esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
###### Abstract  
In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of addressing software or hardware vulnerabilities at a later stage, it is better to address them immediately or during the development phase. Tools such as AIBugHunter provide solutions designed to tackle software issues by predicting, categorizing, and fixing coding vulnerabilities. Essentially, developers can see where their code is susceptible to attacks and obtain details about the nature and severity of these vulnerabilities. AIBugHunter incorporates VulRepair to detect and repair vulnerabilities. VulRepair currently predicts patches for vulnerable functions at 44%. To be truly effective, this number needs to be increased. This study examines VulRepair to see whether the 44% perfect prediction can be increased. VulRepair is based on T5 and uses both natural language and programming languages during its pretraining phase, along with byte pair encoding. T5 is a text-to-text transfer transformer model with an encoder and decoder as part of its neural network. It outperforms other models such as VRepair and CodeBERT. However, the hyperparameters may not be optimized due to the development of new optimizers. We reviewed a deep neural network (DNN) optimizer developed by Google in 2023. This optimizer, the Evolved Sign Momentum (LION), is available in PyTorch. We applied LION to VulRepair and tested its influence on the hyperparameters. After adjusting the hyperparameters, we obtained a 56% perfect prediction, which exceeds the value of the VulRepair report of 44%. This means that VulRepair can repair more vulnerabilities and avoid more attacks. As far as we know, our approach utilizing an alternative to AdamW, the standard optimizer, has not been previously applied to enhance VulRepair and similar models.  
Keywords:VulRepair; T5 transformer; LION optimizer; AdamW; software vulnerabilities +
Footnote †: journal: Applied sciences
## 3 Experiment and Discussion  
Our experiment was conducted at BL3 of SACLA [15]. A schematic of the experimental setup is shown in Figure 1. XFEL pulses with a central photon energy of 9070 eV were focused with the two-stage focusing system using platinum/carbon multi-layer mirrors. The silicon (553) analyzer crystal was set at 1.2 m from the focal point, and an MPCCD detector was placed on a 0.3-m-long detector arm. Thus, the total length from the focal point to the detector \(L\) was 1.5 m, as used in the estimation in Section 2. Figure 2a shows a typical single-shot spectrum of an XFEL pulse, demonstrating that the spectrometer is capable of covering the complete pulse spectrum. In Figure 2b, we show the enlarged spectrum around the peak maximum of Figure 2a. The typical width of observed spikes is 390 \(\pm\) 30 meV in full width at half maximum (FWHM), which is sufficiently larger with respect to the resolution of the spectrometer of 80 meV. Based on this result, we estimated the XFEL pulse duration with a method described in [10]. Here, an energy chirp of the electron beam of 6 \(\times\) 10\({}^{-5}\) fs\({}^{-1}\) was used as the simulation parameter. By comparing the measurement with a simulation, the pulse duration was estimated to be 7.7 \(\pm\) 1.1 fs in FWHM. From the evaluation of Figure 2a, the longitudinal mode number was around 60.  
Figure 1: Experimental setup. The X-ray divergence was increased with the two-stage focusing optics, in which multi-layer elliptical mirrors were used as a second K-B optics. The spectral profile analyzed by a silicon (553) crystal is recorded with a MPCCD detector.  
Figure 2: (a) Spectrum of the XFEL beam measured with the spectrometer; (b) enlarged spectrum around the central region of Figure 2a.
## 6 EEG Signal Pre-Processing and Feature Extraction
### EEG Signal Pre-Processing
#### 6.1.2 EEG Signals Segmentation  
The brain produces signals rich in information, not limited to emotions. These signals are too complex to be analyzed in their entirety. To enhance the effectiveness of detection techniques, a method known as window-based segmentation is utilized. This approach breaks down the signals into smaller, more manageable segments for analysis. This segmentation helps in reducing noise and simplifying the detection process. The duration of the segmentation window can vary, typically ranging from 0.5 s [89] to 60 s [103], depending on the specific requirements of the study or research.
## 3 Thermal and Innovative Technologies for PPO Inactivation
### Thermal Treatment  
Thermal processing is a traditional food preservation method that employs heat to preserve and process food products. The inactivation of polyphenol oxidase (PPO) by heat treatment is the most effective and standard method to control enzymatic browning. Heat is used in this treatment to destroy microorganisms and inactivate enzymes. However, the thermal treatment causes undesirable changes in food qualities, such as loss of colour, flavour, texture, and nutrients [26]. Blanching is a common thermal treatment used to inactivate enzymes in fruits and vegetables. Blanching is a water-intensive technology and is deemed as uneconomical technology [32]. It involves rapid heating of fruits and vegetables to a predetermined temperature and holding it for a set period, usually 1 to less than 10 min [29]. The most common method of blanching is hot water blanching, during which the products are immersed in hot water (70 to 100 \({}^{\circ}\)C) for several minutes [33; 34].  
Figure 4 shows studies on the thermal inactivation of PPO enzymes that have been carried out by various authors [28; 35; 36; 37; 38; 39]. The effect of temperature on PPO stability is commonly investigated by incubating the enzyme at a higher temperature. Processes from 70 \({}^{\circ}\)C to 80 \({}^{\circ}\)C for 5-25 min were seen to provide more than 80% of PPO inactivation. However, studies by [PERSON] [35] on sugarcane and [PERSON] [28] on strawberry PPOs show that at a lower temperature of 60 \({}^{\circ}\)C, 80% of PPO inactivation can also be achieved. This proves that different fruits can react at different temperatures and times to achieve similar PPO inactivation. Different technologies used to produce juice and puree, different fruit cultivars, and different methods used to determine PPO activity could explain the differences in the inactivation after processing.  
Figure 4: Inactivation of PPO (\(\geq\)80%) by thermal treatment [28; 35; 36; 37; 38; 39].
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
## 4 Discussion
### Limitations of the Proposed System  
As in all engineering systems, there are limitations, and they must be considered. They include:  
1. The proposed system will only work in regions where an EEW is available, such as Japan, parts of the USA, Mexico, and Europe. Also, if the EEW is unable to provide a warning signal, the proposed system will not function.
2. If the epicenter happens to be in close proximity to the structure, there will be insufficient time for the deployment of the scissor lift brace.  
A certain amount of excavation is needed to house the mechanical system in the ground in retrofit scenarios.  
4. The proposed system confers lateral stiffness to the structure; such an increase must be analyzed and the subsequent increase in the base shear must be tolerated.
# Mental Workload Classification Method Based on EEG Independent Component Features
## 2 Materials and Methods
### Mental Workload Classification Method Based on EEG ICs Features
#### 2.2.3 Classification  
In this paper, different classifiers separately using LDA [35], back propagation (BP) neural network (BPNN) [36], KNN [37], and SVM [38] algorithms will be discussed and compared in Section 4. The SVM classifier was finally selected as the classification method for different EEG processing methods. The extracted four energy features of EEG ICs of the three loads were used as the input of SVM to classify the mental workload. The grid search method was selected for different subjects to obtain the optimal model parameters. The search space of the kernel function was [linear, rbf, sigmoid]. C was the penalty coefficient, which indicates the tolerance of the error, and the search space was [0.001, 0.01, 1, 10, 30]. Gamma was the parameter required to select rbf as the kernel function, which indicates the number of support vectors, and its search space was [0.001, 0.01, 0.1, 1]. Cross-validation was performed in the classification algorithm to ensure the robustness of the model and reduce overfitting.
## 1 Introduction
### Fast and Efficient Algorithms for Real-Time Implementation  
Furthermore, there exists another common challenge by utilizing the CS-based methods, _i.e._, we need fast and efficient algorithms for real-time system implementation, particularly for digital electronic circuits (e.g., ARM, FPGA, DSP) [30]. From previous researches, sparse recovery techniques can be roughly divided into two families, that are greedy methods (e.g., MP [31], OMP [32], GP [33]) and optimization based methods (e.g., \(l_{1}\) optimization [34], smoothed \(l_{0}\) optimization [35], non-convex optimization [36]). Generally speaking, greedy methods have the advantages of lower complexity, faster speed, less storage requirement, and flexible implementation compared with optimization based methods, and are considered the most suitable candidates for hardware implementation. However, their performances are inferior to those of the optimization based methods, such as \(l_{1}\) norm minimization basis pursuit de-noising (BPDN) [34].  
As the coordinate descent (CD) search has an inherent property of being low complexity when signals are sparse, [PERSON] _et al._ have successfully exploited dichotomous CD (DCD) iterations for solving LS [37], RLS [38] and MVDR beamforming problems [39] using FPGAs for real-time implementation. In addition, to solve the reweighted \(l_{1}\) minimization problem, they have developed a greedy algorithm called H\(l_{1}\)-DCD [40], _i.e._, homotopy DCD method with reweighted \(l_{1}\) penalty, which has shown a high recovery performance with relatively low complexity. It turns out that the overall complexity of the algorithm is comparable to that of MP. Moreover, homotopy iterations result in high accuracy of the solution, even higher than that of the YALLl algorithm. Meanwhile, [PERSON] and [PERSON] _et al._[28] have utilized the low complexity homotopy approach combined with CD search for locating underwater acoustic sources by solving the multi-frequency BPDN problem. The proposed method is evaluated by applying to simulated and real experimental data. As far as we know, previous studies [28,37,38,39,40] are mainly focusing on homotopy DCD with convex regularizations, while there are seldom researches linking to homotopy DCD with non-convex regularizations.
## 3 The Human Role in Teleoperation
### Human Factors, Tasks, and Telepresence
Technological and Human Perceptual Factors: Effect on Skill-Based Behavior and Immersive Teleoperation Design  
As mentioned in Section 2, the activities of human operators rely on three types of performance, namely skill-, rule-, and knowledge-based behavior. The higher the involvement of rule- and knowledge-based layers, the higher is the cognitive workload requested of those operators. Perceptual disturbances can lead to a higher intervention of more rational behaviors, contrasting the reactive mode of skill-based behavior [25]. Thus, it is important to keep activities at the skill-based behavior level where perceptual signs are essential to lower the workload. The present section discusses the technological and human perceptual factors with relevance for immersive teleoperation design to minimize workload. Given that human vision provides over 70% of all the sensory information used in the interaction with the world, we start by presenting the ideal display specifications in Table 1, considering the human eye limits. The comparison of these references with those of current visual mediation systems allows the identification of critical factors for teleoperation. Reliable perception is determinant for HRI task performance, the visual feedback being a major source of information. Actually, users perform better in simple or visually less complex environments (e.g., structured spaces, interactions with few objects and at similar depths, few concurrent tasks) [41; 42]. Related works aiming to minimize workload through visual features' enhancement show positive results [43; 44; 45]. As the task becomes visually more demanding, the involvement of other sensory channels, such as tactile, haptic, and auditory, can contribute to maintaining performance [46]. However, these studies also demonstrate that technological mediation of the visual sensory channel can limit visual features and consequently degrade the user's performance and increase the user's workload. We present some findings concerning visual interface design that aim to mitigate the \begin{table}
\begin{tabular}{l c c} \hline
**Display Propriety** & **Range Value** & **Ref.** \\ \hline Latency & \(<\)7–15 ms & [33; 34] \\ \hline Angular Pixel Density & \(>\)60–200 pixels/degree & [35; 36] \\ \hline Field of View & 210\({}^{\circ}\) (H) \(\times\) 135\({}^{\circ}\) (V) & [37] \\ \hline Frame Rate & \(>\)1800 Hz & [38] \\ \hline Color & 10 Millions & [39] \\ \hline Dynamic Range & 1:10\({}^{9}\) & [40] \\ \hline \end{tabular}
\end{table} 
Table 1: Ideal display specifications to match the human eye limits. mentioned problems. Table 11, at the end of this paper, presents a summary of the specs for mediation teleoperation technology based on human capability requirements and based on related works' specs findings for a given a task.
## 2 Problem Description  
The observation mission scheduling assigns an EOS and a corresponding observation time window (OTW) to each observation mission. In the EOS system, an initial observation plan for static observation missions is firstly generated at the beginning of the scheduling horizon. Meanwhile, a large number of dynamic observation requests for real-time observation information are randomly submitted to the EOS system during the scheduling horizon. To fully utilize the resources of the EOS system to serve these dynamic missions, this paper investigates the EOS dynamic mission-scheduling problem. An example of dynamic observation mission scheduling is illustrated in Figure 1. The dynamic observation plan, represented in red, is generated for the dynamic observation missions based on the initial observation plan, which is shown in blue. We can easily find that the EOS system can execute more missions by dynamic scheduling. In addition, the profit and stability are vital for the EOS system, and the perturbation significantly affects stability of the EOS system. Thus, the observation profit and perturbation are optimized simultaneously in this paper. This section first introduces variables and assumptions for the multi-objective dynamic scheduling problem (DMP-PP) and then formulates the multi-objective dynamic mission-scheduling mathematical model.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Adaptive Filtering Methods  
Removing noise and artifacts from the ECG signal is crucial for its correct and accurate analysis, especially in the context of ventricular fibrillation (VF) detection and prediction. Adaptive filtering methods, such as the Kalman filter and the RLS filter, provide effective solutions for removing noise and artifacts from the ECG signal. These techniques allow for improved signal quality, essential for accurate detection and prediction of ventricular fibrillation. Studies in the literature demonstrate the effectiveness of these methods, highlighting their potential in clinical applications and real-time monitoring. An example of the use of adaptive filtering in this area can be found in the paper [37], which uses an adaptive filter in combination with a support vector machine (SVM)-based recognition system to detect abnormal heartbeats associated with ventricular fibrillation.
## 4 Discussion  
### Contribution of Meteorological Factors to ET\({}_{0}\) Variations  
Neural network models usually need a variety of meteorological factors to train and test [38]. However, adequate weather parameters are often inaccessible in many developing countries [3]. Determining dominant influencing factors helps improve model working efficiency and popularity. Previous studies showed that variables such as air temperature and solar radiation explained approximately 70% of the contribution to ET\({}_{0}\) variations [39,40]. Our study showed that the correlation between ET\({}_{0}\) and air temperature and between ET\({}_{0}\) and solar radiation was highest, while \(\Delta\)T, RH and U\({}_{2}\) had significantly low \(\tau^{2}\) (Figure 6A). The result was consistent with the findings of [PERSON] et al. (2019), who regarded energy-related factors as the most influencing variables for ET\({}_{0}\)[35]. In previous studies, several scholars considered diurnal temperature range (\(\Delta\)T, \({}^{\circ}\)C) a dominant input variable for calculating ET\({}_{0}\)[7,41], which was inconsistent with our study. In this study, \(\Delta\)T showed less correlation at most sites. The \(\tau^{2}\) between \(\Delta\)T and ET\({}_{0}\) was 0.50 in Sunya and Haikou, where their \(\Delta\)T was less than 10 \({}^{\circ}\)C in most seasons due to a tropical climate [42]. Smaller \(\Delta\)T usually indicates closer values to T\({}_{\max}\) and T\({}_{\min}\). When \(\Delta\)T becomes larger, ET\({}_{0}\) shows more correlation with T\({}_{\max}\) and T\({}_{\min}\) but less with \(\Delta\)T because large data fluctuation is considered undesirable in the correlation analysis [43,44]. In this study, U\({}_{2}\) was the least correlated factor. The reason may be that wind speed is not a dominant factor in the atmospheric energy cycle [45,46,47]. Besides, U\({}_{2}\) is further impacted by land use and topography, making accurate data difficult to obtain [48]. This is why several simplified ET\({}_{0}\) models do not include U\({}_{2}\) as an input variable [49,50].
# Privately Generated Key Pairs for Post Quantum Cryptography in a Distributed Network
## 3 Privately Generated Key Pairs for Post-Quantum Cryptography
### Initial Step: Enrollment of Each Terminal Device  
_Root of Trust_  
Each terminal device or client needs to either carry with them or possess at least one CRP system establishing a root of trust. The controlling entity needs to have a database of Crypto-Tables tracking these CRP systems. This must be achieved through an initial step, also called an enrollment cycle. This operation must be performed in a highly secure environment, like the ones in which secret keys are downloaded onto banking cards and other secured tokens. Examples of enrollment cycles are as follows:  
Figure 3: Root of trust with challenge–response–pair mechanism.  
* The Crypto-Tables are generated from the PUFs contained in tokens by a trusted service provider operating like an HSM. The CRPs are carefully measured, the tokens are delivered to the clients, and the Crypto-Tables are transmitted to the CA.
* The clients must visit their bank, which is equipped to generate sets of CRPs from the facial images. The one-wayness of the CRP mechanism is such that the facial images cannot be reconstructed from the CRPs.
* A service provider generates Crypto-Tables from sets of digital images by encrypting them, hashing them, and applying an extended output function (XOF). The CA receives the Crypto-Table while the clients receive the encrypted files.  
After the completion of the enrollment cycle, the CA keeps cryptographic tables for each device to generate the same CRPs as the roots of trust, with a shared response. Malicious insiders at the CAs or HSMs are expected to create a security breach. The proposed protocol has the objective to mitigate the impact of potential attacks in the following way: The clients never disclose their private keys. For many applications, the main role of the CA is limited to the validation of the digital signatures with the public keys. Therefore, a loss of control of the Crypto-Table does not necessarily compromise a financial transaction. However, such a loss of control is not desirable and a two-way authentication of the CA should be considered.
## 2 Materials and Methods
### Meteorological Data  
The main meteorological parameters--precipitation and air temperature--were collected from synoptic, climatological, and rainfall stations located throughout Poland (see Figure 2) for the period 2001-2019 (data from 233 stations in the case of air temperature and from 1313 stations in the case of precipitation). The data were obtained from the Institute of Meteorology and Water Management's National Research Institute (IMGW-PIB) and further processed at the Institute of Geodesy and Cartography. The aim of processing was to obtain the spatial distribution of the meteorological data measured at the stations.  
Meteorological data collected at the stations were interpolated to obtain an image database at a 1 km spatial resolution. Air temperature data were interpolated using the method of ordinary kriging, reducing first the temperature values to sea level, adjusting the spherical model of the semivariogram for each day, and then estimating the final values at the real height above sea level. Interpolation of precipitation was done by applying the method of the average value weighted with distance (i.e., inverse distance weighting (IDW)), and 50 km was assumed as the maximum distance of the stations' impact.