esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Analysis of the Adjustment Effect of RMVSC-STATCOM in Inductive Operation  
To verify performance under inductive operation conditions, a capacitive load of \(P_{\text{L}}+jQ_{\text{L}}\) = 10 MW\(-j\)10 Mvar was applied with a simulation duration of 1 s. At 0.5 s, the RMVSC-STATCOM was connected and began tracking the reactive power command for compensation. The three-phase voltage and current waveforms before compensation are shown in Figure 11a. There is a phase difference between voltage and current, the current leads the voltage, and the power factor is 0.707. Upon activation of the RMVSC-STATCOM at 0.5 s, and after tracking the compensation command, it absorbed 0.995 Mvar of reactive power. Following compensation, the reactive power on the power source side was nearly 0, the power factor improved to nearly 1, and the voltage and current phases aligned. The three-phase voltage and current waveforms after compensation are shown in Figure 11b.  
The tracking of compensated reactive power \(Q\) relative to the reactive power command value \(Q_{\text{ref}}\) is shown in Figure 11c. From 0 to 0.5 s, RMVSC-STATCOM was not involved in regulation, and the reactive power command value was 0, meaning the compensated reactive power \(Q\) was also 0. At 0.5 s, RMVSC-STATCOM was activated. After a response time \(t_{\text{r}}\) of 0.106 s, the change in \(Q\) reached 90% of the difference between the reactive power  
Figure 10: Simulation results of RMVSC-STATCOM in capacitive operation conditions; (**a**) three-phase voltage and current waveforms before compensation; (**b**) three-phase voltage and current waveforms after compensation; (**c**) reactive power tracking; (**d**) power factor waveform.  
command value \(Q_{\text{ref}}\) and the initial value. After a regulation time \(t_{\text{s}}\) of 0.155 s, \(Q\) remained within 95% to 105% of the reactive power command value \(Q_{\text{ref}}\). The overshoot was 0, with an absorbed reactive power of 10 Mvar, and the steady-state error relative to the reactive power command value was 0. The power factor waveform is depicted in Figure 11d, where after the activation of RMVSC-STATCOM, the power factor increased from 0.707 to nearly 1.
# Detection of Pediatric Femur Configuration on X-ray Images
###### Abstract  
In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children's knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images  
Selection of keypoint sets connected to those features.
* Design of the optimal detection algorithm, which enables the proper estimation of keypoints on X-ray images; the proposed algorithm includes a specially tailored estimator consisting of adaptive threshold and deep CNN.
* Proposition of the bone coordinate system directly corresponding to its configuration.  
The accuracy of the proposed method is defined as a root mean squared error (RMSE) between the estimated configuration of the bone and the reference coordinate system. The overall accuracy of the presented method is evaluated on the publicly available LERA dataset [3]. The dataset consist of lower extremity radiographs of adults gathered by the Stanford University School of Medicine.  
The contribution of this paper is threefold. From a medical perspective, we provide a complete solution to obtain femur configuration on two-dimensional X-ray images. From a robotics point of view, the trajectory of femur configuration could be used to define the kinematic model of the joint (to be incorporated in rehabilitation robotic aid). From an artificial intelligence point of view, we provide the optimal estimator architecture to solve a regression task for medical images.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Pre-Processing Module  
#### 4.1.1 Protocol Name and Dissector Extraction  
The extraction system first investigates each Wireshark dissector file to locate the protocol registration function according to Wireshark's protocol registration mechanism and then extracts the protocol name and protocol handle from it. The protocol name, protocol handle, and associated protocol dissector file name are stored in a data structure called \"protocol table\". Subsequently, the extraction system locates all dissector registration functions and extracts all dissector names, each dissector's main parsing function and related protocol handle from the dissector file, and then the dissector information is associated with the related protocol according to the protocol handle. The dissector information and its associated protocol name are stored in a data structure called \"parser table\". The purpose of the association is that in some dissector files, there may be several protocol-dissector pairs, for example, in the \"packet-mbtcp.c\" dissector file, there are several dissectors including \"mbtcp\" and \"mbrtu\" that are associated with \"Modbus TCP\" and \"Modbus RTU\" protocol each. By establishing this association, each protocol and its corresponding dissector can be accurately found by accessing the protocol table.
## 5 Heuristic Approaches
### Fix-and-Relax Heuristics
#### 5.1.2 Adaptation  
As the developed models are time-indexed, the partitioning of the decision variables follows the time horizon, distinguishing the FW \(t=0,\ldots,a_{k}-1\) from the OW \(t=a_{k},\ldots,b_{k}\) and the AW \(t=b_{k}+1,\ldots,T\) where \(T\) is the horizon.  
As the considered problem includes sequence-dependent setup times, which contribute to the high complexity of the problem, the heuristic approach aims at estimating a simplified version of the problem with constant setup times \(\hat{s}_{j}\) for orders \(j\) that will possibly be produced in the AW. Therefore, the following binary decision variables are introduced in \(Pr_{k}\):  
* \(\alpha_{j}\): equals 1 if order \(j\) is scheduled between \(0,\ldots,b_{k}\); 0 otherwise;
* \(\beta_{j}\): equals 1 if order \(j\) is scheduled between \(b_{k}+1,\ldots,T\); 0 otherwise  
These decision variables split the set of jobs \(0,\ldots,n\) into two disjoint subsets \(A\) and \(B\). Jobs of \(A\) are sequenced in the OW while jobs of \(B\) are scheduled after \(b_{k}+1\) (Figure 3).  
Figure 3: Illustration of the developed strategy on an example of \(n=5\) jobs with \(\sigma=9\) and \(\delta=4\).  
During the procedure at step \(k\), the binary decision variables \(x_{jt}\) or \(z_{jt}\) such that \(t=a_{k},\ldots,b_{k}\) are only considered, which reduce in width the subproblem. This is indicated in Figure 3 when the horizontal axis is graduated. Moreover, the decision variables \(x_{jt}\) or \(z_{jt}\) associated with already sequenced jobs are evicted from \(Pr_{k}\). This reduces in height the subproblem. The set of non-processed jobs at each step \(k\) is denoted as \(J_{k}\). Only the decision variables of the last processed order \(l\) in the previous subproblems are kept in \(Pr_{k}\) in order to have the correct setup to make the connection with the next job. Thus, the model shrinks in size iteration by iteration. At each step \(k\), the sub-problem model \(Pr_{k}\) integrates the following constraint:  
\[\sum_{j\in I_{k}}\beta_{j}(\tilde{s}_{j}+p_{j})\leq T-b_{k} \tag{29}\]  
Constraint (29) evaluates that the production of orders, and their setup operations in the AW cannot last more than \(T-b_{k}\) time units. Setup operations of produced orders in the AW have a fixed duration defined by \(\tilde{s}_{j}\). Constraint (29) is, in a way, similar to a time-budget constraint, with an allowance of \(T-b_{k}\) units of production and setup.
## 5 Results and Analytical Discussion
### Discussion and Methodological Considerations  
#### 5.4.1 Experimental Refinements and Performance Assessment  
The initial findings, which revealed nearly perfect accuracy for certain models, required careful review especially with regard to DDoS attack detection. The confusion matrix data gave more consistent and thorough insights by showing particular errors in classifying different types of DDoS. This detailed study identified flaws that were not obvious in aggregate measures, emphasizing the importance of going beyond surface-level performance data for a more thorough examination. For example, even with CNN's general accuracy of 99.80%, the confusion matrix found specific challenges in differentiating DDoS_ICMP from DDoS_UPD attacks. However, these optimized results must be interpreted with caution regarding real-world deployability. The consistent high performance across diverse algorithms suggests possible dataset-specific optimization that may not translate to operational IoT environments, where traffic exhibits greater variability, noise, and unpredictable device behaviors than captured in controlled datasets. KNN variants similarly struggled particular DDoS kinds even with their apparently high overall accuracy. This result is especially important, since it shows how great overall accuracy could hide significant misclassifications at the level of specific attack type. Therefore, it becomes essential to evaluate model performance not only by means of general indicators but also by closely analyzing behavior over several attack types.  
The final DDoS detection results were much influenced by several experimental changes:  
Feature normalization: particularly for SVM and DL models, the change from standard scaling to min-max scaling clearly enhanced model performance for DDoS detection. Comparatively to multiple misclassifications seen with conventional scaling, the confusion matrix for RF with min-max scaling revealed flawless categorization for all DDoS_HTTP, DDoS_TCP, and DDoS_UDP cases.  
Hyperparameter optimization: models such as LSTM gain from well tuned hyperparameters for DDoS detection including learning rate scheduling with early stopping. By use of early stopping, the LSTM model demonstrated convergence at epoch 44, therefore preventing overfitting and optimizing performance.  
Architectural refinements: in CNN's architecture, five convolutional layers with max-pooling operations helped hierarchical feature extraction, which proved especially successful for different DDoS attack variants with subtle variations in network traffic patterns.
# Boosting Targeted Black-Box Attacks via Ensemble Substitute Training and Linear Augmentation
## 5 Evaluation
### Success Rate and Distortion
#### 5.4.1 Single Model Attack  
In this part, we generally compare the effect of our method with the Jacobian-based Augmentation attack. Firstly, we generate the FGSM attack [6] implemented in the paper of Jacobian-based Augmentation [15]; here, we set the hyper-parameter \(\varepsilon\) 0.2 and craft adversarial examples using  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Query Times**} & \multicolumn{6}{c}{**Substitute Model Architecture**} & \multirow{2}{*}{**Mean Accuracy**} \\ \cline{3-3} \cline{5-8}  & & **A** & & **B** & & **C** & **D** & **E** \\ \hline \multirow{4}{*}{MNIST} & 600 & 86.83\% & 85.88\% & 85.09\% & 83.61\% & 87.64\% & 85.81\% \\  & 1200 & 91.16\% & 90.91\% & 90.89\% & 92.81\% & 91.78\% & 91.51\% \\  & 2400 & 96.00\% & 95.89\% & 95.14\% & 96.39\% & 95.13\% & 95.71\% \\  & 4800 & 97.33\% & 96.85\% & 96.47\% & 97.29\% & 96.31\% & 96.85\% \\ \hline \multirow{4}{*}{GTSRB} & 300 & 11.14\% & 5.72\% & 8.20\% & 6.48\% & 5.24\% & 7.36\% \\  & 600 & 22.20\% & 8.71\% & 9.58\% & 9.51\% & 7.62\% & 11.52\% \\ \cline{1-1}  & 1200 & 42.38\% & 32.73\% & 33.62\% & 40.42\% & 41.87\% & 38.20\% \\ \cline{1-1}  & 2400 & 67.98\% & 74.07\% & 75.21\% & 64.39\% & 75.62\% & 71.45\% \\ \cline{1-1}  & 4800 & 75.59\% & 79.52\% & 82.14\% & 74.36\% & 78.99\% & 78.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of the substitute models via ensemble training. Compared with Figure 4, we can find that our ensemble training method doesn’t decrease the accuracy of substitute models. Therefore, the ensemble attack is feasible through these models according to the analysis of the importance of the substitute models’ accuracy.  
the substitute models we trained previously. In addition, we get the result shown in Table 3 that our attack has better performance in the success rate when the perturbation stays the same.  
After simply comparing the non-targeted result, we continue to generate the targeted adversarial examples via a C&W attack method. Table 4 shows \(L_{2}\) distance of perturbation and the success rate. As the table shows, Jacobian-based Augmentation fails in generating a targeted attack. It is explainable that Jacobian-based augmentation failed here. Given an adversarial example \(x^{*}\) that substitute models successfully misclassify as the target class \(t\), the target model may recognize \(x^{*}\) as another class because of the low accuracy of the substitute model.
## 3 Methodology
### Feature Selection
#### 3.3.2 Relief-F Algorithm  
The Relief algorithm [19] is a generic method initially developed for classification problems with binary classes. It attempts to estimate the quality of predictors and of how well their values distinguish between instances near each other. For a randomly selected training instant \(R_{i}\), the Relief algorithm finds its two nearest neighbours: one from the same class called the nearest hit \(H\), and the other from the different class, called the nearest miss \(M\). It updates the quality estimation \(W[P]\) for all predictors \(P\) depending on their values for \(R_{i}\), \(M\), and \(H\). If instances \(R_{i}\) and \(H\) have different values of the predictor \(P\), then the predictor \(P\) separates two instances with the same class, which is not desirable, so the quality estimation \(W[P]\) is decreased. On the other hand, if instances \(R_{i}\) and \(M\) have different values of the predictor \(P\), then the predictor \(P\) separates two instances with different class values, which is desirable, so the quality estimation \(W[P]\) is increased. The whole process was repeated \(m\) times. The Relief-F algorithm [20] is an improved version of the Relief algorithm used for classification problems with more than two classes. It employs more than a single nearest neighbour and can handle missing predictor values. The Relief-F algorithm is another extension to handle regression problems. In contrast to the majority of heuristic methods for estimating the quality of predictors, which assume the conditional independence of the predictors, relief algorithms can determine the quality of the predictors with high dependencies between themselves.
## 1 Introduction
The experience and importance gained in mining on Earth, in terms of technique, excavation methods, treatment, etc., will be transferred to the new circles of Lunar mining, with modifications according to the Lunar environmental conditions as well. Navigation and positioning, which play important roles in mining today, will also have a special importance in the connection between external and in-mine control networks [13]. In line with that, a Lunar navigation system is one of the requirements which must be taken seriously into account when missions coming from different programs occur together and when the construction of the habitats, launching pads, and the extractions from potential Lunar mining sites start. Various needs for a high-precision positioning and navigation system will arise in mining and civil engineering fields, such as to precisely calculate the quantity of extracted ore, to build infrastructure, and to superpose objects (e.g., on top of each other or nearby). The problem of Lunar navigation is treated mostly in projects and papers investigating landing on the Moon's surface and the navigation of robotized Lunar vehicles in limited areas. Accuracy demands for those applications are far from the mm or even cm level, but rather close to a few hundred meters [14]. Nevertheless, the suggested methodologies can be considered as a good starting point for designing the Lunar navigation network. Most of the suggested approaches apply and improve navigating concepts already known on the Earth, namely, inertial systems or navigating by image processing. None of those technologies can be applied as-is on the Lunar surface because of special instrumentation requirements and specific environmental conditions. The huge difference between the Earth and the Moon, in the sense of gravity, atmosphere, temperature, humidity, etc., makes replication of classical Earth-related surveying methodologies very hard to implement on the Moon. This paper elaborates on the methodology of achieving cm-level accurate positioning in real time on the Moon, which is validated by presented numerical results.
# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
## 4 Results
### Psychometric Data Classification  
The data used for testing included psychometric tests (20 input features), associated with analyzing social behavior, memory activities, language delay, and all altered behavioral factors in FASD children. In addition, the dataset included other developmental diseases, making the classification process more difficult. This paper used two models of dense networks using this type of data. The first model archived an accuracy of 75.55%. However, the model did not show significant improvements, despite the variation in the layers and neurons in the layers. In the second model, a feature layer was added, achieving an accuracy of 88.46%.  
Once the network configuration was chosen, training and testing or validation behavior are shown in the model precision and loss functions. The loss function with a high result indicates that the neural network has a poor performance and a low result, and that it is doing a good job. Figure 2(a) shows the accuracy for each of the dataset. In terms of the number of that were aspects found and related to the number of individuals evaluated. We attempted to find a model with no significant difference between the labeled data and the prediction. Nonetheless, with the implemented configuration, the training data increased accuracy. In addition, the validation data also increased in accuracy.  
Figure 2(a) shows the loss function on the psychometric data. In this case, the loss function has a decreasing curve in both training and testing. This result suggests that the network may have issues classifying some data not used during training but not significantly.  
The confusion matrix (Figure 4) shows the performance of the model with test data. The model has a good performance in classifying children with FASD since the correctness is 38.46% over 42.31% in positive hits (FASD having FASD), representing 90.9%, and it has 7.69% over 42.31% false positives (a control having FASD). It should be noted that the total percentage of children diagnosed with FASD is 42.31%. The model allows classifying FASD in most cases. In classifying control patients, the model classifies 51.0% over 57.69% of control patients who are really in control (86.67% of the cases control). And it fails in 3.85% to classify patients with FASD as controls (13.32% mistake of cases control).  
\begin{table}
\begin{tabular}{l r r r} \hline \hline
**Test** & **ANN Accuracy** & **Zhang’s Model Accuracy** & **Difference** \\ \hline Psychometric & 88.46 & 78.26 & 10.2 \\ Antisaccade & 74.07 & 65 & 9.07 \\ Prosaccade & 72.24 & 61 & 11.24 \\ Memory-guided saccade & 88.0 & 55 & 33 \\ DTI & 75 & 67.39 & 7.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy model performance comparison.  
Figure 3: Precision and Loss using feature dense layer on psychometric data.
# Color-Adjustable Devices Based on the Surface Plasmons Effect
## 1 Introduction  
Color is a visual effect on light, produced by the eyes, brain, and our life experiences. The reflection, diffraction, scattering and absorption of light via objects provide us with extremely vivid information, especially in color. Surface plasmons (SPs), which include surface plasmon polaritons (SPPs) and localized surface plasmons (LSPs), are surface electromagnetic waves formed by the collective oscillation of free electrons in metals interacting with the incident light field [1; 2; 3]. It makes light in specific wavelength ranges be absorbed or radiated, and thus, a material presents different colors for human eyes. Due to the fact that the resonance frequency of a surface plasmon's material depends largely on its structure and size, we can adjust it by changing the three-dimensional size and the pattern of the structure [4; 5]. In recent years, many studies have focused on subwavelength structures of different geometries, such as the detuned square ring [6], cone holes [7], double-overlapped annular apertures [8], and hybrid structures [9]. With the development of micro-nano processing technology and characterization methods, artificially producing metal nano-microstructures to generate color has become the main method of generating structure colors in recent years [10; 11]. Compared to chemical dyes, artificial microstructured materials are recyclable, easy to fabricate, and durable [12]. In addition, its local field enhancement effect can break through the diffraction limit and improve imaging resolution. These characteristics make the surface plasmon's structure color in ultra-high resolution imaging [13; 14; 15], inverse design [16], CMOS digital integrated circuits [17; 18; 19], light emitting diodes [20], steganography [21], and many other fields in which they have major applications [22; 23; 24; 25; 26; 27; 28]. For example, it presents a metamaterial absorber based on vanadium-dioxide, which realizes adjustable functions in multiple and wide bands [29]; the same avatar pattern can display different color combinations by changing the angle of the polarizer [30]. In general, studies change the period and material of a micro-nano structure to adjust the position of resonance frequency so that the devices,based on the enhanced optical transmission (EOT) effect of surface plasmons, can achieve better performance. The above methods can adjust the performance of the color rendering, but it is expensive and not reusable.  
In this paper, we propose an adjustable coloring device based on the surface plasmon's structure color, which is composed of a template and metal nanoparticles. The template consists of a layer of metallic aluminum film, deposited on a quartz substrate. Then, a series of periodic circle-hole arrays are designed on the aluminum film. Adding different nanoparticles into the circle holes can cause the resonance frequency in the visible light ranges to move, thereby achieving different color displays. Moreover, we can also make the color change by filling dielectric materials with different refractive indices in the hole of the template.