esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Preserving these local varieties is important because their cultivation is not widely spread due to their ease of hybridization and gradual rather than simultaneous harvesting, which leads companies to discard them when selecting varieties to cultivate. The Mugnoli of Pettorano sul Gizio are local varieties of Brassicaceae obtained via the hybridization of cabbage, belonging to the _Brassica oleracea_ [PERSON] family, with turnip, which is part of the _Brassica rapa_ [PERSON] family. Therefore, it is a plant species that exhibits characteristics of both the cabbage and the turnip from which it originated [18]. Other common Abruzzo cultivars are: Guardiagrele turnip, curly kale from Lama dei Peligni, \"Rapa senza testa\", \"Cima dell'Osento\". These species cultivated in Abruzzo are the subject of our study, which is based on the characterization and classification of these local varieties to protect and enhance their value [19]. The Guardiagrele turnip is a variety of _Brassica napus_ cultivated in the town of Guardiagrele (Abruzzo, Central Italy), from which it takes its name. The curly kale of Lama dei Peligni (Abruzzo, Central Italy) is a species belonging to the family _Brassicaceae oleracea_ var. _sabellica_. Among the _Brassica rapa_ species is \"Rapa senza testa\", rich in numerous leaves without a central stem. Lastly, \"Cima dell'Osento\" is cultivated near the Osento River in Abruzzo (Central Italy). These local varieties were analyzed and compared alongside two commercial Brassicaceae varieties: \"Cima 90\" San Marzano\" and \"Cima Grande\". These varieties were analyzed using infrared spectroscopy [20]; this technique, being rapid, non-destructive, and green, allowed us to obtain results quickly while keeping the analyzed sample intact. Data were processed using chemometric approaches such as Soft Independent Models of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) to highlight similarities and differences between the considered species despite their different family origins and their ability to hybridize [21; 22]. These chemometric algorithms made it possible to classify the various sample classes with good efficiency [23; 24], enriching the study conducted so far on these landraces, which are interesting due to their rich chemical composition, nutritional value, and genetic heritage [25; 26; 27].
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 1
#### 3.1.1 Case 1: Analysis of 10-1000 Frequency Range (Hz)  
Referencing Table 7, deviations in this range are governed by magnetic properties and primarily indicate core movement, magnetostriction effects, or mechanical looseness. A stable core should exhibit minimal deviation from the fingerprint, as the magnetic circuit is dominant in this frequency domain.  
Figure 5a shows that the measured response remains well-contained within the Six Sigma control limits (\(UCL\), \(LCL\)), with only minor fluctuations between 600 Hz and 900 Hz. The \(C_{p}\) and \(C_{pk}\) process capability indices remain above the critical threshold of 1.33, confirming high stability in core structural integrity.  
From Table 4, a \(C_{p}\geq 1.75\) places the core in the \"Highly Stable\" category, confirming that the core remains securely clamped without significant displacement. Additionally, Figure 5b presents range variations well below \(UCL_{R}\), reinforcing mechanical consistency within the core structure.  
Remarks: The core remains intact and stable, with no signs of movement or clamping pressure loss.
# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
###### Abstract  
Due to the widespread distribution of coronavirus and the existence of a massive quantity of data on social networking sites, particularly Twitter, there was an urgent need to develop a model that evaluates users' emotions and determines how they feel about the pandemic. However, the absence of resources to assist Sentiment Analysis (SA) in Arabic hampered the completion of this endeavor. This work presents the ArSentiCOVID lexicon, the first and largest Arabic SA lexicon for COVID-19 that handles negation and emojis. We design a lexicon-based sentiment analyzer tool that depends mainly on the ArSentiCOVID lexicon to perform a three-way classification. Furthermore, we employ the sentiment analyzer to automatically assemble 42K annotated Arabic tweets for COVID-19. We conduct two experiments. First, we test the effect of applying negation and emoj rules to the created lexicon. The results indicate that after applying the emoj, negation, and both rules, the F-score improved by 2.13%, 4.13%, and 6.13%, respectively. Second, we applied an ensemble method that combines four feature groups (n-grams, negation, polarity, and emojis) as input features for eight Machine Learning (ML) classifiers. The results reveal that Random Forest (RF) and Support Vector Machine (SVM) classifiers work best, and that the four feature groups combined are best for representing features produced the maximum accuracy of (92.21%), precision (92.23%), recall (92.21%), and F-score (92.23%) with 3.2% improvement over the base model.  
sentiment analysis; Twitter; Arabic lexicon; Arabic annotated datasets; COVID-19; negation; emoticons +
Footnote †: journal:  
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# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### Strain Energy Components
#### 3.1.4 The Detection Algorithm  
The algorithm of detection of the locking phenomena introduced into the standard three-step adaptive strategy proposed by [PERSON] [40] is presented in Figure 1. The original part of the standard adaptation includes three steps: initial (\(i=1\)), intermediate (\(i=3\)) called also \(h\)-step, and final (\(i=4\)) called also the \(p\)-step. Either local \(h\)-refinement or local \(p\)-enrichment is performed within each element of the latter two steps, based on the estimated approximation error values obtained from the equilibrated residual method of [PERSON] and Oden [43; 44]. The modification of the initial mesh (\(i=2\)) is performed as the second step when necessary. This additional step is composed of two stages: the detection of the phenomenon, and the changes in the mesh which consist in the global \(p\)-enrichment performed within all elements of the thin- or thick-walled structure or such a part of the complex structure.  
The details of the additional step modifying the initial mesh are presented in Figure 2. At first, we search for the thick- or thin-walled parts of the generally complex structure. Such a structure can be composed of thick- and thin-walled parts, solid parts, and transition parts joining the previous two types of geometries. Of course, simple geometries composed of a single thin- or thick-walled part are also possible. Then, for each element of such a thin- or thick-walled part, the interelement stresses on the element boundary have to be calculated. Subsequently, for two local problems described with Equations (30) and (31), the terms of these equations are generated and the equations are solved. Next, one has to sum the element energies from (32) and compare two sums for two types of the local problems. Finally, if the criterion for locking detection is met, one has to confirm this result with the criterion for bending dominance detection (29). If it is fulfilled, then the modification of the initial mesh is performed, which is based on \(p\)-enrichment of all elements of the thin- or thick-walled part of the structure. Such elements of the modified structure are all equipped with the longitudinal order of approximation equal to \(p=p_{max}=8\). Next, the standard steps, \(i=3\) and \(i=4\), of \(hp\)-adaptation can be performed.  
It should be underlined that calculation of the interelement stresses, performed in the above algorithm, is skipped in this paper as it is performed in the standard way presented in [43; 44; 46; 54; 55].  
We also apply the standard \(p\)-enrichment algorithm performed at the modification stage of the additional adaptation step (\(i=2\)). Such modification does not require any additional actions in comparison to the standard \(p\)-adaptation [41; 42].  
Figure 1: Three-step adaptive procedure extended with the fourth modification step.  
Figure 2: Flow diagram of the overall algorithm for the locking detection.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 2 Methodology
### The 5G Signal Design of SRS
The term \"comb-2\" indicates that SRS tones are positioned at the rate of every second subcarrier, thereby resulting in a higher density of occupied subcarriers. Conversely, a comb-4 reduces the density of occupied subcarriers, as SRS tones are placed at every fourth subcarrier, impacting the granularity of positioning measurements. The selection of comb size is contingent upon a delicate equilibrium between signal overhead, positioning precision, and robustness against multipath effects. In densely populated urban areas, a finer comb (e.g., comb-2) may be advantageous to enhance resolution and mitigate NLOS-induced errors. The SRS resource comprises symbols transmitted through the NR grid. Analogous to DL-PRS, UL-SRS resources for positioning are transmitted through a single antenna port and can initiate from the NR uplink slot. These SRS elements can span 1, 2, 4, 8, or 12 consecutive OFDM symbols, which is appropriate for all TRPs participating in positioning procedures. The comb size \(K_{TC}\) for the SRS placement, it is 2, 4, and 8 according to 3 GPP R17 V17.1.0. The configuration of resource element offset patterns can be developed in accordance with the parameters \(K_{TC}\) and \(N_{symb}^{SRS}\), where the comb offset can range from \([0,K_{TC}-1]\).
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Crack Growth and Linkage in Pre-Cracked Rock Disks
#### 3.5.2 Scaling Effect and Crack Coalescence Mechanisms  
As noted by [PERSON] et al. [49], fracture evolution in disordered materials can transition between brittle and ductile regimes owing to strain-localization effects. Although CSCBD tests primarily exhibit brittle failure mechanisms, the presence of shear interactions in mixed-mode scenarios (\(\beta=30^{\circ}\)-\(45^{\circ}\)) suggests local plasticity effects analogous to the strain band formation observed in ductile fracture models. Furthermore, the energy release rate surpassed the critical threshold for crack coalescence, confirming that linkage occurred through localized stress redistribution rather than purely brittle failure criteria. The numerical and experimental fracture modes [49] are compared in Figure 18, revealing a high degree of consistency between the simulated crack paths and the empirical observations. The SW-DVIB model successfully captured the transition from Mode I tensile fracture at lower inclinations to Mode II shear-dominated failure at larger angles, reinforcing its applicability for brittle material characterization.  
The findings presented in this section substantiate the robustness of the SW-DVIB framework in accurately predicting the fracture mechanics behavior. By explicitly incorporating crack inclination effects, the model effectively reproduced experimentally validated fracture-toughness dependencies and scaling phenomena. The agreement between the numerical results and experimental benchmarks strengthens the confidence in the predictive accuracy of the model, ensuring its relevance for failure characterization in brittle geomaterials.
## 2 Materials and Methods
### Testing the Recorded Lenses  
The recorded lenses were tested at three locations on the lens: at the center of the lens, 3 mm to the left of the center and 3 mm to the right of the center. Figure 3 illustrates how the Bragg positions for the two gratings vary because of the different wavelengths, and how they differ at the three probing positions. Testing involved illumination with an unexpanded He-Ne beam of about 1 mm in diameter and rotation of the sample through a wide angular range while monitoring the intensity changes in the transmitted or first-order beam. Since in all cases studied here, higher order beams are negligible, it was often more useful to monitor the zero order or transmitted beam so that the angular position, diffraction efficiency and FWHM information was available in a single scan. Except where otherwise stated, all tests were carried out in a computer-controlled system with a Newport rotation stage and photodetector and a \(\uplambda=633\) nm He-Ne probe laser. Figure 3 shows an illustration of the different conditions for Bragg matching at three positions on the lens. The wave vectors for the input and output beams at both wavelengths are shown along with the grating vector for each position on the lens for each of the two wavelengths.  
Figure 3: (**a**) Ewald Sphere illustration of the different conditions for Bragg matching at three positions on the lens and (**b**) Bragg matching condition for each position, \(-3\) mm, center and \(+3\) mm shown separately for clarity.  
Figure 2: Experimental set-up for both the recording of a cylindrical holographic lens in green and probing using a He–Ne laser. NDF—neutral density filter; HWP—half-wave plate; SF–spatial filter; CL—collimator lens; A—variable aperture; BS—beam splitter.
## 3 Results and Discussion
### Single Galvanometer Scanner Model
\begin{table}
\begin{tabular}{c c c c c} \hline
**Surface** & **Type** & **Radius (mm)** & **Thickness (mm)** & **Glass** & **Semi-Diameter (mm)** \\ \hline Object & Standard & Infinity & 0 & & 0 \\
1 & Standard & Infinity & 20 & & 0 \\ Stop & Standard & Infinity & 20 & & 1.0 \\
3 & Coordinate break & & 0 & & 0 \\
4 & Coordinate break & & 0 & & 0 \\
5 & Standard & Infinity & 0 & Mirror & 7.087 \\
6 & Coordinate break & & 0 & & 0 \\
7 & Coordinate break & & 0 & & 0 \\
8 & Standard & Infinity & \(-\)40 & & 5.00994 \\
9 & Coordinate break & & 0 & & 0 \\
10 & Coordinate break & & 0 & Mirror & 7.105 \\
11 & Standard & Infinity & 0 & Mirror & 7.105 \\
12 & Coordinate break & & 0 & & 0 \\
13 & Coordinate break & & 0 & & 0 \\
14 & Standard & Infinity & 28.8 & & 5.306 \\
15 & Standard & \(-\)568.068 & 8.0 & C79-80 & 16.8 \\
16 & Standard & \(-\)44.924 & 2.342 & & 17.8 \\
17 & Standard & \(-\)41.135 & 7.0 & Silica & 18 \\
18 & Standard & 129.864 & 9.991 & & 21 \\
19 & Standard & \(-\)32.099 & 9.0 & C79-80 & 21.2 \\
20 & Standard & \(-\)84.939 & 3.375 & & 27.5 \\
21 & Standard & \(-\)55.865 & 9.0 & Silica & 28 \\
22 & Standard & \(-\)43.469 & 0.543 & & 31 \\
23 & Standard & \(-\)981.242 & 15.0 & C79-80 & 37 \\
24 & Standard & \(-\)75.330 & 0.502 & & 39 \\
25 & Standard & 265.039 & 13.0 & Silica & 46.5 \\
26 & Standard & \(-\)153.780 & 18.545 & & 46.5 \\
27 & Standard & 208.267 & 12.0 & C79-80 & 46.5 \\
28 & Standard & 714.772 & 156.905 & & 46.5 \\ Image & Standard & Infinity & - & & \\ \hline \end{tabular}
\end{table} 
Table 1: The design parameters of a 1D scanning system using two galvanometers.
## 2 Materials and Methods
### Simulation Conditions  
The walking model received the kinematic variables as input. The knee model was attached to the walking model through a rigid connection between femur and thigh. The only flexion/extension was imposed on the knee, while the remaining degrees of freedom were determined by forward dynamics computation. The Kutta-Merson integration algorithm was adopted with rendering frequency of 100 frames/s and integration step ranging from 0.010 to 0.001 s; configuration tolerance was about 0.001 mm for displacements and 0.05\({}^{\circ}\) for rotations. To understand the role of the knee morphology and kinematics over the tension of the ligaments, a reference simulation was run without external loads and muscle forces. Then the ground reaction forces, the moments and the muscle forces were applied to the knee model, and the changes in ligament tensions were analyzed. In a second series of simulations, the forces of one of the three muscle groups were sequentially set at zero: the quadriceps muscles (NoQuad), the hammstrings muscles (NoHam) and the gastrocnemius muscles (NoGas). This allowed us to infer about the effect of each muscle group on the tension of the ligaments. The changes produced by the elimination of each muscle group were quantified by measuring the average difference of the ligaments' tensions between the \"All muscles\" condition and the condition in which a single muscle group was removed.  
Figure 5: Muscle forces applied to the model by the muscle actuators. Dashed lines represent the sum of the muscle forces belonging to the respective muscle group: Quadriceps (Quadr), Hamstrings (Hamst), Gastrocnemius (Gastr).  
The analysis was carried out in the phases in which each muscle group was dominating: the first contraction of quadriceps occurred from 0% to 30% of the stride cycle, and this phase was named the Loading Phase; from 31% to 80% the second contraction of quadriceps occurred and was named the Knee Flexion Phase; from 81% to 100% the dominating muscles were the hamstrings, and this phase was named Knee Breaking Phase; from 20% to 60% of the stride cycle the gastrocnemius muscles contraction occurred and the phase was named the Support Phase.
## 5 Results and Discussion
### Organic Matter Maturity  
Petroleum evolves from organic material over time as part of the more extensive thermal conversion process of organic material [50]. The nature of the organic matter and the degree of thermal conversion determine the hydrocarbon concentration and distribution in a given source [6; 51]. In this study, Tmax and PI are used as an index of thermal maturity. The key to efficiently using maturity parameters is to evaluate the obtained data carefully, and it is preferable to get many maturity parameters wherever available [40]. After that, a comparison will be made to ensure that the data are comparable or contrasted. By increasing maturity, the volume and content of hydrocarbons are produced by a specific kerogen change [41].  
According to [PERSON] [41] and [PERSON] et al. [52], oil production from source rocks began at a Tmax of 435 to 465 \({}^{\circ}\)C and a PI of 0.2 to 0.4, organic matter is immature when Tmax is less than 435 \({}^{\circ}\)C and a PI less than 0.2, and gas generation from source rocks begins at a Tmax of 470 and a PI greater than 0.4. Tmax values in the examined samples ranged from 442 to 476 \({}^{\circ}\)C, while the PI values ranged from 0.22 to 0.93. (Table 1). Based on these findings, kerogen classification diagrams based on the HI versus Tmax plot [52] were created, which are used to determine kerogen type and maturity (Figure 5A). The results showed that the Montney Formation samples tested had a wide maturity range, spanning from immature to postmature type IV kerogen. The plot of Tmax vs. PI further supports this conclusion (Figure 5B).  
Figure 4: (**A**) pseudo van Krevelen plot illustrating the kerogen type. (**B**) Cross plot of TOC vs. GP demonstrating the generating potential of the tested samples.
## 2 Materials and Methods
### Models of Fractured Carbonate Rock  
The two models of porous-cracked limestone considered to find a correlation between connectivity factor \(f\) and crack's characteristics are shown in Figure 1. The first one (Type 1 model) is a double porosity model (Figure 1a). It consists of an isotropic matrix and two types of voids--pores and randomly oriented cracks saturated with a fluid. Crack aspect ratio \(\alpha_{\text{crack}}\) varies from \(10^{-5}\) to \(0.1\), and crack volume fraction (crack porosity) \(\phi_{\text{crack}}\) does not exceed \(2\%\). Pore aspect ratio \(\alpha_{\text{pore}}\) is equal to \(1\). The aspect ratio of calcite grains is also equal to \(1\).  
The second model (Type 2 model) is a so-called \(f\)-model (Figure 1b) [29,36], which takes into account the connectivity of the voids via a special parameter \(f\). In contrast to the double porosity model here only spherical saturated pores are embedded in the matrix. An empirical factor \(f\) reflects the connectivity of the pores and varies from \(0\) to \(1\). Pore volume fraction \(\phi_{\text{pore}}\) changes from \(0\) to \(20\%\) for both models.  
Considering the carbonate rock model, we use the calcite matrix (isotropic polycrystal) with brine- and gas-filled voids. The bulk and shear moduli of the matrix are equal to \(72.0\)and 32.0 GPa respectively. The brine bulk modulus is 2.5 GPa, and the gas bulk modulus is 0.006 GPa. The shear modulus of fluids is zero.
## 4 Results & Discussion
### Comparison of Case 1  
To investigate the influence on the streamwise distance from the cooling holes to the front of the first-row rib, contours of turbulence kinetic energy for different distances are shown as Figure 4, which presents the process of cooling impingement. The results show that the maximum turbulent kinetic energy reduced from 21,440 m\({}^{2}\)/s\({}^{2}\) to 20,830 m\({}^{2}\)/s\({}^{2}\), when the space increases 20 mm to 60 mm in these three groups. It can be sure that the collision of the coolant flow and the turbulent kinetic energy become weak at the front of the first-row rib, when the horizontal position of the rib is designed away from the impact cooling hole. But, the total area of turbulent kinetic energy on the thermal surface is not changed with various streamwise distance.  
Figure 3: Validity of the grid number for air weighted average temperature.  
According to the above formats, the outlet flow rate temperature is calculated and is shown in Table 2. Due to the mass flow is conserved, the outlet flow rate temperature (\(\lambda_{out}\)) can present heat taken away in the coolant chamber. In the Table 2, Negative value indicates outflow cooling chamber direction. The results show that the outlet flow rate temperature is almost the same, when the streamwise distance increases from 20 mm to 60 mm. So, the quantity of heat exchanged is almost not affected by different spaces in the coolant chamber. It can be considered that the thermal protection on the transition piece has not been significantly improved. Therefore, it also can be concluded that the streamwise distance is not a significant factor affecting the heat transfer coefficient.
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 3 Experiments
### The BLEU Scores for Each Methods  
In addition, BLEU scores [23] were measured using five-fold Monte Carlo cross-validation [29] to compare the performance of text generation. Table 3 shows the BLEU scores for 1 to 4 g. Note that BLEU-n is the average BLEU score for 1 to n. The proposed method from this paper outperforms the BLEU scores of other methods, as indicated in the table.  
To ensure the validity of the performance, every baseline and the proposed method in this work were compared using a \(t\)-test. Therefore, the \(t\)-test was conducted three times for the three baselines, namely CNN-LSTM, DE-LSTM, and DE-LSTM-ATTENTION, against OURS. The hypothesis for each test is as follows:  
* Null hypothesis: The BLEU-4 score of OURS is the same as that of the baseline.
* Alternative hypothesis: The BLEU-4 of OURS significantly outperforms the baseline.  
Figure 5: Retrieval results for the query ’normal EEG in wakefulness and brief stage 2 sleep’. Yellow highlights indicate matching descriptions between the query and the impressions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** \\ \hline CNN-LSTM & 0.2802 & 0.2079 & 0.1684 & 0.1397 \\ DE-LSTM & 0.3615 & 0.2699 & 0.2176 & 0.1796 \\ DE-ATTENTION-LSTM & 0.3098 & 0.2358 & 0.1936 & 0.1623 \\ OURS & 0.3929 & 0.2890 & 0.2304 & 0.1884 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average BLEU scores for each method.  
For each \(t\)-test, the significance level (alpha) is set to 10%. Subsequently, the \(p\)-values of CNN-LSTM, DE-LSTM, and DE-LSTM-ATTENTION are 1.0031\({}^{-06}\), 0.0967, and 0.0037, respectively. Therefore, the null hypothesis is rejected for the baselines.