esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Technologies of 6G for Smart Grid Applications
### mmWave and THz Communication  
Furthermore, mmWave and THz technologies possess narrow beams and small wavelengths, leading to enhanced transmission speeds, increased throughput, improved spectral efficiency, and expanded capacity in 6G systems. In contrast, these waves have limited transmission distances.  
The basic concept behind using mmWave and THz-based cells is to offer enhanced data rates, energy efficiency, capacity, and spectral efficiency while reducing latency. Achieving this involves minimizing the distance between the users and the BS, thereby reducing propagation loss and interference, as well as ensuring high-quality connections. Capacity and quality of service (QoS) are increased by integrating mMIMO with small cells. On the other hand, additive white Gaussian noise (AWGN) is reduced [93]. In addition, THz communication provides low-latency and broadband applications, like virtual reality (VR), ultra-HD videos, and augmented reality (AR).
## 3 Results and Discussion
### Near-Phase-Transition Zone Delineation Based on NMR Testing Results  
The drastic change in unfrozen water content is a key reason for the intense variation in soil thermal conductivity in the near-phase-transition zone [46]. To reveal the temperature variation trends in unfrozen water in sandy soil, 10 groups of specimens were tested using NMR. The dry density of specimens varied from 1.86 g/cm\({}^{3}\) to 2.18 g/cm\({}^{3}\), with an average of 2.06 g/cm\({}^{3}\), and the water content range was 9.2%-21.3%, with an average of 14%.  
Figure 8 shows the measurement results of unfrozen water in sand specimens at different temperatures. It can be seen that the unfrozen water content in sand specimens changes drastically at temperatures around the near-phase-transition point. In this range, spanning about 3 \({}^{\circ}\)C, the unfrozen water content decreases from 15.5% of the initial water content to 2.9% (light red area in Figure 8). Considering the huge reduction in the proportion of unfrozen water, the soil thermal conductivity will change greatly in this narrow temperature range, so we define this temperature interval as the drastic phase transition zone. Conversely, at temperatures away from the phase transition point (light blue area in Figure 8), the proportion of unfrozen water is only 18.7% of the initial water content; thereby, it is defined as a stable phase transition zone.  
The relationship between unfrozen water content \((W_{u})\) and temperature (\(T\)) was fitted using the following formula:  
\[W_{u}=a_{0}|T|^{b_{0}}, \tag{11}\]  
where \(a_{0}\) and \(b_{0}\) are the fitting parameters.  
Figure 8: Test results of unfrozen water of sand specimen at different temperatures.  
Table 6 shows the results of fitting the relationship between the unfrozen water content and the temperature of the testing samples. It can be seen that the \(R^{2}\) of each sample is above 0.9, indicating the formula fits well. Furthermore, in order to determine the temperature limit (\(T_{\text{limit}}\)) of the drastic and stable phase transition zones, the slope at every temperature measurement point on the fitted curve was calculated. The temperature corresponding to the average slope was used as the temperature limit of the near phase and stable phase transition regions. As shown in Table 6, the range of \(T_{\text{limit}}\) is \(-3.24\)\(-\)\(-\)\(-\)\(4.04\)\({}^{\circ}\)C, and its average value is \(-3.5\)\({}^{\circ}\)C. Accounting for the rate of change in unfrozen water content can synchronously reflect the trends in soil thermal conductivity. Thus, in this work, the average temperature limit of unfrozen water was used as \(T_{\text{limit}}\) to analyze the thermal conductivity in the near-phase-transition zone.
# Seismic Assessment of Historical Masonry Buildings at Different Scale Levels: A Review
## 3 Small-Scale Approaches
### Finite Element Methods  
In the last few years, thanks to the huge development of IT tools, the numerical finite element method (FEM) has become increasingly important for evaluating the structural behaviour of existing structures--including historical buildings. Being generally flexible to reproduce a variety of situations, the FEM is an indeed appealing tool, even if, in the caseof complex situations such as for historical buildings, the accuracy is often combined with high computational effort and numerical issues.  
The FE approach includes, as is known, the two steps of modelling (FEM) the considered structure and analysing its behaviour (finite element analysis--FEA), which are investigated in the following.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 4 Concept
### Workflow Design  
Derived from the objectives and design principles developed above, Figure 1 was created. The workflow was created to explain the sequential operations from user input to final response delivery. This section examines each step of the Business Process Model and Notation (BPMN) model, providing a detailed understanding of the decision making processes and interaction with the databases. The process is initiated when a user enters a question. This is the primary interaction between the human and system, and triggers the chatbot's response mechanism. The chatbot internally generates a prompt that encapsulates the user's query, preparing the system for subsequent analysis and response. The system evaluates the chat history to determine if a similar query has been previously addressed, which optimizes the response time by avoiding redundant database queries. If the system finds a relevant instance in the chat history, the chatbot constructs an answer in natural text form, ready to be presented to the user. If the chat history does not provide a satisfactory response, the system moves on to assess the feasibility of a database query with available databases. This decision is crucial, since it involves choosing the correct database for executing queries. If the underlying databases are not sufficient to answer the questions, an \"Unsuccessful Response\" message is generated. However, if it is possible to answer with the underlying database, the respective database schema for constructing an accurate and efficient query is retrieved. Using the user's prompt and the retrieved database schema, the chatbot proceeds to generate a structured query. This query is tailored to retrieve the relevant information from the database in response to the user's initial question. If the query execution fails, the chatbot generates an error prompt and reproduces improved queries to retry with optimized Cypher queries. Upon a successful query execution, the chatbot generates an answer from the query result. This answer is then formatted into a natural text response that can be easily understood by the user. In case the chatbot cannot generate a successful response from either the chat history or the database query, it generates an unsuccessful response. The final step in the chatbot's operational process is presenting the response to the user. Whether the response is a direct answer, an error message, or a notification of an unsuccessful query, the system communicates the outcome clearly and effectively to the user, maintaining transparency in the interaction.  
Figure 1: Artifact: workflow of the chatbot.
## 3 Results and Discussion
### Physicochemical Properties of the Samples  
#### 3.2.1 FTIR Analysis  
FTIR was involved in exploring the surface nature of various samples. As shown in Figure 3, all of the samples exhibited six characteristic peaks in the range of 400 and 2000 cm\({}^{-1}\), suggesting no significant effect of loading Co-Fe mixed oxides on the surface functional groups of ZSM-5. As demonstrated in the previous research, the bands at 796 and 1233 cm\({}^{-1}\) are related to the symmetric stretching vibrations of Si-O-Si [36]. Moreover, the peaks at 448 and 1102 cm\({}^{-1}\) corresponded to the Si-O-Si asymmetric stretching vibrations. Conversely, the characteristic peak at 543 cm\({}^{-1}\) was assigned to the double six-membered ring, which indicates the existence of micropore structure in ZSM-5 [17; 36]. Moreover, the bending vibration of hydroxyl species accounted for the appearance of the peak at 1632 cm\({}^{-1}\)[37].  
Figure 3: FTIR spectra of a series of samples.  
Figure 2: (**a**) Removal of mercury over various samples; (**b**) calculation of adsorption capacity for the pseudo-first-order model and pseudo-second-order model.
# The Impact of Blenderized Tube Feeding on Gastrointestinal Symptoms, a Scoping Review
## 2 Methods
### Study Eligibility  
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist [12] represented in the Supplementary Table S2, and after removing duplicates, all full-text articles were screened by two independent researchers; any discrepancies were solved in a consensus meeting. The articles were included if (a) they reported GI symptoms in pediatric and adult populations fed via blenderized tubes, (b) were freely available, and (c) written in English. Articles only assessing the nutritional value of formulas and review articles were disregarded.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 4 Preventive and Corrective Lost Circulation Measures
### The Procedure of Spotting and Squeezing the Plug in the Thief Zone  
If mud loss occurs during drilling, it is first necessary to determine the severity of the losses and the location of the loss zone so the pill/plug can be efficiently placed where needed. After that, the implementation of corrective measures is approached. To cure severe or total losses in the case of fractured formations, different types of plugs/pills can be used, including cement, cement-bentonite, reverse gunk, cement-gilsonite, diesel-oil-bentonite-cement, thixotropic cement, a thermally activated oil gelling pill, a polymer-based cross-linked pill (PCP), a chemically activated cross-linked pill (CACP), or a High-Fluid loss, High-Strength (HF/HS) pill [1; 19; 22; 23; 36; 91; 97]. The generally recommended procedure of their pumping and squeezing into the thief zone is shown in Figure 6 and may vary slightly depending on the type of pill/plug used. If the zone of mud loss is also a production zone, pill/plug LCMs should be used that will not cause permanent damage to the production zone, i.e., those that are degradable or soluble in acid.  
Figure 5: Plugging the thief zone with the thixotropic cement slurry: (a) loss of mud during drilling, (b) pumping and spotting of a thixotropic cement slurry into thief zone, (c) the situation in the well after hardening of the cement slurry into set cement of sufficient strength to withstand various stresses in the future.
## 4 XAI Methods in Medicine  
### Classification of Explainability Approaches  
Several taxonomies have been proposed in the literature to classify XAI methods and approaches, depending on different criteria [40; 41; 42] (Figure 3).  
First, there is a clear distinction between auxiliary techniques that aim to provide explanations for either the model's prediction or its inner workings, which are commonly called post hoc explainability methods, and AI models that are intrinsically interpretable, either because of their simplicity, the features they use or because they have a straightforward structure that is readily understandable by humans.  
Secondly, we can distinguish interpretability techniques by their scope, where explanations provided by an XAI approach can be local, meaning that they refer to particular predictions of the model, or global, if they try to describe the behaviour of the model as a whole.  
Other differentiations can be made between interpretability techniques that are model specific, because they have requirements regarding the kind of data or algorithm used, and model agnostic methods, that are general and can be applied in any case. Intrinsically interpretable models are model specific by definition, but post hoc explainability methods can be generally seen as model agnostic, though some of them can have some requisites regarding the data or structure of the model.  
More classifications can be made regarding how characteristics of the output explanations are displayed (textual, visual, rules, etc.), the type of input data required, the type of problem they can be applied to [42] or how they are produced [30].  
However, since these classifications overlap [43], in the following sections we have chosen to go over the different techniques included in the reviewed literature structured under the most popular taxonomy: interpretable models and post hoc explainability methods (RQ1).
## 3 Results
### Spatial and Temporal Distribution of Grassland NPP in Inner Mongolia
#### 3.2.3 The Change Trend Distribution of Grassland NPP
These three places maintained a significant growth trend from 2000 to 2010 and 2010 to 2020. Furthermore, the area of grassland NPP decline greatly reduced during this period, with less than 1% of the grassland showing a significant decreasing trend. The grassland NPP growth status greatly improved in Xilingol and along the Greater Khingan Range. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Study Period** \\ **(Years)** \\ \end{tabular} } & \multicolumn{2}{c}{**OLS Method**} & \multicolumn{2}{c}{**Theil–Sen Method**} \\ \cline{2-5}  & **Growth Rate** & \multirow{2}{*}{_p-Value_} & **Growth Rate** & \multirow{2}{*}{_p-Value_} \\  & **(g C/(m\({}^{2}\).yr))** & & & \\ \hline
2000–2010 & 0.27 & 0.85 & \(-\)0.47 & 1.00 \\
2010–2020 & 3.14 & 0.22 & 2.55 & 0.28 \\
2000–2020 & 2.43 & \textless{}0.01 & 2.16 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Growth trend of grassland NPP in the different periods based on the two-tailed significance test method.
## 4 Discussion and Conclusions  
### Challenges to Fielding a UA Capable of A3R  
While SWaP-C decreases for many of the sensors discussed in this paper and the capabilities of CV systems continue to increase, the possibility of a UA conducting A3R in the future is not a question of if but when. The current DoD acquisition lifecycle consisting of requirements, contracts, development, test and evaluation (T&E), deployment, and sustainment is not optimized to expeditiously acquire a LES, such as the CV system required for a UA to complete A3R. In 2022, the Department of Air Force/Massachusetts Institute of Technology (DAF/MIT) AI Accelerator program published a guidebook to AI Acquisitions, which represented one of the first DoD guidebooks to address many of the concerns related to acquiring a LES [106].  
Specifically addressing the T&E portion of the acquisition lifecycle, many challenges exist for adequately evaluating the performance of a UA conducting A3R to include the complexity of the system; safety; security; and lack of methods, tools, and infrastructure, policy, standards, or metrics [107]. In 2020, the USAF published their first guidance to address many of these issues [53], but additional research will be required to create manuals for all services within the DoD.  
Luckily, different government organizations, such as the European Union Aviation Safety Agency (EASA), have begun publishing concepts for assuring neural networks, which could be used to provide perception to a UA [91]. Additionally, civilian standard organizations that include the American Society for Testing and Materials (ASTM) [52] andSociety of Automotive Engineers (SAE) International [108] have begun to address many of the same concerns the DoD has identified as well.  
While, from a sensor standpoint, a UA capable of A3R could very well happen in the near future, extensive research must be completed, and challenges addressed prior to the successful fielding of a UA capable of A3R. Given the severity to mission success of a UA failing to complete A3R, resources must be committed immediately to begin to address the challenges noted above and to ensure a successful acquisition.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 5 Technological Innovation of Probiotics in Functional Foods
### Biofilm Delivery Systems  
Biofilm delivery systems represent an innovative probiotic delivery method that overcomes traditional dosage form limitations by leveraging microbial self-assembly properties to construct protective carriers, significantly enhancing application efficacy and safety [146]. The formation of probiotic biofilm delivery systems involves multiple key stages. The initial stage involves the colonization of probiotics on specific surfaces, mediated by surface proteins and polysaccharides, which facilitate preliminary adsorption and attachment. Subsequently, bacteria synthesize and secrete extracellular polymers (EPS), which intertwine to form a complex extracellular matrix. This matrix provides structural support for the biofilm and forms a physical barrier to protect internal bacterial cells. As the biofilm matures, cells communicate via QS mechanisms to synergistically regulate the synthesis of EPS and other key components, thereby optimizing biofilm stability and functionality. During biofilm development, an internal network of water channels often forms to facilitate nutrient penetration and metabolic waste excretion, thereby maintaining internal homeostasis [147]. In the food industry, [PERSON] et al. [148] successfully applied biofilm-state _Lactobacillus_ from dynamic cultures to ice cream production, resulting in 35% improved product hardness, a 28% reduced melting rate, and the maintenance of \(>\)10\({}^{7}\) CFU/g viability after 28 days of storage at 4 \({}^{\circ}\)C compared to traditional free-state strains, thereby quantifying biofilm's freezing tolerance. EPS in biofilms regulates the immune response of macrophages by interacting with TLR2 and TLR4 on the macrophage surface. This interaction suppresses the production of pro-inflammatory cytokines, such as TNF-\(\alpha\), thereby alleviating inflammatory responses, regulating the intestinal microenvironment, enhancing the barrier function of the intestinal mucosa, improving intestinal permeability, and promoting overall intestinal health [149]. With the aim of achieving lasting NEC protection, offering a new paradigm for functional food development and the clinical treatment of necrotizing enterocolitis in preterm infants, [PERSON] et al. [150] enhanced biofilm formation in _L. reuteri_ using dextran microspheres loaded with maltose, achieving a reduction in morbidity from 45% to 18% in a single-administration experimental model and a 62% decrease in the intestinal permeability index, while preserving intestinal flora alpha diversity. This study demonstrates that single biofilm-state probiotic administration can provide long-term therapeutics through the biofilm's physical barrier effects, metabolite slow-release properties, and environmental response mechanisms.
## 3 Results and Discussion
### Reduced Model Equation for Phenol Conversion (\(Y_{1}\)) and Hydrogen Yield (\(Y_{2}\))
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Response** & **Model terms** & **Effect** & **Coefficient** & **F-Ratio** & \(p\)**-Value** \\ \hline \multirow{8}{*}{Hydrogen yield (\%)} & Model Equation (9) & - & 15.7346 & 60.13 & 0.000 \\  & \(A\) & 8.806 & 0.00426 & 39.93 & 0.000 \\  & \(B\) & \(-6.519\) & \(-3.82292\) & 21.88 & 0.000 \\  & \(C\) & 4.431 & 22.1563 & 10.11 & 0.004 \\ \cline{1-1}  & \(D\) & \(-7.644\) & 0.61562 & 30.08 & 0.000 \\ \cline{1-1}  & \(E\) & 19.644 & 1.35042 & 198.67 & 0.000 \\ \cline{1-1}  & \(AE\) & 5.019 & 0.004182 & 12.97 & 0.002 \\ \cline{1-1}  & \(BE\) & \(-3.581\) & \(-2.98437\) & 6.6 & 0.017 \\ \cline{1-1}  & \(CE\) & \(-5.506\) & \(-1.37656\) & 15.61 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The regression coefficient value of the reduced model for \(Y_{2}\). Figure 8: Parity charts of predicted versus observed responses for the reduced models of phenol conversion (\(Y_{1}\)) (**a**) and hydrogen yield (\(Y_{2}\)) (**b**) with a 95% prediction band. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Response** & **Model Terms** & **Effect** & **Coefficient** & **F-Ratio** & \(p\)**-Value** \\ \hline \multirow{8}{*}{Phenol conversion (\%)} & Model Equation (8) & - & \(-1.87079\) & 121.1 & 0.000 \\  & \(A\) & 17.704 & 0.0062524 & 78.52 & 0.000 \\  & \(B\) & \(-12.201\) & \(-3.13\) & 37.29 & 0.000 \\  & \(C\) & 8.478 & 4.9969 & 18.01 & 0.000 \\ \cline{1-1}  & \(D\) & \(-11.43\) & 0.23866 & 32.73 & 0.000 \\ \cline{1-1}  & \(E\) & 41.859 & 1.18218 & 438.95 & 0.000 \\ \cline{1-1}  & \(AE\) & 10.552 & 0.00879326 & 27.89 & 0.000 \\ \cline{1-1}  & \(BE\) & \(-7.508\) & \(-6.25647\) & 14.12 & 0.001 \\ \cline{1-1}  & \(CE\) & 4.986 & 6.23214 & 6.23 & 0.021 \\ \cline{1-1}  & \(DE\) & \(-7.779\) & \(-1.94471\) & 15.16 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The regression coefficient value of the reduced model for \(Y_{1}\).
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
###### Abstract
[32] explored multiscale image patching centered on anatomical landmarks to improve training efficiency, while [PERSON] et al. [33] proposed a hybrid network that extracts multi-level discriminative features from entire MRI volumes, balancing global and local structural information. Some approaches have focused on isolating specific brain structures prior to classification. [PERSON] and [PERSON] [34], as well as [PERSON] and [PERSON] [35], investigated targeted regions, such as the hippocampus, to enhance AD and MCI differentiation. [PERSON] and [PERSON] [36] introduced a sparse regression module to identify critical cortical areas, such as the posterior temporal lobe, prior to DL-based classification, improving accuracy through spatially constrained feature extraction. Although effective, these targeted methods often involve complex segmentation steps that may limit scalability. Despite these advances, significant challenges persist in deploying 3D DL models in clinical contexts. These include high computational costs, memory demand, and the need for large labeled datasets. Many current models also underutilize complementary features across views, extracting data from isolated patches, which may result in loss of global context. To address these trade-offs, our study focuses on optimizing 2D CNN architectures for efficiency and generalizability while laying the foundation for future integration of volumetric and attention-based techniques. Although 2D CNNs offer a practical and high-performing baseline for dementia classification using MRI, the field is rapidly evolving toward more sophisticated architectures that integrate spatial, temporal, and multimodal information. As research progresses, future models are likely to incorporate attention mechanisms, region-specific modeling, feature fusion strategies, and 3D analysis to further enhance diagnostic accuracy and clinical applicability.