esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Cokriging  
Cokriging [23; 24] on the other hand is used when the target variable is under-sampled so as it is not possible to ensure acceptably accurate estimates. An improvement to the estimation accuracy may then be provided by considering the spatial correlations of this variable to other, more adequately sampled attributes. In order to estimate the value of an attribute \(Z_{0}\) in a point \(x_{0}\) of space using a set of values measured on \(n_{0}\) nearby points and additionally a different set of measurements of other auxiliary parameters \(Z_{i}\) (\(i=1,p\)) spatially correlated to \(Z_{0}\), a linear combination of all these data is used again. If \(n_{i}\) (\(i=1,p\)) is the number of sampled points near \(x_{0}\) and if the \(p+1\) attributes are considered, the cokriging estimator is  
\[Z_{0}^{*}(x_{0})=\sum_{i=0}^{p}\sum_{a=1}^{n_{i}}\lambda_{\alpha}^{i}Z_{i}(x_{ a}). \tag{3}\]  
A minimization analogous to ordinary kriging is then employed to determine the coefficient matrix \(\left\{\lambda_{\alpha}^{i}\right\}\), which characterizes the spatial variability of all attributes by defining the set of direct and cross variograms \(\left\{\gamma_{ij}(h)\right\}\). The generic cross variogram between attributes \(Z_{i}\) and \(Zj\) equals again the semicovariance of the pair increments by distance \(h\):  
\[2\gamma_{ij}(h)=cov\Big{[}(Z_{i}(x+h)-Z_{i}(x)),\,\Big{(}Z_{j}(x+h)-Z_{j}(x) \Big{)}\Big{]}. \tag{4}\]Variograms are brought into the estimations by means of a theoretical function that ensures mathematical consistency. The most commonly used model is the linear model of coregionalization, where all direct and cross variograms are deduced from linear combinations of \(n\) elementary direct variograms \(\{\gamma_{k}(h),k=1\) to \(n\}\), i.e.,  
\[\gamma_{ij}(h)=\sum_{k=1}^{n}b_{ij}^{k}\gamma_{k}(h)\ with\ b_{ij}^{k}=b_{ji}^{k},\ \forall k, \tag{5}\]  
where \(\forall k\) the matrix of coefficients \(\left\{b_{ij}^{k}\right\}\) is conditionally non-negative definite.
## 3 MAC Protocols for CRBANs
### Cognitive Radio for Medical Body Area Networks Using Ultra-Wideband
It is similar to MAC defined for IEEE 802.15.4, except for the definition of multiple phases in the time division multiple access (TDMA) frame. Here, the standard defines exclusive access phases (EAPs) for high-priority devices, such as emergency signals related to a patient's vital signs, random access phases (RAPs) based on slotted Aloha, contention access phases (CAPs) and Type I/II phases based on polling. The ECMA-368 MAC provides a distributed reservation-based channel access mechanism, as well as a prioritized contention-based channel access mechanism. The architecture of the ECMA-368 MAC is fully distributed. All devices provide all required MAC functions and optional functions as determined by the application. No device acts as a central coordinator. Channel time is divided into superframes, with each superframe composed of two major parts: the beacon period (BP) and the data period. The BP is organized into slots and is used to achieve network synchronization, exchange reservation and scheduling information for medium access purposes. During the data period, devices send and receive data using prioritized contention access (PCA) or in reservations established using the distributed reservation protocol (DRP). Figure 6: Multiband orthogonal frequency-division multiplexing (MB-OFDM). Although beacons make network operations significantly simpler, it should be noted that beacon-less operation is possible, as well, by relying on header extensions within the physical layer service data unit used for piggybacking control information. The CRC sends the following information to the IR-UWB devices: schedule of sensing and data reporting and input of selected pulse shapes according to the results of sensing. The 802.15.6 MAC operates using TDMA and supports beacon mode, where the BNC sends broadcast information towards the WBAN devices followed by a TDMA frame. The beacon can be modified to include shaping information (e.g., pulse shape, code parameter, _etc._) for IR-UWB devices. This protocol functionally adopts IEEE 802.15.6 for communication in intra-WBAN tiers and ECMA-368 in inter-WBAN tiers. The distributed reservation-based channel access mechanism is also supported in addition to the prioritized contention-based channel access mechanism. Of course, the design of the MAC protocol for a CR network is among the challenging tasks if the network needs to operate in a distributed fashion [25]. Further research is needed to manage the multiple radio access technologies (RATs) available in CRC.
## 4 Methods
### Architecture of HistAENN and Two-Step Learning  
A local histogram with low number of bins (SWLH) is applied for image preprocessing and the window size is \(21\times 21\) pixels. This preprocessing is responsible for local rotational invariance. The architecture of applied neural network is variable with two phases and corresponding configurations (Figure 4).  
During the first phase, HistAENN works as an autoencoder [8; 51; 52] with input vectors (histograms) and exactly the same output vectors. A bottleneck in the inner part of an autoencoder (FC10 together with RELU10) forces data clustering. Two parts of an autoencoder are achieved after training: encoder and decoder, which correspond to the input-to-bottleneck and bottleneck-to-output parts of neural network. This phase uses 100,000 random fragments of the image for training, so this autoencoder is obtained by unsupervised learning.  
The encoder is reused in the second phase with fixed weights. The outputs of the encoder are connected to the new neural network which is a classifier. Inputs are vectors (histograms), as in the previous phase. The classifier uses \(N\) independent outputs which are assigned to particular classes.  
The position of maximal value from the output vector is a recognized class number. Every position of the sliding window corresponds to a single output class number.  
Desired outputs are manually assigned labels by the user before training HistAENN, so the number of training pairs is much smaller (e.g., 500 for each class) compared to the number of training cases in the first phase. This phase uses the supervised learning principle.  
A typical testing using an additional data set is not used during both phases. It is intentional, because the segmented image is evaluated by a human. Test cases could deliver quality information to the user, but this is not discussed in this paper. This paper is related to the automatic evaluation of variability of HistAENNs and fractal estimators. The application of test cases leads to the rejection of badly fitted HistAENNs. The acceptance of such HistAENNs is required for the increasing of variability range.  
The processing of entire image begins after the end of the second phase of training. The obtained segmentation could be accepted by the user or not, because it is semisupervised learning with a Human in The Loop. We observed that additional iterations are not so frequent, and they are related in most cases to human labeling errors.
## 4 Results and Discussions
### Characterization of Polypropolee Nanocomposite
#### 4.1.1 XRD Analysis  
X-ray diffraction (XRD) analysis of PP-GNP composites typically reveals distinct crystalline patterns corresponding to both polypropylene (PP) and graphene nanoplatelets (GNP), as presented in Figure 4a. The XRD patterns commonly show characteristic peaks associated with the \(\alpha\)-form of PP at specific 20 angles, such as 17.1\({}^{\circ}\) (040), 18.4\({}^{\circ}\) (130), and 20.9\({}^{\circ}\) (111). When graphene nanoplatelets (GNPs) are incorporated into PP, additional peaks may appear or intensify, indicating changes in crystallinity or orientation. These changes are often attributed to GNP's role as a nucleating agent, promoting \(\alpha\)-PP crystal formation or influencing the crystallization behavior of the polymer matrix. At lower GNP concentrations, XRD patterns may show minimal changes compared to pure PP. There might be slight shifts or intensifications in the peaks corresponding to the \(\alpha\)-form of PP, indicating the initial nucleating effects of GNPs. XRD patterns often exhibit more pronounced changes to increase the GNP weight fraction, including additional peaks or shifts in peak positions. Enhanced intensities in \(\alpha\)-PP peaks indicate improved crystallization due to the GNPs acting as an effective nucleating agent. The appearance of new peaks associated with GNPs may indicate intercalation or changes in GNP orientation within the PP matrix. Overall, PP-GNP XRD analysis provides valuable insights into the structural modifications induced by GNPs in polypropylene composites, highlighting their impact on crystalline phases and potentially enhancing material properties.  
It can observed from Figure 4b that the combination of PP and MWCNT exhibits diffractions that are similar to JPCD (00-056-1718) and XRD levels. MWCNT shows  
Figure 4: XRD Analysis of (**a**) GNP/PP composites and (**b**) MWCNT/PP composite.  
two peaks at 44.22\({}^{\circ}\) (1 0 0) and 26.14\({}^{\circ}\) (0 0 2), which indicates the interplanar spacing of 2.05 A and 3.41 A. On the other hand, the PP shows the \(\alpha\)-form at 20 angles 18.4\({}^{\circ}\) (130), 20.9\({}^{\circ}\) (111), and 17.1\({}^{\circ}\) (040) patterns. The peak matching \(\alpha\)-crystal reflections in PP/MWCNTs 3.0 wt% was observed but disappeared with the addition of filler due to MWCNTs' strong \(\alpha\)-nucleating effect.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 4 Analysis of Optimization Strategies
### Performance Analysis of Integrity Parameter
The service accuracy could be estimated using the product of PDOP and UURE. As shown in Table 6, average service accuracy is respectively 0.93 m, 0.88 m, and 0.65 m in scenarios 2 to 4, which have 8.9%, 13.7%, and 36.3% improvement compared with the results in scenario 1. Although a small number of LEO satellites could not obviously improve the PDOP, the service accuracy could be effectively improved.
# A LiDAR-Based Backfill Monitoring System
## 3 Methods
### Backfill Progress Computation  
Poisson surface reconstruction is to generate high-quality 3D surface models from a given set of disordered point cloud data [33]. By calculating the normal vector of the point set and applying the Poisson surface reconstruction algorithm, meshes of the tunnel and the backfill body are obtained, which can facilitate the subsequent backfill volume calculation and visual monitoring of the backfill progress.  
The mesh is regarded as a tetrahedron composed of multiple triangular faces and the coordinate origin. The volume of each tetrahedron can be obtained through the following vector operations [34]:  
\[V=\frac{1}{6}[v_{1}\cdot(v_{2}\times v_{3})] \tag{4}\]  
where \(v_{1},v_{2},v_{3}\) are the coordinate vectors of the three vertices that define the tetrahedron.  
When encoding all vertices of a mesh into triangles, the computer stores all the vertices sequentially in a list. Triangles are defined by vertex indices, which point to specific vertices in the list. For example, a triangle might be defined by the indices [0, 2, 5], indicating that it is composed of the 1 st, 3 rd, and 6 th vertices in the vertex list. The order of the triangle vertices (clockwise or counterclockwise) determines the direction of the normal vector, which affects the orientation of the face. According to the right-hand rule, if the vertices of a triangle face are arranged clockwise, the face is oriented toward the origin, representing the front. The signed volume of tetrahedrons formed by the fronts and the origin are positive, and tetrahedrons formed by the backs and the origin are negative (Figure 6). By calculating the sum of the signed volumes of all tetrahedrons, the result is the volume of the entire mesh.  
Figure 5: Moving sampling method.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.15. Deployment Support Network (DSN)  
An alternative to either accessing the nodes individually, or accessing them over the sensor network, is to provide a parallel network specifically for maintenance [27]. Accessing the nodes individually for a software update is normally impractical for two reasons: the large number of nodes, and the inaccessibility of the nodes. Accessing them over the wireless sensor network itself has several disadvantages: it relies on the network being operational, it has an impact on the performance of the sensor network, and it depletes node energy supplies. A DSN of small, mobile, temporarily attachable nodes can provide a solution to some of these problems. Providing virtual connections from a host PC to the individual nodes allows normal host tools to be used in updating the software.  
DSN is essentially an architecture rather than a specific implementation for software updates. The concept could provide high reliability, out-of-band support for autonomic operation in networks where the cost was justified. An open question is whether DSN could be provided over-the-air using an independent network overlay.
## 6 Conclusions
### Future Research Directions
The following abbreviations are used in this manuscript:  
MAPE Mean Absolute Percentage Error  
RMSE Root Mean Square Error  
LSTM Long Short-Term Memory  
DNN Deep Neural Network  
LSTM-DNN Long Short-Term Memory with Deep Neural Network  
ARIMA Autoregressive Integrated Moving Average  
VARMA Vector Autoregressive Moving Average  
STARIMA Spatiotemporal Autoregressive Integrated Moving Average  
SVR Support Vector Regression  
SVM Support Vector Machine  
GBRT Gradient Boosting Regression Trees  
KNN k-Nearest Neighbor  
SSNN State-Space Neural Networks  
DBN Deep Belief Networks  
RNN Recurrent Neural Network  
RW Random Walk  
FFNN Feedforward Neural Networks  
SAE Stacked Autoencoders  
CNN Convolutional Neural Network  
GRU Gated Recurrent Unit  
eRCNN error-feedback Recurrent Convolutional Neural Network  
PeMS Caltrans Performance Measurement System  
FCL-Net Fusion Convolutional Long Short-Term Memory Network  
DNN-BTF Deep Neural Network-Based Traffic Flow Prediction  
DST-ICRL Deep Irregular Convolutional Residual LSTM  
BDIGRU Bi-Directional Isometric Gated Recurrent Unit  
STANN Spatial-Temporal Attention Neural Network  
SLIT Short-Term and Long-Term Integrated Transformer  
DST-GraphSAGE Dynamic Spatial-Temporal Graph Convolutional Network  
SA-LSTM Self-Attention Long Short-Term Memory  
k-NN k-Nearest Neighbors  
LASSO Least Absolute Shrinkage and Selection Operator
## 2 Problem Statement  
We consider a typical symmetrical dual-lane intersection with four approaches and departures, as depicted in Figure 1. Each approach consists of a straight lane and a left-turnlane. Right-turn lanes are omitted here for simplicity since they have no impact on the collaboration results of the intersection. Here, we define a cooperative zone (CZ) with the scope of the four approaching lanes under the radius \(R\). An intersection coordination unit (ICU) deployed in the center of the intersection assists the vehicles in sharing information in the CZ.  
We assume that all vehicles are connected, and equipped with GPS or BDS, vehicle-to-infrastructure (V2I), and vehicle-to-vehicle (V2V) communication devices to exchange real-time information with the ICU and other vehicles. Each vehicle in the CZ transmits four necessary properties to the ICU, i.e., vehicle ID, vehicle type, origin-destination, and vehicle state (position and speed). (1) A vehicle ID will be automatically assigned to the vehicle when entering the CZ; (2) There are two vehicle types considered in this study: AVs and MVs. We consider high penetration rates of AVs (no less than 70%) in this paper to decrease traffic insecurity factors and alleviate drops in traffic efficiency caused by MVs; (3) The origin-destination of a vehicle means which edges the vehicle enters and departs from in the road network; (4) The vehicle state is the crucial information used to predict the future trajectory of the vehicle, which is transmitted to the correlative neighboring vehicles. At each time step, the ICU receives the information of the four properties of all vehicles in the CZ and produces a communication topology according to the conflict relationships of the vehicles. Through V2V communication, AVs exchange the information of the predicted trajectories with their neighboring vehicles based on the communication topology delivered from the ICU, and then optimize their movements. In addition, the behaviors of lane changing and overtaking are required to only operate before entering the CZ. To simplify the problem, we neglect communication delays, package losses, communication constraints, and computation delays, such that the states of all vehicles are updated simultaneously within a common global clock.  
This paper aims to design a scheme for connected and mixed-automated vehicles to cooperatively pass through a signal-free intersection, as shown in Figure 1. The priority order for all vehicles passing through the intersection follows the \"FCFS\" rule. It is worth noting that only AVs in the CZ can be controlled. MVs and AVs beyond the CZ follow their own driving behaviors. Thus, we focus on regulating the longitudinal movements of AVs on the approaching lanes by utilizing the predicted trajectories of their neighboring vehicles. The coordination issue of connected and mixed-automated vehicles at a signal-free intersection will be addressed in the following section by optimizing the trajectories of AVs under a distributed framework.  
Figure 1: A scenario of connected vehicles passing through a signal-free intersection.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 7 Fault Location Finding Methods
### Support Vector Regression-Based Approach for FL  
Regression problems can be solved via SVM by introducing \(\epsilon\), insensitive loss function. This technique is known as support vector regression (SVR). SVR retains the properties of SVM such as over-fitting data possibilities are minimized by selecting discriminative functions based on principles of structural minimization. The global solution can also be obtained by training as a convex optimization problem [142; 143]. In [144], noise removal and offset reduction is done by stationary WT (SWT). The special determinant transform function is used to extract features from 2 to 5 SWT coefficients. And then radial basis kernel SVR corresponding to fault-type is employed after classification attained by SVM. Eleven different kinds of features are obtained for fault estimation through the spatio-temporal prediction HST matrix in [95]. It is implemented by replacing the Gaussian window of ST with the hyperbolic window as an asymmetrical window to extract features from current and voltage signals. Distinctive fault features are extracted using wavelet packet decomposition (WPD) with Db1 mother wavelet from the first half-cycle of post-fault voltage samples [145].
## 3 Practical Implementation
### Metasurface Implementation Details  
In this section, the implementation details of a SW-guiding MTS are given, in which two TM and TE modes are guided, decoupled and phase-balanced (Figure 6). Pixel B, presented in the previous Section 3.2, has been employed to implement a SW-guiding surface with a total radius of \(R=50u=157\) mm\(=7\lambda_{0}\). At each position on the surface, the metallic elliptical patch is printed on top of the grounded dielectric, based on a cartesian lattice meshgrid (unit cell dimension \(u=3.14\) mm). Each patch is appropriately rotated, in such a way that the minor axis of the ellipse meets the impinging  
Figure 5: Frequency dispersion properties for TM and TE modes when impinging on pixel B at \(\phi=0^{\circ}\). Curves agree with the frequency dispersion of the modes for pixel C within \(\phi=90^{\circ}\) angle.  
angle of the SWs propagating from the center of the structure, i.e., is aligned with \(\hat{\rho}\). The structure is excited by a magnetic dipole oriented towards the \(y\)-axis.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 4 Detection and Classification Method
### Vehicle Type Detection  
In addition to license plate detection, the detection of the object containing the license plate was also implemented. This detection was performed using the pretrained YOLOv12 model for this task. Identifying the object surrounding the license plate is important for verifying the authenticity of the image and for detecting intrusion attempts, such as caseswhere a person is holding a license plate in their hand or situations where only certain categories of vehicles (e.g., based on weight) are permitted to pass.  
To detect objects that may have license plates, a standard list of classes from the COCO dataset was used. However, within the application, only categories corresponding to vehicles that typically carry license plates are relevant, such as car, motorcycle, bus, and truck.  
The original COCO list contains many more object categories, but for this project only those directly related to the intended purpose were retained. Of course, the selection can be easily adapted if new requirements arise or if a different dataset is used.  
It is important to emphasize that, for this stage, the object containing the previously detected license plate was selected directly, thereby avoiding processing other irrelevant objects in the image and reducing execution time.  
After the license plate is localized in the image, it is checked whether it lies within a previously detected object. If the license plate is fully contained within the boundary of such an object, that object is considered to represent the vehicle to which the plate is attached.  
The object type is determined based on the category assigned by the model, and this information is included in the final output displayed alongside the recognized license plate number. This ensures that only relevant objects (those actually containing a license plate) are considered, thereby optimizing both the accuracy and performance of the algorithm.  
The final result is displayed on the Figure 8: each license plate is highlighted with a colored bounding box (green for Romanian plates, red for foreign plates), and next to it appears the recognized text, confidence score, and the detected vehicle type. Thus, the system not only automatically extracts the license plate numbers but also associates them with the correct visual context--that is, the vehicle to which they belong.