. When \(K=2\), it achieves a best MIoU score of 86.74% and F1-score of 84.51%. It is noted that the performance is reduced to 82.28% in terms of F1-score with \(K=0\) and 79.41% with \(K=1\); the comparison results show that the separate use of BCE (\(K=0\)) or dice (\(K=0\)) loss function cannot obtain the optimal results for road extraction. As listed in Table 2, the performance with \(K=0.2\) is also better than the simple combination of \"BCE + Dice\", which obtains improvements of 1.48% on the MIoU score and 0.75% on the F1-score.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Settings** & **\#Params** & **F1-Score** \\ \hline Backbone (with ResNet18) & 80 M & 81.79\% \\ Backbone (with ResNet34) & 118 M & 84.51\% \\ Backbone (with ResNet50) & 830 M & 84.92\% \\ Backbone (with ResNet101) & 902 M & 85.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance and parameters comparison of ResNet series models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Settings** & **MIoU** & **F1-Score** \\ \hline BCE + Dice & 85.26\% & 83.76\% \\ \((1-K^{\text{ \text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\To further illustrate the influences of different weight combinations between dice and BCE loss function, we plot the changes in the loss value during training with respect to the number of epochs, as shown in Figure 7. It can be seen that the loss function curve with \(K\) = 0.2 is smoother than another, which indicates that there is a reasonable allocation of loss weights. To better optimize our model, we adopt \(K\) = 0.2 in the method evaluations.