esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, a mesoscopic model has been established by using coupled CFD-DEM solver _SediFoam_ to reproduce the vacuum consolidation process of the slurry, in which the PVD (Prefabricated Vertical Drain) membrane, the slurry particles, and the tracers are described by the DEM, and the pore water is governed by the CFD method. Eight computational cases that cover a broad range of material parameters governing the PIV model tests on vacuum consolidation of the dredged slurry have been designed and investigated by the established CFD-DEM model. The representativeness of the PIV tracer is evaluated by comparing the statistic displacement of the tracer to that of the slurry particles. The following conclusions can be drawn:  
1. For tracers that are made by carbon powders of mass density 2000 kg/m\({}^{3}\) or by sand grains of mass density 2650 kg/m\({}^{3}\), the stable displacement ratio (\(D_{\rm t}/D_{\rm s}\)) between the tracer and the slurry particle varies between 0.935 and 1.137 if the tracer/slurry particle diameter ratio \(d_{\rm t}/d_{\rm s}\) and the initial void ratio \(e_{0}\) of the slurry varies in the ranges of 1.6-2.5 and 2-3, respectively. Thus, the representativeness of the tracer is acceptable if compared to the ideal value \(D_{\rm t}/D_{\rm s}=1\), since the largest difference is about 14%.
2. For tracers made by the carbon powders of diameter within 1.8 times the median size of the slurry particles, the representativeness of the PIV tracers can be significantly improved since the displacement ratio \(D_{\rm t}/D_{\rm s}\) varies in the small range of 0.935\(\sim\)1.05, i.e., the largest difference is only about 6.5%.
3. Generally speaking, the representatives of the PIV tracers on the displacements of the slurry particles can be improved for tracers of \(d_{\rm t}/d_{\rm s}\) closer to 1 and for slurries of lower initial water content, i.e., lower initial void ratio \(e_{0}\).  
Conceptualization, L.S. and Z.F.; methodology Y.Y., J.W. and L.S.; software, G.W.; validation, Z.F., Y.Y. and G.W.; formal analysis L.S.; writing--original draft preparation, Z.F.; writing--review and editing, L.S., Z.Y., K.Y. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural and Science Foundation of China, grant number 51879234.  
The authors declare no conflict of interest.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 3 Details of Selection of Parameters
### Parameters of the Highest PSNR of Decrypted Image  
As introduced in Section 2.4, after decrypting the image with only the encryption key, some pixels in _lED_ cannot be recovered to original value since their bit planes were replaced by auxiliary information. Hence, the PSNR of the decrypted image is mainly affected by these unrecoverable edged pixels. Thus, under a given embedding rate _GER_, the smaller the amount of auxiliary information is, the fewer edged pixels need to be replaced, and the higher the PSNR is. Therefore, the optimal combination (_m_*, _T_*) is selected as Equation (18) to obtain the highest PSNR of the decrypted image under a given embedding rate.  
\[\left\{\begin{array}{c}(m^{*},T^{*})=\arg\ min(l_{clm})\\ s.t.\ PER\geq GER\end{array}\right. \tag{18}\]
## 4 Results and Discussion
### Baselines and Implementation Details  
Regarding the choice of baseline, we compare GDUI against both the unconditional diffusion model and the diffusion model guided by a classifier trained on ground truth [5]. When comparing with baselines, we use the same backbone and hyperparameters, ensuring a fair comparison. As the training and sampling steps of diffusion models are typically positively correlated with the sampling results [13], we keep the training and sampling steps consistent at 500 steps when comparing. The details of the initial clustering implementation are as follows. During the representation learning phase of clustering, we employ MoCo-v2 [52] and use ResNet34 [53] as the backbone for feature learning in clustering. Additionally, both the clustering head and classification head consist of two fully connected layers.
## 2 Methods Used for Integrated Monitoring and Management of Diet-Related Diseases
### Methods for the Development of the Tailored RHMS
#### 2.3.1 Smart Monitoring Environment
* _Medical Blackbox_ (designed and developed as part of the RO-SmartAgeing project) [102]:
* Functionality: integrates diverse sensors into an Arduino-Mega-based platform for comprehensive health monitoring;
* Key sensors:
* SpO2 sensor: measures pulse rate and oxygen saturation, vital for respiratory health monitoring;
* Alcohol detection sensor: high sensitivity to alcohol vapours, relevant for addiction rehabilitation and monitoring;
* ECG sensor: facilitates clinical-grade electrocardiogram measurements for cardiovascular health assessment;
* Color sensor: detects colour variations in urine strips, aiding in kidney function assessment;
* Temperature Sensor: enables accurate and remote temperature monitoring. * Healthcare application: Essential for patients with chronic respiratory, cardiac, or renal conditions, allowing healthcare professionals to remotely assess vital parameters. Enables timely intervention and reduces the need for frequent clinic visits. * _Ambiental Blackbox_ (designed and developed as part of the RO-SmartAgeing project) [103]:
* Functionality: integrates various sensors into an Arduino-Uno-based platform for environmental monitoring;
* Key sensors:
* Motion sensor: detects movement changes, valuable for patient activity monitoring and fall detection;
* Gas sensor: identifies combustible gases, smoke, and volatile organic compounds, crucial for indoor air quality assessment;
* Temperature and humidity sensor: monitors environmental conditions, aiding in patient comfort and health;
* Light sensor: measures ambient light levels, supporting circadian rhythm assessment;
* Air quality sensor: detects CO\({}_{2}\) levels, providing insights into indoor air quality. * Healthcare application: Ensures optimal conditions for patients. Monitors changes in patient activity and environmental parameters, contributing to enhanced patient well-being. * _Gaitband_ (designed and developed as part of the RO-SmartAgeing project) [104]:
* Functionality: integrates an accelerometer, gyroscope, and Wi-Fi module for real-time posture and gait analysis; it captures movement data, enabling precise gait analysis;
* Healthcare application: Enables continuous monitoring of posture and gait, providing valuable insights for treatment adjustments and personalized care plans. Remote accessibility optimizes patient care, especially in telehealth scenarios. Currently, the Withings BPM Core, Withings Move ECG, Withings Body+, and Withings Sleep Analyzer are integrated into the RO-SmartAgeing system, NeuroPredict Platform, and HepatoConect system; Withings Thermo, Fitbit Charge 5, Glucometer CareSens, Medical Blackbox, Ambiental Blackbox, and Gaitband are integrated into the RO-SmartAgeing system and NeuroPredict Platform; Withings Body Scan, MySignals EMG, Moodmetric Smart Ring, and Sensoria Smart Socks are integrated into NeuroPredict Platform.
## 3 AE Location Method and Verification of Its Accuracy  
AE location technology is the first step of exploring the microcrack propagation and forms the premise for studying the macroscopic fracture and stability of the rock. The location of a microfracture source is mainly calculated according to the time difference in elastic wave arrival at the sensors in different positions. Currently, AE location methods mainly include the least squares method, simplex method, Geiger algorithm, and joint inversion method [19]. Here, the AE location was calculated by applying the Simplex algorithm. The drop-rail test was conducted to verify the accuracy of the Simplex algorithm. By detecting the onset time of AE signals and performing the classification and location calculation on the AE signals, the drop-rail position was compared with the actual location result to analyse the error incurred thereby. Ten drop-rail operations were performed at preset drop-rail positions to extract the onset time of drop-rail signals. Furthermore, the location was calculated by using the Simplex algorithm. According to the comparison between the result of the AE location and the actual location, it can be attained that more  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Test** \\ **Plan** \\ \end{tabular} } & \multirow{2}{*}{**Model Number**} & \multirow{2}{*}{**P2/MPa**} & \multirow{2}{*}{**P1/MPa**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{\
\begin{tabular}{c} **Mass of** \\ **Blasting** \\ **Operations** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Lateral** \\ **Pressure** \\ **Coefficient** \\ \end{tabular} } \\ \hline
1 & N1–1, N1–2 and N1–3 & 16 & 32 & 6 & 1 & 2.0 \\
2 & N2–1, N2–2 and N2–3 & 16 & 24 & 6 & 1 & 1.5 \\
3 & N3–1, N3–2 and N3–3 & 16 & 16 & 6 & 1 & 1.0 \\
4 & N4–1, N4–2 and N4–3 & 16 & 8 & 6 & 1 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental blasting schemes.  
Figure 2: Test model and loading system.  
than 90% of location errors are within 10 mm and the applied location algorithm satisfies the test requirement.
## 3 Results and Discussion  
### Proximate Components and Cooking Losses  
The proximate components and cooking loss of horse loin cooked using the sous-vide method at different cooking durations and temperatures are summarized in Table 1. The proximate composition of conventionally boiled meat (control group) and sous vide-cooked meat included moisture content ranging from 62.64 to 67.25%, crude protein from 26.38 to 30.72%, crude fat from 6.14 to 8.49%, ash content from 1.04 to 1.23%, and cooking loss from 22.88 to 37.58%. No significant differences were observed between the control and treatment groups in terms of moisture, crude fat, or ash content. The treatment groups had significantly higher crude protein content than that in the control group (26.38%), except for the 65 \({}^{\circ}\)C for 12 h treatment group (28.14%), and the protein composition of the treatment groups was influenced by the cooking temperature (\(p\) = 0.0099). The study revealed that the crude protein content was 19.8-21.17%, slightly higher than the values reported by [PERSON] et al. [31], but similar to the 28.1% collected by the United States Department of Agriculture [32]. The cooking loss in all treatment groups was significantly higher than that in the control group (22.88%), and the cooking loss in the treatment groups was affected by the sous-vide cooking temperature (\(p\) = 0.0003). As the temperature increased, there was a corresponding increase in weight loss. The results of this study were generally consistent with those previously reported in studies on beef [8; 33], lamb [8], and pork [34]. Myofibrillar proteins retain most of the moisture, and according to [PERSON] et al. [8], these proteins denature and shrink as the temperature rises between 40 \({}^{\circ}\)C and 90 \({}^{\circ}\)C. Additionally, collagen will shrink at temperatures ranging from 56 to 62 \({}^{\circ}\)C, leading to an increase in cooking loss.
## 3 Experimental Results
### Event-Related Potential (ERP) Analysis
 } & \multirow{4}{*}{Simple} & Pre- & & 2.826 \(\pm\) 1.16 & 2.147 \(\pm\) 1.45 & 2.972 \(\pm\) 2.41 & 173 \(\pm\) 14 & 167 \(\pm\) 10 & 174 \(\pm\) 13 \\  & & Interruption & 4.320 \(\pm\) 2.69 & 2.675 \(\pm\) 1.47 & 2.650 \(\pm\) 1.69 & 178 \(\pm\) 16 & 175 \(\pm\) 14 & 175 \(\pm\) 14 \\  & & Recovery & 4.333 \(\pm\) 1.87 & 2.792 \(\pm\) 1.89 & 3.366 \(\pm\) 2.28 & 176 \(\pm\) 15 & 176 \(\pm\) 10 & 178 \(\pm\) 15 \\ \cline{2-9}  & \multirow{4}{*}{Complex} & Pre- & & 4.216 \(\pm\) 2.92 & 2.769 \(\pm\) 2.58 & 2.106 \(\pm\) 1.16 & 178 \(\pm\) 15 & 178 \(\pm\) 14 & 175 \(\pm\) 16 \\ \cline{2-9}  & \multirow{4}{*}{Complex} & Interruption & & 5.304 \(\pm\) 3.88 & 2.947 \(\pm\) 1.65 & 2.765 \(\pm\) 1.67 & 178 \(\pm\) 15 & 181 \(\pm\) 14 & 181 \(\pm\) 11 \\ \cline{2-9}  & & Recovery & 5.812 \(\pm\) 3.93 & 3.392 \(\pm\) 2.99 & 2.765 \(\pm\) 1.25 & 182 \(\pm\) 12 & 181 \(\pm\) 15 & 181 \(\pm\) 15 \\ \cline{2-9}  & \multirow{4}{*}{Pause} & Pre- & & 4.316 \(\pm\) 2.99 & 2.046 \(\pm\) 2.23 & 2.037 \(\pm\) 1.98 & 172 \(\pm\) 15 & 173 \(\pm\) 15 & 173 \(\pm\) 13 \\ \cline{2-9}  & & Recovery & 3.948 \(\pm\) 1.97 & 2.273 \(\pm\) 1.88 & 2.038 \(\pm\) 1 latency, an interaction effect between fatigue state and trial type was observed in the frontal region (\(F(2,429)=0.911,p=0.033,\)\(\eta_{p}^{2}=0.014\)), but not in the central (\(F(2,445)=2813,p=0.114\)) or parietal regions (\(F(2,429)=1.257,p=0.286\)). Analysis indicated that differences in peak P200 latency across trial types were significant only in the fatigued state.
## 4 Results
### Simulation Results as a Function of Time  
In the previous section, the figures provided an aggregation of the simulation results observed in time for each value of the number of hops for communicating pairs of nodes one to four hops apart, respectively. Those results are interesting, showing the evolution of the different metrics with the increasing distance between the communicating nodes. The results presented in this section provide a detailed description of the evolution of the metrics' values in time for a communicating pair of nodes located four hops apart. Figure 11 shows these metrics values for the bidirectional videoconference application (A\({}_{Vd}\)BI). It can be seen in Figure 11a,b that even if the source and destination nodes are estimated to be fourr hops apart, TFD-OLSR uses, more often than CBL, a higher number of hops to reach the destination. Though the mean node-to-relay distance (Figure 8), which is higher for TFD-OLSR than for CBL-OLSR, suggests fewer IP hops with TFD-OLSR to reach a given destination, the results show that CBL-OLSR achieves a better organization to optimize this metric. Concerning the end-to-end delay, Figure 11c,d show that the CBL-OLSR results are, most of the time, better than those of TFD-OLSR, probably due to a lower number of hops in the communications.  
Figure 11: Mean number of hops, end-to-end delay, and percentage of received packets in medium (S2) and high (S3) node density scenarios for bidirectional videoconference and two communicating nodes spaced 4 hops apart.
## 5 Inhabitants' Activity Tracking
### Activity Recognition  
\"The process of matching predefined activity classes to the data received from sensors is called activity recognition\" [14], which is a supervised learning approach. The recognition algorithm could be a support vector machine (SVM), decision tree (DT), instance-based learner (IB) or a neural network (NN) [89]. The activity recognition process can be roughly characterized by following steps [82]:* Selection and installation of suitable sensors for monitoring a user's activity together with changes in the environment.
* Collecting, saving, and processing perceived information through data analysis techniques and/or knowledge representation formalisms at proper levels of abstraction.
* Creating computational activity models in a manner that enable software systems/agents to conduct reasoning and manipulation.
* Selection or development of reasoning algorithms to infer activities from sensor data.  
Considering the sensor types for activity monitoring, activity recognition can be divided to two categories [82]:  
* Vision-based activity recognition: It is based on visual sensors deployment, like video cameras, for monitoring a person's behaviour within the environment. The generated sensory data are video sequences or digitized visual data. The methods in this category use computer vision techniques, including feature extraction, structural modelling, action extraction, movement tracking and segmentation, for analyzing visual observations for pattern recognition.
* Sensor-based activity recognition: It is based on the use of sensor technologies to monitor activities. The generated sensory data are mainly the time series of state changes and/or various parameter values that are usually processed through data fusion, probabilistic, or statistical analysis approaches, and formal knowledge technologies for activity recognition.
## 5 Propeller Design for HAPS Operation
### Recent Development on Propeller Design Methodology
A clear performance gap emerges between earlier approaches that rely on low-order models, such as classical BEMT and VLM, which generally achieve efficiencies below 75%, and more recent optimization frameworks that incorporate high-fidelity solvers (e.g., 3D RANS) and advanced techniques such as Bayesian or adjoint-based optimization. These modern methods often yield efficiencies above 80%, with some exceeding 85%, reflecting the importance of accurate aerodynamic modeling and integrated optimization workflows. Interestingly, high efficiency is not exclusive to large-diameter designs. While configurations with \(D>6\) m (e.g., Gutierrez [84], Jiao [85], Zheng [69]) benefit from reduced induced losses, several compact propellers--notably Yang (1.4 m), Yao and Xu [45; 149] (2.0 m)--also achieve 78-82% efficiency, thanks to refined design methods and airfoil tailoring. This underlines the potential of surrogate-assisted and multi-level strategies in overcoming geometric limitations. Overall, the most effective optimization pipelines appear to be those combining physics-based models with data-driven tools, striking a balance between aerodynamic fidelity, computational cost, and adaptability to mission-specific constraints. Given the variability in operating conditions and performance targets, direct comparison remains non-trivial; however, general trends clearly support the use of hybrid and high-fidelity frameworks in future HAPS propeller design. The comparative analysis of optimization techniques for high-altitude propellers highlights a clear evolution toward robust and adaptive methodologies. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Altitude (km)** & **Wind Speed (m/s)** & **rpm** & **Shaft Power** & **Max Thrust** & **Efficiency** \\ \hline
20 & 25 & +43.54\% & +42.35\% & +36.47\% & \(-\)4.06\% \\
15 & 15 & +112.17\% & +112.11\% & +75.06\% & \(-\)17.48\% \\
10 & 10 & +139.89\% & +140.66\% &
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 3 Preliminaries
### Elliptic Curve Cryptography  
Elliptic curve cryptography (ECC) [23] is a public key encryption method that applies the mathematical structure of elliptic curves. An elliptic curve, \(E\), is defined as \(E_{q}(a,b)\): \(y^{2}=x^{3}+ax+b(modp)\), where \(a,b\in F_{p}\), and \(p\) and \(q\) are large prime, and \(4a^{3}+27b^{2}\
eq 0\). Then, we can select an additive cyclic elliptic curve group, \(G\), with the order \(q\) and generator \(P\). The properties of group \(G\) are listed below.  
* Elliptic Curve Discrete Logarithm Problem: Given two random points, \(A,B\in G\), calculating a random value \(k\) satisfying \(A=k\cdot B\) in polynomial time is infeasible.
* Elliptic Curve Computational Diffie-Hellman (ECCDH) Problem: Given three random points, \(A,M,N\in G\), calculating \(mnA\) satisfying \(M=mA\) and \(N=nA\) in polynomial time is infeasible.
# Online Learning Based Underwater Robotic Thruster Fault Detection
## 6 Experimental Results and Analysis
### Quantitative Analysis of Experimental Results  
In order to quantitatively analyze performance of the proposed method, Mean Absolute Error (MAE) of the propeller speed prediction model was calculated. As shown in Table 4, MAE of the proposed online learning-based model is 10.54, 1.70 and 2.91 in the sea trial data simulation, pool test fault detection experiment and sea trial fault detection experiment, respectively. By contrast, MAE of the fixed model is 16.98, 5.53 and 258.23  
Figure 19: Model residuals of the online learning-based fault detection method during sea trial of the portable AUV.  
respectively. MAE of the proposed online learning-based model is at least 37.9% lower than the fixed model.  
It is worth noting that MAE of the new-built online learning-based model is 0.25, 0.44 and 0.24 in the sea trial data simulation, pool test fault detection experiment and sea trial fault detection experiment respectively, which illustrates the effectiveness of the proposed online modeling algorithm.
###### Abstract  
Power consumption management is crucial to maintaining the reliable operation of power grids, especially in the context of the decarbonization of the electric power industry. Managing power consumption of industrial enterprises by personnel proved ineffective, which required the development and implementation of automatic energy consumption management systems. Optimization of power consumption behavior requires comprehensive and reliable information on the parameters of the technological processes of an industrial enterprise. The paper explores the specific features of non-stationary conditions of output production and assesses the potential for power consumption management under these conditions. The analysis of power consumption modes was carried out based on the consideration of random factors determined by both internal and external circumstances, subject to the fulfillment of the production plan. This made it possible to increase the efficiency of power consumption in mechanical engineering production by taking into account the uncertainty of seasonal and technological fluctuations by 15-20%, subject to the fulfillment of the production plan. This study presents a justification for utilizing the theory of level-crossings of random processes to enhance the reliability of input information. The need to analyze the specific features of technological processes based on the probabilistic structure and random functions is proven. This is justified because it becomes possible to fulfill the production plan with technological fluctuations in productivity and, accordingly, power consumption, which exceeds the nominal values by more than 5%. In addition, the emission characteristics are clear, easy to measure, and allow the transition from analog to digital information presentation. The algorithm and methods developed to analyze the power consumption patterns of industrial enterprises can be used to develop automatic power consumption management systems.  
industrial enterprise; technological process; analysis of power consumption behavior; power consumption management; theory of level-crossings of random processes; random function +
Footnote †: journal: _Algorithms_  
**2025**, 18, 49**