esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### ROI Presentation Techniques of Adjacency Matrix  
We investigate three methods of visualizing multiple attributes of the adjacency matrix when selecting the ROI. They are embedded view, multi-view, and juxtaposed view.  
The embedded view usually encodes multiple attributes on edges, which can be embedded into the unit cell (for one edge/two nodes) and meta cells (for cluster). When embedding in the unit cell, due to the small area allocated by the adjacency matrix to each cell, multi-attribute does not have sufficient display room and will block edge coding, which makes it difficult for users to observe. For meta cells, ZAME [5] calculates the attribute values to obtain the final color map, which loses many details. RMC [6] justifies all the attribute information on the edge, making the positional relationship among attributes, nodes, and edges challenging to understand.  
Multi-view uses an auxiliary view to display multivariate, which solves the problem of insufficient display room in the adjacency matrix. Visualization in GraSp [3] uses mobile devices to display graphs on a large display. Although it expands the new display space, it requires frequent view switching when mapping between two graphs. The multi-view technique can also be completed by view switching, completely covering the original visual information and requiring frequent page switching to complete the task. These conditions all lead to user fatigue. Multi-view may also need to be accessed through multiple software, which not only causes window switching but also increases software installation requirements that is not convenient.  
The juxtaposed view displays multiple attributes by placing different encoding views at the side of the original view. This method does not affect the encoding of the original view. However, it is not easy to match specific items [25]. As a result, of the limited display area this technique needs interaction widgets such as sliders for auxiliary observation, or requires the user to switch the view back and forth. For example, Matrix Zoom [26] provides views of different encoding. Some views need scroll-view [27; 28; 29] to show complete content, which is hard to match items. Hence, the interaction process is cumbersome.  
In this work, we place the multivariate information on the side of the ROI to make the relationship among attributes, nodes, and edges more intuitive and use spatial dimensions to display information without blocking any mapping codes in the original view.
## 2 Materials and Methods
### Experimental Procedure
#### 2.1.4 Artificial Neural Networks  
For each geometry, an ANN was trained and evaluated, with and without consideration of the measurement data, using the open-source software Python, as well as its  
Figure 2: Procedure for data preparation.  
machine learning framework PyTorch. Depending on whether the measurement data were considered or not, the number of input neurons was determined by the number of relevant features according to the feature analysis or the number of process parameters. Moreover, a single hidden layer with 5 neurons was chosen. In the past, the number of hidden layers and neurons has been mostly determined using a trial-and-error method. There has been a constant attempt by researchers to replace this time-consuming method with exact approaches to optimize the number of hidden layers and neurons. Nevertheless, in this case, a trial-and-error approach was sufficient, since the data set used to train the ANN was comparatively small and the goal was to compare neural networks with the same design for different input data, instead of optimizing a single ANN for a specific use case. The number of neurons in the output layer was always 3, as the quality categories of all data points were converted into numerical values: good (2), medium (1) and poor (0). Thus, setting the number of neurons in the hidden layer to 5 also ensured that it was between the number of neurons in the input and output layers.  
In addition to the topology of the networks, the activation function, the loss function and the optimization algorithm are essential hyper-parameters that influence the functionality of ANN. This work was based on the comparatively simple and often-used activation function, Rectified Linear Unit [28]. Furthermore, because a categorical target variable was used, the common loss function Cross-Entropy Loss was applied in the training phase to determine the error between the predicted and actual quality categories. As it has proven successful in other applications, especially in classification tasks, the Adam optimizer was selected to update the weights between the neurons [29]. The validation was performed using the train-and-test approach. In this method, the data sets are divided up. In this work, the data were divided up into training, test and validation data, with a percentage ratio of 70/10/20.
## 2 Materials and Methods  
### Imaging Model  
The geometrical model of layover imaging of airborne non-uniform multi-baseline SAR systems is shown in Figure 1. In the one-transmitting multiple acquisition mode, after the SAR image acquired by each antenna of the airborne SAR is processed by image registration and de-chirping, the imaging model of the receiving antenna k in the elevation direction can be expressed as [20]:\[g_{k}=\int\limits_{\Delta s}\gamma(s)\exp(-j2\pi\zeta_{k}s)ds \tag{1}\]  
where \(\Delta s\) represents the range of elevation direction, \(\zeta_{k}\) represents the spatial frequency along the elevation direction, and \(\gamma(s)\) represents the distribution of the scattering coefficient of each scattering point in the elevation direction. Considering the noise, the discrete model in the elevation imaging model is simplified as:  
\[g=A\gamma+\varepsilon \tag{2}\]  
where \(g\) represents the observation vector with \(K\) antennas, and the matrix form in \(A\) is \(A_{kl}=\exp(-j2\pi\zeta_{k}^{*}g_{l})\) with the size of \(K\times N\), \(N\) is the number of corresponding height ranges, \(\zeta_{k}=-\frac{B_{k}\cos(\theta_{l}-\alpha)}{\lambda R}\), \(\theta_{l}\) is the off-nadir angle corresponding to scattering points of \(l\), \(R\) is the reference slant range, \(B_{k}\) is the baseline length corresponding to the \(k\)th antenna, \(\alpha\) is the baseline angle, and \(\lambda\) is the signal wavelength. The relation between elevation direction \(s\) and height direction \(h\) can be expressed as \(s=\frac{h}{\sin(\theta_{l})}\), at this time \(A_{kl}=\exp(j\frac{2\pi\gamma}{\lambda}\cdot\frac{B_{k}\cos(\theta_{l}-\alpha )}{R\sin(\theta_{l})}\cdot h_{l})\), and let \(A=[a(\varphi_{1}),a(\varphi_{2}),\cdots,a(\varphi_{L})]\), where \(a(\varphi_{l})=[A_{1l},A_{2l},\cdots,A_{Kl}]^{T}a(\varphi_{l})=[A_{1l},A_{2l},\cdots,A_{Kl}]^{T}\) is the array direction vector, while \(T\) denotes the transposition operation.
## 1 Introduction  
Research conducted in France within the scope of the CEOS.fr national program has highlighted the importance of taking into account, as of the design stage, Thermo-Hydro-Mechanical (THM) effects in massive structures. These effects cause premature cracking that can significantly undermine the durability and mechanical responses of such structures [1].  
The CEOS.fr program included experiments on large test samples that were used to adopt an international benchmark devoted to modeling the test beam behavior. An interactive workshop--called \"ConCrack2\"--was held in June 2011; its results were published in a report [2], excerpts of which appeared in a special issue of the EJECE journal [3].  
The experimental program featured beams under restrained shrinkage during concrete maturation, through use of two steel struts (RG8 test, see Figure 1). Based on a set-up with extensive instrumentation, measurements were continuously performed for several weeks after casting and, during a second phase, the beam was tested in bending in order to determine process-related THM effects on the mechanical performance of the structure (global stiffness, strength, etc.).  
The purpose of the present study is to propose a comprehensive and efficient modeling tool in order to simulate the entire loading program of the RG8 test. As demonstrated by the benchmark participants [2], the problem to be solved encompasses many coupled phenomena (temperature increase, gradual concrete maturation, creep, restrained shrinkage effects, etc.). Treated by means of a classical FEM description, this approach entails a highly sophisticated numerical simulation [4]. In an effort to drastically reduce the size of the model, the present contribution makes use of a simplified description based on a multi-fiber beam discretization. Such a discretization step has already been proposed by [5] in order to model a massive reinforced concrete beam that is undergoing mechanical loading after free shrinkage. This step has proven its relevance; hence, we are pursuing herein its application to the case of restrained shrinkage. The multi-fiber description includes a THM model [6] coupled with a unilateral concrete damage model [7]. For massive structures, the CEOS.fr program has focused on the need to treat the scale effect. Such a treatment is undertaken herein through the application of a Weibull law that transposes the measured mechanical characteristics of concrete from a standardized test piece (height 32 cm, diameter 16 cm) to the scale of the test beam (5.10 m \(\times\) 0.8 m \(\times\) 0.5 m) [8].  
In this framework, an in-depth comparison between experimental and numerical results obtained during the RG8 test is presented in this paper. More specifically, we are interested in deformations and forces developed in the structure by restrained shrinkage, the times of crack appearance and opening, and the consequences of damage sustained on the residual mechanical performance of the beam. A wide array of results were found to be of critical engineering importance for the crack control of concrete structures.
## 3 Results
### Functional Properties Evaluation
#### 3.4.2 Piezoresistive Behavior
Between 0.5% and 2%, the trend of the GF decreased while the electrical conductivity continued to increase (Figure 11). Figure 10: Piezoresistive response of PCL membrane (**a**), 0.05%GNPs-PCL membrane (**b**), 0.1%GNPs-PCL membrane (**c**), 0.2%GNPs-PCL membrane (**d**), 0.5%GNPs-PCL membrane (**e**), 1%GNPs-PCL membrane (**f**), and 2%GNPs-PCL membrane (**g**). In literature, the theory for piezoresistive materials regarding the percolation threshold is presented as the limit where the electrical conductivity of materials varies in several orders of magnitude (within this range) and where there is very little variation in conductivity before and after that limit, with a tendency to stabilize it after that point. In this way, in this threshold, the maximum conductivity achieved can be obtained for an optimal piezoresistive behavior (higher GF values) [6]. In this specific case, it was confirmed that the sample with 0.5% of GNPs presents the best performance near the percolation threshold (Figure 11).
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 4 Results
### Driving Characteristics Comparison of Micro-EVs and Conventional Vehicles  
The results of evaluating the driving performance of the micro-EVs and the conventional vehicle on narrow roads are presented in Table 8. In all narrow road sections, the micro-EVs had a higher space mean speed compared to the conventional vehicle. Notably, the difference in space mean speed between the micro-EVs and the conventional vehicle was the greatest in Section C, with a difference of 8.3 km/h.  
In Section B, there was less difference in the travel speeds between the micro-EVs and the conventional vehicle. Sections A and C are located in areas with high pedestrian volumes, where drivers had to be more cautious of pedestrians. In contrast, Section B is predominantly surrounded by fields and farmlands, with very few pedestrians to consider while driving. It can be inferred that when pedestrians are present along the road, micro-EVs, with their smaller body size, face less obstruction compared to the conventional vehicle with a larger body. Therefore, the minimal speed difference between micro-EVs and the conventional vehicle in Section B can be attributed to the lower presence of pedestrians that require attention while driving.
## 2 Method
### Expanding the Dataset: Data Augmentation  
Prior to the actual preparation of data for training, we performed data augmentation to expand the dataset. For the data augmentation process we carried out, we applied several transformations to our dataset, including zoom in/out, colorization, and image translation.  
1. Zoom in/out: We adjusted the scale of each image in the dataset by either zooming in or zooming out. This was achieved by enlarging or reducing the image size by a certain percentage. The zoom levels were randomly selected within a predefined range to ensure that our model could recognize features at different scales and distances.
2. Random colorization: To introduce variability in color, we altered the color properties of the images randomly. This included adjusting the hue, saturation, and brightness. This step aimed to train the model to perform well under various lighting conditions and color settings.
3. Image translation: We also randomly shifted the images along the x and y axes. This shift involved moving the image a few pixels horizontally or vertically. This technique helped make the model robust to positional changes, ensuring it could detect and classify objects correctly even if they were not centrally aligned.  
To achieve data augmentation, we exclusively used OpenCV (cv2). OpenCV (OpenSource Computer Vision Library) [15] is a widely used open-source library for real-time computer vision and image processing. Developed initially by Intel, it provides extensive tools for tasks such as object detection, face recognition, and image segmentation. Supporting multiple programming languages, including Python and C++, OpenCV is renowned for its versatility and efficiency, making it a popular choice for developers and researchers in the field of visual data analysis.  
With cv2, we implemented random zoom in and zoom out transformations by resizing the images within a specified range to introduce variability in scale. Additionally, we applied image shifting, translating the images horizontally and vertically by a random number of pixels to ensure positional diversity. These transformations enriched the dataset, enhancing the model's ability to generalize across different scales and positions, thereby improving its robustness and performance. Given that the input was small in size now, we augmented the dataset so each category was double the amount before this step, meaning 1500 images for each category.
## 4 The IDCP Algorithm
### The Algorithm  
Before the iterative task-scheduling process, an initial solution needs to be constructed. For the sake of simplicity, as well as the efficiency of IDCP, we adopt HEFT [34] to obtain the initial solution. As in previous statement, the nodes on the critical path are rescheduled at every iteration, offering better chances for improving the current solution. However, the rescheduling operation on each node is highly likely to change the critical path. Hence, a working set is involved to store the nodes on the current critical path.  
\[S_{n}=\{n_{i}|if\ n_{i}\ is\ on\ the\ current\ critical\ path\} \tag{20}\]  
At the start of every iteration, all the nodes on the current critical path are added to \(S_{n}\). Then IDCP iterates to reschedule all the nodes in \(S_{n}\) and update the current solution. Note that every time a node is rescheduled, the solution is updated and the critical path is changed. Thanks to the working set, IDCP is able to focus on critical path we try to optimize at the beginning.  
Further, to prevent infinite iterations, we introduce two exit criteria. The first one is the number of iterations. The IDCP simply exits after \(NR\) number of iterations. In addition, it may occur that the solution is not improved for \(NT\) consecutive times due to local optimum or global optimum. Either way, we choose to exit IDCP.  
As stated above, three steps are executed at every iteration, namely node selection, node assignment and solution update, which are detailed respectively in the following subsections.
## 2 Research Methodology
### Sampling Techniques and Equipment
In this study, we have determined the physiochemical and microbiological properties of water at various locations during the pre-monsoon, monsoon, and post-monsoon seasons to assess the extent of water contamination and the impact on physiochemical properties. The mean value for each parameter was calculated for the pre-monsoon, monsoon, and post-monsoon periods. These mean values represent the overall condition of each parameter across the different locations and provide a better understanding of the parameters that require collective attention. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S. No.** & **Parameters** & **Units** & **Acceptable Limit** &
\begin{tabular}{c} **Standard** \\ **(BIS \& WHO)** \\ \end{tabular} & **Weightage** \\ \hline
1. & Color & Hazen unit & 5 max & & \\ \hline
2. & Odor & - & Agreeable & & \\ \hline
3. & pH & - & 6.5-8.5 & 8.5 & 0.1428 \\ \hline
4. & Turbidity & NTU & 1 max & & \\ \hline
5. & Nitrate & mg/L & 45 max & 50 & 0.0242 \\ \hline
6. & Total hardness & mg/L & 200 max & 300 & 0.0040 \\ \hline
7. & Iron & mg/L & 0.3 max & & \\ \hline
8. & Chlorine & mg/L & 250 max & 250 & 0.0048 \\ \hline
9. & Fluoride & mg/L & 1 max & 1.5 & 0.8090 \\ \hline
10. & Total dissolved solid & mg/L & 500 max & 1000 & 0.0012 \\ \hline
11. & Calcium & mg/L & 75 max & & \\ \hline
12. & Magnesium & mg/L & 30 max & & \\ \hline
13. & Sulphate & mg/L & 200 max & 250 & 0.0048 \\ \hline
14. & Alkalinity & mg/L & 200 max & 120 & 0.0101 \\ \hline
15. & Total coliform & - & Shall not be detectable & & \\ \hline
16. & _Escherichia. coli_ & - & Shall not be detectable & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physiological and microbiological parameters, their units, and acceptable limits. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **Name of Equipment** & **Parameter Analyzed/Purpose** \\ \hline
1. & pH Meter & pH \\ \hline
2. & Conductivity-TDS-Temp Meter & TDS \\ \hline
3. & Nephelo/Turbidity Meter & Turbidity \\ \hline
4. & Spectro Photometer & Iron, Fluoride, Sulphate, Nitrate \\ \hline
5. & Electronic Balance & For Weighing \\ \hline
6. & Hot Air Oven & For Drying \\ \hline
7. &uffle Furnace & For Drying \\ \hline
8. & Refrigerator & For Sample Storage/Chemicals Storage \\ \hline
9. & Deep Freezer & For Sample Storage \\ \hline
10. & Thermo-hygrometer & Temperature and Humidity \\ \hline
11. & Laminar Air Flow & _Escherichia coli_ and Total Coliform \\ \hline
12. & Biosafety Cabinet & _Escherichia coli_ and Total Coliform \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of equipments.
## 4 Conclusions  
In cold regions, the changes in the physical and mechanical properties of rocks under F-T action often lead to some rock engineering disasters. In this study, the sensitivity of physical and mechanical properties of sandstone in Urumqi to F-T action was investigated. In addition, the evolution of stress thresholds for crack initiation and development in the sandstone with F-T cycles was analyzed based on AE-stress relationships. The main conclusions are as follows:  
1. In the Urumqi region, the elastic modulus of the rock is more sensitive to F-T cycles than other physical-mechanical parameters such as density and strength, which have a half-life of only 27 cycles. Therefore, special attention should be paid to the rock deformation to prevent open-pit mining disasters.
2. The acoustic behavior of the studied sandstone clearly shows the effect of F-T cycles on the AE-stress relationship. As F-T damage increases, the crystallization pressure that was induced by freezing forms cracks, making the crack closure phase more pronounced with larger cumulative AE rate values. Subsequently, with the load increases, micro-cracks begin to develop and expand, forming visible crack damage zones on the surface.
3. The stress thresholds for micro-crack development and macro-crack initiation decrease with the increasing F-T damage. After 30 F-T cycles, the stress threshold for micro-cracks propagation decreases from 20.73 MPa in the natural condition to 5.02 MPa, while that of the macro-cracks damage zone increases from 7% to 29%. The F-T damage has an important impact on the fracture behavior of typical sandstone samples from Xinjiang, China.  
Methodology, J.X. and H.P.; investigation, Z.S.; resources, J.X.; data curation, Z.S. and J.X.; writing--original draft preparation, J.X.; writing--review and editing, J.X. and H.P.; visualization, Z.S. and J.X.; supervision, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51974296, No. 52061135111).
## 3 Experimental Results
### Load-Displacement Response  
The load-displacement curves can be seen in Figure 12. The load values are determined from the test machine and the displacement values are the mean values of the two LVDT sensors. It can be seen that all the specimens exhibit the elastic and hardening deformation stage. During the elastic deformation stage, all the specimens show the similar load-displacement response and the end of the elastic deformation stage is referred to the yielding of the headed bars. In the hardening deformation stage until peak load, the load-displacement curves among all the specimens are also similar since the displacement value in the load-displacement curve is the sum of the tensile extension of the headed bar and the slip between the steel bar and concrete. The value of the slip is much smaller compared to the steel bar tensile extension after yielding, and therefore, the load-displacement curves basically represent the deformation of the headed bar under tension. Additionally, the ductility is various among the specimens with the same design parameters and it is due to the uneven bar stress and rotation of the specimens mentioned above. Additionally, the peak loads vary among different specimens even with the same design parameters, and come along with the stress differences among the headed bars. Since the headed bars are already in the strain-hardening deformation stage during the hardening stage of the specimens, small differences in the rebar stresses are accompanied by large differences in the rebar deformation. So, the ductility of specimens varies.  
Figure 11: STM in simply supported deep beam.  
Figure 12: Load–displacement curves.  
The bearing capacity of CHBC is the maximum load (\(F_{u}\)) applied to the headed bar during loading and its value is equal to the half of the peak load determined from the universal testing machine in this paper. Table 4 summarizes the values of the bearing capacity for CHBC. It can be seen that the bearing capacities for all the specimens are between the yield strength and ultimate tensile strength of the headed bar determined from the materials' tests. It should be noted that the bearing capacities of specimens with steel rupture failure are smaller than the rebar ultimate tensile strength. That is because half of the peak load determined from the universal testing machine is smaller than the load applied on the headed bar which is eventually ruptured. The uneven headed-bar stress of the specimens mentioned above should be responsible for such a situation.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 5 Proposed Solution
### Structure Specification
_Export Data Model_: This component uses multiple data models which facilitates integration with third party tools, especially network orchestrators. The transport of data models takes place via the consumption of APIs.