esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
After analyzing the changes in stiffness during a destructive test based on three methods, including equivalent stiffness, short-term stiffness, and the back-calculated piecewise stiffness from system identification, some conclusions can be drawn, which are as follows:  
1. Before crack initiation, the stiffness remains constant. However, during the early stage of crack extension, the stiffness of the T-beam in the crack region decreases significantly. At this point, the stiffness of this area decreased by 31% compared to the initial value. As the crack continues to extend, the stiffness will gradually decrease until the beam ultimately fails or the steel undergoes tensile yielding.
2. By comparing different standards and experimental data, we identified the applicable scope of different methods. When considering the beam as a whole, it is reasonable to use the equivalent stiffness as its overall stiffness before cracks form in the prefabricated concrete T-beam. Calculating the short-term stiffness of the mid-span section according to the specific code can effectively reflect the damage situation of the cracked section and is more suitable for the non-linear stage.
3. The segmented stiffness prediction method based on system identification predicts the stiffness of T-beams by using multipoint deflection back-calculation. The stiffness obtained by segmented reverse prediction can effectively reflect the damage state of each segmented section throughout the entire failure test process. This method provides valuable insights into the extent of damage in each segment and helps to evaluate the overall structural integrity of the beam.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** Authors [PERSON] was employed by the company China Construction Seventh Engineering Bureau Co., Ltd. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 3 Results
### Substrates  
The type of molecule to be evaluated and its physico-chemical characteristics also offer different challenges for virtual screening, in terms of docking and its ability to discriminate between actives and decoys. For each specific target, the decoys included in the DUD-E were generated by having similar 1-D physico-chemical properties to the actives from which they originated, to remove bias [32]. Hence, to analyze how the different substrate properties affected the discriminating ability of each target, the physical properties of all actives identified in the ligands ranked as the top 1% were evaluated and compared with the other actives that were ranked the worst.  
In this study, four fundamental properties of the ligands were analyzed--the size of the ligands, polarity, charge, and the number of rotatable bonds.  
Figures 5 and 6 present heat maps of the correlation between the substrate properties and their position in the ranking according to the type of target family (proteases and metalloenzymes, respectively). Darker red (+1) yield perfect positive correlation while darker blue (\(-\)1), yield perfect negative correlation. From Figure 5, it is clear that polarity and number of rotational bonds is important for both Vina and is even more distinct for AutoDock, since it presents a positive correlation, that is, as the ranking number increases, the polarity and number of rotational bonds also increase. This means that the molecules with more rotatable bonds and which are more polar, are ranked worst in the list. This leads to the conclusion that more polar and more flexible molecules present a bigger challenge for AutoDock, in particular. For metalloenzymes, the correlation profile is a little bit different from proteases. It is not easy to find a clear tendency because while some targets present a positive correlation for some property, others have a negative correlation for the same property. This could again be explained by the large variability of protein types in this particular family.  
Figure 5: Heat map correlation for proteases between Vina and AutoDock scores and ligand properties, such as fraction of hydrophobic accessible surface area (FASA_H), fraction of polar accessible surface area (FASA_P), fraction of positive and negative accessible surface areas (FASA+ and FASA—), molecular weight (MW), and number of rotational bonds (Rot. B.).
## 1 Introduction
Furthermore, MSC possess immunomodulatory features, as well as multipotent differentiation capacity, and could thus potentially target IVD degeneration from two approaches. Nevertheless, the hypoxic and acidic IVD environment is foreign to MSC and questions remain about their ability to survive and thrive in such a demanding niche [30; 31]. A recent report from the Mesoblast phase II/III clinical trial suggested their MSC-based product was able to alleviate some pain-associated outcomes, but no regenerative changes were observed on MRI modalities [32]. As such, a range of studies has attempted to induce the differentiation of MSC toward an NP cell-like cell phenotype. MSC can be subjected to environmental conditions [33], hydrogel systems [34; 35], genetic manipulation [36; 37], etc. ; aimed to induce more NP cell-like features. Particularly, the application of growth and differentiation factors to support differentiation towards an NP cell-like cell, as indicated by enhanced proteoglycan production rates or enhanced NP cell marker expression levels; e.g., aggrecan, type II collagen, Brachyury, and CD24 [38]. Multiple growth factors are suggested to be able to support NP cell differentiation, including Wnt3a [39], Wnt5a [40], TGF-\(\beta\)1 [41], TGF-\(\beta\)3 [42], GDF5 [43], and GDF6 [44]. Very few bodies of work have compared different growth factor potency for NP cell differentiation. One study by [PERSON] et al. [44] suggested GDF6 media supplementation optimal for MSC to NP cell differentiation, following assessment of GDF5, GDF6, or TGF-\(\beta\)1 media supplementation. Moreover, work by [PERSON] et al. revealed quite eloquently, the potential of GDF6 to differentiate adipose stem cells toward NP cell-like cells [45], and subsequently highlighted a requirement of the SMAD1/5/8 pathway [46]. Alternatively, work from [PERSON] et al. [47] revealed the ability of growth factors to synergistically enhance NP cell differentiation. In their work, they found that only combined application of TGF-\(\beta\)1 and GDF5 was able to create MSC that formed fully chondrogenic pellets, as well as stringently enhance expression of NP cell markers; e.g., ACAN, COL2A1, CD24, PAX1, and OVOS2. Review work has also suggested the application of both Wnt3a or Wnt5a as promising chondrogenic factors, particularly combining Wnt3a with other chondrogenic factors was speculated to hold potential [48]. A wide range of factors has been proposed to enable MSC to NP cell differentiation. However, a large-scale assessment or screening to determine which factors, or even more, which combination of factors, are most potent to induce NP cell features, has not been reported. To optimize growth factor-mediated MSC differentiation, particularly for the creation of cell therapy products, we initiated this study to screen a wide range of growth factor combinations to assess which combination showed most potent in inducing proteoglycan production and NP cell-associated expression levels as indicators of MSC to NP cell-like cell differentiation.
## 2 Results and Discussion
### PTR-ToF-MS: Determination of Volatile Compounds from Spices  
PTR-ToF-MS analyses provided a volatile fingerprint from Paprika and Baklouti peppers, with high mass resolution, short acquisition times and no sample modifications. A total of 56 different peak signals were detected from the two peppers studied. By using the same technique, 101 volatile compounds were identified in six common market spices (black/white pepper, chili paprika, cinnamon, nutmeg and saffron) showing a pattern that varied by spice type [25]. As reported in Table 2, the two pepper accessions exhibited a similar volatile profile, consistent with the findings previously reported by [PERSON] et al. [26] on _C. annuum_ dry matrices. However, compared to the previous study, we observed a higher emission of terpene compounds from both Paprika and Baklouti peppers. Indeed, the PTR results revealed a volatile profile characterized by different classes of terpene compounds, including sesquiterpenes (mostly detected at _m/z_ 205, 123, 109), oxygenated terpenes (mostly detected at _m/z_ 137, 123, 109), and monoterenes (mostly detected at _m/z_ 137, 123, 109). In addition, we observed a greater emission of sulfur compounds (_m/z_ 49, 63, 111) from Paprika compared to Baklouti. These differences between the two studied accessions highlight a distinct chemical composition and potential flavor contributions of these pepper varieties compared to other _C. annuum_ accessions. It is important to note that these compounds have a different odor threshold perception. For example, monoterenes generally have lower odor thresholds and are often perceived at lower concentrations, contributing significantly to the aroma profile even in small amounts. In contrast, sesquiterpenes typically have higher odor thresholds, requiring higher concentrations to be detected. Sulfur compounds, such as thiols, sulfides, and disulfides, often have extremely low odor thresholds (often pungent odors) and can contribute significantly to the overall aroma
## 4 Results
### Multiclass Confusion Matrix Analysis  
To evaluate the performance of both models across multiple traffic categories, normalized confusion matrices were generated using ten classes from the UNSW-NB15 dataset. These matrices express the percentage of correctly and incorrectly classified instances per true class, offering detailed insights into class-wise behavior. As illustrated in Figures 5 and 6, both XGBoost and TabNet demonstrate strong performance in classifying the most prevalent categories, such as Generic and Normal, with high true-positive rates. In contrast, their effectiveness decreases for less frequent classes like Fuzzers, Backdoor, and Worms, where misclassification is notably higher.  
Figure 4: ROC-AUC curves for XGBoost and TabNet models evaluated on UNSW-NB15 dataset.  
Although the general classification patterns are similar, a closer inspection reveals that XGBoost achieves slightly more concentrated predictions on key classes like Generic, while TabNet tends to distribute its predictions more broadly across categories. This behavior may lead to increased confusion among rare classes. These findings underscore the importance of evaluating explainability and class-wise robustness, especially in cybersecurity applications, where the detection of infrequent attacks is critical.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 2 Environmental, Technical, Financial Specifications of the Plant
### Specifications
#### 2.2.1 PV Module  
Currently, the main semiconductor material used to manufacture PV is silicon. The most widely used PV technologies are polycrystalline (p-Si), monocrystalline (m-Si), and amorphous silicon (a-Si). There are also other materials such as CdTe or CIGS/CIS for the production of thin-film PV, including Amorphous Silicon (a-Si), Cadmium Telluride (CdTe),  
Figure 2: Overview diagram of grid-connected PV plant.  
Copper Indium Selenide (CIS), Copper Indium (Gallium), and Copper Indium Selenide (CIS), Di-Selenide (CIGS/CIS), and Heterojuntion with intrinsic thin-film layer (HIT). Due to the low production cost, high efficiency, and decades-old production technology, crystalline silicon photovoltaic cells still dominate the market (Figure 3). Si (crystalline) photovoltaic modules can achieve efficiencies up to 21.9% and 25.6%, respectively [37; 38; 39; 40; 41; 42]. In Europe, the m-Si, p-Si type has a typical yield of 10 \(\div\) 18% [43]. Amorphous silicon (a-Si) has an efficiency of 4 \(\div\) 6% and amorphous silicon (a-Si) thin film PV has a maximum efficiency of 10.5%. The selection of panels with the right capacity needs to be evaluated with many different conditions. Table 2 shows the average efficiency of current PV panel technologies.  
Calculation results of different capacities panel are presented in Table 3 below, calculated using Pvsyst software. In order to improve the efficiency of the plant and reduce the area and installation cost, the proposed plant for Da Mi solar power plant is a 72-cell silicon photovoltaic panel with a large capacity (more than 330 Wp). The panel efficiency is not less than 17%. This range of power and performance is currently popular across the world, with many manufacturers responding.
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 1 Introduction
In this contribution, the focus of this research work is mainly placed on the terrain coverage scenarios containing mountains, and the research objective is to present a novel coverage path planning method conducted in three-dimensional (3D) space and provide the generated coverage trajectory with good properties of conformal coverage effects, trajectory minimization, and obstacle avoidance. In order to address the CPP problem of terrains with sparsely distributed mountains, a UAV coverage path planning method based on the DH (Diameter-Height) model is proposed for mountainous terrain, with the purpose of providing better coverage effects, minimizing the coverage path length, and avoiding obstacles. By taking into account the diameter and height information of the mountain, a simplified mountain model has been proposed and termed as the DH model. In virtue of the proposed DH model, a simplified 3D map can be constructed for the region of mountainous terrain. A region segmentation method is presented for the 3D map of mountainous terrain, thus dividing the surface 3D map into two categories: the surface region of the simplified mountain model and the flat sub-regions excluding the mountain model in the 3D map. The equal-interval spiral ascent path and parallel circular sweeping path are adopted for these two coverage regions, respectively. Finally, in order to achieve the minimum optimization of coverage path length, a greedy strategy-based path connection algorithm is performed, taking into account the obstacle avoidance. The main contributions of this paper are as follows:A simplified mountain model is proposed for convenient 3D map construction of mountain terrain. * A region segmentation method is proposed for the 3D map of mountainous terrain, and the equal-interval spiral ascent path and parallel circular coverage path are adopted for covering the mountainous terrain, taking into account the mountainous features. * A coverage path connection method with trajectory minimization and obstacle avoidance is proposed in the final path connection stage, providing the coverage paths with good properties of energy saving and collision avoidance. * The simulation experiments have been conducted to verify the effectiveness and feasibility of the proposed CPP method based on the DH model, and comparative experiments are constructed to demonstrate that the proposed method excels at UAV energy saving in the coverage scenes of mountainous terrain. The rest of the paper is organized as follows: Section 2 describes the problem statement of the DH model definition and the UAV energy model definition. Then, Section 3 provides a detailed introduction of the proposed CPP method based on the DH model. Experiments and discussions are provided in Section 4. Finally, the paper is concluded in Section 5.
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 2 Methodology  
Equation (1) describes the relationship between observed seismic data, seismic wavelet, and reflectivity:  
\[\mathbf{Y}_{\mathrm{obs}}=\mathbf{T}_{\mathbf{w}}\mathbf{R}, \tag{1}\]  
where \(\mathbf{Y}_{\mathrm{obs}}\) is the multi-channel observed seismic data, \(\mathbf{T}_{\mathbf{w}}\) represents the Toeplitz convolution matrix corresponding to the wavelet \(\mathbf{w}\), \(\mathbf{R}\) is the multi-channel reflectivity. In this work, we focus on the stationary seismic deconvolution and the wavelet is assumed to be stationary. For the i-th trace in the observed seismic data \(\mathbf{y}_{i}\) and the reflectivity \(\mathbf{r}_{i}\), Equation (1) can also be written as:  
\[\left[\begin{array}{c}y_{i,1}\\ y_{i,2}\\ y_{i,3}\\ \vdots\\ y_{i,L}\end{array}\right]=\left[\begin{array}{cccccccc}w_{0}&w_{1}&\cdots&w_ {-(m-1)}&0&\cdots&0\\ w_{1}&w_{0}&\cdots&w_{-(m-2)}&w_{-(m-1)}&0&\cdots\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \cdots&0&w_{m-1}&w_{m-2}&\cdots&w_{0}&w_{1}\\ 0&\cdots&0&w_{m-1}&\cdots&w_{1}&w_{0}\end{array}\right]\left[\begin{array}{c }r_{i,1}\\ r_{i,2}\\ r_{i,3}\\ \vdots\\ r_{i,L}\end{array}\right] \tag{2}\]  
where \(i=1,2,\ldots,N_{tr}\). \(N_{tr}\) is the number of seismic traces, \(L\) is the number of time samples of seismic data, and the length of the wavelet is taken as \(2m-1\). Within the blind deconvolution framework, the objective is to simultaneously invert both the reflectivity and wavelet from seismic data:  
\[\mathbf{w},\mathbf{R}=\underset{\hat{\mathbf{w}},\hat{\mathbf{R}}}{\arg\min} \big{\|}\mathbf{Y}_{\mathrm{obs}}-\mathbf{T}_{\hat{\mathbf{w}}}\hat{\mathbf{R }}\big{\|}_{2}^{2}. \tag{3}\]
## 3 Results and Discussion
### Analytical Performance  
The method performance was verified to validate its use. The formaldehyde calibration curve was prepared at 7 levels of concentration (0.10, 0.25, 0.50, 1.0, 2.5, 5.0, 10 ng mL\({}^{-1}\)) and correlation coefficient was equal to 0.9993. For calculating the limit of detection (LOD) a signal-to-noise ratio equal to 3 was used, and a value of 10 was selected for the limit of quantitation (LOQ). The LOD was 0.15 ng mL\({}^{-1}\) and LOQ was 0.50 ng mL\({}^{-1}\). The recovery found in ultrapure water spiked at level 5.0 ng mL\({}^{-1}\) was 86.1%. The enrichment factor that was calculated as the ratio of the peak area of formaldehyde with and without using the DES-TF-SPME method was equal to 178. Both single sorbent and sorbent-to-sorbent precision are satisfactory with the relative standard deviation values of 3.3% and 4.8%, respectively. This demonstrated that the newly prepared DES-3(1:3) sorbent is suitable for selective extraction of formaldehyde derivative and its stability on mesh support gives the possibility to use it at least 5 times after conditioning. Analytical figures of merit for the proposed DES-TF-SPME/HPLC-UV-Vis method are summarized in Table 2. A chromatogram of a formaldehyde derivative standard was visualized in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tested Parameters** & **Values** \\ \hline Linear range [ng mL\({}^{-1}\)] & 0.5–125 \\ Regression equation & y = 28.37\(\times\) + 1.23 \\ Determination coefficient (r\({}^{2}\)) & 0.9995 \\ Single sorbent RSD [\%] (\(n\) = 4) & 3.3 \\ Sorbent-to-sorbent RSD [\%] (\(n\) = 4) & 4.8 \\ Recovery [\%] & 86.1 \\ Enrichment Factor & 178 \\ LOD [ng mL\({}^{-1}\)] & 0.15 \\ LOQ [ng mL\({}^{-1}\)] & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The figures of merit obtained for the developed method.
## 2 Experimental Details
### Equipment  
All components were weighed with a RADWAG AS 60/220.X2 (Radom, Poland) analytical balance.  
The chemical analysis of the base alloy was performed with a Spectromax (Kleve, Germany) XF-BT spectrometer.  
The activation of SiC particles was achieved by mixing them in the activating solution in a IKA C-MAG HS 7 (IKA-Werke GmbH & Co. KG, Staufen im Breisgau, Germany) magnetic stirrer with heating and ceramic heating plate, which offers excellent chemical resistance.  
The calcination was performed in an electric oven with resistors, UTTIS Romania type (Bucuresti, Romania), model CE4x2x1 with 3.5 kW power.  
The alloy was melted in a laboratory size electric oven with resistors. The centrifugal casting was performed on a vertical axis casting plant, with installation also located in the laboratories of Materials Science Department and presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **Si** & **Fe** & **Cu** & **Mn** & **Mg** & **Zn** & **Ti** & **Pb** & **Al** \\ \hline
[\%] & 8,23 & 1,50 & 0,117 & 0,111 & 0,062 & 0,166 & 0,022 & 0,014 & 89,70 \\ \hline \end{tabular} The sum of the elements is not 100% since the composition includes elements with very low concentrations.  
\end{table}
Table 1: Chemical composition of the Al-Si alloy.  
The shell was coated with a thermoreactive binder (20 mm thickness) in order to decrease the intensity of the thermal exchange with the environment, keeping the liquid alloy as long as possible in the liquid state in order to intensify the embedding process of the SiC particles.  
Structural analysis was performed using an Omnimet-Buehler analysis system equipped with a Nikon microscope (1000\(\times\) maximum magnification) and software programs suitable for quantitative structural analysis.  
The X-ray diffraction patterns were recorded on a Bruker (Billerica, MA, USA) AXS D8 discover XRD (X-ray Diffraction) using a monochromatic \(\mathrm{Al_{K\alpha}}\) X-ray source to identify the phases and constituents.  
The samples were also analyzed with a scanning electron microscope (SEM, Quanta FEI, Eindhoven, The Netherlands) equipped with an X-ray energy dispersion spectroscopy system (X-EDS, INCA Oxford Instruments, Abingdon, UK).  
The dilatometric analysis was performed on a DIL LINEIS L75 Platinum Series dilatometer, within 25-550 \({}^{\circ}\)C temperature range deploying 10 K\(\cdot\)min\({}^{-1}\) heating rate in the dynamic step [37]. The experimental runs comply with the ASTM E228: 2011 standard procedures. Specimens' dimensions were set as 105 \(\times\) 5 mm.  
Differential thermal analysis was performed using a NETZCH STA 449 F3 Jupiter (Selb, Germany). NETZCH Proteus--Thermal Analysis--version 5.2.1 software was used for processing the DTA-TG experiment. The DTA peaks were established on the heating curve along with the transformation enthalpy [28; 29; 38; 39; 40].