esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 4 Experiment and Analysis
### Experimental Settings
#### 4.1.2 Imaging Regions  
This paper uses 100 imaging strips as the minimum limit and selects five different large regions from around the world as the regions to be imaged, namely the Congo (K), India, Australia, the United States, and Antarctica. The boundaries of each region are appropriately simplified, and the distribution of each region is shown in Figure 5. The parameters of each region are shown in Table 2.
# Inspection System for Vehicle Headlight Defects Based on Convolutional Neural Network
## 3 Method for Detecting Defective Vehicle Headlight Parts
### Alignment Calibration Using Major Axis Coordinates (Step 1)
#### 3.1.1 Likelihood-Based Color Model Transformation  
When a vehicle headlight image is captured using the inspection lighting (Figure 5a), there is a shadow due to the structure of the headlight. Thus, the color distribution in the histogram is skewed to one side (Figure 5d) when binarization is performed based on the gray-scale image [16]. Consequently, the interval to be set as the threshold value narrows (Figure 5d 1). Moreover, three threshold values need to be selected when using the hue-saturation-value (HSV) model in order to minimize the influence of light. Only one threshold can be used instead of three since the likelihood and normalization are applied after changing the red-green-blue (RGB) model to the HSV model to minimize the influence of light. As such, a wider threshold setting interval than the gray-scale, image-based setting interval should be used in the proposed method.  
Figure 4: Structure of the defect checker module.  
Figure 5: _Cont._The applied likelihood is shown in Equation (1). The likelihood value is calculated using the HSV value of the sampling coordinate and the HSV value of the input image. Normalization is applied by employing Equation (2) after calculating the likelihood value. The result of this step is shown in Figure 4(c), and the threshold setting interval is shown in Figure 4(e) 2.  
\[L(d)=\frac{1}{\sqrt{2\pi\alpha}}\exp\left(-\frac{d^{2}}{2\alpha^{2}}\right), \tag{1}\]  
\[d=\sqrt{\left(h_{s}-h_{i}\right)^{2}+\left(s_{s}-s_{i}\right)^{2}+\left(v_{s}- v_{i}\right)^{2}},\]  
\[L(y,\;x)=\frac{L(y,\;x)-MNN}{MAX-MIN}, \tag{2}\]  
\[MIN=\min[L(1,\;1)\ldots L(h,\;w)],\]  
\[MAX=\max[L(1,\;1)\ldots L(h,\;w)].\]  
where \(L(d)\) denotes the likelihood result value of the pixel, \(h_{s}\) denotes the hue value of the sampling coordinate \(s\), \(h_{i}\) denotes the hue value of the image coordinate \(i\), \(L(y,\;x)\) denotes the normalized value of the image coordinate \((y,\;x)\), and \((h,\;w)\) denotes the height \(h\) and width \(w\) of the input image.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### XFEM/GFEM Enrichment Functions for LEFM
#### 3.2.3 Kronecker Delta Property  
From Equation (14) it can be easily deduced that for enriched nodes, the FE degrees of freedom will no longer correspond to displacements at the nodes. To restore this desirable property, enrichment functions can be modified such that they vanish at nodal points. A simple way to accomplish that is through enrichment function \"shifting\" [115], which consists of subtracting from the enrichment functions, their values at the nodal points:  
\[\Phi_{I}\left(\mathbf{x}\right)=\Phi\left(\mathbf{x}\right)-\Phi\left( \mathbf{x}_{I}\right) \tag{18}\]  
where \(\Phi_{I}\left(\mathbf{x}\right)\) is the modified enrichment function and \(\mathbf{x}_{I}\) are the spatial coordinates of nodal point \(I\).  
From the above, it becomes clear that shifting results in a different enrichment function for each node. Furthermore, when applied to the jump enrichment functions of Equation (16), it causes the functions to vanish for elements that do not contain the crack, thus simplifying the implementation.  
The Kronecker delta property can also be preserved by employing the stable GFEM [111; 112; 116], a technique where the FE interpolant of the enrichment functions is subtracted from the enrichment functions themselves. The main advantage of this technique however, lies in the fact that it can considerably improve the conditioning of the resulting stiffness matrices.
## 4 Results and Discussion
### Mixture Testing Results and Discussion
#### 4.2.3 Cracking Behavior at Low Temperature  
The results from TSRST test are presented in Figures 11 and 12, showing the mean values for three replicates. The error bars indicate one standard deviation. As can be seen in Figure 11, fracture temperature of the virgin, 3% SBR, and 4%EMAA/3%SBR mixtures are within a few degrees from \(-\)24.0 to \(-\)27.2 \({}^{\circ}\)C, which does not result in an obvious distinction between these mixtures. The 4% EMAA mixture shows a warmer fracture temperature than the other mixture, indicating that the EMAA may have a negative impact on low-temperature cracking. In addition, based on Figure 12, 4%EMAA mixture ranks the second in fracture stress, which indicates a relatively good low-temperature anti-cracking ability to some extent. Therefore, the fact that the EMAA has an effect on low-temperature cracking properties of asphalt mixture needs further investigation. Based on the results of Figures 11 and 12, 4%EMAA/3%SBR mixture shows the best low-temperature cracking property with the highest fracture stress and second lowest fracture temperature. This is probably related to the compatibility and chemical effects between SBR and EMAA. It is in good agreement with previous studies [37] that found that SBR has a great impact on low-temperature properties of asphalt mixtures.  
Figure 11: Fracture temperature (thermal stress-restrained specimen test (TSRST)) of different asphalt mixtures.  
Figure 10: Phase angle of different mixtures.  
The FST results from DCT test are shown in Figure 13. The FST is obtained as fracture energy (G\({}_{\text{f}}\)) divided by fracture strength (S\({}_{\text{f}}\)). It is shown that FST can better characterize the fracture process of asphalt mixture. Among four different mixtures, 4% EMAA mixture has the lowest FST value. It also can be observed from Figure 13 that 4%EMAA/3%SBR mixture evidently shows the highest FST value, which is 32% higher than that of the virgin mixture. Moreover, the variation in FST values as a result of different modification is assessed. As can be seen in Figure 13, the variation of 4%EMAA/3%SBR mixture is only 10.4%. Much higher variations (36.9%, 25.4%, and 14.7%) are observed in the other three mixtures, thus indicating that 4%EMAA/3%SBR mixture shows great homogeneity and integrity compared to the other mixtures.  
Overall, it can be concluded from the TSRST and DCT testing methods that adding EMAA has a negative effect on low-temperature behavior of virgin mixtures. However, EMAA/SBR compound modifier could improve the low-temperature property of mixture. The reasons may be the chemical reactions between the two modifiers, as well as the compatibilization of EMAA on SBR-modified asphalt.
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 3 Results and Discussion
### Registered Monitoring Signal  
An example of a registered signal is shown in Figure 4. It corresponds with the tail pulley rotary speed of \(n_{1}=159\) rpm. On the left, the strain gauge indications are expressed in ADUs, while on the right, they are recalculated to the force units N. However, it should be noted that these are the values of the force pressing directly on the strain gauge surface, not to be confused with the belt tension or the belt pressure on the pulley.  
During the measurement time, some typical registered characteristics appeared, which can be seen in the graph in relation to the damages. The most noticeable one is the \"jump\" of all three strain gauge indications when the belt joint was in contact with the tail pulley after the fourth second of measurement. At that moment, the indication of strain gauge T1 jumped from ca. 700 ADU to ca. 2400 ADU, which corresponded with the drop in the pressing force. Quite predictably, the central strain gauge, T2, experienced a smaller drop in the pressing force, which corresponded with the increase in indications from 400 ADU to 1400 ADU.  
Another obvious characteristic is the one related to the cut UP I in the middle of the belt width. This one reached the pulley at the seventh second of the experiment and caused a significant drop in the pressing force on the central strain gauge, T2, from ca. 42 N to almost 3 N. Gauge T2 was directly under the cut, so the pressing force was very small. Other preset damages had different impacts on each strain gauge, making it possible to identify their presence from a comparison of the three signals.
## 7 Discussion  
This paper introduced alternative strategies for scenarios where traditional knowledge distillation is impractical due to inaccessible model parameters or incompatibility across different deep learning frameworks. We have explored novel methods that circumvent these constraints, focusing on enhancing sentence vector alignment using _FastText_ embeddings, diverging from the established norms of _sBert_ sentence vectors and knowledge distillation techniques.  
Unlike _sBert_, which employs contrastive learning to cluster similar sentences together and separate dissimilar ones, our approach is built on the foundation of sentence vector training with a unique twist. We utilize vectors derived from disparate architectures and employ loss functions that concentrate on sentence similarities, eschewing the contrasting aspect of _sBert_.  
From a knowledge distillation perspective, standard methodologies typically focus on aligning hidden states or reproducing logits between models. Our innovative approach diverges by utilizing the average of pre-logit hidden states, which facilitates the distillation process using just a single vector, as opposed to employing the entirety of pre-logit hidden state vectors.  
Our experiments with dual combined loss functions yielded mixed outcomes. The _distance minimization_ strategy, which adjusts _FastText_ embeddings via an encoder model, unfortunately led to the degradation of word content, resulting in subpar sentence vector production. In contrast, the _distance and perplexity minimization_ model utilizes a more nuanced loss function that combines cross-entropy, for capturing the distribution of words, with cosine similarity, for aligning vectors. This approach highlights the importance of complex loss functions and domain relevance in creating robust models capable of generalizing across diverse linguistic contexts. Furthermore, our in-depth analysis revealed that datasets with a higher domain coverage, denoting more shared vocabulary with the training data, are associated with enhanced performance in classification tasks. This observation reinforces the promise of our proposed models in computational linguistics applications.  
Building on these insights, our research was primarily aimed at information transfer between different sentence embedding models. Here, we discovered an interesting phenomenon: reducing the dimensions of the original _sBert_ model does not affect the sentence evaluation tasks. This stands in contrast to the common assumption that higher dimensions are synonymous with better performance; our findings show that a pared-down model can achieve comparable results. This revelation adds a new layer to our understanding of model efficiency and underscores the potential of dimensionality optimization in natural language processing tasks.
# Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring
## 4 Results and Discussion
### Results  
Figure 2 shows that the REMR performs better in comparison to AODV in terms of energy consumption. One thing that should be mentioned here is that at a certain point in the simulation, the energy consumed by REMR became almost constant. In comparison to that, the AODV protocol continued to consume a large amount of energy.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Routing Protocol** & **AODV** \\ \hline Node Density & 80 \\ \hline Area & 6000 \(\times\) 6000 (m\({}^{2}\)) \\ \hline Node Range & 100–300 m \\ \hline Simulated Time & 35 min \\ \hline Node Type & Multiple mobile nodes with automatic harvesting \\ \hline Traffic type & constant bit rate \\ \hline Evaluation parameters & Throughput, energy consumption, delivery ratio, end-to-end delay \\ \hline No. of Receivers & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3 shows the results of throughput of REMR (edited AODV in the simulation environment) and AODV as implemented in the reference [20]. It is observed that REMR performs better than AODV in terms of throughput due to recursive and energy-efficient implementation in the proposed REMR. This is also because REMR uses multipath routing, whereas AODV uses single-path routing.  
Figure 4 shows the delivery-to-time ratio performance of both REMR and AODV. REMR achieves a better packet delivery ratio than AODV. This is mainly due to the multipath capability of REMR.  
Figure 5 represents the end-to-end delay of a particular node to the sink. REMR shows less delay in sending data from one node to the sink. Though the REMR has more end-to-end delay than the AODV at the start, after a certain amount of time, the delay starts decreasing once the better routing paths are finalized using the multipath approach.  
Figure 3: Throughput of AODV and REMR.  
Figure 2: Energy consumption of AODV and REMR.
## 3 Dataset and Feature Description
### Feature Description
#### 3.2.1 Histogram of Oriented Gradients  
HOG [43] is a texture-based feature descriptor that adopts the histogram of gradients as a statistical measure. The primary idea behind the concept is that any local shape and object can be demonstrated using the gradient intensity distribution or edge direction. As HOG is invariant to geometric transformation, it is widely used in pattern recognition domain. In the computation part, first of all the entire facial image was divided into cells. After that, the gradients were calculated according to Equation (1). In this work, one dimensional gradient was taken. A matrix \(\text{M}=\left[\begin{array}{cc}1&0&-1\\ \end{array}\right]\) was used to calculate the gradient in the X direction denoted by \(\text{Grad}_{\text{X}}\). Subsequently, \(\text{M}^{\text{T}}\) was adopted for gradient in Y direction denoted by \(\text{Grad}_{\text{Y}}\). In this work, the matrix \(M\) was used as a mask which was passed through the entire image and the variable here was the pixel intensity. The gradient was computed by taking the intensity difference of the neighboring pixels in particular direction. In case of \(\text{Grad}_{\text{X}}\), the intensity difference along horizontal neighboring pixels was considered. Similarly, for \(\text{Grad}_{\text{Y}}\) the intensity difference along vertical neighboring pixels was considered. The final gradient direction \(\text{Grad}_{\text{Dir}}\) was calculated as follows:  
\[\text{Grad}_{\text{Dir}}=\ \tan^{-1}\frac{\text{Grad}_{\text{Y}}}{\text{Grad}_{ \text{X}}} \tag{1}\]  
Next, the entire gradient direction domain (lying between \(0^{\circ}\) to \(360^{\circ}\)) was divided into 8 histogram bins. Initially, the entire image was divided into cells. For every cell, the bin count for each histogram bins was obtained. We increment the bin count if the value of the gradient direction falls in the range of a particular bin. This way, we achieve the histogram of the gradients for each cell. Finally, those histograms obtained from each cell were concatenated to get the final feature vector. For the extraction of HOG feature, three image dimensions were considered. HOG feature descriptors were applied on each facial emotion image and the final feature vectors were obtained. The feature size of HOG depends on the image size and the cell dimension. The feature dimension for \(32\times 32,\ 48\times 48\) and \(64\times 64\) images are 324, 900 and 1764 respectively.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 7 Evaluation of _T-Wing_
### Energy Overhead of Reporting Location  
To quantify the energy savings achieved by the adaptive location-reporting algorithm described in Section 4, we first measured the battery life time of the embedded platform described in Section 7.1. We fully charged the battery (2850 mAh) of the mobile platform and then measured the battery life time for each case (report per 15 s, adaptive report, no report) as described in Figure 3a. We assumed that 90 percent of personal life cycle is in the \"Calm State\", while the remainder is in \"Run State\" or \"Walk State\", 2 and 8 percent, respectively [44]. For the measurements of the energy saving effect by the adaptive location-reporting algorithm, the battery life time was measured with turning the Wi-Fi interface and process of web provider off. As shown in Figure 7, our results show that using our adaptive location update protocol, users can greatly increase the total usage lifetime of _T-Wing_.  
Figure 6: Time-series and measurements showing total uploading time through the cellular network.  
Figure 7: Battery life time with varying location update duty cycle.
## 5 System Implementation
### Data-Processing Phase
#### 5.2.2 Wine Record Validation Process
Similar to the errors returned during the off-chain validation process, an error, either classified as (1) modification attack, where both the payload and recovered public address could not be found or matched on-chain, (2) reapplication attack, where the _\"write_counter\"_ or _\"read_counter\"_ are inconsistent with its counterparts stored on-chain, or (3) cloning attack, where an inconsistent tag identifier is found on-chain, would be returned to the app-backend service, and the respective _\"unsuccessful_validation_data\"_ of a corresponding wine record updated with an error message shown on the user interface of _ScanWINE_ application. Once the on-chain validation process is successful, the results and latest _\"IPFS_hash\"_ of the specific wine identifier are then returned to the blockchain service, and the latest version of the wine record subset stored in the IPFS network could then be retrieved, with the _\"IPFS_hash\"_ specified via its IPFS node. The wine record subset retrieved from IPFS network is then compared with its counterpart, stored in the database, interacting with the app-backend service. The validation result is then returned to the app-backend service and the user interface of _ScanWINE_. With the successful validation of the wine record, the overview page of the corresponding wine record is then brought up on the user interface of _ScanWINE_, with wine pedigree data, wine status data and some transaction data fields, such as the unique transaction hash with a corresponding block number with which the registered node can query and confirm the latest block and transaction related to this wine product on a blockchain explorer connected to the blockchain network. The _\"read_count\"_ of the tag is incremented after every tag-reading process, and so its counterparts stored on-chain and off-chain will also be incremented during every validation operation. The faulty and counterfeited wine product, with the error of any classified attack logged in the _\"unsuccessful_validation_data\"_ of its wine record, should, therefore, be rejected and returned to its winemakers or the previously registered nodes of the wine products for further operations.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### Pre-Processing and Canny Edge Detection  
In chest X-ray images, the presence of high-intensity or bright-pixel diaphragm regions, as seen in the posteroanterior view presented in Figure 2, can pose challenges for accurately differentiating possible disease variations. In the medical context, the letter 'R' typically denotes the right side of the patient. This labeling convention is commonly used in medical imaging to specify the side or location of the body under examination. Here, 'R' indicates the right side of the chest. This labeling aids healthcare professionals in accurately interpreting the image, focusing their attention on the specific area of interest, and identifying any potential abnormalities or conditions on the right side of the chest. The use of such conventions facilitates precise analysis and diagnosis, ensuring effective patient care and treatment. To improve the automated diagnosis of lung areas and enhance the detection of disease-related features, a pre-processing step is applied to the X-ray images before further analysis [29].  
In this research, we employ the Canny edge detection algorithm as a pre-processing image technique. Canny edge detection is a popular edge detection algorithm known for its ability to effectively remove noise and preserve essential edge data, thus enhancing the clarity of edges in an image. By accentuating sharp edges and producing a smoother image, the Canny algorithm enables us to extract more meaningful features and information for subsequent processing steps.  
Compared to other edge detection methods, such as the Sobel, Prewitt, and Roberts, the Canny algorithm has demonstrated superior performance in maintaining more usable edge information while reducing noise. This is especially beneficial in chest X-ray images, where subtle abnormalities and disease-related features can be critical for accurate diagnosis.  
Following the Canny edge detection step, we utilize the GRAD Class Activation Mapping (Grad-CAM) technique on the pre-processed images. Grad-CAM allows us to visualize the regions of the image that the CNN model focuses on during the decision-making process. By overlaying the model's attention on the image, Grad-CAM provides insights into the areas that contribute the most to the COVID-19 detection decision. This interpretable visualization not only aids in understanding the model's predictions but also enhances trust and confidence in the diagnostic results.  
Figure 2 illustrates the output image after pre-processing with the Canny edge algorithm. The enhanced edges and reduced noise in the image contribute to a clearer representation of relevant features, ultimately assisting the deep learning model in detecting COVID-19-related patterns with higher accuracy.