esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 2 Related Works
In [70], they introduced a model for multimedia traffic transmission over cognitive radio TDMA networks under secondary collision errors. They used a model based on the redundancy for link maintenance to compensate for any spectrum resource loss due to the primary traffic reclaims. Their model had aimed minimization of the collision effects to increase the Goodput but the system suffers from high complexity. Among all available and frequently used multimedia formats, JPEG2000 has a lot of practical features that make it a viable choice for CRNs [71]. In [71], a new channel allocation algorithm found on JPEG2000 and OFDM (orthogonal frequency division multiplexing) was introduced, which improved the quality of the SU's received image by providing dynamic access (DA) to the available spectrum pool, without violating the interference requirement to the PU. The maximization of Signal to Noise Ratio (SNR) was aimed at this work to get higher QoS but it just limited to JPEG2000 standard. In this work, we also have replaced traditional WSN with Cognitive Radio WSN, so called CRSNs. In the next section, full details of our customized CR-MAC layer for image streaming over smart grids are presented.
## 4 Experiment Results and Comparison
### Data Sets Used in Experiment
#### 4.1.1 Nsl-Kdd  
NSL-KDD is a proposed data collection method intended to address some of the shortcomings of the KDD'99 data set, which is considered one of the most commonly used datasets for anomaly detection. Further description of this dataset can be found in [29]. It is made up of about 4,900,000 single-connection instances. Each instance has 41 features and is classified as either normal or attack. There are four unique attack types, as outlined in Table 3.
## 3 The UDVRP Model
### Kinematic Model  
The vehicle follows a differential-drive kinematic model:  
\[\begin{cases}\dot{x}=v\cos\theta\\ \dot{y}=v\sin\theta\\ \dot{\theta}=\frac{v}{L}\tan\delta\end{cases} \tag{1}\]  
where \((x,y)\) is the position, \(\theta\) is the heading angle, \(v\) is the linear velocity, \(\delta\) is the steering angle, and \(L=1.2\,\mathrm{m}\) is the wheelbase. Motion constraints include \(|\delta|\leq 30^{\circ}\) and \(v\leq 5\,\mathrm{m}/\mathrm{s}\).
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 2 MPC for Current Control of MPS
### Optimization and Deployment  
In FSMPC, the control move for the \(k+1\) period is determined at discrete time \(k\) as \(u^{opt}=\text{argmin}/(k+2)\). In earlier studies, the optimization phase of FCSMPC was performed by exhaustive search. This involved computing the value of \(J\) for each candidate or allowed control move to obtain the minimum.  
The deployment of the method in a DSP is relatively straightforward. A flowchart of the FSMPC algorithm is shown in Figure 3. It is interesting to note that the main blocks are the same regardless of the type of machine (induction, permanent magnet, etc.) and regardless of the number of phases. For any particular case, one needs just to adjust the model and a few parameters.  
The results of FSMPC have been featured in many papers [4]. In the case of MPS, special attention must be given to the different planes in the VSD decomposition. As an example, check the waveforms in Figure 4, where the \(\alpha\)-\(\beta\) components clearly follow their sinusoidal references and the \(x\)-\(y\) components are regulated around zero.  
Despite the flexibility of FSMPC, its practical application is hindered by a significant computational load, especially in MPS, due to the large number of possible control moves. As discussed in the Introduction, a high computational load leads to a reduced sampling frequency, which in turn limits the control bandwidth. Additionally, in the case of single-vector approaches, the longer duration of the applied voltages results in significant distortion from the ideal sinusoidal waveform.  
This issue prompted researchers to explore methods for reducing the time required for the optimization phase. The various approaches developed are categorized in the next section.  
Figure 4: Typical evolution of stator currents for FSMPC.  
Figure 3: Flowchart for FSMPC of stator current.
# Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures
## 3 Results
### Fuzzy Linear Regression Results
\begin{table}
\begin{tabular}{c c c c} \hline
**IM** & \(\mathbf{\lambda_{1,2}}\) & \(\mathbf{\lambda_{1,3}}\) & \(\mathbf{\lambda_{2,3}}\) \\ \hline PGA/(g tan(\(\alpha\))) & 0.93 & 0.95 & 0.98 \\ p FCV/(g tan(\(\alpha\))) & 0.97 & 0.95 & 0.92 \\ p \({}^{2}\) PGD/(g tan(\(\alpha\))) & 0.99 & 0.96 & 0.95 \\ p Tm & 0.98 & 0.95 & 0.97 \\ p PGV/TGA & 0.96 & 0.95 & 0.99 \\ p PGD/PGV & 0.99 & 0.96 & 0.95 \\ A\({}_{\text{RMS}}\)/(g tan(\(\alpha\))) & 0.93 & 0.99 & 0.94 \\ P v RMS/(g tan(\(\alpha\))) & 0.88 & 0.62 & 0.70 \\ p \({}^{2}\) DAs/(g tan(\(\alpha\))) & 0.99 & 0.92 & 0.91 \\ p \({}^{2}\) L\({}_{\text{cs}}\)/(g tan(\(\alpha\))) & 0.95 & 0.98 & 0.96 \\ p \({}^{2}\) L\({}_{\text{cv}}\)/(g tan(\(\alpha\))) & 0.97 & 0.98 & 0.99 \\ p \({}^{2}\) L\({}_{\text{s}}\)/(g tan(\(\alpha\))) & 0.94 & 0.97 & 0.96 \\ p \({}^{2}\)S\({}^{0}\) L\({}_{\text{g}}\) (g tan(\(\alpha\))) \({}^{3/2}\) & 0.93 & 0.95 & 0.98 \\ p \({}^{1,25}\) Ig/(g tan(\(\alpha\))) & 0.96 & 0.99 & 0.95 \\ p \({}^{3}\) SED/(g tan(\(\alpha\))) \({}^{2}\) & 0.95 & 0.99 & 0.96 \\ p \({}^{4}\) bug & 0.99 & 0.98 & 0.97 \\ p CAV/(g tan(\(\alpha\))) & 0.97 & 0.92 & 0.95 \\ p \({}^{2}\) CAD/(g tan(\(\alpha\))) & 0.99 & 0.94 & 0.95 \\ \hline \end{tabular}
\end{table} 
Table 4: The results of estimated similarity ratios for every model of FLR analysis. \begin{table}
\begin{tabular}{c c c} \hline
**Structure** & **RMSE** \\ \hline
1 & 3.88 \\
2 & 4.14 \\
3 & 3.54 \\ \hline \end{tabular}
\end{table} 
Table 3: The results of the RMSE parameter for FLR analysis. \begin{table}
\begin{tabular}{c c c c} \hline
**IM** & **RMSE\({}_{1}\)** & **RMSE\({}_{2}\)** & **RMSE\({}_{3}\)** \\ \hline PGA/(g tan(\(\alpha\))) & 4.17 & 4.23 & 4.25 \\ p PGV/(g tan(\(\alpha\))) & 4.49 & 4.82 & 5.39 \\ p\({}^{2}\) PGD/(g tan(\(\alpha\))) & 8.30 & 5.07 & 5.27 \\ p Tm & 4.82 & 5.08 & 5.09 \\ p PGV/TGA & 4.89 & 5.07 & 5.09 \\ p PGD/PGV & 4.87 & 5.06 & 5.27 \\ A\({}_{\text{RMS}}\)/(g tan(\(\alpha\))) & 4.11 & 4.40 & 4.61 \\ p V RMS/(g tan(\(\alpha\))) & 5.34 & 5.24 & 5.35 \\ p\({}^{2}\) D\({}_{\text{RMS}}\)/(g tan(\(\alpha\))) & 6.97 & 5.07 & 5.27 \\ p\({}^{2}\) L\({}_{\text{cs}}\)/(g tan(\(\alpha\))) & 4.63 & 5.00 & 5.28 \\ p\({}^{2}\) L\({}_{\text{cv}}\)/(g tan(\(\alpha\))) & 4.93 & 5.00 & 5.27 \\ p \({}^{1,3}\)/(g tan(\(\alpha\))) & 4.90 & 4.66 & 4.82 \\ p\({}^{1,50}\) I\({}_{\text{c}}\)/(g tan(\(\alpha\))) \({}^{3/2}\) & 4.69 & 4.44 & 4.61 \\ p\({}^{1,25}\) I\({}_{\text{f}}\)/(g tan(\(\alpha\))) & 5.02 & 4.93 & 5.41 \\ p\({}^{2}\) SED/(g tan(\(\alpha\))) \({}^{2}\) & 5.09 & 5.08 & 5.26 \\ p \({}^{1,3}\)/(g tan(\(\alpha\))) & 4.91 & 5.08 & 5.12 \\ p CAV/(g tan(\(\alpha\))) & 5.16 & 4.78 & 5.24 \\ p\({}^{2}\) CAD/(g tan(\(\alpha\))) & 6.41 & 5.10 & 5.27 \\ \hline \end{tabular}
\end{table} 
Table 5: The results of the RMSE parameter for every model of FLR analysis. high similarity ratios between the models. Furthermore, the significant similarity indicates the effectiveness of dimensionless IMs in accurately predicting the response of rocking systems with different dynamic properties.
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 3 The Proposed Approach
### Analysis Filter Bank  
The analysis FB consists of Audlet filters \(H_{k}\), \(k\in\{1,\ldots,K-1\}\), a low-pass filter \(H_{0}\), and a high-pass filter \(H_{K}\). In total, it consists of \(K+1\) filters. The Audlet filters are defined by  
\[H_{k}(e^{2i\pi\xi})=d_{k}^{\frac{1}{2}}w\left(\frac{f_{s}\cdot\xi-f_{k}}{ \Gamma_{k}}\right)\quad k\in\{1,\ldots,K-1\} \tag{7}\]  
where \(w(\xi)\) is a prototype filter's shape centered at frequency 0. Any symmetric or asymmetric window is an eligible \(w\). The main condition on \(w\) is that its frequency response must decay away from 0 on both sides. The parameters \(\Gamma_{k}=\beta B_{scale}(f_{k})\) and \(f_{k}\) control the bandwidth and center frequency, respectively, of the filter \(H_{k}\). The parameter \(\beta\) allows for the filter bandwidths to be compressed/expanded. Note that when \(\beta\
eq 1\), the bandwidth of the filters \(H_{k}\) deviates from the human auditory filters' bandwidth.  
To determine \(K\) and construct the sets \(\{f_{k}\}\) and \(\{\Gamma_{k}\}\), the first step consists in choosing an essential frequency range \([f_{\text{min}},f_{\text{max}}]\subseteq[0,f_{N}]\), a frequency mapping \(F_{\text{Scale}}\), and a filter density \(V\in\mathbb{R}^{+}\) of filters per Scale unit. The set \(\{d_{k}\}\) is considered arbitrary for now. An optimal choice of downsampling factors \(d_{k}\) is provided in Section 3.1.3.
# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 2 Related Work
### Backbone Network  
The backbone networks of multi-person pose estimation methods are designed to extract keypoint features and instances of people; the accuracy with which they do so largely determines the quality of the prediction results. To ensure the effectiveness of the proposed method, three different backbones architectures, Hourglass [4], Deep Layer Aggregation (DLA) [25], and HRNet [8], are comprehensively considered.  
Hourglass: The stacked Hourglass Network [4] consists of overlapping residual blocks [26], each of which is linked by a skip connection to effectively process and consolidate multi-scale features. With an encoder-decoder architecture and an intermediate supervision process, the Hourglass network shows robust performance in some complex environments, such as in cases with occlusion or cases where similar parts from nearby people are present [27]. The size of this network is quite large, which results in graceful keypoint estimation performance. The structure of an hourglass module is illustrated in Figure 1(a).  
DLA: DLA [25] is an image classification network with hierarchical skip connections, in which aggregation is defined as the combination of different layers throughout a network. DLA uses iterative deep aggregation to symmetrically increase feature map resolution, preventing loss of information in dense predictions. Moreover, DLA hierarchically merges features to create networks with better accuracy and fewer parameters. The structure of a DLA network is illustrated in Figure 2b.  
HRNet: HRNet [8] aims to maintain high-resolution features throughout the entire network. This network can be divided into parallel multi-resolution convolutions and  
Figure 2: Structures of three state-of-the-art backbone networks for human pose estimation.  
repeated multi-resolution fusions. High- to low-resolution convolution streams generate multi-scale feature maps in parallel. The goal of the fusion module is to merge information across multi-resolution representations. The structure of HRNet with three parallel branches is illustrated in Figure 2c.
## 7 Attack and Countermeasure Designs
### Countermeasure Design
In DaD, CHR is used _i._ to configure a countermeasure(s) when the adversary is detected, and _ii._ to determine the severity of the attack which can be used to set different countermeasures. For instance, if the attack is detected in a period (TIME) and continued in the next detection state, the attack can be considered severe. Through simulation experiences, an optimum DaD period check attack time was defined as 0.5 s for the NDNtube simulation. Figure 10 illustrates the severity of the attack in the DaD approach. Because each of the countermeasure methods have different effects, depending on the attack severity, various countermeasure methods can be used to mitigate the attack. By doing this, the NDN content distribution is maintained while mitigating the attacks. DaD is based on three countermeasure methods (unpredictable delay, probabilistic caching, and no-cache). The unpredictable delay is applied in the first detection state and this attack is considered as _minor_. The probabilistic caching is in the second state and this attack is considered as _moderate_. In the last attack check, if the attack continues, the router is set to no-cache for any contents, and this attack is considered as _severe._  
If the attack persists, the no-cache is applied for the next attack states. Otherwise, for a no-attack or attack-withdraw situation, the NDN router (node) sets to the default state for all consumers (e.g., LFU, LRU, or FIFO). Table 1 shows the attack states, auxiliary process, and input parameters for the DaD approach. In this algorithm, the cacheHitTreshold and TIME parameters are previously defined by application needs. To detect an adversary for each interface (face), TIME is used as a check period. For instance, TIME can be in seconds to check faces in the NDN router. To decide whether a router is under attack or not, the CHR is used. For instance, if a face's CHR is greater than the previously calculated cacheHitTreshold, that face is considered an adversary's face. In addition, this process is performed by getCacheHitRatio(), which is used to determine CHR for each interface of the NDN router. Figure 10: DaD attack states and countermeasures for the attack severity. The countermeasure methods are applied if DaD determined the attack (ATTACK_DETECTED). Different countermeasures are used depending on the severity of the attack. This is also identified by the attack period (TIME). In NDNtube, DaD continuously obtains the CHR from each face and analyzes \(\approx\)50 packets (100 packets/0.5 s). If a face's CHR is over 5%, DaD sets the minor attack phase for 3 s and keeps detecting the attacks every 0.5 s. If the attack continues, DaD sets the moderate phase for another 3 s and checks the attack every 0.5 s. If the attack continues, DaD sets a severe phase and keeps it while the attack is detected.
## 2 Materials and Methods
In (**a**,**c**,**d**) subfigures, the two different colors represent recovered (blue) and not recovered (red) patients and in (**b**) subfigure the three different colors represent patient ASIA scores A (blue), B (red) and C (cyan). \[Acc =(a+d)/(a+b+c+d)\] \[Sen =a/(a+b)\] \[Spec =d/(c+d)\text{ and }\] \[Prec =a/(a+c)\]  
where \(a\) is the number of positive (success) cases correctly classified, \(b\) is the number of positive cases misclassified as negative (fail), \(d\) is the number of negative cases correctly classified, and \(c\) is the number of negative cases misclassified as positive. Accuracy shows how good the system is in predicting correctly (success or failure). Sensitivity shows how good the system is in predicting success, while specificity shows how good the system is in predicting failure. Evaluation results, in terms of ML accuracy scores, are presented in Table 1 and Figure 2 (part of). As in statistics, accuracy is the best metric used to evaluate the performance of a classification model. It measures the proportion of all predictions (both positive and negative) that are correctly classified by the ML model [35]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**(a) Ensemble Algorithm (Vote)** & & & & \\ \hline
**Biomark Accuracy (\%)** & **All Together** & ** SSEPs** & **MEPs** & **NCS** & **Clinical Assessment** \\ \hline Motor Recovery & 89.8 & 85.4 & 81.3 & 82.9 & 75.6 \\ \hline AIS index & 84.1 & 74.9 & 72.7 & 74.4 & 63.4 \\ \hline
**(b) Randomforest** & & & & & \\ \hline
**Biomark Accuracy (\%)** & **All Together** & ** SSEPs** & **MEPs** & **NCS** & **Clinical Assessment** \\ \hline Motor Recovery & 93.1 & 91.9 & 86.6 & 85.4 & 75.6 \\ \hline AIS index & 89.0 & 80.1 & 75.2 & 81.3 & 66.3 \\ \hline
**(c) Decision Trees (J48)** & & & & & \\ \hline
**Biomark Accuracy (\%)** & **All Together** & ** SSEPs** & **MEPs** & **NCS** & **Clinical Assessment** \\ \hline Recovery & 81.3 & 79.2 & 77.9 & 67.1 & 73.5 \\ \hline AIS index & 71.9 & 76.0 & 67.0 & 67.1 & 59.8 \\ \hline
**(d) Neural Networks (Multilayer Perceptron)** & & & & & \\ \hline
**Biomark Accuracy (\%)** & **All Together** & ** SSEPs** & **MEPs** & **NCS** & **Clinical Assessment** \\ \hline Motor Recovery & 90.2 & 82.5 & 75.2 & 79.2 & 71.5 \\ \hline AIS index & 78.0 & 68.7 & 69.5 & 65.4 & 57.7 \\ \hline
**(e) Bayes (Naive Bayes)** & & & & & \\ \hline
**Biomark Accuracy (\%)** & **All Together** & ** SSEPs** & **MEPs** & **NCS** & **Clinical Assessment** \\ \hline Motor Recovery & 76.0 & 73.9 & 75.6 & 71.9 & 69.5 \\ \hline AIS index & 62.6 & 58.9 & 58.1 & 61.4 & 56.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outcomes derived from statistical accuracy across various clinical and electrophysiological methods and overall assessment predictions.
## 4 Proposed Methodology
### Expression and Speech Feature Fusion Model with Attention Mechanism
Batch normalization is applied after each convolutional layer to standardize the activations and accelerate training. To prevent overfitting, we incorporate a dropout rate of 0.2 before the final fully connected layer. The model uses cross-entropy loss with label smoothing (\(\epsilon=0.1\)) to improve generalization. The entire fusion model contains approximately 2.7 million trainable parameters, with the majority in the feature extraction networks.
## 2 Related Work
### Action Recognition Methods
#### 2.2.4 Sensor-Based Methods  
The sensor-based action recognition method collects the velocity and direction data of different body parts of players and calculates the features through these data. These eigenvalues are then used to train the machine learning model to achieve action classification. There have also been many meaningful attempts in basketball action recognition. Sensor-based basketball action recognition methods can be represented by [PERSON] et al. [33]. Through the acceleration sensors, gyroscopes, and magnetic sensors worn on athletes, the body movement data of athletes in different action states were collected as action features, including speed, acceleration, angular velocity, and other data. Support vector machine (SVM) was used as the classification algorithm to identify walking, running, jump shots, and other actions. Similarly, [PERSON] [34] used an acceleration sensor, a magnetic sensor, and a wrist gyroscope to obtain motion vector data and classified three different dribble modes according to KNN and random forest. The sensor-based basketball action recognition method is a relatively mature method, and it achieves good results. However, the application prospects for the sensor are limited because it may affect the normal performances of players. Nevertheless, it can save much computing power by pre-calculating features, and the model learning is more targeted and effective compared to training directly on unprocessed data.  
Combining the advantages of the above two methods, we propose a feature-enhanced model named LSTM-DGCN that contains a feature extraction technique and a deep learning method. It can automatically extract effective features from the input joint coordinates through a built-in feature extraction module instead of directly inputting the skeleton graph as the direct feature of GCN. Then, it can select the most important joint angles and joint distances in basketball action recognition as the indirect feature input GCN and use LSTM to mine the temporal relationship between adjacent frames. Compared with the direct application of joint coordinates, LSTM-DGCN is more accurate and requires fewer samples.
## 5 Conclusions  
This paper proposed a classification model using the tomato leaf images segmented for training the VGG-19 with the transfer learning method. This classification model used nine disease types and one healthy type obtained from the Plant Village database which were segmented to extract the original leaf regions and the black backgrounds using the HSV. In addition, the learning rate and epochs were selected in the VGG-19 with the transfer learning to have the best classification network. In particular, the classification accuracy is 99.72%, higher than that of previously proposed works which had the highest ACC at 99.35% and lowest ACC at 94%. Moreover, the training time of the VGG-19 with the segmented images (2.75 s) and (29.30 s) is faster compared with that of the VGG-19 without the image segmentation (2.98 s and 35.27 s). The experimental results using the segmented leaf images and the best choice of the network parameters demonstrated the effectiveness of the proposed model. Moreover, the model with the leaf image segmentation and the VGG-19 architecture with transfer learning can be developed to be applied for classifying other leaf image datasets.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Work** & **Model** & **Transfer Learning** & **Classes** & **Preprocessing** & **ACC** \\ \hline [PERSON] et al. [53] & GoogleNet & Yes & 10 & No & 99.39 \% \\ [PERSON] et al. [31] & GoogleNet & Yes & 10 & No & 99.35 \% \\ [PERSON] et al. [28] & CNN model & No & 10 & No & 98.40 \% \\ [PERSON] et al. [24] & MLP & No & 10 & PCA-WOA & 94.00 \% \\ [PERSON] et al. [54] & CNN model & No & 10 & Transformed into grey images & 98.49\% \\ Our proposed model & VGG-19 & Yes & 10 & HSV color space for image segmentation & 99.72 \% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Tomato leaf disease comparison between previous methods and results.  
**Author Contributions:** Conceptualization, methodology, simulation, and writing original draft, B.-V.N. and T.-N.N.; methodology, supervision, validation and writing--review and editing, T.-H.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by Ho Chi Minh City University of Technology and Education (HCMUTE) under Grant No. T2021-51 TD.  
**Data Availability Statement:** The data presented in this study are openly available at [[https://www.kaggle.com/datasets/emmarex/plantdisease](https://www.kaggle.com/datasets/emmarex/plantdisease)]([https://www.kaggle.com/datasets/emmarex/plantdisease](https://www.kaggle.com/datasets/emmarex/plantdisease)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
## 4 Results and Discussion
However, due to model limitations and environmental variability, residual errors, including multipath and unmodeled biases, persist. The STMCCEKF addresses these uncertainties through its robust Student's t kernel, which down-weights the influence of such non-Gaussian errors during measurement updates, enhancing estimation accuracy in challenging conditions. Figure 2(a) illustrates the probability density function of the Student's t-distribution with degrees of freedom \(v=3\), exhibiting a symmetric, bell-shaped curve similar to the Gaussian distribution but with heavier tails. These heavier tails indicate a higher probability of extreme observations, a characteristic arising from the distribution's ability to model variance uncertainty, making it more robust against the outliers. Figure 2(b) compares the Student's t-distribution (blue) with the Gaussian distribution (red), showing that while both distributions peak at the mean, the Gaussian distribution has a sharper