esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Use Case Study: [PERSON]'s Healthcare Monitoring
### Scenario Overview  
[PERSON]'s array of IoT healthcare devices are integral to her daily health monitoring routine. They capture critical data, such as heart rate, blood pressure, and body temperature. These data are then fed into SIM's Semantic Annotation Component, which utilises a specialised model to assign relevant medical terminology to the raw data, thereby transforming them into structured, semantically annotated data. For example, heart rate readings might be categorised as \"Normal\", \"Elevated\", or \"Low\" based on predetermined thresholds. Upon structuring and annotating [PERSON]'s data, the Data Security and Encryption Module encrypts the information, ensuring its secure transfer to the Blockchain Component. The data are securely stored and safeguarded against unauthorised access and modification. When [PERSON]'s health monitoring app needs to access [PERSON]'s data, it queries the Semantic Query Module. This module utilises a semantic query language to search through the blockchain-stored annotations and retrieve relevant data. For example, it could search for instances where [PERSON]'s annotated heart rate data are labelled as \"Elevated\". The Semantic Analysis and Inference Engine analyses the queried data. For example, continuous instances of \"Elevated\" heart rate could indicate a potential cardiac risk. Insights derived by the analysis engine are then transferred to the Service Integration and Composition Module. This module composes relevant services based on these insights. For instance, if a potential cardiac risk is detected in [PERSON]'s health data, it could trigger a service to alert [PERSON]'s emergency response team and send a health advisory to [PERSON] via [PERSON]'s app. All the information, from the original data to the final insights and services, is visualised using the Semantic Visualisation Component. This allows [PERSON], [PERSON], [PERSON], and [PERSON] to visually monitor the process and outcome, aiding in understanding and decision-making. Lastly, the AI Feedback and Analysis Module captures user feedback and preferences to improve the system. Feedback from [PERSON] about her health monitoring experience, [PERSON] on the app's performance, [PERSON] on emergency response efficiency, and [PERSON] on data analysis is collected and used to refine the system's operations. Figure 5 shows a diagram illustrating how [PERSON]'s data are collected and processed by different modules of SIM to deliver services or trigger alerts. To further demonstrate the practical application of SIM in this scenario, several algorithms that act as a blueprint for implementation are provided. These algorithms outline the step-by-step operational workflow of SIM, showcasing its functionality. Algorithm 1 provides a high-level pseudo-code for SIM implementation for healthcare monitoring. Figure 6 provides a visual guide to the system's process, from data collection to continuous monitoring and improvement.
## 4 Results and Discussion  
### Comparing the Calculated GSI Values to the Chart Values  
Using the entire database, the two RMR-based different equations of GSI determination (Equations (1)-(5)) and GSI field values were analyzed, based on the observations  
Figure 4: (**Left**): near-laminated weathered siltstone with siltstone-siltstone bedding planes (Telfer Gold mine, Australia). (**Right**): planar failure in fresh quartzite with quartzite-quartzite bedding planes (Telfer Gold Mine, Australia). Both have bedding scale anisotropy [28].  
Figure 3: Examples of rock mass classes in various tunnel sections: Chamber bench \(\sim\) 60 m\({}^{2}\) (class **II**), Chamber top heading \(\sim\) 60 m\({}^{2}\) (class **III**), Chamber half top heading 30 m\({}^{2}\) (class **IV**), Research tunnel 33 m\({}^{2}\) (class **V**) (granitic rocks, Bâtaapáti, Hungary).  
during the excavation of the Bataapati radioactive waste repository. Using the two RMR-based equations, Equations (1) and (3), different correlations were found (Figures 5 and 6). Using Equation (1), the obtained values are always higher than the chart values (\(\mathrm{GSI_{chart}}\)). There is a linear relationship between the two values:  
\[\mathrm{GSI_{chart}}=0.793\;\mathrm{GSI_{calc(1)}}+5.974\;(\mathrm{R^{2}}=0.736) \tag{6}\]  
Using Equation (3), the calculated points are nearly equal to the chart points:  
\[\mathrm{GSI_{chart}}=0.932\;\mathrm{GSI_{calc(2)}}-4.166\;(\mathrm{R^{2}}=0.814) \tag{7}\]  
Figure 5: Correlations between Chart and calculated GSI values using Equation (1).  
Figure 6: Correlations between Chart and calculated GSI values using Equation (3).
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation  
In this section, we first present the implementation of our solution in the scenario of a full 5G network, where the gNB is responsible for the IPv6-SRv6 encapsulation. Then, we describe a performance evaluation of the proposed solution, comprising a scalability analysis, an evaluation of the performance in static conditions, and the results in a scenario with container migration.
## 3 FC and FLC  
The FL system considers values between true and false, rather than being confined to binary extremes, thus enhancing traditional logic frameworks. It can process both linguistic and numerical data by mapping from input vectors to a singular output value via an online algorithm. Unlike conventional binary logic, which operates strictly at 0 or 1, FL functions across a continuum from 0 to 1. This allows for more nuanced interpretations of data, aligning well with the mimicking complex, nonlinear relationships and managing situations with varying degrees of certainty. Consequently, FL offers a closer approximation to human reasoning and decision-making processes. Also, it proves beneficial in a variety of applications.  
Fuzzy control (FC) emerges as the predominant field of application. The FLC process is initiated with fuzzification, in which crisp numerical input values are transformed into fuzzy sets based on predefined linguistic variables. This transformation enables the system to manage imprecision and ambiguity effectively. Such a step is crucial for translating real-world sensor data into formats that the FLC can interpret in terms of degrees of truth, rather than binary on/off states. The inference engine assesses the fuzzy inputs along with the rule base, which consists of a collection of if-then rules encoding expert knowledge and decision logic. These rules determine the output of the controller in fuzzy terms, taking into account all possible input combinations and their respective actions.  
Finally, the defuzzification mechanism transforms the set of fuzzy outputs into a crisp value, which is subsequently utilized for control purposes. This output is a single value representing the aggregated result of the fuzzy inference process, optimized to achieve the desired control objective. Consequently, the FLC can make decisions that mimic human reasoning, facilitating sophisticated control strategies in complex and nonlinear systems [21; 22; 23; 24].
## 3 Results and Discussion
### Overview of Metabolites Discovered in Filed Soil (Contaminated Site)
#### 3.2.2 Secondary Metabolites, Antibiotic, and Therapeutic Materials in the Chemical Accident Site  
The carbamoyl phosphate in purine metabolism and streptomycin 6-phosphate in streptomycin biosynthesis was discovered in field soil (Table 4). According to [27], antibiotic synthesis such as streptomycin is related to purine metabolism. Streptomycin metabolism commonly happens with the signaling molecules, (p)ppGpp (guanosine- pentaphosphate) and GTP (guanosine tetra- and pentaphosphates). The purine metabolism induces the signaling materials accumulated by enzymes of purine metabolism aiming (p)ppGpp activation. Moreover, Streptomyces avermitilis joining in streptomycin biosynthesis also was discovered in field soil. These metabolites founded in field soil prove a metabolic and biological relationship. The 2\({}^{\prime}\)-deoxyiosine 5\({}^{\prime}\)-phosphate and streptomycin 6-phosphate can be secondary metabolites showing a trace of toluene contamination. Moreover, another antibiotic metabolite, 2-hydroxy-beta-keto-L-tyrosineyl-[pcp], participating in ciprofloxacin and novobiocin biosynthesis, was also founded in the field soil. Acarbose is an important therapeutic chemical for diabetes (Table 4). Acarbose, a complex oligosaccharide is one of the prominent \(\alpha\)-glucosidase inhibitors generally for type II diabetes therapy, produced by the bacteria genus, actinoplanes, and streptomyces, which were discovered in field soil in this study [27]. This metabolite seems a product of stress responses because it has been studied that Acarbose is a secondary metabolite unrelated to cell growth [28; 29]. This material might be produced as a result of Acarbose and validamycin biosynthesis metabolism. This result gives the drug researchers a chance to isolate therapeutically valuable antibiotic compounds when bacteria encounter chemical stress and toxicity.
###### Abstract
Most existing works are either limited to transmission-level systems or assume simplified balanced conditions [2; 10; 22], overlooking the unique challenges posed by unbalanced, three-phase distribution networks. Furthermore, comprehensive benchmarking across multiple test feeders is often lacking [11; 17; 19], and performance under realistic conditions--such as measurement noise and partial observability--is not often explored in depth [11; 24]. This paper addresses these gaps by developing a PINN-based framework specifically tailored for unbalanced distribution systems and systematically evaluating it on four IEEE benchmark feeders: IEEE13, IEEE34, IEEE37, and IEEE123. In contrast to previous studies that focus on balanced or transmission-level systems, the proposed approach explicitly handles multi-phase unbalanced networks and is validated across diverse grid topologies and operating conditions. The model's performance is assessed not only in noise-free conditions but also under corrupted inputs and incomplete measurement scenarios--two realistic challenges often overlooked in prior PINN applications. Comparative analysis against baseline machine learning models highlights the superior accuracy, robustness, and generalization capabilities of the proposed PINN approach, providing valuable insights into its practical applicability for real-world distribution network monitoring.
# Elastic Modulus Prediction Model of Foamed Concrete Based on the Walsh Formula
## 6 Conclusions  
In this paper, an elastic modulus prediction model of foamed concrete is established based on the derivation of the Walsh formula and inclusion theory. The research can be mainly summarized as the following points:  
1. According to the internal structure characteristics of foamed concrete, the calculation formula is established for elastic modulus of spherical shell by introducing the Walsh formula and spherical pore hypothesis. Moreover, the elastic modulus prediction model of foamed concrete is constructed based on the two-layer embedded model in inclusion theory.
2. The uniaxial compression test of foamed concrete is carried out, and the proposed model is compared with the test data and the existing theoretical models. The verification results show that the elastic modulus values calculated using the proposed model are consistent with the experimental data. The values are also in good agreement with the distribution law of the calculation results calculated by using other theoretical models.
3. The effects of porosity, Poisson's ratio of matrix, and elastic modulus of matrix on the elastic modulus of foamed concrete are analyzed by the proposed model. The results show that the elastic modulus of foamed concrete decreases with the increase  
Figure 16: Variation law of the elastic modulus of foamed concrete with the elastic modulus of matrix.  
in porosity and matrix Poisson's ratio, and increases with the increase in matrix elastic modulus.  
Conceptualization, F.L. and J.H.; methodology, F.L.; validation, F.L. and J.H.; formal analysis, Z.Z. and M.L.; investigation, J.Z.; data curation, F.L. and J.Z.; writing--original draft preparation, F.L.; writing--review and editing, J.H.; supervision, Z.Z. and M.L.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 50908234, and Hunan Provincial Natural Science Foundation of China, grant number 2020 JJ4743.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Buckling Analysis of Piles in Multi-Layered Soils
###### Abstract  
Pile buckling is infrequent, but sometimes it can occur in slender piles (i.e., micropiles) driven into soils with soft layers and/or voids. Buckling analysis of piles becomes more complex if the pile is surrounded by multi-layered soil. In this case, the well-known [PERSON]'s solution for pile buckling is of no use because it refers to single-layered soils. A variational approach for buckling analysis of piles in multi-layered soils is herein proposed. The proposed method allows for the estimation of the critical buckling load of piles in any multi-layered soil and for any boundary condition, provided that the distribution of the soil coefficient of the subgrade reaction is available. An eigenvalue-eigenvector problem is defined, where each eigenvector is the set of coefficients of a Fourier series describing the second-order displaced shape of the pile, and the related buckling load is the eigenvalue, thus obtaining the effective buckling load as the minimum eigenvalue. Besides the pile deformed shape, the stiffness distribution in the multi-layered soil is also described through a Fourier series. The Rayleigh-Ritz direct method is used to identify the Fourier development coefficients describing the pile deformation. For validation, buckling analysis results were compared with those obtained from an experimental test and a finite element analysis available in the literature, which confirmed this method's reliability.  
pile buckling; multi-layered soils; variational approach; Fourier series; Rayleigh-Ritz method; soft soils +
Footnote †: journal: Applied Sciences
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process
### Optimization Algorithm  
The algorithm of the MDP can be applied to update the policy of the mobile node, as shown in Algorithm 1. Each mobile node initializes its policy and transmits its state \(S\) to the edge server. The edge server calculates the reward function \(R\) and the constraint function \(C\) for each mobile node. Then, the edge server solves the LP to derive the optimization solution. The results are inserted into \(A\) and sent to the mobile node. This process is repeated for all the mobile nodes until all the optimization solutions are acquired.  
```
1:Initialize the policy for the agent
2:for\(S\)do
3:\(S=[get(D),get(M)]\)
4:\(r(S,A)\) = RewardFunction()
5:\(c(S,A)=[getSINR]\)
6: Solve \(R(S,A)\) and \(C(S,A)\) by the LP
7:\(List\_add[R(S,A)]\) // Add a result of the LP to List
8:endfor
9:\(A^{*}=Solution(List)\)
10:for\(A\)do
11: Send \(A\) to the agent // Send a result for ResourceAllocation
12:endfor
```  
**Algorithm 1** MDP algorithm
## 2 PV Model Modeling and Problem Formulation
### PV Models' Objective Function  
The primary objective of the extraction phase in the determination of coefficients for various PV models using optimization algorithms is to evaluate the effectiveness of each parameter and identify the most optimal value of each parameter for the specific problem being studied. This method aims to minimize the difference between estimated and measured current data by minimizing error as much as feasible. In order to evaluate the proposed coefficients, the objective function must be established and optimized. The objective function is designed to minimize the difference between the estimated and measured current values to the maximum extent possible. The utilization of the root mean squared error (RMSE) as the objective function is the primary focus of this study. The RMSE is a metric used to evaluate the level of dispersion among predicted and measured results [8; 28].  
\[RMSE(\varkappa)=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\Bigl{[}(f(V_{i},I_{i},\varkappa)- \hat{f}(V_{i},I_{i},\varkappa)\Bigr{]}^{2}}. \tag{10}\]  
In the given context, symbol \(N\) represents the measured current, \(\varkappa\) denotes the coefficients that are to be differentiated, and \(f(V,I,\varkappa)\) represents the error function. In the context of the SDM framework, the error function is formulated as follows:  
\[f(V,I,\varkappa)=I_{ph}-I_{sd}\biggl{[}exp\biggl{(}\frac{V+R_{s}\cdot I}{ \gamma\cdot V_{i}}\biggr{)}-1\biggr{]}-\frac{V+R_{s}\cdot I}{R_{sh}}-I, \tag{11}\]  
where \(\varkappa=[I_{ph},I_{sd},\gamma,R_{s},R_{sh}]\). For the DDM, the error function is presented as  
\[f(V,I,\varkappa)=I_{ph}-I_{sd_{1}}\biggl{[}exp\biggl{(}\frac{V+R_{s}\cdot I}{ \gamma_{1}\cdot V_{i}}\biggr{)}-1\biggr{]}-I_{sd_{2}}\biggl{[}exp\biggl{(} \frac{V+R_{s}\cdot I}{\gamma_{2}\cdot V_{i}}\biggr{)}-1\biggr{]}-\frac{V+R_{s }\cdot I}{R_{sh}\cdot N_{s}}-I, \tag{12}\]  
where \(\varkappa=[I_{ph},I_{sd_{1}},I_{sd_{2}},\gamma_{1},\gamma_{2},R_{s},R_{sh}]\). Finally, the algorithm with the lowest RMSE value is considered preferable.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 4 Simulation Results
### Comparison with the State-of-the-Art Methods
#### 4.2.4 Scenario 4  
In this scenario, we set the parameters of the measurement error distributions as \(\mu_{m}=6\,\)deg, \(\mu_{p}=6\,\)deg, and \(\mu_{n}=4\,\)dB, and the time steps of the sequential measurement as \(T=5\). Figure 7 shows the RMSEs versus the number of anchor nodes, \(N\). From this figure, we can see that the RMSEs of all the considered methods decrease when \(N\) increases. Furthermore, one can see that more anchors lead to better performance for all algorithms, as they provide more reliable information. It also demonstrates the advantage of using combined measurements in hybrid systems over using single measurements in traditional systems.
## 3 Obstacle Vertex Extraction Algorithm Based on Equivalent Path
### Obstacle Vertex Extraction Algorithm (OVEA)
#### 3.3.2 Oblique-Axial Movement  
For oblique-axial movement, we can consider it as the reverse of axial-oblique movement. Due to the reversibility of the path, the obstacle vertices encountered during a forward and backward movement along an SGP are the same. Suppose in an SGP, from grid B to grid M, there are k consecutive oblique movements \(\{\text{m}_{1},\text{m}_{2},\dots,\text{m}_{\text{k}}\}\) and l consecutive axis movements \(\{\text{n}_{1},\text{n}_{2},\dots,\text{n}_{\text{l}}\}\). By reversing this sequence, the path is transformed into one from grid M to grid B: \(\{\text{n}_{1},\text{n}_{1-1},\dots,\text{n}_{1},\text{m}_{\text{k}},\text{n} _{\text{k}-1},\dots,\text{m}_{1}\}\), as shown in Figure 8. The method for axial-oblique movement described in 3.3.1 can then be applied to extract the obstacle vertices encountered and add them to the candidate point set in reverse order.
## 4 Results and Discussion
### Surface Finish  
#### 4.2.1 Profilometry Measurements  
Surface roughness was quantified using profilometry scans on flat plates (\(100\times 100\times 5\,\mathrm{mm}\)). Figure 2 plots the arithmetic mean roughness (\(R_{a}\)) for each material:  
* [noitemsep]
* FDM (PLA): \(R_{a}=12.1\pm 1.2\,\mathrm{\SIUnitSymbolMicro m}\)
* FDM (ABS): \(R_{a}=13.5\pm 1.4\,\mathrm{\SIUnitSymbolMicro m}\)
* SLA (Resin): \(R_{a}=2.2\pm 0.3\,\mathrm{\SIUnitSymbolMicro m}\)  
SLA exhibited significantly smoother surfaces than FDM, with \(R_{a}\) values approximately 80% lower. Statistical analysis (t-test, \(\alpha=0.05\)) confirmed that the difference in \(R_{a}\) between SLA and each FDM group was highly significant (\(p<0.001\)). This result underscores SLA's advantage in applications requiring fine surface finishes, such as molds, dental models, or aesthetic prototypes.
# Worldwide Coverage Mobile Systems for
## 4 Ku-Band Antennas Design Techniques
### Double Focus Antenna --Offset Gregory  
This antenna is shown in Figure 5. The structure of this antenna includes an additional sub-reflector that improves its features showing a higher efficiency and gain. For example, the efficiency of a double focus offset Gregory with an offset of 90 cm is equivalent to that achieved by a regular offset antenna whose offset is 110 cm.  
One of the drawbacks of Gregory offset antennas is the higher implementation cost. On the other hand, the design with much more severe specifications provides around 12% of additional gain. Despite its size being reduced as compared to the regular offset antenna, it is not a realistic candidate for world-wide coverage mobile communications. As a design example of this kind of antenna, Table 10 introduces the most relevant specifications (Technical data of a Masterfocus/Fibo antenna).  
Figure 4: Toroidal antenna.
## 3 Experiment I: Minimal SBR for Full Sentence-Level Intelligibility
### Results  
Of the 14 participants, two did not complete all the conditions due to slow progress; hence their data is excluded from the results presented below. Data of another participant was also discarded after an outlier analysis. Therefore, the mean \(\text{SBR}_{min}\) levels shown in Figure 3 as open circles are based on the remaining 11 participants. The measured \(\text{SBR}_{min}\) levels fall into the range of \(-9.4\) to \(3.5\) dB across all backgrounds and talkers. A two-way repeated measures ANOVA with within subject factors (5 background sounds \(\times\) 4 talkers) revealed a significant effect of both background type [\(F(4,40)=52.670,p<0.001,\eta^{2}=0.538\)] and talker [\(F(3,30)=67.694,p<0.001,\eta^{2}=0.594\)], as well as a significant two-way interaction between background type and talker [\(F(12,120)=2.665\), \(p<0.01,\eta^{2}=0.130\)]. This suggests that the minimum SBR which listeners require varies significantly across various background sounds and speech content produced by different talkers.  
Post-hoc pairwise comparisons with Bonferroni correction further confirmed the visual impression that stationary backgrounds (SSN and BAB) lead to higher \(\text{SBR}_{min}\) than fluctuating background (CS) for speech produced by all talkers [all \(p<0.05\)], except for M8. This is consistent with the findings reported in the literature (e.g., [11; 13]). Furthermore, by removing the vocal track from SNG, which to some extent functions as competing speech and potentially introduces both energetic and informational masking to intelligibility, \(\text{SBR}_{min}\) for all talkers in MSC substantially decreased compared to that in SNG [all \(p<0.01\)] with M8 being an exception. \(\text{SBR}_{min}\) for the speech of M8 did not vary as dramatically as for other talkers across different backgrounds presumably because of its low intrinsic intelligibility  
Figure 3: Measured (circle) and predicted (cross) lowest speech-to-background ratio (\(\text{SBR}_{min}\)) in speech-shaped noise (SSN), babble (BAB), competing speech (CS), song (SNG) and the same song with vocal track being removed (MSC), grouped by talker. Error bar indicates 95% confidence interval of the mean. Predicted \(\text{SBR}_{min}\) corresponds to BiDWGP of 0.5 in each condition.  
that had been observed in the pilot test, leading to the relatively high \(\text{SBR}_{min}\) at which the intelligibility to listeners usually converges. Comparing across the four talkers, it is evident that speech produced by M8 had a higher \(\text{SBR}_{min}\) than other talkers in SSN, CS and MSC backgrounds [all \(p<0.05\)].