esa-sceva/satcom-chunk-collection huggingface dataset:
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 7 Conclusions  
V2V communication holds immense promise for revolutionizing the ITS by enabling safer, more efficient, and more connected vehicles. Firstly, it is evident that V2V communication technologies, including mesh networks, edge computing, blockchain, and AI dynamic allocation, offer significant improvements. The strengths and limitations of V2V-enabled communication architectures are thoroughly discussed. The hybridization and integration of multiple technologies present opportunities for achieving optimal performance and resilience in dynamic vehicular environments. Future research efforts should focus on exploring synergies between different technologies and developing hybrid solutions that leverage the strengths of each approach. Moreover, addressing key challenges such as security, privacy, interoperability, scalability, and reliability is essential for realizing the full potential of V2V sustainable communication. By investing in research and development initiatives aimed at overcoming these challenges, stakeholders can accelerate the adoption and deployment of V2V communication technologies on a global scale.  
**Author Contributions:** Conceptualization, S.C. and M.A.N.; methodology, M.A.N.; software, S.C.; validation, S.C., M.A.N. and Y.M.; formal analysis, M.A.N.; investigation, S.C.; resources, S.C.; data curation, S.C.; writing--original draft preparation, M.A.N.; writing--review and editing, M.A.N.; visualization, M.A.N.; supervision, S.C.; project administration, S.C.; funding acquisition, M.A.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research grants given to [PERSON] from the Projects of Talents Recruitment of GDUPT (NO. 2022 rcyj2015), in Guangdong Province, China.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
In this paper, a novel shape control method for carbon fiber-reinforced composite (CFRC) reflectors with piezoelectric lead zirconate titanate (PZT) actuators is proposed. First, a finite element model considering higher transverse shear deformation with independent voltage degrees of freedom is formulated. Based on the finite element model, an optimal shape controller is developed for minimizing the root mean square (RMS) error. Then, the placement of the actuators has been optimized by using a single-objective and a multi-objective optimization method. For the single-objective optimization, an optimization method that combines the Genetic Algorithm with the gradient project method is proposed to minimize the residual RMS error. Two numerical examples are given to verify the feasibility of the proposed method. For the multi-objective optimization, both the residual RMS error and the energy consumption are considered as optimizing objectives. The proposed method can contain more widely distributed non-dominated solutions compared with the method of the weighted sum of the objective functions. The obtained non-dominated solutions can significantly reduce the energy consumption by slightly increasing the RMS error. Numerical examples are provided to verify the feasibility of the proposed optimal control method.  
Conceptualization, methodology, writing--original draft, software, validation, K.W.; methodology, writing--review and editing, W.Y.; methodology, writing--review and editing, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China, grant number 62001374.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company HTYS Information Technology Co., Ltd., Shanghai Branch. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
## 4 Simulation Result
With the increase of energy consumption and the decrease of the PDR, the average energy consumption of PICS is worse than DBR when the network number of nodes is less than 300. Except the case that the network node number is 100, the PRCS protocol always consumes the minimum average energy because it only requires one-hop neighbor information and achieves good PDR performance. **D. PRCS/PICS with different neighbor information update interval**: We compared the PRCS and PICS protocols performances with varied beacon update interval \(T=\{80,150,250\}\). The simulation results of PDR, average energy consumption and end-to-end delay are presented in Figure 9. By increasing the beacon update interval, the PDR performances of both PRCS and PICS are decreased because the neighbor information is fading over time. The outdated information leads to increased latency in PRCS protocol. In PICS, when the beacon update interval is \(T=250\), the average candidate number is fewer than for \(T=150\), as shown in Figure 9d. It leads to a smaller holding time at each hop. Then, its end-to-end delay is lower than for \(T=150\). With decreasing of the beacon update interval, the energy consumption by neighbor information exchanging will increase, which makes both PICS and PRCS cost more energy when \(T=80\), especially with more nodes in the network (e.g., more than 200 nodes in PICS and more than 150 nodes in PRCS). Figure 9: PRCS/PICS with different neighbor information update interval. (**a**) Packet delivery ratio; (**b**) End-to-End delay; (**c**) Energy consumption per node and packet; (**d**) Average candidate number.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
[32] provided the design of optimized or near-optimized LS by experimentally estimating the mechanical strengths and failure mechanisms and found that with the increase of inclination angle of struts and relative density of LS, the mechanical properties of beams increased. [PERSON] et al. [33] proposed a concurrent optimization method to optimize natural frequencies of additive manufacturing-fabricated LS, which increased the first natural frequency (_freq1_) of the optimized structure, 38.5% higher than that of the original structure. [PERSON] et al. [34] used a simple basic lattice shape composed of pillars, and only optimized its density distribution by setting the pillar diameter as design variable, and treating steady-state pressure and temperature reductions as multiobjective functions. [PERSON] et al. [35] investigated the optimal designs of novel LS filled with thin tubes based on multiobjective crashworthiness optimization procedure. Lattice member diameter and tube thickness were set as design variables, and minimizing peak crash force and maximizing specific energy absorption were chosen as design objectives. The research revealed that the optimized BCC LS hybrid designs generally had a better crashworthiness performance than BCC LS with vertical strut counterparts with the same design objectives. As outlined above, optimization methods of LS with two or more objectives of mechanical or heat transfer, or both, greatly expanded the application of LS in heat pipes and tubes. However, few studies on optimization method of LS concerned both heat transfer and mechanical performances used at region \"1\" of turbine blades, as shown in Figure 1. Although, in [36], [PERSON] et al. developed an effective optimization method of LS concerning both heat transfer and mechanical performances, the application background was for composite structure instead of turbine blades. Therefore, the aim of this study was to construct an optimization model of PLS and X-type LS (XLS) to obtain the optimal LS used at the trailing edge of turbine blades based on functions establishment between geometric parameters (diameter and inclination angle of LS) and representative parameters (Nusselt number, relative density, the first-order natural frequency, and elastic modulus). The boundary conditions were simplified from turbine blades, such as thin-wall structure; applying a constant heat flux on the target surface; mass flow inlet; and pressure outlet. The results show that the heat transfer and mechanical (lightweight, vibration, and bearing load) performances of the optimal LS are better than that of the initial LS obtained from [36] i.e., \(Nu\) increases by 24.1% and \(\overline{p}\) decreases by 31% in the optimal LS of the first selected problem; and \(Nu\) increases by 28.8% while \(freq1\) and \(\overline{\rho}\) are almost unchanged in the optimal LS of the second selected problem, compared to initial LS.
## 5 Conclusions  
By theoretical analysis and computer simulation, the equivalent physical model of a long-distance submarine cable is built. On this basis, a simplified model of a single-node CSO undersea DC power system is established. According to the simplified model, the steady-state equations of a single-node CSO undersea DC power system are derived. The steady-state equilibrium operation point criterion of the system is obtained, and has been verified in the medium-voltage experimental prototype of the single-node CSO undersea DC power system. Considering the characteristics of long-distance submarine cable, the Lyapunov function of the single-node CSO undersea DC power system is constructed by the mixed potential function method. The large-signal stability criterion of the system is derived according to the mixed potential function third theorem, and the validity of this criterion is verified by computer simulations and experiments. The factors affecting the large-signal stability are theoretically analyzed, and the conclusions are proved by computer simulations and prototype experiments: For a single-node CSO undersea DC power system, increasing the output voltage of the shore station PFE or the capacitance of the submarine cable is beneficial to enhancing the large-signal stability, and so is decreasing the power consumption of the undersea station or the inductance of the submarine cable. Furthermore, the simulation proves that the conclusion is also applicable to the large-signal stability of multi-node CSO undersea DC power systems.  
Conceptualization, F.L.; methodology, Y.J. and F.L.; software, Y.J.; validation, F.L.; formal analysis, Y.J.; investigation, Y.J. and F.L.; resources, F.L.; writing--original draft preparation, Y.J.; writing--review and editing, Y.J. and F.L.; visualization, Y.J.; supervision, F.L.; funding acquisition, F.L.  
This research was funded by the Shanghai Science and Technology Commission for the fund of Shanghai science and technology innovation action plan, grant number 16 DZ1205000.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & \(\mathbf{U_{S}/kV}\) & \(\mathbf{P_{L}/kW}\) & \(\mathbf{R_{0}/\Omega}\) & \(\mathbf{L_{0}/mH}\) & \(\mathbf{C_{0}/\mu F}\) \\ \hline \#1 & 10 & 10 & 10 & 4 & 2 \\ \#2 & 10 & 10 & 10 & 4000 & 2 \\ \#3 & 10 & 10 & 10 & 4 & 0.001 \\ \#4 & 6 & 10 & 10 & 4 & 2 \\ \#5 & 10 & 50 & 10 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Five Saber parameter sets for large-signal stability simulation of a multi-node CSO undersea DC power system.  
Figure 12: The voltage transient processes in five parameter sets.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture  
Figure 1 demonstrates the system model used in this study. We begin by taking the raw labeled images from the dataset and pre-processing them, which mainly comes in the form of selecting classes and undersampling, similar to previous pre-processing performed in [16]. This only consists of normalizing the pixel-values between 0 and 1, which is a well-known, simple image pre-processing technique [27]. We then train several ViT-based classifiers specifically developed for mobile device applications on these data, as will be explained further in Section 3.2. These models will be further quantized based on the aforementioned quantization techniques. Upon evaluating the performance of these models, an appropriate model is chosen for deployment on a mobile device.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Dataset** & **Model** & **Quantization** & **Performance** & **Model Size** & **Inference Time** \\ \hline [PERSON] et al. [21] & Malignant & ReducedFireNet & F32 & F1-score: 0.958 & 42.8 KB & Not reported \\ [PERSON] et al. [23] & COVID-19 & DDSM++ & F16 & Accuracy: 0.936 & Not reported & 280 ms \\ et al. [23] & X-ray lung & EfficientNetB2V2 & QAT & F1-score: 0.975 & 9.9 MB & 470 ms \\ et al. [25] & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of existing work on edge deployment for medical diagnostics.  
Figure 1: The proposed system model demonstrating the methodology used in this study, showing the dataset and the ViT-based classifiers’ training process, followed by the quantization of these classifiers for deployment on edge devices.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 3 Technological Background  
The next sections describe the details of the technologies related to this paper that are more relevant to the simulations performed. First, the main parameters involved in H.264 video encoding are explained. Second, the low-rate part of the IEEE 802.15.5 standard is introduced.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Main Contributions** & **Routing** & **MAC** & **Energy-Saving** \\ \hline \multirow{3}{*}{[PERSON] _et al._[12]} & Throughput & \multirow{3}{*}{One-hop} & 802.15.4 compliant & \multirow{3}{*}{Duty-cycle} \\  & enhancement & & & \\  & Cross-layer optimization & & & \\ \hline \multirow{3}{*}{[PERSON] _et al._[13]} & Architecture for QoS & Hierarchical & \multirow{3}{*}{Not applicable} & \multirow{3}{*}{Not applicable} \\  & provisioning, congestion & (clustered) & & \\  & control & & & \\ \hline \multirow{2}{*}{PT [15]} & Congestion control, & Hierarchical & \multirow{2}{*}{OMAC} & CSMA-CA \\  & determine link capacity & & & \\ \hline \multirow{3}{*}{[PERSON] _et al._[16]} & \multirow{3}{*}{Selectively drop packets} & Asymmetric, & \multirow{3}{*}{Not specified} & Energy-aware \\  & & Hierarchical & (clustered) & \\ \hline \multirow{3}{*}{[PERSON] _et al._[17]} & Prioritized multipath & Multipath & \multirow{3}{*}{Not specified} & Energy-aware \\  & forwarding & (path selection by QoS) & & \\ \hline \multirow{2}{*}{[PERSON] _et al._[19]} & Interference-minimized multipath & Multipath & \multirow{2}{*}{802.11(DCF)} & \multirow{2}{*}{Not specified} \\  & & enhancement & & \\ \hline \multirow{3}{*}{MR2 [20]} & Incremental, & Multipath & \multirow{2}{*}{802.15.4} & Turn off \\  & interference-aware multipath & (throughput & \multirow{2}{*}{interfering devices} \\  & multipath & enhancement) & \\ \hline \multirow{2}{*}{GEAM [21]} & Interference-aware multipath & Multipath (geographic & \multirow{2}{*}{802.11} & \multirow{2}{*}{Not specified} \\  & multipath & greedy forwarding) & \\ \hline \multirow{2}{*}{[PERSON] _et al._[22]} & Interference-minimized multipath & Multipath (throughput & \multirow{2}{*}{802.15.4} & \multirow{2}{*}{Not specified} \\  & & enhancement) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work overview.
## 4 Data and Methods
### Design of the Performance Evaluation
#### 4.6.1 Proof of Concept
The distance weighting function was configured according to the findings of the performance evaluation by means of the perfect data source, and it was therefore fixed at 0.0. The amount of cloud cover was varied from 10% to 30% following the same premise as in the evaluation with the perfect data source. The findings are documented in Section 5.1.
# Motion Sickness in Mixed-Reality Situational Awareness System
## 1 Introduction  
Motion sickness is an uncomfortable phenomenon experienced by individuals in response to Virtual Reality (VR) environments or motion stimuli. This phenomenon necessitates reliable measurement methods to assess its impact. There are ways to accurately measure motion sickness in users. A very objective method is to measure body sway amplitude, blood pressure, body temperature, sweating, change of skin conductivity, heart rate, EGG, and EEG, as these can indicate motion sickness [1]. These measurement methods are great for obtaining very accurate Motion Sickness (MS) readings for a single participant but the setup is not very portable and may not provide accurate results on in-field setups. As an alternative method, subjective user-based questionnaire scores have widely been used and been proven to be reliable [2]. Two significant instruments employed in this context are the Simulator Sickness Questionnaire (SSQ) and the Motion Sickness Susceptibility Questionnaire (MSQ) [3].  
A large contributor to MS is latency in VR, and how it breaks immersion [4]. There are multiple types of latencies that can be explored in a MRSAS system, two of the main ones being glass-to-glass latency and motion-to-picture latency. The former describes the latency between a change in the camera input being reflected on the VR Head-Mounted Display (HMD). The latter describes the latency between motion of the user and this being reflected in the HMD. Motion-to-picture latency and its effects on MS have been widely explored and is the primary consideration for most VR applications; as such, all additions made to a VR system in the hopes of reducing MS should be weighed against their computational cost and, by extension, their effects on latency [5].
## 2 Generation of Discrete Trajectory Points  
In surface machining, the tool-tip trajectory is typically a parametric curve. While optimizing the tool axis vector along this curve, it is necessary to discretize it. However, the resulting volume of discrete trajectory points can significantly affect the efficiency of the optimization methods. Therefore, it is important to design a data source for tool axis vector optimization that reduces the amount of data while maintaining accuracy.  
The essence of discretization is to replace curves with straight lines. There are many discretization criteria to discretize the curve, such as the distance between the chord and the curve. However, considering the calculation burden, a method based on the error between the chord length and arc length of the curve is proposed. (The curve used in this paper is a B-spline interpolation curve, and the node vector \(u_{i}\) is a vector composed of multiple nodes which represent how to segment the B-spline curve.)  
The concrete algorithm, as shown in Figure 1, is provided as follows:  
Step 1: Set the initial node vector of the parametric curve \(u_{1}=0\). Set node vector step length \(\Delta u=0.0001\) and the variable \(b=\Delta u\). Go to Step 2;  
Figure 1: The method of curve discretization by arc length and chord length relation.  
Step 2: Using Equation (1) to obtain the node vector of the \(n\)-th trajectory point, if \(u_{i}\geq 1\), then \(u_{i}=1\), get the coordinates of the last trajectory point \(P_{i}\), and end the generation of discrete trajectory points. If \(u_{i}<1\), go to Step 3;  
\[u_{i}=u_{i-1}+\Delta u \tag{1}\]  
Step 3: According to the node vector \(u_{i}\), \(P_{i}\) is calculated. Judge whether the straight-line distance \(L_{i-1}\) and the distance \(S_{n-1}\) along the curve between point \(P_{i}\) and \(P_{i-1}\) satisfy the condition. If yes, the coordinates of the trajectory point \(P_{i}\) are saved and return to step 2. If not, go to step 4;  
Step 4: Use the dichotomy method to reassign \(\Delta u\): Determine whether \(L_{i-1}\) and \(S_{i-1}\) satisfy condition 2. If yes, set \(b=\frac{1}{2}b\), \(\Delta u=\Delta u-b\). If not, set \(\Delta u=\Delta u+b\). Return to Step 2;
## 1 Introduction  
Human-computer interaction (HCI) bridges the communication gap between humans and computers [1]. Hand Gesture Recognition is one type of HCI that predicts the class and moment of execution of a given hand action (gesture) [2]. Surface electromyography (sEMG), which captures the electrical activity of muscles, is one possible input for these models [3, 4]. On the contrary, electromyography (EMG) is a type of physiological signal recording that detects electrical signals from muscle fibres in response to gestures or movements [5]. The brain creates electrical impulses that are sent to muscle fibres through the spinal cord and nerve fibres to activate movements [6, 7]. EMG-based research for medical assistance and smart gadgets, such as stroke assessment [8], analysis of neural impairments [9], heartbeat analysis [10], prosthetics [11], rehabilitation [12, 13], physical training assessment [14, 15], sports analytics [16], movement recognition and analysis [17], affective computing [18], human-machine interfaces [19, 20], text input for the disabled [21], bio-signal fusion [22], and general healthcare [23], has been active. To aid rehabilitation from a distance, interactive forms for telerehabilitation can be used to measure the development of patients' range of motion (ROM) in real time using artificial intelligence algorithms, by manipulating the angles of action of limbs about a joint [24]. [PERSON] et al. [25] then explored an artificial intelligence approach built into the MediaPipe website to measure range of motion with a universal goniometer and a digital angle meter. [PERSON] et al. suggested a WebRTC-based telerehabilitation system framework capable of managing video and audio streams based on network status and available bandwidth to ensure real-time communication performance [26].  
The novelty of the study lies in the combination of these elements:  
* The study developed a robot-assisted hand-motion therapy system.
* The system utilizes machine learning approaches to recognize and describe hand motion patterns in healthy people.
* The system also tracks rehabilitation progress.  
The study used surface electrodes to collect electromyographic (EMG) data from the forearm flexion and extension muscles, which were used as input parameters to the machine learning algorithms to recognize seven hand gestures.  
The study's contributions include demonstrating the technical feasibility of measuring and recognizing finger movement patterns and the potential use of this approach for monitoring rehabilitation progress or other medical processes. The study also analysed nearly 10,500 individual measurements of the specified gestures, which were assigned to a specific movement class and used to develop a classification algorithm. The study findings could potentially lead to the development of more effective and personalized stroke therapy programs that improve hand mobility and function.  
The remainder of the paper are as follows. Section 2 discusses the measurement and classification methodology. Section 3 presents and analyses the results. Section 4 provides a discussion. Section 5 outlines the conclusions.
## 2 Results and Discussion
### Amino Acid Sequence and Mass Spectrometry Analysis of APs (RSHP-A\(\rightarrow\)D)  
Using a Protein/Peptide Sequencer and electrospray ionization mass spectrometer (ESI-MS), the amino acid sequences of RSHP-A, RSHP-B, RSHP-C, and RSHP-D were determined to be Val-Pro-Arg (VPR), Ile-Glu-Pro-His (IEPH), Leu-Glu-Glu-Glu-Glu (LEEEE), and Ile-Glu-Glu-Glu-Gln (IEEEQ), respectively. The measured MWs of RSHP-A, RSHP-B, RSHP-C, and RSHP-D were 370.46 Da ([M + H]\({}^{+}\) 371.68 Da), 494.55 Da ([M + H]\({}^{+}\) 495.96 Da), 647.64 Da ([M + H]\({}^{+}\) 648.72 Da), and 646.66 Da ([M + H]\({}^{+}\) 647.29 Da) (Figure 5 and Table 1), which agreed well with their theoretical masses of 370.44 Da,