esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Sustainability and Environmental Impact  
Environmental implications from IoV-related technologies are the focus of critical research in the ecosystem of the IoV and the smart cities. This involves finding ways to both consume less energy [112] and emit feweremissions, requiring innovative transportation solutions that effectively address these challenges. This research adds by analyzing IoV's environmental track to provide this contribution to the vision of smart cities by orienting the development of technology towards a greener eco-friendly direction for the environment in current urban design.
## 4 Cluster-Based Multipath Routing in WMSN  
This section describes the routing operation of our proposed cluster-based multi-path routing protocol for WMSNs, called SCMR, which is based on the hierarchical structure of multiple paths established depending on hop count and received signal strength (along with measured SNR \(\&\) BER) as an indication on the link quality and distance between the nodes. SCMR depends on the local information exchanged among the nodes to establish the routes to the sink and does not require any coordination measurement equipments or position message exchange.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Regression Models  
In this paper, the SVR-based machine learning technique is adopted to implement the complex nonlinear mapping relationship between quality-aware features and subjective evaluation values, which has been depicted in Figure 2. The SVR is frequently used to pool high-dimensional data. The predictive value \(Q_{T}\) of textual regions of an SCI is calculated as  
\[Q_{T}=Fun_{T}(F_{T}) \tag{31}\]  
where \(F_{T}\) denotes the features of textual regions of this SCI which are extracted in (7) and \(Fun_{T}\) represents a regression model which has been trained beforehand by employing the SVR. Here, the \(\varepsilon\)-SVR [47] is used to conduct the regression model learning and \(Fun_{T}\) is given as  
\[Fun_{T}(x)=\sum_{j=1}^{J}\left(\beta_{j}-\beta_{j}^{*}\right)K\left(x_{j},\ x \right)+b \tag{32}\]  
\[K\left(x_{j},\ x\right)=e^{-\rho\left[x_{j}-x\right]^{2}} \tag{33}\]  
where \(\beta_{j}\) and \(\beta_{j}^{*}\) (\(0\leq\beta_{j}\), \(\beta_{j}^{*}\leq C\)) denote the Lagrange multipliers, \(C\) represents the tradeoff error parameter, \(b\) is a bias parameter, \(J\) represents the number of support vectors, \(x_{j}\) denotes the \(j\)th support vector, \(x\) denotes the feature vector of textual regions, \(K\left(x_{j},\ x\right)\) is a radial basis function (RBF) kernel and \(\rho\) denotes the width of the RBF kernel. More detail about the \(\varepsilon\)-SVR can be found in [47].  
Similarly, we can derive the predictive value \(Q_{P}\) of pictorial regions of an SCI as  
\[Q_{P}=Fun_{P}(F_{P}) \tag{34}\]  
where \(F_{P}\) represents the features of pictorial regions of SCIs extracted in (30) and \(Fun_{P}\) denotes the trained regression model.
## 4 Proposed Online Method for Impedance Characterization of Electrical Machines
### Step 3--Search for dominant frequency components
#### Step 5--Combining impedances  
To take advantage of the precision of both plots around the different resonance frequencies, we will concatenate these two characterizations. The superposition of the two curves is shown in Figure 20. Despite the relative clarity of the full impedance characterization, we can see that it remains fairly noisy, which is why it is necessary to perform a cleanup step on the raw data.  
Figure 19: Raw frequency impedance characterization obtained by thresholding simple voltages (**a**) and currents (**b**).  
Step 6--Cleaning the raw impedance characterization  
To extract a more precise impedance characteristic from the raw pattern generated in the previous step, we need to perform a \"cleaning\" step. The strategy adopted is to cluster the various points gravitating around multiples of the switching frequency. In this study, an interval of 5% on either side of these multiples was considered. Thus, the impedance amplitude and phase for each of these multiples is defined as the average of the values within this interval. If no point is close to one of the switching frequency multiples, then no value is assigned to it.  
By applying this strategy to the raw impedance characterization obtained in Figure 20, we obtain a much cleaner frequency spectrum, especially at very high frequencies. To complete the resulting spectrum, the value of the impedance calculated at the fundamental excitation frequency (\(f_{e}\)) is added. This new characterization is shown in Figure 21.  
Step 7--Model storage and aging data collection  
Finally, for aging monitoring purposes, it is essential to accurately evaluate the various parallel and series resonances of this improved impedance characterization. Since each point represents a multiple of the switching frequency, it is not possible to precisely assess these  
Figure 21: Improved frequency impedance characterization.  
Figure 20: Complete raw frequency impedance characterization.  
resonance values. There are two ways to recover these informations. The first is to interpolate these profiles and extract local extremes in order to analyze their evolution. The second is to model the impedance frequency behavior of the machine phases using the various approaches presented in Section 3.2 and look for behavioral deviations synonymous with aging.  
Figure 22 shows the impedance behavior obtained by interpolating the improved characterization calculated in step 6. The type of interpolation used is the \"Akima spline\" [57]. The first two parallel and series resonances can be properly characterized, and data can be stored to monitor the health of the winding. Finally, the overall behavior at very high frequencies is more erratic and fails to accurately identify the other resonances.
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 3 Asynchronous Flipped SCMA Scheme
### Complexity Analysis of AF-SCMA  
Computational complexity is an important metric to evaluate the performance of an algorithm. For the proposed scheme AF-SCMA, the computational complexity can be analyzed from the aspects of pilot sequence detection and SCMA decoding process. For pilot sequence detection, we adopt the sliding correlation technique to detect the pilot sequence in a sliding window with the size of \(W\). For each iteration round, the complexity of sliding correlation is about \(O\left(\left(n_{p}W/\tau_{p}\right)log_{2}\left(n_{p}W/\tau_{p}\right)\right)\) by using fast Fourier transform (FFT) [33], where \(n_{p}\) is the length of the pilot sequence in bit and \(\tau_{p}\) is transmission duration of the pilot sequence. For the SCMA decoding process, the computational complexity is related to the decoding algorithm. In this paper, we adopt the typical but efficient MPA to decode the SCMA codewords. Thus, the decoding complexity for a chunk is about \(O\left(M^{d_{r}}\right)\)[34], where \(M\) is the size of the codebook and \(d_{r}\) is the number of interference codewords occupying the same subcarrier. To decode a packet, the decoding process will repeat \(N_{chunk}\) times, where \(N_{chunk}\) is a small positive integer in practice (no bigger than 10). Therefore, the decoding complexity for a packet is about \(O\left(M^{d_{r}}\right)\). Please note that based on the conventional MPA, some improved algorithms have been put forward to further reduce the SCMA decoding complexity. However, researching SCMA decoding algorithms with lower complexity is out of the scope of this paper, which is left for future work.
# Proving Fixed-Point Theorems Employing Fuzzy \((\sigma,\mathcal{Z})\)-Contractive-Type Mappings
## 3 Main Results
: The existence of the fixed point follows from Remark 2, Part \((a)\), and Theorem 5, and the uniqueness of the fixed point follows from Theorem 6. **Corollary 3**. : _Let \((\mathcal{K},\mathcal{M},*)\) be an \(\mathcal{M}\)-complete FMS and \(\sigma:\mathcal{K}\times\mathcal{K}\times(0,\infty)\to[0,\infty)\). Assume that \(T:\mathcal{K}\to\mathcal{K}\) is a \(\sigma\)-\(\psi\)-fuzzy contractive mapping satisfying the following assumptions:_  
\((i)\) _T is_ \(\sigma\)_-admissible;_ \((ii)\) _There exists_ \(\alpha_{0}\in\mathcal{K}\) _with_ \(\sigma(\alpha_{0},T\alpha_{0},t)\leq 1\)_, for each_ \(t>0\)_;_ \((iii)\) _For each sequence_ \(\{\alpha_{n}\}\) _of_ \(\mathcal{K}\) _with the property that_ \(\sigma(\alpha_{n},\alpha_{n+1},t)\leq 1\)_, for each_ \(t>0\)_, there exists_ \(k_{0}\in\mathbb{N}\) _such that_ \(\sigma(\alpha_{n},\alpha_{m},t)\leq 1\)_, for each_ \(m,n\in\mathbb{N}\) _with_ \(m>n\geq k_{0}\)_,_ \(t>0\)_;_ \((iv)\) _If_ \(\{\alpha_{n}\}\) _is a sequence in_ \(\mathcal{K}\) _such that_ \(\lim_{n\to\infty}\alpha_{n}=\alpha\in\mathcal{K}\) _and_ \(\sigma(\alpha_{n},\alpha_{n+1},t)\leq 1\)_, for each_ \(n\in\mathbb{N}\) _and_ \(t>0\)_, then_ \(\sigma(\alpha_{n},\alpha,t)\leq 1\)_._ _In addition, assume that_ \(\lim_{n\to\infty}\inf_{m>n}\mathcal{M}(\alpha_{n},\alpha_{m},t)>0\)_, for all_ \(x\in\mathcal{K}\) _and_ \(t>0\)_. Then,_ \(T\) _admits a fixed point._  
Proof. : In view of Theorem 5 and Remark 2 part \((b)\), we need only to show that the quadruple \((\mathcal{K},\mathcal{M},T,\xi)\) owns the property \((S^{\prime})\), where \(\xi(l,s)=\psi(s)\), for each \(l,s\in(0,1]\) and \(\psi\in\mathbb{V}\). Assume that \(\{\alpha_{n}\}\) is any Picard sequence for all \(n\in\mathbb{N}\) such that for each \(n\in\mathbb{N}\) and \(t>0\)\(\inf_{m>n}\mathcal{M}(\alpha_{n},\alpha_{m},t)\leq\inf_{m>n}\mathcal{M}(\alpha_{n+1 },\alpha_{m+1},t)\) with \(0<\lim_{n\to\infty}\inf_{m>n}\mathcal{M}(\alpha_{n},\alpha_{m},t)=\alpha(t)\). Then, by the definition of \(\psi\), we have  
\[\lim_{n\to\infty}\inf_{m>n}\xi(\mathcal{M}(\alpha_{n+1},\alpha_{m+1},t), \mathcal{M}(\alpha_{n},\alpha_{m},t))=\psi(\alpha(t)).\]  
Since \(T\) is a \(\sigma\)-admissible mapping, one easily can show that \(\sigma(\alpha_{n},\alpha_{n+1},t)\leq 1\), for each \(n\in\mathbb{N}_{0}\) and \(t>0\). Applying Condition \((iii)\), there exists \(k_{0}\in\mathbb{N}\) such that for each \(m,n\in\mathbb{N}\) with \(m>n\geq k_{0}\), \(t>0\), we have  
\[\sigma(\alpha_{n},\alpha_{m},t)\leq 1.\]  
The \(\sigma\)-\(\psi\)-fuzzy contractivity of the mapping \(T\) gives rise to  
\[\psi(\alpha(t)) \leq\lim_{n\to\infty}\inf_{m>n}\sigma(\alpha_{n},\alpha_{m},t) \mathcal{M}(T\alpha_{n},T\alpha_{m},t)\] \[\leq\lim_{n\to\infty}\inf_{m>n}\mathcal{M}(T\alpha_{n},T\alpha_{m},t)=\alpha(t),\]  
that is, \(\psi(\alpha)\leq\alpha(t)\) which implies that \(\alpha(t)=1,\forall\,t>0\), and hence, we have  
\[\xi(l,s)=\frac{s}{l}\text{ \ and \ }\sigma(x,y,t)=1+\frac{|x-y|}{t}.\]0  
This completes the proof.
## 1 Introduction
White Sharks are most commonly distributed throughout sub-tropical and temperate regions [41]. In Australia, they are distributed throughout southern waters, from southern Queensland on the east coast to the North West Cape in Western Australia [42]. Studies on the presence and movements of juvenile and sub-adult (180-320 cm total length--TL) White Sharks have found that they occur mainly in coastal, continental shelf, or slope waters [37; 38; 43; 44; 45] with larger sharks using more pelagic environments [46], often aggregating around continental islands [47; 48; 49]. Along the Australian east coast, juvenile and sub-adult White Sharks have complex patterns of movements with high individual variability, yet exhibit seasonal site fidelity to a few locations [37; 44]. Although juvenile and sub-adult White Sharks are present along the east coast year-round, they exhibit higher occupancy in nearshore areas in central and northern NSW from June to November [37; 50], which has implications for human-wildlife conflict management. Following a cluster of shark bites in northern NSW in 2014/2015, the NSW Government established the NSW Shark Management Strategy (SMS), which trialed a suite of new and emerging shark bite mitigation measures with the objective of increasing protection for water users while minimizing harm to target and non-target species ([[https://www.sharksmart.nsw.gov.au](https://www.sharksmart.nsw.gov.au)]([https://www.sharksmart.nsw.gov.au](https://www.sharksmart.nsw.gov.au)), accessed on 17 July 2023). One of these new shark bite mitigation measures was SMART (Shark-Management-Alert-in-Real-Time) drumlines, which were developed on Reunion Island in 2013 [51]. They are traditional drumlines coupled with a Global Positioning System (GPS) enabled buoy that automatically provides near real-time alerts via satellite communications to the presence of a captured animal via email, phone call, and text message. This enables a rapid response time, reducing the likelihood of injuries or fatalities for the captured animals. SMART drumline trials in NSW have successfully caught all three target species [52; 53]. Catch protocols require responders (scientists or contractors) to attend to captured animals within 30 min to maximize survival. Analysis of blood physiology from White Sharks caught on SMART drumlines indicates that the capture process is relatively benign and that the response times in NSW are appropriate to minimize long-term negative impacts on released White Sharks [52; 54]. However, one of the concerns of stakeholders is that sharks released from SMART drumlines may remain inshore post-release, effectively negating the shark bite mitigation measure. The primary aim of this study is to quantify the short-term post-release movements and the longer-term fate of White Sharks after capture, tagging, and release from SMART drumlines. This study will provide critical information to support the utility of this process as a non-lethal bather protection tool.
# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 3 Proposed Method
### Network Architecture Design
#### 3.1.1 Generator Network Architecture  
The generator network architecture is shown in Figure 2.  
The generator network architecture consists of regular convolution layers (Conv) and dense connection layers (Dense). The concatenated infrared and visible images are fed into the generator network. The first three layers are regular convolution layers, where the input layer and the second layer have a kernel size of \(5\times 5\), and the third layer has a kernel size of \(3\times 3\). Dense 1 to Dense 6 are introduced dense connection blocks, where the input of Dense 3 is the sum of the outputs from Dense 1 and Dense 2; the input of Dense 4 is the sum of the outputs from Dense 1, Dense 2, and Dense 3; the input of Dense 5 is the sum of the outputs from Dense 1, Dense 2, Dense 3, and Dense 4. Moreover, a standard convolutional layer with a kernel size of \(7\times 7\) is employed to link the output of the input layer to the outputs of Dense 1 to Dense 5, which then acts as the input for Dense 6. Ultimately, the merged outcome is achieved using another standard convolutional layer with a kernel size of \(1\times 1\). Batch normalization and activation functions are incorporated  
Figure 1: Fusion framework.  
Figure 2: Generator network architecture.  
into every layer of the generator network architecture, with the output layer utilizing tanh as the activation function, while the remaining layers utilize LeakyReLU.  
The parameters related to the generator network architecture are presented in Table 1, with K denoting the size of the convolution kernel, S the stride, and P the padding of the convolution kernel; N1 and N2 denote the quantity of input and output channels, respectively.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 3 GSFedSec: GS-Based SA for Privacy Preservation in FL
### System Setup
#### 3.1.6 Secure Aggregation  
After receiving updates from participating devices, the server collects all updates. These updates are padded with \(S_{i}\). To cancel the masks \(S_{i}\) collected from all clients, the server needs an unmasking key, which the admin sends along with the latest GS. Then, the Server aggregates all updates to obtain a new updated model. The formula for SA can be expressed as given in Equation (12):  
\[\omega_{t+1}\leftarrow\Sigma_{i=1}^{K}\frac{n_{i}}{n}w_{t+1}^{i}, \tag{12}\]  
where \(\eta\) is the fixed learning rate, \(K\) is the number of clients, \(C\) is the fraction of clients participating in the process, \(\omega\) is the model parameter, \(\omega_{t}\) is the current model, and \(\omega_{t+1}\) is the subsequent model. We define \(n=\Sigma_{i}n_{i}\), where \(n\) is the total number of samples in the dataset. Algorithm 1 outlines the procedures at the server, clients, and admin of the proposed algorithm.  
```
1:A Server Executes:
2:initialize \(\omega_{0}\)
3:for each iteration round \(t\in N\)do
4:\(S_{t}\) = set of \(n\) clients
5: send \(S_{t}\) to the admin
6:for each client \(i\in S_{t}\)do
7:\((\omega_{t+1}^{i},\sigma_{Upd}^{i})\gets ClientUpdate(i,w_{t})\)
8: decrypts \(\sigma_{Upd}^{i}\)
9: verifies \(\sigma^{i}\): Equations (10) and (11)
10:endfor
11:\(w_{t+1}^{i}\leftarrow\Sigma_{i=1}^{K}\frac{n_{i}}{\pi}w_{t+1}^{i}\)
12:endfor
```  
**B. ClientUpdate(\(i,w_{t}\)):**  
```
1:\(\sigma\leftarrow\mathit{AdminGenerates}(x_{i})\)
2:\(B\leftarrow\) split \(P_{i}\) into batches of size \(B\)
3:for each client \(i\in S_{t}\)do
4:for batch \(b\in B\)do
5:\(\omega_{t+1}^{i}\leftarrow\omega_{t}^{i}-\eta\triangledown(\omega;b)\)
6:\(\sigma_{Upd}^{i}\gets ClientSign(\omega_{t+1}^{i})\)
7:endfor
8:endfor
9:return encrypted \((\omega_{t+1}^{i},\sigma_{Upd}^{i})\) to the server
```  
**C. ClientSign(\(\omega\)):**  
```
1:compute tracing variables: Equations (2a) and (2b)
2:compute binding variables: Equations (4a)-(4c)
3:compute challenger: Equation (5)
4:compute signing variables: Equations (6a) and (6b)
5:generate group signature: Equation (7)
6:encrypt model update: Equation (8)
7:add secret key \(S_{i}\): Equation (9)
8:return\(Upd_{se}\) to ClientUpdate(\(i,w_{t}\))
```  
**D. AdminGenerates(\(x\)):**  
```
2:generate \(S_{n}=PRG(n)\)
3:divide \(S_{n}\) into \(n\) parts
4:for each client \(i\) from \(1\) to \(m\)do//\(m\) = total number of clients
5:\(A_{i}\gets g^{1/(\tau+x_{i})}\in G_{1}\)
6:for each \(i\) from \(1\) to \(n\)do//\(n\) = number of clients selected for FL
7:\(A_{i}S_{i}=A_{i}+S_{i}\)
8:endfor
9:endfor
10:return\(A_{i}S_{i}\)
```  
**Algorithm 1** GSFedSec.
# Evaluation of the Aerodynamic Effect of a Smooth Rounded
## 2 Methodology: Wind Tunnel Experimental Tests  
In this section, the methodology applied to study the flow around the train under crosswind by means of wind tunnel tests is described.  
To study the phenomenon, wind tunnel tests were performed in compliance with the European Standard for crosswind assessment EN14067-6:2018 for high-speed trains and TSI. In this case, the relative wind yaw angle given by a particular combination of train and wind speed, is reproduced in the wind tunnel test by the equivalent case in which the wind velocity acts on the static model of the train with an absolute yaw angle \(\beta_{\mathrm{w}}\).  
The studied vehicle is the Italian ETR1000, the high-speed train of the Italian railway system, operating at the speed of 300 km/h. The train convoy is composed of eight coaches in fixed composition for a length of 202 m. In line with other modern high-speed trains, the cross section of the ETR1000 is characterised by the presence of a closed smooth rounded roof. The train nose, on the contrary, is characterised by the presence of sharp edges. For this study, the first car of the ETR1000 was reproduced in 1:15 scale, and then, to correctly reproduce the boundary conditions, one-half of the second vehicle has been added to the tested model.  
Forces and moments are measured by means of a dynamometric balance, rigidly connected to the first vehicle model, while 160 pressure taps are placed on the surface of the scaled model to measure pressure distribution.
# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 4 Experiments
### Ablation Studies
#### 4.3.1 Ablation Study of ICSA  
The intra-clip self-attention (ICSA) module is designed to capture short-term motion features, where \(n\) denotes clip length. When \(n\) takes different values, the ICSA module can capture short-term motion information at varying granularities. As shown in Figure 9, \(n=1\) denotes the number of successive frames within a clip. When \(n=1\), the ICSA module models joint correlations within a single frame, whereas \(n=3\) enables the capture of inter-joint dependencies across three consecutive frames.  
As shown in Figure 9, on the NTU RGB+D dataset, when \(n=1\), the recognition performance of the proposed MTC-Former achieves 89.0% (X-Sub) and 93.8% (X-View). As \(n\) increases, the model's recognition performance reaches its optimal level at \(n=3\), with accuracy rates of 90.3% (X-Sub) and 95.0% (X-View), achieving improvements of 1.3% (90.3% vs. 89.0%) and 1.2% (95.0% vs. 93.8%), respectively, compared to ICSA\({}_{n=1}\). However, when \(n>3\), the recognition performance of the model gradually declines.  
When \(n=3\), the model achieved the best performance because it captures a sufficient range of temporal dynamics without incorporating irrelevant information. Too small n (e.g., 1 or 2) fails to reflect temporal variations, leading to underutilized motion cues. Too large n (e.g., 4 or more) includes redundant or noisy temporal data, which may distract the attention mechanism.  
Figure 8: Top-1 recognition curves with hyperparameters on the X-Sub setting of NTU RGB+D skeleton dataset, including **(a)** the dilation ratio in MSTI and **(b)** the number of heads in MHSA.  
Figure 9: Top-1 recognition accuracy curves with varying clip lengths (n).
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 5 Discussion and Future Research  
In this study, we modify the structure of OpenPose, which is a widely used bottom-up HPE model, to additionally extract features from depth images and progressively fuse them with features for color images. The additional use of depth images significantly reduces instances where joints of different individuals are mistakenly assigned to the same skeleton in occluded scenes. Furthermore, compared to conventional early fusion or late fusion approaches, in which features are integrated either at the feature extraction stage or at a later stage, our proposed progressive fusion further improves the accuracy of individual skeleton detection. Within the proposed HPE model, global features are derived from color images, while depth images serve as a valuable source of local 3D topological information. Despite the exclusion of occluded joints from the OKS calculation, we observed that the influence of other joints on the remaining joints is reduced. This indicates that the proposed method can maintain pose estimation consistency by suppressing body-to-body interference under the occlusion scenes. While this study presents performance results on a collective set of occlusion samples, future work examining the impact of different occlusion severities may provide further insight into the robustness of the proposed model.  
Although NTU RGB+D 120 provides enough body-to-body occlusion scenes, it was optimized for action recognition. Consequently, the skeleton annotations may have lower precision. This can limit the accurate evaluation of the proposed method.  
The proposed feature fusion method was applied to OpenPose, but it can also be extended to the latest SOTA bottom-up HPE models. Furthermore, the HPE performance can be improved by employing advanced feature extractors such as HRNet or CSPNeXt instead of VGG. While the proposed method uses \(1\times 1\) convolution operations for feature fusion, introducing more sophisticated fusion techniques such as attention-based fusion or cross-modal gating could further enhance the representation capacity.