esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Fluid Dynamics Model for Traffic Simulation  
In order to introduce the way traffic evolution is treated in the application of fluid dynamics models, let us introduce some basic concepts that will be used in the following. A road network can be considered as a finite number of roads whose intersections are the junctions, or crossroads. A graph \(G=(N,E)\), is used to represent such elements, \(N\) being the set of nodes related to the road junctions, while \(E\) is a set of arcs, or links, associated with the roads. The relationship between each element in \(N\) and \(E\) and their corresponding objects in the network is biunivocal, hence in this work we will use interchangeably nodes, junctions or crossroads, and roads, links or arcs.  
In this particular case of link-and-node traffic network model, we deal with a continuous in space and time representation of the network, hence each arc \(e_{i}\) is associated with a finite linear interval \([a_{i},b_{i}]\), and the extreme points of this interval correspond to the head and tail nodes of the arc. This will turn out to be useful when dealing with the visual representation of the output. Indeed notice that each extreme point corresponds to a node in \(N\). Then, given a geo-referencing for the entire set of nodes \(N\), any point \(p_{i}\) belonging to the interval \([a_{i},b_{i}]\), associated with link \(i\in E\), can be expressed by means of the coordinates of the first extreme point of the link, namely \(a_{i}\), and the width of the interval \([a_{i},p_{i}]\). We will implicitly use this concept in the output visualization on a GIS (Geographic Information System, see, e.g., [24]), when using the dynamic segmentation of arcs as described in Section 3.2.  
For each node \(v\in N\), we define the _forward star_\(FS(v)\subset E\) as the set of arcs whose tail is node \(v\), and the _backward star_\(BS(v)\subset E\) as the set of arcs whose head is node \(v\). The number of ingoing arcs is given by \(|BS(v)|=bs_{v}\), and, similarly, the number of outgoing arcs \(|FS(V)|=fs_{v}\). A node \(n\in N\) is a source if \(bs_{n}=0\), while is a sink node if \(fs_{n}=0\). The macroscopic traffic variables considered in the model are the density of vehicles, the average speed of cars and the flow. Define \(\rho=\rho(t,x)\in[0,\rho_{max}]\) as the density of cars, being \((t,x)\in\mathbb{R}^{2}\) and \(\rho_{max}>0\) the maximum density of cars on the road. Define \(f(\rho)=\rho\cdot v\) as the function representing the flow of cars, given by the density times the local average speed of cars \(v=v(t,x)\). We assume, as done in most cases, that \(v\) is a decreasing function depending on the density only, and that the corresponding flow is a concave function. Moreover, we assume that \(f\) have a unique maximum and, for ease of notation, that \(\rho_{max}=1\).
## Conclusions  
The traffic forecast problem is very challenging. This problem consists of many complex factors. The complex factors included multiple spatial-based dependencies and the temporal-based dependencies. To make the prediction accurate, with spatial-temporal correlations, also external factors influence it. Across the whole neural network stack, to extract the spatial-temporal features, we proposed the spatial-temporal-based convolutional block. In the first level, we proposed a novel multi-branch-based model, and the model utilized 3D CNN with different periodicity branches to extract the spatial-temporal features. In the second level, our feature embedding branch consists of the attention-based LSTM to embed the features and combine with the units of the residual layer. The feature embedding branch captures the inflow and outflow of the citywide traffic flow. The external factor branch contains some external factors to make an influence on the traffic flow. In this way, to make predictions more accurate than the previous baseline method, fused with external factors. Our task is to predict simultaneously, our spatial-temporal convolutional branch capture the multiple spatial and temporal dependency together with external factors. Our prediction accuracy is improved 55% to 60% as compared to previous baseline methods. However, our study is still not perfect and has some limitations. From the following aspects, as a future study point of view, we will consider improving our accuracy more. We will explore more graphical knowledge of road networks, calculate travel time estimation, and route recovery with citywide traffic flow. We will divide the city-states according to semantic states. We will include the impact of COVID-19 in the traffic flow.  
**Author Contributions:** Conceptualization, Z.U.A., H.S., and Z.Y.; funding acquisition, H.S.; methodology, Z.U.A., H.S., and Z.Y.; project administration, Z.U.A.; resources, H.S.; software, Z.Y. and A.A.; supervision, H.S. and Z.Y.; validation, Z.U.A., H.S., and Z.Y.; visualization, Z.U.A.; writing--original draft, Z.U.A. and A.A.; writing--review and editing, Z.U.A., H.S., Z.Y., and A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work sported by the National Science Foundation of China grants 61672417 and 61876138, and Shaanxi key research and development program 2020 KW-002. Any opinions, findings, and conclusions expressed here are those of the authors and do not necessarily reflect the views of the funding agencies..  
**Acknowledgments:** Data retrieved from Kaggle and T-drive-trajectory.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 3 Results
### Number of Surface Area Observations per Dataset  
The number of PlanetScope observations varied throughout the year and varied across different OFRs (Figure 4). The months with the highest number of PlanetScope images were November-December 2020 (-17) and March-April 2021 (-14), while the months with the lowest numbers were July-September 2020 (<10) and February 2021 (<3) (Figure 4A). In addition, most of the OFRs (~60%) had 80-100 PlanetScope observations per year (Figure 4B). Basemap images were processed at a daily cadence, and we considered a new Basemap observation every time a new image composite was used. In this regard, the number of Basemap images followed a similar pattern found for PlanetScope; however, the mean number of Basemap observations per month was higher than of PlanetScope in 10 out of the 12 months analyzed, and most of the OFRs (~75%) had 90-120 Basemap observations per year (Figure 4A,B).  
Planet Fusion images were derived from real and synthetic pixel values, and the number of real and synthetic observations varied throughout the year (Figure 4C). The number of images derived from real pixels reached its peak (~13-15) between November and December 2020, and the lowest numbers were found in February 2021 (~2) and between May and June 2021 (<5). In general, most of the OFRs had ~ 80-100 real observations per year. The number of mixed images (i.e., composed by real and synthetic pixels) tended to be < 10 for all months, and most of the OFRs had <50 mixed observations per year (Figure 4C,D). The number of synthetic images is higher than real and mixed observations for all months of the year, and the highest values (~22-26) occurred in July 2021 and May-June 2020, with the lowest values between November and December 2020 (~14-16)  
Figure 4: Number of surface area observations per month for PlanetScope and Basemap (**A**) and for Planet Fusion real, mixed, and synthetic (**C**). Frequency distribution of the total number of observations per OFR per year for PlanetScope and Basemap (**B**) and for Planet Fusion real, mixed, and synthetic (**D**).  
(Figure 4C). In addition, most of the OFRs had \(\sim\)250-260 synthetic observations per year (Figure 4D).
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 3 Method  
In this work, vibration signals were obtained from both healthy bearings and bearings with different faults. By utilizing vibration analysis techniques, the fault classification of rolling bearings was achieved. The schematic flowchart of this method is shown in Figure 2.
## 4 Wireless Body Area Networks (WBANs)
### Wearable Wireless Body Area Network (WWBAN)
#### 4.2.1 Wearable Smart Shirt Application  
A number of wearable physiological monitoring systems have been developed to monitor the health status of the individual wearer or the elderly [17,57-61]. A wearable physiological monitoring system called 'Smart Vest' to monitor various physiological parameters such as electrocardiograms, photoplethysmographs, heart rate, blood pressure, body temperature and galvanic skin response has been developed [58]. Figure 5 shows the overall system architecture of the wearable smart shirt for ubiquitous health and activity monitoring, which consists of a shirt with integrated wireless sensor nodes, a base station and a server personal computer for remote monitoring.  
Figure 5: The system architecture with a wearable smart shirt.  
The smart shirt can measure electrocardiogram and acceleration signals for continuous monitoring and real time healthcare was designed and developed in [59]. The shirt contains sensors for continuous monitoring health data and conducting fabrics acting as electrodes to capture the body signals. The measured physiological electrocardiogram data and physical activity data are transferred to an _ad-hoc_ network using the IEEE 802.15.4 communication standard for base-stations and a server PC for remote monitoring. A wrist worn wearable medical monitoring and alert system targeting high-risk cardiac/respiratory patients has been developed to monitor physiological parameters such as electrocardiograms, heart rate, blood pressure, skin temperature, _etc_. [17].
# Micro-Focusing of Broadband High-Order Harmonic Radiation by a Double Toroidal Mirror
## 1 Introduction
This sensor allows the determination of wavefront aberrations after focusing, and thus provides convenient feedback for the alignment of the focusing optics. The focus location and spot shape can be estimated by performing back-propagation calculations. In this work, we use two toroidal mirrors in a Wolter configuration to focus broadband high-order harmonic radiation between 20 and 50 eV. This design allows us to use a large demagnification of approximately 35 between the HHG source, located at 6 m from the optics, and the image position, at 170 mm from the mirror pair. We optimize the focusing of an XUV beam with an XUV wavefront sensor, using single-shot measurements, and back-propagate the measured intensity profile and wavefront to the focal plane which yields a spot of 3.6 \(\times\) 4.0 um\({}^{2}\) FWHM. These results are consistent with ray-tracing simulations that predict a minimum size of 3.0 \(\times\) 3.2 um\({}^{2}\) FWHM. In Section 2, we describe the experimental setup, including the XUV generation and focusing geometry. Wavefront measurements and focal spot optimization are presented in Section 3. We conclude in Section 4.
# Research on the Vehicle Steering and Braking Stability Region
## 2 Stability Analysis of 5 DOF Steering and Braking Vehicle Model  
### Vehicle Model  
To study the stability of the plane motion of vehicles, this paper does not consider the influence of suspension systems and load transfers in steering and braking conditions, that is, the vertical, pitch, and roll motions of vehicles are ignored. This paper mainly analyzes and calculates the dynamic stability region under vehicle braking. If a double-track model is used, the degrees of freedom of the system model will reach seven. Through previous research, we have found that as the degrees of freedom of the system model increases, the difficulty of accurately solving the equilibrium point greatly increases. Moreover, the dynamic situation in this study can also be effectively analyzed through a 5 DOF model. In addition, for front wheel steering vehicles, although the actual steering angles of the two tires are different due to the differential (usually the inner tire has a larger steering angle), this is not the focus of this study. We simplify the double-track model to the single-track model, which can better study how changes in steering angles affect the dynamic mechanism of vehicle steering and braking. Based on the classical 3 DOF steering model [11], the longitudinal force and braking torque of the tires are introduced. The schematic diagrams of the single-track model considering the longitudinal force of the tire and the wheel with braking torque are shown in Figure 1.  
Where \(X_{CoG}\) and \(Y_{CoG}\) represent the \(X\)-axis and \(Y\)-axis of the vehicle coordinate system, and \(X\) and \(Y\) represent the \(X\)-axis and \(Y\)-axis of the geodetic coordinate system. When the vehicle velocity is low, the air resistance is small, and the wheel rolling resistance is the main driving resistance. While the vehicle is in high-velocity conditions, air resistance surpasses wheel rolling resistance and becomes the main resistance [26]. The direction of air resistance is always opposite to the driving direction, so it is necessary to introduce symbolic functions into the model to describe the longitudinal and lateral air resistance of the vehicle body. Finally, the 5 DOF dynamic system equation can be given as follows:  
\[\left\{ \begin{array}{l}\ddot{v_{y}}=-v_{x}\omega+\frac{F_{lf}\sin\delta_{f} +F_{sf}\cos\delta_{f}+F_{sr}-\text{sgn}(v_{y})\cdot C_{\text{air},x}A_{\text{L },y}\frac{\rho}{2}v_{y}}{m}\\ \dot{\omega}=\frac{(F_{lf}\sin\delta_{f}+F_{sf}\cos\delta_{f})_{lf}-F_{sr} \text{ln}\,t}{I_{z}}\\ \dot{v_{x}}=v_{y}\omega+\frac{F_{lf}\cos\delta_{f}-F_{sf}\sin\delta_{f}+F_{lr }-\text{sgn}(v_{x})\cdot C_{\text{air},x}A_{\text{L},y}\frac{\rho}{2}v_{x}}{m} \\ \dot{\omega_{f}}=\frac{-\text{sgn}(\omega_{f})\cdot T_{bf}-R_{e}\cdot T_{bf}} {I}\\ \dot{\omega_{r}}=\frac{-\text{sgn}(\omega_{r})\cdot T_{bf}-R_{e}\cdot T_{bf}} {I}\end{array} \right.
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 2 Developing Telecommunication Services--From INAP to CAMEL
Then, CAMEL was extended for UMTS core networks: Phase 3 was defined for 3 GPP Release 99 and Release 4 as a common 2G/3G specification. Phase 4 was defined as part of 3 GPP Release 5. Each CAP phase provides the set of operations and procedures needed to support the corresponding CAMEL phase requirements, as defined in the specifications [40, 41, 42, 43, 44]. In-line with other GSM specifications, the later phases build new features on top of the previous phases. Many services could be created using CAMEL. It has been particularly successful in cases when subscribers were roaming outside their home network. Good examples are prefix-free dialing, when the dialed number is the same no matter in which country the call is placed by the subscribers, parallel alerting of multiple devices, and advanced voicemail services. CAMEL has enabled call-unrelated services too, for example, for mobility or messaging [45, 46].
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 3 Strategies for Scanning the Impact Area  
Due to security issues, for every ballistic rocket launched towards the sea, a foreseen impact point and trajectory are determined in order to establish an exclusion area both in the air and sea where there is a significant probability of impact. In this section, two scanning strategies are proposed for a multi-UAV system aiming at the exclusion area surveillance in such a way that the area can be checked while ensuring both minimal energy consumption of the UAVs, as well as stability and reliability of the network connecting the aircrafts.  
When establishing the appropriate scanning strategy, there are some aspects regarding the scanning purpose that must be taken into account. For instance, one of such requirements may include providing an aerial image of the entire scanning area. Therefore camera aperture, altitude and flight speed are key examples of relevant parameters for planning the mission. Besides the aspects that are inherent to the task to be executed, there are also other important considerations related to the resources required for executing the mission. Thus, a good scanning strategy should define routes that ensure minimal energy consumption in order to provide maximum flight autonomy, as well as keep the aircrafts in close proximity to ensure a stable communication link between them, which further minimizes the probability of failure.  
For the specific application analyzed in this paper, the impact area may vary from a few to hundreds of square kilometers and, depending on the rocket itself, the area of impact can be located close to the coast or a few hundred kilometers offshore. In this scenario, the group of UAVs to be assigned to the mission should be as small as possible to ensure at least four hours of flight autonomy. At least one of the aircrafts must be capable of keeping a reliable communication link with the required bandwidth and also the ability to communicate with the BS from any point in the impact area.  
When the area to be covered is small, a single UAV can be assigned to the mission [21]. In this case, two simple strategies are found in the reference literature: spiral and back-and-forth methods [44].
## 4 Evaluation
### Node-Level Encryption Performance and Variability  
To evaluate the internal scalability, we examine how encryption and communication workloads are distributed across slave nodes as the number of participating nodes increases. As the number of nodes increases, additional slave nodes are introduced into the  
Figure 6: Encryption Performance comparison between AES (Original) and Proposed (PQC + QKD + AES) across multi-node.  
Figure 7: Decryption Performance comparison between AES (Original) and Proposed (PQC + QKD + AES) across multi-node.  
encryption pipeline. We analyze whether these slave nodes exhibit consistent encryption and communication performance, and whether increasing the number of nodes affects per-node performance variability or leads to workload imbalance.  
Figure 8 shows the encryption and communication time for each slave under 2, 4, and 8-node configurations (excluding the master) using 70 GB Datasets. Each slave is responsible for encrypting its assigned subset independently using AES-CTR and transmitting the encrypted data back to the master via MPI. As shown in the figure, node-level encryption and communication times remain highly consistent across all configurations. For 3 Nodes (1 master and 2 slaves), the two slaves completed encryption in 115.89 and 120.79 s, and communication in 5.20 and 5.23 s, respectively. With 4 nodes, encryption times across slaves ranged from 46.66 to 47.06 s, and communication from 3.44 to 3.64 s. In the 8-node configuration, all slaves completed encryption within 19.65 to 19.95 s, and communication from 1.47 to 1.55 s.  
These results demonstrate that our offset-aware partitioning and parallel AES processing strategy achieve balanced workload distribution. The low variance in encryption time across slaves indicates that no single node becomes a bottleneck, even as the number of participating slaves increases. In addition, communication overhead increases predictably with node count, reflecting the benefits of using non-blocking MPI transfers and streaming batch transmission. Notably, increasing the number of nodes reduces per-node encryption time nearly linearly, as the total file is divided into smaller subsets. This ensures that the proposed design supports efficient scaling without incurring synchronization overhead or load imbalance.
# Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
## 3 HGCELM
### Hypergraph Convolutional Regression  
Based on the hidden hypergraph embedding \(\mathbf{Z}\), we use a hypergraph convolutional regression layer to predict labels. Formally, the layer can be written as:  
\[\mathbf{Y}=\mathbf{S}\mathbf{Z}\mathbf{\beta}. \tag{13}\]  
To solve \(\mathbf{\beta}\), the above equation can be rewritten as the following ridge regression problem:  
\[\underset{\mathbf{\beta}^{*}}{\arg\min}\|\mathbf{\beta}\|_{F}s.t.,\widetilde {\mathbf{Y}}=\mathbf{S}\mathbf{Z}\mathbf{\beta}. \tag{14}\]  
Here, \(\widetilde{\mathbf{Y}}=[\mathbf{Y}_{\mathcal{T}};\mathbf{Y}_{\mathcal{U}}]\) is an augmented training target matrix. Since \(\mathbf{Y}_{\mathcal{U}}\) is invisible during training stage, thus it is set to be a 0 matrix. Let \(\mathbf{M}\) be a diagonal mask matrix with its first \(N_{\mathcal{T}}\) diagonal elements \(M_{ii}=1\) and the rest equal to 0. We further rewrite Equation (14) as  
\[\underset{\mathbf{\beta}^{*}}{\arg\min}\frac{1}{2}\Big{\|}\mathbf{M}\Big{(} \widetilde{\mathbf{Y}}-\mathbf{S}\mathbf{Z}\mathbf{\beta}\Big{)}\Big{\|}_{F} ^{2}+\frac{\lambda}{2}\|\mathbf{\beta}\|_{F}^{2}. \tag{15}\]  
It is easy to prove that Equation (15) holds a optimal solution and its closed form is expressed  
\[\mathbf{\beta}^{*}=\Big{(}\mathbf{Z}^{T}\mathbf{S}^{T}\mathbf{M}\mathbf{S} \mathbf{Z}+\lambda\mathbf{I}\Big{)}^{-1}\mathbf{Z}^{T}\mathbf{S}^{T}\mathbf{ M}\widetilde{\mathbf{Y}}. \tag{16}\]  
The labels of the unlabeled data points can be determined depended on the obtained \(\mathbf{\beta}^{*}\), which is given by  
\[\widetilde{\mathbf{Y}}_{\mathcal{U}}=[\mathbf{S}\mathbf{Z}]_{\mathcal{U}} \mathbf{\beta}^{*}. \tag{17}\]  
We show the overall learning steps of the proposed HGCELM in Algorithm 1. Since there is no iteration in HGCELM, it would be computationally efficient for training such model.