esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Instantiating M-String
### Implementation  
Finally, we implemented the M-String abstraction as a LART domain. The implementation, with examples and documentation of domain usage, can be found online on the supplementary page [[https://divine.fi.muni.cz/2020/mstring](https://divine.fi.muni.cz/2020/mstring)]([https://divine.fi.muni.cz/2020/mstring](https://divine.fi.muni.cz/2020/mstring)). The LART domain is a C++ library that implements abstract semantics of M-String operations presented in Section 5. Such a library is then linked to the transformed program allowing the program to perform abstract analysis with model-checker DIVINE. An abstract domain definition in LART consists of a C++ class that describes both the representation (in terms of data) and the operations (in terms of code) of the abstract domain.  
In the case of M-String domain, this class contains 2 attributes: an array of _bounds_ and an array of _characters_, as outlined in Section 7.2 and depicted in Figure 3. The class has two type parameters: the domain to use for representing segment bounds and the domain to represent individual characters (i.e., the content of segments). A specific instantiation is then automatically derived by the C++ compiler from the classes which represent the type parameters and the parametric class which represents M-String values.  
As a minimal set of operations, the M-String domain implements all requisite aggregate operations: these are lift, update and access. Furthermore, the implementation provides an optimized version of string operations described in Sections 5: strlen, strcpy, strcat, strcmp and strchr. These operations reduce the loss of abstraction precision that would arise if only the abstraction of accesses and updates from strings were used.  
Since C strings are stored, in fact, as shared, mutable character arrays, the implementation of the M-String domain needs to reflect the sharing semantics of such arrays. If multiple pointers exist into the same abstract string, modifications through one such pointer must be also visible when the string is accessed through another pointer. Moreover, the pointers do not have to be equal: they may point to different suffixes of the same string. Therefore, the representation of pointers to abstract strings must treat the _object_ and the _offset_ components separately (see also Section 6.3), and the representation of the _offset_ component must be compatible with the bound domain \(\overline{\mathbf{B}}\).
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 5 Comparison of the Algorithms
### Quantitative Comparison
#### 5.1.4 Scenarios with Probability Distribution and Obstacles  
Finally, the last scenario type is analyzed. Similarly to the previous ones, 50 scenarios are generated, and for each one, five different combinations of probability distribution and obstacle arrangements are created. Again, each algorithm is tested for 100 initial conditions. In Figure 18a,b, the efficiency and fitness distribution have been represented. For both performance measurements, A1 clearly outperforms the other algorithms.
## Materials and Methods
### Tensile Test Procedure  
The drying and wetting specimen preparation paths were considered separately for the purpose of examining the effect of water content variation on loess' tensile behavior. Accordingly, two methods were adopted in order to prepare the loess specimens for the tensile tests. The first method was to prepare a batch of specimens by gradually increasing their water contents and this is hereafter referred to as the wetting method (WM). The other method, termed the drying method (DM), involves firstly preparing several specimens with a water content that is close to the liquid limit and then air-dry them to different target water contents.  
For the WM, the loess was dried in an oven at 105 \({}^{\circ}\)C, crushed and then sieved through a 2 mm sieve. Different amounts of distilled water were added to the sieved soil in order to achieve the target water contents (i.e., 6%, 10%, 14%, 18%, 22% and 26%). The wet soil was subsequently kept in a sealed container for 24 h so as to ensure water equalization. The tensile specimens were prepared with the mold that is shown in Figure 2 and they were compacted to three different dry densities (i.e., 1.4, 1.6 and 1.8 g/cm\({}^{3}\)) using light compaction equipment. For the DM, the sieved soil was firstly moistened to a water content that was approximately equal to its liquid limit and then it was compacted within the tensile mold to the desired dry densities (i.e., 1.4, 1.6 and 1.8 g/cm\({}^{3}\)). After that, the compacted specimens were carefully transferred to desiccators in order to reduce their water contents. The specimens were weighed at intervals until the target water contents were attained (i.e., 6%, 10%, 14%, 18%, 22% and 26%). Finally, all of the prepared specimens were subjected to the tensile test at a constant displacement rate of 0.1 mm/s along the horizontal direction.  
As is illustrated in Figure 1, the tensile force (\(F\)) that was mobilized was measured by the load cell while the tensile displacement (\(S\)) was recorded by LVDT. The tensile stress (\(\sigma\)) and strain (\(\epsilon\)) were determined using the following equation [37], wherein \(A\) is the cross-sectional area of the neck part of the specimen and equals 9.6 cm\({}^{2}\) and \(L\) (100 mm) is the total length of the specimen.  
\[\sigma=F/A;\ \epsilon=S/L \tag{1}\]  
In the process of the tensile test, the tensile stress (\(\sigma\)) increased gradually until a peak value was achieved. This peak stress is defined as the tensile strength (\(\sigma_{t}\)) of the specimen and represents the maximum stress that the specimen can resist against before it succumbs to tensile failure [38]. Furthermore, parallel tests were conducted on three duplicated specimens for each dry density and water content in order to ensure the repeatability of the tests.
## 1 Introduction  
Traffic crashes remain a significant problem for both developing and developed societies due to the material and moral losses they cause [1]. The occurrence of traffic crashes is influenced by various factors, including driver, vehicle, and road characteristics, as well as environmental and behavioral aspects. While human errors, such as speeding and careless driving, account for over 90% of traffic crashes [2], researchers have also highlighted the role of non-human factors, including road conditions, environmental factors, and vehicle types [3, 4, 5]. Understanding these contributing factors is critical for developing effective countermeasures, which require the collection and analysis of detailed crash-level data. Statistical methods have been traditionally used to analyze crash severity and its contributing factors. However, their reliance on strict assumptions and limited predictive power has motivated researchers to explore more flexible approaches, particularly Machine Learning (ML) and Deep Learning (DL). These methods have become prominent tools to overcome the limitations of statistical models, offering improved accuracy and flexibility.  
This study proposes a hybrid approach combining the feature extraction capabilities of a Deep Neural Network (DNN) with the classification performance of a Random Forest (RF) model. By combining the strengths of both methods, the approach addresses challengessuch as the limited interpretability of DL models and the constrained feature handling of traditional classifiers. The effectiveness of the hybrid model was evaluated using an original crash dataset from Mersin, Turkiye, obtained from the General Directorate of Security (GDS) database. Its performance was compared with that of well-known methods, including k-Nearest Neighbor (kNN), XGBoost, standalone RF, and DNN, using metrics such as accuracy, F1-Score (micro, macro, and weighted), and ROC-AUC. The results demonstrate the superiority of the hybrid DNN+RF approach, achieving a high performance (e.g., 0.92 accuracy, 0.89 F1-macro, 0.91 F1-micro score) and providing valuable insights for traffic safety practitioners.  
The remainder of the paper is structured as follows: Section 2 presents a brief review of the related literature. Section 3 describes the dataset in detail, including data balancing techniques and the feature selection process. Section 4 provides an in-depth explanation of the proposed hybrid model. Section 5 presents the experimental results. Finally, Section 6 concludes the paper with a summary of the key findings.
## 4 Results and Analysis
### Compare of Heat Fluxes Calculated Using Different LST Datasets
#### 4.3.2 Comparison of the DisTrad-LST and GWR-LST Heat Fluxes
For example, at EC15 on 8 July and 13 September, the GWR-LSTs are significantly overestimated compared to the EC-LSTs and the DisTrad-LSTs. Based on Equations (2)-(4), \(R_{n}\) and G are underestimated, whereas H is overestimated. Thus, in conjunction with Equation (1), the GWR-LST LE is significantly lower than the other two. However, in most cases, when the SHDI value is low (less than 0.2) and the underlying surface of the footprint area contains a single land cover type, the difference between the two methods is less than 10 W\(\cdot\)m\({}^{-2}\).
## 7 Results and Performance Analysis
### TriagE-NLU Evaluation
A higher BLEU score indicates better fluency and structural alignment, reflecting the model's ability to create coherent and faithful clinical representations. TriagE-NLU outperformed all baseline models in both accuracy and BLEU score, achieving 0.92 accuracy and a BLEU score of 0.87, reflecting strong reliability in both classification and structured sequence generation tasks. To further illustrate TriagE-NLU's advantages, we analyzed challenging cases involving multilingual phrasing and overlapping symptoms. For example, in the Romanian input \"sotul meu e ametti si nu-si poate misca bratul drept\" (\"my husband is dizzy and cannot move his right arm\"), mT5-Base fails to generate a structured frame, while XLM-R + BiLSTM misses the neurological context due to lack of metadata integration. In contrast, TriagE-NLU accurately extracts both neurological.dizziness and limb.weakness.right slots and recommends activating the stroke protocol. Additionally, mBART + MLP, which lacks a semantic head, cannot identify onset times or structured symptoms, limiting interpretability. These limitations stem from architectural constraints: mT5-Base lacks an explicit classification head, mBART + MLP is not designed for structured generation, and XLM-R + BiLSTM relies on frozen embeddings and cannot leverage patient metadata. TriagE-NLU's dual-head architecture and integrated metadata encoding allow it to outperform these baselines in both accuracy and clinical interpretability across diverse, multilingual inputs. These results highlight TriagE-NLU's robustness across multilingual, multimodal input scenarios.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 3 Experiments and Results
### Parameter Setup  
In deep learning neural networks, both hyperparameters and network architecture are crucial factors. Hyperparameters refer to those parameters that need to be manually set, and they significantly influence the model's performance and convergence speed. Improper choices can lead to issues such as failure to converge or overfitting. Therefore, selecting appropriate hyperparameters is paramount. The hyperparameters and network architecture settings in this paper are shown in the Table 1.
## 4 Experiment and Result Analysis
### Comparison Experiments and Analysis
It is seen that the proposed model can achieve the best improvement since the accident data are incorporated via the random walk method based on a multi-graph attention mechanism. However, LSTM, due to its focus only on the temporal features, performs poorly in extracting spatial relationships from external features, indicating that accident data can be used to extract useful local semantic information variations and apply them to traffic flow prediction.
## 3 Time-Frequency Representation  
Signal analysis can be carried out in the time or frequency domain by Fourier transform or inverse transform. However, the Fourier transform is a kind of overall transform, which is only suitable for stationary and deterministic signals, and cannot reflect the changes of signal frequency characteristics over time. To analyze the time-varying frequency information of a signal, time-frequency representation (TFR) is needed. TFR transforms the signal from single time/frequency domain into a time-frequency 2D feature image [25], which reflects the time-frequency joint characteristics of the signal. A WiFi time-frequency image is shown in Figure 10.  
Since the non-parametric time-frequency analysis method does not require prior knowledge of the signal, the time and frequency resolution obtained does not depend on the specific signal, and is more suitable for the scenario of SOP perception. Commonly used non-parametric time-frequency analysis contains linear and nonlinear methods [26; 27]. Typical linear analysis includes STFT, Continuous wavelet transform (CWT), etc., and typical nonlinear analysis includes Wigner-Ville distribution (WVD), Cohen Classes, etc.
# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 2 Gaussian Process
RBF is infinitely differentiable. That means the GP with this kernel has a mean square derivatives for all orders. Despite that RBF is very smooth, strong smoothness assumptions are unrealistic and the using kernel from Matern family is recommended [1]. The Matern kernel family is a kind of generalization of RBF function. The general formula for Matern covariance functions is given by (9):  
\[k\big{(}x_{i},x_{j}\big{)}=\frac{1}{\Gamma(v)2^{v-1}}\left(\frac{\sqrt{2v}}{ l}d\big{(}x_{i},x_{j}\big{)}\right)^{v}K_{v}\left(\frac{\sqrt{2v}}{l}d\big{(}x_{ i},x_{j}\big{)}\right), \tag{9}\]  
where \(K_{v}\) is a modified Bessel function, \(l\) is length-scale parameter and \(v\) is a parameter, which allows to change smoothness of function, which give an ability to flexibly control the function in relation to the one we want to model. Also \(d\big{(}x_{i},x_{j}\big{)}\) is the Euclidean distance. Of particular note are frequently used parameter \(v\) with values of \(3/2\), which is used for learning functions, which are at least once differentiable and 5/2 for functions at least twice differentiable [1]. Additionally, worth mentioning is the Exp-Sine-Squared kernel function (also called periodic kernel, proposed by [PERSON] [15]). Main differential here is a possibility to catch periodicity of a given functions and of course model them. Formula (10) represents Exp-Sine-Squared function. \[k\big{(}x_{i},x_{j}\big{)}=\exp\left(-\frac{2\sin^{2}\big{(}\pi d\big{(}x_{i},x_ {j}\big{)}/p\big{)}}{l^{2}}\right). \tag{10}\]  
In addition to \(l\) length-scale parameter we have \(p\) as a periodicity parameter, which allows flexibility with respect to periodicity. This kernel comes in handy when we would like to model some kind of seasonality of our problem. As mentioned, each of the selected covariance functions has a set of free parameters called hyperparameters whose values are need to be determined. Properly handling this task is one of the main difficulties in a GP usage. As can be seen in the next part of this paper, currently the ML-II approach is widely used, where we calculate parameters by a sort of marginal likelihood optimization. Its advantage is ease of use and uncomplicated computation process with a major drawback of less accuracy. For instance, it can suffer from a multiple local maxima problem. There are also solutions for calculating hyperparameters such as statistics methods such as hierarchical Bayesian models. More on this topic can be found in next chapter [1]. Equation (3) is widely used across all the literature, sometimes in different forms, but always all comes to this formula. GP has a variety of fields which can be used, where machine learning and statistics methods are applicable. GP works best under the difficult circumstances, where building a model could be a thought task, because of hard to obtain data. The overall usage and how it is applied in nowadays problems is described in detail in the following sections.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 2 Materials and Methods
### Noise
#### 2.3.4 Noise Addition to EGMs  
First, spike perturbations were added to the EGM signals, using 15 realizations of random spike trains, each with a pulse width of a single sample and probabilities \(P_{s}\) of \([0.01,0.05,0.10,0.15,0.25]\). The methodology presented in [4] was adopted to generate five databases of EGMs for each value of \(P_{s}\). A 60 Hz PLI was added to the original EGMs, with frequency fluctuations of [\(\pm 1\%\), \(\pm 5\%\)] and signal-to-noise ratios (SNRs) of [\(-5\), \(0\), \(5\), \(10\), \(15\)] dB. The methodology of [19] was implemented to generate five databases of EGMs for each value of SNR. Finally, five datasets containing EGMs with loss of samples were generated by adopting \(\eta(\%)=\{0.01,0.05,0.1,0.15,0.25\}\), and for the loss of resolution perturbation. Five EGM datasets were generated using the following test values: \(\beta(\%)=\{0,75,93.5,97.3,99\}\). Figure 4 depicts examples of each type of noise.  
Table 1 summarizes the configuration of each perturbation applied to the EGMs. The methodology of [4] was considered to generate the perturbed EGMs databases. Furthermore, twelve additional perturbed EGM databases were created by combining different types of noise and considering the variability generated in different feature types to evaluate the proposed framework under various scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Database** & **Loss of Samples** & **Spikes** & **PLI** & **Loss of Resolution** \\ \hline
1 & 0.01 & 0.01 & \(-5\) dB & 0\% \\
2 & 0.05 & 0.05 & 0 dB & 75\% \\
3 & 0.10 & 0.1 & 5 dB & 93.5\% \\
4 & 0.15 & 0.15 & 10 dB & 97.3\% \\
5 & 0.25 & 0.25 & 0.15 dB & 99\% \\ \hline \end{tabular}
\end{table}
Table 1: Configuration of EGM perturbations for each validation database.
# Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis
## 3 Methodology and Framework
### Execution Process  
The execution process consisted of two stages. (1) Dynamic analysis. The Cuckoo Sandbox was used to monitor the behavior of each file while it was running on the guest system, and then produced a behavior report with the Java server object notation (JSON) format after the process was completed. (2) Memory Analysis. At this stage, the Volatility tool was used to analyze each memory image and produce a memory analysis report with the JSON format. At the end of the execution process, two reports were generated: Behavior and memory reports for each tested sample. Thus, the total number of generated reports was 3200 for entire data set.
# Deep Eutectic Solvent Pretreatment and Green Separation of Lignocellulose
## 2 Deep Eutectic Solvent in Separation of Lignocellulose  
Various types of deep eutectic solvents (DESs) have proven effective in facilitating the separation of lignocellulosic materials [38]. Nonetheless, mechanical and chemical pretreatments have been employed in conjunction to enhance the separation efficiency and refine the process conditions, including the duration and temperature of the separation [39]. Table 1 provides an overview of the typical DES-based lignocellulosic separation systems that have been utilized in recent years, highlighting their key features and applications. It is noteworthy that type II DES (ChCl:AlCl3:6H2O/FeCl3:6H2O) and type III DES (ChCl:lactic acid/p-TsOH) are commonly employed in the lignin removal process. This is due to the limited low-melting-point range of non-hydrated metal halides forming type I DES, which results in higher costs compared to metal salts meeting the requirements of type II DES. The preparation of type III DES is simple, relatively unreactive with water, and cost effective. Additionally, many are biodegradable in line with Green Chem principles. The discovery of type IV DES has demonstrated that transition metal halides can also form eutectic crystals. However, their high melting point makes them challenging for use in lignocellulosic separation [40; 41]. Among these, type III DES has shown outstanding in effectively removing lignin [42]. By exploring the separation of lignocellulosic with different DESs, researchers have observed that various classes and ratios of DES can effectively separate lignocellulosic from different plant biomass. The specific separation effect is influenced by both the raw materials and types of DES used. Increasing the separation temperature and extending the separation time can improve the removal rate of lignin. Different chemical and mechanical pretreatments assist in enhancing the efficiency of lignocellulosic separation. Figure 1: Schematic representation of a eutectic point on a two-components phase diagram. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Substrate** & **Solvent (mol)** & **Assisted** & **Condition** & \begin{tabular}{l} **Dalignification** \\ **Rate** \\ **(a)** \\ \end{tabular}  & \begin{tabular}{l} **Lignin in** \\ **Solid Residue** \\ **(a)** \\ \end{tabular}  & \begin{tabular}{l} **Collulose in** \\ **Solid Residue** \\ **(a)** \\ \end{tabular}  & \begin{tabular}{l} **Heniculose** \\ **(a)** \\ \end{tabular}  & \begin{tabular}{l} **Cellulose** \\ **Feversion** \\ **Feversion** \\ **(a)** \\ \end{tabular}  & **Ref. ** \\ \hline Com stover & ClCl: LA (1:2) & Microwave-assisted & 800 W,45 s & 79.60 & 9.22 & 65.78 & 5.03 & 75.11 & [56] \\ \hline Supgaseous hygiene & ClCl: LA (1:2) & \ \begin{tabular}{l} Hydrothermal \\ pretreatment \\ \end{tabular}
## 4 Results and Discussion  
### Near-Field Flow Field Characteristics  
#### 4.1.1 Drag Coefficient of Different Pantograph Diversion Strategies  
The following tables show the drag coefficients of the three calculated models, where Table 4 shows the drag coefficients in total and Table 5 shows the drag coefficients of each pantograph component. It can be found that the drag coefficients of the whole computational model decrease to different degrees when the sinking platform is installed on the train roof or after the pantograph fairing is installed. As shown in Table 4, compared with the pantograph exposed on the roof alone, the drag reduction rates of the above two cases are 2.93% and 5.42%, respectively. To obtain a better idea of how the total drag coefficients changed, it is necessary to analyze the drag of each pantograph part. As shown in Table 5, the drag coefficients of the base frame, insulator 1, and camera are dropped when the sinking platform is installed on the train roof, due to the reduction in their windward area. When the pantograph fairing is installed, most of the components are shielded by the fairing, especially the base frame, insulators, and camera; therefore, their drag coefficient decreases sharply. Although the drag coefficient of the fairing is comparable to the entire pantograph, as shown in Table 4, the total drag coefficient of the train model with three cars roof is greatly reduced after the installation of the fairing. As a consequence, the pantograph fairing could be a good alternative in terms of drag optimization for higher-speed train.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Head Car** & **Middle Car** & **Tail Car** & **Pantograph** & **Fairing** & **Total** \\ \hline Case 1 & 0.1022 & 0.0754 & 0.0762 & 0.3162 & / & 0.5700 \\ Case 2 & 0.1022 & 0.0833 & 0.0776 & 0.2902 & / & 0.5533 \\ Case 3 & 0.1008 & 0.0693 & 0.0829 & 0.1443 & 0.1418 & 0.5391 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computed drag coefficients of three cases.  
Figure 8: Sketch of the computational mesh. (**a**) Y-plane of the computational mesh; (**b**) enlarged view of the pantograph area; (**c**) enlarged view of the pantograph.