esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Performance Discussion
The prediction\(-\)driven algorithm significantly reduced the satellite peak loads compared with its non\(-\)predictive counterpart, indicating a lower likelihood of system congestion. In particular, under high\(-\)load conditions, the ARTHF leverages traffic forecasts to proactively assign TTGs with high expected demand to underutilized satellites. This predictive load\(-\)balancing strategy mitigates congestion and enhances the overall fairness, as evidenced by the improved Jain's index and reduced peak loads. The communication quality was evaluated using the average SNR across all TTGs, as shown in Figure 8. The prediction\(-\)driven algorithms demonstrated a significant increase in the average SNR compared with both its non\(-\)predictive counterparts, highlighting a core advantage of traffic forecasting. By anticipating future traffic demands, the algorithm enables differentiated handover strategies. TTGs with predicted high future demand can be assigned to satellites prioritized for load capacity, even if the instantaneous SNR is slightly compromised. Conversely, TTGs with a lower predicted demand can be allocated to satellites offering optimal signal quality without significantly impacting the overall network load. This flexibility allows the system to better optimize the overall communication quality. Notably, in low\(-\)load scenarios, the ARTHF framework naturally  
Figure 5: Handover times over time and average handover times. LSTM\(-\)based traffic prediction with an attention\(-\)enhanced rainbow DQN, the framework enables proactive load balancing and alleviates congestion under high\(-\)load conditions, as reflected in the improved Jain's fairness index and reduced peak loads shown in Figures 6 and 7. It also preserves high communication quality in low\(-\)load scenarios, as indicated by the enhanced SNR in Figure 8, while consistently achieving lower handover frequencies across diverse traffic scenarios, as illustrated in Figure 5. These findings, supported by the realistic and dynamic traffic patterns of the Milan dataset, highlight the robustness and adaptability of ARTHF for practical use in dynamic satellite\(-\)to\(-\)ground communication systems.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### System Model and Assumptions  
We study a large-scale wireless sensor network whose nodes are densely deployed and organized in a cluster-based topology. After the nodes are deployed, they do not change their initial locations and remain stationary during the operation of the network. The reason for choosing a cluster-based hierarchical model is that it is most appropriate for multi-hop communication and assists the common en-route filtering paradigm [7]. When the density of sensor nodes is high enough, cluster merge methods can be used to include no less than \(L\) nodes in the cluster [7; 29]. There are several techniques available to make clusters, merge clusters, choose CHs and produce cluster IDs [26]. We assume that the sensing range of sensor nodes is larger than their transmission range, and every CH uses a longer transmission range than that of the member nodes in the cluster [7; 8]. All nodes are assigned node IDs prior to network deployment. A simple cluster formation scheme is assumed; that is all nodes within one hop distance join to form a cluster, and the nodes that have the smallest IDs in the clusters are elected to be CHs [7]. For the sack of simplicity, it is assumed that sensor nodes cannot be compromised for a very small interval of time during which the formation of clusters, key distribution, and discovery of paths are performed [7; 11]. We further assume that the network is exposed to onlyFRIA and FVIA [7; 11]. Since the nodes are spatially correlated, clusters located adjacent to each other contend with each other whenever an event happens, and the wining cluster prepares the report. Each member node checks the report for consistency with its observations and appends a MAC to it. After having received all the votes, e-CH randomly picks a preset number of votes, including its own vote, and affixes them to the report and transmits the report to the BS. Compromised intermediate verification nodes can treat false votes on a report as true votes, and they can similarly invalidate true votes. Compromised nodes can be exploited to launch several other types of attacks; however, a discussion of all other possible attacks, their impact, and countermeasures is beyond the scope of this paper.
## 8 Conclusions  
This experiment studied the applicability of cellular Internet for communication of multicast R-GOOSE messages over GRE and mGRE tunnels. These tunnels were established with real networking devices connected to a commercial cellular network. The performance of the proposed tunnels was analyzed for various configurations: unsecured (plaintext R-GOOSE) and secured (signed R-GOOSE) messages as well as unsecured (GRE/mGRE over IP) and secured (GRE/mGRE over IPsec) communication paths. Although tunneling and security protocols impose extra communication headers and processing delays, these delays can be compensated by novel cellular technology characteristics. This confirms that cellular Internet can be considered for transmitting multicast R-GOOSE messages in P2M communication-based protection scenarios. In our experiment's location, the 4G communication had higher performance than 5G communication. Since the implementation of the 5G network is not in a mature stage, the more proper comparison between 4G and 5G performances could be conducted in the future when 5G network implementation is complete. In the future work, this experiment can be extended to a larger geographical area where sender and receivers located in different cities obtain statistical data on the spatial variation of the communication delay and investigate the impact of 5G coverage. From a statistical analysis point of view, the future work is to analyze the measured data to find out the parameters of the underlying statistical mechanism and create a forecast model for the communication delay.  
Information exchange and integration are the key enablers for designing novel smart grid wide-area protection applications. In P2M applications, multicast communication enables efficient integration of the substations that exchange protection data via the Internet. Cellular technology is considered as a cost-effective solution for the inter-substation communication of multicast R-GOOSE messages over the Internet. In this context, deterministic communication and cybersecurity must be noticed. According to the measurement results, security mechanisms caused insignificant delay when compared to communication latency. Therefore, secured configurations are preferred ones because they not only provide real-time communication but also protect R-GOOSE messages against cyber-attacks. This ensures reliable functioning of the wide-area protection applications.  
Conceptualization, P.J. and A.S.; Methodology, P.J.; Software, A.S.; Supervision, S.R.; Writing--original draft, P.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was financially supported by \"Distributed management of electricity system--project number 322673\" funded by the Academy of Finland.  
**Data Availability Statement:** The data used to support the reported results are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix B Three-Dimensional Finite-Difference Model Based on Fluid-Solid Coupling Theory
### Post-Processing  
Since our 3D finite-difference model takes into account the topography of the Three Gorges Reservoir area, we evenly extracted the maximum shear-strain calculation results at 500 m deep underground to analyze the crustal stress-strain field, as shown in Figure 9. Figure 9 shows the calculation results under self-weight conditions, which represent the crustal stress-strain state before the water impounding. However, Figure 9 shows the calculation results under three water impounding stages, which were used to discuss the influence of the direct reservoir water load.  
Compared with before the water impounding, the study area will produce a relatively larger pore water pressure field after the water impounding. For the fluid-solid coupling problem, the distribution of the pore water pressure field can directly reflect the reservoir water infiltration. Therefore, we chose the A-A\({}^{\prime}\) section, a triangular structure near the intersection of the Xiannvshan faults, the Jiuwanxi faults, and the Yangtze River, to extract the numerical calculation results of the pore water pressure field at the section location and performed post-processing on it. In addition, since the model before water impoundment only considers its weight, it is meaningless to post-process the results of pore water pressure before the water impounding. Figure 11 shows the pore water pressure distribution at the A-A\({}^{\prime}\) section at the 135 m, 156 m, and 175 m water levels.
# IoT Privacy and Security Challenges for Smart Home Environments
## 5 Some Existing Security Support for IoT
### 6 LoWPAN and Security  
The Institute of Electrical and Electronics Engineers (IEEE) has defined the 802.15.4 standard for wireless personal area networks (WPANs). IEEE 802.15.4 defines how the physical and media access control layers should operate under the low-bandwidth, low-cost, low-speed and low-energy conditions typical of these networks. As such, 6 LoWPAN [(23)] is a light-weight protocol designed by the IETF to allow IPv6 packets to be transferred over IEEE 802.15.4 wireless networks.  
Figure 2: The comparison between IETF IoT and TCPIP protocol stacks.  
The Internet Protocol Security (IPsec) suite has defined Authentication Headers (AH) and Encapsulating Security Payloads (ESP) to enable data integrity, confidentiality, origin authentication and anti-replay protection for IPv6 packets. The authors in [24] proposed compressed AH and ESP features for 6 LoWPAN to implement IPsec and thus provide end-to-end secure communications between wireless devices.  
An enhanced authentication and key establishment scheme for 6 LoWPAN networks (EAKES6 Lo) has been proposed by the authors in [25]. EAKES6 Lo is divided into two phases to improve the security of 6 LoWPAN networks. The two phases are: (1) system setup; and (2) authentication and key establishment. In Phase 1, the symmetric cryptography mechanism Advanced Encryption Standard (AES) is used to encrypt the data transfer in the network. To verify the integrity of the data, hash function Message Digest Algorithm 5 (MD5) or Secure Hash Algorithm (SHA) is employed. In Phase 2, six messages will be exchanged to finalize the authentication and key establishment process and establish a mutual authentication.  
Thus, glowPAN provides a template for secure wireless communications, even for resource-constrained devices.
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 3 The Proposed Algorithm
### Watermarking Extraction  
The procedure of watermark embedding based on SIFT, SVD, and APBT is shown in Figure 5.  
Step 1. 4 \(\times\) 4 block-based APBT is carried out on the received image after attacks \(I_{\mathrm{a}}\) and the first four coefficients of each block are picked out according to the zig-zag sequence.  
Step 2. According to keys \(K\), all feature points are performed by Step 1 to obtain the received watermarked coefficients matrix \(M_{\mathrm{cw}}^{*}\). To differentiate matrix in Section 3.2, superscript \(*\) is noted for received matrices.  
Step 3. SVD is performed on \(M_{\mathrm{cw}}^{*}\) using Equation (15), and \(S_{\mathrm{ww}}^{*}\) is recorded for the next step:  
\[M_{\mathrm{cw}}^{*}=U^{*}S_{\mathrm{ww}}^{*}(V^{*})^{\mathrm{T}}. \tag{15}\]  
Step 4. \(S_{\mathrm{w}}^{*}\) is retrieved by Equation (16):  
\[S_{\mathrm{w}}^{*}=U_{\mathrm{w}}S_{\mathrm{ww}}^{*}(V_{\mathrm{w}})^{\mathrm{ T}}. \tag{16}\]  
Figure 5: Watermark extraction procedure of the proposed scheme.  
Step 5. The extracted watermark is generated for coefficients matrix \(M_{\text{cw}}^{*}\) in Step 3, which is shown in Equation (17):  
\[W_{\text{e}}^{*}=(S_{\text{w}}^{*}-S)/\alpha. \tag{17}\]  
Step 6. The inverse Arnold transform is performed to extract the watermark \(W_{\text{e}}\).  
This completes the watermark extraction process.
## 3 Results and Discussions  
### The Drying Performance of the Drying System  
In the present work, the _Mc_ was measured at 30 min intervals, and the measured _Mc_ was used to calculate the _DR_; the variations in _Mc_ and _DR_ with drying time \(t\) for different \(\alpha\) are depicted in Figure 3; as can be seen from the figure, over the six drying cycles of the drying process, the _Mc_ gradually decreases with the increase of \(t\) for different \(\alpha\). It can be seen from the variation trends of _DR_ that the _DR_ for the different \(\alpha\) in the first cycle obtained the maximum values, which might be due to the fact that the evaporated water in this period could be regarded as free water, and the dehydration characteristic could be summarized as free water evaporation [21]; thus, moisture was quickly removed from the corn. Moreover, it also can be seen from Figure 3 that in the first drying cycle, the _DR_ under \(\alpha\) of 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\) were similar and obviously higher than that of the others. In the second and third drying cycles, the _DR_ under the \(\alpha\) of 75\({}^{\circ}\) and 90\({}^{\circ}\) were similar and obviously higher than that of others, while in the fourth and fifth drying cycles, the _DR_ under the \(\alpha\) of 90\({}^{\circ}\) was obviously higher than that of others, which might be due to the fact that the moisture in the material was mainly expressed as bound water, and thus, more energy was needed to overcome the binding energy between the moisture molecules and the adsorption site, and higher hot air temperature was needed to evaporate the same amount of water [30]. Accordingly, by comprehensively considering the heat exchange performance of the HE and based on the analysis in the above Section 3.1, a variable temperature drying process could be developed as _Tair_ of 76.5 \({}^{\circ}\)C in the 1 st drying cycle (\(\alpha\) = 45\({}^{\circ}\)), _Tair_ of 95.2 \({}^{\circ}\)C in the 2 nd and 3 rd drying cycles (\(\alpha\) = 75\({}^{\circ}\)), _Tair_ of 100.3 \({}^{\circ}\)C in the 4 th and 5 th drying cycles (\(\alpha\) = 90\({}^{\circ}\)). Another drying operation followed by the obtained optimized drying process was conducted, and the drying performance was compared with the drying performance under the \(\alpha\) of 75\({}^{\circ}\) and 90\({}^{\circ}\) (the final _Mc_ of only two of the drying processes was down to the required moisture content of 14 %d.b.); the comparison curves are shown in the following Figure 4.  
Figure 3: The variation of moisture content and drying rate with drying times for different valve openings.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 2 Background  
### Network Traffic Anomaly Detection  
With the continuous expansion of the network scale and the increasing diversification of attack techniques, ensuring the stability and security of network systems has become a core task in the protection of information infrastructure. As a critical component of the cybersecurity defense system, network traffic anomaly detection aims to identify abnormal traffic that deviates from expected communication patterns by analyzing network behavior, thereby uncovering potential malicious activities such as DDoS attacks, port scanning, data breaches, and APTs. In recent years, the rise of data-driven approaches has led to the widespread adoption of machine learning and deep learning techniques in the field of network security, significantly improving the accuracy of anomaly detection. However, the inherent characteristics of network traffic--such as high dimensionality, an unstructured form, and dynamic variability--pose substantial challenges for effective modeling and inference. As a result, developing efficient, accurate, and generalizeable anomaly detection mechanisms remains a pressing research focus in contemporary cybersecurity.
## 4 Numerical Simulation Analysis
### Influence of Burial Depth
#### 4.4.2 Internal Force Responses  
It has been previously demonstrated that the side wall is the weak area in the structure; therefore, shear force of side wall 3 is chosen to be analyzed and the results are shown in Table 14, in which the static values are defined as static computational results under the effect of gravity, and dynamic values are defined as dynamical computational results under earthquake motions.  
Comparing shear force amplifications at the top and bottom of side walls in different buried depths, it can be concluded that dynamic load has a greater influence on the top of side walls when the buried depth is 1.2 m when the shear force amplifications of the top of the side wall are larger than those of bottom. With the increase of buried depths, dynamic loads have a greater effect on the base of side walls, and the shear force amplifications on the top of the side wall are less than those of the bottom, which causes shear forces within the bottom of the wall to reach the maximum values under static and dynamic loads. Thus, the bottom of the side wall is the weak part of the underground substation. Therefore, it should be given more attention during the seismic design of such a system.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 3 Experimental Data and Analysis Indicators  
### Observation Data of CAPS System  
The CAPS system was in the test period in April 2021. The system uses two GEO satellites and a SIGSO satellite, namely APSTAR-7 (76.5\({}^{\circ}\) E), CHINASAT-10 (110.5\({}^{\circ}\) E), and ASIASAT-3S (147.5\({}^{\circ}\) E), to form a navigation constellation. The measured data of the CAPS system from 17:00 to 18:00 on 1 April 2021, with a sampling interval of one second, were collected at the Wuqing station in Tianjin, China. The measured pseudo-ranges in this data are corrected by virtual clock technology, which eliminates or greatly reduces correlated errors, including ephemeris errors, clock errors, errors caused by atmospheric delays, etc. We simulate the pseudo-range of multi-frequency measurement by adding random noise that satisfies the Gaussian distribution on the basis of the single-frequency measured pseudo-range. The position coordinates of the observation station are obtained by using the long-term static relative positioning method of commercial RTK.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 2 Materials and Methods  
### Simulation Approach  
In the literature, there is no univocal definition of the terms hybrid simulator, AR simulator, and mixed reality simulator. In performing this review, we have categorized the simulators using the following definitions:  
* Augmented reality (AR) simulator: an interactive simulator in which the real-world environment is enhanced by computer-generated content perceived by the user using different senses. In these simulators, the specifically designed physical component could be absent (using only pre-existing elements of the real-world environment, such as the ground, a wall etc.), passive (not actively participating in the simulation), or active (providing/enabling specific functionalities in the simulation).
* Hybrid simulator: an interactive simulator in which the system integrates both a virtual and a physical module. In these simulators the physical parts are always present, but they could play either a passive or active role in the simulation.
* Mixed reality (MR) simulator: an interactive simulator in which real content (physical objects) and virtual information (computer-generated content) are merged so that they can interact with each other in real time. In these simulators the physical parts can interact with the virtual content (and/or vice versa).  
Therefore, as an example, a simulation system could have been classified as AR and hybrid but not as MR if the system integrated both virtual content and physical parts but did not enable any virtual physical interaction.  
The results of the classification according to these definitions are given in Section 3.10, which reports the associate statistical data collected by answering statistical questions SQ2 (\"How many AR simulators are there?\"), SQ3 (\"How many MR simulators are there?\"), and SQ4 (\"How many hybrid simulators are there?\").
# Novel Thermal Management Strategy for Improved Inverter Reliability in Electric Vehicles
## 3 Proposed Thermal Management Strategy
### Power Loss and Highest Virtual Junction Temperature Estimation
Similarly, the average conduction losses of the body diode during a modulation period are calculated as [38]:  
\[P_{cond,D}=\frac{1}{T_{sw}}\int_{0}^{T_{sw}}V_{SD}(i_{SD},T_{vj,D})i_{SD}(t)dt, \tag{11}\]  
where \(V_{SD}(i_{SD},T_{vj,D})\) is the body diode forward voltage (Figure 2c), \(i_{SD}(t)\) is the current circulating through the body diode, and \(T_{vj,D}\) is the diode virtual junction temperature. Note that in SiC MOSFETs the reverse recovery energy loss \(E_{RR}\) is negligible; thus, diode switching losses \(P_{sw,D}\) have not been considered. Additionally, as both MOSFET and body diode share the same die, it has been considered that \(T_{vj,MOS}\simeq T_{vj,D}\simeq T_{vj}\). When executing the thermal management strategy online (Figure 1), highest average power losses of a power device (\(P_{device,h}=P_{cond,MOS}+P_{sw,MOS}+P_{cond,D}\)) are estimated at each modulation period from the precalculated 4-D LUT, according to the operation point of the power inverter. Then, fluid temperature \(T_{fluid}\) is measured, and \(T_{vj}\) is calculated from the equivalent Foster network (Figure 3) whose thermal resistances \(R_{i}\) and capacitances \(C_{i}\) have been obtained form the datasheet (Figure 2d). Note that the \(R_{th}\) nomenclature used follows the AQG 324 Automotive Qualification Guideline. A detailed explanation regarding \(R_{i}\) and \(C_{i}\) estimations can be found in [39]. The transient thermal impedance \(Z_{th,JF}\) of the circuit of Figure 3 can be expressed in the Laplace domain as follows:  
\[Z_{th,JF}=\sum_{i=1}^{n}\frac{R_{i}}{1+\tau_{i}s}, \tag{12}\]  
where \(n\) is the number of the RC equivalent circuits and \(\tau_{i}=R_{i}C_{i}\). For the digital implementation of the foster network, such continuous transfer function is transformed into the \(z\) domain, with a sample-time of \(T_{sw}\), and \(T_{vj}\) is numerically obtained.