esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Driving Factors Determination  
Ten featured variables with the greatest impact had been ranked in the RF model (Figure 6). These variables included topographic factors (DEM, aspect, slope), meteorological factors (T, Rh, VP), vegetation factors (SIF), and air quality factors (CO, NO\({}_{2}\), SO\({}_{2}\)), among which the importance of SIF followed the DEM, aspect, slope, and T (Figure 6), which indicated the necessity to incorporate vegetation photosynthesis into the NAI model spatio-temporally.  
In the path analysis, when all land types were considered, the path model for the NAI values explained 77.65% of their variance (df = 9, F = 18.57, RMSE = 0.054) (Figure 7a1). The positive effects of T and SIF and the adverse effects of CO on the NAIs were the combinations of direct and indirect effects. The indirect effects of DEM on the NAI were mediated by SIF and T, which led to the positive effects of DEM on the NAIs (Figure 7a2). The indirect effects of slope and aspect on the NAIs were mediated by SIF, which also led to positive effects on the NAIs (Figure 7a2).  
Figure 5: Model training results (**left**) and verification results (**right**) of the RF algorithm.  
Figure 6: Importance of each feature in the model.  
Figure 7: _Cont_.  
Similar to all land types, the model under forest land, urban and built-up land, and cropland also presented a good prediction with 84.23%, 71.23%, and 75.57% explanation for NAI variances. Furthermore, the direct and indirect effects of terrain features were quite similar to that of all land types for forest land (Figure 7b1), which also produced a positive effect on NAIs. The direct and indirect effects for urban and building land and cropland tended to be different from that under all land types, especially for cropland, whose DEM influenced the NAI via the mediation of T and SIF, producing negative effects (Figure 7d1,d2). Meanwhile, slope had extraordinarily higher contributions to NAIs on cropland than the other land types (Figure 7d2).  
SIF was one of the most important factors affecting NAI concentration (except for urban and built-up land). In forest land and cropland, the total contribution of SIF to NAIs was 0.126 and 0.115, respectively. CO was another important factor affecting NAI concentration, which was mainly negatively correlated with NAIs, while T, SIF, and RH affected NAIs through CO. In all land types, the direct effect of RH on NAI was not significant. In addition, on construction land, the total contribution of T and CO to the NAI concentration was the largest (0.043 and, \(-\)0.033, respectively), and SIF was not the main factor affecting NAIs.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 2 Feature Point-Based Change Detection Method  
This section explains the feature-based, unsupervised change detection proposed in this paper. Figure 1 shows the processing sequence of our method. We perform pre-processing on input satellite images, extract feature points from them, and apply feature matching. Concurrently, we apply pixel-based change detection. The two processing sequences are merged by creating change magnitudes of the features. Finally, changed features are determined.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Malicious Connection Defense
The implemented code snippet after our FREC alerts is:  
\[\text{FREC alerts}\leftarrow\text{Solve(14)}\] \[GenerateReport(R)\leftarrow\text{Solve(18)}\] //check for a hostcompromised host \[\text{while}\;FR_{C_{i}}==\text{malicious}\;\{  
\(|r^{\prime}(t)-L|<\epsilon\). We use \(\dot{S}(t)\) for the time derivative of \(\bar{\tau}^{\prime}(t)\) with continuous-time \(S(t)\), and because we expect abrupt changes in values, we consider the \(\bar{\tau}^{\prime}(t)\) of discrete-time \(S^{m}\) with the superscript indicating time step of \(S\). We apply two discrete-time models to obtain the granularity of attack and defense effects reflected in the visual representation. We derive the first discrete-time model from a continuous-time model:  
\[\dot{S}_{t}=\beta_{i}(1-S_{i})\sum_{j=1}^{n}E_{ij}S_{j}-\delta_{i}x_{i} \tag{20}\]  
where \(x_{i}\) denotes the attack level in host \(i\), \(\beta_{i}\) denotes the attack rate, \(\delta_{i}\) denotes the defense rate, and \(E_{ij}\) denotes the edge weights between hosts. Afterward, we apply Euler's method [60] to Equation (20) to derive the second discrete-time model with time index \(m\) and a random sample \(Z>0\) as:  
\[S_{i}^{m+1}=S_{i}^{m}+Z\left(\beta_{i}(1-S_{i}^{m})\sum_{j=1}^{n}E_{ij}S_{j}^{ m}-\delta_{i}S_{i}^{m}\right) \tag{21}\]  
To achieve 3 and 4, we use the granularity obtained from Equation (21) to display blue lines with single-width sizes for normal connections and red with double-width sizes for anomalous connections. The granularity depth represents the severity of the attack, i.e., the ratio of compromised host connections to total network connections, or the effectiveness of defense measures, i.e., the ratio of benign host connections to total network connections. This choice of colors leverages common associations of red with danger and blue with safety, thereby utilizing established psychological principles to enhance user comprehension and response efficiency. The width is an additional layered property to help users who might be color blind, primarily when visualizing an extensive network with many connections. From the visual system, we assume that initially, when network service discovery is made, everything is normal, and host connections are displayed in blue. Later, when an intrusion occurs, hosts connected to a malicious host are indicated with red color as described in Algorithm 1. Because real attacks might not occur simultaneously in a network, we introduce all induced malicious hosts simultaneously for a finite experiment setup. The granularity of the network connection colors, i.e., blueish or reddish, changes _on the fly_ as attacks are detected and as our defense module neutralizes the malicious connections.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### AI-Driven Data Services (DaaS)
#### 4.2.4 User Experience Layer (UEL)  
The UEL serves as the principal interface between end-users and the ViMindXAI platform, facilitating access to intelligent video analytics services. It delivers multimodal, intuitive, and role-sensitive interfaces that allow users to perceive, explore, and act upon intelligent video analytics. Although the core computational logic resides in lower architectural layers, the UEL bridges these capabilities with user-facing insights, enabling informed decisions across domains such as public safety, healthcare, and operations. It is designed with modularity and cognitive adaptability in mind. To maximize usability across diverse environments, the UEL supports deployment on web, tablet, and AR/VR interfaces, requiring only minimal local inference. This ensures responsive and accessible user interaction, even in bandwidth-constrained or mobility-oriented scenarios. The UEL is structured around two core components: User Intelligence Interfaces (UII) and End-User Groups.  
**(1) User Intelligence Interfaces (UII)**  
This module includes three intelligent components designed to support user interaction and visualization:  
* **Immersive Monitoring Interface:** The Immersive Monitoring Interface provides real-time AR/VR-based visualizations for operational monitoring and training, utilizing platforms such as Unity, Meta Quest, and HoloLens. It supports environments that demand high spatial and contextual awareness.
* **Adaptive AI Dashboard:** Presents personalized, role-specific dashboards with real-time updates and explainability features such as GradCAM and SHAP. Integrates with standard BI tools like Tableau, Grafana, Kibana, and Power BI.  
Conversational access is enabled through seamless integration with the SVIE in the DaAL, users are enabled to interact with analytics through natural language queries, contextual prompts, and voice-based commands, supporting intuitive human-AI interaction. This facilitates intuitive human-AI communication across roles and contexts.
# Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
## 3 Proposed Data Embedding Method
### Data Extraction  
The embedded data can be extracted when decoding the processed video. Here, the same key \(\kappa\), which is only known to the authorized parties, is utilized to identify the blocks selected for data embedding, i.e., which one is selected/skipped. The embedded data are then retrieved from the intra-coded block by extracting the _parity_ of the intra prediction mode in BL, as well as the first \(\xi\) bits from the intra prediction mode in EL. Specifically, the embedded data in EL are derived from the coded mode and the spatial decoded mode of the spatially co-located block of the reference layer by using the bitwise-XOR operation. Similarly, the parity bits of the MVP indices are extracted from the inter-coded blocks in all coded layers.  
The SHVC reference software SHM-12.0 [33] was modified to implement the proposed data embedding techniques. Here, we adopted the QP parameter settings as suggested in the SHM test conditions (CSTC) [34], i.e., QP = 22, 26, 30, and 34. Experiments were conducted by using two layers of spatial scalability for Low Delay P (LDP) and All Intra (AI) settings with a group of pictures (GOP) structure of 4, i.e., IPPPIPP\(\cdots\). The remaining parameters were set to the SHM default configuration. The experiments were conducted by using a PC with AMD Ryzen 5 3500U 2.10 GHz CPU and 8 GB of RAM running on a 64-bit Windows 10 operating system. Six standard video test sequences for SHVC from [35; 36] as shown in Figure 5 were considered to evaluate the performance of the proposed data embedding method. A pseudo-random number generator was employed to generate a sequence of binary numbers (i.e., 0's and 1's), which was then embedded as the payload by using both the intra and inter picture prediction techniques. Unless specified otherwise, all available embedding venues were utilized, i.e., embedding at the maximum rate. Using SHVC compressed video as the baseline, the processed videos (containing embedded payload) were evaluated. It was verified that the resulting bit streams were still format compliant and they could be decoded by SHVC decoders while the embedded data could be extracted correctly for all settings. All results were collected by processing the first 200 frames in each video test sequence. It is crucial to note that the AI setting is considered here to capture the performance of embedding data by using the intra prediction mode, while the LDP setting captures the performance of embedding data by using both the intra prediction mode and the motion vector predictor.  
Figure 4: MVP candidates where ‘X’ is the current block to be coded.
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 3 ST-GCN
### The Way the Sampling Area Is Divided  
The domain of each node is divided into regions for spatial graph convolution. ST-GCN has three partitioning strategies, as shown in Figure 3. Body joints are indicated by orange dots, and in Figure 3, red dashed circles are the adjacent area \(B(v_{ti})\) with the sampling function \(p\) using distance D = 1.  
The unilabeling partitioning strategy is depicted in Figure 3a. The adjacent region \(B(v_{ti})\) of root node \(v_{ti}\) is not divided.  
Figure 3b is the distance partitioning strategy. The adjacent region \(B(v_{ti})\) of the root node \(v_{ti}\) is divided into two regions: the root node 0 (green dots) and the remaining neighbor nodes (blue dots). Each region is assigned a different weight.  
The spatial configuration partitioning strategy is shown in Figure 3c. This strategy can be broadly classified into centripetal and centrifugal motions according to the motion of the body parts. Take the bone center of gravity as the reference point, which is indicated by a black cross. The \(B(v_{ti})\) is divided into three regions: (1) the root node itself (green dot); (2) the centripetal set (blue dot)--the neighboring nodes that are closer to the gravity center of the skeleton than the root node; (3) the centrifugal set (yellow dots)--the centrifugal group. Each region is assigned a different weight.
## 2 Improved RRT Algorithm
### Extended Target Bias  
The RRT algorithm is characterized by the random search of the space. Such search path planning brings huge advantages, but it also brings the disadvantages of the RRT algorithm [24]. Before the algorithm obtains the path solution, the random tree search is completely random. The random tree often expands to some places far away from the target, and the algorithm efficiency is not high. In order to improve the efficiency of the algorithm, this paper adopts the extended goal bias strategy. By artificially guiding the generation of random points, the target point \(X_{goal}\) is selected with a certain probability as the random point \(X_{rand}\) in the loop. In order to maintain the ability of random trees to expand the unknown space, the probability \(P\) is usually not selected as too large, usually 0.05-0.1. \(P=0.1\) is used in this paper. The comparison of the path generated by the basic RRT algorithm and the target-biased RRT algorithm with probability \(P=0.1\) is shown in Figure 2. As shown in Figure 2, the basic RRT algorithm has many sampling points, and the path distance is long. As shown in Figure 2, the improved algorithm has fewer sampling nodes and the path distance is short. Therefore, the improved algorithm reduces the path length and the number of sampling points, which improves the efficiency of the algorithm.
## 5 Conclusions  
In order to make better use of OTFS channel sparsity and reduce the complexity of channel estimation, a GAMP-SBL channel estimation algorithm is proposed in this paper. We first restate the input-output relationship of the OTFS system in the DD domain and derive a channel model that can reflect the sampling points in the Doppler domain. Secondly, the channel estimation problem is transformed into a sparse basis coefficient recovery problem using BEM, which greatly reduces the parameters of channel estimation. Finally, the GAMP algorithm is used to achieve reliable channel quantization estimation in the OTFS system and avoid matrix inversion in SBL. In particular, (1) under different SNR values, the algorithm is significantly superior to the channel estimation technique based on a single pulse in the simulation, and the matrix operation is converted into scalar computation using the message passing framework, which greatly reduces the algorithm complexity. (2) The channel state information obtained by the model is more accurate. Thus, the proposed algorithm can be applied to multi-scatterer scenarios, such as V2X, and the simulation and experimental results also have superior BER performance in 3 GPP-specified vehicle networking scenarios, which verifies the effectiveness of the proposed algorithm.  
**Author Contributions:** Conceptualization, Y.L. and P.L.; methodology, Y.Z. and Y.L.; software, X.L.; validation, X.L.; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); data curation, L.C.; writing--original draft preparation, X.L.; writing--review and editing, Y.L. and C.W.; visualization, X.L.; supervision, Y.Z., P.L., S.W. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Chongqing, China (No. CSTB2023 NSCQ-MSX0025) and Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region (No. 2022 GXZDSY014).  
Figure 4: The BER performance comparison with velocities of 121.5–685 km/h, i.e., \(f_{d}=3\)–15%.  
Data Availability Statement: Due to the privacy implications of the research project, this article cannot currently provide research data.  
Conflicts of Interest: Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] were employed by the company State Grid Chongqing Information and Telecommunication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System
## 2 Improvement of the Contract Net Protocol Model
### The Pheromone Flow Model  
The information between the manager agent and the contractor agent is transmitted by a pheromone. According to the different roles of the agent at this time, the pheromone information released is also different [20]. The manager agent can release the task pheromone to the environment and the contractor agent can calculate the bidding pheromone according to the perceived task pheromone and release it to the public environment. Task pheromones and bid pheromones in the environment are constantly changing and remain for a period of time, as shown in Figure 2 below. Suppose there are \(m\) contractor agents and \(n\) tasks need to be completed, after four stages of pre bid-tender-bidding-winning, the classical contract net protocol and dynamic contract net protocol need more communication times, while the improved contractor net protocol in this study reduced the number of communication times of the classic contract net protocol, \(2 mn+2n\) to \(mn+2n\) times, reducing the number of communication times by \(m\times n\)[21; 22].
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 4 Simulation Results and Discussions
### Packet Drop Comparison  
The results illustrated in Figure 7 indicate that the MBMQA possesses the lowest packet drop percentage compared to the other schemes (i.e., 55% lower than MP-OLSRv2 and 35% lower compared to MEQSA-OLSRv2) at the 60 m/s speed of the device. The higher number of packets dropped in other schemes is due to the absence of the backlog queue length awareness of devices for selecting the best route. In contrast, the MBMQA routing scheme intelligently selects devices with a lower queue length to reduce the chance of traffic overhead and packet drop.
# Different Formulations to Solve the Giesekus Model for Flow between Two Parallel Plates
## 4 Results
### High Weissenberg Number  
The HOS formulation was implemented with the log-conformation technique for flow simulation, considering high values of the Weissenberg number. The SW formulation [10] was not able to converge to the solution when considering \(Re\) and \(Wi\) higher than \(8000\) and \(10\), respectively.  
However, the formulation based on the independent variable change was able to obtain solutions for any values of \(Re>0,Wi>0\) and \(0<\alpha_{G}<0.5\), with the only restriction to use the dimensionless number \(\beta>0.01\).  
Figures 10-12 show the variation of the streamwise velocity \(U(y)\) and the three components of the extra-stress tensor \(T_{xx}\), \(T_{xy}\) and \(T_{yy}\) in the wall-normal direction \(y\) for different dimensionless numbers of \(Re,\beta,\alpha_{G}\), and with Weissenberg number \(Wi=150,300\), and \(500\).  
Figure 9: Same as in Figure 1 for dimensionless numbers: \(Re=2000\), \(\beta=0.01,\alpha_{G}=0.3\) and \(Wi=2\).  
Figure 11: _Cont._  
Figure 10: Same as in Figure 1 for dimensionless numbers: \(Re=2000\), \(\beta=0.25,\alpha_{G}=0.1\) and \(Wi=150\).  
One may observe that the results obtained by the different formulations are in agreement. An interesting behavior presented by the HOS formulation can be observed. The results obtained by this formulation, for high Weissenberg numbers, show oscillations for high Weissenberg numbers.  
Figure 11: Same as in Figure 1 for dimensionless numbers: \(Re=8000\), \(\beta=0.5,\alpha_{G}=0.3\) and \(Wi=300\).  
Figure 12: Same as in Figure 1 for dimensionless numbers: \(Re=5000\), \(\beta=0.75,\alpha_{G}=0.2\) and \(Wi=500\).  
the extra-stress tensor components in the channel center, and as the Weissenberg number increases, the oscillations are more pronounced.  
Oscillations usually appear in simulations of flow between parallel plates close to the wall and not in the channel center. However, for high values of the Weissenberg number, a discontinuity is observed in the extra-stress tensor components in the channel center. With the HOS formulation, even using the log-conformation technique to solve the problem of the high Weissenberg number, oscillations were found in the extra-stress tensor components in this region. The use of mesh refinement in the wall-normal direction smooths these oscillations. It is also possible to adopt a stretched mesh, refining only in the region of the channel center to reduce these oscillations and the computational cost.
# Influence of Baffle Location on the Diodicity of Tesla Valves
## 3 Results and Discussion
### Comparison of Experimental and Simulation Results  
This study conducts a comparative analysis of the TV's diodicity before and after structural optimization, thereby validating the feasibility of the optimization methodology through experimental analysis. Figure 17 presents a comparison of the diodicity in its original and optimized configurations. Observations from the figure indicate that the incorporation of baffles into the TV, as proposed in this study, significantly enhances its diodicity. Consistent with the experimental data, simulation results demonstrate an increasing trend in diodicity values in conjunction with an increasing flow rate. Throughout the operational range examined, the simulation error is maintained at less than 10%.  
Figure 16: (a) Diodicity trend of Tesla valves with baffle positioned above the outlet (cases 3, 6, 7). (b) Diodicity trend of Tesla valves with baffle positioned below the outlet (cases 4, 5, 8).  
During the experiment, the main discrepancy between the experimental and simulation results was found to be due to the non-ideal smoothness of the TV's flow channel surface and the minute air bubbles adhering to the valve surface. To mitigate these uncertainties, we utilized a peristaltic pump to significantly reduce the presence of bubbles in the fluid and employed 3D printing technology to manufacture the valve model with an improved surface finish. Upon comparing the revised experimental results with the simulation results, the discrepancies observed were minimal, thereby confirming the reliability of the numerical computation results presented in this study.