esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Core Insights and Critical Reflections
### Critical Commentary
#### 4.6.4 Challenges in the Global Collaboration Landscape: From Technological Silos to Global Synergy  
The dominance of countries and regions such as China, the United States, and the European Union reflects the strength of their regional research ecosystems. At present, global collaboration in the UAV-RS field exhibits a \"strong hubs, weak bridges\" pattern: while China and the U.S. have formed dense internal research networks, the bridgingconnections between them remain disproportionately weak relative to their immense academic output. Although China and the United States lead in UAV-RS research productivity, our analysis also reveals significant geographic imbalances. In particular, countries in Africa, Southeast Asia, Latin America, and parts of South Asia show notably low levels of academic participation in this field. We identify three key reasons and characteristics underlying this disparity:  
(1) Structural and institutional barriers, including limited research infrastructure, restricted access to high-end UAV platforms and sensors, lack of technical training opportunities, and high financial barriers to publishing in high-impact international journals. (2) Many countries face import restrictions, high equipment costs, and complex regulations, which make it difficult to acquire and maintain a stable UAV-RS operational system. Moreover, the absence of localized open-access remote sensing datasets and standardized workflows in these regions hampers their integration into the global knowledge system. This leads to a skewed research agenda--despite facing urgent environmental, agricultural, or disaster-monitoring challenges, these regions are underrepresented in global academic discourse. (3) Language barriers, limited access to English-dominated academic platforms, and a lack of targeted support from international funding agencies further exacerbate the issue. As a result, \"knowledge blind spots\" have emerged in the development of UAV-RS technologies, marginalizing the practical needs of developing countries.  
This phenomenon of \"technological silos\" presents a serious challenge to scientific progress. Global issues such as climate change, food security, and biodiversity loss require transnational, seamless data sharing and methodological consensus. The current fragmented model of cooperation not only hinders joint efforts to address these challenges, but also widens the digital divide between countries and regions, limiting the global accessibility and equitable distribution of advanced technologies.
## 3 Adhesion Performance
### Pull-Off Test  
A pull-off test was conducted using Nano and Micro Tribometer UMT-2 (Campbell, CA, USA). MSAMS specimen in the form of discs of 3 mm diameter was used as a dry adhesive. The specimen was attached to an aluminum block, fixed on the force transducer. The specimen and block were perpendicular to the load direction. The specimen was brought into contact and detached by linear motion of the translation stage, which was oriented perpendicularly to the glass surface (average roughness \(Ra\) = 3.3 \(\pm\) 2.1 nm) (Figure 3a). The pull-off force was measured, while the translation stage moved at different attach velocities (\(V_{0}\)) with the same preload and the same detach velocity (\(V_{t}\) = 0.2 mm/s). Contact time between the MSAMS specimen and the glass substrate was 5 s.  
The pull-off force measured between the PVS specimen and the glass substrate is presented in Figure 3b. The result reveals that the pull-off force on the glass substrate is not only independent of the load force [10], but also relies on the load velocity. The result indicates that for the robot, the footpad of this kind does not need much load control: the pad just needs to contact the surface, in order to produce substantial adhesion force and meet the requirement of the attaching phase. This characteristic of MSAMS is extremely beneficial for applying it at zero gravity, when the robot cannot rely on gravity to load the adhesive pad.
## 5 Evaluation Metrics and Cross-Validation for Cardiovascular Disease Prediction
### Evaluation Metrics  
When assessing the performance of cardiovascular disease prediction models, it is essential to use a range of evaluation metrics that provide a comprehensive view of their effectiveness. Specifically, assessing the performance of machine learning models in cardiovascular disease prediction is crucial for determining their effectiveness in clinical applications. Various evaluation metrics have been used to measure the model's performance and to evaluate the effectiveness of classifiers [176]. These metrics are computed using the confusion matrix. The common evaluation metrics used to assess the performance of machine learning models in cardiovascular disease prediction, including accuracy, recall (sensitivity), specificity, precision, F1-score, Matthews correlation coefficient (MCC), the area under the curve (AUC) and receiver operating characteristic (ROC) curve [29; 62; 176; 177; 178; 179; 180]. In cardiovascular disease prediction, evaluation criteria are crucial [17]. Table 3 describes the key of each of the evaluation metrics as well as provides the mathematical formulation of each of the evaluation metrics, which can be used to evaluate the effectiveness of machine learning algorithms for cardiovascular disease. The abbreviations that are commonly used in cardiovascular disease prediction are given as follows. TP (True Positive): When the predicted output is identified as true positive (TP), it indicates that the subject with cardiovascular disease is correctly classified, confirming the presence of cardiovascular disease. TN (True Negative): In the case of a predicted output classified as true negative (TN), it signifies the accurate classification of a healthy subject, correctly identifying them as not having cardiovascular disease. FP (False Positive): If the predicted output is false positive (FP), it implies the misclassification of a healthy subject, incorrectly indicating that they have cardiovascular disease. FN (False Negative): When the predicted output is false negative (FN), it indicates the misclassifica  
Figure 2: Structure of the data preprocessing and machine learning model applications. tion of a subject with cardiovascular disease as healthy, incorrectly suggesting the absence of cardiovascular disease. Different medical conditions and scenarios require different trade-offs between sensitivity and specificity. For example, in a cardiac emergency setting, high sensitivity may be more important to detect as many cases as possible. In contrast, in routine screenings, a balance between sensitivity and specificity may be more appropriate. Imbalanced datasets could also pose a technical challenge to the prediction model. Imbalanced datasets are common in healthcare applications, including cardiovascular disease prediction. Hence, it is also important to address the challenges of imbalanced datasets to ensure that cardiovascular disease prediction models are both accurate and clinically relevant. Specifically,
## 6 Conclusions  
The objective of this study is to quantify the relationship between braking and the potential of truck skidding based on a data- and model integrated-driven method. Firstly, the road-vehicle interaction model was built considering the dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, based on the \"HighD\" data set and employing the principles of cluster analysis, Python and Matlab were utilized to extract the data of driver behavior microcharacterization, vehicle attributes, and road properties. Thirdly, by parameterizing model inputs, the transient dynamic response of trucks was analyzed and the potential of truck skidding was predicted, and the MSBDRs were recommended.  
The results show that:  
1. The skidding potential differs significantly between truck axles during braking on horizontal curves, with the rear axle demonstrating higher susceptibility.  
2. Under any driving condition, there is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers.  
3. The MSBDR is 4.5 m/s\({}^{2}\) on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s\({}^{2}\), 3 m/s\({}^{2}\),and 2 m/s\({}^{2}\),  
Although there are important discoveries revealed by this study, there are also limitations. Some aspects were neglected, such as the influence of difference of per-wheel force on research results.  
The data for this study were primarily obtained from the HighD data set in Germany. Further research could focus on the variations in braking characteristics among drivers from different countries to determine if these should be considered in models.  
T.X.: methodology, investigation, software, formal analysis, data curation, writing--original draft, writing--review and editing. J.X.: conceptualization, validation, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, CHD, grant number 300102214110.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.
## 3 Simulation Analysis Using SSA for GREATEM Signal De-Noising
### De-Noising Simulation Analysis for Quasi-Two-Dimensional Earth Model  
In order to further explore the de-noising effect of the SSA algorithm, we used a quasi-two-dimensional earth model for research. The theoretical model is shown in Figure 9a, the surrounding rock resistivity was 100 \(\Omega\)-m; the resistivity of the low-resistance layer was  
Figure 8: Comparison of signals before and SSA de-noising.  
Figure 7: The separated signals after SSA. (**a**) GREATEM signal; (**b**) frequency spectrum of GREATEM signal; (**c**) noise; (**d**) frequency spectrum of noise.  
10 \(\Omega\cdot\)m, the depth was from 150 m to 220 m, and the simulation data inversion result is shown in Figure 9b. After adding the higher harmonic noise and a little white noise to the simulation data, before and after de-noising were inverted. Figure 9c is the inversion result before de-noising. Due to the higher harmonic noise and the white noise, the inversion result can not reflect the actual model, and the position and thickness of the low-resistance layer have large deviations after inversion. Figure 9d is the inversion result after de-noising by the SSA algorithm, consistent with the simulation data inversion result in Figure 9d, proving the method's effectiveness.
## 4 Out-of-Plane Flexural Behavior of SC Composite Beams with Different Shear Connection Ratios
### Load-Displacement Curves  
The load-displacement curves of six SC composite beams with different shear connection ratios are shown in Figure 9. The load corresponding to the theoretical flexural capacity of the SC composite beam with the yield tensile steel plate is 315 kN, which was determined according to equations for calculating the bending moment of a doubly reinforced concrete beam stipulated in GB 50010-2010. For low shear connection ratio, the ultimate capacity of SC composite beams with 0.7 shear connection ratio is 193.0 kN, which is far below the theoretical value. The displacement corresponding to ultimate capacity is 33 mm. As shown in Figure 9a,b, the load-displacement curves of SC composite beams with low shear connection ratios are approximately linear.  
Figure 9: Load-displacement curves of SC composite beams with different shear connection ratios.  
When the shear connection ratio reaches 1.3, a short plateau occurs in the load-displacement curve. The yield capacity is 314.5 kN, which is close to the theoretical value, and the displacement corresponding to the yield capacity is 50 mm. The ultimate capacity is 323.4 kN, the displacement corresponding to ultimate capacity is 67 mm, and the ductility could be 1.32. It could be due to the uneven interfacial shear distribution, the ultimate capacity of the SC composite beam with a 1.0 shear connection ratio is lower than 315 kN. The load-displacement curves of SC composite beams with middle shear connection ratios are variable, as shown in Figure 9c,d.  
When the shear connection ratio reaches 2.0, a significant plateau occurs in the load-displacement curve. The yield capacity is 317.5 kN, and the displacement corresponding to yield capacity is still 50 mm. The ultimate capacity is 367.8 kN, the displacement corresponding to ultimate capacity is 165 mm, and the ductility can be 3.3. The yield capacities and yield displacements of SC composite beam with 1.7 shear connection ratio are similar with those of a SC composite beam with a 2.0 shear connection ratio. The load-displacement curves of SC composite beams with high shear connection ratios are stable and full, as shown in Figure 9e,f. The most economic shear connection ratio could be between 1.3 and 1.7 for a SC composite beam with a 6.67 shear span versus depth ratio.
# Lightning Protection of Photovoltaic Systems: Computation of the Developed Potentials
## 5 Discussion  
To achieve an accurate picture of the lightning performance of the system under study, several issues need to be addressed, concerning different parameters (separation distance,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Grounding System**} & **Depth of Rod A** & **Depth of Rods B** & **Depth of Rods C** \\  & **[m]** & **[m]** & **[m]** \\ \hline gs1 & 1 & 1.5 & - \\ gs2 & 3.2 & 1.5 & - \\ gs3 & 4 & 1.5 & - \\ gs4 & 5 & 1.5 & - \\ gs5 & 3.2 & 3.2 & - \\ gs6 & 4 & 1.5 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examined grounding systems (gs) characteristics (see Figure 9 for rods A, B, and C).  
Figure 10: Maximum potential for soil structure D and grounding systems of Table 2.  
Figure 9: 3d simulation model indicating the structure of the examined grounding systems.  
depth of rods, soil structure, and grounding grid) that determine the developed potentials. According to the above simulations, the following outcomes are concluded:  
* The increase of the separation distance, s, has greater impact on the developed potential in the case of soil structures with greater upper layer resistivity compared to those with a lower one. (Figures 5 and 6).
* The installation of a perimeter grounding grid can significantly decrease the developed touch voltages (Figure 7). An installed perimeter grounding grid increases the grounding area and leads to a lower grounding resistance value and, consequently to diminished developed touch voltages.
* The lowest values of the developed potential are achieved in the case of non-attached LPS system with conductive isolator (Figure 8). This result contributes to the design of grounding systems, as the reduction of hazardous overvoltages is a significant criterion for the selection of a lightning protection system.
* The installation of rods on the perimeter grounding can contribute to decreasing the developed potential (Figure 9) considering the soil structure. In case the soil resistivity of the upper layer is higher, compared to the bottom layers, the installed rods' length should be such that part of them is buried beyond the upper layer boundaries. On the contrary, in case of a lower soil resistivity value of the upper layer, a horizontal grounding area increase would restrict the developed overvoltages rather than buried electrodes.  
The results of the present work, the mounting position of the PV park, the geometrical--structural characteristics of the LPS, and the soil resistivity must always be taken into account in order to construct an efficient lightning protection system in a techno-economical way. Future work can include the examination of various soil structures (multi-layer horizontal or vertical with different values of resistivity), separation distances, and depths of rods and perimeter grounding grids in order to achieve the optimal design of the LPS and to minimize, as far as possible, the developed potentials, touch and step voltages.
## 5 Feasibility Study Through System Simulations
### Radio Channel Model  
A simple radio channel model is used for the proposed scenario, consisting of the power path loss for LOS radio signal propagation and shadow fading.  
The path loss for LOS radio signal propagation, that is, without the obstruction of walls or floors, is calculated using the path loss model for indoor scenarios [223]:  
\[L=20\log_{10}(f)+N_{PL}\log_{10}(d)-28, \tag{7}\]  
where the path loss \(L\) is in dB, \(f\) is the frequency in MHz, \(d\) is the distance between the transmitter and the receiver in meters, constrained to \(d>1\) m, and \(N_{PL}\) is the power loss coefficient. Here, we will work with \(f=2600\) MHz, and \(N_{PL}=30\), which is a typical power loss coefficient for this frequency range used in office and residential scenarios.  
The shadow fading statistics are modeled using a random variable \(S\) following a normal distribution with zero mean and the standard deviation \(\sigma\)  
\[S\sim\mathcal{N}(0,\sigma^{2}). \tag{8}\]  
A typical value for the standard deviation for the selected frequency range and scenario is \(\sigma=3\) dB.  
The radio channel is modeled by  
\[H=L+S. \tag{9}\]  
The received power at a receiver, \(P_{RX}\), is calculated through a linear transformation in the dB domain, subtracting the effects produced by the channel on the transmit power:  
\[P_{RX}=P_{TX}-H, \tag{10}\]  
where \(P_{TX}\) is the transmit power of the transmitter in a dB unit, for example, in dBm. Here, we assume a constant transmit power.  
In RSS-based fingerprinting, the received power \(P_{RX}\) is used as a fingerprint. However, because our study is based on system simulations, we simplify the calculations by proceeding as follows. Instead of using the received power as the fingerprint, we use the numeric value of the effects produced by the channel, \(H\), directly as an equivalent to the \(P_{RX}\) fingerprint. This is because (1) we normalize the datasets of the RSSs input into the NN to the zero mean scaled by the standard deviation of the RSSs; therefore, a constant transmit power will simply disappear. (2) The added shadow fading is a random variable with a zero mean normal distribution, and (3) the weights in the first layer of the NN have the degrees of freedom to change the sign of the input data if needed. Therefore, we avoid extra computations for calculating the received power and use Equation (9) to generate the fingerprints instead. The fingerprints are a function of the distance \(d\) between the receiver and the transmitter (base station) for a fixed frequency \(f\). In addition, there is a shadow fading component considering a fixed standard deviation \(\sigma\) in the shadow fading and a fixed power loss coefficient \(N_{PL}\).
###### Abstract
[15] conducted a comparative analysis using both a 2D axisymmetric model and a full 3D model of a hydrogen tank. Their findings indicated that the results from the 2D and 3D models closely aligned during the charging and discharging phases, wherein hydrogen is injected into or released from the tank at high velocities, weakening the effects of buoyancy. This study focuses on a specific application of hydrogen tanks in \"tube trailers\", which are specialized vehicles designed for the transportation of high-pressure hydrogen gas from production sites to refueling stations. These trailers house multiple tanks, known as \"tube skids\", vertically installed and interconnected to store and transport significant quantities of hydrogen, as depicted in Figure 1. This configuration offers enhanced flexibility in vehicle integration compared to traditional horizontal tube skids, and it facilitates rapid hydrogen release upwards from the vehicle in case of an emergency. Figure 1: Hydrogen transport system (tube trailer). Given the considerable duration required to fully charge a vertically structured tank, the numerical analysis had to accommodate a broader time range than that typically associated with rapid filling. Additionally, the process of hydrogen introduction at the tank's bottom at comparatively slow velocities suggested a pronounced effect of buoyancy on the internal flow patterns within the tank. Accordingly, this study developed a numerical analysis model based on the commercial software ANSYS Fluent. The model is designed as a 2D axisymmetric framework to effectively simulate the entire charging process of a single tube skid by incorporating the influence of gravity and buoyancy effects. Utilizing this numerical model, the study established criteria for charging tube skids, analyzing the impact of five charging and environmental conditions on the temperature distribution within the tube skids. The analysis yielded temperature data, from which the sensitivity of each variable to temperature increases in the tank was determined. Subsequently, temperature prediction formulas for different regions within the tank were derived based on the numerical analysis results.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Grains and Seeds
#### 5.1.3 Pharmaceuticals  
The removal of the antibiotic norfloxacin in real water samples was tested using rice and coffee husks with similar conditions and particle sizes [71]. The results showed that the maximum removal was achieved in distilled water (96.9 and 99.6%) after 1 h at pH 6.2, rather than in municipal samples (81.4 and 95.6%). Particularly, the highest qm value was observed for coffee husk (33.56 vs. 20.12 mg/g). Another pharmaceutical recovered from the water with an alternative biosorbent was sulfamethoxazole, which is commonly used to treat bacterial infections. The biological adsorbent consisted of carbonized and sulfonated coffee wastes, and although no removal efficiency was reported it exhibited higher sulfamethoxazole biosorption capacity (256 mg/g) at room temperature [100]. Spent coffee biochar (NaOH-activated) displayed efficient interactions with naproxen (qm = 263.3 and 269 umol/g), diclofenac (qm = 97.1 umol/g), and ibuprofen (qm = 74.0 and 76.1 umol/g) in lake water and wastewater effluent at pH 7 and 25 \({}^{\circ}\)C [38]. The pristine activation of this biomaterial improved the adsorptive removal of naproxen (344.4 umol/g) and diclofenac (202.9 umol/g) in wastewater, as well as ibuprofen in lake water (86.2 umol/g) and wastewater (124.1 umol/g). The complete recovery (100%) of diclofenac through a TiO2 composite obtained from _Argania Spinosa_ tree nut shells was found to follow the Langmuir and pseudo-first order models for 50 mg/L analgesic and 0.1 g/L adsorbent at 25 \({}^{\circ}\)C [52]. In this study, carbamazepine and sulfamethoxazole were taken up using the same biomaterial with lower removal efficiencies (85 and 67% respectively) and adsorption capacities (105.3 and 125 mg/g respectively) under the same experimental conditions. Sodium diclofenac was retained using AC from cacao pod husk, with an efficiency of 76%; this value increased to 93.6% when decreasing biochar dosage (0.25 g) and increasing adsorbate concentration at neutral pH [51]. As shown in Table 3, the adsorption kinetics for these pharmaceuticals followed the pseudo-second order reactions and other isotherm models, as well as Langmuir.