esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Generally the camera/object states in a tracking system can be divided into \(4\) categories [9]: 1) Stationary Camera, Stationary Object (SCSO), 2) Stationary Camera, Moving Object (SCMO), 3) Moving Camera, Stationary Object (MCSO), 4) Moving Camera, Moving Object (MCMO). In the case of visual target tracking by autonomous vehicles, both the camera and object move with respect to each other and it is the MCMC state. Before going into the algorithms details, the applications of visual target tracking for autonomous vehicles navigation are summarized in the next section.
## 4 Experiments
### Comparison of Algorithm Performance
#### 4.3.2 Experimental Results and Analysis  
The Table 1 shows that the overall performance of the proposed algorithm surpasses that of the original algorithm.  
1. Accuracy: The proposed algorithm achieved a maximum accuracy of 93.83%, representing an improvement of 15.51% over the original algorithm, with an average increase of 14.97%. This success can be attributed to three key factors: the dynamic threshold calculation method, based on the improved OTSU algorithm, enables the algorithm to adaptively adjust the threshold according to varying image backgrounds, leading to enhanced segmentation; the continuity detection method precisely identifies the start and end points of the foreground, effectively excluding impurities and other interference factors; and the improved image center point selection method, which utilizes a multi-center point strategy, makes the algorithm more resilient to noise variations in complex background images.
2. Precision: The proposed algorithm achieved a maximum precision of 98.71%, reflecting an increase of only 1.02% over the original algorithm. This modest improvement is attributed to the algorithm's heightened sensitivity to noise and impurities, despite maintaining high accuracy. The lowest precision, recorded at 96.40%, represents a 2.70% decrease compared to the original algorithm, which was due to occasional misjudgments when processing specific types of images.
3. Recall: The proposed algorithm achieved a maximum recall rate of 91.48%, representing an improvement of 16.72% over the original algorithm, with an average increase of 17.33%. This significant improvement is primarily attributed to the continuity detection-based method for determining the segmentation start and end points, which effectively excludes impurities and interference.
4. Processing Speed: The proposed algorithm's processing speed was slightly reduced by 0.585 images per second, approximately 1.34%. However, considering the significant improvements in precision and recall rates, this minor decrease in processing speed is a worthwhile trade-off.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### PCA-Based Weight Determination Framework
#### 2.4.1 Evaluation Matrix Construction  
For \(n\) provinces, \(T\) time points (e.g., 96 points at 15 min resolution or 24 points at hourly resolution), and 3 primary indicator scores for each province at each time point, we construct an evaluation matrix:  
\[X_{t}=\begin{bmatrix}x_{11,t}&x_{12,t}&x_{13,t}\\ x_{21,t}&x_{22,t}&x_{23,t}\\ \vdots&\vdots&\vdots\\ x_{n1,t}&x_{n2,t}&x_{n3,t}\end{bmatrix},t\in\{1,2,\ldots,T\} \tag{13}\]  
where \(x_{ij,t}\) is the score of the \(j\)-th primary indicator for the \(i\)-th province at time point \(t\).
## 4 Conclusions and Recommendations  
The most relevant conclusion of this research is that plug-in electric vehicles are the only efficient motor from the point of view of polluting emissions. Plug-in electric buses can make the same kilometers emitting the lowest level of polluting gases.  
Based on research results, the answer to the main research question is:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Initial Eigenvalues**} \\ \cline{2-4}  & **Total** & **\% of Variance** & **\% Accumulated** \\ \hline F1 & 2.879 & 95.975 & 95.975 \\ F2 & 0.121 & 4.018 & 99.993 \\ F3 & 0 & 0.007 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: First principal component analysis values for inputs: total variance explained.  
Figure 3: The graphical representation of the results of the DEA model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alternative (DMU)** & **Inputs: F1** & **Outputs: Km/N\({}^{\circ}\) Bus** & **Efficiency** \\ \hline Diesel & 0.87377 & 50,443.39 & 0.60012 \\ GNC & 0.90508 & 50,443.39 & 0.52036 \\ Diesel hybrid & 0.35636 & 50,443.39 & 0.75428 \\ Plug-in electric & \(-1.14098\) & 38,318.97 & 1 \\ Induction electric & \(-0.99424\) & 43,415.78 & 0.88654 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Input and output values for each alternative (DMU).  
Answer to the Research Question: The most efficient alternative fuel technology, in terms of air pollution, for urban public buses, is the plug-in electric.
## 5 Conclusions  
This study performed experiments to evaluate the rotational stiffness that can occur in the horizontal members of pocket-type connections. Upward rotation and lateral rotation experiments were performed on 30 specimens each, and the results of the authors' previous downward rotation experiments were used to analyze the failure shapes and moment-rotation curves of the specimens as well as to make the following conclusions. 1. There were differences in the failure shapes of pocket-type connections according to the rotation direction. In the case of the downwardly rotated specimens, the main failure shape occurred in the pins and pockets, which have low yield strength, rather than the steel pipes. In the upwardly rotated specimens, deformation or failure occurred in some of the top parts of the pocket owing to the bending of the pin; however, the deformation of the pocket was negligible, and cave-in phenomena occurred in the part of the vertical steel pipe that made contact with the end of the horizontal steel pipe. In the laterally rotated specimens, there was little torsional deformation of the pins, but there was deformation in the rotation direction of the pocket, and in some of the specimens, cracks occurred in the bent part of the pocket. Owing to the differences in the failure phenomena, the maximum moment capacity of the upwardly rotated specimens was the largest, followed by that of the downwardly rotated specimens and the laterally rotated specimens in that order. 2. A bilinear rotational stiffness model is appropriate for the moment-rotation curves that result from each rotation direction of the pocket-type connections. Based on the moment-rotation curves, it was determined that the connections correspond to semi-rigid connections in the Eurocode 3 connection classification system when the rotation direction is downward or upward, and they can be considered nominally pin connections during structural analysis when the rotation direction is lateral. 3. Considering the rotational stiffness during the upward rotation of the pocket-type connections, the initial stiffness (\(k_{1}\)) was found to be 47.138 kNm/rad, and the second stiffness (\(k_{2}\)) was found to be 2.461 kNm/rad, while the initial stiffness of the downward rotation was 28.378 kNm/rad, and the second stiffness was 1.450 kNm/rad. In contrast, the initial stiffness of lateral rotation was 3.144 kNm/rad, and the second stiffness was 0.577 kNm/rad. A bilinear model can be proposed in which the initial and second rotational stiffness values of upward rotation are, respectively, 1.66 and 1.70 times that of the downward rotation, and the initial and second rotational stiffness values of lateral rotation are respectively 0.11 times and 0.4 times that of the downward rotation.
## 3 Algorithm for Multi-Station Satellite Orbit Optimization in Common-View
### The Implementation of Optimal Satellite Orbit Algorithm in Common View  
#### 3.2.1 Decision Variables  
According to the system architecture and problem description above, the following decision variables in the common view affect the optimal satellite orbit design problem between numerous stations:  
\[i=\begin{cases}i_{com},&\text{Common visible orbital inclination of a ground laser station}.\\ i_{uncom},&\text{Non-common visible orbital inclination of a ground laser station},\end{cases} \tag{8}\]  
\[\Omega=\begin{cases}\Omega_{com},&\text{Common visible right ascension of ascending node}\\ \Omega_{uncom},&\text{Non-common visible right ascension of ascending node}\end{cases} \tag{9}\]  
The parameters \(i\) and \(\Omega\), respectively, represent the orbital inclination of the satellite orbit and the right ascension of the ascending node. These parameters are crucial for determining the orientation of the satellite's orbital plane. When two or more designated ground laser stations can simultaneously observe the satellite, the parameters \(i\) and \(\Omega\) denoted as \((i_{com},\Omega_{com})\). In other cases, they are denoted as \(i_{uncom}\) and \(\Omega_{uncom}\).
## 3 Challenges in FSO Communication
### Atmospheric Channel Effects Phenomena  
FSO communication is a promising technology with notable benefits, yet it encounters several hurdles that impede its broad implementation. This section examines the primary challenges in FSO communication, such as atmospheric interference, alignment and pointing precision, range restrictions, and security issues. Addressing and overcoming these obstacles is essential for creating robust FSO communication systems that deliver reliable and efficient wireless communication across diverse applications.
## 3 Background: Merkle Patricia Tries in Ethereum State Management
### Width-16 Structure Design Rationale  
Ethereum's choice of a width-16 MPT structure represents a careful architectural decision balancing performance, efficiency, and practical implementation concerns. This design choice is fundamental to understanding the state management system's behavior and optimization potential.  
The width-16 structure processes Ethereum addresses (160-bit values) in 4-bit chunks (nibbles), aligning naturally with hexadecimal representation. This approach offers several key advantages:  
1. Optimal Bit Manipulation: Processing 4 bits at a time provides efficient CPU operation alignment;
2. Memory Access Patterns: 16-way branching creates node structures that align well with common memory page sizes;
3. Storage Density: Reduced tree height compared to binary tries while maintaining manageable node sizes;
4. Implementation Efficiency: Hexadecimal representation simplifies debugging and development.  
These design choices directly impact path length distributions and proof sizes, which form the core focus of our analysis. The width-16 structure creates a specific probabilistic framework for address distribution and path sharing, leading to the characteristic behaviors we examine in subsequent sections.
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 3 System Model  
As shown in Figure 1, in this paper, we investigate an EC system composed of a single user and multiple ESs. In this system, the set of ESs is defined as \(\mathcal{M}=\{1,2,...,m,...M\}\), where each ES is equipped with an access point (AP). The user can select a designated AP to upload its dependent task to the corresponding ES. Moreover, these ESs are interconnected via wired connections. At a specific time slot, the user generates a dependent task. Given the limited resources and energy of the user's device, it is infeasible to complete the task execution within the desired time and energy consumption ranges. Consequently, the user offloads this dependent task to a proxy node. This proxy node then assigns the dependent task to other ESs that are incentivized to participate in task computation. Notably, this proxy node could be the ESs closest to the user.  
The working architecture of the system in this article is shown in Figure 2, in which the user and the ESs construct an incentive mechanism through the Stackelberg game. Within this mechanism, the user takes the role of leader and proposes an incentive strategy, which is designed to encourage the ES to participate in task computation by offering a reward. The proxy node publicizes the user's incentive strategy, and, if the ES (follower) responds to the user's incentive strategy, it informs the proxy node of the amount of resources it can contribute. The proxy node then informs the user of the ES's response strategy. Next, the user offloads the dependent task to the proxy node, which, by assuming a greedy strategy, calculates that each ES needs to compute \(i\in[\left\lceil\frac{C}{N}\right\rceil,C]\) sub-tasks, where \(C\) is the number of sub-tasks in the dependent task and \(N\) is the number of ESs encouraged to participate in task computation. The proxy node iterates through the values of \(i\) to distribute resources. Following this, based on each resource allocation strategy, a task distribution plan is obtained using reinforcement learning that is based on S2S neural networks. Upon completion of the task, the proxy node returns the results to the user. If the user verifies the results as correct, the reward is given to the proxy node, which then distributes the reward according to each ES's ratio of resource contribution. This process can be repeated if extended to a multi-user scenario.  
Figure 1: System architecture diagram.  
Figure 2: System work architecture diagram.
## 2 Physical Model  
In this paper, a numerical method was adopted for the two cylinders with roughness strips. The physical model of the elastically supported two staggered cylinders is shown in Figure 1. The vibration direction of each cylinder was limited to the \(y\)-direction, which was perpendicular to the direction of inflow (\(x\)-direction) and the axis of the cylinder. The cylinder in the vibration system was a flexible bluff body with a diameter of \(D\) and a length of \(L\). The elastic coefficient of the vibration system was \(K\), and the structural damping caused by friction is \(C\). The upstream surface of the cylinder was symmetrically attached with two rough bands along the axis. The thickness and coverage of each rough band were 0.847 mm and 16-degree. The angles of the two rough bands were different--the upstream cylinder was \(\alpha=\pm 20^{\circ}\), and the downstream cylinder was \(\alpha=\pm 30^{\circ}\)[15]. The Reynolds number range considered in this paper was \(Re=\) 2000-14,000, and the corresponding reduced velocity (\(U*=U/(f_{n}\cdot D)\), \(f_{n}=\sqrt{K/(m+m_{a})}/2\pi\) was the natural frequency) range was \(2\leq U^{*}\leq 14\). When the two tandem cylinders were fixedly arranged; the spacing in flow direction was \(d=2D\)-7\(D\); when the two cylinders were staggered with elastic supports, the cross-flow direction spacing was \(T=\) 0\(\sim\)1.0\(D\), and the flow direction spacing \(d\) was fixed at 2\(D\). In this paper, the density and kinematic viscosity of liquid water at 15 \({}^{\circ}\)C were considered. The system parameters of the upstream and downstream cylinders were identical, as shown in Table 1.
## 6 Conclusions  
This paper investigated the performance of 5G-satellite integrated networks using the two-ray and free-space path loss (FSPL) models, with an emphasis on IoT applications. The two-ray model, which accounts for both direct and reflected paths, highlighted significant oscillations in received power at short distances due to interference effects. For instance, at 50 km and a 300 km satellite altitude, the received power reached \(-115\) dBm, but it dropped to \(-136\) dBm at 1000 km altitude, illustrating that lower altitudes are preferable for high-power, short-range applications. The analysis showed that higher  
Figure 12: Atmospheric attenuation validation and comparison.  
Figure 13: Rain attenuation validation for 32 GHz. The proposed model aligns closely with ITU-R tabulated values for various rain intensities.  
frequencies increase path loss, especially at long distances. At 1000 km, the received power at 24 GHz was approximately \(-125\) dBm, compared to \(-130\) dBm at 32 GHz, emphasizing the importance of frequency selection in achieving extended coverage. Channel capacity, influenced by bandwidth, was also shown to decrease with distance: at 3000 km, a 50 MHz bandwidth provided 300 Mbps, whereas 10 MHz offered only 60 Mbps, demonstrating the trade-off between bandwidth and range. An outage probability analysis revealed that higher SNR thresholds lead to greater outage probabilities, particularly at long distances. For an SNR threshold of 15 dB, the outage probability at 3000 km reached 25%, highlighting the challenge of maintaining reliable connections over long distances. In summary, our findings suggest that network configurations must be optimized for satellite height, frequency, bandwidth, and SNR requirements to achieve reliable 5G-satellite IoT connectivity across various scenarios. Future work could explore adaptive algorithms to further enhance network performance in dynamic environments.  
Conceptualization, M.U. and S.C.E.; methodology, M.U.; software, M.U. and F.E.; validation, M.U., S.C.E., S.E. and F.E.; formal analysis, M.U.; investigation, M.U.; resources, M.U. and S.C.E.; data curation, M.U.; writing--original draft preparation, M.U.; writing--review and editing, M.U., S.C.E., S.E. and F.E.; supervision, S.C.E.; project administration, S.C.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts of interest.
## 1 Introduction  
### Background  
Seismic isolation is widely applied to buildings for earthquake protection in earthquake-prone countries [1]. Unlike in the case of traditional (non-isolated) earthquake-resistant structures, seismic isolation ensures that the behaviors of building structures are within the elastic range and reduces acceleration in buildings during large earthquakes [2]. Therefore, this technique is applied to not only new buildings, but also existing reinforced concrete and masonry buildings, including historical structures [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. The isolation layer consists of isolators and dampers. Isolators support the gravity loads of the superstructure. The horizontal flexibility and centering capability of seismically isolated buildings is also achieved by isolators [1]. Dampers provide the energy dissipation capacity for reducing the seismic responses. Although it is very common to use hysteresis dampers (e.g., steel dampers) and/or oil dampers in the isolation layer, there are several studies about the innovative seismic isolation systems in recent years, e.g., [15, 16, 17, 18, 19, 20, 21, 22].  
In general, there is some degree of irregularity in building structures, whether they are newly designed or preexisting. Therefore, the problem of torsional response due to plan irregularities needs to be studied for base-isolated structures, as well as non-isolated structures. Several researchers have investigated the seismic behavior of base-isolatedbuildings with plan irregularities [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]; these include fundamental parametric studies using idealized models [23; 24; 25; 26; 27], as well as studies using more realistic frame building models [28; 29; 30; 31; 32; 33; 34; 35]. Most of these studies are based on nonlinear time-history analyses [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. However, there are a few investigations that examine the applicability of nonlinear static procedures to base-isolated buildings with asymmetry. [PERSON] and [PERSON] [36] examined the applicability of the extended N2 method [37; 38], which is one of the variants of the nonlinear static procedures, to the base-isolated asymmetric buildings. They found the nonlinear peak response of base-isolated asymmetric building can be predicted by the extended N2 method. However, they examined only those whose superstructure was regular in elevation. Therefore, further investigations are needed for the applicability of the nonlinear static procedures, especially for base-isolated building with plan and elevation irregularities.
# Convolutional Neural Network (CNN)-Based Frame Synchronization Method
## 3 Proposed CNN-Based Frame Synchronizer  
In this section, we propose a new synchronization method using a CNN classifier as shown in Figure 4. The CNN classifier generates the estimated arrival time, i.e., \(\hat{n}_{0}\), from the input signals that is the correlator output signals, \(z[n]\) in (3). The detailed procedure of the proposed CNN classifier is depicted in Figure 5, and the specific parameters for it are summarized in Table 1. For a simple description of the proposed method, we set the window size by \(10,000\), i.e., \(W=10,000\). Note that the proposed method can be applied to an arbitrary size of time window by slightly modifying the CNN structure. Figure 4: Overall block diagram of the proposed convolutional neural network (CNN)-based frame synchronization. Figure 3: Example of the correlator output, \(z[n]\), when \(W=10,000\) and \(SNR=0.9\) dB. Figure 5: Diagram of the proposed CNN-based synchronization block in Figure 5, when \(W=10,000\). (**a**) Correlation output normalization. (**b**) 2D-training signal generation. (**c**) Convolutional layer 1. (**d**) Convolutional layer 2. (**e**) Convolutional layer 3. (**f**) Fully-connected layer. (**g**) Label vector. (**h**) Estimate of packet arrival time. The first step is a training sample normalization step as shown in Figure 5a, in which the correlator output signal, \(z[n]\), is normalized such that its maximum value is one as follows,  
\[y[n]=\frac{z[n]}{z_{\max}}=\frac{z[n]}{\max_{n^{\prime}\in[0,W-1]}z[n^{\prime}]},\;\forall n\in[0,W-1]. \tag{5}\]  
Next, the \(1\times 10,000\) 1D signals are converted to \(100\times 100\) 2D training signals by performing matricization operation with a row-major order and its dimension is \(\sqrt{W}\). Precisely, as shown in Figure 5b, the first 100 samples become the first row, the next 100 samples become the second row, and so forth. The converted 2D-training signals can be represented by a black and white (monochrome)