esa-sceva/satcom-chunk-collection huggingface dataset:
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 3 Proposed Method
### Hillshade Simulation with 3D Spatial Information  
As suggested by [PERSON] and [PERSON] [5], spatial shade simulation on previous 3D spatial terrain information is necessary to improve black ice estimation. A national digital evaluation model (DEM) was generated at 1 m intervals with a national map on a scale of 1/5000 provided by the National Geographic Information Institute [22] and local maps on a scale of 1/1000 provided by the National Spatial Information Portal [23]. As shown in Figure 2, the two maps show contour lines that connect similar elevations. Based on the maps, DEM was generated with ArcGIS v10.1. The solid green lines represent the contour lines from the national map, whereas the shading behind these lines is the constructed DEM based on the contour lines. The high-elevation areas are brighter, whereas the low-elevation areas are relatively darker.  
The altitude and azimuth angles are required to detect areas with shadows at specific times of the day using the DEM. Therefore, this information is developed using data from the Korea Astronomy and Space Science Institute (KASCI) [24]. The KASCI provides hourly information on the altitude and azimuth angles of the sun, along with sunrise and sunset times at a specific place and time, in the form of graphs and tables (Figure 3).  
Shadow occurrences can be simulated by using the hillshade analysis function in ArcGIS and analyzing the DEM information with the altitude and azimuth angles of the sun (Figure 4). The results of the simulated shadow occurrences at 07:00, 09:00, and 11:00 (i.e., after sunrise) on 1 March 2020, are shown in Figure 4.  
Figure 2: Digital elevation model extracted from 1/5000 and 1/1000 national map ver 2.0.
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
#### 4.3.7 Validation Tools and Database  
The aim of an operational validation system is to ensure that satellite-based EO products can be validated and inter-compared using standardised methodologies, following community-endorsed best practices against a set of globally representative FRM. This framework was first prototyped within the CEOS-LPV sub-group, resulting in the On Line Validation Exercise (OLIVE) project [175]. The ambition of the OLIVE tool was to provide a centralised platform, allowing any user to perform a full validation exercise on a new product, deriving common quality assessment metrics and plots. OLIVE quality assessment was focused on coarse resolution land products, mainly FAPAR and LAI. The validation was based on inter-comparison with reference satellite datasets over BELMANIP-2 (BEnchmark Land Multisite ANalysis and Intercomparison of Products) globally distributed sites [176] and direct validation against the DIRECT ground-based dataset [177]. The OLIVE tool is currently not maintained and needs to be fully redesigned with more advanced open-source language and state-of-the-art code architecture [150]. Furthermore, the BELMANIP-2 list of sites needs to be updated, using more recent land cover maps and optimising the sites to be better representative of S2's resolution [3]. A similar concept of a web-based validation platform was recently developed in the frame of CEOS-LPV, focusing on land surface albedo products [178]. Another successful example of an operational validation tool is the ESA Atmospheric Validation Data Centre (EVDC) [179], which follows the same philosophy of becoming a one-stop-shop, providing centralised open access to satellite sub-settings and spatiotemporal collocated ground-based reference data of atmospheric variables. The development and maintenance of similar tools is considered as the final step for enabling operational validation at global scale of satellite EO data, i.e., to reach stage 4 in CEOS-LPV terms, as recently pointed out in an overview paper [150].
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 4 \(\boldsymbol{\
### White Noise in \(a_{\ell q}\)  
White noise is injected initially in the carrier-smoothed pseudorange measurement error model of the SKF to deal with transient errors in position estimates. During this initial period, the modeled \(\sigma^{(\ell)}(i)\) is given by  
\[(\sigma^{(\ell)}_{\text{init}}(i))^{2}=(\sigma^{(\ell)}(i))^{2}+(\sigma^{( \ell)}_{\text{wh}}(i))^{2} \tag{30}\]  
If the injected white noise variance, \((\sigma^{(\ell)}_{\text{wh}}(i))^{2}\), is \(\mathcal{K}_{\text{wh}}\sigma^{(\ell)}(i))^{2}\), \((\sigma^{(\ell)}_{\text{init}}(i))^{2}\) becomes equal to \((\mathcal{K}_{\text{wh}}+1)(\sigma^{(\ell)}(i))^{2}\). \(\mathcal{K}_{\text{wh}}\) is a known scale factor as white noise is artificially included. With this, Equation (26) is changed to  
\[a_{\ell q}=\left(\exp^{(-\beta_{\ell q}^{\text{mod}}\Delta t)}\frac{\sigma^{ (\ell)}(i)}{\sigma^{(\ell+1)}(i)}\right)\dots\left(\exp^{(-\beta_{\ell q}^{ \text{mod}}\Delta t)}\frac{\sigma^{(\ell+q-1)}(i)}{\sigma^{(\ell+q)}(i)} \right)\left(\frac{\sigma^{(\ell)}(i)}{\sigma^{(\ell)}_{\text{init}}(i)} \right)\left(\frac{\sigma^{(\ell+q)}(i)}{\sigma^{(\ell+q)}_{\text{init}}(i)}\right) \tag{31}\]  
Replacing the last two terms on the right,  
\[a_{\ell q}=\left(\exp^{(-\beta_{\ell q}^{\text{mod}}\Delta t)}\frac{\sigma^{ (\ell)}(i)}{\sigma^{(\ell+1)}(i)}\right)\dots\left(\exp^{(-\beta_{\ell q}^{ \text{mod}}\Delta t)}\frac{\sigma^{(\ell+q-1)}(i)}{\sigma^{(\ell+q)}(i)} \right)\frac{1}{(\mathcal{K}_{\text{wh}}+1)} \tag{32}\]  
Similarly, the \(\ell\)th diagonal term of \(\boldsymbol{\Psi}_{\text{sub}}^{i}\) is given by  
\[a_{\ell 0}=\left(\frac{\sigma^{(\ell)}_{\text{act}}(i)}{\sigma^{(\ell)}(i)} \right)^{2}\frac{1}{(\mathcal{K}_{\text{wh}}+1)}\leq 1 \tag{33}\]  
Its lower bound is \(\mathcal{K}_{\text{min}}^{\prime}/(\mathcal{K}_{\text{wh}}+1)\). Using Equation (29), \(a_{\ell q}\) is bounded by  
\[a_{\text{min}}^{q}\frac{\mathcal{K}_{\text{min}}}{(\mathcal{K}_{\text{wh}}+1)} \leq a_{\ell q}\leq a_{\text{max}}^{q} \tag{34}\]
## 4 MARL-Based Routing Solution  
This model is designed to route data flows by creating paths from the talker to the listener, following the shortest possible path formed based on the total delay of the links. While the primary objective is to establish the shortest paths, alternative routes with slightly higher delays may be used if sufficient resources are unavailable along the shortest path. However, these alternative routes must always comply with the maximum delay bound specified in the service channel request. The episode length in this model is variable, ending either when the destination TSN switch is reached or when the maximum delay bound is exceeded. This model is defined as an MDP, which is described in detail below.
## 3 Proposed Method
### Spatial Self-Attention Augmented Graph Convolution
#### 3.3.1 Spatial Graph Convolution  
For the spatial dimension, we use adjacency matrices of three types: static adjacency matrices (\(A_{1}\)), globally-learned adjacency matrices (\(A_{2}\)), and adaptive adjacency matrices (\(A_{3}\)). \(A_{1}\) is a \(N\times N\) hard-coded adjacency matrix of graph representing the physical structure of the human intra-body connections, \(A_{2}\) is also an \(N\times N\) adjacency matrix, which is learned by initializing a fully-connected graph according completely to the training data. The matrix and the parameters of the model are optimized together during training process. The matrix element can be any value, which can not only indicate whether there is a connection between two joints, but also the strength of the connection. \(A_{3}\) is learned an adaptive graph for each sample to represent the strength of the connection between  
Figure 4: Spatial self-attention augmented graph convolution combines an improved ST-GCN block and a self-attention module to enhance spatial graph convolution.  
Figure 3: Spatial temporal self-attention augmented graph convolutional block. It is internally composed of a spatial self-attention augmented graph convolution (SAA-Graph, as shown in Figure 4) followed by a temporal convolution (TCN) [1] and batch normalization.  
two vertices. We embed the input twice by using two sets of learned weights, then we transpose one of the embedded matrices and take the dot product between the two and normalize to get the adaptive adjacency matrix, similar as [4].  
Each adjacency type is applied with its own graph convolution operation (GCN) by using individual weights instead of replacing the original \(A_{1}\) with \(A_{2}\) or \(A_{3}\). Then, the output of GCN applies a \(1\times 1\) convolution as a learnable reduction measure for weighting the stack output and provides the required number of output channels. In this way, the model can increase flexibility without reducing the original performance.  
For the spatial dimension, the graph convolution operation is formulated as  
\[GCN_{l}(f_{l}) =\sum_{i=1}^{3}{(D_{k}^{-\frac{1}{2}}(A_{l}+I)D_{k}^{-\frac{1}{2}} )f_{in}W_{i}},l=1,2,3; \tag{1}\] \[GCN(f_{out}) =Concat(GCN_{1},GCN_{2},GCN_{3}). \tag{2}\]  
where \(A_{l}\) is adjacent matrixs, \(D_{k}\) is a degree matrix, \(I\) is an identity matrix describing the self-connection of joints, \(f_{in}\) is the set of joints, and \(W_{i}\) is trainable parameter of the neighbor subset. \((D_{k}^{-\frac{1}{2}}(A_{l}+I)D_{k}^{-\frac{1}{2}})\) means a normalization of the \(A_{l}+I\).
# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 2 Materials and Methods
### Experimental Setup and Dataset  
In this paper, insulator images captured from various transmission lines across different voltage levels in Shenzhen, along with an open-source insulator images dataset, are processed to create a dataset comprising insulators exhibiting flashover, breakage, and self-explosion. The dataset comprises a total of 1923 images. We used the LabelImg annotation program to annotate the insulator images, and the XML file with bounding boxes' locationsand labels is generated through the annotation. Examples of self-explosion, breakage, and flashover are shown in Figure 5. Next, the dataset is augmented using the mosaic augmentation method. This involves randomly rotating, cropping, scaling, and applying color dithering to four images, which are then combined into one new image. Consequently, a total of 4000 augmented images are generated. An example of a data-augmented sample image is shown in Figure 6, while the distribution of labeled samples is shown in Table 1.  
The training set, validation set, and test set are divided into three parts in the ratio of 8:1:1, and the original size of the image ranges from 1152 \(\times\) 864 to 7360 \(\times\) 4921 pixels. Then, the image size is fixed to 640 \(\times\) 640 pixels after mosaic data augmentation, which is used as the original input for training the model. Before the training starts, the k-means clustering algorithm is used to cluster 9 centers of the length and width of the bounding box in the dataset as the initial anchors of the model, and reasonable anchors can make the model converge faster during training. Finally, the anchors (the length and width of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Sample Size** &
\begin{tabular}{c} **Proportion of Total** \\ **Sample (\%)** \\ \end{tabular} \\ \hline Insulator & 17,734 & 44.4\% \\ Self-explosion & 2720 & 6.8\% \\ Breakage & 7924 & 19.8\% \\ Flashover & 11,556 & 28.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the sample in the dataset.  
Figure 5: Sample diagrams of insulator state. (**a**) self-explosion; (**b**) breakage; (**c**) flash over.  
Figure 6: Image after data augmentation.  
initial bounding box) are [17; 15; 28; 26], [53; 36; 89; 70], [62; 244], [283; 112], [109; 311], [184; 378], and [387; 252].
# Enhanced and Combined Representations in Extended Reality through Creative Industries
## 2 Related Work
### Cutting-Edge Technologies  
The degree of immersion offered by XR technologies is determined by the user's level of isolation in the extended worlds, as well as the number of senses with which the user participates in those experiences [40, 14]. In AR, the user's simultaneous interaction with the real world and augmentation elements through their portable device has a limited degree of immersion. However, AR solutions are usually the most affordable and readily accessible due to the equipment, as displayed in [89]. In MR, the degree of immersion is greatly enhanced as the user interacts with the elements of the augmented space through the head-mounted display and gestures. The cost of acquiring MR equipment has, until recently, been a factor limiting the spread of their use. However, in recent years, significant progress has been made in incorporating mixed reality elements into devices that provide highly immersive experiences in VR environments [90, 91, 92, 93]. The current trend seems to lead to the development of XR devices that will include haptics and special devices for reproducing smells. In other words, multi-sensory XR devices will lead to the pinnacle of immersion [94]. However, the ease of use and the usefulness of the applications to be developed should be a high priority.  
Artificial intelligence is treated with caution by many and offers additional possibilities in everyday life and the field of research. In the sector of cultural heritage, leveraging AI, machine learning (ML), and advanced algorithms could be used for digitization, reuse, and knowledge enhancement [95, 96, 97]. AI algorithms, including deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), could be applied for automated artifact recognition, textual analysis, and contextual understanding [95, 98]. In other words, these technologies enable the efficient and accurate categorization, indexing, and annotation of digital repositories, facilitating easy access and reuse [99]. In addition, ML algorithms contribute to data mining, trend analysis, and predictive modeling, empowering heritage professionals to derive valuable insights and trends from extensive datasets [100]. Integrating AI with 3D scanning and reconstruction techniques allows for the generation and preservation of artifacts in digital spaces [101]. Moreover, AI is used to provide effective training and upsfilling for users. Explainable AI (XAI) is suitable for providing reasonable explanations to non-technical users [102].
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Implementation Aspects and Associated Configuration and Monitoring Processes
#### 3.2.1 Wireless Communications and Networking and Support for Monitoring the MANET Nodes  
The platform can be realized using a wide range of wireless communications technologies. The implementation is based on IEEE 802.11 due to the wide availability of versatile and low-cost hardware for its support, but other alternative technologies could  
Figure 6: State transition diagram for: (**a**) mobile node; (**b**) fixed source node; (**c**) fixed destination node.  
have been used instead. The operation of the MANET platform is insensitive to the underlying wireless technology, as all three types of messages (beacon, routing message, ACK) exchanged between nodes, as discussed in Section 3.1, are encapsulated in UDP packets. The connectionless nature of the UDP protocol matches well with the characteristics of the interactions in the MANET environment.  
In the implemented platform, each node is equipped with two distinct radio interfaces (both Wi-Fi). The first one is setup in ad hoc mode and supports the UDP-based message exchanges between peer MANET nodes, as just explained. The second interface operates in infrastructure mode and connects the node to an independent auxiliary network of star topology, so that the status of the MANET nodes can be monitored through a gateway. Figure 7 depicts this dual connectivity feature. Typical Linux tools supporting SSH and/or VNC sessions can be leveraged for monitoring. As an example, Figure 8 displays a screenshot capturing status parameters during the realization of an experiment.  
It is noted that the auxiliary network may also be used to enable an NTP-based clock synchronization between the traffic source and destination nodes, avoiding the need for using RTCs in these nodes, as already discussed in Section 3.1.4.
# Transfer Convolutional Neural Network for Cross-Project Defect Prediction
## 6 Discussion
### Time and Memory Overhead of TCNN  
In this section, we evaluate the time and memory overhead of TCNN on the six pairs of CPDP tasks used in Section 6.2. The experimental environment was a Windows 10, 64-bit, Intel Xeon 3.9-GHz server with 16 GB RAM. We adopted the NVIDIA GTX1070 to accelerate the training process of the neural network model.  
Figure 6 presents the model training time of TCA, TCA+, DBN, CNN, DPDBBN, DPCNN, and TCNN on each CPDP task. We did not include LR, NNFilter, and DG in the figure because their training time was less than 100 ms. As shown in Figure 6, the training time of TCNN was reasonable, e.g., on average, it took about 23 seconds to train a model. In summary, TCNN model training time was longer than that of LR, NNFilter, DG, TCA, TCA+, DBN, and DPDBN and was close to that of  
Figure 5: Performance of TCNN under different parameter settings.  
CNN and DPCNN. Although it took more time to train TCNN, the prediction performance of TCNN was better than other models. We believe that the training time of TCNN is still acceptable.  
In addition, in our experiment, it took at most 1.6 GB of memory and 1.9 GB of video memory for the TCNN to generate the DL-generated features for both the training data and the test data. The above executing requirements are relatively easy to achieve, so we believe that our TCNN method is applicable in practice.
## 3 Materials and Methods
### SPD Multi-Metric
#### 3.1.3 Measurement  
The surface and the protection level analysis are integrated. The overall method calculates the actual protection level by integrating the risk analysis for the attackable points with the efficacy of the deployed defense mechanisms.  
The outcome is a triple vector of \(\langle\)_Security, Privacy, Dependability_\(\rangle\) representing the total SPD of the system. The final SPD vector reflects the perfect separation (100) subtracting the weighted sum of the limitations' impact:  
\[\begin{array}{l}S=100-\big{[}W_{V}\times L_{V}+(W_{W}\times L_{W}+W_{C}\times L _{C}+W_{A}\times L_{A}),\\ P=100-\big{[}W_{D}\times L_{D}+(W_{W}\times L_{W}+W_{C}\times L_{C}+W_{A}\times L _{A}),\\ D=100-\big{[}W_{E}\times L_{E}+(W_{W}\times L_{W}+W_{C}\times L_{C}+W_{A}\times L _{A})\big{]}\end{array} \tag{8}\]  
where \(W\)- represents the threat bias of each limitation type (as in Microsoft RASQ [53]).  
The SPD evaluation is a function of separation among the possible threats and the defended assets. Each property's value falls in the range of 0-100, with 0 represents no defense and 100 full coverage and separation among the assets and threats. It has to be noted that the absolute SPD of \(\langle\)100,100,100\(\rangle\) does not guarantee absolute protection. This designates that all required defense mechanisms have been deployed for all possible interaction points and set correctly, based on the current set of known limitations (e.g., CVE bulletins or CERT reports). Higher rating reveals redundancy of defense mechanisms, as well as, higher operational/implementation expenditures.  
More information on the SPD multi-metric and the performed analysis can be obtained in the initiatory article [60].
###### Abstract  
The agri-food processing industry predominantly relies on fossil fuels, contributing significantly to greenhouse gas emissions and extensive water use. Climate change requires a conversion of food processing technologies towards sustainability. Our research focuses on testing and validating pulsed electric field (PEF) technology as a mild processing method for stabilizing freshly squeezed, not from concentrate, blood orange juice. Experiments were carried out on a continuous pilot plant endowed with a patented treatment chamber that ensures a constant flow of product without \"hot spots\" for a homogeneous treatment. Once the operative conditions of the process in terms of energy density were optimized, PEF-treated blood orange juice was tested in order to evaluate the effects on physico-chemical parameters (total soluble solids, total acidity, pH, CIE L*, a*, b*, C*, h color indices, cloud, bioactive compounds (ascorbic acid, total anthocyanins, total and individual flavanoens), antioxidant activity (ORAC units, total phenolic content), microbial communities (aerobic mesophilic viable count, yeasts and molds, acid-tolerant microorganisms), residual enzymatic pectinmethylesterage activity, and sensory attributes (flavor, off-flavor, off-odor, color, intensity of odor, acidity, sweetness, bitter, freshness, cooked flavor). Then, in order to simulate the commercial refrigerated distribution of PEF-treated blood orange juice and define its refrigerated shelf-life, the PEF-treated freshly squeezed, not from concentrate, blood orange juice was subjected to physico-chemical, antioxidant, microbiological, and sensory evaluations over twenty days of refrigerated storage at 4 \(\pm\) 1 \({}^{\circ}\)C. The PEF treatment effectively ensured excellent microbial inactivation and enhanced the nutritional and health characteristics of the juice, thereby extending its shelf-life. This study demonstrates the significant potential of the PEF treatment to produce blood orange juice suitable for a new retail segment--freshly squeezed juices with superior health quality, fresh-like characteristics, and extended refrigerated shelf-life.  
blood orange juice; pulsed electric field; sensory attributes; pectinmethylesterase; bioactive compounds +
Footnote †: journal: Applied sciences