esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of \(E_{nt}=E_{c}/E_{m}\) Ratio on Sound Radiation Fields  
Figure 10 illustrates the influence of the parameter \(E_{nt}\) on sound power level for power-law indexes, \(k=1,5,\) and \(10\) similar to as reported by researchers [57]. The effects of modulus ratios on sound power level are documented in this research. A frequency shift is observed in the stiffness control region (first mode--the region of interest) with an increase in modulus ratios.  
Figure 10: SPL’s peak shift with \(E_{nt}\) for power-law index, \(k=1\) and \(10\).  
Figure 9: Behavior of variation in \(k\) with SPL peaks (**a**) first peak, (**b**) second peak, (**c**) third peak, and (**d**) fourth peak.  
As \(E_{rat}\) increases, a decrease in sound power level peaks is observed. \(E_{rat}\) denotes the elasticity ratio of ceramic and metal constituents. To better understand the effect of \(E_{rat}=E_{c}/E_{m}\) on the peaks of sound power level, Figure 11 is presented. Obviously, the sound power level decreases with an increase in modulus ratios, and SPL peaks shift towards the right. When the power-law index, \(k\) increases, and the SPL radiated by the P-FGM plate increases. The findings suggest that the parameter \(E_{rat}\) and the power-law index, \(k\), could be used as design variables to reduce the sound radiation.
## 3 Simulation
The mean PSNR of 31 wavebands was calculated and its value was above 31 dB, which means that the image had a good spatial quality and was similar to the reference image. SAM and SMAPE evaluate the spectral differences of recovered spectra and reference spectra. SAM has the same expression as Equation (5). A mean SAM of 512 \(\times\) 512 pixels was used for comparison, and a small value was found to be better. The SAM metric is concerned with the spectral orientation, while the SMAPE metric measures the absolute differences. The SMAPE is expressed in percentages, and a small value denotes a good match. From the PSNR, SAM, and SMAPE values in Table 1, it can be seen that there was a tendency for these values to become worse as the threshold increased. When the \(th\) = 0.5, the PSNR and SAM results were still better than those of the point-by-point optimization method, while the SMAPE result was slightly worse. Therefore, it is best that the threshold value does not exceed 0.5; otherwise, the reconstructed spectral difference will continue to increase.
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 2 Literature Review
### Summary  
The energy efficiency methodologies of WSN involves several techniques like routing, clustering, and data aggregation processes. Nature inspired and metaheuristic optimization methods were widely used for making such energy efficient algorithms. However, those optimization algorithms may not be suitable for problems of WSN and customizing such optimization algorithms have become a complex part. Also, the computational cost and the requirement of peripheral devices improve the energy utilization of such optimization algorithms. In some applications, an uneven energy utilization can be observed, and it may result in sudden network failure. Similarly, optimization-based data aggregation systems have chances for missing the useful information generated by the sensors. Hence the machine learning- based approaches are introduced in the recent years to improve the energy efficiency of WSNs. Machine learning methodologies are designed to improve energy efficiency through anomaly detection, intelligent routing, cluster head selection, and data compression. It is achieved by leveraging adaptive learning and data-driven insights on WSN. Analyzing data and energy consumption patterns also improve the performance of WSN by predicting the energy shortages in advance. K-means clustering and fuzzy logic systems seem to be the popular algorithms that are incorporated in ML- based WSNs for optimizing the cluster head selection process. The neural network-based approaches improve the latency by observing the real-time conditions and data transmission patterns simultaneously. However, the ML algorithm might consume more energy than traditional optimization approaches, because of its nature on analyzing multiple data at the same time.  
The proposed work aims to address the energy consumption issues of the previous methods, by using a data compression technique. The traditional optimization and ML-based algorithms are structured to analyze a huge amount of data for taking certain decisions, like routing path or cluster head selection. Therefore, the computational complexity of such models may take an additional consumption of energy for the operation of such complex algorithms. The proposed work addresses this issue by implementing an intelligent data compression technique using a popular ML technique called Variational Autoencoders. In traditional works, the huge sensor data were considered only for analysis; whereas in the proposed work, the huge sensor data is compressed directly, and it results in the minimal data transmission, to save the energy consumption on data transmission. VAEs were originally designed for compressing image data transmission, and it is customized in this work for compressing the numerical sensor data by adjusting their encoder and decoder properties. This adjustment allows the VAEs to observe numerical input features with an optimal compression and reconstruction accuracy.
## 2 Background Concept and Problem Modeling
### Collision Detection in Workspace Using the Oriented Bounding Box (OBB)  
The workspace denoted by \(\mathbf{W}\in\mathbb{R}^{3}\) is defined as a space in which the robot manipulator works. In order for the robot manipulator to move in the workspace, it is necessary to check if it is guaranteed \(q_{t}\in\mathbf{Q}_{\text{\tiny ini}}\). To check this, collision detection methods are used [25; 27]. In this paper, the OBB (oriented bounding box) [28] is employed. The OBB is a more accurate collision detection method than the AABB (axis aligned bounding box) [29]. The OBB method models objects (i.e., possible obstacles) in the workspace in the form of a rectangular box. Then, a collision test between the manipulator and an obstacle is performed using the two boxes of modeled objects. It is shown in [30] that a total of 15 calculations are performed using each of the three faces of the two boxes that use collision detection and the nine edges between the two boxes. Therefore, each box can be expressed as a robot manipulator and an obstacle, and collisions with all obstacles are detected using the OBB method repeatedly [31]. If all these steps are passed, it can be sure that collision is free, i.e., \(q_{t}\in\mathbf{Q}_{\text{\tiny ini}}\).
## 3 Materials and Methods
### Key System Purposes
#### 3.3.2 User Interactivity  
As shown in Figure 6, this interactivity allows users to provide multiple input points to refine the segmentation results, with the system processing these inputs in real time to generate more accurate masks. This allows for the following benefits:  
* Enhances the overall user experience, providing convenience and immersion.
* Enables intuitive interactions, such as selecting digital icons or scrolling pages.
* Allows for error refinement, potentially improving user comfort.
* Supports iterative tasks, making the segmentation process more flexible and accurate.  
Through implementing interactive segmentation, our system not only promotes user engagement but also paves the way for developing more sophisticated interactive capabilities in AR applications.  
Figure 5: The VPS (visual prompt selection) framework operates through the following pipeline: (**a**) An AR camera captures the real-world scene observed by the user, transmitting it to the system as a frame. (**b**) The object detector conducts localization and categorization, identifying objects within the image. The outputs are pairs of bounding boxes and labels. (**c**) These bounding boxes are processed in the prompt encoder, and together with image embeddings from the image encoder, the decoder generates masks. (**d**) A mask is generated for each object. (**e**) Using these final masks and label information, AR systems distinguish non-rigid objects from the background. This enables the placement of digital icons located in the background while keeping the display of non-rigid objects.  
Figure 6: The VPS (visual prompt selection) framework’s workflow with user interaction is as follows: (**a**) Real-world images are captured. (**b**) Object detection is performed, generating bounding boxes. (**c**) User interactivity is allowed with the introduction of the Segment Anything Model. User input isaccepted through hand tapping or flicking, and this input is treated as a point. The coordinates are then passed to the prompt encoder. (**d**) SAM consists of three key components: an image encoder, a prompt encoder, and a mask decoder. The image encoder generates image embeddings, which are subsequently sent to the mask decoder. The prompt encoder in SAM can simultaneously process both boxes and points, enabling the specification of the intended object. The mask decoder uses image and prompt embeddings to generate an output mask. (**e**) The final output is a user-intent mask.
# An Efficient Palette Generation Method for Color Image Quantization
## 2 Related Work  
Vector quantization has been successfully used in image compression. There are many techniques which have been proposed to speed up the codebook search in vector quantization [18; 19; 20; 21; 22; 23; 24; 25]. [PERSON] and [PERSON] [22] proposed a kick-out condition for the fast codeword searching algorithm. In this article, this fast searching method is called by Wu-Lin method. For a color pixel in the color image, the Wu-Lin method can efficiently determine the closest color in the palette.  
For the color pixel \(x=(x_{1},x_{2},x_{3})\) in an image and a color \(y=(y_{1},y_{2},y_{3})\) in the palette, the square Euclidean distance is defined as follows:  
\[\text{SED}(x,y)=\sum_{j=1}^{3}\Bigl{(}x_{j}-y_{j}\Bigr{)}^{2}=||x||^{2}+||y||^{ 2}-2\sum_{j=1}^{3}x_{j}y_{j} \tag{1}\]  
The goal is to find a palette color \(y\) such that it minimizes Equation (1). Given the color pixel \(x\), \(||x||^{2}\) is a common term for all palette colors. Therefore, the goal is also to find a palette color \(y\) such that it minimizes  
\[\text{SED1}(x,y)=||y||^{2}-2\sum_{j=1}^{3}x_{j}y_{j} \tag{2}\]  
According to the Cauchy-Schwarz inequality, we have  
\[||x||\times||y||\geq\sum_{j=1}^{3}x_{j}y_{j} \tag{3}\]  
Therefore, the following inequality is always true:  
\[\text{SED1}(x,y)\geq||y||^{2}-2||x||\times||y||=||y||(||y||-\,2||x||) \tag{4}\]  
If a palette color \(y\) satisfies the test condition  
\[||y||(||y||-\,2||x||)\geq SED1\_min \tag{5}\]where \(SED1\_min\) denotes the minimal SED1 between the color pixel \(x\) and the closest palette color \(y_{\min}\) found so far, then \(SED1(x,y)\geq\ SED1\_min\) can be derived from Equations (4) and (5). Therefore, the palette color \(y\) is not the closest color.  
The color palette is sorted in ascending order of length. Assume that \(c1\) and \(c2\) are two colors in the palette. The following two properties are important. The first property is that if \(||c1||<||x||\) when the palette color \(c1\) is not the closest color; the palette color \(c2\) is also not the closest color if \(||c2||<||c1||\). The second property is that if \(||c1||>||x||\) when the palette color \(c1\) is not the closest color; the palette color \(c2\) is also not the closest color if \(||c2||>||c1||\).  
If the color pixel \(x\) = (125, 65, 130), the value of \(||x||\) is about 191.70. Figure 1a shows the sorted palette of \(K\) colors. Assume that the color \(c\) is the initial searched palette color for the color pixel \(x\). In this study, the color \(c\) is the palette color with the closest length value to \(x\), and the binary search technique is employed to find the color \(c\). In this case, the palette color \(c\) = (73, 58, 167). It can be computed that the value of \(||c1||\) is about 191.26. Therefore, Figure 1b shows the searching order of colors in the palette.
## 2 Methods and Steps
### Data Sample Processing Method
#### 2.3.1 Data Missing Processing Method Based on the RKNN Algorithm
After analyzing and processing the sample data using the time series prediction method, the sample number has significantly increased. Finally, after the successful implementation of the data acquisition scheme, six kinds of data information related to truck operation, such as elevation, speed, driving distance, instantaneous diesel consumption, cumulative diesel consumption, and diesel consumption during driving, can be collected (the environment-related parameter data mainly plays an auxiliary role in analysis). Taking the single transportation process of an open-pit mining truck as an example, a total of 837 sets of single factor variable data were obtained through the data acquisition device, and a total of 5859 sets of sample data were obtained, among which 856 sets of data were missing in the 5859 sets of data, and the collection accuracy was only 85.3%. After processing by the RKNN algorithm, the data sample size can reach about 5460, and the accuracy is increased by 7.9% (due to the limitation of the research space, the specific operation process will not be described). The running status data of some mining trucks are shown in Table 1.  
Among them, \(X_{1}\) represents the elevation; \(X_{2}\) represents the traveling speed, _m/s_; \(X_{3}\) represents the distance traveled, \(m\); \(X_{4}\) represents the instantaneous diesel consumption, \(L/s\); \(X_{5}\) represents the cumulative diesel consumption, \(L\); and \(X_{6}\) represents the diesel consumption during driving, \(L\).
# Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning
## 5 Experimental Analysis
### Experimental Configuration  
The experiment's code was implemented using the PyTorch deep learning framework. Before training, the network model loads pre-trained weight parameters from the COCO dataset, this transfer learning strategy greatly reduced the training time [16].  
In the actual experiments, the training iteration epochs are set to 50, with a training image batch size of 8. The Adam optimization algorithm is employed, with an initial learning rate set to 0.0013. Additionally, the cosine vector annealing strategy is utilized for learning rate decay, with a weight decay coefficient of 0.0005. The first three rounds of training serve as a warm-up period.  
Multi-scale training is adopted to enhance the network's robustness during training. During verification, images are scaled to 512 by 512 pixels. The experimental equipment parameters are detailed in Table 1 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CPU Configuration** & **Model Number** & **Xeon Silver 4210R** \\ \hline \multirow{3}{*}{GPU Configuration} & Internal memory & 192 GB \\  & Model number & NVIDIA GeForce GTX 1080 Ti \\  & Core frequency & 1582 MHz \\  & Video memory frequency & 11,000 MHz \\  & Video memory capacity & 11 GB \\  & Video memory width & 352 bit \\  & CUDA Core & 3584 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental equipment parameters.  
During the experiment, the input image size was set to 512 \(\times\) 512. The k-means clustering algorithm and genetic algorithm were utilized to obtain nine types of anchor frame data of different scales based on the KAIST dataset, as illustrated in Table 2 below [17].  
The KAIST dataset is a large dataset used for pedestrian detection tasks. It comprises 95,328 images, each containing a visible image and a corresponding long-wave infrared image, totaling 103,128 intensive annotations. It serves as a widely used benchmark in the field of multi-spectral pedestrian detection, providing abundant resources for the research and development of pedestrian detection algorithms.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 3 Methods
### Classification and Accuracy Assessment  
The classification was implemented for five land cover classes: cork oak, shrubs, grass, soil, an shadows. There are several object-oriented classification machine learning algorithms implemented in OTB, both unsupervised and supervised (e.g., RF, SVM, normal Bayes, decision tree, k-nearest neighbors). In our case, two supervised methods were tested: RF and SVM. These commonly used classifiers, as also specified in [PERSON] et al. [43] and [PERSON] et al. [25], were deemed to be the most suitable supervised classifiers for GEOBIA because their classification performance was excellent. In supervisedalgorithms, decision rules developed during the training phase are used to associate every segment of the image with one of the preset classes. Supervised classification has become the most commonly used type for the quantitative analysis of remotely sensed images, as the resulting evaluation is found to be more accurate when accurate ground truth data are available for comparison [25; 44; 45].
## 4 Computational Experiments
### Efficiency of the Algorithm on Problems of Higher Dimension
\(Q^{<4>}\) (red): target request flow rate, \(Q^{<7>}\) (blue): number of routes, \(Q^{<8>}\) (black): calculation time (ms). Figure 7: Modelling results for 20 target requests. Request intensity varies from 1 to 50 in steps of 0.1. All results are shown. \(S^{<4>}\) (red): target request flow rate, \(S^{<6>}\) (blue): transmission ratio, \(S^{<7>}\) (black): number of routes, \(S^{<8>}\) (pink): calculation time (ms). Let us analyze the average time for calculating the result for cases when it was possible to transmit all requests with the required intensity. We evaluated the algorithm performance in three modes. In the first mode, the ratio of the number of routes to the number of requests is in the range from 1 to 1.2 (Figure 9). The average running time of the algorithm for one experiment was 11.061 ms, the minimum time was 7 ms, and the maximum time was 18.5 ms. The number of experiments falling within this range was 1957. In the second mode, the ratio of the number of routes to the number of requests was in the range from 1.2 to 1.5 (Figure 10). The average running time of the algorithm for one experiment was 17.236 ms, the minimum time was 13.3 ms, and the maximum time was 68.3 ms. The number of experiments falling within this range was 216. Figure 10: Change in calculation time and the number of total number of routes when the condition of the 2 nd mode is met. \(M1^{<3>}\) (red): number of target requests, \(M1^{<7>}\) (blue): number of routes, \(M1^{<8>}\) (black): calculation time. Figure 9: Change in calculation time and the number of total number of routes when the condition of the 1 st mode is met. \(M1^{<3>}\) (red): number of target requests, \(M1^{<7>}\) (blue): number of routes, \(M1^{<8>}\) (black): calculation time. In the third mode, the ratio of the number of routes to the number of requests exceeded 1.5 (Figure 11). The average operating time was 23.009 ms, the minimum time was 19 ms, and the maximum time was 34.8 ms. The number of such experiments was 42. If we consider the worst of the results obtained before averaging for all 4900 experiments, for this experiment the algorithm execution time was 334 ms. In this experiment, a network was simulated in an overloaded state and the algorithm was unable to find the transmission routes for all the information. However, even this comparatively large amount of time is more than acceptable for modeling and control systems.
## 2 The Unified Forms of Code Biases
### Inter System Clock Bias  
Similarly, in multi-systems, due to the hardware delays (\(bias_{P_{i}^{sys}}^{r}\) and \(bias_{P_{i}^{sys}}^{s}\)) between the two systems being unequal, the reference receiver clock errors are unequal between the two systems, and the satellite clock has been corrected by using the DCB files published by the IGS organization. Compared with the reference system (GPS in the experiment), the observation equations in another system (BDS in the experiment) are as follows.  
\[P_{1}^{sys}= \rho^{sys}+cdt_{P_{CIF2}^{ref}}-cdT_{P_{CIF2}^{sys}}+T_{w}+ \delta I^{sys}\] \[+c(\alpha^{sys}bias_{P_{1}^{sys}}^{r}+\beta^{sys}bias_{P_{2}^{ sys}}^{r}-(\alpha^{ref}bias_{P_{1}^{ref}}+\beta^{ref}bias_{P_{2}^{ref}}^{r}))+ \varepsilon_{P_{1}^{sys}} \tag{21}\] \[P_{2}^{sys}= \rho^{sys}+cdt_{P_{CIF2}^{ref}}-cdT_{P_{CIF2}^{sys}}+T_{w}+\gamma _{2}^{sys}\delta I^{sys}\] \[+c(\alpha^{sys}bias_{P_{1}^{sys}}^{r}+\beta^{sys}bias_{P_{2}^{ sys}}^{r}-(\alpha^{ref}bias_{P_{1}^{ref}}+\beta^{ref}bias_{P_{2}^{ref}}^{r}))+ \varepsilon_{P_{2}^{sys}}\] (22) \[L_{1}^{sys}= \rho^{sys}+cdt_{P_{CIF2}^{ref}}-cdT_{P_{CIF2}^{sys}}+T_{w}-\delta I ^{sys}-\lambda_{1}^{sys}\delta N_{1}^{sys}\] \[+c(\alpha^{sys}bias_{P_{1}^{sys}}^{r}+\beta^{sys}bias_{P_{2}^{ sys}}^{r}-(\alpha^{ref}bias_{P_{1}^{ref}}+\beta^{ref}bias_{P_{2}^{ref}}^{r}))+ \varepsilon_{L_{1}^{sys}}\] (23) \[L_{2}^{sys}= \rho^{sys}+cdt_{P_{CIF2}^{ref}}-cdT_{P_{CIF2}^{sys}}+T_{w}-\gamma _{2}^{sys}\delta I^{sys}-\lambda_{2}^{sys}\delta N_{2}^{sys}\] \[+c(\alpha^{sys}bias_{P_{1}^{sys}}^{r}+\beta^{sys}bias_{P_{2}^{ sys}}^{r}-(\alpha^{ref}bias_{P_{1}^{ref}}+\beta^{ref}bias_{P_{2}^{pref}}^{r}))+ \varepsilon_{L_{2}^{sys}} \tag{24}\]  
where \(\delta\rho^{sys}=\rho^{sys}+cdt_{P_{CIF2}^{ref}}-cdT_{P_{CIF2}^{sys}}+T_{w}\). Compare with reference system, there is a receiver clock error compensation in another system called inter system clock bias (ISB) as follows. The estimated ISB in the actual PPP is the receiver component of ISB.  
\[ISB_{sys-ref}=cdt_{P_{CIF2}^{sys}}-cdt_{P_{CIF2}^{ref}}=c(\alpha^{sys}bias_{P _{1}^{sys}}^{r}+\beta^{sys}bias_{P_{2}^{sys}}^{r}-\alpha^{ref}bias_{P_{1}^{ref}} ^{r}-\beta^{ref}bias_{P_{2}^{ref}}^{r}) \tag{25}\]
###### Abstract  
The maintenance of oil pumps is a complex task for any operating organization, and for an industrial enterprise in the oil and gas sector of the economy, this issue has a high degree of urgency. One of the reasons for this is a wide spread of pumping equipment in all areas of oil and gas enterprises. At the same time, an aggressive environment, uneven load, remote facilities, and harsh climatic zones (especially in the areas of the Arctic region or production platforms) are factors that make it relevant to develop special systems that help or simplify the maintenance of pumping equipment. Dynamic modeling is one of the modern technologies which allows for solving the urgent issue of assessing the technical condition of equipment. It is the basis of systems that carry out diagnostics and prognostic calculations and allow for assessing the dynamic state of objects under various conditions of their operation, among other functions. Augmented reality technology is a technology that allows for reducing the time for equipment maintenance by reducing the time for searching and processing various information required in the maintenance process. This paper presents an investigation of the effectiveness of an augmented reality and a dynamic simulation system collaboration in oil pump maintenance. Since there is insufficient research on the joint application of these two technologies, the urgent issue is to prove the effectiveness of such collaboration. For this purpose, this paper provides a description of the system structure, gives a description of the development process of the augmented reality system application and tests the application using Microsoft HoloLens 2.  
digitalization; augmented reality; dynamic simulation; oil pump; maintenance