esa-sceva/satcom-chunk-collection huggingface dataset:
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 4 Results and Discussion  
Sections 4.1 and 4.2 report and discuss the results directly obtained from the MT-InSAR analysis of COSMO-SkyMed SAR images of the area of interest. Specifically, Section 4.1 interprets the displacement time series and velocity of the PSs identified on the bridge and its access lines, while Section 4.2 interprets their consistency with the expected response to temperature variation. On the other hand, Sections 4.3 and 4.4 investigate and discuss the correlation between the displacement time series and environmental loads. Specifically, Section 4.3 focuses on the correlation of displacements with the variations in air temperature, while Section 4.4 focuses on the correlation of displacements with the variation of the water level of the Po River measured in proximity to the bridge. They also investigate whether it is possible to identify different bridge spans based on the opposite longitudinal deformation of the bridge cantilevers in the floodplain and the effect of water level variation on the response of the bridge piers in the riverbed.
## 4 Cryptanalysis of Son et al.'s Son et al. (2019) Protocol
### TA Circumvention Attacks  
In a secure VANET system, vehicles must register with the TA before accessing services. However, the following attack scenario demonstrates a vulnerability. An RSU administrator can impersonate a TA and complete the vehicle registration process independently. This breach is possible because the legitimate TA uses a shared secret key \(k\) with the RSU to verify the vehicle registration information uploaded to the blockchain. An RSU with knowledge of \(k\) can bypass the TA, create registration data, and upload it to the blockchain. The attack process unfolds as follows:  
1. [label=Step 0.,leftmargin=*,noitemsep,topsep=0 pt]
2. The vehicle selects \(ID_{i},PW_{i}\) and \(x_{i}\), computes \(HPW_{i}=h(ID_{i}||PW_{i})\) and \(RPW_{i}=HPW_{i}\oplus h(x_{i})\), and sends \((ID_{i},RPW_{i})\) to the RSU, not the TA.
3. Upon receiving \((ID_{i},RPW_{i})\), the RSU generates \(r_{i}\) and computes \(RID_{i}=h(ID_{i}||RPW_{i}||r_{i})\).
4. The RSU then uploads \(h(RID_{i}||k)\) to the blockchain.  
This process effectively completes the registration of the vehicle by the RSU, allowing the vehicle to authenticate interactions with all RSUs using their registered \(RID_{i}\). Consequently, \(\mathcal{A}\) can launch attacks upon bypassing the TA.
# Photovoltaic Roundabouts for Enhancement of Self-Sufficiency and Resiliency
## 2 Materials and Methods
### Evaluation of the Critical Diameter  
The final phase of the methodology used in this study was to evaluate the self-sufficiency of the roundabouts in terms of the power required for lighting. In particular, a dimensional parameter was introduced for the geometry of the roundabouts, which represents the threshold for achieving the aforementioned self-sufficiency.  
This parameter, called \"critical diameter\", was defined as the value of the inscribed diameter at which the balance between the energy generated by the photovoltaic system and the energy required for the artificial lighting of the roundabout is guaranteed. This means that choosing an effective diameter larger than the critical diameter guarantees the generation of a surplus of electricity that can be used for other purposes (e.g., charging electric vehicles, reselling energy to commercial companies, etc.).  
The critical diameter values were estimated for all configurations that were the subject of the previous assessments. In addition, the final discussions were articulated with reference to two specific critical diameter values:  
1. Lower critical diameter: value of the critical diameter corresponding to the balance between the energy required by the lighting configuration with the lowest consumption among the configurations simulated in the previous point and the electricity generated by the photovoltaic systems considered.
2. Upper critical diameter: value of the critical diameter corresponding to the balance between the energy required by the lighting configuration with the highest consumption among the configurations simulated in the previous point and the electricity generated by the photovoltaic systems considered.
## 4 Method
### Feature Extraction in Closeness and Period  
To consider both spatial and temporal characteristics, ADNet uses a dense block (Figure 3b) to consider spatial dependencies in the convolution kernel in a dense block. It takes two types of inputs for each dense block: one sampled in units of \(p\) time steps, and the other sampled in units of \(q\) days. This design enables each dense block to specialize in two types of predictions: closeness prediction, which captures short-term temporal trends, and periodic prediction, which models long-term periodic patterns based on date. Additionally, by utilizing dense blocks composed of convolution layers, batch normalization, and ReLU activation functions, the model can leverage deeper network architectures. If we define \(X_{C}^{0}\) as the initial input, and \(f_{L}()\) as \(L\)-th dense block, we can express the output of \(L\)-th dense block in closeness \(X_{C}^{L}\), and the input of this block can be expressed as  
\[X_{C}^{L}=f_{L}\left(X_{C}^{0}\oplus X_{C}^{1}\oplus\cdots\oplus X_{C}^{L-1} \right), \tag{1}\]  
where \(\oplus\) denotes concatenation.  
Figure 1: Analysis of (**Left**) temporal characteristics and (**Right**) spatial characteristics of Telecom Italia dataset (Internet).  
Similarly, the output of the \(L\)-th dense block in period \(X_{P}^{L}\) and input of this block can be expressed as  
\[X_{P}^{L}=f_{L}\left(X_{P}^{0}\oplus X_{P}^{1}\oplus\cdots\oplus X_{P}^{L-1} \right), \tag{2}\]  
The first convolutional block (Figure 3a) and the last convolutional block (Figure 3c) are not densely connected to other dense blocks. The first convolutional block connects input to feature extraction, and the last one connects feature extraction to attention module.  
Figure 3: The details of blocks in feature extraction stage. (**a**) The first convolution block, (**b**) dense block, and (**c**) the last convolution block.  
Figure 2: The framework of proposed method. The model receives \(p\) inputs to extract closeness features and \(q\) inputs to extract period features.
## 6 Conclusions  
In this work, the authors presented various performance metrics for wireless sensor networks using AODV, DSR, ZTR and the proposed MPH routing protocol. Besides, we compared the energy efficiency of MPH with that of AODV, DSR and ZTR. We tested our MPH protocol in a simulator using conditions taken from a real environment and evaluated its performance for 10 days for 10 specific nodes chosen randomly. The simulation results were compared with the corresponding measurements gathered from the real network. This comparison showed that the simulator was well programmed and gave results very close to those of the real network under the conditions and parameters used. This proved that it was a reliable tool to simulate different conditions of a WSN.  
We then analyze metrics that have a direct impact on the energy consumption of a network. Retransmissions, retries to access the channel, the number of discovered routes in the tables of the nodes, the recovery time after a failure in some links of a topology that already has valid routes: all these are parameters that affect the local and global energy consumption. The MPH protocol was designed for a scenario where there is a single, robust, high processing node, called the coordinator node, and the other nodes have low memory and low processing capabilities. The aforementioned metrics were calculated and analyzed for this particular scenario, and the performances of AODV, DSR, ZTR and MPH were evaluated under the same conditions of topology and network design, using an energy model proposed by us. The MPH protocol showed great advantages over the other protocols regarding network reconfiguration under adverse conditions. This was so because MPH has a reactivity mechanism to cope with changes in the network and enjoys a smooth route maintenance mechanism that enters into action when the links are connected intermittently. In addition, the developed energy model presents better performance under the MPH protocol, because this protocol always guarantees the shortest route to the destination, which translates into lower packet delays and fewer packet retransmissions, because there are fewer intermediate nodes and also because MPH has more valid routes, so that packets are forwarded correctly most of the time.  
In this paper, we also compared the performance of MPH with that of a simple routing algorithm, such as ZTR, in order to show that MPH is a highly competitive protocol in WSNs, with good performance as measured by the results in the metrics considered and with an energy consumption equivalent to that of a less complex routing algorithm, such as ZTR. Besides, we could show that even though MPH presents more elaborate neighbor tables, this does not represents higher complexity compared with a simple solution in WSNs, such as ZTR.
## 5 Analysis of the Forecasting Results
### Single-Step Forecasting
#### 5.1.1 Analysis of the Proposed Method and Conventional Models  
The forecasting results of the proposed model and conventional models are shown in Tables 4 and 5. The original time series and preprocessed data obtained by the EEMD technique is shown in Figure 3. By comparing the results illustrated above, the following conclusions are made:  
The fluctuation and instability of the original time series are clearly shown in Figure 3. Additionally, in Figure 3, the reconstructed wind speed data in which the signals with high frequency have been removed obviously shows reduced noise of the raw data. By comparing the forecasting results of the new preprocessed data with the original data from Tables 2 and 3, the model achieves a high performance when using the de-noised wind speed data. The MAPE values of the CSOSVM model decrease by 5.88%, 5.56%, 2.39%, and 7.36%, respectively. Using the PSOSVM model for comparison, the MAPE values also decrease by 6.55%, 6.88%, 5.34%, and 5.71% in four datasets, respectively. Therefore, the EEMD technique has good validity.  
The proposed hybrid approach achieves better performance than the conventional methods in wind speed prediction. From Table 4, the MAPE value of BPNN, RBFNN, ARIMA, and the proposed model in wind turbine 1 are 8.68%, 10.32%, 8.41%, and 4.79%, respectively. In the other three datasets the proposed method
## 1 Introduction
The thicker Rogers RT5880 material was selected for its low-loss properties, while the increased thickness and the use of Rogers 4350B material help reduce the Q factor of the equivalent resonant circuit, thus broadening the frequency band. Our innovative logarithmic function approach for constructing the microstrip polarization conversion radiating cell results in a smoother impedance transition, which not only improves the bandwidth of the LCPC but also enhances the purity of the reflected CP EM wave. In addition, the incorporation of a \(\lambda/4\) (\(\lambda\) is the wavelength corresponding to the operating frequency) microstrip sector-branch DC bias circuit effectively mitigates the impact of the DC source on the impedance characteristics of the high-frequency radiation components. By applying different DC bias voltages to adjust the dielectric constant of the LC, the MS maintains stable and controllable multi-band LCPC and polarization-selective conversion performance. The paper is organized as follows. Section 2 provides a detailed description of the structural design of the MS unit cell and introduces the characteristics and tuning mechanism of the LC. Section 3 presents the simulation results of the MS unit cell and analyzes the LCPC mechanism. Section 4 evaluates the performance of the MS by comparing and validating the results obtained from both the simulation and experimental measurements. Finally, Section 5 briefly summarizes the main contributions of this study and draws conclusions.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ4--Edge Intelligence Application Domains
#### 4.4.2 Surveillance  
Surveillance applications such as seismic imaging [69], air pollution prediction [42], smoke detection [146] and human activity recognition often require systems that can monitor ongoing activities in nearly real-time through object tracking and detection [155], action and activity recognition, event detection, and scene understanding [156; 157; 158]. Therefore, latency is a critical aspect of this domain, which validates the recent works in this field using edge intelligence as an alternative to the cloud.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Domains** & **Works That Approach the Theme** \\ \hline Industry (8) & [8,31,51,53,68,92,95,122] \\ Surveillance (5) & [42,69,81,120,146] \\ Security (4) & [9,29,71,147] \\ Intelligent Transport Systems (ITS) (13) & [26,40,43,45,51,52,56,58,75,148–151], \\ Health (14) & [13,25,28,47,48,51,67,70,84,91,101,117,152,153] \\ Energy Management (4) & [38,50,51,94] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Application domains and corresponding works.  
Figure 7: Publications by domain application.
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 3 ED and PTAT Block Description
### Temperature Compensation through the PTAT Block
From (1), assuming \(V_{DS,M4}\geqslant 3V_{T}\), the bias current of \(M4\) is  
\[I_{M4}=I_{S}\left(\frac{W}{L}\right)_{M4}e^{\frac{V_{DS,M4}}{nV_{T}}}. \tag{14}\]  
Substituting (14) in (13), and again assuming the difference in \(V_{DS}\) between the diodes and \(M4\) not to significantly affect \(V_{TH}\),  
\[r_{DS}=\frac{V_{T}}{I_{M4}}\frac{\left(\frac{W}{L}\right)_{M4}}{\left(\frac{W} {L}\right)_{MD}}. \tag{15}\]  
Due to the 1:1 mirror \(M1\)-\(M2\), the bias current of \(M4\) is equal to that of \(M3\) and is  
\[I_{M4}=I_{M3}=\frac{nV_{T}}{R_{PTAT}}\ln\frac{\left(\frac{W}{L}\right)_{M3}}{ \left(\frac{W}{L}\right)_{M4}}. \tag{16}\]  
Therefore, combining (16) with (15), it is possible to prove that  
\[R_{in}=\frac{r_{DS}}{N}=\frac{1}{N}\frac{\left(\frac{W}{L}\right)_{M4}}{\left( \frac{W}{L}\right)_{MD}}\frac{R_{PTAT}}{n\ln\frac{\left(W/L\right)_{M3}}{\left( W/L\right)_{M4}}}, \tag{17}\]  
which shows that \(R_{in}\) is theoretically not a function of \(T\). A simple start-up circuit, not shown in Figure 2 for the sake of simplicity, is also included to push the PTAT block away from its zero-current stable DC operating point and push it to the actual DC operating point it was designed for.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 2 The Considered Class of Flexible Job Shops and Their Representation
### Evaluation of Solution Performance  
The solution of the above problem produces the make-span:  
\[MS=\max_{\forall i,\forall j\in A_{i}}\bigl{\{}\bigl{(}t_{iSij}+d_{iSij}|x_{ ij}=1\bigr{)}\bigr{\}}-\min_{\forall i,\forall j\in A_{i}}\bigl{\{}\bigl{(}t_{i1j} |x_{ij}=1\bigr{)}\bigr{\}} \tag{19}\]  
In Equations (14)-(16), the decision variables \(x_{ij}\) are absent; then, by choosing for each \(t_{ijk}\) with \(x_{ij}=0\), their earlier starting time, constraints (14)-(16) are satisfied by these \(t_{ijk}\) variables. Then, in the case in which all the release time are to zero, (19) can be rewritten more simply:  
\[MS=\max_{\forall i,\forall j\in A_{i}}\bigl{\{}t_{iSij}+d_{iSij}\bigr{\}} \tag{20}\]  
In the general case, the make-span for machine \(l\) is given by  
\[MS_{l}=\max_{i,kj}\bigl{\{}t_{ikj}\bigl{|}x_{ij}=1\;\;\text{and}\;l_{ikj}=l \bigr{\}}-\min_{i,k,j}\bigl{\{}t_{ikj}\bigl{|}x_{ij}=1\;\;\text{and}\;l_{ikj}= l\bigr{\}} \tag{21}\]  
and the total processing time of machine \(l\) is given by  
\[PT_{l}=\sum_{i=1}^{n}\sum_{j\in A_{i}}x_{ij}\sum_{k\in\{1,\ldots,S_{i}\},\;l_{ ikj}=l}d_{ikj} \tag{22}\]  
Assuming that the machines once started are shut down only when their last operation is completed, their idle time where a residual amount of energy is used is given by  
\[IT_{l}=MS_{l}-PT_{l} \tag{23}\]  
It can be observed that the resolution of problem (9)-(16) will provide a scheduling of minimum total energy without considering the consequences for its make-span. If the jobs had no due times and the working period [0, \(T\)] was sufficiently large, the solution of problem (9)-(16) will consist of selecting each job's machine assignment of minimum energy \(E_{ij}\). A way to introduce the make-span minimization objective can be obtained by setting, at smaller values, the due times of the different jobs or their global maximum value \(\max_{i}\bigl{\{}dt_{i}\bigr{\}}\) according to the production context. Successive resolutions of the problem with decreasing due times will make it possible to highlight the influence of the make-span on the energy performance of the workshop for a given production plan \(J\). This influence should be analyzed to enable the identification of characteristic patterns within some families of production plans, which will allow, once again by using artificial intelligence techniques such as Machine Learning [39; 40], to guess correctly the feasibility of the adopted due times before embarking on the numerical resolution of the optimization problem.
# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### DI Models
Several state-of-the-art counting methods (i.e., MCNN, CSRNet, SANet and CAN) were trained with and without augmenting the datasets. The results of all the tests on data augmentation outperformed the ones on real data. [PERSON] et al. [50] focused their attention on the specific problem of Remote Photoplethys-mography Estimation (rPPG), in order to detect blood volume changes from videos of human faces. They propose a data augmentation methodology to generate synthetic videos of the face of a subject that represents a target rPPG signal. The facial synthetic videos are generated by two Encoder/Decoder deep networks. The inputs needed by the models are images of the face of the subject and the target rPPG signal. The authors evaluate their model on PURE and UBFC-RPPG datasets over several metrics (e.g., Pearson correlation coefficient (R), Mean absolute error (MAE), and Root mean square error (RMSE)). They trained on three different data: the source data, the data augmented with traditional methods (i.e., random rotation, brightness, and saturation), and the data augmented with the proposed method. The model trained using the proposed method for data augmentation improved the performances on all the metrics. Lastly, the authors of [59] presented a model able to generate synthetic images of facial expressions. The model is composed of two GANs: a MotionGAN able to generate a series of synthetic facial landmark sequences that represent facial expressions; and a TextureGAN that generates novel facial expressions videos from the generated landmark sequences together with a source natural face image. The performances of MotionGAN in data augmentation were tested on the CASIA dataset, training an LSTM network to recognise facial expressions. The LSTM trained on the original training set achieved an accuracy of 87.5%, while the same network trained on an augmented dataset using MotionGAN achieved 92.7%.
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 3 ULCF Life Prediction for the Q345 qC Base Material and Weld Joints
### ULCF Predictions for Base Material and Weld Joints
#### 3.2.3 Fatigue Life Prediction Based on the CVGM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(\Delta\epsilon_{t}\)/\%} & \multicolumn{3}{c}{**Predicted**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & \(N_{f}^{p}\) & \(N^{p}\)\({}_{r}\) & \(N^{p}\)/\(N^{p}\)\({}_{r}\) & \(N_{f}\) & \(N_{r}\) & \(N_{f}\)/\(N_{r}\) \\ \hline
7.0 & 60 & 83 & 72.3\% & 77 & 82 & 93.9\% \\
8.0 & 48 & 66 & 72.7\% & 59 & 63 & 93.7\% \\
9.0 & 40 & 56 & 71.4\% & 44 & 48 & 91.7\% \\
10.0 & 33 & 42 & 78.6\% & 35 & 37 & 94.6\% \\ \hline \end{tabular}
\end{table} 
Table 10: Relationship between fatigue and fracture lives of base metal specimen in CVGM. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(\Delta\epsilon_{t}\)/\%} & \multicolumn{3}{c}{**Predicted**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & \(N_{f}^{p}\) & \(N^{p}\)\({}_{r}\) & \(N^{p}\)\({}_{f}\)/\(N^{p}\)\({}_{r}\) & \(N_{f}\) & \(N_{r}\) & \(N_{f}\)/\(N_{r}\) \\ \hline
7.0 & 27 & 39 & 69.2\% & 37 & 41 & 90.2\% \\
8.0 & 22 & 32 & 68.8\% & 32 & 35 & 91 the number of cycles to initial damage and the post-fracture path. The error between the predicted and test values of the weld specimen is greater than that of the base metal specimen.
## 4 Numerical Case Study--Assessment of SSI Effects on a Steel Building
### Moment Resisting Frame  
#### 4.1.1 Fixed-Base Moment Resisting Frame  
The numerical model of the moment resisting frame (MRF) is modelled in accordance with the data given in [43; 44]. A simple frame model without composite connections and slabs was made using frame elements. The first period of vibration (\(T_{1}\)) for this model equals to 1.0 s [43]. Since literature [43; 44] provides detailed information about the geometry, material model, mass distribution, and lateral force distribution, it was therefore possible to calibrate the numerical model to match the capacity curve to the one presented in [44], as showed in Figure 8.  
Material non-linearity was considered in the form of lumped plasticity, consisting of plastic hinges in columns and beams, modelled in accordance with Eurocode 8--Part 3. Plastic hinges were modelled with a trilinear moment-rotation relationship, while the yielding (\(M_{\mathrm{y}}\)) and plastic moment (\(M_{\mathrm{p}}\)) were determined for each cross section and floor  
Figure 7: Case study building geometry (cm).  
separately and assigned manually as presented in Table 1. The building loads were assigned to the frame model as uniform loads. The loads were composed of the self-weight, considered by the software, and linear vertical loads on the frames due to actions on the slabs, additional 45.0 kN/m of dead load and 27.0 kN/m of live load. In order to match the capacity curve presented in [43] an identical triangular lateral load distribution to Castro and Elghazouli [43] was used. The effective seismic mass of the building was composed of 100% dead load and 30% of live load.  
In Table 1 the \(M_{y}\) and \(M_{p}\) are given, along with the corresponding yielding rotation (\(\theta_{y}\)) and plastic rotation (\(\theta_{p}\)). Since the building was designed following the capacity design principle, the inelastic deformations first occur in the beams (strong columns-weak beams principle).