esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Mechanical Properties
#### 3.3.2. Consequence of Nano Filler Loading  
The composition of the fillers, polymer, polymer filler adherence, cross-linking chemicals, and preparation techniques influence the mechanical characteristics of a composite. As a result, any enhancement in the characteristic is assessed in comparison to a polymer matrix that has experienced the same procedure. The fillers are saturated at room temperature by the polymer solution, which is then hardened using a cross-linking agent. Tensile, flexural, and impact properties typically improve as the nano filler loading in the formulation increases. Figure 10 demonstrates the average values of mechanical strength based on nano Al\({}_{2}\)O\({}_{3}\) particles. The adherence between the epoxy and the filler reduces beyond a specific limit of filler content based on the treatment methods, leading to a decline in the strength of the end goods [38].  
The overall tensile strength of the material during the 4 weight percentage of nano Al\({}_{2}\)O\({}_{3}\) is 59.94 MPa, the bending is 149.52 MPa, and the impact is 37.0 KJ/m\({}^{2}\). The composite's elasticity decreased somewhat, with a filler loading of 6%. That decrease is attributed to the filler's difficulty sustaining stresses transferred by polymeric composites, as well as poor interfacial interaction resulting in meaningless gaps among fillers and substrate elements, resulting in unsatisfactory outcomes [39]. Whenever the filler loading is excessive, the polymers have a more challenging time entering the tiny diminishing spaces among the fillers, resulting in poor soaking and a decrease in load-transfer effectiveness at the filler-resin contact. The epoxy resin adheres well to various surfaces and may be strengthened further by adding fibres or nanoparticles. Figure 11 Demonstrates the mean values of bending strength based on the different input parameters.
## 1 Introduction
Due to the possibility of misclassification, more vibration signal fusion needs to be considered. In addition, the sensor installation location to reduce the redundancy of fusion data was studied [23; 24]. The proposed multi-vibration sensor signal fusion method can be used for different rotor faults. Based on the established multi-sensor spatial position correlation model and signal energy correlation model, the time deviation and adaptive weights of different sensors are estimated to achieve the different adaptive fusion of faults. The fault diagnosis method of SAE is used to classify the fused fault data. Compared with the data classification results of unfused single sensor data and other fusion methods, the results show the superiority of the fusion method. The main innovations are as follows:  
1. A model of spatial correlation of multiple vibration sensors is proposed;
2. Propose a method of spatial and temporal multi-correlation fusion of vibration sensors;
3. The proposed method can handle different types of rotor faults and can effectively implement fault diagnosis. The organization of the rest of this article is as follows: The second part introduces the basic wavelet noise reduction theory, linear normalization method and Hilbert-Huang transform. The fourth part verifies the STMF method and the comparative analysis with other fusion methods, which verifies the superiority of the STMF method. The fifth part is the conclusion. The overall framework and ideas of the content in the article are shown in Figure 1. First, wavelet denoising was performed on the original fault data and a linear normalization method was used to standardize the data. Then, the data are fused and the weight is determined by the correlation of the multi-sensor spatial position coordinates and the energy correlation of each sensor. Finally, the SAE method was used to intelligently diagnose faults with the fusion fault data.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 4 Anomalous Vehicle Behavior Detection Using mSSA  
The fundamental idea of the proposed approach lies in an intuitive observation. Most vehicles on roads actually follow traffic rules and behave legitimately. Therefore, vehicles that act significantly different are more likely violating the rules. Consequently, it is reasonable to identify vehicles that present different characteristics from others as objects-of-interest, and more analysis should be conducted on them. The movement of a vehicle can be described using its trajectory information, which consists of an ordered sequence depicting positions, speeds and directions of the vehicle over time. Following this rationale, the anomaly vehicle detection problem is reformatted as a problem of finding the object of changes \(v_{c}\) from the set of objects \(\mathbb{V}_{K}\).  
For the convenience, Table 1 summarizes the notations used in this paper.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Notation** & **Description** \\ \hline \(\mathbb{X}_{N}\) & time series with length \(N\) \\ \hline \(X\) & trajectory matrix constructed from \(\mathbb{X}_{N}\) \\ \hline \(\mathbb{X}_{N}^{(k)}\) & time series with \(k\) channel \\ \hline \(M\) & window length \\ \hline \(l\) & the number of eigenvalues selected \\ \hline \(X_{base}^{(n)}\) & base matrix at \(n\)th iteration \\ \hline \(X_{test}^{(n)}\) & test matrix at \(n\)th iteration \\ \hline \(\mathcal{D}_{n,J,r,s}\) & distance between base and test matrix \\ \hline \(\mathcal{X}_{K,N}^{(S)}\) & time series of \(S\) characteristics for \(K\) vehicles with length \(N\) \\ \hline \(\mathbb{V}_{K}\) & set of \(K\) vehicles \\ \hline \(X_{n,N}^{(j)}\) & trajectory matrix of time series of \(j\)th characteristic of \(n\)th vehicle \\ \hline \(\mathcal{X}_{base,N}^{(j,l)}\) & base vehicle time series for distance calculation \\ \hline \(\mathcal{D}_{n,N}^{(j,l)}\) & distance from each vehicle to the base vehicle time series \\ \hline \(P_{n}\) & anomaly score for \(n\)th vehicle \\ \hline \(h\) & threshold to determine anomalies \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used in algorithms.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 4 Experiments  
### Dataset and Setting  
At present, there are no publicly available datasets for the task of pointer meter reading recognition. To address this issue, we collected 380 images with the size 1024 \(\times\) 1024 from the internet and two manufacturing plants in Shandong, each of which has at least one pointer meter. Figure 5 shows six example cases from this dataset. In order to achieve accurate image labels, two annotators performed the label operation, and their annotations were averaged to produce the final label of each image. The annotation information includes the position, height, and width of each bounding box and the positions of the pointer and different scales. Considering that our pointer meter dataset has a limited number of images, we first pre-trained both the meter dial location network and the reference point segmentation network based on the COCO dataset [56]. Then we further fine-tuned the weight parameters of these two networks based on our meter dataset. All the meter images were randomly sampled and divided into two sets with a ratio of 8:2, namely, one set for model training and the other for model testing. For data augmentation in the training phase, we applied the commonly used random rotation and resizing operations, which can enhance the performance of our model and also alleviate the overfitting problem caused by the small dataset. Note that all the deep models in the experiments were trained with the same data augmentation techniques. In addition, we conducted all experiments on a PC equipped with an Intel Core i7-10700k CPU (Santa Clara, CA, USA), 32 GB memory, and an NVIDIA GeForce GTX2080 Ti GPU (Santa Clara, CA, USA).
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods  
### Preliminaries  
**Definition 1 (Traffic network).** _The traffic network is defined as a graph, \(G=\{V,E,A\}\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\)represents nodes with \(|V|=\mathrm{N}\), \(N\) is the number of nodes, \(E\subseteq V\times V\)represents edges illustrating the connections between node pairs, and \(A\in\mathbb{R}^{N\times N}\)is the adjacency matrix of the traffic network, where \(A_{ij}=1\) denotes a connection between node \(i\) and node \(j\), otherwise \(A_{ij}=0\). According to the typical stability of traffic network, \(G\) is assumed to be constant in this research._  
**Definition 2 (Traffic flow tensor).** _In the traffic network, the traffic flow of \(N\) nodes at time \(t\) is defined as \(X_{t}\in\mathbb{R}^{N\times F}\), where \(F\) represents the dimension of traffic flow features. The traffic flow tensor at all time slices, \(T\), of all nodes is defined as \(\mathcal{X}=(X_{1},X_{2},X_{3},\cdots,X_{T})\in\mathbb{R}^{T\times N\times F}\)._  
The task of traffic flow prediction is to forecast future traffic flow given historical observations. Formally, given the observed traffic flow tensor, our aim is to find a function that predicts the following \(T^{\prime}\) time steps' traffic flow from the previous \(T\) time steps' historical observations as follows:  
\[[X_{t-T+1},X_{t-T+2},\cdots,X_{t};G]\overset{f}{\rightarrow}[X_{t+1},X_{t+2}, \cdots,X_{t+T^{\prime}}] \tag{1}\]
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was conducted using SPSS 25.0 (Statistical Package for the Social Sciences Inc., Chicago, IL, USA). The normality of data distribution was assessed through the Shapiro-Wilk test. A three-way repeated measures ANOVA (3 Sound \(\times\) 2 Vision \(\times\) 2 Surface) was employed to examine the influence of Sound (no sound/nature sound/noise sound), Vision (OE/CE), and Surface (firm/foam) on CoP\({}_{\rm{Vm}}\) values. To assess the practical significance of statistically significant differences, effect sizes for each outcome measure were computed using the partial eta squared (np\({}^{2}\)) formula. Effect sizes were categorized as small (0.01 \(<\) np\({}^{2}\)\(<\) 0.06), moderate (0.06 \(<\) np\({}^{2}\)\(<\) 0.14), or large (np\({}^{2}\)\(>\) 0.14) [49; 50]. To account for multiple comparisons, a Bonferroni adjustment was conducted. The level of statistical significance was set at \(p<\) 0.05.
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 5 Application Case  
Since the signal to noise ratio \(\gamma\) in (23) is related to the maximum link distance, we can compute this distance in a system using the LoRa technology where the communication link is under the Nakagami-\(m\) distribution.  
We choose the device SX1272 [18] as reference. For instance, the datasheet report that the receiver sensitivity is \(P_{RX}=-137\) dBm for \(B=125\) kHz and \(SF=12\). Using (23), we can set a target bit error probability of \(P_{b}=10^{-4}\) and analyze what is the impact of the Nakagami-\(m\) factor in the maximum link distance.  
We choose Okumura-Hata [20] as our path-loss model with urban area parameters. The heights of the base station and mobile stations are 40 meters and 1 m, respectively. The system operates in the 900 MHz frequency, which agree with the limits presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(SF\)} & \multicolumn{2}{c}{**SNR = \(-30\) dB**} & \multicolumn{3}{c}{**SNR = \(-10\) dB**} & \multicolumn{3}{c}{**SNR = \(10\) dB**} \\ \cline{2-11}  & **1** & **3** & **5** & \(\boldsymbol{SF}\) & \(\boldsymbol{m}\) & **1** & **3** & **5** & \(\boldsymbol{SF}\) & \(\boldsymbol{m}\) & **1** & **3** & **5** \\ \hline
**12** & 0.7 & 2 & 0.0 & **12** & 0.5 & 5.0 & 4.0 & **12** & 2.1 & 11.0 & 4.0 \\ \hline
**10** & 1.5 & 8.0 & 0.1 & **10** & 2.2 & 1.0 & 1.2 & **10** & 0.2 & 7.8 & 80.0 \\ \hline
**8** & 65.6 & 1.9 & 1.5 & **8** & 0.1 & 0.2 & 2.6 & **8** & 23.4 & 0.6 & 13.3 \\ \hline
**6** & 25.4 & 0.7 & 0.2 & **6** & 2.7 & 2.3 & 1.9 & **6** & 0.9 & 4.6 & 3.3 \\ \hline
**4** & 21.7 & 0.4 & 0.7 & **4** & 0.8 & 13.0 & 4.2 & **4** & 2.4 & 1.2 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table with percentage error values between (18) and (23). The value of each cell corresponds to the number times \(10^{-6}\) %.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Region** & **Frequency (MHz)** \\ \hline EU & 863–870 \\ \hline US & 902–928 \\ \hline AU & 915–928 \\ \hline CN & 779–787 and 470–510 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regions and Operating Frequencies for LoRa devices [18].  
Figure 3: Bit error rate for the Rician case. The continuous lines is given by (38) and stars are given by the Matlab simulations. Analytic AWGN curve is provided for reference.  
Figure 4 presents the maximum link distance for different values of \(SF\) and \(m\). When we compare these curve against the AWGN curve (no fading case) given in (14), we notice that the distance decreases from 5 km to 200 m for \(SF=12\), which indicates a loss of about 96% of link range. For scenarios where the Nakagami-\(m\) parameter ranges from 2 to 6, the link range loss reduces to 80% and 60%, respectively. For \(m=10\), the performance becomes closer to the AWGN performance.
## 2 Inter-DCI Link Modeling and OP Estimation
### Equivalent Model of the Inter-DCI Link Supported by MCF  
The system equivalent model of the inter-DCI link supported by MCFs is shown in Figure 1. Only the receiving part of the interfered core \(n\), also designated as core under test (CUT), is represented in Figure 1, as only the performance of the CUT impaired by ICXT is assessed. For the interfered core \(n\), and for each interfering core \(m\) (with \(m\)=1,..., \(N_{i}\)), a transmitter is considered, generating a different PAM4 signal independent from the PAM4 signals generated in the other transmitters. Then, in each transmitter, the PAM4 symbols are sampled and passed through an electrical filter that models the frequency limitations of the electrical part of the transmitter. Each optical modulator performs a linear electro-optical conversion without chirp and has a finite extinction ratio \(r\).  
The weakly-coupled MCF model considers the CUT and \(N_{i}\) interfering cores. To focus on the analysis of the impact of ICXT on the performance of very high capacity inter-DCI links, the CDC is designed to perfectly compensate the CD induced in the CUT.  
After the CDC, the signal is optically amplified by an EDFA and an optical filter reduces the amplified spontaneous emission (ASE) noise power. The EDFA gain is set to fully compensate the losses introduced by the MCF and the CDC module. The DD optical receiver includes a PIN photodetector, electrical noise addition due to the electrical circuitry of the receiver, filtering, and the decision circuit to decide on the transmitted PAM4 symbol.
## 2 Experimental Investigation  
### Surface Acoustic Wave Using Inter-Digital Transducer  
The precursor to the damage is quantified using the nonlocal damage index developed through non-local field theory. To demonstrate the applicability of the proposed formulation, experimental investigation is conducted using a SAW and longitudinal waves in Lithium Niobate crystal, as discussed below. Interdigital electrodes were fabricated on a LiNbO\({}_{3}\) single crystal. The LiNbO\({}_{3}\) crystal was cleaned using acetone, isopropanol, and trichloroethylene for 10 min at a temperature of 60 \({}^{\circ}\)C. Then, the wafer was subjected to ultrasonic cleaning treatment for 10 min using de-ionized water. In order to remove the moisture content, the wafer was baked at 120 \({}^{\circ}\)C. After cooling the wafer, the photoresist was applied to the surface using the spin coating technique at 4000 rpm for 55 s. Later, the wafer was soft-baked for 10 min at about 125 \({}^{\circ}\)C. After soft-baking, the wafer was exposed to UV light (15 mW/cm\({}^{2}\)) for 2 s. The exposed area of the resist becomes soluble to the developer. This developing procedure continued until the section which was exposed to UV light was completely etched. Then, IDT structures were coated with 0.12 um gold by thermal evaporation. Then the lift-off technique was adopted to realize the structures. The fabricated individual IDT sensors were bonded to the LiNbO\({}_{3}\) substrate using the ultrasonic wafer bonding method. The schematic diagram of the fabricated IDT and its dimensions is shown in Figure 1a. The experimental setup of for the IDT for SAW propagation in the LiNbO\({}_{3}\) substrate is shown in Figure 1b.
# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 1 Introduction
In addition, the above neural network prediction model is only for the ideal wheelset, without considering the influence of wheelset repair on the prediction results. Wheelset parameters will fluctuate after rotational repair, and the wheelset restores other parameters to a safe range by reducing the wheel diameter value. If the prediction accuracy is not enough, the wheel diameter will be wasted, resulting in an increase in operation cost [9]. Moreover, the above prediction model does not select the neural network method from the aspect of wheelset data characteristics. The prediction accuracy is not high and cannot provide accurate guidance and reference for wheelset maintenance planning. Therefore, this study first took into account the wheelset rotational repair situation and verified that the prediction model was still good before and after rotational repair. Secondly, considering that the wheelset parameters are time-based parameters, the LSTM neural network was introduced, the dropout layer was used to optimize the prediction model, and the three most critical parameters of the wheelset were determined by investigating and sorting out the reasons for the actual wheelset rotational repair operation. All of them were modeled and predicted. The prediction of key parameters of the wheelset has a high accuracy, which can improve important support and guidance for future wheelset maintenance arrangement and preventive maintenance, preventing trains not running on time due to too many wheelsets requiring maintenance. Recently, there are too many wheelset maintenance instances in the Shanghai Metro, which leads to the train not going further, so it is necessary to arrange wheelset maintenance in a planned way. This paper proposes a prediction model for the key parameters of the wheelset based on the LSTM neural network. Through the analysis of the causes of wheelset wear and relating data, the key parameters of the wheelset are determined: wheel diameter, flange thickness, and flange height. Based on the LSTM neural network, through data processing, model fitting, cross-validation, and other machine learning processes [10], a prediction model for the key parameters of the wheelset is constructed. Real measured data of the wheelset are inputted into the LSTM prediction model and then compared with the BP (back propagation) prediction model and GRU (gate recurrent unit) prediction model. The results confirm that the LSTM model maintains high prediction accuracy even when the data fluctuate significantly after wheelset's reprofiling.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 4 Analysis and Simulation  
In this section, we describe the experiments and analysis performed in our simulation environment, a setting which allows us to investigate the effect of (1) the drone's altitude, (2) the drone's speed, (3) the terrain over which the drone flies, and (4) ambient light on our method's performance. The simulation environment was designed so that we could examine our solution in a controlled environment, without external disturbances, and demonstrate a proof of concept of our solution in various field conditions.  
Figure 1: Correlation as a function of distance for location #1 (a neighborhood in Manhattan) for an altitude of 200 m (the blue points indicate the simulation results, and the red line represents the linear regression).  
Figure 2: The implementation of the proposed method, using an example in which we obtained five frames (\(frame_{0}\) to \(frame_{4}\)) and tried to predict the distance for \(frame_{5}\) (\(i=0\), \(n=4\)).  
In this setting we can also test our proposed method anywhere in the world by using Google Earth. To predict the next position, we graphed the correlation vs. distance, built a function suitable for the graph, and learned how it behaves. A test window is defined as the number of points needed to construct a linear regression function so that \(f(correlation)=distance\_prediction\).  
We used the simulator to conduct various experiments aimed at assessing the influence of the specific factors (1-4 in the previous paragraph) on the method's performance. The following metrics were used to evaluate the performance: the root-mean-square error (RMSE), \(R^{2}\), and mean absolute error (MAE).
## 5 Results and Discussion
### Reliability Analysis
#### 5.3.2 Probability of Corrosion Initiation  
Figure 14 shows the corrosion initiation probability curves for the different loading cases. As expected, the probability of initiation to corrosion (PIC) increases with time and loading. This is due to the accumulation of chloride ions in the area near the reinforcements during the exposure time. Cracks in the concrete facilitate the access of oxygen and water, necessary for the oxidation reactions and the formation of rust [52]. For example, for the experimental exposure conditions, the times to reach PIC = 0.6, are 996, 575 and 505 days for, respectively, the tests without loading, with static loading and with cyclic loading. Indeed, the exposure conditions of tests are extreme because the values of the surface chloride concentration are very high [16]. The lifetime is significantly reduced for the loaded tests. These results are in agreement with experimental observations which indicate that, for certain rebars, corrosion signs have been observed at the end of the tests (\(t=388\) days).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Mean (Years)** & **Standard Deviation (Years)** \\ \hline Unloaded & 2.99 & 2.39 \\ Static load & 1.89 & 1.52 \\ Cyclic load & 1.68 & 1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean and standard deviation of corrosion initiation time.  
Figure 13: Probability density of corrosion initiation time.
## 4 Modulation Schemes  
The most conventional modulation scheme for OCC is OOK. However, a high flickering rate is required for the LEDs to achieve high data rates. Moreover, the camera should be able to receive the data stream, leading to the necessity for cameras to have high sampling rates. The currently available commercial cameras are mostly built to operate at 30 frames per second (fps). To satisfy the [PERSON] criterion, the LED flickering rate must not exceed 15 Hz, a frequency too low to be detected by the human eye (the cut-off frequency for the human eye is 100 Hz [24]). Therefore, an appropriate modulation scheme should be chosen for OCC.  
The modulation schemes are selected on the basis of service scenarios. This section provides a short description of existing OCC modulation schemes categorized according to the data rate and communication distance. Table 1 summarizes the proposed and implemented modulation techniques for OCC and their features. It is worth mentioning that the implementation of different modulation schemes will have different requirements. The survey was performed to categorize the implemented modulation schemes on the basis of their required features and possible application scenarios.