esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Influences of Structural Parameters  
In this section, the influences of structural parameters (\(b\), \(\gamma\), \(c\), \(\omega\)) on the cavity expansion responses were further investigated. Herein, the soil property parameters in Table 1 and the state parameters in the case of \(R=1\) were chosen, and the calculation results were displayed in terms of effective stress path (ESP) in the \(p^{\prime}\)-\(q\) plane.  
The isotropic compression destructuring index \(b\) controls the decrease of the additional voids ratio \(\Delta e\) of structured soils, which represents the destructuration caused by the isotropic compression. As suggested by Carter and Liu [35], the values of \(b\) depend on soil structure and are generally \(b\geq 1\) for soft clay and \(b<1\) for stiff clays. Herein, \(b=0.25\), \(1\), and \(30\) were adopted to analyze the influences of \(b\) on the ESPs. The calculated results for \(b=0.25\), \(1\), and \(30\) were shown in Figure 14. As \(b\) increases, the ESP experiences a lower stress level, indicating that the stress release becomes more significant.  
The shearing destructuring index \(\gamma\) reflects the influences of shear stress on the destructuration of soils. For the several natural and artificial structured soils reported in Carter and Liu [37], the values of \(\gamma\) are in the range of 0.5-2.1. Herein, \(\gamma=0.5\), \(1\), and \(2\) are adopted to analyze its influences on the cavity expansion responses. The calculation results are displayed in Figure 15, from which the same variation trends as Figure 14 can be observed. Obviously, \(\gamma\) together with \(b\) make prominent contributions to the destructuration of soils.  
Figure 14: Effects of structural parameter \(b\) on the effective stress paths.  
Figure 15: Effects of structural parameter \(\gamma\) on the effective stress paths.  
The structural parameter \(c\) is defined as the stable value of the additional voids ratio \(\Delta e\) when \(p^{\prime}\) is sufficiently large during the isotropic compression. Actually, the soil structure is almost completely removed under extremely large pressure, which means that the value of \(c\) is very small. Taking the natural Eastern Osaka Clay reported in Adachi et al. [42] as an example, Carter and Liu [37] concluded that the value of \(c\) for this clay can be assumed as zero. The parametric study shows that little differences occurred between various \(c\) values.  
The structural parameter \(\omega\) describes the flow rule, which is normally taken as 1 when the experimental data is insufficient, as suggested by Carter and Liu [37]. Actually, the values of \(\omega\) also have little effect on the results.
## 1 Introduction  
High Reynolds number flows \(Re\gtrsim 10^{6}\) frequently appear in the aerodynamic design of aircrafts and spacecrafts. In the computational fluid dynamics (CFD), Reynolds-averaged Navier-Stokes (RANS) simulation achieves remarkable success in steady flows. However, RANS simulation is difficult to apply to unknown unsteady flows. Large eddy simulations (LES) are rather reliable in simulations of unknown unsteady flows.  
In the turbulent boundary layer of \(Re\gtrsim 10^{6}\) flows, LES struggles to satisfy the sufficient grid resolution requirement due to the vortex scale decreasing in the inner layer of the turbulent boundary layer, which is about 10% of the turbulent boundary layer thickness. [PERSON] and [PERSON] [1] estimated that the total number of the computational grid required in a wall-resolved LES is proportional to \(Re^{13/7}\).  
The near-wall treatment such as RANS-LES hybrid simulation [2]; detached eddy simulation (DES) [3]; and wall-stress model [4; 5; 6; 7; 8] have success with the prediction of the unsteady high Reynolds number flows. The wall-stress model using one-dimensional equations in wall-normal coordinates for turbulence boundary layer was introduced by [PERSON] and [PERSON] [5; 7; 8]. This model yields accurate wall shear stress and heat flux using instantaneous values from the LES. Shear stress and heat flux are provided to the LES in the form of flux across the wall in this model.  
The Cartesian cut-cell method [9; 10; 11; 12; 13] subdivides the object surface into small flat planes inside the cubical computational cells. In other words, these cells are divided by small cut planes into the fluid side and the object side. The interaction between the object and the fluid is given by wall flux vectors across the cut plane. Due to this sharp interface treatment and the robust grid generation of the Cartesian mesh, the Cartesian cut-cell method is one of the most promising methods in CFD. However, the Cartesian cut-cell method and other Cartesian mesh solvers [14; 15; 16] struggle to satisfy the required grid resolution for boundary layers because rectangular cells with an extremely large aspect ratio used in boundary fitted mesh are not available with these methods. To overcome this difficulty, an appropriate near-wall treatment is required. The wall-stress model by [PERSON] and [PERSON] is expected to be one of the most compatible wall treatment techniques with the Cartesian cut-cell method.  
The present study performed numerical simulations of the flow past a triangular column and 30P30N high-lift airfoil configuration using the Cartesian cut-cell method with the wall-stress model. The numerical results of the present study revealed the prediction capability of the wall-stress model to the Cartesian cut-cell simulation of unsteady flows.
###### Abstract  
Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to SME realities--particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the technology-organization-environment (TOE) framework with selected attributes from the diffusion of innovations (DOI) theory to examine adoption dynamics through a dual structural and perceptual lens. Empirical insights from sectoral and regional contexts are also incorporated. Ten critical challenges are identified and analyzed across the TOE dimensions, ranging from data access and skill shortages to cultural resistance, infrastructure limitations, and weak governance practices. Notably, the framework is expanded to incorporate responsible AI governance and democratized access to generative AI--particularly open-weight large language models (LLMs) such as LLaMA, DeepSeek-R1, Mistral, and FALCON--as emerging technological and ethical imperatives. Each challenge is paired with actionable, context-sensitive solutions. The paper is a structured, literature-based conceptual analysis enriched by empirical case study insights. As a key contribution, it introduces a structured, six-phase roadmap methodology to guide SMEs through AI adoption--offering step-by-step recommendations aligned with technological, organizational, and strategic readiness. While this roadmap is conceptual and has yet to be validated through field data, it sets a foundation for future diagnostic tools and practical assessments. The resulting study bridges theoretical insight and implementation strategy--empowering inclusive, responsible, and scalable AI transformation in SMEs. By offering both analytical clarity and practical relevance, this study contributes to a more grounded understanding of AI integration and calls for policies, ecosystems, and leadership models that support SMEs in adopting AI not merely as a tool, but as a strategic enabler of sustainable and inclusive innovation.  
artificial intelligence (AI); small and medium-sized enterprises (SMEs); technology-organization-environment (TOE) framework; diffusion of innovations (DOI); digital transformation; AI adoption challenges; innovation management
# Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
## 3 Experiment
### Feature Extractions
#### 3.2.1 Unigram  
We extracted the word features from the text to construct a learning model. The first step was to derive words after preprocessing. We used the Bag-of-Words technique to tokenize each word into a DocumentTermMatrix that indicates how many times the word appears in each article. The Bag-of-Words technique transforms the sentence into a set of numeric vectors as follows. First, the word set is built by collecting words from all sentences in all documents. Then, a sentence is expressed as the word counts in word order. For example, in \"symptoms appear after at least two\" and \"it was two years of unnecessary stress,\" these two sentences generate the word set, {symptoms, appear, after, at, least, two, it, was, years, of, unnecessary, stress}. Two sentences are expressed as the counts of each word as {1,1,1,1,1,1,0,0,0,0,0,0} and {0,0,0,0,0,1,1,1,1,1,1,1}, respectively. The numeric vector is derived for a document by aggregating the word count for all sentences in a document. The document term matrix (DTM) is represented in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gender** & **Numbers of Messages** & **Ratio (\%)** \\ \hline Male & 8954 & 40 \\ Female & 6078 & 27 \\ N/A & 7363 & 33 \\ Total & 22,395 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The gender information disclosed in an AIDS-related web forum.  
We extracted the document term matrix in two ways, the word occurrence and the occurrence frequency, to test which feature extraction method is efficient.  
To reduce the feature set, we set the threshold value to the number of appearances for extracting word features. We incrementally increased the threshold value from 5 to 15 by 5s and checked the classification performance.
## 4 Discussion
### Limitations and Future Research  
Despite the valuable results provided by this study, several limitations should be acknowledged to contextualize the findings and guide future research. First, the analysis focused exclusively on group-stage matches of the FIFA Club World Cup 2025, with knockout-stage fixtures deliberately excluded to avoid inconsistencies arising from extra-time periods. While this decision enhanced data consistency, it limited the generalizability of the findings to the full competition structure, particularly the high-pressure and tactically distinct knockout rounds. Second, although a wide range of technical, tactical, and physical performance indicators were examined, the analysis did not incorporate contextual, tactical, psychological, or environmental factors that may significantly influence match outcomes. For instance, home advantage, the presence or absence of fans, weather conditions, opponent strength, and travel demands were not considered, despite prior research suggesting their impact on team performance. Furthermore, team-specific tactical configurations, such as formation choices, in-game adjustments, or strategic pressing schemes, were not captured by the available data. Psychological variables such as player motivation, team cohesion, and leadership dynamics were also excluded, although these factors may be particularly influential in closely contested matches. In addition, the decision to dichotomize match outcomes into win versus non-win categories, while analytically practical, may have obscured significant differences between draws and losses. Finally, although the application of ROC curve analysis introduced a novel methodological dimension to football performance modeling, future research could expand this approach by incorporating advanced predictive techniques, such as machine learning algorithms, or adopting mixed-methods frameworks that integrate both quantitative and qualitative data. Such advancements would enhance the ecological validity, interpretability, and predictive capacity of performance models in elite football contexts.  
Future studies may benefit from conducting comparative analyses across club-level and international competitions, while also integrating gender-based perspectives to en rich understanding of contextual performance influences. The inclusion of player-level psychological metrics, such as decision-making efficiency, stress responses, mental fatigue, or team dynamics, alongside situational variables (e.g., home vs. away status, match importance, or climatic conditions), would offer a more holistic view of the determinants of success. Moreover, longitudinal designs following teams across multiple seasons or tournaments could shed light on how key performance indicators evolve in response to team restructuring, managerial strategies, or player development trajectories.
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Commercialization Perspective  
Efficiency and stability enhancement of OSC are the major challenges in the commercialization of OSC. For improvements in efficiency, several approaches are employed, such as broadening in the absorption of the solar light spectrum, exciton diffusion/dissociation, transport of charges, and collection of charges. On this front, organic materials with broad absorption ranges have been used, and tandem structures of materials have complementary absorption spectra. Improvement in the diffusion/dissociation efficiency can be improved by bulk heterojunction. The formation of nanoscale morphologies can improve the charge transport and charge collection. Moreover, inverted configurations cause interface stability and the degradation of the device and hence improve the overall stability of OSC.  
In the last decade, significant progress has been made in the field of OSC. Material design and chemistry of the input materials along with fabrication techniques are the major fronts to address in the context of commercialization. In the last couple of years, a major breakthrough in OSC technology was the achievement of more than 20% PCE. Thickness-intensive photovoltaic properties, high performance, air-insensitivity, green solvents, and no requirement of extra treatment are the required features in the context of commercialization. All of these features must be addressed well from a commercialization perspective. All-polymer OSCs are stable, use green solvents, and achieve a fairly high efficiency that makes them promising candidates for commercialization. Moreover, the introduction of a high-performance interface layer between the complementary sub-cells of tandem OSCs is a promising approach to achieve high efficiency. Generally, photovoltaic materials have complex chemistry and hence are expensive. The exploration of low-cost photovoltaic materials is one of the major interests in context of commercialization. The applications of OSC in portable devices and building-integrated materials will be a great evolution, and we anticipate OSC may constitute a major share in the future of commercial products for renewable solar energy generation [115].
## 1 Introduction
[14] modified the cross-based local support region construction by adding two additional thresholds and new constraint rules. On the contrary, the adaptive support weight approach, which was first presented by [PERSON] and [PERSON] [20], adaptively adjusts the support weights for pixels according to color similarity and spatial distance. Even though the adaptive support weight approaches achieved an outstanding performance, they suffered from high computational complexity. Therefore, several fast approximations of the bilateral filter [21; 22] were then developed, but at the price of quality degradation. In order to reduce the computational complexity without quality degradation, [PERSON] et al. [13; 23] and [PERSON] et al. [24] proposed to aggregate matching cost using the guided image filter (GF). Compared with the bilateral filter, the guided image filter proposed by [PERSON] et al. [25; 26] can preserve the edges better and can be implemented with linear computational complexity independent of the window size. The guided filter has demonstrated great potential in stereo matching applications that have limited computation resources, especially in the real-time systems [13; 27; 28]. Even though the guided filter-based cost aggregation methods can achieve good performance in many cases, it is still not satisfactory enough for discontinuous regions and textureless regions since its fixed square size support window used by the traditional guided image filter. In this paper, a local stereo matching algorithm with effective matching cost computation method and cost aggregation strategy was proposed. The proposed new matching cost combines enhanced image gradient-based matching cost with improved census transform-based matching cost and is proved to be more robust to radiometric changes. In matching cost aggregation step, we construct a cross-based adaptive shape support window for each pixel and implement a modified guided filter based on this adaptive shape region to improve the reliability of the disparity map, especially for low texture structures. The winner-take-all strategy is then carried out to obtain the optimal disparity for each pixel. Finally, multi-constraints-based disparity refinement is applied to further improve the disparity map and eventually get sub-pixel accuracy disparity map. The remainder of this paper is organized as follows. The proposed matching cost computation method and matching cost aggregation strategy are first described in Section 2. Section 3 presents the experimental results and discussions about the method and results. Finally, Section 4 concludes this paper.
## 1 Introduction
### Measurements of EMF Exposure  
The criteria for assessing exposure to EMF provided by international safety guidelines refer to the unperturbed EMF (and evaluated omnidirectional) in the location under consideration [8; 9; 11; 12; 13; 14]. Typically, standardized EMF measurements aimed at evaluating human RF EMF exposure are performed to determine the spatial distribution of the electric field (E-field) in the analyzed location, based on measurements using narrow- or broad-frequency band sensitive measurement equipment, typically employing small dimension ball-probes containing several E-field sensors to develop omnidirectional measurement results. Depending on the geometric configuration between the \"human body--EMF meter--EMF source\", deformations of EMF spatial distribution may cause an increase or decrease in the EMF level, in the locations where the measurements are taken. This phenomenon is observed because human organisms have greater conductivity than the air and both the person whose exposure is being assessed and the person performing the measurements, may influence the spatial distribution of the evaluated EMF. Requirements for minimizing this impact of the human body, which can significantly increase the uncertainty of the EMF exposure evaluation, are specified in standardized protocols regarding environmental measurements, e.g., EN 50413 [15]. Usually, keeping some distance between the measurement device (EMF-sensitive probe) and the nearest human body is advised. Applying measures minimizing the impact of the human body on EMF distribution is particularly important in exposure cases, when the spatial distribution of RF EMF is highly dependent on the location of humans and technical objects in the area under question, such as in exposure to low-MHz range (Figure 2).  
This standardized technique of evaluating EMF is particularly useful when EMF sources have a flat level of emission during the measurements, and when the location of the person whose exposure is being assessed is fixed towards EMF source. When workers are moving near EMF sources, assessing their exposure in the time domain is difficult. Determining the spatial distribution of EMF over a large area is also difficult. Personal  
Figure 2: An example of cross-section (frontal and sagittal plane) of spatial distribution of the E-field near an insulated human body model for the exposure scenario: transverse direction of EMF propagation (from right to left of the model), vertical E-field polarization (from top to bottom), at 100 MHz frequency; linear scale; orange color applied to the level of 10 V/m [authors archive].  
exposure monitoring using on-body EMF recorders (data loggers) can provide more reliable measurement results in both cases.
## 4 Simulation  
To verify the feasibility and effectiveness of the proposed SOC balancing control strategy, a two-terminal MMC-SCES transmission system was built in PSCAD/EMTDC using the efficient simulation method [42]. The system architecture is depicted in Figure 6. Active and reactive power control were applied to the MMC-SCES, while DC voltage and reactive power control were employed on the MMC. Additionally, a conventional circulating current suppression controller (CCSC) was implemented to mitigate the double-line-frequency circulating current within the system [41]. The system parameters are provided in Table 1. Furthermore, the traditional balancing method was utilized for comparison [24].  
Figure 6: Two-terminal transmission system with MMC-SCES.
## 3 Methods and Data
### Data  
The data used include climate, elevation, population, land use, and vegetation ecological water. The elevation data were from STRM. The rest of the data includes two parts, the details are as follows. All data were resampled to resolution of 1 km by the bilinear interpolation method.  
1. Climate. The temperature and precipitation data for the base period were obtained from ERA5-Land monthly average reanalysis data. As the study area is not large and the data needed for predicting future vegetation ecological water should include radiation, temperature, precipitation, wind speed, and humidity, the CNRM-CM6-1-HR model in the CMIP6 dataset under SSP1-2.6, SSP2-4.5, and SSP5-8.5 was chosen for including all the above elements and having a relatively small spatial resolution [(32)].
2. Land use. The land use data for the base period were obtained from the European Space Agency, the land type accounted for the largest proportion of a grid and was taken as the land use type for that grid. The land use data for the near future period and distinct future period under SSP1-2.6, SSP2-4.5, and SSP5-8.5 were from Land Use Harmonization 2 (LUH2) [(33)].
3. Population. The population data for the base period were sourced from the WorldPop population density dataset. According to Our World in Data, the population growth rate in Kenya from 2020 to 3030 is about 2.0%, from 2030 to 2059 is about 1.3%, and from 2060 to 2099 is about 0.3%. The population growth rate in Tanzania from 2020 to 3030 is about 2.8%, from 2030 to 2059 is about 2.1%, and from 2060 to 2099 is about 1.0%. The average population growth rate of Kenya and Tanzania was taken as the population growth rate of MRB. Therefore, the population growth rate of the MRB from 2020 to 3030 is about 2.4%, from 2030 to 2059 is about 1.7%, from 2060 to 2099 is about 0.7%, and the population density is shown in Table 3.  
Vegetation ecological water. The vegetation ecological water of the base period was from the research results [25]. The vegetation ecological water under SSP1-2.6, SSP2-4.5, and SSP5-8.5 of the near future period and distinct future period were calculated by the RF algorithm with elevation and CMIP6 data. The minimum R\({}^{2}\) in all scenarios is 0.74, indicating good results (Figure 2).