esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### The Throughput of the System  
It is very essential for us to analyze the throughput for the system, especially the terrestrial user \(D\). According to [18], the definition of throughput for two time slot networks can be expressed as:  
\[\mathrm{T=}\frac{R_{s}}{2}\times\left[1{-}P_{out}\left(\gamma_{0}\right)\right]. \tag{39}\]  
where \(R_{s}\) is the target rate of the system.  
By substituting the analytical and asymptotic OP expressions into Equation (39), the analytical and the asymptotic expressions of throughput are derived. In order to reduce the length of the paper; hence, we do not give the final expressions here.
## 6 Conclusions  
This paper proposes a MASK modulation power and signals synchronous transmission system based on PSFB. The proposed method reduces the system's volume without a DC-DC modulation circuit. The proposed method reduces the voltage fluctuation to 5% to minimize the impact of voltage fluctuation on the load. Through the application of a demodulation circuit combined with DSP, it can transmit multiple signals, which are not only limited to '0' and '1'. Based on this experimental platform, a 16-band signal can be transmitted, and in different applications, the signal transmission rate may be further improved using this method. This method covers the general audio range and has the prospect of popularization and application.  
Figure 11: A 1500 baud rate 16 ASK modulation information transmission diagram: (**a**) Demodulation circuit waveform; (**b**) ADC conversion voltage data diagram.  
Figure 12: Signal demodulation waveform.  
**Author Contributions:** Conceptualization, X.Z. and X.P.; methodology, X.Z.; software, X.P.; validation, X.Z., X.P. and Y.G.; formal analysis, Z.C.; investigation, R.H.; resources, M.X.; data curation, X.Z.; writing--original draft preparation, X.P.; writing--review and editing, X.Z.; visualization, Y.G.; supervision, Z.C.; project administration, R.H.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52077153, and the Natural Science Foundation of Tianjin, grant number 20 JCYBJC00190.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of a Four-Axis Robot Arm System Based on Machine Vision
## 3 Vision-Guided Design of the Four-Axis Robotic Arm System
### Vision Control Algorithms  
As the most typical representative of one-stage target detection algorithms [25], the YOLO algorithm can be used in real-time systems due to the fast running time, which is based on deep neural networks for object recognition and localization. To ensure that high-speed detection is possible on embedded platforms, the YOLOV7 algorithm is used in this paper, which is the most advanced algorithm in the current YOLO series and surpasses the previous YOLO series in terms of accuracy and speed [26; 27]. In addition, in this paper, we use the TensorRT optimizer, a deep learning inference engine introduced by NVIDIA [28; 29], which can accelerate the model during inference and improve the recognition speed of the model for images as well as the read-in and read-out efficiency [30; 31]. Currently, the model can be applied in common deep-learning frameworks [32].  
After the deployment method is determined, the dataset is produced with the robotic arm recognizing and grasping objects, mainly including 8 common objects such as cell phones, glasses, tea cups, and Bluetooth headphones. Firstly, a total of 3 datasets were made to train the model for the pictures of the photographed objects, but the final trained model had poor anti-interference ability and low detection accuracy due to the single background of the photographed objects. After several attempts, a model with better detection accuracy was trained by increasing the background of the images, the number of images, and the number of training sessions. After the dataset was created, the target annotation was labeled. After all the images in the dataset are labeled, the overall dataset is created, and the original dataset is classified according to a certain ratio. In this paper, a ratio of 7:3 is used to divide the dataset into a training dataset and a test dataset.  
After the dataset is divided, the images in the dataset need to be trained. First, YOLOv7 is downloaded, and data inference is performed on a cloud-based server to ensure the inference rate. The parameters of the server are pytorch1.10, python3.8, CUDA11.3, RTXA4000 GPU, 16 GB video memory, 12-core Intel(R) Xeon(R) Gold 5320 CPU @ 2.20 GHz, and 32 GB memory. According to the above server configuration, the corresponding training parameters are set. The main parameters include input image size (640 \(\times\) 640), number of training iterations (200), batch size (16), learning rate (0.01), and momentum (0.937). The changes in various values during the training process as the number of iterations increases is shown in Figure 13. It can be seen from Figure 13 that after 200 training iterations, the values of high precision and recall do not fluctuate greatly, and the changes in each value tend to be smooth, indicating an excellent training result.
## 1 Introduction
[15] used a back propagation neural network to tackle nonlinear mapping and complex internal mechanisms. Road traffic crashes are non-negative, integer, and random event counts [16]. Count data models, such as negative binomial (NB) and Poisson models, have been widely used to analyze traffic accident data for the satisfaction of underlying distribution assumptions of traffic accidents [16]. A cross-sectional or panel count data model cannot take into account the temporal structure which existed in the time series data. Serial correlation will largely distort modeling results if variables are highly correlated to each other. A quantitative research method, called an autoregressive integrated moving average, or ARIMA, utilizes series data for the period, perhaps to properly comprehend the set of numbers or forecast future patterns. If a mathematical analysis forecasts possible trends using data from the past, it is said to be autoregressive. The modification of ARIMA, known as Seasonal Autoregressive Integrating Movement Estimate, or Seasonal ARIMA, specifically enables unitary series information for the period with a constant term. The autoregressive integrated moving average (ARIMA) model is commonly used in time series temporal analysis with serial correlation. ARIMA and Seasonal-ARIMA have been used to model time series count data and enjoyed many applications for the past years [16]. In Box-Jenkins equations, partial autocorrelation graphs are a frequent technique for logistic regression. The synchronization among X_t and X_[t-k] that is not taken into consideration by delays 1 to k \(-\) 1 is known as the incomplete hysteresis at lag k.  
* However, the ARIMA model cannot be applied for non-negative integer-valued data such as traffic accidents, since the underlying assumption of errors in the ARIMA model is normal distribution. * To model traffic accident distribution and take account into time series temporal structure, this paper proposes an integer-valued autoregressive (INAR) count data model with fusion of multi-sources data including speed, traffic flow, vehicle types, and weather information to forecast daily crash happening at study Freeway in China. * Selection of NB or Poisson model was discussed according to probability integral transform histogram (PIT). * Autocorrelation (ACF) and Partial Autocorrelation (PACF) plots were used to check time series stationarity. * Prediction error of 0.7 was achieved according to RMSD.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 5 Localization in Smart Applications
### Smart Transportation and Mobility  
Recent advances in linking vehicles to the Internet have given rise to ease and safety in transportation facilities. An idea of the Internet of Vehicles (IoV) connected with the Internet of Energy (IoE) characterizes imminent inclinations towards smart transportation and significance. Likewise, generating novel dynamic environments built on conviction,safety, and accessibility for transport applications will guarantee customer concerning communications and services. Intelligent transport schemes have been extensively studied over the last few decades for providing advanced and motivated facilities for traffic organization and steering security concerns. [PERSON] et al. [84] have developed a proximate vehicle map building procedure for localization, in which the accuracy is highly reliant on the location outcomes of every vehicle. Actually, maximum vehicles conveying approaches are omnidirectional, affecting the information sharing among the vehicles when their accurate location is not easily sensed. The parking lot and gate monitoring scheme proposed in [85] uses a wireless sensor network and active RFID. Gate monitoring is an economical and simple model in which RFID tags are assigned to subscribed users or provided dynamically at the entrance to the transitory users. [PERSON] et al. [86] developed a multi-objective grey wolf optimization-based model for optimal localization in smart parking. The optimization algorithm is used to minimize localization error. The objective functions have included the distance and topological constraints. ITS developed in [87] permits to trace disabled users. It affords flexibility by uniting stereo entity recognition with RFID and Bluetooth technology to improve pedestrians' operational abilities for interacting with transportation infrastructure. A cloud-based smart car parking scheme for smart cities uses the particular software progression method. It reduces disturbances and increases convenience and safety.
## 4 Results and Discussion
### Case Study 2
#### 4.3.3 Comparison of RTO with the Simplex Method  
The results found using the RTO are differentiated, and the results procured by the simplex method are shown in Table 12. The RTO outmatch the simplex method in getting the optimal values of TMS and in an aggregate net gain of times. It gives a benefit of 10.5312 s over the simplex method, with the same starting parameters gathered for this contextual investigation. This gain in all out net time gain is adequate given that it is a small system. For all the ideal and optimal values for TMS, the RTO performed exceptionally compared to the simplex method, in terms of complete net time gain. Furthermore, it limits the aggregate working time up to optimal value and will keep up appropriate coordination even in a fault condition. For all the coordination conditions, the optimal value acquired by RTO for all the relays will fulfill the coordination limitations. Additionally, no infringement has been discovered with respect to the coordination requirements.  
Figure 12: Convergence characteristic representation for Case 3.  
Figure 13: Optimized total operating time with the literature for Case 3.
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
## 6 Conclusions  
This study proposed a design for the headtube of an electric scooter to improve driving safety using topology optimization. Conventional electric scooter designs emphasize material changes or ergonomic adjustments to enhance the riding comfort. In contrast, the proposed approach leverages topology optimization under multiple loading conditions, resulting in a structurally optimized headtube with enhanced stiffness and impact resistance. The headtube structure was reconstructed by applying compliance minimization optimization to achieve an optimal structure, effectively reducing the strain energy. The results demonstrate that the optimized design achieves at least a 51% improvement in collision performance compared to conventional commercial models, highlighting the practical benefits of topology optimization in real-world applications. These findings emphasize the importance of considering multiple loads in the design process, as this leads to a more reliable and safer structure capable of withstanding complex impact scenarios. Future research should explore complete 3D topology optimization, fatigue analysis, and material constraints to refine the structural performance and manufacturability of optimized electric scooter components.  
Conceptualization, M.G.K. and J.J.K.; methodology, M.G.K. and J.W.C.; software, M.G.K. and J.W.C.; validation, M.G.K. and J.J.K.; formal analysis, M.G.K. and J.W.C.; investigation, M.G.K. and J.W.C.; resources, J.J.K.; data curation, J.J.K.; writing--original draft preparation, M.G.K. and J.W.C.; writing--review and editing, J.W.C. and J.J.K.; visualization, J.W.C. and J.J.K.; supervision, J.J.K.; project administration, J.J.K.; funding acquisition, J.J.K.; All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE). (P0020536, HRD Program for Industrial Innovation).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank the technical support from Ansys Korea.  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this paper, an optimized LMS algorithm based on the attenuation of the step of the convergence parameters is proposed, and we called this novel algorithm the DTV-LMS algorithm. The DTV-LMS algorithm can solve the problem of the strong SI of RF domain in the CCFD system by cooperatively controlling the autocorrelation values between the time factor and the error signal to update the step size of the algorithm and provide a better quality of service for 5G communication networks. The proposed DTV-LMS algorithm is compared with the SVSLMS, MVSSLMS, VSSLMS algorithms and used to verify the superiority of the proposed algorithm by analyzing the convergence speed, the ICR, and the channel tracking capability among these algorithms. The final ICR value of the DTV-LMS algorithm proposed in this paper is 78.21 dB approximately, and the final ICR values of the SVSLMS, MVSSLMS, and VSSLMS algorithms are about 74.98 dB, 74.76 dB, and 74.15 dB. Meanwhile, the convergence speed of the algorithm is also obviously improved compared to the other reference algorithms mentioned in this paper, according to the experimental results. The theoretical analysis and simulation results show that the proposed algorithm achieves a good balance between convergence speed and SSE, which can maintain a small value of SSE at a faster convergence speed, and it also has a good channel tracking capability when dealing with burst noise interference. Therefore, it can be better applied to a 5G wireless communication environment.  
However, we experimented with simulations under ideal conditions in this study--for example, the effects of nonlinear interference are not considered, so it has certain limitations. We are committed to researching and solving possible problems in real-life scenarios in future work. In addition, our test  
Figure 10: Impact of the time interval on ICR.  
cases also have limitations, and we should enrich our test cases in the next work. Since some residual SI signals are still not eliminated after SI cancellation at the RF domain, we will also research how to eliminate residual SI completely in the digital domain in our next work. All in all, we still have many things to improve, and we will work hard to find the perfect way to solve them in our future work.  
[PERSON] conceptualized the study and provided resources. [PERSON] designed the experiments, conducted data analyses, wrote the original draft of the manuscript. [PERSON] and [PERSON] reviewed and improved the manuscript.  
This research received no external funding.  
The authors declare that there is no conflict of interests regarding the publication of this article.
# Experimental Evaluation of Rigidity Center
## 3 Results and Discussion
### Results of Group D Buildings  
Figure 21 shows the test results of the scaled and revised versions of real buildings. D1 with an L-shaped shear wall at the south-east corner resulted in significant torsion in a clockwise direction when loaded in S-N and W-E directions. D2 with a huge L-shaped shear wall again resulted in significant torsion, and the torsion was in a counterclockwise direction. D3 and D4 had both asymmetrically placed shear walls and floor plans, which ended up exhibiting considerable torsion in the opposite direction of the strong axis of the shear walls. Although D4 had shear walls located symmetrically in S-N and W-E directions separately, torsion was observed, and this was attributed to the global locations of the shear walls that were greater in number in the north-east part of the plan.  
Figure 22 reveals the comparative results of the RC and E, and Table 10 summarizes the results for the second floor. As can be seen, in the square-planned D1 building and the trapezoidal-planned D5 building, the test results were almost equal to the results of ETABS. However, in D2, ETABS calculated the rigidity center at the junction of the huge L-shaped shear wall, but in the test results, the rigidity center was found again on the shear wall aligned parallel to the S-N axis; this again was shifted to the south because of the shear wall at the south-east corner. The same conclusion can be drawn in D3 because E was at the corner of the huge shear wall, whereas it shifted through to the south-east due to the additional shear walls in this direction. It is interesting that although the N-E part of D4 had more shear walls, both the test results and ETABS calculated rigidity close to the mass center, as the shear walls were symmetrical in a separate axis.  
Figure 21: Test results of Group D.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RC from ETABS**} & \multicolumn{2}{c}{**RC from Test**} & \multicolumn{2}{c}{**Difference, \%**} \\ \cline{2-7}  & \(x_{r}\)**, mm** & \(y_{r}\)**, mm** & \(x_{r}\)**, mm** & \(y_{r}\)**, mm** & \(\Delta x_{r}\) & \(\Delta y_{r}\) \\ \hline D1 & 27.1 & 247.8 & 27.5 & 247.1 & 0.15 & 0.23 \\ D2 & 272.4 & 38.7 & 267.7 & 72.4 & 1.56 & 11.24 \\ D3 & 20.7 & 28.7 & 45.4 & 56.9 & 8.25 & 9.40 \\ D4 & 104.0 & 160.8 & 119.6 & 152.0 & 6.82 & 3.81 \\ D5 & 120.5 & 229.8 & 125.8 & 226.4 & 1.79 & 1.15 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Rigidity center comparisons of Group D.  
Figure 22: Rigidity centers of Group D.
## 2 Automatic Identification of Analysis Methods
### [PERSON] proposed the Support Vector Machine (SVM) [23], a pattern recognition method with distinct advantages for nonlinear mapping in small sample sets and limiting over-learning, making it particularly well-suited to small-sample data processing. SVM has been widely used to diagnose mechanical defects when there are a few problem samples [24]. The PSO, paired with the SVM approach, is used in this study since the algorithm's efficiency must be addressed to finish the fault diagnostic procedure. Several training sample points \(x\), and corresponding labels \(f(x)\), are provided for the learning process. The training sample can be transferred into a higher-dimensional feature space and separated using a hyperplane, as follows:  
\[f(x)=\omega x+b \tag{4}\]where \(\omega\) is an m-dimensional coefficient vector, and b denotes the constant term. The values of \(\omega\) and \(b\) should be chosen to optimize the hyperplane, the distance between positive and negative samples. As a result, SVM is an optimization problem with the following formula:  
\[\min_{\omega,C,\xi}\frac{1}{2}\omega^{T}\omega+C\sum_{i=1}^{N}\xi_{i} \tag{5}\]  
where \(\xi\) is the relaxation factor and \(C\) is the punishment factor. Then the Lagrange coefficient is introduced, translating the problem to a quadratic programming issue with the following equations:  
\[\begin{split}\max W(a)=&\sum_{i=1}^{N}a_{i}-\frac{ 1}{2}\sum\limits_{i=1}^{N}\sum_{j=1}^{N}a_{i}a_{j}f(x_{i})f\big{(}x_{j}\big{)}K \big{(}x_{i},x_{j}\big{)}\\ &\text{s.t.}\sum\limits_{i=1}^{N}a_{i}f(x_{i})=0,0\leq a\leq C \end{split} \tag{6}\]  
The main kernel functions in SVM have a linear, polynomial, sigmoid, and radial basis kernel function (RBF), where \(K(x_{i},x_{j})\) is the kernel function that can be used to transform samples into higher-dimensional feature space. The RBF function is the most suitable and widely used function. As a result, RBF is employed as the kernel function in this paper, which is defined as:  
\[K\big{(}x_{i},x_{j}\big{)}=e^{-g\left|x_{i}-x_{j}\right|^{2}},g>0 \tag{7}\]
## 5 Parameter Analysis
### End-to-End Latency  
In a 6 TiSCH network, end-to-end latency in transmission depends on the slotframe size and the PDR of the links along the communication path from the source to the root node. Links with lower PDR values require more retransmissions, thereby increasing the overall latency. The end-to-end latency \(EL_{t}\) in the proposed AAS approach, for a path of length \(P\), is expressed as the summation of latencies introduced at each hop from the source node to the root and is determined as  
\[EL_{t}=\sum\
olimits_{h=1}^{P}\left(T_{h}+\frac{L_{hs}+E_{Txh}\cdot SF}{PDR_{ kl}}\right) \tag{10}\]  
where \(T_{h}\) denotes the number of time slots between the Rx cell from a child and the Tx cell to the parent for a node \(k\) at hop distance \(h\). \(E_{Txh}\) represents the average number of retransmissions required at that hop, and \(PDR_{kl}\) is the PDR of the link between node \(k\) and its parent \(l\). In scheduling schemes such as Orchestra, ALICE, and A\({}^{3}\), end-to-end latency is a function of the slotframe size. In contrast, the proposed AAS technique expresses latency solely as a function of the segment size \(L_{hs}\). When retransmissions are required, the latency increases by an amount equivalent to the slotframe size.