esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 4 Simulation and Results
### The Influence of the Number of Recommenders on the Trust Evaluation  
In the process of trust evaluation for a certain SP, the SR needs to collect the recommendation opinions from the intermediate nodes to form a more accurate trust point of view. As shown in Figure 7, when the number of the recommenders is 0, it indicates that the trust value of SP to form the viewpoint of the SR is completely evaluated based on direct experience. Along with the number of recommenders gradually increasing, the SR  
Figure 5: Bidirectional trust evaluation of SR and SPs.  
Figure 6: Trust evaluation of SR with different RPE and RTS.  
can collect more recommendation opinions. From the experimental results of this group, it can be seen that when the number of recommendation opinions is equal to or greater than 6, the SR's trust evaluation opinion on SP tends to be stable, and the SR can more accurately identify the honest and trustworthy SP while avoiding wrongly delegating malicious or negative SPs. Therefore, in order to better evaluate the trustworthiness of the SP, the SR needs to obtain as many recommendations from intermediate nodes as possible during the service delegation process.
## 5 Discussion
### Geological Interpretation
#### 5.1.2 Landslides and Erosion Landforms
By referring to the displacement rates observed in 2014-2020 at each mass movement area, it turns out that, while the active state of the IP3 landslide body (polygon B in Figure 9a) is confirmed and the intensity of the process belongs to the category of very slow processes (i.e., velocity of 16 mm/year to 1.6 m/year; according to [92]), the hazard classification of the IP2 landslide bodies (polygons A and C in Figure 9a) should be revised from dormant to active with extremely slow (i.e., velocity less than 16 mm/year) and very slow intensity, respectively, if considering their vertical rates (while estimates along the steepest slope direction suggest that both processes occur with very slow intensity). On the other side, given that most of these landslide bodies are calanchi, the proposed reclassification based on the found motions has to be interpreted in the context of the typical behavior of these landforms, which are characterized by washing and weathering processes, and conditioned by local rainfall regime. Future in-depth analysis of these processes could also account for local precipitation records to investigate any potential correlation with the deformation observed in the satellite results and highlight impacts on landslide triggering and/or reactivation.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 6 Conclusions  
This study explores a deep learning surface defect detection method based on a segmentation network. The proposed method consisted of two stages. First stage: the architecture includes a lightweight encoder and a multi-scale fusion mechanism. The multi-scale fusion mechanism incorporates the feature maps generated by each layer of the encoder into the loss function to improve the learning process. On the magnetic tile  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Defect Type** & **Pos** & **PosNeg** \\ \hline Blackhole & 88.8(4) & 95.8(5) \\ Fray & 100.0(0) & 94.2(3) \\ Crack & 100.0(0) & 96.6(3) \\ Break & 81.8(3) & 91.4(6) \\ Uneven & 100.0 (0) & 94.3(3) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results on the magnetic tile dataset (number of misclassified examples in parentheses).  
Figure 9: Examples of true positive and true negative (**top two rows**) as well as false positive and false negative (**bottom two rows**) detection on magnetic tiles using the proposed method. The classification scores for each example are displayed in the top-left corner.  
dataset, with Params and Flops being 36.675 and 19.714, respectively, the MIoU and F1 scores are 66.94 and 74.8. This study demonstrates that the model outperforms its baseline model. Second stage: involves an additional decision-making network built on top of the segmentation network to predict whether the entire image contains anomalies. It performs well on Fray and Crack, with no misclassified examples, while maintaining a high accuracy in determining negative test examples. Other defect types also exhibit high accuracy.  
Existing defect detection algorithms based on deep learning primarily leverage the powerful fitting capability of neural networks. However, these methods do not consider the geometric constraints of defects. Therefore, combining the geometric features of cracks with the learning capability of convolutional neural networks to achieve more precise defect detection will be a future research direction. In addition, we will further study the detection robustness of the deep learning algorithm.  
Conceptualization, [PERSON] and Y.H.; methodology, [PERSON]; software, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and T.J.; visualization, [PERSON] and Z.H.; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number: 2023 YFF1103405.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
###### Abstract
[17] proposed a suboptimal iterative algorithm that uses a new iterative rank minimization method to optimize all variables in each iteration jointly. This algorithm simplifies the URLLC services and yields a significant performance gain. [PERSON] et al. [18] went further to investigate the coexistence of eMBB and URLLC services in RIS-assisted cellular networks. It can be seen that there are fewer studies related to RIS technology for achieving the coexistence of eMBB and URLLC services in wireless networks. Based on the above analysis, RIS technology which can reshape the wireless propagation environment and the coexistence multiplexing issue of URLLC and eMBB are both essential components in the future 6G network. Combining the two for efficient operation to fully utilize the network's potential and realize the coexistence of heterogeneous services is the focus of our current attention. The main contributions of this paper are as follows:  
* We study the problem of achieving efficient multiplexing URLLC and eMBB in RIS-assisted wireless networks. The resource allocation problem in the coexistence scenario is formulated as two optimization problems, while the performance tradeoff between different services is considered. * The eMBB allocation problem is solved in two stages. The first stage determines the RB allocation. The second stage jointly optimizes the power allocation and RIS phase shift matrix. However, since the RIS optimization takes a long time, the RIS phase shift matrix is pre-configured. * For URLLC services, we propose a heuristic URLLC allocation algorithm based on pre-configured RIS. The pre-configured RIS phase shift matrix is sequentially substituted into the URLLC allocation problem to obtain the optimal power and frequency allocation strategy to maximize the URLLC packet reception rate. * Simulation results show that the heuristic algorithm based on RIS pre-configured proposed in this paper achieves a better URLLC packet reception rate than other allocation methods while satisfying the delay requirements. Then, the performance of four different types of RIS configurations is compared to prove the effectiveness of RIS. The composition of this paper is as follows: Section 2 presents the system model. Section 3 presents two optimization problems, i.e., the eMBB and URLLC allocation problems. Section 4 converts and solves these two problems separately and introduces the optimal RIS phase shift matrix design. Section 5 conducts simulation experiments and analyzes the experimental results. Section 6 concludes the whole paper and considers the outlook for this work.
###### Abstract
In comparison, one can identify the highest frequency of intensities via a histogram of the image, and data are embedded relying on this maximum value in the histogram [16; 17; 18]. This method is termed the histogram shifting-based approach that gives a high payload depending on the highest frequency of intensities in the histogram. RDH in natural images [19] and RDH in encrypted images [20] are the two widely explored types of RDH. A basic workflow of RDH in an encrypted image is shown in Figure 3. In an encrypted domain, data can be reversibly embedded either over an encrypted image or through encryption. In the first case, two parties are involved at the sender's end. They are the content owner and the data hider. Here, image encryption is performed by the content owner, and hiding data becomes the responsibility of the data  
Figure 1: Data-hiding approaches. hider. In this scenario, the details of the original image are concealed from the data hider, while in data hiding through encryption, both the encryption and data-hiding processes are handled by a single party. The proposed \(MPSA\) scheme is an RDH scheme in encrypted images. The contributions that are specific to the proposed scheme are as follows:  
Figure 3: Flow diagram of reversible data-hiding scheme in encrypted image. Figure 2: Flow diagram of reversible data-hiding scheme. Retainment of entropy and histogram: The bulk of the existing RDH schemes in encrypted images predominantly alters the pixel distributions in the encrypted image during data hiding. However, this manuscript proposes an RDH scheme called the \(MPSA\) scheme that retains the entropy and histogram. It is noteworthy to mention that entropy and histogram are the two efficiency parameters to judge the efficiency of any image encryption process. The proposed scheme is well poised with entropy and histogram retainment even after data hiding and, hence, does not alter the efficiency of the image encryption process. 2. Embedding capacity is independent of the structural properties of the cover image: The \(MPSA\) scheme is completely independent of the structural properties of the cover image. This ensures a fixed embedding rate by the proposed \(MPSA\) scheme. 3. Real-time performance: The practical computation time incurred during the embedding and extraction phase is much less, proving its real-time applicability. 4. Less overhead at the receiver side: Machine learning models are explored in RDH to improve the efficiency of the recovery process. However, these trained models must be shared with the receiver before the image transmission. In comparison, the \(MPSA\) scheme introduced in this paper does not have such overheads since it is based on some statistical measures. Furthermore, the manuscript discusses literature works on RDH schemes in the encrypted images in Section 2, the \(MPSA\) approach in Section 3, an illustration of the work with an example for better understanding in Section 4, and the results obtained from the proposed work, with a detailed comparative study in Section 5. The manuscript is concluded in Section 6 with a few insights into future works.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 3 SatSINS  
The SatSINS is composed of three modules: \"Prior to reaching ice edge\", \"Approaching ice edge\"; and \"In ice\" (Figure 3). Each module is bounded with time, ice condition and ship location.  
The addressed ice conditions include SIE (to determine when the vessel will enter the ice-covered water), SIC, ice types, approximate SIT in the bay (to determine the arrival time to the final destination) and the local-scale sea ice variability near the coast (to ensure smooth arrival to the destination). Ice extent, concentration and variability are mainly remote sensing issues, while ice thickness is mostly a thermodynamics issue that can better be estimated using a thermodynamic model (unless thickness develops as a result of ice crushing and piling in highly dynamic regimes). [PERSON]'s Law of ice growth [20] was used in this study to estimate the land-fast ice thickness.  
The analysis of sea ice conditions took place at three phase that signifies different scales of information in relation to the location of the vessel relative to the ice cover. The first is when the vessel is 2-3 weeks away from the ice, the second is when the vessel is approaching the ice edge (about one  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CHINARE** & **Sensor** & \begin{tabular}{c} **Pixel Spacing** \\ **(m \(\times\) m)** \\ \end{tabular} & \begin{tabular}{c} **Acquisition Time** \\ **(Coordinated Universal Time, UTC)** \\ \end{tabular} & \begin{tabular}{c} **Polarization** \\ **Mode** \\ \end{tabular} & \begin{tabular}{c} **Beam** \\ **Mode** \\ \end{tabular} & \
\begin{tabular}{c} **Swath Width** \\ **(km \(\times\) km)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\(28^{\text{th}}\) (2011)} & \multirow{4}{*}{\(75\times 75\)} & \multicolumn{4}{c}{19:25} \\  & & & 22 November 2011 & & & \\  & & & 19:16 & & & \\  & & & 25 November 2011 & & & \\  & & & 18:39 & & & \\  & & & 26 November 2011 & & & \\  & & & 19:42 & & & \\  & & & 27 November 2011 & & & \\ \hline \multirow{4}{*}{\(30^{\text{th}}\) (2013)} & \multirow{4}{*}{\(50\times 50\)} & \multicolumn{4}{c}{22:45} \\  & & & 27 November 2013 & & & \\  & & & 15:14 & HH + HV &
\begin{tabular}{c} ScanSAR \\ Wide \\ \end{tabular} & \(500\times 500\) \\ \cline{1-1}  & & & 29 November 2013 & & & \\ \hline \hline \end{tabular}  
* * SAR imagery was not ordered in 2012 because sea ice concentration (SIC) was less than 70%.  
\end{table}
Table 1: Synthetic aperture radar (SAR) imagery used for ice navigation in Prydz Bay (2011–2013) *.  
week from reaching the final destination) and the third is when the vessel navigates through the pack ice. The analysis procedures are summarized in the flow chart (Figure 3). Each module represents one analysis phase. Data downloading and analysis were performed by the team members residing at BNU as land coordinators.
###### Abstract  
Many functional food ingredients improve intestinal barrier function through their colonic fermentation products short chain fatty acids (SCFAs). Effects of individual SCFAs have been well studied, but the effects of SCFA mixtures-colonic fermentation products have been rarely investigated. Therefore, this study used an EnteroMix semi-continuous model to simulate the colonic fermentation of three widely used food ingredients, polydextrose, lacitol and xylitol in vitro, and investigated the effects of their fermentation products on impaired colonic epithelial barrier function through a mucus-secreting human HT29-MTX-E12 cell model. Fermentation of polydextrose and lacitol produced mainly acetate, while fermentation of xylitol produced mainly butyrate and resulted in a much higher butyrate proportion. All fermentation products significantly improved intestinal barrier repairing as measured by increased transepithelial electrical resistance and decreased paracellular permeability. Among these, xylitol fermentation products exhibited better repairing effects than that of polydextrose and lacitol. Correlation analysis showed that the repairing effects were attribute to butyrate but not acetate or propionate, implying that in the fermentation products butyrate may play a major role in improving intestinal barrier function. Our results suggest that functional food ingredients that mainly produce butyrate during fermentation may be of more value for improving gut health related to chronic diseases.  
short-chain fatty acids (SCFAs); in vitro colonic fermentation; intestinal barrier function; lacitol; polydextrose; xylitol
## 2 Materials and Methods  
Firstly, the ingots of Cu\({}_{0.01}\)Bi\({}_{2}\)Te\({}_{2.7}\)Se\({}_{0.3}\) and Hf-doped Cu\({}_{0.01}\)Bi\({}_{2}\)Te\({}_{2.7}\)Se\({}_{0.3}\) (Cu\({}_{0.01}\)Bi\({}_{2.7}\)Hf\({}_{x}\)Te\({}_{2.7}\)Se\({}_{0.3}\), \(x\) = 0.05, 0.075, 0.1, 0.125) were synthesized by using a conventional melt-solidification process. According to the stoichiometric compositions, the starting raw elements (high purity Cu, Bi, Te, Se, and Hf) were weighed and vacuum sealed (\(\sim\)10\({}^{-3}\) Pa) in quartz tubes to prevent oxidation during the melting. The mixtures in the sealed quartz tubes were melted in a rocking furnace (rocking angle \(\sim\)120\({}^{\circ}\)) for 5 h at 1453 K and cooled to room temperature. The acquired ingots were pulverized into powders by using a ball mill for 10 min and screened with a 325 mesh (\(\sim\)44 \(\upmu\)m) sieve. The powders were compacted by SPS under 30 MPa for 2 min at 753 K in a dynamic vacuum. Dense polycrystalline bulks (11 mm in diameter and 13 mm in thickness) with a relative density \(\sim\)96% were fabricated.  
Phase formation behavior was confirmed by X-ray diffraction (XRD, Smartlab, Rigaku, Japan) with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5418 A). From the SPSed discs, bar-type samples (2 mm \(\times\) 2 mm \(\times\) 8 mm) for measurement of the electronic transport properties and cuboidal samples (10 mm \(\times\) 10 mm \(\times\) 1 mm) for measurement of thermal diffusivity (\(\lambda\)) were cut in a plane perpendicular and parallel to the SPS pressing direction, respectively. Thus, all three parameters (\(\sigma\), \(S\), and \(\lambda\)) were measured along the same direction. Temperature-dependent \(S\) and \(\sigma\) were measured from 300-480 K by using a TE property measurement system (ZEM-3, ULVAC-RICO, Japan). The Hall carrier concentration (\(n_{\text{H}}\)) and Hall mobility (\(\mu_{\text{H}}\)) were obtained by Hall effect measurement at room temperature in the van der Pauw configuration (magnetic field of 0.5 T, HMS-S5500, Ecopia, South Korea). The temperature-dependent \(\kappa\) values were estimated from density (\(\rho\)), specific heat capacity (\(C_{\text{p}}\)), and \(\lambda\). Between 300-410 K, the \(C_{\text{p}}\) was measured by using a physical properties measurement system (PPMS, Quantum Design, San Diego, CA, USA) and the Dulong-Petit fitting was used to estimate the \(C_{\text{p}}\) up to 480 K. The temperature-dependent \(\lambda\) values were measured under vacuum by using the laser-flash method (TC-1200 RH, ULVAC-RICO, Japan).
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.9 Mutual Authentication  
The GCS receives a login request message (\(\{HID_{M},V_{M},R_{1},T_{1}\}\)) from a mobile device and authenticates the mobile device by verifying \(V_{M}^{*}=V_{M}\). The drone receives an authentication request message (\(\{C_{j},V_{G},R_{2},R_{3},T_{2}\}\)) from the GCS and authenticates the GCS by verifying \(V_{G}^{*}=V_{G}\). The GCS receives an authentication message (\(\{R_{4},Auth_{D},T_{3}\}\)) from the drone and authenticates the drone by verifying \(Auth_{D}^{{}^{\prime}}=Auth_{D}\). Finally, the mobile device receives an authentication message (\(\{R_{5},R_{6},Auth_{GCS},T_{3},T_{4}\}\)) from the GCS and authenticates the GCS and drone by verifying \(Auth_{GCS}^{{}^{\prime}}=Auth_{GCS}\). Consequently, the proposed protocol ensures mutual authentication among mobile devices, drones, and the GCS.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 1 Introduction
A 72-h GNSS Toolkit 3 (Zhongke Microelectronics, Hangzhou, China) evaluation of satellite reception in Figure 1 revealed stable visibility of 5-12 BeiDou satellites in Nanjing (vs. 4-11 for GPS), with most sustaining >25\({}^{\circ}\) elevation angles. Comparable carrier-to-noise ratios (C/N\({}_{0}\)) of GPSs confirm BeiDou's positioning robustness in Asia-Pacific regions, establishing a physical-layer foundation for complex-terrain performance. Figure 1: Satellite reception test results (left: C/N\({}_{0}\); right: visible satellites; red: BeiDou; blue: GPS; green: GLONASS). Nevertheless, hardware-layer anti-interference capabilities necessitate complementary software-layer optimization. Although measurement accuracy continues to improve, most area measurement instruments employ static memory pre-allocation--a strategy ill-suited to dynamic sampling point variability, risking memory waste or stack overflow [14]. In weak signal environments with intermittent connectivity, this approach may also cause sampling queue fragmentation. This study innovatively integrates dynamic memory management with BeiDou positioning: a FreeRTOS-based linear incremental allocation strategy enabled on-demand resource distribution while preventing memory fragmentation. During signal interruptions, Kalman prediction automatically filled data gaps, establishing a dual \"hardware anti-interference + software fault-tolerance\" safeguard. Table 1 benchmarks this system against existing solutions. In summary, to address demands for high precision, portability, and stability in area measurement, this paper presents an instrument integrating a domestic BeiDou positioning chip and an STM32 microcontroller (STMicroelectronics, Paris, France). The system leverages BeiDou positioning, FreeRTOS real-time operation, and dynamic memory management to significantly enhance flexibility and resource efficiency. Through precise data processing and adaptive resource strategies, it maintains high reliability even in unstable signal environments, overcoming traditional GPS limitations in signal-degraded areas. Users obtain rapid area measurements by traversing target perimeters with the handheld device, offering technical support for land resource planning, agricultural management, and surveying applications.
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 2 BN Model for the Monitoring and the Error Recovery
### Integration of the Embedded System Constraints  
In the context of embedded systems, the constraints related to the system cannot be ignored. It is important to capture these system constraints and to integrate them at the monitor level for considering a realistic solution. The system constraints include resource constraints, timing constraints and the energy consumption ones. The resource constraints of the vibration example can be associated to the application as shown in Figure 5.  
We show in Figure 6 that depending on resource availability, we can choose to activate the stabilization for the application (case when resource \(>\)30%) or not if there are not enough resources available (case resource \(<\)30%).  
Figure 4: The probability to activate is the stabilization to maintain a good QoS of the application depending on the evidence on IMU and wind.  
Figure 5: BN for Vibration Including Resource Constraint.  
The timing constraints can be inserted also in the BN model, connected to the resource information. Actually, the resource needed for the application implementation can increase to provide an application with a better performance because parallel functions need generally more resource to operate in a parallel way. This can be modeled with the parameters of the BN model as shown in Figure 7. To operate at 250 MHz, we need more resources (\(>\)30%) than if we operate at 100 MHz.  
A BN model for HM specifications allows to take into account the uncertainty of the context of the mission, based on sensor information. Nevertheless, for a non-expert in BN models, the HM specifications for diagnosis or error mitigation are not so easy to elaborate. We propose in the next section to simplify the specifications of HM by catching the main error contexts, the main systems constraints and main mitigation solutions by means of simple FMEA-like tables. These tables enable to define the underlying BN models based on a pattern driven approach.