esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
MCSA has been applied in different types of electrical motors, including induction machines, permanent magnet motors, and SynRMs [18], although it has recently been reported that this approach can lead, in some circumstances, to false indications [19]. In addition, it is known that the current loops of the electronic drive can influence the amplitudes of the harmonics of the stator currents, thus hindering the diagnosis based on an analysis of the stator currents harmonics [20]. Air gap asymmetry faults may be also detected by analyzing the ZSVC (zero-sequence voltage component). This approach is advantageous since the ZSVC can be decoupled from the effects of the electronic drive and the controller [20]. Although the fault detection method based on the ZSVC involves an accessible central point of the stator windings, this arrangement is frequently applied in fault-tolerant motor drives. The results presented in [20; 21] for PMSMs (permanent magnet synchronous machines) prove that the ZSVC approach has a better accuracy and sensitivity than the analysis of the stator currents spectra, particularly when the motor operates under low-speed conditions. In the technical literature, there are many works that analyze the effects of air gap eccentricity faults, but few of them are related to PMa-SynRMs [9; 22; 23]. Most of the works dealing with fault detection in PMa-SynRMs are either based on an analysis of the torque developed by the machine [4; 24], although this method has several drawbacks due to its nonlinear relationship with the severity of the fault, or apply an off-line approach [25], with the associated disadvantages. Instead, this work focuses on an analysis of the spectral content of both the line currents and the ZSVC in five-phase fPMa-SynRMs under the effects of air gap asymmetries, which permits the application of a straightforward on-line fault diagnosis strategy. The finite element method (FEM) hasbeen applied to analyze the different machines, since FEM is a recognized simulation method that allows an accurate electromagnetic analysis [26] and assists in the optimal design process of electrical machines [27]. FEM also allows one to compute the values of key machine variables, and thus, permits an analysis of the behavior of suitable parameters to be used in fault detection approaches to characterize the motor failures under analysis [20]. The detection of failures in PMaSynRMs is a hot topic due to the growing interest of these machines for vehicular traction applications. The particular geometry of such motors hinders the on-line detection of eccentricity faults. This paper proves that when selecting favorable operating points, the detection is possible. This paper also proposes an on-line eccentricity fault detection approach, which includes the calculation of fault indicators from the spectral content of the stator currents and the ZSVC. The calculation process of such indicators presents a low computational burden, which is a requisite for on-line applications.
# Investigation of Nanocutting Characteristics of Off-Axis 4H-SiC Substrate by Molecular Dynamics
## 3 Results and Discussion
### Influence of Cutting Speed  
The temperature of the off-axis 4H-SiC workpiece during cutting with different cutting speeds is presented in Figure 11. It can be seen that the temperature of the 4H-SiC workpiece increases with the increase of the cutting speed. The larger the cutting speed is, the fewer the time costs and the less the heat dissipates. And it results in the increases the workpiece temperature. The effect of the cutting speed on the workpiece temperature is evident, while the effect on the structure transformation and the cutting force isn't. The cutting speed may affect structure transformation and cutting force through temperature. However, the simulation time was relatively small (about 600 picoseconds) and the effects were not captured.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 3 The Proposed Method
### YOLOv8n-CBAM-EfficientNetV2 Modules  
#### 3.1.1 EfficientNetV2 Submodule  
EfficientNetV2 is an improved network based on EfficientNetV1, which optimises the performance of the network in terms of parameters such as depth and width. EfficientNetV2 is capable of adaptively balancing the depth and width of the network as well as the resolution of the input image, which effectively reduces the number of parameters and the complexity of the model training, thereby markedly accelerating the training speed of the model [21].  
The EfficientNetV2 network incorporates a number of Fused-MBConv and MBConv modules. The Fused-MBConv module is frequently employed in the shallow layers of the network, owing to its high efficiency. In contrast, the MBConv module is better suited to the deeper layers, owing to its depth and width tunability. Figure 4 shows the specific structure of these two modules. The MBConv module comprises two \(1\times 1\) convolutions, one \(3\times 3\) depth-separable convolution, and one SE attention module, as illustrated in Figure 4a. The Fused-MBConv module unifies the originally independent \(1\times 1\) point-by-point convolution and \(3\times 3\) depth convolution into a single \(3\times 3\) convolutional structure, as illustrated in Figure 4b. This fusion design reduces the computational burden of the model. The combination of Fused-MBConv and MBConv in EfficientNetV2 allows for the exploitation of the high efficiency of Fused-MBConv and the tunability of MBConv, thereby achieving a real-time performance improvement in the wake detection process.  
Figure 3: Network structure of the YOLOv8n model. C, K, and S, respectively, represent the number of output channels of the convolutional layer, kernel size, and stride. Nearest indicates that the nearest interpolation algorithm is used in the Upsample module. D represents the dimension that needs to be concatenated in the Concat module. Nc represents the number of target categories in the experimental dataset.
## 3 Models
### Adversary Model  
The adversary model assumed in this study is adopted from Reference [5]. It is assumed that the adversary is well equipped and it has adequate resources such as computation capabilities, memory and unlimited power. It is equipped with antenna and spectrum analyzers to enable it to monitor the communication between sensor nodes when it is within a certain detection range. It is capable of analyzing the traffic pattern. The adversary is mobile, it is initially located in the locality of the sink node to listen for arriving packets. The benefit of initial location at the sink node is to increase the probability that the adversary will overhear packets due to the large volume of packet traffic at the destination sink node. When a packet arrives at the sink node, the eavesdropping adversary will overhear and start the back tracing attack by moving 1 hop towards the source node until it locates the source node. According to Kerckhoff's principle, the adversary is aware of the routing strategy which is used in the network and it assumes the transmission range similar to that of sensor nodes. When the adversary detects a packet, it can analyze the angle of arrival of the signal and the received signal strength to identify the immediate sender node. It then performs back tracing attack by moving to the immediate sender node location without delay. Once at the immediate sender node, the adversary continues to analyze the traffic pattern between the node and its neighboring nodes. It targets to capture information such as sender node ID, packet type, and sequence number. It continues to analyze the traffic pattern and back trace the packet routes until it arrives at the source node location to locate the asset. The adversary is capable of capturing all packets within its detection range and it never fails to detect a packet. It performs passive attacks and avoids interfering with the functioning of the nodes in network. To avoid getting noticed by the network administrators, adversary does not interfere with the packets transmission and does not destroy the sensor node equipment. The adversary is a cautious adversary. It uses its computational power to limit its waiting time at any immediate sender node. At an immediate sender node, it observes a waiting timer and if the timer expires, the adversary will roll back to its previous immediate sender node and resume the traffic analysis and packet detection process. In addition, the adversary stores the information of all the visited immediate sender nodes to evade revisiting the nodes. By doing do, it reduces the probability of getting trapped in a loop.
## 3 Neural Inverse Optimal Control
### Application to All-Terrain Tracked Robot Control  
Considered as the most important type of mobile robots, a tracked robot runs on continuous tracks instead of wheels which develop a thrust higher than a wheeled robot. This kind of robots is ideal for working in tasks under rough terrains. Among the applications tracked robots can achieve are urban reconnaissance, forestry, mining, agriculture, rescue mission scenarios, autonomous planetary explorations [23; 24; 25].  
A tracked robot consists of the following state variables [17; 26; 27] position \(x\), position \(y\), position \(\theta\), velocity 1, velocity 2, current 1 and current 2. In this work, we focus on the controller tracking performance for \(x\), \(y\) and \(\theta\) (Figure 5) for given references \(x_{r}\), \(y_{r}\) and \(\theta_{r}\). The objective is to improve the NIOC results presented in [17] by using GCO to find the optimal parameters of the controller. These parameters are included in the matrices \(\mathbf{P}_{1}\) and \(\mathbf{P}_{2}\) defined in (11) and (12) respectively. The \(\mathbf{P}_{1}\) and \(\mathbf{P}_{2}\) are symmetric positive definite matrices, therefore we can define the set of variables \(\{\psi_{1},\psi_{2},\psi_{3},\psi_{4},\psi_{5}\}\) for the optimization problem with the definition in (17).  
\[\mathbf{P}_{1}=\left[\begin{array}{ccc}\psi_{1}&\psi_{2}&\psi_{3}\\ \psi_{2}&\psi_{1}&\psi_{4}\\ \psi_{3}&\psi_{4}&\psi_{1}\end{array}\right]\quad\mathbf{P}_{2}=\left[\begin{array} []{ccc}\psi_{5}&0\\ 0&\psi_{5}\end{array}\right] \tag{17}\]  
Figure 4: The interior of the Modified HD2® Treaded ATR Tank Robot.  
Figure 3: Modified HD2® Treaded All-Terrain Tracked Robot (ATR) Tank Robot Platform with wireless communication.  
The lower bound of the search space is given by \(\{1,1,1,1,1\}\) and the upper bound is given by \(\{4\times 10^{7},4\times 10^{7},4\times 10^{7},4\times 10^{7},4\times 10^{4}\}\). Next, the objective of the optimization is to get a better control tracking, in order to achieve this, we minimize the sum of the Root Mean Square Error (RMSE) in every state. This idea is described by (18), where \(n\) is the number of samples of the reference and estimated functions, we are using \(n=3335\) for real-time experiments and \(n=5000\) for simulation experiments.  
\[f=\min_{\psi_{1},\psi_{2},\psi_{3},\psi_{4},\psi_{5}}\sqrt{\frac{\sum_{i=1}^{n }(\hat{q}-x)^{2}}{n}}+\sqrt{\frac{\sum_{i=1}^{n}(\hat{q}-y)^{2}}{n}}+\sqrt{ \frac{\sum_{i=1}^{n}(\hat{\theta}-\theta)^{2}}{n}} \tag{18}\]  
Then, the GCO algorithm will find optimal values that minimize (18). For the following experiments we use a GCO algorithm in five dimension running 150 iterations using 30 B-cells (4500 executions) for a test of 10 seconds; we set the parameters \(F=1.25\) and \(CR=0.7\). We include graphics for one simulation test using the all-terrain robot model presented in [17], and the results of one experimental test using the modified HD2(r) Treaded ATR Tank Robot Platform with wireless communication (Figure 3) presented in [17].
## 6 Conclusions  
We propose novel relative radiometric normalization algorithms based on pseudo\(-\)invariant polygon features with single\(-\)band and multiple\(-\)band regression, called polygon\(-\)single and polygon\(-\)multi methods. In comparison to normalization algorithms based on pseudo\(-\)invariant point features, hyperspectral features, and histogram matching, the proposed methods are more accurate when using contemporaneous Sentinel\(-\)2A and Landsat\(-\)8 OLI image radiometric normalization. They are followed by histogram matching, pseudo\(-\)invariant point features\(-\)based normalization, and hyperspectral library\(-\)based normalization. The comparative results in each band show that polygon\(-\)multi has the best scatter fitting, especially for bands 2 and 3. Polygon\(-\)multi and polygon\(-\)single are the most accurate (R\({}^{2}\) = 0.9948, 0.9945 and RMSE = 0.0095, 0.0097, respectively) for each pixel comparison. The reflectance frequency of the whole image also illustrates that polygon\(-\)multi and polygon\(-\)single have the best shape fitting and are close to the histogram matching algorithm. Our multiple\(-\)band regression analysis indicates that the adjacent bands have a large influence and the fit of pseudo\(-\)invariant features using multiple\(-\)band data achieved is higher and provides more accurate normalized reflectance than that with a single band. Using the mean reflectance of selected polygons to replace point reflectance can effectively eliminate the influence of abnormal (artificial) points, making the regression equation more universal. The proposed algorithms have distinct advantages over other methods, especially for the normalization of artificial surface pixels.  
Methodology, L.C., Y.M. and Y.Y.; Validation, Y.L. ([PERSON]) and Y.L. ([PERSON]); Investigation, H.Z. and Y.L. ([PERSON]); Writing--original draft, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of Special Investigation on Basic Resources of Science and Technology grant number 2019 FY202501 and National Natural Science Foundation of China grant number 41802246 and 41971306.  
Not applicable.  
The datasets that were analyzed in this study can be found publicly here: [[https://scibub.copernicus.eu/](https://scibub.copernicus.eu/)]([https://scibub.copernicus.eu/](https://scibub.copernicus.eu/)) (accessed on 26 September 2019) and [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 22 September 2019). The dataset presented in this study can be available on request from the author.  
Figure 13: Using all pixels of two images to fit scatter plots.  
**Acknowledgments:** The authors would like to thank the United States Geological Survey and European Space Agency for freely providing remote sensing images.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 1 Introduction and Related Works
### Contributions  
The main contributions of this paper are summarized as follows.  
1. A joint resource allocation and pairing scenario in D2D communications is investigated. A number of D2D receivers are considered for the reuse of cellular network resources. For each D2D receiver, there exists a number of potential D2D transmitters, and a stable fuzzy pairing criteria is proposed for the selection of the best transmitter 2. The set of transmitters is considered as a fuzzy set in such a way that a fuzzy degree is assigned to each node, concerning the data rates and battery levels in connection with their potential receiver. We claim that this fuzzy membership function can be considered as a measure of the stability of the connections. 3. To examine the proposed method, this method is compared with three other pairing methods, so that the three parameters of stability, fairness, and sum rate in different modes such as changing the D2D search radius and increasing the number of D2D transmitters and receivers for all four methods are investigated. 4. The proposed method for D2D pairing, in addition to reducing the traffic load of the cellular network, leads to more stable connections with a higher quality of service. One of the significant applications of this method is that bulky content can be transferred more reliably.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Causative Attack Methods
#### 4.2.1 Gradient Ascent  
The Gradient Ascent (GA) method is a causative attack proposed by [PERSON] et al. [58] to significantly decrease the Support Vector Machine (SVM) classification accuracy by inserting crafted data into the training dataset. The method identifies the values associated with local maxima in the model's test error. The authors utilize an incremental learning approach, which seamlessly fine-tunes data point parameters, thus enabling them to achieve an optimal solution by introducing carefully crafted data.  
The attacker aims to discover a point \((x_{c},y_{c})\) that, when added to the training dataset \(D_{tr}=\{x_{i},y_{i}\}_{i=1}^{n}\), \(x_{i}\in\mathbb{R}^{d}\) maximally decreases the SVM's classification accuracy. The attacker proceeds by drawing a validation dataset \(D_{val}=\{x_{k},y_{k}\}_{k=1}^{m}\) and maximizing the hinge loss function \(L(x_{c})\) of the SVM classifier induced on the validation dataset \(D_{val}\) and trained on \(D_{val}\cup(x_{c},y_{c})\) as per following Expression (16).  
\[\underset{x_{c}}{max}L(x_{c})=\sum_{k=1}^{m}\left(1-y_{k}f_{x_{c}}(x_{c}) \right)_{+}=\sum_{k=1}^{m}(-g_{k})_{+} \tag{16}\]  
where \(g_{k}\) is the margin constraints impacted by \(x_{c}\) and defined by the Expression (17).  
\[g_{k}=\sum_{j\
eq c}Q_{kj}\alpha_{j}(x_{c})+Q_{kc}(x_{c})\alpha_{c}(x_{c})+y_{ k}b(x_{c})-1 \tag{17}\]  
here, \(\alpha\) represents the dual variables of the SVM, which correspond to each training data point. \(Q_{ss}\) denotes the margin support vector submatrix of \(Q\).  
The authors use the gradient ascent technique to iteratively optimize the non-convex objective function \(L(x_{c})\). This optimization procedure presupposes the initial selection of an attack point location \(x_{c}^{(0)}\) and in each iteration updates the attack point using the formula \(x_{c}^{p}=x_{c}^{p-1}-tu\), where \(p\) is the ongoing iteration, \(u\) is a norm-1 vector indicating the attack direction, and \(t\) denotes the magnitude of the step.  
Although this method is a first-order optimization algorithm that only requires the gradient of the objective function calculation, it is sensitive to the starting parameter settings. In case the initial values are too far from the optimal values, the algorithm will most probably converge to a local maximum than a global maximum, or will slowly cover an optimal solution especially, when the objective function is highly non-convex.
## 4 Influence of Temperature in Clock Skew  
[PERSON] _et al_. [14] demonstrate that temperature variations affect the clock skew estimation in synchronization protocols. The following paragraphs show the above mentioned behavior on FTSP.  
Using a star-based WSN topology with one root and four standard motes, experiments have demonstrated the influence of temperature variations in the clock skew of a tuning-fork oscillator. The root mote is responsible for the synchronization of all other motes. Once the experiment has started and all the motes are synchronized, a base station receives the local time of each mote in order to generate statistical data about the synchronization error.  
In the work of [PERSON] _et al_. [3], the topology [15] was forced with software mechanisms. In this work, the same configuration to create the topology has been followed. By using the above mentioned topology, a good signal coverage between all the motes and the base station is ensured.  
The root mote sends synchronization beacons cyclically, with a \(5\) s period. The base station broadcasts another beacon to obtain the local time of each mote in a \(1\) s frequency rate. The linear regression is computed with three elements to obtain a good temporal proximity between data.  
The experiment has a duration of \(0.5\) h, obtaining the following results:  
* Between time \(0\) s and \(800\) s, the temperature was fixed for all the network. For this period, the average synchronization error was about \(1.90\)\(\mu\)s.
* At \(800\) s, a temperature variation of 15 \({}^{\circ}\)C is provoked in just one of the motes of the network. As can be observed in Figure 1, the average synchronization error starts to increase, until instant \(2,000\) s.
* The test ends with an average synchronization error of \(4.10\)\(\mu\)s.
* These results show a decrease of the performance of about \(215.78\)%.  
The obtained results show a clear influence of temperature in the synchronization error. This is because of the change in the frequency of the oscillator due to temperature variations. The frequency change affects the clock skew estimation, and thus, the average synchronization error increases. These results enforce the postulates of [PERSON] [4], who stated that \"temperature variations change the frequency of oscillation\". Therefore, it is necessary to improve the clock skew estimation, taking into account the temperature variations.  
Figure 1: Temperature influence in Flooding Time Synchronization Protocol (FTSP).
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 2 Methodology
### Signal Model
The converted time \(t\) can be expressed as \(t=t_{m}+\tau\), where \(t_{m}\) is the number of integer cycles, called slow time, and \(\tau\) is the time in a cycle, called fast time. Generally, the fast time \(\tau\) and slow time \(t_{m}\) are selected based on the signal characteristics of GNSS. The fast time \(\tau\) has a length of one PRN code cycle (such as 1 ms for both BDS B1I and B3I), and the slow time \(t_{m}\) is the number of code cycles. The ACF function after signal compression is also a periodic function, that is \(X(t_{m}+\tau)=X(\tau)\). \[s_{c}(\tau,t_{m})=A_{3}\cdot X(\tau-\Delta t)\cdot dec(f_{d})\cdot\exp\{-j\cdot 2 \pi\cdot f_{d}\cdot(t_{m}+\tau-\Delta t)\} \tag{7}\]