esa-sceva/satcom-chunk-collection huggingface dataset:
# Event Detection of Muscle Activation Using an Electromyogram
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Estimation Accuracy (Mean \(\pm\) Std in Msec.)**} \\ \cline{2-7}
**Subject** & \multicolumn{3}{c}{**Onset**} & \multicolumn{3}{c}{**Offset**} \\ \cline{2-7}  & **Proposed** & **eDTA**[17] & **nDTM**[14] & **Proposed** & **eDTA**[17] & **nDTM**[14] \\ \hline
1 & 13.02 \(\pm\) 11.17 & 18.77 \(\pm\) 15.00 & 87.46 \(\pm\) 71.12 & 23.98 \(\pm\) 22.49 & 74.31 \(\pm\) 64.96 & 275.46 \(\pm\) 83.76 \\ \hline
2 & 23.71 \(\pm\) 15.78 & 32.60 \(\pm\) 20.90 & 66.20 \(\pm\) 15.49 & 33.41 \(\pm\) 22.01 & 40.81 \(\pm\) 38.55 & 58.39 \(\pm\) 49.49 \\ \hline
3 & 35.93 \(\pm\) 22.53 & 27.83 \(\pm\) 25.59 & 62.93 \(\pm\) 16.46 & 26.77 \(\pm\) 35.98 & 174.77 \(\pm\) 72.61 & 71.71 \(\pm\) 62.71 \\ \hline
4 & 37.27 \(\pm\) 20.49 & 36.99 \(\pm\) 66.03 & 82.60 \(\pm\) 30.41 & 37.51 \(\pm\) 66.60 & 91.96 \(\pm\) 120.47 & 134.93 \(\pm\) 103.09 \\ \hline
5 & 58.17 \(\pm\) 52.12 & 126.24 \(\pm\) 138.60 & 85.03 \(\pm\) 248.30 & 222.51 \(\pm\) 169.26 & 547.49 \(\pm\) 397.46 & 243.96 \(\pm\) 121.18 \\ \hline
6 & 67.57 \(\pm\) 67.80 & 78.70 \(\pm\) 433.05 & 66.96 \(\pm\) 150.99 & 198.66 \(\pm\) 88.37 & 214.31 \(\pm\) 419.42 & 259.33 \(\pm\) 213.06 \\ \hline
7 & 78.84 \(\pm\) 115.66 & 112.82 \(\pm\) 182.47 & 62.36 \(\pm\) 132.80 & 272.29 \(\pm\) 111.78 & 493.12 \(\pm\) 571.60 & 477.45 \(\pm\) 537.98 \\ \hline
8 & 81.00 \(\pm\) 217.04 & 157.54 \(\pm\) 264.78 & 307.89 \(\pm\) 459.48 & 289.27 \(\pm\) 203.44 & 485.15 \(\pm\) 461.15 & 321.89 \(\pm\) 272.70 \\ \hline
9 & 90.33 \(\pm\) 82.46 & 189.53 \(\pm\) 151.18 & 230.72 \(\pm\) 315.83 & 289.51 \(\pm\) 134.38 & 364.49 \(\pm\) 192.19 & 344.83 \(\pm\) 151.68 \\ \hline
10 & 134.84 \(\pm\) 130.39 & 179.92 \(\pm\) 151.50 & 176.94 \(\pm\) 192.35 & 323.46 \(\pm\) 203.20 & 496.47 \(\pm\) 182.11 & 476.66 \(\pm\) 123.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the proposed method and two conventional methods for the 10 subjects. Figure 9: The performance comparison of the proposed algorithm and two conventional methods. (**a**) Results of onset detection for the best case (subject #1). (**b**) Results of offset detection for the best case (subject #1). (**c**) Results of onset detection for the worst case (subject #10). (**d**) Results of offset detection for the worst case (subject #10).
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 8 Experimental Results
### Overall Performance Including BLE Communication Overhead
In addition, we also evaluated the computational complexity of our application in the case of revealing a theoretical number of 500 attributes (which is a highly inflated value) from a cryptographic credential: see Figure 11. The required verification time, including the BLE communication overhead, takes up to 3 s for most of the tested smartphones. The measurements therefore show that our application can be practical even in the case of a high number of attributes contained in the cryptographic credentials.
## 6 Algorithms and Frameworks in Federated Learning
### Federated Averaging (FedAvg)  
Federated Averaging, or FedAvg, addresses the need for effective decentralized machine learning [5]. As data become more distributed across devices, centralizing them for training becomes impractical due to bandwidth and privacy issues. FedAvg provides a solution by allowing local model training on each device, while synchronizing through a global shared model, denoted as \(w_{t}\), where \(t\) indicates the communication round. Each client uses methods like Stochastic Gradient Descent (SGD) for local updates, and these updates are averaged at the server to iteratively improve the global model. Recent evaluations of FL algorithms highlight FedAvg's performance. [PERSON] et al. utilized the MNIST dataset, a standard for digit classification, to benchmark algorithms in a client-server setup [82]. Their comparative analysis, focusing on the communication efficiency and convergence, demonstrated FedAvg's strong performance, particularly with client communication limited to 10,000, under IID data conditions. This empirical evidence supports FedAvg's effectiveness in various FL scenarios.
## 3 Component Selection and Criteria  
In general, the critical factor in the aeronautical field is the weight, as it plays an influential gyroscopic role in the construction of all components. All primary systems, such as propulsive, aerodynamic, and flight-control systems, depend on the aircraft weight. Hence, the component selection of an aircraft must be considered from the perspective of weight. Similarly, this study also determined the components of the octocopter by relying on weight factors. Initially, a standard trial-and-error approach was implemented, in which fixed-mass procedures were followed. In a fixed-mass methodology, two types of masses play a vital role in UAV component selection: the overall mass of the UAV and the payload mass. The payload weight-based approach was chosen in this work, owing to the clear application plan for the UAV. In this targeted application, the essential supporting parameters (such as the storage tank capacity, speed of the fluid, design of the nozzle sprayer, center of gravity variations, endurance of the spray process, and recycling process) are known, and guided the determination of the payload weight. Generally, the payloadweight of the UAV is comprised of secondary and primary categories, as expressed in Equation (1).  
\[\mathrm{W_{PI}}=\mathrm{W_{Primary\ Payload}}+\mathrm{W_{Secondary\ Payload}} \tag{1}\]  
In this work, the storage capacity of the sea water container is assumed to have a weight of 1000 g. In the secondary payload category, the nozzle sprayer and its holding devices contribute significantly. The weight of the nozzle sprayer was estimated to be 50 g. A glass fiber-reinforced polymer (GFRP)-based composite material is suggested for the construction of a C-D duct-based storage tank so that the approximate weight of the tank is 200 g. Thus, the payload weight is estimated as 1.25 kg.
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 1 Introduction
Through field tests, [PERSON] [23], [PERSON] [24], and [PERSON] [25] monitored geogrid deformation, pore water pressure, the settlement of piles and soil between piles, and the lateral displacement of foundations. On this basis, the load transfer mechanism and settlement deformation law were analyzed. The above research on the load transfer law mainly analyzed its influence on the load transfer by changing a single variable. Due to the single evaluation index, it was impossible to clarify the embankment load transfer law under the influence of various factors, or to determine the contribution of the soil arching effect and the membrane effect to the load transfer. From the above analyses, it can be seen that the research on the load transfer mechanism of pile-supported embankments still has the following problems: At present, the commonly used load transfer calculation method is, first, to use the relevant theory of the soil arching effect to calculate the load acting on the horizontal reinforcement. Then, the reinforced body is used as the research object for carrying out the force analysis and the deformation calculation. However, the soil arching effect and the membrane effect are interactive. The deformation of the reinforced material depends on the size of the load; that is, the degree of the soil arching effect. However, the existence of reinforced materials will affect the magnitude of differential settlement, and then affect the soil arching effect. Therefore, it is of great significance to clarify the contribution of the soil arching effect and the membrane effect to the load transfer mechanism. In the study of the load transfer law, the main research method at present is single-factor analysis of each influencing factor, without considering the interaction between various factors. Response surface optimization design can determine the optimal design from a range of design variables under given constraints. Therefore, based on the single-factor test, this paper first clarifies the contribution of the soil arching effect and the membrane effect to the load transfer, as well as the degree of the load transfer under the combined action of the soil arching effect and the membrane effect. The sensitivity of each influencing factor was evaluated. Additionally, based on the response surface optimization test, the multiple regression equation of the influencing factors and the evaluation index was established, the interaction relationship between the embankment filling properties and the grid strength was analyzed, and the optimal parameter combination was solved.
###### Abstract  
Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle-road collaboration scenarios.  
Keywords:expressway; ETC data; over-the-horizon; vehicle detection of potential threats; intelligent driving +
Footnote †: journal: Applied Sciences
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 5 Performance Parameters
### Throughput  
The throughput of MRHOF is reduced drastically under attacks for Scenario I, as observed from Figure 10. This is because nodes select the malicious nodes as parents, and the affected normal nodes have a throughput of 0 kbps because their packets do not reach the sink node. This shows that a part of the network is paralyzed for sending packets. Moreover, in MRHOF, there is no defence mechanism against attacks, and route is stablished without considering any of the trust metrics in contrast with the trustworthy route establishment in SMTrust, thus enhancing the throughput in the SMTrust-based RPL protocol. SMTrust, SecTrust, and MRTS offer higher throughput because of the embedded attack detection mechanisms. In addition, the nodes' throughput remains greater than zero, thus increasing the throughput value overall. The average throughput value of SMTrust outperforms SecTrust in Scenario I, with a percentage increase of 32% and 80.6% under Blackhole and Rank attacks, respectively. The logic behind improved results of SMTrust is that it employs six different trust metrics as compared to other works, such as, success rate, recommended trust, historical observations, energy level, and location and link stability.  
Figure 9: Comparison of Packet Loss Rate (%).  
Moreover, the difference in the quantification, and weighted computation of the overall trust index, as explained in Section 3, logically justifies its better performance.  
In comparison with MRTS, SMTrust shows negligibly lesser throughput, a difference of 0.16 kbps, under Blackhole attack, and a higher packet loss rate, a difference of 4.3%, under Rank attack. This is because MRTS uses an IDS approach for attacks detection and isolation, as well as a hardware security chip embedded in each node. This is justified by the fact that SMTrust employs simplified equations to compute trust metrics and trust index, thus avoiding integration of IDS or hardware separately. This means that there is no requirement of hardware for embedding SMTrust in an IoT system. Similarly, SMTrust significantly outperforms MRHOF under attacks in Scenario II and III, as shown in Figure 10. Thus, indicating the importance of trust model and crucial selection of trust metrics for trustworthy topology creation and defence against routing attacks.
## 5 Performance Evaluation
### Experiments' Scenario and Parameters  
To evaluate the performances of our system in the same conditions as most of the ZigBee systems, we conducted the tests in an indoor environment. In these experiments, the network is composed of 13 ZigBee sensor nodes. We used only FFD devices that can act as routers or end-devices. This is due to the the fact that the topology can change at any time, and end-devices may become routers in a new topology. The scenario is to deploy our network inside the building at the University of Rabat,The system is programmed in such a way that all sensor nodes measure data every 2 s and transmit these toward the coordinator.  
The end-device nodes sense and transmit data to the parent node; meanwhile, the routers can receive data from neighbors and transmit these toward the coordinator using the links defined in the topology. Figure 7 shows the deployment of nodes over the building and also shows the links between nodes in one of the topologies constructed during this experiment.  
The detailed parameters of the experiments and their corresponding values are summarized in Table 3.  
The coordinator sends received data from the network to a computer that represents the control system. Figure 8 shows the coordinator area. The connection between coordinator and the control system computer is wired. This can be explained by two reasons:  
1. The wireless connection will consume much energy because all of the network's data will be sent through this connection.
2. To ensure that collected data coming from nodes arrive safely at the control system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experiment Parameters** & **Values** \\ \hline Deployment distribution & Unique \\ Deployment area & 200 m \(\times\) 40 m \\ Number of FEDs & 13 \\ Number of RFDs & 0 \\ Number of nodes & 13 \\ Initial energy & 1 (J) \\ Number of coordinators & 1 \\ Maximum indoor distance between adjacent nodes & 24 m \\ Sensing interval & 2 s \\ Packets size & 127 bits \\ Time \(\tau\) & 2 min \\ Time \(\tau^{\prime}\) & 4 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters. FFD, full function devices; RFD, reduced function devices.  
Figure 7: Sensor network deployment and topology over the building of the university.
## 3 Test System
### Test Language and Tools  
TTCN-3 is a testing framework that has been used for testing many communication protocols for decades. It defines the TTCN-3 test scripting language, architecture of a test system and the interfaces between the components. Figure 6 depicts a typical TTCN-based test system architecture.  
In a TTCN-3 framework, the TTCN-3 Executable (TE), compiled from a testing script written in TTCN-3 language, interacts with supporting components via TCI (Test Control Interface) or TRI (Test Runtime Interface) to control the testing procedure. The components above TCI in the figure are self-explanatory. Below, the Platform Adapterallows TE to utilize services from the operating system, such as the timer. The System Under Test (SUT) Adapter (SA) actually is involved in the message exchange between the test system and the SUT. The separation of testing logic (TE) and communication implementation (SA) makes the testing architecture flexible and extensible for various situations.  
Most commercial and non-commercial TTCN-3 tools implement most components and interfaces of the testing framework. The user has to write a testing logic in TTCN-3 language. Although the tools support some SAs, most cases require the user to implement SA to customize the interaction with SUT for the specific communication technology. We used a commercial TTCN-3 tool, and we developed the SAs for Ethernet, PLC, 61850, the codec for EXI encoding C++, as well as the testing scripts in TTCN-3.
## 4 Mathematical Model
### Propeller  
In this paper, the propeller model was written as a regression model based on the static thrust test data of the sample propeller used in the actual experiment in order to minimize the error of the propeller model for effective verification of proposed electric propulsion system model. The propeller model comprises formulas for thrust and torque calculation and submodels for thrust and torque coefficients. In this study, the thrust and torque coefficient model was developed as a regression model based on the momentum theory and actual performance test, and only the static thrust condition was considered. This was done in order to validate the proposed electric propulsion system model as correctly as possible and it is based on the static thrust data that were obtained from the actual experiment for commercial propeller samples.  
According to the standard definition of propeller aerodynamic coefficients, the propeller thrust and torque can be calculated using Equations (5) and (6) [16], where \(T_{prop}\) and \(Q_{prop}\) are the propeller thrust and torque, respectively; \(C_{T}\) and \(C_{Q}\) are the thrust and torque coefficients, respectively; \(\rho_{air}\) is the air density; \(n\) is the propeller speed in revolutions per second; and, \(D\) is the diameter of the propeller.  
\[T_{prop}=C_{T}\cdot\rho_{air}\cdot n^{2}\cdot D^{4} \tag{5}\]  
\[Q_{prop}=C_{Q}\cdot\rho_{air}\cdot n^{2}\cdot D^{5} \tag{6}\]  
The aerodynamic coefficient of the propeller is expressed as a function of geometrical features, Reynolds number, tip speed, and advance ratio. The geometrical features can be represented as the diameter of the propeller and the pitch ratio. The Reynolds number and tip speed are determined while using the diameter and speed of the propeller, and the advance ratio is not considered in the static thrust condition. Thus, the aerodynamic coefficients of the propeller under static thrust conditions are modeled as a function of diameter, rotational speed, and pitch ratio, as shown in Equation (7), in which \(C_{T_{o}}\) and \(C_{Q_{o}}\) are the static thrust and static torque coefficients, respectively; and, \(k\), \(a\), \(b\), \(c\) are the coefficients of the regression model.  
\[\left[C_{T_{o}},C_{Q_{o}}\right]\approx k\cdot n^{a}\cdot D^{b}\cdot\left(P/D \right)^{c} \tag{7}\]
## 4 Outline of the Approach
### Process of Concept Alignment for Interoperability in Heterogeneous Sources
#### 4.5.3 Two-Tier Matching Strategy  
The process of concept alignment is based on a two-tier matching strategy that consists in two phases: element level matching and structure level matching.  
The purpose of the process of concept alignment is to determine the mappings between two concept schemes at the granularity of concept elements. This means finding the alignment _A'_ given the pair of schemas \(X\) and \(U\) where \(X\) can be any concept scheme constructed from a profile supplier/consumer document and \(U\) will always be the ubiquitous user model concept scheme (as explained in 4.5.1). We want to determine the all the semantic relations \(R\) from the triplet \(<\)\(c_{s}\),\(c_{t}\),\(R\)\(>\) from all concepts in \(X\) to all concepts in \(U\) as shown in Equation (3):  
\[R\big{(}c_{s},c_{t}\big{)}\forall c_{s}\in C_{s},\forall c_{t}\in C_{T} \tag{3}\]  
We are considering two types of links between two concepts of different concept schemes based on SKOS mapping properties that establish associative, and interchangeability mappings. A hierarchical mapping link between two concepts, established by the properties skos:broadMatch and skos:narrowMatch are not useful for this work since interchange and reuse is intended.The property skos:relatedMatch is used to state an associative mapping link between two concepts.  
The most important mappings, for our purposes, are interchangeability mappings. The skos:closeMatch property states that the two linked concepts are \"sufficiently similar that they can be used interchangeably in some information retrieval applications\" [43] skos:exactMatch links two concepts, \"indicating a high degree of confidence that the concepts can be used interchangeably across a wide range of information retrieval applications skos:exactMatch is a transitive property, and is a sub-property of skos:closeMatch\" [43].  
In the element level matching step, the concepts are directly compared to each other without considering the hierarchy structure and values. The goal of element level matching is given concept \(c_{s}\) of the source concept scheme \(X_{s}\), finding the best concept label c\({}_{tb}\) from a set of concept candidates for alignment in the target concept scheme \(X_{T}\).  
In the structure level matching step, the context of the source and target concepts (neighbors of the concepts in the hierarchy) are considered. The ultimate goal of this process is determine the one-to-one mappings between the concept \(c_{s}\) of the source concept scheme \(X_{s}\) and the best concept c\({}_{tb}\) from the set of labels \(C_{t}\) of the target concept scheme \(X_{T}\). From this phase we can also obtain decision recommendations for the inclusion of new concepts, sub collections and collections in the ubiquitous user model concept scheme allowing it to evolve over time.