esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Environment  
### Simulation Setup  
The core objective of our simulation is to study performance of the vehicular networks in presence of malicious nodes performing MITM attacks. To facilitate our simulations, we used Veins, which is an open source framework and is used widely for simulations of vehicular networks [39; 40]. Veins is  
Figure 4: Attacker Pattern (**a**) Distributed (**b**) Fleet.  
built on top of two popular simulators: SUMO (traffic simulator) [41] and OMNET++ (discrete event simulator) [42] as depicted in Figure 5. SUMO provides traffic patterns for specific realistic map while OMNET++ provides various modules (application layer, DSRC and PHY layer) to ensure realistic network behavior. A small patch \"TraCI\" is used for communication between OMNET++ and SUMO [43]. Whenever, an event (accident information) is triggered in OMNET++, TraCI enables the vehicles in SUMO to change their route by sending out respective commands.  
1. **Scenario 1:** Attackers are distributed randomly across the network, and
2. **Scenario 2:** Attackers are present together in a fleet structure.
## 4 Results and Discussion
### Time Series Data Prediction Results  
The primary purpose of this experiment is to evaluate the performance of different models on stock price prediction and electronic health record data. By introducing multiple evaluation metrics, including precision, recall, accuracy, F1-score, memory usage, and frames per second (FPS), this study compares the proposed method with other classic models, as shown in Tables 1 and 2 and Figure 8. These metrics provide a comprehensive assessment of each model's performance on various datasets, considering not only prediction accuracy but  
From the results, the proposed method shows the highest precision, recall, and F1-score on both the stock and electronic health record datasets, achieving a precision of 0.94, a recall of 0.89, and an accuracy of around 0.92. In comparison, traditional machine learning models like SVM and Random Forest achieve relatively similar precision and recall but show significant drawbacks in memory usage and FPS metrics. For tasks involving time-series data, deep learning models such as RNN and LSTM display strong modeling capabilities for time dependencies, with relatively high precision and F1-scores, while maintaining lower memory consumption and slightly higher FPS than the proposed method, demonstrating their strengths in computational resource management. However, supported by multi-head attention mechanisms and hash tree structures, the proposed model can more precisely extract critical features from time-series data, an advantage that is particularly beneficial for handling high-dimensional data and complex feature extraction tasks. This structural advantage allows the proposed method to maintain prediction accuracy while leveraging a more complex architecture to provide deeper learning and protection of data details. This balance renders the proposed model especially valuable for tasks that demand high precision and data security.
## 3 Results and Discussion
### Modeling Study
#### 3.2.2 The Deep Bed Filtration Model  
For the deep bed filtration model, the time evolution of the parameter \(\lambda\) with varying filtration velocity is indicated in Figure 4A. Here, similar \(\lambda_{0}\) values of 0.536, 0.590, and 0.575 m\({}^{-1}\) were obtained for filtration velocities of 20, 30, and 40 m/h, respectively. This demonstrates that \(\lambda_{0}\) is independent of the filtration velocity and SS concentration, in agreement with the results of a previous study [39]. Moreover, the \(\lambda\) values obtained in the present study are seen to vary in the ranges of 1.031\(-\)1.525, 1.088\(-\)1.510, and 1.001\(-\)1.313 m\({}^{-1}\) at filtration velocities of 20, 30, and 40 m/h, respectively, which is also in agreement with previously reported ranges of 0.8-269.24 m\({}^{-1}\) (for 0.24\(-\)0.36 mm Ottawa sand) [40] and 140-191 m\({}^{-1}\) (for 0.227 mm sand) [47]. However, the results in Figure 4B indicate that the correlation between the \(\lambda\) and \(\sigma\) parameters that was proposed in Equation (6) is absent. This is attributed to the variation in the SS concentration of the effluent due to the significant heterogeneity in the amount of influent SS. The \(\lambda\) value is influenced by time as well as pore space; hence, \(\lambda\) varies with variations in the operation time, bed depth, and pore structure of the media layers [48].  
Figure 3: The variation in the kinetic model parameters with varying filtration velocity.
## 2 Proposed Method
### Traffic Sign Candidate Detection  
In order to remove obvious non-traffic signs above road areas, we utilize the height (\(z\)) information of extracted road points in the LiDAR data to obtain traffic sign candidates. We perform the following steps to filter out the road and low elevated objects and extract traffic sign candidate regions:  
1. Employ the image to global coordinate projection on road regions extracted from each color image to find their corresponding road regions in its geo-referenced LiDAR data. The projection is performed by using six affine parameters to map the road pixel \(i\) at location \((x_{i},y_{i})\) in an image to its associated point \(l\) at location \((x_{l},y_{l})\) in LiDAR data.
2. Calculate the histogram of the height values \(z^{\prime}s\) of extracted road points in LiDAR data.
3. Find the center of the histogram bin with the maximum number of points and set this center value as the threshold \(T_{z}\).
4. Filter out the points whose \(z\) values are less than \(T_{z}+T_{0}\), where \(T_{0}\) is an empirically determined offset value to remove vehicles or any low elevated objects on the road.
5. Use the Euclidean distance-based clustering algorithm to segment remaining high elevated objects to traffic sign candidates.
6. Remove small traffic sign candidates containing less than \(T_{1}\) points.
7. Employ the global to image coordinate projection on traffic sign candidates extracted from each geo-referenced airborne LiDAR data to find their counterparts in its color image. This projection is the inverse of the image to global coordinate projection.  
It should be noted that the two thresholds, \(T_{0}\) and \(T_{1}\), have to be empirically determined based on the resolution and density of the LiDAR data set. We use the image to global coordinate projection as defined in Equation (4) to map a pixel \(i\) at location \((x_{i},y_{i})\) in an image to its associated point \(l\) at location \((x_{l},y_{l})\) in LiDAR data.  
\[\begin{bmatrix}x_{l}\\ y_{l}\end{bmatrix}=\begin{bmatrix}R(0)&R(1)&R(2)\\ R(3)&R(4)&R(5)\end{bmatrix}\begin{bmatrix}x_{i}\\ y_{i}\\ 1\end{bmatrix} \tag{4}\]  
We use the global to image coordinate projection as defined in Equation (5) to map a point \(l\) at location \((x_{l},y_{l},z_{l})\) in LiDAR data to its associated pixel \(i\) at location \((x_{i},y_{i})\) in image data.  
\[\begin{bmatrix}x_{i}\\ y_{i}\\ 1\end{bmatrix}=\begin{bmatrix}R(0)&R(1)&R(2)\\ R(3)&R(4)&R(5)\\ 0&0&1\end{bmatrix}^{-1}\begin{bmatrix}x_{l}\\ y_{l}\\ z_{l}\end{bmatrix} \tag{5}\]  
where \(R(0)=0.0,R(1)=-0.076,R(2)=0.079,R(3)=0.0,R(4)=4.230\times 10^{5}\), and \(R(5)=4.506\times 10^{5}\).
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 3 Network Architecture of B5G for Reducing the Interference  
The new network architecture is a combination of several technologies, including HetNet, D2D, UDNs, UAVs, beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), mm-wave, etc. Architectural improvements are necessary to ensure that the new radio is compatible with a conventional network. However, diverse technology models' design and contemporary practice result in massive interference in each other's signals. These susceptible interferences affect the performance of the entire network [41; 42].  
From a technological perspective, B5G will merge terrestrial wireless communication, satellite communication, and direct communication over short distances. Concurrently, B5G will incorporate communication, perception, computing, navigation, and other emerging technologies. By leveraging the management of intelligent mobility and the methods of control, B5G will create a new architecture of the 3D core network that can combine those systems and assist universal ubiquitous coverage of very high-speed communications, comprising communications on the earth, in space, in air, and over the sea [7; 43]. Figure 2 shows the architectural advancements used in B5G networks [23].  
Although this 3D core network design has the potential to overcome conventional coverage restrictions and eventually establish an unparalleled universal coverage, some challenges should be solved to improve the performance of the B5G networks [21]. Interference is the most significant factor that influences the capacity and QoS provided to end-users. Therefore, in the B5G network, it is essential to explore how interference can be canceled using traditional interference cancellation techniques, such as successive interference cancellation (SIC) and parallel interference cancellation (PIC), or key enabling technologies such as M-MIMO, intelligent beamforming (IB), resource allocation (RA), etc. [1; 44].
# Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario
###### Abstract
In addition, the selection algorithms of existing link adaption techniques are basically through brute-force search, with high algorithmic complexity. Furthermore, these techniques lack in solving the distance adaption and multipath interference problems simultaneously. In this paper, the VLC channel characteristics in the IIoT scenarios, including large-scale fading and multipath-related characteristics, are analyzed based on the ray-tracing simulation. Moreover, a two-step approach to optimizing the multipath interference is further proposed. The main contributions can be listed as follows:  
* Based on the ray-tracing simulation, the large-scale fading and multipath-related characteristics, including the channel impulse response (CIR), the optical path loss (OPL), and the root mean square (RMS) delay spread (DS), are analyzed and modeled through the distance-dependent and the statistical distribution models. Through analyzing the results, the channel characteristics are dependent on the propagation distance under the single transmitter (TX). Furthermore, the degree of the time domain and spatial domain dispersion in the IIoT scenario is higher than that in the typical indoor environments (e.g., conference room and corridor). * The effect of the density of surrounding objects on the VLC channel characteristics is analyzed under the single TX. Furthermore, the large-scale fading and multipath-related characteristics at different user heights are compared under the multiple TX cases. We find that the user surrounded by the denser objects can receive much more multipath components, leading to the smaller OPL and larger RMS DS. Furthermore, the larger OPL and the smaller RMS DS can be observed at the receiver (RX) with a low height. * The link adaption method is further proposed to optimize a multipath interference, which leads to the ICI. The luminary adaptive selection and delay adaption techniques are both combined in the method. Four parameters, including the SNR, the RMS DS, the CIRs, and the BER of the direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system are simulated to verify the performance of the link adaption method. The verification results indicate that our proposed method has a significant optimization for multipath interference. The rest of this paper is organized as follows. Section 2 introduces the simulation setup, including the simulation scenario and DCO-OFDM system. The analysis and modeling of the VLC channel characteristics in the IIoT scenario are analyzed and modeled in Section 3. The link adaption method and the verification results are shown in Section 4.
###### Abstract
\] (45)  
where \(k_{2}^{*}=\min\limits_{i=1,\dots,N}\{k_{i}^{v},k_{i}^{\theta},k_{i}^{\omega}\}\), \(\beta_{2}^{*}=\min\limits_{i=1,\dots,N}\{\beta_{i}^{v},\beta_{i}^{\theta}, \beta_{i}^{\omega}\}\), and \(\kappa_{2}=k_{2}^{*}(3N)^{-\beta_{2}^{*}}\). From (45), we have  
\[\dot{V}_{4}\leq-\kappa_{4}V_{4}^{\frac{\beta_{2}^{*}+1}{2}}.\] (46)  
The convergence time is  
\[T_{5}\leq\frac{V_{4}^{\frac{1-\beta_{2}^{*}}{2}}}{\kappa_{2}(1-\beta_{2}^{*})},\] (47)  
According to Lemma 1, subsystem (37) is finite-time stable, implying that \(\underset{t\to T_{4}}{\lim}\hat{v}_{i}=v_{id}\), \(\underset{t\to T_{4}}{\lim}\hat{v}_{i}=\theta_{id}\), \(\underset{t\to T_{4}}{\lim}\hat{\omega}_{i}=\omega_{id}\). Hence, the tracking error \(\|\delta_{ie}\|\) is bounded. \(\square\)
+++  
In Theorems 1-4, the stability of the observer subsystem, the kinematic subsystem, and the dynamic subsystem is analyzed. Through the following theorem, the stability of the closed-loop cascade system is synthesized:  
**Theorem 5**. : _Consider a network of nonholonomic mobile robots with dynamics (1) and (2) with FFESOs (7) and (15), the kinematic control laws (25), (26), the path-updating law (27), and the dynamic control laws (30), (33), and (35). If Assumptions 1-3 are satisfied, the distributed formation control can be achieved, and the errors in the closed-loop systems (36) and (37) are finite-time uniformly ultimately bounded stable._  
Proof. : According to Theorems 3 and 4, the system cascaded by subsystems (36) and (37) is finite-time uniformly ultimately bounded stable. From Theorems 3, the error vector satisfies \(\|E_{1}\|\leq\frac{\|Y^{*}\|}{\kappa_{1}}\), which means that there exist positive constants \(\epsilon_{1}\) and \(\epsilon_{2}\) such that (3) and (4) are satisfied, which completes the proof. \(\square\)  
**Remark 3**. : _A single-path-guided formation control scheme is proposed in this article, which makes multiple mobile robots keep the desired formation while tracking the parameterized path with a specified speed. Compared with the results in [7; 8; 9; 10; 11; 12; 13], where each mobile robot was assigned a desired parameterized path, the single-path-guided formation control scheme proposed in this article saves communication and computing resources._
# Modeling the Directional Clumping Index of Crop and Forest
## 2 Modeling and Evaluation
### Simulation Method of Directional Clumping Index
#### 2.2.3 Forest Directional Clumping Index Simulation  
The whole forest canopy was segmented into small inclined cylinders along the view direction, while considering the leaf distribution heterogeneity (Figure 3). Average LAI can be calculated for each cylinder according to the tree distribution pattern, i.e., the predefined leaf volume density and cylinder height (related to view zenith angle, \(\theta\)). Assuming leaves distribute homogeneously within each cylinder, \(\Omega(\theta)\) is 1 for each cylinder at any zenith angle. The cylinder gap fraction \(\overline{p_{i}}\) can be calculated according to Beer's law as a function of \(u\),  
\[\overline{p_{i}}=\exp\biggl{(}-\frac{G_{\theta}}{\cos\theta}\ u\ H\biggr{)} \tag{15}\]  
where the canopy height is converted to cylinder height through \(\cos\theta\), and \(G_{\theta}=0.5\). The average gap fraction, \(\overline{p(\theta)}\), of the whole canopy can be calculated as the average over all cylinders, and \(\Omega\) can be obtained from (3).  
Key parameters for the simulation include the average LAI of each tree, tree height, crown depth, crown radius, tree density, and parameters defining the tree distribution pattern and research area size.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 4 Communication in Distributed Systems  
The second important setting in which data compression plays an increasingly important role in PDE solvers is communication in distributed systems. The ubiquitous approach for distribution is to partition the computational domain into several subdomains, which are then distributed to the different compute nodes. Due to the locality of interactions in PDEs, communication happens at the boundary shared by adjacent subdomains. A prime example are domain decomposition solvers for elliptic problems [62].  
The inter-node bandwidth in such systems ranges from around 5 GB/s per link with high-performance interconnects such as InfiniBand down to shared 100 MB/s in clusters made of commodity hardware such as gigabit ethernet. This is about one to two orders of magnitude below the memory bandwidth. Consequently, distributed PDE solvers need to have a much higher arithmetic density with respect to inter-node communication than with respect to memory access. In domain decomposition methods, the volume of subdomains in \(\mathbb{R}^{d}\) with diameter \(h\) scales with \(h^{d}\), as does the computational work per subdomain. The surface and hence communication, however, scale only with \(h^{d-1}\), such that high arithmetic intensity can be achieved by using sufficiently large subdomains--which impedes on weak scaling and limits the possible parallelism. Consequently, communication can become a severe bottleneck.  
Data compression has been proposed for increasing the effective bandwidth. [PERSON] and [PERSON] [10] consider lossless compression of floating point data streams, focusing on high throughput due to low computational overhead. Combining two predictors based on lookup tables trained online from already seen data results in compression factors on par with other lossless floating point compression schemes and general-purpose codes like GZIP, at a vastly higher throughput. Being lossless and not exploiting the spatial correlation of PDE solution values limits the compression factor, however, to values between 1.3 and 2.0, depending on the size of the lookup tables. [PERSON] et al. [12] present a transparent compression layer for MPI communication, choosing adaptively between different lossless compression schemes. Again, with low redundancy of floating point data, as is characteristic for PDE coefficients, compression factors below two are achieved.  
Higher compression factors can be achieved with lossy compression. As in in-memory compression, analytical a priori error estimates can provide valuable guidance on the selection of quantization tolerances. Here, however, the inter-node communication bandwidth is relatively small, such that the computational cost of a posteriori error estimators might be compensated by the additional compression opportunities they can reveal--an interesting topic for future research.
Although numerous optimization algorithms have been successfully applied to the parameter extraction of software effort estimation systems, it is still indispensable to introduce a novel algorithm to achieve higher accuracy and consequently, improve the total efficiency of different estimation methods. Accordingly, the main objective of this study is to extract the parameters of the various effort estimation methods by exploiting the importance of merging uniform initialization, chaotic mapping, and a personal learning strategy with PSO algorithm. The purpose of proposed PSO variant is to obtain good performance by addressing the premature convergence and trap into local optima drawbacks. However, the challenges of premature convergence and easy trapping in the local optimum solution still persist. Therefore, in this research, a modified chaotic-based PSO with uniform particle initialization (MUCPSO) is proposed to enhance the comprehensive performance of the standard PSO algorithm. The MUCPSO balances the global and local search to enhance the diversity of initial swarm by forming them dispersedly while avoiding premature convergence by introducing three additional schemes. Firstly, initialized particle or swarm initialization is generated by uniform approach. Secondly, the linear inertia weight is replaced by an introduction of the nonlinear chaotic inertia weight map. Thirdly, a personal learning strategy to enhance the global and local search to avoid trap in local optima. The MUCPSO is finally examined and compared with standard PSO (SPSO) [14], and the following two latest PSO variants: chaotic based PSO (CPSO) [24], uniform chaotic initialized PSO (UCPSO) [23], and one nature-inspired optimization, i.e., Genetic Algorithm (GA) [25] by using three software effort estimation methods as the benchmark functions: Use Case Points, Agile, and COCOMO. One main contribution of this study is the improvement of the standard PSO by introducing three parameters such as the uniform approach for the diversity of particle initialization, nonlinear chaotic inertia weight map to balance between the exploration and exploitation phase, and personal learning strategy to enhance the global and local search in order to avoid trapping in local optima. The remainder of this paper is arranged as follows: Section 2 present a related work; Section 3 presents details of standard particle swarm optimization; Section 4 presents details of MUCPSO as the proposed algorithm; Section 5 details materials and methods; Section 6 presents and discusses the experimental results and Section 7 discuss the conclusion and recommends future works of this study.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Segmentation in Smooth Muscle  
Meanwhile, in smooth muscles, the general challenge is to measure the thickness of a specific muscle type rather than a complete skeletal muscle [38]. Smooth muscles are located on internal organs and do not have transverse lines, unlike skeletal muscles [59]. The network architecture used in smooth muscle segmentation does not require modifications such as those with skeletal muscles.
# Study on Mechanical Properties and Application of a New Flexible Bolt
## 6 Engineering Application  
As shown by the static tensile test and cyclic dynamic load test conducted, the mechanical property of flexible bolts remains stable under the conditions of static and dynamic load. Due to the relatively large friction between the locking casing and steel-strand bolt, no slipping and damage occurs. Accordingly, it can be concluded that flexible bolts can adapt to the stress fluctuation and the impact of dynamic load in the process of driftage.  
Currently, the flexible bolts have been popularized and applied in the Yaoqiao Coal Mine in Jiangsu Province. Through the feedback of engineering practice in the coal mines, the control effect of roadway surrounding rock is very good, and the roadway efficiency is greatly improved. Next, this paper introduces the geological situation and monitoring condition of the coal mine in detail.