esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study presents hybrid particle swarm optimization with quasi-Newton (HPSO-QN), a hybrid optimization method for accurately identifying mechanical parameters in two-mass model (2 MM) systems. These systems are commonly used to model and control high-performance electric drive systems with elastic joints, which are prevalent in modern industrial production. The proposed method combines the global exploration capabilities of particle swarm optimization (PSO) with the local exploitation abilities of the quasi-Newton (QN) method to precisely estimate the motor and load inertias, shaft stiffness, and friction coefficients of the 2 MM system. By integrating these two optimization techniques, the HPSO-QN method exhibits superior accuracy and performance compared to standard PSO algorithms. Experimental validation using a 2 MM system demonstrates the effectiveness of the proposed method in accurately identifying and improving the mechanical parameters of these complex systems. The HPSO-QN method offers significant implications for enhancing the modeling, performance, and stability of 2 MM systems and can be extended to other systems with flexible shafts and couplings. This study contributes to the development of accurate and effective parameter identification methods for complex systems, emphasizing the crucial role of precise parameter estimation in achieving optimal control performance and stability.  
parameter identification; two-mass model; electric drive systems; particle swarm optimization; quasi-Newton method; hybrid optimization; stochastic algorithms; mechanical parameters; optimal control performance; elastic joints +
Footnote †: journal: _Algorithms_  
[EMAIL_ADDRESS]
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 2 Methodologies
### Prediction Modeling Phase
#### 2.2.1 CNN-BiLSTM  
It is very reasonable to use 1D convolution to extract features of one-dimensional time series such as PM2.5 concentration. In the CNN layer, the rectified linear unit (_relu_) function, given in Equation (1), is used as an activation function, which can avoid neuron death by modifying the negative value and solve the gradient vanishing and exploding problems [25]. Additionally, the input matrix of each training set is 720*13. The 720 is obtained by multiplying 24 by 30, where 24 means that a day contains 24 samples in total, and 30 means that a total of 30 days of samples are entered. Additionally, the 13 is the number of variables included in each sample. After extracting features through the CNN layer, the shape of the output matrix is reduced to 720*12. In the BiLSTM layer, the output of CNN is input into BiLSTM, and the generated shape is 720*8.  
\[relu(x)=max(0,x) \tag{1}\]
## 4 Numerical Studies
### Effects of Different Key Factors on Single-Vehicle Simulation
When a vehicle drives in the WCA or decelerates, its remaining power increases significantly. When the vehicle accelerates, the remaining power decreases considerably. For the BPS, the remaining powers of the vehicles at the terminal moment in scenarios A, B, and C are equal to 0.5017, 0.5014, and 0.5010 kWh, respectively. Therefore, it can be concluded that the higher the initial speed of the vehicle, the greater the power consumption in the CA. Simultaneously, the remaining power at the terminal time decreases with an increase in the initial speed. The WCL length and charging efficiency exert a stronger impact on the SOC, and the charging distance and charging efficiency have a significant effect on the power replenishment of CAEFVs. The numerical results obtained for the different scenarios are listed in Table 6. Here, \(t_{f}\) is the terminal moment, \(T_{cha}\) is the charging time, \(E_{cha}\) is the charge replenished by the vehicle in the CA, \(E_{con}\) is the pure energy consumption, \(E_{rec}\) is the charge recovered by the regenerative braking system, and SOC is the remaining battery capacity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & \(\mathbf{t_{f}}\left(\mathbf{s}\right)\) & \(\mathbf{T_{cha}}\left(\mathbf{s}\right)\) & \(\mathbf{E_{cha}}\left(\mathbf{Ws}\right)\) & \(\mathbf{E_{con}}\left(\mathbf{Ws}\right)\) & \(\mathbf{E_{rec}}\left(\mathbf{Ws}\right)\) & **SOC** \\ \hline A–C & 70 & 43.34 & 858,132 & 349,735 & 114,547 & 0.502 \\ B–C & 70 & 45.88 & 908,424 & 438,177 & 143,841 & 0.502 \\ C–C & 70 & 47.79 & 946,242 & 520,801 & 184,543 & 0.502 \\ A–B & 56 & 28.76 & 569,448 & 326,037 & 106,991 & 0.501 \\ B–B & 53 & 28.41 & 562,518 & 396,600 & 152,478 & 0.501 \\ C–B & 50 & 27.72 & 548,856 & 499,761 & 190,914 & 0.501 \\ A–T & 42 & 14.34 & 283,932 & 147,738 & 26,886 & 0.500 \\ B–T & 41 & 14.56 & 288,289 & 249,521 & 72,473 & 0.500 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Numerical results obtained for different scenarios. Figure 3: Results obtained for nine different scenarios.
## 4 Case Verification and Analysis
### Visual Analysis of Model Decision Areas  
Generally, convolution neural network (CNN) is a kind of dark box technique, the global optimal solution is constantly approximated through theoretical derivation and gradient propagation methods. In order to visualize the learning progress of CNN, [PERSON] et al. proposed a class activation mapping (CAM) [29; 30; 31] visualization tool, which introduces a kind of CNN to generate heatmaps of input images. Using the visualization tool, the high-activation areas, which have significant impact on the classification and localization results, are shown, and it is possible to visualize whether the high-response areas of the model lie in the core of the target. Taking insulators as an example, the image class activation mapping process is shown in Figure 9.  
According to Figure 9, the insulator image is input to the CNN and many feature maps are extracted through multiple convolution layers (Conv). The last convolution layer is connected to the global average pooling (GAP) layer and the visual CAM heatmaps of insulators are generated by weighting and summing the feature maps. The softmax layer is used to output results.  
Figure 9: The class activation mapping process of an insulator image.  
\begin{table}
\begin{tabular}{c c c} \hline
**ECA-YOLOX-Tiny** & **YOLOV4-Tiny** & **SSD-MobileNet** \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._Combined with corresponding network models, insulator images can be input into the detection model, and then the CAM heatmaps are adopted to visualize and analyze the decision areas of each model. Based on the above process, the internal representation learned by the CNN can be visualized to identify the largest activation areas in the images for target localization. In the CAM heatmaps, the darker color areas have a greater influence on the network determination results. The reliability of the defect detection model can be evaluated by comparing the output results of the high-response decision areas with the actual boundary situation of the insulator defect areas and the multi-target insulators.  
The YOLOV4-Tiny model is compared with ECA-YOLOX-Tiny, and the CAM heatmaps of two models are shown in Table 10 (for defective insulators) and Table 11 (for normal insulators). Comparing the pictures of YOLOV4-Tiny in Table 10, the ECA-YOLOX-Tiny model is more accurate in locating the defect areas of defective insulators, and its decision areas have a higher response level, while the YOLOV4-Tiny model has a certain location offset. It can be seen from the pictures in Table 11 that the high-response areas of classification and localization lie in the core of the insulators in these two models, and the high-response areas are basically consistent with the geometric center of the insulators. Usually, the target response degree is consistent with the model detection results. In comparison, the high-response areas of ECA-YOLOX-Tiny are larger and more responsive than YOLOV4-Tiny, especially for the insulators with small overlapping targets, obscured by tower poles and with high-similarity backgrounds.
## 3 Case Studies
### Battle on Neretva VR
#### 3.3.3 Workflow  
The application is created in Unity 3D from the following assets: VR videos of two museum and two partisan headquarters scenes, virtual environments of bridge surroundings  
Figure 6: Battle on Neretva application structure.  
with 3D models of airplanes, detonator, anti-aircraft machine gun, the improvised bridge and the wounded soldier in the trolley. The virtual environment of the bridge was modelled based on the drone photogrammetric model of the real location. The VR videos were directed according to the new VR film language grammar, using the possibilities of user's freedom to look around. Comrade [PERSON] was recorded on green screen and superimposed in the VR video of the museum scene. Using make up we tried to make the actor as similar as possible to the historical character. He studied the way of talking and mannerisms of [PERSON] recreating them in his speech. In sound postproduction we coloured his voice to achieve the maximum resemblance. The folley sounds, environment sound and the audio of actors were mixed into ambisonic format together with the original music. The workflow of application creation is showed in Figure 7.  
Since we were satisfied with end results of \"Nine dissidents\" movie and viewers were pleased with what they saw, we decided to apply most of these experiences in \"Battle of Neretva VR\". We again used smaller space adding to the tense atmosphere, and also the space was converted into partisan headquarters. All the lamps in the room were placed in a way that they could both be parts of the set, and help with lightning it. One important difference we implemented was that the camera is now actually the viewer and other actors play towards it, where in \"Nine dissidents\" it was just \"unseen\" observer. This added another layer of immersion.  
The Museum will install the application in the space called VR showroom, where it will be available for visitors on Oculus Quest headsets. We believe that it will motivate the visitors to come and complete their visit of the exhibition with experiencing the battle and becoming its participants.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
###### Abstract  
Integrated Sensing and Communication (ISAC) systems have emerged as a key component for Sixth Generation (6G) networks, enhancing resource efficiency and enabling diverse applications. Currently, ISAC systems have been recognized as a leading trend for future standardization, i.e., International Mobile Telecommunications (IMT)-2030. As in the previous IMT-2020 standardization, the emphasis has been on developing a methodology for assessing network conditions, with one of the crucial approaches incorporating system-level simulations. However, within this framework, there has been a notable absence of proposed abstractions for the physical layer of ISAC systems, which are valuable for system-level simulators. The physical abstraction process helps reduce computational simulation costs, enabling efficient and rapid evaluation of system conditions. Therefore, this paper aims to fill this gap by outlining the key aspects and metrics recommended for a physical layer abstraction in sensing applications within ISAC frameworks. Applying physical abstraction in the context of target localization and detection algorithms may enable an initial understanding and evaluation of ISAC system performance. These algorithms are proposed as an example of simulating the sensing functionalities to be abstracted, which are based on a stochastic geometric channel model. Orthogonal Frequency Division Multiplexing (OFDM) symbols play a crucial role in target position estimation. The findings show that doubling OFDM symbols improves the detection probability by 3 dB in terms of Signal to Noise Ratio (SNR). Finally, the proposed Physical Layer Abstraction (PLA) method produces performance metrics as figures and lookup tables tailored for system-level simulators.  
Integrated Sensing and Communication; Sixth Generation; physical abstraction; target detection; target localization +
Footnote †: journal:  
0000-0002-4001-1000]S. A. Inca
###### Abstract  
As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical Tracking Velocimetry (OTV) algorithm is a promising strategy to address surface flow monitoring. Moreover, the lightweight nature of OTV coupled with computational optimizations makes it suited even for its deployment in situ to perform measurements at the edge with cheap embedded devices without the need to perform offload processing. Despite these notable achievements, the actual practical deployment of OTV in remote environments would require cheap and self-powered systems enabling continuous measurements without the need for cumbersome and expensive infrastructures rarely found in situ. Purposely, in this paper, we propose an additional simplification to the OTV algorithm to reduce even further its computational requirements, and we analyze self-powered off-the-shelf setups for in situ deployment. We assess the performance of such set-ups from different perspectives to determine the optimal solution to design a cost-effective self-powered measurement node.  
Keywords:optical tracking velocimetry; OTV; surface flow monitoring; computer vision; river surface velocity; particle tracking; energy consumption +
Footnote †: journal: Applied Sciences
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 2 Methodology
### Inclusion and Exclusion Criteria  
In order to refine the results of the keyword search, a set of inclusion and exclusion criteria was applied to select studies relevant to the objectives of this review. Initially, only articles that described programmable data plane implementations within 5G and beyond architectures were included. Specifically, studies that utilized the P4 programming language were considered, as it is widely recognized as the de facto standard in the current state of the art. Subsequently, a second criterion was applied to limit the selection to studies published between 2018 and 2022 as well early work from 2023. The reason for selecting this time frame is that it corresponds to the period during which the vast majority of research on programmable data planes in telecommunication networks was conducted. Prior to 2018, publications on this topic were scarce and primarily focused on pre-5G technology. It is worth noting that certain 4G LTE applications were considered within this review given their compatibility with 5G and beyond systems.  
The primary search yielded 224 research articles, which were further reduced to a final set of 61 articles that met the aforementioned criteria and were deemed relevant to the objectives of this review.
## 1 Introduction
Most of the theories of uncertainty propagation are, however, focused on explaining the behavior of medium and high Earth orbit navigation constellations, seldom involving LEO mega-constellations. A well-known and robust approach used to study the uncertainty propagation problem is the Monte Carlo simulation [21], and the distribution of terminal states is obtained through the steps of randomly taking a large number of samples, high-precision simulation, and statistical analysis. The numerical approach-based Monte Carlo simulation has a long computation time and high hardware requirements. A possible solution to the problem at hand is applying the semi-analytical dynamics model, which does not need to propagate the orbital elements of the satellite or the position and velocity vectors of the satellite to obtain the position information of the satellite at a specific time and therefore has a smaller computational memory and computational load [22]. This paper aims to develop an overarching framework to reveal the uncertainty propagation law of the phase of LEO mega-constellations. The perturbation solution is modeled in Section 2. Section 3 describes the Monte Carlo uncertainty propagation approach. Section 4 carries out a numerical simulation. Conclusions are drawn in Section 5.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Single Element Flexible Antennas for the Sub-6 GHz 5G Band
#### 4.1.1 Slot/Slit Based Flexible Antenna
This is due to the fact that with an increase in the dielectric constant, the resistive part of the input impedance reduces, and the reactance becomes capacitive. Figure 9d shows the impact of bending on the antenna return loss. In another approach in [50], a CPW antenna using an LCP substrate that combines two strips, one each on the radiating as well as the ground plane, is utilized for achieving a multiband operation, as shown in Figure 10a. The proposed antenna has three operating bandwidths 2.38-2.79 GHz, 3.27-4.05 GHz, and 4.80-8.44 GHz, respectively, as shown in Figure 10b. The average gain of 0.65 for 2.4 GHz, 2.26 for 3.5 GHz, and 2.6 dBi for 5.5 GHz are achieved. The antenna has a directional radiation pattern in both the E-plane and H-plane. The bending analysis of the antenna under the two radii (R = 10 mm and R = 50 mm), is shown in Figure 10c,d, respectively. It is observed that there is a slight variation in the \(S_{11}\) of the antenna in all of the frequency bands. The SAR of the antenna is evaluated by placing the antenna at a distance of 5 mm from the model, which consists of several layers of tissue with different thicknesses. The maximum SAR values are found to be 0.93, 0.89, and 1.92 for 10 g tissue for frequencies of 2.4, 3.5, and 5 GHz, respectively, as shown in Figure 10e. Figure 10: _Cont._  
Figure 9: Bowtie slotted antenna: (**a**) Fabricated prototype; (**b**) Gain analysis; (**c**) Radiation Pattern; (**d**) Impact of the bending. Reprinted from ref. [49]. Figure 10: Multiband antenna on a LCP substrate: (**a**) Fabricated prototype; (**b**) S-parameter; (**c**) Measured and simulated S-parameter along the H-plane bent on the R = 10 mm; (**d**) Measured and simulated S-parameter along the H-plane bent on the R = 50 mm; (**e**) Simulated SAR value of 10 g of tissue on a human tissue model at 5 GHz. Reprinted from ref. [50].
## 5 Estimation Equation for Maximum Load Bearing Capacity Considering Buckling Position
### Applicability of Estimation Equations
#### 5.3.2 Comparison with Static Loading Experiments
The legend is the same as in Figure 12. Taking the buckling position into account, the estimate was smaller than the experimental value by 2% for C-008-030, but larger by 6% for C-009-032, by 3% for C-1.5 DS, and by 6% for C-2D-S. The errors were within 10% for all cases regardless of the loading method, showing that the equations can provide estimates with high accuracy. On the other hand, if the buckling position is not taken into account, the estimation may result in errors as large as 10% as in C-008-030. This shows that the accuracy of the estimation equations improved, and the maximum load bearing capacity can be estimated with an error of about 5% when buckling position is taken into account. It was also found that without performing numerical analysis, the estimation formula shown in Equation (9) can be used to obtain the required \(P_{\rm u}\) to estimate maximum load bearing capacity and can give maximum load bearing capacity estimates without producing large errors. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & \multicolumn{2}{c}{**[PERSON] et al. [9]**} & \multicolumn{2}{c}{**[PERSON] et al. [6]**} \\
**Model** & **C-008-030** & & **C-009-032** & **C-1.5D-S** & **C-2D-S** \\ \hline Diameter of steel tube \(D\) [mm] & & 400 & & 318.5 \\ Thickness of steel tube \(t_{\rm s}\) [mm] & 5.73 & & 5.77 & 6.90 \\ Height of steel tube \(L\) [mm] & & 1750 & & 1800 \\ Radius thickness ratio & 0.08 & & 0.09 & 0.058 \\ parameter \(R\) & & & & 0.407 \\ Slenderness ratio parameter \(\lambda\) & 0.30 & & 0.32 & 0.27 & 0.35 \\ Concrete filling ratio \(L_{\rm c}/L\) & & 0.34 & & 0.20 \\ Axial force ratio \(n\) & & 0.15 & & 0.20 \\ Buckling position & & Bottom part & & Bottom part \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental model specifications for comparison.
## 3 Reconstructing Missing Blocks in Autocovariance Matrices
### Case I: \(r>q\)
Note that \(H(F^{*})^{i}X=C_{q+i}\) for \(i>0\), i.e., \(H_{1}(F^{*})^{i}X=\left(C_{q+i}^{ff}\quad C_{q+i}^{fs}\right)\) and \(H_{2}(F^{*})^{i}X=\left(C_{q+i}^{sf}\quad C_{q+i}^{ss}\right)\). Therefore, \(J=\theta X\) and, in particular, \(J_{2}=\theta X_{2}\), where \(J_{2}\) and \(X_{2}\) are the submatrices formed by the last \(n_{2}\) columns of \(J\) and \(X\), respectively. Note that, keeping Hamilton-Cayley in mind, \(\theta\) has been defined such that its rank does not change when some rows of \(H(F^{*})\)are added to \(\theta\), for some \(i>nr\). Moreover, \(J\) has been defined only with available autocovariance blocks. If we rearrange the rows of \(X_{2}\) such that \(\theta X_{2}=\left(\theta_{a}\ \theta_{b}\right) \begin{pmatrix}X_{2n}\\ X_{2b}\end{pmatrix} \), we make it such that \(X_{2b}\) is the submatrix containing the unknown blocks. To calculate \(X_{2b}\), we solve the following system of linear equations:  
\[J_{2}-\theta_{a}X_{2n}=\theta_{b}X_{2b}, \tag{6}\]  
for Condition vi, (6) has as a unique solution  
\[X_{2b}=\left(\theta_{b}^{t}\theta_{b}\right)^{-1}\theta_{b}^{t}(J_{2}-\theta_ {a}X_{2n}).\]  
As a consequence, we have \(C_{i}^{ss}\) for \(i=q-r+1\), \(\ldots\), \(q\) and we can calculate \(C_{i}^{ss}\) for \(i>q\), considering that \(C_{i}=A_{1}C_{i-1}+\ldots+A_{r}C_{i-r}\) for \(i>q\). Therefore, Theorem 1 has been proven. \(\square\)  
The following Corollary 1 is a consequence of section (a) of the previous Theorem and of the Theorem in [9]. Corollary 1: _Suppose \(r>q\) and that Conditions I, ii and III hold. In this case: rank(\(A_{r}\): \(B_{q}\)) = \(n\) and (a(z), b(z)) is left coprime iff rank\(C_{nr}(F,\ G^{*})\) = \(nr\)._  
Note that, unlike [1], we can consider rank \(A_{r}<n\) or rank \(B_{q}<n\).
## 2 Materials and Methods
### Proposed Model
#### 2.3.6 Artificial Neural Network (ANN)  
1. Multilayer Perceptron (MLP)  
A multilayer perceptron is a thinking structure that is formed by connecting neurons to each other with synaptic connections, which is inspired by the human brain and has a learning algorithm, similar to neural networks in biological systems. An MLP represents a feed-forward artificial neural network (ANN) model, which maps sets of input data onto a set of suitable outputs. Figure 7 presents the architecture of MLP. An MLP contains multiple layers of nodes in a directed graph, with every layer completely connected to the following one. Apart from the input nodes, every node constitutes a neuron, or a processing element, with a nonlinear activation function. The MLP uses a supervised learning technique that is called back-propagation in order to train the network [52].  
2. Convolutional Neural Network (CNN).  
Deep learning is generally applied by utilizing the neural network architecture. With deep learning, the model learns and abstracts the relevant information automatically as the data passes through the network. The term \"deep\" denotes the number of layers in the network, i.e., the higher the number of layers is, the deeper the network is. Layers are interconnected through nodes, or neurons, with every hidden layer by utilizing the previous layer's output as its input.  
Figure 6: The architecture of a GRU recurrent neural network [51].  
Figure 7: The architecture of a multilayer perceptron (MLP) [53].  
The most popular deep learning network is a convolutional neural network (CNN, or ConvNet). The CNN includes an input layer, an output layer, and several hidden layers. Convolution, activation or using a rectified linear unit (ReLU), and pooling represent the three most frequent layers. The above-mentioned operations are performed repeatedly over tens or hundreds of layers, with every layer learning for the purpose of detecting various features in the input data [53]. Figure 8 shows the general framework of convolution neural networks (CNNs).