esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Optimal Control Law of Autonomous Unmanned Surface Vessels
## 4 Discussion
### Control-Parameter Setup  
Control parameters that satisfied the existence conditions in Equation (7) for the proposed nonlinear optimal control design are indicated in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \multirow{2}{*}{\(a_{2}\)} & 0.002 \\  & 0.005 \\  & 0.008 \\ \hline \multirow{2}{*}{\(\mathbf{\Gamma}_{2}\)} & \(\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\\ \end{array}\right]\) \\ \hline \multirow{2}{*}{\(q_{211}\)} & \(50,000\) \\  & \(q_{211}\) & \(q_{211}\)I\({}_{3\times 3}\) \\ \(\mathbf{Q}_{222}\) & \(\mathbf{I}_{3\times 3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Control parameters for optimal control design.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(V_{\omega}\) (m/s) & 3.96 & \(C_{X\omega}(\gamma_{R})\) & \([-0.8\ 1]\) \\ \(C_{Y\omega}(\gamma_{R})\) & \([-0.7\ 1]\) & \(C_{N\omega}(\gamma_{R})\) & \([-1.05\ 1.05]\) \\ \(\rho_{air}\) (kg/m\({}^{3}\)) & 1.1644 & \(\rho_{w}\) (kg/m\({}^{3}\)) & 1025 \\ \(g\) (m/s\({}^{2}\)) & 9.8 & \(N\) & 1000 \\ \(\beta\) & \([-\pi\ \pi]\) & \(A_{i}\) & 3 \\ \(\phi_{i}\) & \([0\ 2\pi)\) & \(\lambda_{i}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modeling parameters of environmental disturbances (wind, wave, and current).
## 3 Related Works
### Watermarking algorithms based on Singular values and vectors  
In 2008, [PERSON] _et al_. [41] proposed a hybrid block-based watermarking scheme. The host image is divided into four blocks and the watermark (i.e., a binary image) is embedded twice: firstly in the largest SV of upper-left block (previously segmented into sub-blocks) by means of quantization, and secondly in the bottom-right block (segmented as well into sub-blocks), by using the method proposed by [PERSON] _et al_. [42]. To enhance the security of the scheme the watermark is permuted before the embedding process. A quantization table formed with the largest SV of each block of the original image is used in extraction. The algorithm is robust against common and geometric attacks. However, by modifying only the upper-left and bottom-right blocks of the cover image, some images may show artifacts at the block's borders of the watermarked image (i.e., strong luminance differences between upper-left corner and upper-right or bottom-left corners).  
It is important to mention that all the previously cited watermarking schemes based on singular vectors don't preserve the orthogonality property of the \(U\) and \(V\) matrices, since the transformation used to embed the watermark is nonlinear. It is very difficult to embed the watermark into the singular vectors of the host image while preserving the orthogonality, because it is necessary to devise an orthogonal transformation for watermark insertion and, at the same time, to preserve the quality of the watermarked image.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.3 RF Results  
The study experimented with random forest for both scenarios and the results are shown in Table 8. The results were comparable to the SVR and XGBoost models while training significantly faster than both. A randomized grid search was used to optimize the  
Figure 19: SVR actual vs. predicted EPA NO\({}_{2}\) correlation plot using TROPOMI and date features for the study’s first scenario. Figure 20: SVR actual vs. predicted EPA NO\({}_{2}\) correlation plot using TROPOMI and date features for the study’s second scenario. hyperparameters for the scenario and set of features used in each experiment. Table 8 lists the settings for different experiments. Figure 21 displays the decision tree generated from RF experiment #2 (Table 8). The tree split started with the \"dayofyear\" feature by taking the middle point value of the year. TROPOMI NO\({}_{2}\) and date features split all subsequent nodes. Each split was performed with values higher than a threshold the model set. A sample was taken from the dataset to calculate the MSE of the predicted EPA NO\({}_{2}\) error distance from the regression line until a leaf (terminal) node was constructed, determining the optimal EPA NO\({}_{2}\) prediction. Figure 22 corresponds to RF experiment #4 (Table 8). The plot mostly shows underestimation for the test set line over actual values. The feature set utilized resulted in performance slightly behind RF experiment #3. The figure showcases the model's inability to learn the patterns even with different feature sets listed in Table 8.  
Figure 23 corresponds to RF experiment #12 (Table 8). The plot graphs the second-best result behind SVR's similar experimental setup for the study's second scenario to show a good prediction fit over actual EPA NO\({}_{2}\). Specific points in the yellow line pick up some extreme values, while others are estimates.
## 2 Experimental Work
The actual piping diagram of the subsea jumper is depicted in Figure 4. The high-speed camera required high accuracy, and the Mini UX100 system developed by Japan's FASTCAM company was chosen. Its capture speed (full frame) was up to 4000 fps. The high-speed camera had to shoot when the light intake was low. To ensure there was enough light intensity for the high-speed camera to catch the rigid jumper inner gas-liquid flow, an LED fill light was installed on the side of the pipeline. Eight pressure sensors were inserted into the elbow portion of the jumper, as can be seen in Figure 4, to track pressure changes in various locations. The S-20.0-2.5 series pressure sensor developed by Germany's Wika company was adopted. It has an accuracy of 0.1% and a working range from 0 to 2.5 MPa. The pressure sensor was threaded to the rigid jumper via NPT. It emitted a voltage signal of 0-5 V, and the sampling rate was set to 1000 Hz. An NI-USB-6210 series acquisition card developed by America was used, and the upper computer was equipped with an NI data acquisition system for data acquisition, storage, and export. As Figure 5 shows the jumper section slug flow-induced vibration experimental system. To prevent the flanges and transparent windows on the jumper from affecting the vibration characteristics of the pipe, the same-size rigid jumper was selected for the vibration experiments, and fixed constraints were set on both ends. The LC0103 has a built-in IC piezoelectric accelerometer to measure vibration, and the accelerometer was installed in different positions to obtain the vibration characteristics of the pipeline. The sensor sensitivity was 50 mV/g, and the frequency range was 0.35-10,000 Hz. The LC3101 data-collecting device developed by SANYO was used to enable multi-channel sampling, and the obtained data were then transmitted via a USB interface to the host computer for monitoring in real time as well as saving. Figure 4: Actual piping diagram of the jumper.
# Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure
## 2 Related Work
### Discussion  
Previous work in this field mainly focused on short-term person re-identification. Meaning; a model can re-identify a person using images/videos taken on the same day, but the model's performance will dramatically deteriorate when trying to re-identify the same person after a long period if the persons' clothing changes [20]. Most of the previous work relies heavily on clothing features because models extract features from the entire body image. Long-term re-identification has been understudied by the scientific community due to the limited availability of large-scale public datasets [20].  
To improve recognition even after a subject has changed clothes, we experiment with a multi-modal approach inspired by [8]. Instead of feeding the entire input image of a person to one neural network, we consider the upper region of the human body, which includes hair, face, neck, and part of the chest as the first modality, and the remaining body figure as the second modality.  
The idea of multi-modal learning stems from how humans learn; when a number of our senses are heightened and engaged during learning, we tend to learn better and retain more information. Building models that learn from different sources of information and then fusing their output improves the overall learning process thus, improving performance.  
The upper region of the human body contains information that does not change dramatically over time compared to the body figure changing rapidly with the clothes variation. Therefore, two separate models were built to learn appropriate features from each part. We feed each modality (face and body) to different neural networks that can extract features from each modality and combine the results of both neural networks. The first modality was extracted from soft bio-metric features.  
By definition, soft bio-metrics are the characteristics of a human extracted from images or videos. Soft biometrics are behavioral and physical characteristics used to identify, verify, and describe human subjects. Some examples of soft bio-metrics are height, weight, and facial measurements. To obtain the first modality, after extracting the facial region, we included the hair, face, neck, shoulders, and a portion of the chest of a human subject. Generally, the previously mentioned regions tend not to change as drastically over time as clothing, so they were grouped. After that, the facial region was extended to incorporate part of the hair, neck, shoulders, and a portion of the chest to increase the features extracted from this region. The second modality is the remaining body figure.
## 3 The Concept of the ASI  
Many projects have been developed on the subject of monitoring and security analysis to assist the System Operator manage information overload. Those projects have provided elegant solutions that are real-time measured. However, they require wide-area measurements and high-speed communication links between the measured points.  
As an alternative approach, ASI is proposed as a device designed to recognize the advent of a possible system stress at its location and alert the system operator accordingly. There are four principal characteristics that define the ASI, namely autonomous, selective, accurate and intelligent. These characteristics are critical to the reliable operation of the autonomous stress indicator and to the stress-alert system which it embodies. These characteristics are now described in detail.
## 4 Harmony Search Algorithm and Its Variants
### Improved Harmony Search Algorithm  
The Improved Harmony Search (IHS) algorithm was introduced in 2007 by [PERSON] et al. in [39]. IHS was developed in an effort to improve the convergence of the solutions by dynamically varying \(PAR\) and \(BW\) at every new improvisation \(k\) as explained by Figure 8.  
The probability of path adjustment is increased by increasing \(PAR\) linearly as:  
\[PAR(k)=PAR_{\min}+\left(\frac{PAR_{\max}-PAR_{\min}}{NI}\times k\right) \tag{21}\]  
Figure 8: Improvisation process in Improved Harmony search (IHS) algorithm.  
Figure 7: Improvisation process of a new harmony vector in Harmony Search (HS) algorithm.  
where \(k\) is the iteration or the current improvisation and \(PAR_{\max}\) and \(PAR_{\min}\) are the maximum and minimum adjustment rate, respectively.  
At the same time, the degree of adjustment is decreased by decreasing \(BW\) exponentially as:  
\[BW(k)=BW_{\max}\exp\left(\frac{1}{NI}\times\ln\left(\frac{BW_{\min}}{BW_{\max}} \right)\times k\right) \tag{22}\]  
where \(BW_{\max}\) and \(BW_{\min}\) are the maximum and minimum bandwidth, respectively.  
Similarly to the HS algorithm, the IHS algorithm follows the same steps with the inclusion of the calculation of \(PAR(k)\) and \(BW(k)\) as detailed in pseudocode of Algorithm 2:  
```
1. Define parameters of IHS algorithm: \(n\), \(HMS\), \(HMCR\), \(PAR_{\max}\), \(PAR_{\min}\), \(BW_{\max}\), \(BW_{\min}\), \(NI\).
2. Initialize HM with random harmony vectors using (19) and calculate the cost function of each vector.
3. Calculate the \(k^{\text{th}}\) pitch adjustment rate \(PAR(k)\) and bandwidth distance \(BW(k)\).
4. Improvise a new Harmony vector \(\textbf{X}_{new}\) based on the three rules of Figure 8.
5. Update the HM with \(\textbf{X}_{new}\) if \((f\left(\textbf{X}_{new}\right)<f\left(\textbf{X}_{w}\right))\) as \(\textbf{X}_{w}=\textbf{X}_{new}\).
6. If the maximum number of improvisations \(NI\) is reached then stop the program and return the best harmony vector \(\textbf{X}_{B}\). Otherwise, go back to step 3.
```  
**Algorithm 2**Improved **A**Sear **Algorithm**  
This method strongly enhances the capabilities of the IHS algorithm in terms of precision once the algorithm has converged to an interesting region of the search space. However, this method introduces the problem of bounds selection (i.e., \(PAR_{\max}\), \(PAR_{\min}\), \(BW_{\max}\) and \(BW_{\min}\)). Moreover, the fact that the parameter \(PAR\) continues to increase without settling even when an interesting region is reached made this method questionable.
## 5 Results and Discussion
### Control Synchronization of Two Control Schemes
As can be seen in the fourth stage, when motor 1 operates with velocity control, the synchronous velocity of the motors rises slowly and finally stabilizes around 1680r/min, and the synchronous velocity increases by about 85r/min. Figure 15b shows the phase difference curves between motors. The phase difference respectively has fluctuations when the synchronous velocity changes, and the rest time stability near 0\({}^{\circ}\) except for the second stage. In a similar way with Figure 10, the phase differences approach \(\pm\)180\({}^{\circ}\) according to Figures 9a and 10. However, the absolute phase difference of the second stage in Figure 15b is smaller than that in Figure 11b. This is because the two-ERs system in Figure 11b is more symmetrical than that in Figure 15b, which conforms to the variation in Figures 9 and 10. Comparing the acceleration amplitudes of two positions in Figure 15c-f, it can be seen that the vibration system implements the circular trajectory motion. In the first and third stages, the acceleration amplitudes of \(x\)- and \(y\)-directions of two positions are approximately equal in control synchronization, and the \(y\)-direction is 90\({}^{\circ}\) ahead of \(x\)-direction. However, the acceleration amplitudes of two positions in the fourth stage are greater than those in others, because the synchronous velocity in the fourth stage is greater than those in others. According to the experimental analysis in Figure 15, the phase controller can be used on different slave motors to achieve the synchronous motion of three ERs with the zero phase difference, which proves the effectiveness of the proposed control scheme. Due to the coupling characteristic of the self-adjusting of the vibration system, the acceleration amplitudes of the vibration system are affected in the cases of vibration synchronization and control synchronization.
## 1 Introduction
The normalized residuals \(\left\{\frac{\gamma_{i}-\widehat{\gamma_{i}}}{s_{i}}\right\}\) (here \(\widehat{\gamma}_{i}\) being the datum value predicted by the model) are expected to follow a standard normal distribution. Yet, residuals of geomagnetic observations of an arbitrary type often display a sharper distribution, sometimes much closer to that of the so-called Laplace distribution (e.g., [2; 3; 4; 5]). We suggested in [1] that residuals may be incorrectly normalized and, therefore, their common statistical distribution is a mixture of Gaussian distributions. In particular, we demonstrated in [1] several examples of the variability in \(\sigma\) determination that indeed lead to the non-Gaussian shape of the histogram. Thus, we assume that the observable residual \(\theta\) is a mixture of individual Gaussian random variables with zero expectations and random variances \(\beta^{2}\). In the present short note, we argue that the distribution of the random variable \(\beta\) can be well approximated by the lognormal distribution with pdf  
\[f_{\beta}(t)=\frac{1}{t\sqrt{2\pi}}\exp\left[-\frac{\ln^{2}t}{2s^{2}}\right]\]  
We also provide the method that recovers the value of \(s\) in the real data case.
## 4 Experiments and Analysis
### Results Analysis and Discussion
#### 4.4.4 Analysis of Results on the SDAA Dataset
Later, it increased with the number of epochs and became constant after epoch 30. Apart from these analyses, our proposed model significantly combatted overfitting in all different datasets. As a result, the training dataset's accuracy is slightly higher after the 20 th epoch than the validation or test dataset in all datasets--while, for initial epochs, the test data's accuracy is a bit higher. Figure 4 likewise shows the loss value to confirm the combatted overfitting effect of our model on the four Twitter datasets. From the figure, the loss of our models in training and validation sets shows a decreasing trend with the increase of epochs and finally reaches a lower value and becomes stable. For instance, in Figure 4a, the loss of SD4A validation data starts from 0.5106, steadily decreasing to 0.1764 for 10 Epochs, and then becomes stable for all other iterations. Figure 4b presents the loss trend of training and validation  
Figure 3: Performance comparison obtained by different methods on the SD4A dataset (**a**); US-Airline dataset (**b**); Sentiment140 dataset (**c**); Sentiment140-MV dataset (**d**). of the US-airline dataset. It provides a rapid loss curve of 10 iterations starting from 0.4669 to 0.1835 on the validation set and becomes stable. Figure 4c,d belongs to the loss rate of sentiment140 and Sentiment140-MV, respectively. The loss validation rate starts from 0.6919, decreasing the trend to 0.3293 for sentiment140. Similarly, loss validation for Sentiment140-MV starts from 0.7091 and steadily decreases to 0.2289. In both, the loss trends for the validation set become stable after 20 epochs. In the result evaluation of Figures 3 and 4, our proposed ACL-SA network model converges after the mentioned number of epochs with consistent accuracy and loss. Thus, these analyses provide evidence that the ACL-SA network reduces the overfitting problem and attains adequate accuracy. Our proposed model performs better than the other baseline algorithms on all datasets based on the experimental and comparative analyses. Furthermore, the model minimizes the losses and increases the accuracy of the model.
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 3 Methodology
### Loss Function  
Most of the previous salient object detection methods have always used cross-entropy for training loss. When these methods capture target objects, it is difficult to obtain complete details, resulting in blurred boundaries or incomplete detection results. Inspired by [PERSON] et al. (2019), a hybrid loss is constructed to supervise the network and to learn more detailed information concerning boundary location and structure capture. The fusion loss is composed of binary cross-entropy (BCE) ([PERSON] et al., 2019), boundary intersection over union (boundary IoU) ([PERSON] et al., 2019), and structural similarity (SSIM) ([PERSON] et al., 2019). Therefore, the total loss of NURCNet is defined as follows:  
\[\mathcal{L}_{all}=\sum_{k=1}^{K}\mathcal{L}_{bce}^{(k)}+\mathcal{L}_{iou}^{(k) }+\mathcal{L}_{ssim}^{(k)} \tag{1}\]  
Figure 5: Refinement network structure diagram.  
where \(K\) denotes the total number of the outputs. As described in Sections 3.2 and 3.3, our NURCNet is deeply supervised with five outputs, i.e., \(K=5\), including four outputs from the encoder-decoder network and one output from the refinement network.  
The BCE loss [37] is one of the most often used losses in binary classification and segmentation, defined as follows:  
\[\mathcal{L}_{\text{bce}}=-\sum\limits_{r,c}G_{r,c}log(S_{r,c})+(1-G_{r,c})\log (1-S_{r,c}) \tag{2}\]  
where \(G_{r,c}\in\{0,\,1\}\) is the ground truth label of the pixel (\(r\), \(c\)) and \(S_{r,c}\) is the predicted probability of being defective object.  
The SSIM [39] is originally proposed for image quality assessment, and it captures the structural information of an image. Let X= {\(x_{\text{Y}}\);  j = 1,..., \(N^{2}\)} and Y= {\(y_{\text{j}}\);  j = 1,..., \(N^{2}\)} be the pixel values of two corresponding patches cropped from the predicted probability map \(S\) and the binary ground truth mask \(G\), respectively; the SSIM is defined as follows:  
\[\mathcal{L}_{ssim}=1-\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{ xy}+C_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)} \tag{3}\]  
where \(\mu_{x}\), \(\mu_{y}\) and \(\sigma_{x}^{2}\), \(\sigma_{y}^{2}\) are the mean and standard of \(x\) and \(y\), respectively, \(\sigma_{xy}\) is their covariance, and \(C_{1}=0.01^{2}\) and \(C_{2}=0.03^{2}\) are used to avoid dividing by zero.  
Boundary IOU [38] loss is adopted to further penalize the inaccurate classification, and it is defined as follows:  
\[\mathcal{L}_{iou}=1-\frac{\sum\limits_{r=1}^{H}\sum\limits_{c=1}^{N}S_{(r,c)} G_{(r,c)}}{\sum\limits_{r=1}^{H}\sum\limits_{c=1}^{W}\left[S_{(r,c)}+G_{(r,c)}-S_{(r,c )}G_{(r,c)}\right]} \tag{4}\]
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 2 Materials and Methods
### Adapting Air Mobility Communication Design in Broken-Line UAV Corridor Topologies
This comprehensive parameter estimation framework ensures that the Markov reliability models accurately reflect real-world system behavior while accounting for uncertainty and temporal variations in system characteristics.
## 2 Proposed Routing Scheme  
In this section, the proposed heterogeneous protocol called HMGEAR is explained. This scheme builds on the homogeneous routing protocol suggested in [34].  
In this scheme, the network divides the sensor nodes into four logical regions on the basis of predefined threshold distance. The Base station and a gateway node are placed outside the sensing field and at the center of the network, respectively. The nodes whose distance from either the Base station or gateway node is less than the predefined distance are assigned to Regions 1 and 2, respectively. In this case, they transmit their data to either the Base station or gateway node using direct communication approach. These are the homogeneous nodes. However, if their distance is more than the predefined threshold distance and closer to the gateway node they are placed in Region 3, else in Region 4, as illustrated in Figure 1. These are the heterogeneous nodes. Elections are conducted in these two regions and heads are selected based on the residual energy of the nodes. Data are transmitted to the BS through a multi-hop communication technique (see [10], for example) from these regions. The gateway node receives the final report from CHs in Region 3, aggregates the data before transmitting them to the BS. The nodes in each region transmit their data based on the energy threshold set out for them, below which the nodes cannot transmit data but sleep to conserve their energies.