esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental/Numerical Evaluation of Steel Trapezoidal Corrugated Infill Panels with an Opening
## 3 Results  
### Behavior of Specim 1  
The infill shear plate behaved elastically during the first four cycles with 1, 2, 3 and 4 mm displacements, respectively. Buckling of the shear plate was detected in cycle 5 with 6 mm displacement and 0.38% drift angle. Distortion of the shear plate was observed first on the left side of the panel near the top of the opening and then in the bottom right corner in the direction of the large diameter of the panel during cycles 5 and 6. In cycles 7 and 8, new distortions were developed in the direction of the other diameter of the shear panel.  
The first tear was seen on the left side near the top of the opening in cycle 15 with 25 mm displacement and 1.59% drift angle. During cycle 19 with 30 mm displacement and 1.91% drift angle, the second tear was monitored in the top right corner. The third and fourth tears were detected during cycles 23 and 25 with the same displacement of 35 mm and drift angle of 2.22% in the top and bottom left corners, respectively. By continuing the loading, the number of tears and their dimensions both increased, and cracks were developed in welded connections, especially the welded connections of the infill plate to the beams and columns. In cycle 39 with 70 mm displacement and 4.45% drift angle, the shear force increased to 385 kN, which was the maximum recorded shear force during the loading of this specimen. Yielding of the columns' external flange was clearly observed at their bottom end in cycle 39. Moreover, during cycles 43-46 with 80 mm displacement and 5.12% drift angle, conditions of the columns at their top end, as well as conditions of the top beam at the points of its connections to the columns, indicated yielding clearly. Finally, the loading was stopped by cycle 48 with 100 mm displacement and 6.37% drift angle. Figure 10 shows the first specimen (specimen 1) at the end of loading. As illustrated in Figure 10, diagonal tears in the four corners of the opening and several tears located close to the welded connection of the infill plate to the top beam were remarkable.  
Figure 10: Specim 1 at the end of loading.
## 2 Preliminaries
### Elliptic Curve Cryptography  
ECC is a public-key cryptosystem based on the algebraic structure of elliptic curves over finite fields. Let \(F_{p}\) denote a finite field of large prime order \(p\). An elliptic curve \(E\) over \(F_{p}\) is defined by the Weierstrass equation \(y^{2}=x^{3}+ax+b\mod p\), where \(x,y,a,b\in F_{p}\).  
**Definition 1** (**Elliptic Curve Discrete Logarithm Problem)**.: _Given a base point \(P\in E(F_{p})\) and another point \(Q=x\cdot P\), it is computationally infeasible to determine the unique integer \(x\)._  
**Definition 2** (**Elliptic Curve Computational Diffie-Hellman Problem)**.: _Given points \(Q_{1}=a\cdot P\) and \(Q_{2}=b\cdot P\), it is computationally infeasible to compute the point \(Q=abP\) in probabilistic polynomial time without the knowledge of either a or \(b\)._
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 7 Conclusions  
In summary, this study investigated the short-landing technology, and the proposed control strategy effectively addressed the short-landing problem for a flying-wing UAV. The research results are as follows:  
1. The paper proposes a complete control strategy to solve the short-landing problem for a flying-wing UAV, using 3 BSD thrust-vector control. The frequency-domain-based approach is used to allocate the lift fans, thrust vectors, and aerodynamic control surfaces, resulting in a practical and computationally efficient solution.
2. The equilibrium points for the velocity and angle of attack are determined through trim analysis based on the maximum linear and angular acceleration analysis to establish the command values for velocity and angle of attack.
3. Different flight modes were considered, and the study designed an ESO-based angular rate controller and sink rate controller. The three-axis decoupling control is realized via a SISO design technique, and the ESO-based sink rate controller shows better disturbance rejection capability when considering gusts of wind.
4. A flight boundary protection method utilizing an additional angle of attack command is introduced, which enhances both the precision and safety of landings. Monte Carlo simulations were conducted to validate the effectiveness of the proposed method.  
Figure 27: Monte Carlo simulation trajectory.  
Figure 28: Landing error diagram: (**a**) CEP error and (**b**) histogram with a distribution fit for CEP.
# Nonlinear Random Forest Classification, a Copula-Based Approach
## 1 Introduction  
Dimension reduction is a major area of interest within the field of data mining and knowledge discovery, especially in high-dimensional analysis. Recently, the issue of machine learning has received considerable attention; hence, a number of researchers have sought to perform more accurate dimension reductions in this issue [1, 2]. While dimension reduction tries to reduce the dimension of data by selecting some functions of the original dataset, feature selection is one of its special cases, which selects the most important features among all of them. There are many areas of statistics and machine learning that benefit from feature selection techniques. From the statistics point of view, [PERSON] et al. (2013) [3] and [PERSON] (2008) [4] have applied feature selection for multivariate time series. [PERSON] et al. (2008) [5] have investigated feature selection and regression in high-dimensional problems.  
It is known that selecting the most important and relevant features is the main aim in decision tree/random forest algorithms. Although there are many classification approaches proposed in the literature, they rarely deal with the possible existence of nonlinear relations between attributes. On the other hand, note that mutual information-based filter methods have gained popularity due to their ability to capture the non-linear association between dependent and independent variables in a machine learning setting. Mutual information based on a copula function will be a good choice to carry out a feature selection in which the results are stable against noises and outliers [6, 7]. So, one of the major aims of this work is using feature selection in a classification context based on a copula function, especially in random forest classification.  
Random forests are commonly used machine learning algorithm, which are a combination of various independent decision trees that are trained independently on a random subset of data and use averaging to improve the predictive accuracy and control over-/ under-fitting [8, 9, 10, 11]. In this work, in order to extract the most important features in random forest, we use associated copula of features. In this regard, the connection copula between the exploratory variables as well as the associated copula of exploratory attributes andthe class labeled attribute are considered. The rest of the paper is organized as follows: we review preliminaries and introduce our method in the next section; we illustrate our algorithm considering simulated data as well as two real datasets in Section 3; finally, Section 4 is devoted to some concluding remarks.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 9 Evaluation
### Analysis for Large-Scale Scenarios
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Requirement** & \multicolumn{2}{c}{**Proposed Solution**} & **Comments** \\ \hline \multirow{8}{*}{Adaptive consumption constrained to availability} & - & Use of management mechanisms to adapt consumption to generation: (i) time-shifting capabilities on energy demands; (ii) prioritization of energy supply according to the priority level of the energy demand; and, (iii) the rejection of energy demands if available supply is insufficient to cover all consumption. & The adaptation process must be carried out constantly, and one or all of the proposed management mechanisms can be used for this purpose. \\  & - & Implementation of the management mechanisms through algorithms, which must be deployed in infrastructures with high computational capacity due to the complexity involved in the adaptive energy management process. & \\ \hline \multirow{8}{*}{Consumer-side participation} & - & Availability of user-side connectivity and the use of IoT technologies to participate in the energy management ecosystem. & The participation of the ECs in the energy management process is established through contracts with the ES. These agreements detail all of the technical aspects that are related to the energy demands (e.g., priority) and the possible incentives (e.g., reduce bills) that the ECs can receive by modifying consumption patterns to the availability. \\  & - & Dynamic behavior by implementing algorithmic management strategies through VFNNs forming SFCs. These VFNNs can be created, activated, or modified on demand according to the conditions of consumption and generation. & The NFV technology could be deployed on the operations centers available in current smart grids. Additionally, the communications standards, such as SCADA, could migrate to programmable solutions such as SDN. Through these updates, current energy systems could begin their transition to adaptive consumption management. \\ \cline{1-1}  & - & The NFV technology could be deployed on the operations centers available in current smart grids.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 2 Pseudo-Range Correction  
PRC data contain a variety of types of errors related to range errors between the receiver and the satellite. Among the error sources, ionospheric and tropospheric delays are the two most significant ones. Since they are caused by atmospheric composition, the amount of a ranging error varies depending on the degree of solar activity and the length of the signal transmission. Therefore, PRC values also vary according to observation time and elevation angle of the satellite. In addition, PRC has a feature that is repeated periodically, because GNSS satellites orbit at a regular period.  
Figure 1a shows PRC values of all visible GPS satellites for one day (23 April 2016) from the reference station at Inha University, South Korea (37.449\({}^{\circ}\) N, 126.656\({}^{\circ}\) E). As shown in Figure 1a, the relative maximum or minimum PRC is different according to the observation time. The reason for this difference is ionospheric error, which is strongly affected by the Sun. During 4:00-9:00 Universal Coordinated Time (UTC), which corresponds to 13:00-18:00 local time (LT) in South Korea, magnitudes of PRC are the largest. In particular, the PRC of some satellites shows PRCs larger than 50 m from 6:00 to 8:00 UTC (15:00-17:00 LT). On the other hand, it is less than 20 m, and some go down to 3 m during the period 12:00-21:00 UTC (21:00-06:00 LT). Thus, observation time is one of the important factors for determining PRC values.  
Figure 1b shows PRC values of the GPS Pseudo Random Noise (PRN) No. 1 satellite for seven consecutive days (1-7 April 2016), i.e., for a week. As shown in Figure 1b, the shape of long-term PRC  
data displays the upside down U patterns repeatedly with a certain time difference. PRCs increase higher than 25 m at 9 to 10 UTC, when the satellite is observed for the first time, but become smaller as the elevation angle of the satellite becomes higher. PRC values were in the range of 3-4 m at 12 to 13 UTC, when the elevation angle was the highest. After that, however, it becomes larger again as the elevation angle decreased. This phenomenon was repeated every day with similar shapes for a week. As such, PRC is different according to the elevation angle of the satellite, and revealed a repetitive shape. Therefore, the orbital period of a satellite is one of the most important factors that determine the repeatability and periodicity of PRC. Figure 1 shows these characteristics.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 4 Simulation Results
### Comparison of the Number of Cluster Heads  
To demonstrate that it is necessary to consider the improvement when nodes closer to the BS do not participate in clustering formation, the simulation results of the cluster heads number for the IEE-LEACH-B and IEE-LEACH protocols are provided in this section. Unlike the LEACH and IEE-LEACH-A protocols, IEE-LEACH-B and IEE-LEACH consider the energy when selecting the CH. Thus, IEE-LEACH-B and IEE-LEACH can select the optimal number of cluster heads.  
Figure 10 shows the number of cluster heads versus the number of rounds. The maximum number of cluster heads for IEE-LEACH occurs after approximately 700 rounds, and the number of cluster heads is zero after 2600 rounds. However, the maximum number of cluster heads for IEE-LEACH-B occurs in approximately 500 rounds, and the number of cluster heads is zero after 2300 rounds. We find that the number of cluster heads extends up to 2600 rounds for IEE-LEACH as compared to 2300 rounds in IEE-LEACH-B, which is due to the improvement by which the nodes closer to BS do not participate in the cluster formation. The number of cluster heads for IEE-LEACH-A and LEACH disappears after 2050 rounds and 1800 rounds, respectively. It is obvious that IEE-LEACH and IEE-LEACH-B prolong the network lifetime compared with IEE-LEACH-A and LEACH because IEE-LEACH-B and IEE-LEACH consider the energy adjustment parameter when selecting the CH. In addition, the proposed IEE-LEACH protocol generates more CHs than IEE-LEACH-B throughout the 3500 simulation rounds, which can reduce the energy consumption of the WSN nodes. Thus, the proposed scheme is efficient in terms of saving energy and extending the lifetime of the WSN.
## 4 Training and Evaluation of Threat-Detection AI Model
### Training Protocols and Evaluation Measures  
The YOLOv4 and DINO models were trained using the NVIDIA _Train Adapt Optimize_ (_TAO_) version 3.0 framework, which is natively based on _Tensorflow_ and _PyTorch_. For the YOLOv4 model, the CSPDarkNet53 network pretrained on the Google OpenImages dataset [29] with 49,468,412 trainable parameters was used as the backbone architecture. For the training, the number of bounding boxes per cell was set to three, the object confidence threshold was set to 0.01, and the NMS clustering threshold was set 0.5. It was trained for 1000 epochs to ensure convergence. The DINO model was trained with the FAN_Tiny backbone for 200 epochs. For all the model training, the transfer learning approach was adopted to avoid the requirement of a huge dataset, large computational resources, and long training times for training the model from scratch. All the models were trained using a desktop machine equipped with two NVIDIA T4 Graphical Processing Units (GPUs) with 16 GB of memory each. The main parameters for the training process are summarized in Table 3. The model hyperparameters were manually tuned to achieve the best results.  
The trained model was evaluated using validation and test datasets with images unseen during the training process. The split of train-val-test (i.e., 70-20-10) was used to ensure that the model was not overfitting. As an evaluation metric, the Mean Average Precision (mAP) was used, which is based on the IoU (i.e., a measure of the overlap between the predicted bounding box and the actual bounding box (\"ground truth\") created during the annotation of the dataset). A prediction by the AI is deemed correct if the IoU between the predicted bounding box and the ground truth is above a given threshold \(t\). The mAP is a metric in \([0,1]\) summarizing the performance of the AI for different overlapping thresholds \(t\). A value close to 1 indicates an accurate model.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology  
At present, gesture recognition based on FMCW radar mainly uses deep learning neural networks for classification and recognition. However, there are still some shortcomings. Using a single neural network for classification and recognition of multiple types often results in insufficient accuracy. Due to the simultaneous input of all gesture data during training, this results in a large amount of input data, a complex network model, and lower recognition efficiency. Conventional research typically emphasizes direct training and recognition using collected gesture datasets and overlooks gesture segmentation issues, resulting in a lack of practicality.  
Figure 7: Gesture HRRP after CFAR detection. The yellow parts are the detected gesture signals.  
To overcome the shortcomings, the commonly used gestures are categorized into two types: simple trajectory gestures and complex trajectory gestures. Simple trajectory gestures which have linear motions can be directly recognized by solving parameters. Therefore, a state machine method can be employed for recognizing simple trajectory gestures. Complex trajectory gestures cannot be directly recognized, so the neural network method can be employed for recognizing them. Finally, by combining the recognition results of the two methods, the final recognized gesture is obtained. The diagram of recognition process is illustrated in Figure 8.  
The specific processing flow is as follows. Initially, the millimeter-wave radar acquires the IF signal of the hand target through the transmitting and receiving of electromagnetic waves which contains target information such as distance, velocity, azimuth, and elevation angle. Secondly, the IF signal is processed to estimate the gesture parameters. Thirdly, MTI processing and OS-CFAR detection are conducted on the IF signal to detect the start and end positions of the gesture in order to segment the complete gesture movements. Fourthly, preprocessing is applied to the acquired gesture trajectory, which involves median filtering and uniform resampling to accommodate the various operating habits of different users. After preprocessing, the gesture trajectory is recognized to obtain the result. About simple trajectory gestures, using a neural network may lead to complexity and inefficiency. Directly judging the motion trajectories is more suitable for recognizing such gestures. In addition, trained neural networks can only recognize pre-set gestures, making it difficult to expand gesture types. When it comes to complex trajectory gestures, direct classification using parameters can lead to confusion. Therefore, neural networks are employed to identify specific gestures in three dimensions: distance, azimuth, and elevation. Finally, the classification results of the two methods are combined and the recognized gesture type is output.
# Single-Shot Global and Local Context Refinement Neural Network for Head Detection
## 4 Experiments and Analysis
### Dataset and Evaluation Metrics  
We conduct extensive research on the Brainwash [38] and HollywoodHeads [10] head detection datasets. The Brainwash dataset contains 82,906 labeled head boxes across 11,917 images. There are 10,917 total images in the training set, 500 images in the verification set, and 500 images in the test set. It is a large dataset obtained from crowd scenes utilizing video footage at a fixed interval. The HollywoodHeads dataset is the largest dataset currently used for head detection. It consists of 224,740 images from 21 Hollywood movies, with 369,846 heads in total. The training, validation, and test sets contain 216,719, 6719, and 1302 annotated images, respectively.  
The evaluation metric adopted in testing is the standard average precision (AP). Furthermore, the IoU threshold is 0.5.
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 3 Vector-CFNN
### Vector-Based Convolution  
Although the CFNN can reduce many learnable parameters by replacing fully connected layers, matrix kernels can also produce a great deal of parameter redundancy by the convolution operation [24]. Vector-CFNN employs a vector-based convolution instead of a conventional convolution. For instance, a convolutional layer with a kernel size of \((k\times k,c)\) can be convoluted by a kernel size of \((k\times 1,m)\) followed by a kernel size of \((1\times k,c)\). Figure 3 presents a schematic of a vector-based convolution. It can effectively reduce parameters and floating-point operations per second without suffering the loss of recognition accuracy rate.
## 3 Materials and Methods
### Methods
#### 3.2.2 Depth Feature Extraction Module  
In most medical image segmentation networks, the down-sampling operation is usually used to extract low-level semantic information from images. The down-sampling operation block structure in the U-Net network has been widely used in different medical segmentation models. As shown in Figure 4a, this module is connected by two consecutive CBRs (Conv 3 \(\times\) 3 + BN + ReLU), and ordinary convolution is used to extract image features. In order to obtain a larger receptive field and extract a more effective feature map, a CBR3 \(\times\) 3 + BN + ReLU convolution is usually superimposed on the basis of Figure 4a, and the structure is shown in Figure 4b. However, if the convolution is stacked blindly, the effective receptive field will decrease to a certain extent, and at the same time, the number of parameters will increase, and the amount of calculation will increase. To solve this problem, a depth feature extraction module is proposed, as shown in Figure 4c. Depth separable convolution is introduced, and a new feature map with the same number of input feature channels is obtained by using 7 \(\times\) 7 channel-by-channel convolution, and then the feature map output is obtained by convolution operation with the obtained new feature map through 1 \(\times\) 1 point-by-point convolution [33]. Compared with ordinary convolution, depth separable convolution can extract deeper feature information and reduce the number of network parameters. At the same time, a residual connection is introduced to avoid gradient disappearance and improve network performance. The depth feature extraction module can reduce network parameters, improve network performance, and extract image semantic information more accurately.