esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** & **Specificity** & **Binary Accuracy** \\ \hline DWT & 0.922 & 0.919 & 0.92 & 0.98 & 0.966 \\ MFCC & 0.929 & 0.929 & 0.929 & 0.982 & 0.965 \\ CWT & 0.947 & 0.947 & 0.947 & 0.987 & 0.981 \\ DWT/MFCC & 0.91 & 0.899 & 0.905 & 0.978 & 0.946 \\ DWT/CWT & 0.939 & 0.937 & 0.938 & 0.985 & 0.973 \\ MFCC/CWT & 0.939 & 0.932 & 0.935 & 0.985 & 0.985 \\ ALL & 0.955 & 0.955 & 0.955 & 0.989 & 0.992 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative table between precision, recall, F1 score and specificity achieved by each proposed model: the independent networks, a combination of pairs of them and the complete model. Figure 8: Models’ performance: The graph shows the precision, recall, F1 score, specificity and binary accuracy achieved by each proposed model: the independent networks, a combination of pairs of them and the complete model based on Table 2.  
Figure 10: DL performance results (20% of dataset as training/testing set): (**A,B**) Confusion matrices for multiclass and binary classification, respectively. Figure 9: DL performance results (20% of dataset as test set): (**A,C**) Precision, recall, F1 score and specificity of the complete model for the testing set in a multiclass and binary classification, respectively. The vertical axis shows only the percentage from 0.8 to 1.0 to facilitate the visualization of the results. (**B,D**) Confusion matrices for multiclass and binary classification, respectively. Finally, Figure 11 shows the performance of each network and the combinations used for each of the classes evaluated through the same metrics. It is possible to observe that although the performance varies between the networks, the results are consistent between each of the proposed configurations, where the precision and recall for each class are spliced. Along with the intermediate dropout layers, these results confirm that the networks were correctly generalizing the features that define each class and not memorizing the patterns for each signal.
## 5 Discussion  
The fuzzy control system is thought of as a state feedback control system, taking into account three state variables as the control variables: speed error, acceleration and rotational speed, and mechanical torque. [PERSON]'s fuzzy regulator structure, triangle, and trapezoid membership functions; maximum-minimum inference method; and defuzzification method with center of gravity were chosen. This structure ensures the strongest nonlinear character of the regulator. A base with 27 rules was developed. The transient response characteristics in response to the step signal applied to the input have an asymptotic aperiodic character. The position rise time is slightly shorter than in the case of linear control. The position overshoot is zero. The control system is input-output stable. Other types of membership functions and other inference methods can be chosen as research perspectives.  
The neural predictive control structure resulted after several repeated tests using various combinations of parameters and various sets of data input-output of the process for training the neural network model to obtain a small error in validating the neural model. The neural network parameters model resulted after several successive trainings, aiming to minimize the training mean squared error.  
The neural model reference control structure presented resulted also after several repeated tests, using various combinations of parameters, and various sets of data input-output of the process for training the neural network model and for training the neural controller, aiming to minimize the training mean squared errors and to obtain small errors in validating the neural model and neural controller.  
The response of the neural control systems to a set of random step signals applied to the input was determined. The transient response characteristics in response to the step signal applied to the input have an asymptotic aperiodic character. The position rise time is slightly shorter than in the case of linear control. The position overshoot is zero.  
As research perspectives, for both neural control structures, other training parameters of the neural networks, other structures, a different number of neurons per layer, and other activation functions can be chosen in order to minimize the training errors. At each training, however, other values of weights and polarizations will be obtained.  
In this study the positive aspects of deep learning include the possibility to obtain precise models of the controlled process method and linear methods are small. The paper demonstrates that if the motion process has a linear model, the values of the performance criteria of the regulation have close values, and good quality of the regulation is ensured. However, if the motion process is nonlinear, with the help of fuzzy and neural control methods, better values of the control performance criteria can be obtained compared to linear methods, ensuring robustness to the identification of the parameters and to the action of the disturbing load torque.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 4 Taxonomy of Vulnerability Detection Approaches and Findings Analysis
### Graph-Based Approach
#### 4.2.2 Findings Analysis  
In this second approach, the Funded method combines a graph-based learning set and automated data collection to detect vulnerabilities in code sources. This study achieved a high accuracy (92%) in function-level vulnerability detection, which surpasses matching-based methods. Building on this principle of Funded, VulSPG incorporates rich semantics and explicit structural information to enhance the performance of vulnerability detection in target code functions, achieving a slightly improved detection accuracy of 93.8%. Next, the method based on the Gremlin framework manages asset component trees and their related vulnerabilities in assessing the vulnerability process. Although no specific accuracy metrics are provided for this method, it offers a qualitative improvement by efficiently mapping vulnerabilities to asset components. Based on graph structure representation, the analytic graph focused on real-time monitoring, defense preparation, and response to incidents, although concrete performance metrics like accuracy are not explicitly reported. The threat knowledge graphs method, which reveals hidden relationships within CPE, CVE, and CWE, show promise with a good Mean Reciprocal Rank (MRR) score of 0.424, indicating their effectiveness in uncovering vulnerabilities. Recently, the GRACE method combined graph structure and LLM to improve software vulnerability detection. The method resulted in a significant improvement in F1 scores, 14.82%, 24.64%, and 73.8% across three different datasets, showcasing its effectiveness in various contexts. Lastly, the attack graphs method integrates network topology and reachability graphs to detect new attack paths, especially in dynamic IoT environments. This technique shows efficiency in dynamically adjusting IoT and significantly improves the detection of new attack paths. Based on prior findings, the GRACE method demonstrates significant potential in this area.  
In practical cases, the method based on the threat knowledge graph uses knowledge graph embedding (TransE) to predict more associations between CVE, CPE, and CWE. An amount of 465 affected products were analyzed by this method, which revealed only 11 false positive predictions. More specifically, using a threat knowledge graph to analyze the CVE-2021-21348 before 4 August 2021, which affects the Java library XStream, the model predicted various other products, such as Debian Linux and Oracle, among others, which were only revealed after the cutoff date.  
Thus, the graph-based approach combines several graph methods to build a global dependance and relationship between concerning elements to detect vulnerabilities and improve cybersecurity awareness. The following subsections provide more details on the provided features.
## 3 Results  
This section presents the results of experiments carried out to evaluate the effectiveness of our proposed method. In Section 2.2.1, we introduced the core techniques, random patch extraction (RPE) and class-balanced sampling (CBS). Here, we compare them against two baselines, fixed patch extraction (FPE), which uses a fixed offset per parcel, and no class balancing (NCB), which preserves the original class distribution, by performing an ablation study that involves gradually adds RPE, CBS, and the combined RPE + CBS on top of the baselines.  
Specifically, we (i) measure model performance using accuracy, the macro F1 score, and Kappa to determine how our techniques affect overall classification performance; (ii) examine class-wise F1 scores to check whether minority classes (e.g., ginseng and tree nursery) benefit from RPE and CBS; (iii) evaluate patch-level predictions to gauge the model's direct performance, as well as parcel-level predictions to see how aggregation (i.e., combining patches) enhances the final outcome; and (iv) analyze computational efficiency in terms of training time and memory usage across different configurations.  
As noted in Section 2.3, all experiments were performed under both 1\(\times\) (no upscaling) and 2\(\times\) (bilinearly upscaled) settings. In the 2\(\times\) environment, bilinear interpolation  
Figure 9: Inference -phase patch coverage comparison between original (1\(\times\)) and upscaled (2\(\times\)) input images, showing how upscaling affects patch layout (orange boxes).  
enlarges the image, reducing each patch's spatial context but increasing the number of overlapping patches per parcel, thereby compensating for the loss at the patch level via aggregated predictions.
## 3 Method
### Dynamo Script Overview and Implementation
#### 3.2.7 Extraction of the Environmental Performance Output Using North-South and East-West Lengths  
The algorithm shown in Figure 8 was developed to understand the impact of a building's facade on its environmental performance. In particular, buildings with many south-facing facades can benefit from environmental advantages such as lower heating costs due to their increased absorption of solar heat.  
To concretely implement this algorithm, the following steps should be considered:  
* The extraction of the building's facade: first, extract the outline of the building from its 3D model.
* Determine the directionality of the facade: calculate the vector or normal vector of each facade to determine its directionality.
* Classify the facade as north-south or east-west: Based on the calculated directionality, each facade is classified as either north-south or east-west. To do this, compare the angle between the directionality of each facade and the standard directions of north-south or east-west.
* Calculate the length of the facade: compute the length of the classified facade.
* Calculate the total length: sum the total length of the facades classified as north-south and east-west.  
Figure 8: Visual programming of environmental performance output.  
The data extracted in this way can be used to evaluate the environmental performance of a building. In particular, buildings with larger south-facing facades can utilize solar heat more effectively. Therefore, this information can be used to improve the energy efficiency of buildings.
## 3 Results and Discussion
### Accuracy  
Accuracy (Table 3) was determined by using quality control samples in replicates at different levels (16 and, 40 ng/mL) for serum and one level for food (100 ng/mL) and urine (200 ng/mL). Relative errors were between 1.0 to 13% for food, 0.3 to 15% for serum and 2 to 10% for urine. Only the accuracy of enterodi in food was above the value recommended by the Food and Drug Administration (FDA) (<15% CV other than lower LOQ [45]) with 18%. Previous methods (Supplementary Tables S1-S3) have presented similar acceptable accuracy levels for food (bread [44]), serum [32; 36; 37; 44] and urine [32; 40; 44].
# Direct Temperature Measurements of Cardiac Stent during MRI Examinations
## 1 Introduction
One of these examined RF heating effects of stents in a 7 Tesla MRI scanner was observed using electromagnetic field simulations and phantoms with properties that mimic myocardium [22]. The authors then validated the simulation results with experimental RF heating paradigm using fiber optic probes and MR thermometry on an agarose phantom containing coronary stents. In this case, a single-pulse experiment, exceeding the regulatory limits, was performed using a set up very different to standard clinical cardiac MRI protocols. In another similar study [23], RF-induced heating of coronary stents at 7.0 T is examined using electromagnetic field simulations. For the assessment of RF-induced temperature changes, RF heating experiments are executed using fiber optics and MR thermometry for 2D temperature mapping. Even in this study, this was not used as a standard clinical protocol in the experimental test. Heating of a metallic coronary stent in MR was studied theoretically and experimentally also in [24]. In this study, the stent was modelled as a tubular flow heater to calculate the time-dependent temperature during an MRI examination. The model was tested experimentally by performing laboratory magnetic resonance heating in a standard 4.7 T vertical-bore NMR spectrometer. Once again, the exposure paradigm was far away from the clinical standard procedure. Interestingly, the time-dependent stent's temperature was evaluated at different RF irradiation power levels. [PERSON] et al. [25] performed experiments and computer modeling of heating of a cardiovascular stent by RF fields in a 1.5 T MRI birdcage coil fed by a quadrature power divider to allow the coil to produce circularly polarized B1 fields. The local (point) SAR was calculated from the initial linear temperature rise. However, again, the exposure conditions were very different to a clinical cardiac MRI examination paradigm. RF-induced heating on cardiac stents has been evaluated also in vivo and post-mortem on pigs [26] during MRI in a 3T scanner using a sequence with a console SAR of approximately 5.5 W/kg, achieved by overriding the system SAR monitor, to test the hypothesis that the in vivo condition significantly reduces the RF-induced temperature rise near a medical device. All of the above studies differed in terms of exposure conditions, and none of them used a standard clinical protocol typical of a comprehensive cardiac MRI examination. In this context, we developed a dedicated measurement set-up by focusing our target on the direct measurement of the heating of a cardiac stent during an MRI examination, using a cardiac standard protocol. The main object of our study was to assess the temperature rise in exposure conditions as close as possible to reality. Starting from temperature measurements in three different points, maximum temperature change, local SAR, and thermal dose CEM43\({}^{\circ}\)C are calculated along the one-dimensional size of the stent.
###### Abstract  
Low impact on the environment and low cost are the key drivers for today's technology uptake. There are many concerns for cement production in terms of negative environmental impact due to greenhouse gas (GHG) emission, deficiency of raw materials, as well as high energy consumption. Replacement of the cement by appropriate additives known as supplementary cementitious materials (SCMs) could result in reduction in GHG emission. Limestone-calcined clay cement (LC\({}^{3}\)) is a promising binder in the concrete sector for its improvements to environmental impact, durability, and mechanical properties. On the other hand, the advantages of fiber-reinforced concrete such as improved ductility, versatility, and durability have resulted in increasing demand for this type of concrete and introduction of new standards for considering the mechanical properties of fibers in structural design. Thus, using natural fibers instead of synthetic fibers can be another step toward the sustainability of the concrete industry, which is facing increasing demand for cement-based materials. This review studies the potential of natural Kenaf fiber-reinforced concrete containing LC\({}^{3}\) binder as a step toward green cementitious composite. While studies show that energy consumption and GHG emission can be reduced and there is a significant potential to enhance mechanical and durability properties of concrete using this composition, adjustment of the mix design, assessing the long-term performance and standardization, are the next steps for the use of the material in practice.  
supplementary cementitious materials; natural fiber; LC\({}^{3}\); kenaf fiber; mechanical properties; durability; calcined clay; limestone 2020 10 357 10.3390/app10010357
## 4 Results
### Results of Global Test Set  
To be more general, PSNR and SSIM for all test samples from the test set through different networks are listed in Table 1. CNN-based approaches achieve obviously better performance than TR, with PSNR and SSIM increased by more than 3 dB and 0.15, respectively. In comparison, among three CNN models, AB-U-Net performs the best satisfactory bone reconstruction. The evaluation indexes of U-Net are lower than others embedded with attention blocks. We believe that the attention mechanism is helpful to enhance model sensitivity and prediction accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Test Set (760 Samples)**} \\ \cline{2-3}  & **PSNR (dB)** & **SSIM** \\ \hline Time Reversal & 12 \(\pm\) 2.16 & 0.65 \(\pm\) 0.09 \\ U-Net & 15.6 \(\pm\) 1.97 & 0.81 \(\pm\) 0.00 \\ Attention U-Net & 15.67 \(\pm\) 1.95 & 0.81 \(\pm\) 0.00 \\ AB-U-Net & 15.83 \(\pm\) 2.04 & 0.82 \(\pm\) 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSNR and SSIM under reconstructed images on Test Set.  
Figure 6: Comparison of structural similarity (SSIM) map based on different CNN architectures: (**a**) Time Reversal, (**b**) U-Net, (**c**) Attention U-Net and (**d**) AB-U-Net. Yellow corresponds to reconstructed areas with low SSIM, which indicates the poor reconstruction performance.  
There are perturbations of TR method between different bone samples, where the standard deviation of PSNR and SSIM go to 2.16 dB and 0.09 in the test set. Interestingly, after TR-obtained images processed by CNN, better robustness is achieved, i.e., a negligible variation of SSIM in Table 1. This result demonstrates that the post-processing method is more robust and shows its possibility to improve the image quality caused by the object structure and experimental conditions.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results
### Tl  
TL is defined as the decrease of the signal intensity through the path from the sender node to the receiver node. Diverse empirical expressions to measure the TL have been developed. The Thorp formula defines the signal TL (Equation (25)).  
TL for the proposed scheme is shown in Figure 12. The results show that the TL of 3D-SM is greater than DDRP. In 3D-SM, the number of transmission is greater, so the TL depends on the distance between the sender and receiver. In this scheme, the transmissions are direct, and distance varies from the minimum value to the maximum (transmission range). The nodes far from the MS also directly send data, which increases the TL in the scheme. In DDRP, the data from the source to the receiver are sent through multi-hop transmission at the minimum distance. This decreases the TL.  
Figure 11: Path loss. (**a**) when all nodes die; (**b**) when first node dies.  
The TL of AUV-PN is higher due to clustering. This is the cost paid to achieve better throughput and less delay.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Iterative Training Algorithm
#### 3.5.3 Algorithmic Realization  
In the course of knowledge dissemination, inter-model gradients can impede one another, thereby curtailing each model's capacity to learn independently. This phenomenon, known as gradient contention, occurs when gradients produced by different models or tasks conflict with each other, leading to ineffective optimization. Specifically, when gradients push shared model parameters in opposing directions, they may neutralize each other's effect, hindering both models' learning processes. To preclude gradient contention across models and, at the same time, enable effective knowledge transmission and reinforce each model's autonomy in optimization, we devise an iterative training algorithm. This algorithm partitions loss optimization into several phases and carries them out in a sequentially alternating fashion. Initially, the procedure optimizes Model A's supervised loss \(L_{sup}^{A}\) while holding Model B's parameters static, thus allowing Model A to refine its performance under the supervision of true labels; subsequently, it optimizes Model B's supervised loss \(L_{sup}^{B}\) while freezing Model A's parameters. Thereafter, the framework employs the transfer losses \(L_{trans}^{A}\) and \(L_{trans}^{B}\) to impose knowledge constraints on Models A and B, respectively, using pseudo-labels to guide inter-model refinement. See Algorithm 1 for details. This staggered iterative update scheme safeguards each model's independent feature learning, using pseudo-labels to facilitate a more adaptable form of collaboration. In the end, after repeated iterations, the predictions from both models increasingly align, culminating in a synergistic improvement in overall performance. This algorithm deftly circumvents gradient contention while bolstering both models' learning capabilities and operational robustness.