esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols
### DEC Protocol Implementation  
The simulation and analysis for the original DEC and DEC-KM protocols were performed using MATLAB for the same parameters and initial conditions. The simulation environment involves three sets of nodes containing 50, 100 and 200 nodes, initially randomly distributed in the 100 m \(\times\) 100 m field. The network is heterogeneous, and the nodes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** & **Description** \\ \hline \(x\times y\) & 100 m \(\times\) 100 m & Area of network, dimensions \\ \hline \(n\) & 50 & Number of nodes in the network \\ \hline \(R_{max}\) & 5000 & Maximum number of rounds \\ \hline \(P_{opt}\) & 0.1 & The probability of a node to become CH \\ \hline \(E_{dec}\) & 50 nJ/bit & Energy dissipation per bit \\ \hline \(E_{\beta}\) & 10 pJ/bit/m\({}^{2}\) & Energy dissipation for free space \\ \hline \(E_{ang}\) & 0.0013 pJ/bit/m\({}^{4}\) & Energy dissipation for multipath delay \\ \hline \(E_{Rx}\) & 50 nJ/bit & Receiving energy of sensor \\ \hline \(E_{D}\) & 5 nJ/bit/message & Data aggregation energy \\ \hline \(P_{x}\) & 0.1 & Probability of a node to become cluster head \\ \hline \(L\) & 4000 bits & Packet size \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used to implement the DEC and DEC-KM protocols in MATLAB.  
have different energy levels. The Base Station (BS) is located in the centre of the network. The nodes were organised into 10 clusters. All simulations were performed for 50 nodes, and only the simulations presented in Section 5.2.5. in Figures 10 and 11 and in Table 6 were carried out for three networks consisting of 50, 100 and 200 nodes.  
The other parameters used for all simulations are shown in Table 1. The results obtained for the DEC protocol are shown in Figure 4.  
The network contains a set of nodes of different energy levels that are uniformly randomly sorted and divided into 10 clusters. All nodes communicate with the base station located in the centre of the network via cluster heads marked as black circles.  
In connection and processing, all nodes have similar capabilities, but their energy levels are different because the level of energy consumption is different from node to node; therefore, some nodes, for example, CH, need more energy than the normal nodes.  
The clustering of the network shown in Figure 4 has its drawbacks. There are long distances between the nodes in some cluster areas, for example, in CA5, CA6 and CA7. Two clusters (CA1 and CA2) contain only one node.
## 5 Testing Results
### Average Throughput Performance  
The throughput can be utilized to record the sum of organized information transmission in a specific period of time. A much higher throughput provides higher proficiency, which suggests little delay within the information transmission, a quicker transmission speed, and greater sensitivity to outside impacts. Figure 4a,b demonstrate the trade-off between an average throughput and the allotted time. Two different scenarios have been created to test and validate the performance of the proposed EDB-CPS and contending methods: IA-CPS, BS-MIC, and StreamLAB. In the first scenario, no malicious nodes are generated; in the second scenario, 5% of the nodes are malicious have been created. Figure 4a demonstrates the result of the first scenario, and Figure 4b shows the result of the second scenario. Figure 4a shows that the proposed EDB-CPS was able to receive a 445.2 kb/s throughput during the maximum 30 min, while the contending modes demonstrate much less throughput as compared to the proposed model. The BS-MIC, StreamLAB, and IA-CPS produced 441.3 kb/s, 438.3 kb/s, and 437.9 kb/s of throughput, respectively.  
When the malicious nodes were generated, the throughput of the proposed model marginally reduced, while other contending modes reduced more throughput as compared to our proposed system (EBS-CPS).  
The results demonstrate that the proposed system yields an approximately 443.2 kb/s throughput, while StreamLAB, IA-CPS, and BS-MIC obtained throughputs of 434.2 kb/s, 432.3 kb/s, respectively. Thus, the proposed system shows better throughput in both scenarios: without and with malicious nodes.  
Throughout of the single node \(T_{p}\) can be obtained as  
\[T_{p}=\sum_{i=1}^{m}\frac{\eta_{d}^{k}}{\delta_{p}^{2,i}}, \tag{7}\]  
where \(m\) is the total number of participating devices in this system, \(\rho\) is the density of nodes, \(\delta_{p}^{2,i}\) is the single-hop delay of each node in many cases, and \(\eta_{d}^{k}\) represents the amount of data sent from the node to the adjacent actuator. The above equation only shows the amount of data sent during the allotted time because the data sending and receiving rates are the same in our case. Due to the distribution randomness in the WSN, the throughput can be approximately signified by the average throughput of a single sensor device and the  
Figure 4: (**a**) Average throughput performance without malicious nodes of the proposed EDB-CPS and the contending models: IA-CPS, BS-MIC, and StreamLAB. (**b**) Average throughput performance with 5% malicious nodes for the EDB-CPS and contending models: IA-CPS, BS-MIC, and StreamLAB.  
number of sensors participating in the entire network. Thus, the throughput of the entire network can be obtained as  
\[T_{p}=S\cdot T_{s}=2\pi Z\rho\cdot\frac{1}{Z}\int_{0}^{Z}\frac{\omega_{d}^{k}}{ \delta_{p}^{2,\mu}}dk, \tag{8}\]  
where \(S\) represents the total number of sensors in the network (the product of the sensor device density and the area of the whole network), while \(T_{s}\) calculates the average throughput of a single sensor. This formula can estimate the throughput of the whole network.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
###### Abstract
A line segment detector, a line matching algorithm and frequency difference-based cable force estimation were integrated in order to eliminate the impacts of UAV motion and to improve the robustness of the proposed method. Smartphones among all the other mobile devices integrated a wide range of advanced technology such as embedded sensors, growing storage capability, computational power and network connections. For this reason, a group of studies utilized smartphones as the smart terminal to collect dynamic properties of stay cables. A series of consecutive studies [21; 22; 23; 24; 25; 26] proposed a wireless cable force measurement method by applying the built-in acceleration sensor in an iPhone; developed a cable force measurement software, Orion-Cloud Cell (i.e., Orion CC), based on the IOS; and further developed a two-mode acquisition program and a command control program to allow the synchronous collaboration of multiple iPhones in vibration testing. [PERSON], et al. [27] developed a micro-electromechanical (MEMS) acceleration sensor and linked it to the smartphone for cable force measurement. The experimental results showed that the resolution and sampling resolution of the sensor directly affected measurement accuracy, while manual and environmental excitation affected the reliable identification of natural frequencies. A summary of previous studies on cable force estimation methods is presented in Table 1.  
To conclude, the use of digital image processing technology in cable force measurement has advanced drastically in recent years, and the feasibility and economic efficiency of this technology have been verified. Compared to conventional sensor-based methods, this technology is intrinsically simple in terms of operation, requires no preinstalled sensors and can achieve reliable accuracy. However, most of the above-mentioned studies used fixed industrial cameras/camcorder to collect vibration videos of stay cables or built-in sensors in mobile devices to monitor dynamic responses of stay cables, based on which cable forces were estimated. Few studies used mobile devices for image data collection and analysis, with the exception of [20; 24]. This paper proposed a lightweight cable force measurement method based on smartphone, which can realize multi-project, multi-cable surface, multi-person and real-time monitoring of cable force in a visual vibration environment at any time. The proposed method is more convenient, economical and efficient compared to previous studies.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Message Dissemination  
We model the message dissemination in the network to consider vehicle-to-vehicle communications. Every second, some vehicles send the data to other vehicles. So, we have to estimate how many vehicles are going to send the data every second. For that, the RSU distribution is important.  
Figure 9(a),b illustrate the difference between two different distributions of RSUs. Each color represents one step of dissemination (i.e., the list of the new segments where the data are disseminated), for example, Step 1 is marked in red, Step 2 is marked in blue, etc. In the second case (Figure 9(b)) more exchanges will be needed (seven steps) to cover the whole network than for the first case (Figure 9(a)) for which only four steps are needed. Additionally, the number of segments reached at step two is different: 24 (Figure 9(a)) versus 6 (Figure 9(b)).  
Table 4 presents the parameters needed to describe the messages dissemination.  
The first time, we select a segment of the city, which is composed of two junctions at its extremities and the road that links them. Then, we will extrapolate the results to the entire network (city) (Figure 11).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Description** \\ \hline \(L\) & Length of a segment (m) \\ \(X_{C}\) & Indicate the presence (1) or the absence (0) of \\ \(S_{X}\) & an RSU on the junction C \\ \(S_{X}\) & Probability for a segment to have \(X\) RSU \\ \(M_{X}\) & Minimum number of V2V communications \\ \(M_{X}\) & needed to cover a segment for configuration \(X\) \\ \(H_{X}\) & Highest number of V2V communications \\ \(H_{X}\) & needed to cover a segment for configuration \(X\) \\ \(HM_{X}\) & Average number of V2V communications \\ \(H\) & needed to cover a segment for configuration \(X\) \\ \(O_{X}\) & Number of segment linked to \(X\) other segments \\ \(N_{0}\) & Number of segments with one RSU \\ \(N_{RSU}\) & Number of segments with two RSUs \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dissemination parameters.  
Figure 11: Segment.  
Figure 10: (**a**) Best dissemination scenario; (**b**) Worst dissemination scenario.  
**Definition of \(L\)**  
Figure 11 schematizes the generic configuration of a segment, with  
\[L=2*R_{C} \tag{14}\]
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 3 Results and Discussion
### Model Performance Comparison
#### 3.2.1 Comparison of ShuffleNetV2 Series Models  
Similar to ShuffleNetV1, the number of channels in each block of the ShuffleNetV2 is scaled to create networks of different complexity, and denoted as ShuffleNetV2_0.5, ShuffleNetV2_1.0, ShuffleNetV2_1.5, and ShuffleNetV2_2.0. Among them, _0.5_, _1.0_, _1.5, and _2.0 are scaling factors which is related to the number of output channels in each layer of the network. Their experimental results on the datasets are illustrated in Table 2.  
It can be seen from the data in the Table 2 that using the same experimental platform and hyperparameter settings, ShuffleNetV2_2.0 achieves a higher classification accuracy than the other three models, although the amount of parameters and FLOPs is relatively high. We choose to further improve ShuffleNetV2_2.0 after comprehensive considerations and conduct other related experiments based on the frequency domain image datasets.
## 4 Conclusions  
In the last five years, there has been an exponential increase in the number of patents registered by major technological players, such as Microsoft, Apple, Intel, Facebook, IBM, and Bytedance, with a wide range of technologies being invented. This scenario implies the emergence of new devices and equipment concepts. As a result, over the next two or three years, the focus of research will shift toward investigating the potential applications of newly introduced devices. Additionally, the reviewed studies showed that several of the laboratory-tested proposals have the potential to be validated by industry, now in production contexts.  
We observed two significant contradictions. Despite a relative consensus in the literature on the many benefits of integrating virtual reality and digital human modeling to assess physical ergonomics in the early stages of product development, currently, virtual reality and digital human modeling are seldom combined in the same ergonomics analysis, and the majority of cases continue to focus on analyzing pre-designed production processes, with all resources (devices, buildings, equipment, etc.) completely deployed.  
In our review, we identified potential new research opportunities. We suggest further research on a potential gap between researchers' expectations, that virtual reality is a current trend in digital human modeling, and the actual adoption of the technologies in the field. We support the assertion in [30], that future research should investigate why virtual reality tools are not extensively utilized, and what is required to make these technologies accessible to digital human modeling tool users. We also suggest that further research should focus on the reason for the lack of studies integrating digital human modeling and virtual reality in the same ergonomic analysis.  
Because of the remarkable advancements in technology, a growing number of firms are considering virtual reality and digital human modeling for ergonomics evaluations, without fully understanding their overall benefits and constraints [20].  
Our review outcomes, concerning the present patent scenario--the current application fields, methods, and hardware and software used--as well as the recent advances, challenges, and opportunities in the combination of virtual reality and digital human modeling throughout product development, may provide a reference for practitioners and researchers as they continue to develop novel solutions for the early detection of a physical ergonomics issue.  
Conceptualization, methodology, investigation: A.G.d.S., M.V.M.G., I.W.; writing--original draft preparation, and writing--review and editing: A.G.d.S., I.W. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank for financial support from the National Council for Scientific and Technological Development (CNPq). IW is a CNPq technological development fellow (Proc. 308783/2020-4).  
Not applicable.  
The authors declare no conflict of interest.  
Methods, hardware, and software referred to in each article reviewed.
## 6 Performance Evaluation  
The performance evaluations for registration of the IoT device, registration of the publishing client and discovery by the IoT client are presented. Each evaluation is tested by 20 times for the round-trip time (RTT) and the deviation is very small according to the average.  
Figures 16 and 17 shows the evaluation results of the network communication delays for registering the IoT device and WSP. For registering the IoT device, the IoT device sends the RAML data to the RD and the RD returns the acknowledgement to the IoT device. Similarly, for registering the WSP, the publishing client is the client for registering the WSP. Figure 18 shows the evaluation results for the discovery by the IoT client. The IoT client requests to the RD and the RD responds the information of registered entities. The RTTs for each registration is done in the same hardware and network environment. The RTT is collected in the IoT device through the time difference between the time for sending the first request and the time for receiving the last response.  
Figure 16: Round-trip time (RTT) for registration by IoT device.  
Figure 17: RTT for registration by publishing client.  
Figure 18: RTT for discovery by IoT client.  
The evaluation result for the IoT device registration, that illustrates the RTTs are between 488 ms and 652 ms, the average is 523.6 ms, and the standard deviation is 37.57 ms. The evaluation result for the WSP registration illustrates the RTTs are between 544 ms and 679 ms, the average is 570.3 ms, and the standard deviation is 30.26 ms. The evaluation result for the IoT client discovery illustrates the RTTs are between 345 ms and 505 ms, the average is 398.05 ms, and the standard deviation is 39.92 ms.  
Through the performance evaluation, the results illustrate that the interactions take time. The first reason may be the network where the elements communicate. The communication is supported by the OCF IoTivity which is the communication solution based on the CoAP. The CoAP is novel protocol for the constrained environment. Therefore, the implementation of CoAP may not be as mature as HTTP. The problem may be occurred because of the delay of discovery by the IoT client. For example, in the exhibition, there are many people using the WiFi to build the network. The WiFi speed shall be bad even if the signal is strong. If the client requires the discovered result to be presented quickly, then the OCF based discovery may not be sufficient. We will implement the HTTP based discovery scheme to support stable interaction.
## 1 Introduction
proposed a high-accuracy position control method based on an active disturbance rejection control (ADRC): this overcame the problem of the high-frequency noise incurred by the traditional speed-calculating method based on a differentiator and ensured a high-accuracy position control of the system [12]. Although the aforementioned studies have made great progress in decreasing errors and disturbance, they fail to simplify the control system when improving the overall performance in practice. For example, many control methods (such as [3, 4, 5, 6, 7, 8]) are proposed on the premise that the GS has been stabilized by the PD controller (the PD controller is realized by an analogue integrated circuit), which will lead to the limitation of the application scope of control methods and the improvement of hardware requirements. The sliding-mode variable-structure control satisfies the control requirement for the motor of a GS due to its rapid response, simple structure, and insensitivity to parameter changes and perturbations [13]. Although the sliding mode controller is used in [3], the method is only suitable for a continuous domain, and the upper bound of external disturbance is required. Therefore, in the present research, DSVC+DDC is proposed. The DSVC can suppress the parameter perturbation better and improve the response performance. On the one hand, the DDC can compensate for the external disturbance online and improve the control accuracy and speed; on the other hand, it also allows for an independent tuning with the sliding mode controller, which loosens the limit of the traditional assumed upper bound of the external disturbance to the limit of the change rate of the external disturbance. To address the slow step-responses of a GS with disturbance and parameter perturbation, this paper proposes a novel approach through mathematical model derivation and conducts step-response experiments with the GS to verify the effect of this approach.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 3 Advantages of Voxel-Based Representation  
Based on a review of the literature, we found that voxel-based representation, a flexible and precise method for three-dimensional space representation, has been successfully applied in various fields, such as fire modeling [26], indoor navigation [27], and manufacturing simulation [28]. The following discusses the advantages of voxel-based representation:  
Firstly, voxel models have significant advantages in storing and managing three-dimensional geographic information, supporting more comprehensive 3D spatial analysis. Compared to traditional 2D GIS data, voxel models not only represent terrain changes accurately but also convert urban elements traditionally modeled as 2D surfaces (such as buildings and roads) into 3D voxel structures. This transformation not only reveals urban features and spatial connections that are difficult to detect with 2D data but also significantly benefits navigation-related activities. Voxel models provide finer-grained information for path planning and obstacle detection, enhancing the decision-making capability of navigation systems in complex environments, helping researchers and urban planners better understand and analyze complex urban spaces [29; 30].  
Secondly, voxel models excel at handling spatial topology. Each voxel has a well-defined topological relationship with its surrounding voxels, which provides reliable data support for path planning, spatial connectivity analysis, and material propagation studies. In navigation applications, voxel representation can effectively describe the 3D shape of obstacles, allowing path-planning algorithms to avoid obstacles and optimize travel paths more accurately. Moreover, voxel models also have strong adaptability, enabling real-time environmental updates and path adjustments, enhancing navigation systems' robustness in complex and dynamic environments [26; 27].  
Finally, voxel models' flexibility makes them particularly well-suited to navigation tasks that require dynamic updates and rapid responses. By simplifying the representation of complex 3D environments, voxel models can quickly adapt to constantly changing environments, particularly in systems like autonomous driving and drone navigation, where real-time perception and decision making are essential. Voxel technology significantly improves the efficiency and accuracy of path planning and obstacle avoidance.  
These advantages highlight the important role of voxel integration in navigation-related activities, improving the precision, safety, and real-time responsiveness of path planning. With the continued advancement of computational power and big data technologies, voxel technology is expected to provide more efficient and precise solutions for future intelligent navigation systems.