esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study presented a modified YOLO-v3 detection model for insulator fault detection in aerial images. Firstly, a novel dataset named 'InSF-detection' was established, consisting of 1331 aerial images with one fault or multiple faults. Secondly, to achieve insulator fault detection in aerial images with complex backgrounds, on the basis of the YOLO-v3 model, Cross Stage Partial Network, and DenseNet, a Cross Stage Partial Dense YOLOv3 (CSPD-YOLO) model was proposed in this paper. CSPD blocks were adopted into the feature extraction network to increase feature reuse and propagation in the low-resolution feature layers, a structure of feature fusion was adopted into the CSPD-YOLO model to obtain multi-scale features of insulator faults, and an improved loss function and the k-mean++ clustering algorithm were adopted into the CSPD-YOLO model to improve the detection accuracy of insulator faults. Finally, the proposed CSPD-YOLO model, YOLO-v3, the model in the literature [38], and YOLO-v4 were carefully trained and tested on the established dataset. Experiments on insulator fault detection were conducted on the 'InSF-detection' dataset, and experimental results of the compared models showed that the average precision (AP) values of YOLO-v3, the literature model [38], YOLO-v4, and the CSPD-YOLO model were 93.31%, 95.07%, 96.38%, and 98.18%, respectively, proving that the proposed CSPD-YOLO model was superior to YOLO-v3, the model in the literature [38], and YOLO-v4. In addition, in the CSPD-YOLO model, the values of precision (P), recall (R),  
Figure 12: Experimental results of insulator fault detection with a power tower background.  
and F1 score were higher than those of the compared models. The average detection time of the CSPD-YOLO model (0.011 s) was slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s); however, the proposed CSPD-YOLO model could still be applied for the real-time detection of insulator faults with diverse scenes. Consequently, the CSPD-YOLO model achieved good performance in insulator fault detection amid diverse background interference.  
In future studies, the proposed CSPD-YOLO model will be utilized in UAV for the detection of on-line faults on high-voltage transmission lines.  
Writing, finished the experiment and this paper, C.L.; methodology, Y.W.; experiments and analysis, J.L.; data collection and analysis, Z.S.; labeling the ground truth for each image in our dataset, H.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Founding of China under grant 61573183; Major Natural Research Project of Anhui Provincial under grant KJ2019 ZD63; Excellent Young Talents support plan in Colleges of Anhui Province under grant gxyq 2019109, gxggnfx 2019056.  
Not applicable.  
The authors wish to thank the editor and reviewers for their suggestions and thank [PERSON] for his guidance.  
The authors declare no conflict of interest.
## 3 State of the Art in Digital Twins and Cyber-Ranges
### State of the Art in Cyber-Ranges  
While the above definition intentionally accepts the many known uses of CRs, a look at the state-of-the-art technology reveals most available products essentially target the market of training services. The following is a non-exhaustive list of commercially available Cyber-Ranges and a description of their main characteristics:  
* Airbus Cyber-Range is a proprietary development from Airbus Cybersecurity. It exists in three versions, as a fixed turnkey platform, as a mobile platform and as a service in the cloud. It has a drag and drop design interface for network modeling, a large catalogue of attacks and equipment templates and a growing OT modeling and simulation capacity. It is customizable without professional services [109].
* Hynesim CR is proprietary development from Diateam. It exists in fixed, mobile and cloud versions, has a drag and drop design interface and includes a Learning Management System (LMS) enabling to customize user training path [110].
* Malice CR is a proprietary development from Sysdream. It exists in fixed, mobile and cloud versions, provides an industrial virtualization layer, a LMS and a modern web management interface [111].
* CyberBit CR is a proprietary development from CyberBit Ltd. It exists as a fixed or mobile platform, includes a large attack catalogue and powerful scenario engine, is customizable without the need of professional services and includes an industrial virtualization layer [112].
* Palo Alto's CR is a proprietary development from the eponym company. It exists in fixed, mobile and cloud versions and has a scenario engine and a drag and drop interface. It supports a structured training and certification program [113].
* Ravello CR is a proprietary development from SimSpace. It exists in fixed, mobile and cloud versions and provides an industrial virtualization layer, a modern web management interface, a drag and drop design interface, a hardware network traffic generator, a scenario engine and a LMS. It is customizable without professional services [114].
* Cisco CR is a proprietary development from Cisco. It is available as fixed platform or in the cloud. It provides an industrial virtualization layer, a large attack catalogue and a scenario engine [115].
* Cdex CR is a proprietary development from Vector Synergy. It provides an industrial virtualization layer, an attack catalogue and a scenario engine [116].  
As this list suggests, most CRs are proprietary products, as the dominant VM technologies are. Few CRs have developed a community approach to support exchange of templates or scenarios by users, with adequate access control and user-rating mechanisms to ensure legality of use and quality of content. Although many vendors pretend to address equally IT and OT infrastructures, most CRs on the market currently do not integrate OT modeling beyond network level. OT CRs typically include virtual or physical PL equipment and SCADA (Supervisory Control and Data Acquisition) software as well as traffic representative of industrial protocols.
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 3 Method
### Vibration System Simulator
#### 3.1.2 Using FIR Filters to the Build Simulator  
The simulator can be viewed as a five-input/four-output filter. The input signals are denoted as \(x_{0}(n)\), \(x_{1}(n)\), \(x_{2}(n)\), \(x_{3}(n)\), and \(x_{4}(n)\), where \(x_{0}(n)\) represents the force signal of the exciter, and \(x_{1}(n)\), \(x_{2}(n)\), \(x_{3}(n)\), and \(x_{4}(n)\) represent the force signals of the four electromagnetic actuators. The required output signals are recorded as \(d_{1}(n)\), \(d_{2}(n)\), \(d_{3}(n)\), and \(d_{4}(n)\), which represent the acceleration signals, respectively. \(y_{j}(n)\) is the estimated value of the expected output (\(d_{j}(n)\), j = 1, 2, 3, 4).  
Obviously, there are \(5\times 4\) = 20 channels between the input and output, each of which can be represented by an FIR filter. We can suppose that \(\hat{C}_{ij}\) represents a filter between \(x_{i}(n)\) and \(d_{j}(n)\), where i = 0, 1, 2, 3, 4 and j = 1, 2, 3, 4.  
Note that \(d_{1}(n)\) is the open-loop response under \(x_{0}(n)\), \(x_{1}(n)\), \(x_{2}(n)\), \(x_{3}(n)\), and \(x_{4}(n)\) excitation. Therefore, the estimation of \(d_{1}(n)\) can be expressed as follows:  
\[\begin{array}{lll}y_{1}&=&\omega_{01}^{T}(n)x_{0}(n)+\omega_{11}^{T}(n)x_{1 }(n)+\omega_{21}^{T}(n)x_{2}(n)+\omega_{31}^{T}(n)x_{3}(n)\\ &+&\omega_{41}^{T}(n)x_{4}(n)\end{array} \tag{9}\]  
where \(y_{1}(n)\) is the estimated value of \(d_{1}\), and \(\omega_{i1}(n)\) is the filter weight of the filter \(C_{i1}\). It can be found that \(y_{1}(n)\) is the superposition of the outputs of five FIR filters. Similarly, the estimated values of \(d_{2}(n)\), \(d_{3}(n)\), and \(d_{4}(n)\) are expressed as follows:  
\[\begin{array}{lll}y_{2}&=&\omega_{02}^{T}(n)x_{0}(n)+\omega_{12}^{T}(n)x_{1 }(n)+\omega_{22}^{T}(n)x_{2}(n)+\omega_{32}^{T}(n)x_{3}(n)\\ &+&\omega_{42}^{T}(n)x_{4}(n)\\ &y_{3}&=&\omega_{03}^{T}(n)x_{0}(n)+\omega_{13}^{T}(n)x_{1}(n)+\omega_{23}^{T} (n)x_{2}(n)+\omega_{33}^{T}(n)x_{3}(n)\\ &+&\omega_{43}^{T}(n)x_{4}(n)\end{array} \tag{10}\]  
Figure 4: Schematic diagram of an FIR filter.  
\[y_{4} = \omega_{04}^{T}(n)x_{0}(n)+\omega_{14}^{T}(n)x_{1}(n)+\omega_{24}^{T} (n)x_{2}(n)+\omega_{34}^{T}(n)x_{3}(n)\] \[+ \omega_{44}^{T}(n)x_{4}(n)\]  
Formulas (10)-(12) can be written as a reduced version of Formula (13), which is the simulator of the vibration system.  
\[Y(n)=W(n)X(n) \tag{13}\]  
\[Y(n)=[y_{1}(n)y_{2}(n)y_{3}(n)y_{4}(n)]^{T} \tag{14}\]  
\[X(n)=\left[x_{0}^{T}(n)x_{1}^{T}(n)x_{2}^{T}(n)x_{3}^{T}(n)x_{4}^{T}(n)\right] ^{T} \tag{15}\]  
\[W(n)=\begin{bmatrix}\omega_{01}^{T}(n)&\cdots&\omega_{41}^{T}(n)\\ \vdots&\ddots&\vdots\\ \omega_{04}^{T}(n)&\cdots&\omega_{44}^{T}(n)\end{bmatrix} \tag{16}\]  
The simulator consists of 20 single-input/single-output filters. The matrix \(W(n)\) contains the weights of 20 filters, where \(W_{ij}(n)\) is the weight vector of the filter \(C_{ij}\): i = 0, 1, 2, 3, 4 and j = 1, 2, 3, 4.
## 6 Results
### Madrid-Las Tablas Traffic Scenario (MLT)  
This traffic scenario was first outlined in [34]. The traffic network is depicted in Figures 4 and 5 and consists of 971 nodes and 1583 links covering one of the largest districts in Madrid, Spain. It spans over 24,300 hectares with approximately 43,000 residents plus a daily floating job population of around 38,000 office workers. The district also has major commercial and financial hubs, leading to significant traffic influx and outflow during business hours. Its borders are defined by major expressways to the north, south, and east, while railways block the western side. The scenario contains a synthetic traffic demand of 3500 vehicles generated to simulate congested areas across the network. It is divided into 7 flows of 500 vehicles each, as depicted in Figure 5 and Table 3.  
Dj-kSP TWM Maps Generation  
Due to the size, complexity, and variety of parameter combinations that generate the path flow sets for the MLT network, we will develop in-depth relevant experiments pointing to the significant variations around it. The reference experiment uses \(K=6\) semi-disjoint paths with an index of \(\phi=0.6\) (there can only be a maximum of 40% overlap between paths) and a distance radius of \(R=5\). We concentrate on the \"Edge-Cost\" \(DjC_{n}\) removal policy by affecting the fundamental criterion for creating TWMs, which are the weights of the edges.  
Figure 6 illustrates the Dj-kSP obtained for the flow (\(N3\to N5\)) using the disjoint criteria for edges in common, \(Sim_{EiC}(\tau_{a},\tau_{b})\), applying different edge removal policies (\(DjERC_{maxW}\), \(DjERC_{minW}\), \(DjERC_{minS}\), \(DjERC_{maxS}\)). It is remarkable to observe that the ESX-C algorithm sometimes cannot find the target of \(K\) Dj-kSP with the applied disjoint level constraints (not enough paths that meet the requirements).  
For instance, Dj-kSP process for the MLT scenario applying \(Sim_{EiC}(\tau_{a},\tau_{b})=60\%\) with \(DjERC_{minW}\) and K = 6, will return only 17 path-flows. The same scenario with \(Sim_{EiC}(\tau_{a},\tau_{b})=30\%\) will return 41 path-flows.  
Figure 5: Logical view with the traffic commodities depicted in red (created with Matlab R23A [71]).
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 3 The Proposed Method
### Optimization of SVR Parameters  
SVR is a regression algorithm that maps data into a high-dimensional space and fits data by identifying an optimal hyperplane, thereby enabling accurate estimation of target values. It is capable of effectively addressing nonlinear problems, exhibiting strong generalization capability and demonstrating robustness to noise and outliers. In the SVR model, the regularization parameter \(C\) and the kernel function parameter \(\gamma\) are two critical hyperparameters that jointly determine the complexity and generalization performance of the model. The regularization parameter \(C\) controls the model complexity, striking a balance between training error and generalization capability. A larger \(C\) causes the SVR to overly focus on the accuracy of the training set, leading to overfitting. A smaller \(C\) may prevent the model from capturing the characteristics of the training data well, resulting in underfitting. \(\gamma\) is essential for mapping the input data into high-dimensional space. A smaller \(\gamma\) means a larger influence range for the data points, which can lead to underfitting. A larger \(\gamma\) means a smaller influence range for each data point, which may lead to overfitting. In Reference [45], PSO is employed to optimize the parameters of the SVR model, but the resulting computational burden remains significantly high, limiting its practical applicability. Therefore, this research introduces a genetic algorithm, which gradually approximates the optimal \(C\) and \(\gamma\) estimates by imitating the process of biological evolution and conducting extensive searches in the solution space. The detailed steps are described as follows.
## 5 Method and Design
### Overall Design  
When simulating various scheduling algorithms in a multiprocessor environment, a simplified system suffices compared with a comprehensive operating system like Windows or Linux. We have omitted features such as memory management, I/O operations, and program execution details from our model. The exclusion of these elements was a deliberate choice driven by their complexity. We focus solely on the minimal essential information needed to compute metrics and appropriately schedule jobs for their designated duration. The methods outlined below are essential for facilitating the simulation of various schedulers.  
* **Job generation:** a transition within the Petri net will generate jobs using specific values outlined in Section 3.1. The characteristics of the jobs will also depend on the scheduling algorithm employed. Jobs may be generated through random, static, or external dataset-based methods. Subsequently, all generated jobs will be placed in a global queue accessible to all CPUs.
* **Queues:** freshly generated jobs are deposited into the global queue, following the previously outlined procedure. Each CPU is equipped with its dedicated local queue. For a system comprising \(n\) CPUs, there will be \(n\) local queues, resulting in a total of \(n+1\) queues, including the global queue. Jobs are transferred from the global queue to the local queue with the least number of jobs. In scenarios where multiple local queues possess an equal minimum number of jobs, the decision regarding which local queue will receive the job will be left to GPenSIM. As a result, the quantity of jobs in each local queue remains relatively consistent.
* **Redistribute jobs:** in the presence of an idle CPU, it should be able to migrate a job from one local queue to another. Job migration is contingent upon the global and local queues' lack of jobs. Under these conditions, a job from another local queue will return to the global queue and undergo fair redistribution.
* **Scheduling:** CPUs select executable jobs from their respective local queues to run within a specified time frame. The scheduling algorithm remains consistent across all CPUs, focusing on the jobs within its local queue. The selection of a particular job is contingent upon the implemented scheduler. Certain scheduling algorithms may require additional fields in the jobs to facilitate the proper selection of the next job.
* **Time:** each job encompasses several fields necessary for metric calculations, as detailed in Section 3.1, contingent on having access to time. GPenSIM employs a clock called the _global timer_, a discrete value incremented by 1/4 of the shortest firing transition [24]. The global timer in GPenSIM will be used when assigning time to jobs. The rationale behind this choice will be explained later as we delve into the advantages and disadvantages of utilizing this built-in global timer.
## 2 Basic Theory
### Gramian Angular Field (GAF)  
GAF converts one-dimensional time series data into two-dimensional images and converts polar coordinate system data while preserving the correlation between each time point. This is suitable for analyzing data over brief lengths of time. The fundamental theory of Gramian angular field (GAF) can refer to the relative investigations [48; 49; 50]. If the time series data \(\mathrm{X=[x_{1},x_{2},x_{3},\ldots,x_{n}]}\), and then using the normalization method to scale all x values to the range of \([-1,1]\), the formula would be as follows [48]:  
\[\mathrm{\ddot{x}_{i}=\frac{(x_{i}-\mathrm{max(X)})+(x_{i}-\mathrm{min(X)})}{ \mathrm{max(X)-min(X)}}} \tag{5}\]By calculating the arccosine using the time point as the radius and scale value, the polar coordinate system is generated [48]:  
\[\begin{cases}\theta_{\mathrm{i}}=\cos^{-1}\left(\tilde{\mathrm{x_{i}}}\right)&-1 \ \leq\tilde{\mathrm{x_{i}}}\ \leq\ 1\\ \mathrm{r_{i}}=\frac{\mathrm{i}}{\mathrm{n}}&\mathrm{i}\ \in\ \mathrm{n}\end{cases} \tag{6}\]  
\(\theta_{\mathrm{i}}\) is the opposite value of the cosine function. \(\mathrm{r_{i}}\) is the radius of \(\mathrm{x_{i}}\) on the polar coordinate system. The data of [\(-\)1, 1] corresponds to the cosine angle which varies between [0, \(\pi\)]. The time correlation can be observed from the angle. The Gramian angular summation field (GASF) and Gramian angular difference field (GADF) can be obtained by calculating the sum/difference of the trigonometric function. This paper uses GASF, which is defined as follows [48]:  
\[\mathrm{GASF}=\begin{pmatrix}\cos(\theta_{1}+\theta_{1})&\dots&\cos(\theta_{1} +\theta_{\mathrm{n}})\\ \cos(\theta_{2}+\theta_{1})&\dots&\cos(\theta_{2}+\theta_{\mathrm{n}})\\ \vdots&\ddots&\vdots\\ \cos(\theta_{\mathrm{n}}+\theta_{1})&\dots&\cos(\theta_{\mathrm{n}}+\theta_{ \mathrm{n}})\end{pmatrix} \tag{7}\]
# Key Factors of the Initiation and Development of Polygonal Wear in the Wheels of a High-Speed Train
###### Abstract  
The polygonal wear of train wheels occurs commonly in rail transport and increases the wheel-rail interaction force dramatically and has a bad effect on the safety and comfort of the train. The mechanism of polygonal wear needs to be studied. The characteristics of test data measured from 47,000 sets of polygonal wheels of high-speed trains were analysed statistically. The analysis shows that, in the entire use life cycle of the wheels, the order (wavelength) and development speed of polygonal wear are different; they correspond to different wheel diameters because of wear and re-profiling. A prediction model, which considered the flexibility of the wheelset for the polygonal wear of the wheels of high-speed trains, was developed to explain this phenomenon. This theoretical model analyses the initiation, development, and characteristics of polygonal wear. The analysis includes the effect of the high-frequency flexible deformation of the wheelset, train operation speed, and wheel diameter variation. This study suggests that, if the wheel perimeter is nearly an integral multiple of the wavelength of severe periodic wear along the wheel circumference, the polygonal wear on the wheel can develop quickly. Furthermore, the wavelength of the periodic wear of the wheel relies on the operation speed of the train and wheelset resonant frequency. Therefore, the initiation and development of polygonal wear on wheels depends on the operation speed, wheel diameter, and the resonant frequencies of the wheelset. This conclusion can be applied to research concerning measures associated with the suppression of polygonal wear development.  
operating speed; polygonal wear; resonance; train-track rigid-flexible coupling dynamics model; wheel diameter +
Footnote †: journal: Applied Sciences
## 2 End-to-End Speech Recognition  
### Connectionist Temporal Classification (CTC)  
A CTC network uses a recurrent neural network (RNN) and CTC error criterion to directly optimize the prediction of a transcription sequence. To deal with the issue that output length is shorter than input speech frames, CTC adds a blank symbol as an additional label to the label set and allowsrepetition of labels or blank across frames. The CTC model predicts the conditional probability of the whole label sequence as:  
\[L_{CTC}=-lnp(\mathbf{y}|\mathbf{x})=-ln\sum_{\mathbf{\pi}\in\mathcal{B}^{-1}(\mathbf{y})}p(\mathbf{ \pi}|\mathbf{x}), \tag{1}\]  
With the conditional independence assumption, \(p(\mathbf{\pi}|\mathbf{x})\) can be decomposed into a product of posteriors of each frame. Thus, Equation (1) can be written as:  
\[L_{CTC}=-ln\sum_{\mathbf{\pi}:\mathbf{\pi}\in\mathbf{L}^{\prime},B(\mathbf{\pi}_{1:T})=\mathbf{y }}\prod_{t=1}^{T}p(\pi_{t}|\mathbf{x}). \tag{2}\]  
where \(\mathbf{y}\) denotes the output label sequence. \(\mathbf{y}\in\mathbf{L}\) and \(\mathbf{L}\) is the label set for ASR. \(\mathbf{x}=(\mathbf{x_{1}},\dots,\mathbf{x_{T}})\) is the corresponding feature sequence, \(t\) is the index of frame, and \(T\) is the total number frames. \(\mathbf{\pi_{t:T}}=(\pi_{1},...,\pi_{T})\) is the frame-wise CTC output symbol path from 1 to \(T\). Each output symbol \(\pi_{t}\in\mathbf{L}^{\prime}\) and \(\mathbf{L}^{\prime}=\mathbf{L}\cup blank\). \(p(\pi_{t}|\mathbf{x})\) is the probability of output symbol of CTC network at time \(t\). A many-to-one mapping \(B\) is defined as \(B:\mathbf{L}^{\prime}\to\mathbf{L}\) to determine the correspondence between a set of paths and the output label sequence.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### Effect of Rough Surface  
One of the main difficulties for detecting buried objects with GPR is the strong random clutter produced by the reflection at the rough air-soil interface (due to impedance mismatch). If the interface is rough, this reflection cannot be easily removed and it clearly worsens the detection capability of the system. Therefore, it was necessary to analyze the performance of the different configurations with a rough surface. This analysis was performed with FDFD, considering a rough surface with an average height of 2 cm and a correlation length of 60 cm.  
The SAR images for the multimonostatic and multibistatic configurations with a buried dielectric target are shown in Figure 13. In the multimonostatic configuration, the target is hardly detected (similarly as for the flat surface). In the multibistatic configuration, the target is still detected, although there are some stronger reflections at the air-soil interface, which worsens the quality of the SAR image. The inversion results for the multistatic configuration are similar to the multibistatic arrangement. The target detection is improved relative to the multimonostatic configuration because, in the multistatic and multibistatic configurations, rays specularly reflected from the ground surface do not reach the receiver. Using a forward-stationed drone receiver also offers advantages of higher signal strength and closer angular proximity to the maximum target-scattered response, relative to a vehicle-mounted receiver [16]. Thus, it can be concluded that the proposed multibistatic configuration allows to detect dielectric targets even under rough surfaces.
## 2 Background and the Related Work
### Traffic Classification Approaches  
The process by which the network protocols or applications that are present in a network are identified and then classified is known as network traffic classification. Network management, service measures, network architecture, security monitoring, and advertising have all made extensive use of traffic classification. Knowing what kinds of network applications flow through a network is a crucial challenge for internet service providers (ISPs). Through this technique, internet service providers or network operators can manage the overall performance of a network. Traditional techniques for classifying internet traffic include those that use ports, payloads, and ML. The ML approach is currently the most commonly utilized method [24].