esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Operationalization of the XR Framework for Safety Management
### Validation Through Implementation Examples  
The validation of the proposed XR framework was conducted through a series of illustrative implementation examples integrated throughout this section. These examples represent practical scenarios that reflect typical safety management tasks across the construction lifecycle, allowing the framework to be tested in real or simulated contexts. Each example demonstrates the applicability of specific components of the framework--ranging from BIM model integration, safety data structuring, and planning coordination, to immersive walkthroughs, safety training, and real-time inspection using AR. The examples were selected to cover different project stages (design, planning, execution, and monitoring), various XR technologies (VR, AR, and MR), and a range of safety-related activities (e.g., hazard identification, PPE verification, and site inspection).  
By applying the framework to these diverse cases, its internal coherence, logical structure, and adaptability to different project types and digital maturity levels were assessed. The implementation examples also served to verify the technical feasibility of integrating BIM, safety data, and XR interaction, while illustrating how XR environments can enhance safety awareness, user engagement, and proactive decision-making. Although this validation does not replace large-scale empirical testing, it provides a strong conceptual and functional validation of the framework. The practical insights obtained from these examples support its relevance and replicability in real-world construction settings, laying the foundation for future pilot projects and standardisation efforts.
# Framework for Malware Triggering Using Steganography
## 3 Methodology
### Geolocation Techniques
#### 3.1.3 Wi-Fi Positioning Systems  
Wi-Fi positioning systems (WPS) [30] identify the location by using terrestrial-based wi-fi access points (APs). It detects existing wi-fi signals in the vicinity of a wi-fi-enabled mobile device and calculates the device's current location. Wi-fi positioning does not require the establishment of a wi-fi network connection. Several commercial wi-fi positioning systems have been developed. Of the various commercial systems, Skyhook [31] and Google's geolocation API [32] were chosen for our work. Since its inception, the Skyhook system has been the longest-running and has had the most widespread adoption on many devices. The Google system has also provided good accuracy and comprehensive coverage in several studies.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Description** \\ \hline Latitude & The device’s geographic latitude coordinate is measured in decimal degrees. \\ \hline Longitude & The device’s geographic longitude coordinate is measured in decimal degrees. \\ \hline Altitude & The device’s geographic height was measured meters above the WGS 84 ellipsoid. \\ \hline Accuracy & The accuracy of the latitude and longitude coordinates in meters. It must be supported by all implementations. \\ \hline AltitudeAccuracy & The accuracy of the height (altitude coordinate), is specified in meters. \\ \hline \multirow{2}{*}{Heading} & The device’s direction of travel, measured in degrees, where \(0^{\circ}\leq\) heading \(<360^{\circ}\), counting clockwise relative to true north. \\ \hline Speed & The device’s current ground speed is measured in meters per second. \\ \hline \end{tabular}
\end{table}
Table 1: Coordinates object properties.  
Figure 3: The permission in the W3C Geolocation API.  
To discover and collect nearby access points (wi-fi signals), we used the node-wifi-scanner utility. Scan results, also known as fingerprints, include a list of APs and their corresponding received signal strength information, where the signal strength is measured in dBm. Once we have identified the wireless access points, we will send a POST request to a wireless location service using fetch. The request body must be in JSON format (JavaScript Object Notation). There is an attribute called wi-fiAccessPoints in the body of the POST request that accepts an array of objects and must have two or more media access control (MAC) addresses. When the service is successful, it returns a location coordinate (latitude and longitude) and the accuracy in meters. If Google's WPS service fails to geolocate, based on the MAC address data of the access point, it returns an IP geolocation result. Therefore, when Google returns IP responses, we consider this a failure. Although the service does not explicitly indicate an error, any responses based on IP geolocation can be identified by comparing them to a query with no AP MAC inputs or setting the \"considerp\" object to false to disable IP geolocation. If Skyhook cannot determine a device location, it returns a specific \"location not found\" message.
## 4 Evaluation of the UHPFRC Single-Story Modular House Manufacturability
### Design Loads and Materials Used  
The design loads considered in this study included dead, live, and wind loads. The dead load was selected to encompass basic framing loads, permanent fixtures, and building finishes. The live loads were set at 1.0 kN/m\({}^{2}\) for the roof and 2.0 kN/m\({}^{2}\) for the bedroom and living room spaces. The wind loads were determined based on the basic wind velocity (V\({}_{0}\)) of 25.4 m/s, importance factor (I\({}_{w}\)) of 0.95, and wind topographic factor (K\({}_{z\text{d}}\)) of 1.0 in a local area. The material properties, including the reference compressive strength of the concrete (120 MPa), elastic modulus (41 GPa), reference cracking tensile strength (f\({}_{\text{crk}}\)) of 4.6 MPa, reference tensile strength (fctk) of 7.0 MPa, material modulus of 0.80, and unit weight of 25.5 kN/m\({}^{2}\), were adopted from the Structural Design Guidelines of Fiber-reinforced SUPER Concrete (2019) [48]. The yield strength of the rebar used was 400 MPa.
## 1 Introduction
[PERSON] carried out grinding operations [20] to get the AE signal, and then used autoregressive modeling and discrete wavelet decomposition for feature extraction. The optimal features were obtained by three different feature selection methods, and the superiority of the extraction and selection method was proved through comparative experiments. There are many kinds of traditional feature selection and dimensionality reduction methods, such as Principal Component Analysis (PCA), the Pearce coefficient method, etc. [21; 22; 23; 24]. Most of these methods carry out feature fusion and reconstruction, or abandon certain features according to the correlation between features [25; 26]. These methods are feasible when the feature dimension is large enough; however, the evaluation of the correlation between features is difficult to carry out, and the accuracy of those methods will decrease when the feature dimension is less, or correlation between the features is fuzzy. In this study, the AE signal of sandstone is taken as the research object in combination with the petrophysical characteristics of low-permeability reservoirs. Here, we propose a method for feature selection by using the fluctuation law of single feature parameters and clustering analysis. This method doesn't need to use the correlation between features. The research emphasis is on the analysis of the parameters' own fluctuation law and the correlation between cluster labels and original labels. The reservoir rocks are porous, and the AE signal feature has fewer dimensions (usually less than 100). The uncertainty of correlation between features is higher in comparison with other types of signal; thus, the two-step method is especially suitable for feature selection in low-permeability reservoir rock, and then, the energy security in the process of underground exploration can be improved. The remainder of this paper is organized as follows. The flow of the two-step algorithm is shown in Section 2. In the first step, the operation of standardized, mean, and tolerance calculationsare completed in turn. In the second step, the algorithm-switching rule is proposed according to the silhouette value criterion. Then, the label correlations are calculated, and the AE parameters are selected through the quantitative relationship of the threshold. Section 3 presents the parameter selection experiment. The experimental system and samples are described. Six parameters are extracted from the AE signal, and the two-step method is used to conduct parameter selection. Section 4 concerns moisture identification and algorithm comparison. The moisture identification results of two other selection algorithms are compared with the two-step method. The rationality of the two-step method in low-permeability rock moisture identification is tested. In addition, the signal variability and handling method in actual engineering applications are also stated in Section 4. Section 5 summarizes the method process, clarifies the application scenario and proposes the future work plan.
## 2 Methodology for the 3D Quality Assessment of UUSR
### 3D Geological Modelling
#### 2.2.2 Spatial Interpolation of Indicators  
Kriging interpolation is widely used in geological and geotechnical assessments considering both the distance and degree of variation between known data points when estimating values in unknown areas [43; 44]. In order to create the 3D geological model attributed with the data of geotechnical indicators \(c\), \(\varphi\), \(k\), the geostatistical method (Kriging) is employed to interpolate geotechnical test data onto the 3D geological structure model. Considering the significant differences in engineering properties between cohesive soil and sandy soil, it is not appropriate to perform a unified interpolation of their geotechnical parameters. Therefore, for cohesive and sandy soils, the lower boundary interfaces are divided into upper and lower parts, and attribute interpolation calculations are conducted independently for each part.  
For hydrogeological indicator \(q\), a spatially discontinuous variable parameter, is assigned by a zoning value based on the YSW, considering the spatial extent and geological constraints. The zoning value can be divided into several grades according to the specific YSW. Each node inside the 3D geological model is assigned by its zoning grade in an aquifer and 0 in an aquitard.  
Regarding the spatial distance indicators ds and \(dg\), taking the example of \(ds\), if a node inside the model is located above the soft soil layer, the ds would be the vertical distance from that point to the soft soil layer. If the node is inside the soft soil layer, the ds would be 0. If the node is located below the soft soil layer, the ds could be assigned a value of 999, indicating that the presence of the upper soft soil layer has no influence on the tunnel for this node.  
Finally, each node inside the geological model is associated with a dataset of indicators, including \(c\), \(\varphi\), \(k\), \(q\), \(h\), \(dg\), and \(ds\).
## 3 Ways to Assess Psoriasis Severity and Therapeutic Strategies for Psoriasis Treatment
### Therapeutic Strategies in the Treatment of Psoriasis
#### 3.2.3 Systemic Treatment  
In patients presenting psoriatic lesions classified as moderate to severe psoriasis, topical therapies, or phototherapy, prove ineffective [33]. In such patients, retinoids, immunosuppressive agents and biologic drugs are included in the therapy [19]. Many types of biologic drugs are used in treatment, and the basis of their mechanism of action is to block the activity of pro-inflammatory factors such as TNF-\(\alpha\), IL-17, IL-23, as well as neutralize lymphocytes [19; 27]. An important agent in the fight against psoriasis is methotrexate, which has been used since 1972 and belongs to agents that exert an immunosuppressiveeffect [34]. Its mechanism of action is based on competitive inhibition of dihydrofolate reductase, which inhibits the synthesis of purine and pyrimidine nucleotides [34]. According to the authors [34], methotrexate given in low doses (\(<\)25 mg per week) reduces the proliferation of lymphoid cells and consequently alleviates the course of the disease. Other drugs in the same group, the use of which has been approved by the FDA, include the equally frequently used apremilast and cyclosporine [29; 34]. A representative of retinoids effective in the treatment of psoriasis is azacitretin, a vitamin A derivative whose mechanism of action is not fully understood [34]. Its main function is modulation of epidermal differentiation and proliferation, as well as anti-inflammatory and immunomodulatory effects. According to studies presented by the authors [34], the use of azretthin in combination with phototherapy is more effective than monotherapy.
## 4 Experiments and Results  
### Experimentation Details  
The primary focus of the experiments was to evaluate the effect of the social media-based clustering process on the quality of the recommendations. The system was not compared directly to other tourism recommenders, as there are not any similar approaches combining clustering and association rules. We used the final data set of tweets posted in the city of Barcelona, obtained after the pre-processing and filtering steps detailed in Section 3.2. The data set consisted of 37,302 tweets and 6066 users. It was then partitioned by users--80% users in the training set and 20% in the test set--which is one of common methods used in machine learning studies.  
The training set was run through the stages of the proposed system. Firstly, the clustering features were extracted from the tweets of the users of the training set and then they were clustered using the parameters described in Section 3.2.2. Secondly, the association rules for POIs were mined with respect to each cluster, as described in Section 3.4. The clusters for which no rule was discovered in this stage were removed, so they were not considered when assigning users to clusters. Finally, the recommendation process was carried out as described in Section 3.5. The popularity ratio and preference ratio for each association rule with respect to every individual user in the test set were computed. The weighting vector was adapted for each user based on his/her interest in popular POIs. Then, all the rule evaluation metrics were combined using weighted averaging and the final selection (Algorithm 3) was carried out. It was decided to recommend 10 items to each user in the test set. POIs related to Gastronomy, Accommodation and Transportation were not considered, as we decided to focus the recommendations purely on touristic activities (a total of 1363 in the considered data set). These 1363 POIs were only considered for recommendations when clustering was not applied. After the clustering stage, the pool of POIs to select from was, on average, approximately 80 POIs per cluster, with very little overlap between pools. The average pairwise Jaccard similarity coefficient of all cluster pools was 0.129. The next subsection describes the metrics used to evaluate the quality of the recommendations.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network
### Control Unit
#### 3.1.3 Repeater  
Volcanic areas usually show a complex orography characterized by deep ravines and a large number of obstacles such as cinder cones. For this reason, a repeater had to be developed in order to extend the gateway coverage, acting as a mid-point between the gateway and the end devices. (Figure 2).  
To reduce the development time, the repeater was based on the end device hardware adapted by removing the temperature acquisition module, the RTD, and the MAX31685. Therefore, the repeater is composed by the microcontroller PIC16 LF1788, the Ra-02 LoRa module, and a 2-dBi antenna (Figure 5). This device is in receiving mode most of the operation time (see Section 3.2.3), so it has a higher consumption (Table 6) than the end devices. Therefore, it was adapted to be powered by a 12-V battery along with a solar panel.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Li-Ion Rechargeable Battery** &
\begin{tabular}{c} **Lithium Thionyl Chloride** \\ **Non-Rechargeable Battery** \\ \end{tabular} \\ \hline Type & 18650 & AA \\ \hline Nominal Voltage & 3.7 V & 3.6 V \\ \hline Charging Voltage & 4.2 \(\pm\)0.05 V & - \\ \hline Discharge Cut-Off Voltage & 2.75 V & 2.0 V \\ \hline Capacity & 2600 mAh & 2400 mAh \\ \hline Operating Temperature Range & \(-\)20 \(\rightarrow\) +60 \({}^{\circ}\)C & \(-\)55 \(\rightarrow\) +85 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 4: Battery specifications.  
Figure 5: Repeater. Configuration (**a**) and general hardware schema (**b**).
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis  
The performance of the proposed SRAM is assessed using various metrics, including read static noise margin, read and write access time, word line write trip voltage, hold power, and area. Additionally, the performance is compared with other existing designs measured under the same conditions. The performance metrics were calculated under a VDD = 1 V and 90-nm CMOS process.
## 3 Results and Discussion
### Comparison of ANN, ANFIS, and SVR  
The present study attempted to investigate the potentials of ANN, ANFIS, and SVR modeling techniques to estimate the \(MR\) of parboiled hulls. In order to have a measurement of the efficiency of the developed equations, three statistical functions were used (i.e., _MSE_, _MAE_, and \(R^{2}\)). Table 6 shows the values of ANN, ANFIS, and SVR models. Accordingly, the ANFIS model provided higher accuracy than ANN. Furthermore, the predicted results against the actual values for the Gaussian membership function and the ANN are presented in Figure 9. [PERSON] et al. (2018) and [PERSON] et al. (2018) in a comparative application of ANN and ANFIS to predict the \(MR\) of onion and blackberry, respectively, reported that the ANFIS model performs better in predicting the \(MR\). [PERSON] et al. (2018) and [PERSON] et al. (2018) also used ANN- genetic algorithm and ANFIS--to predict the \(MR\) of basil seed and apricot crops. They showed that the ANFIS model performed better due to the higher \(R^{2}\) value. Similarly, [PERSON] et al., (2018) reported that the ANFIS model was slightly better than multilayer feed-forward neural networks models for the prediction of \(MR\), energy, and exergy of Cantaloupe Slice in a microwave dryer. [PERSON] et al. (2018) reported that the ANN model was more accurate than the ANFIS and mathematical model for the prediction of stevia leaves in an infrared-assisted continuous hybrid solar dryer. However, [PERSON] et al. (2018) and [PERSON] et al. (2018) for drying quince and _Echium amoneum Fisch. & C. A. Mey._, respectively, were shown that compared to ANN and mathematical models, ANFIS is reported as the most suitable model for \(MR\) prediction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{\(MR\)} \\ \cline{2-4}  & **ANN** & **ANFIS** & **SVR** \\ \hline _MSE_ & 0.0059 & 0.0036 & 0.0004 \\ _MAE_ & 0.0423 & 0.0388 & 0.0182 \\ \(R^{2}\) & 0.9991 & 0.9995 & 0.9997 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of prediction behavior of parboiled hulls drying in an IR-HA drying using ANN, ANFIS, and SVR.  
Figure 8: Predicted \(MR\) data vs. real values, and for quadratic algorithm the SVR method.  
Although the training and evaluation of the SVR method were different from the other two models and the calibration results were reported with all the data in Table 6, the mean square error was significantly lower than the ANN and ANFIS results.
## 2 Materials and Methods  
### Materials  
Methanol (HPLC grade), formic acid, Folin-Ciocalteu's phenol reagent and sodium carbonate was acquired from Panreac (Barcelona, Spain). The 2,2-Diphenyl-1-picrylhydrazyl (DPPH) reagent, methanol (99.8%) and some polyphenol standard patterns (phloroglucinol, protocatechuic acid, epicatechin, quercetin, myricetin, ferulic acid, p-coumaric acid, rutin, gentisic, caffeic and catechin) were purchased from Sigma-Aldrich (Steinheim, Switzerland). Chlorogenic acid and vanillic acid polyphenol patterns were provided by HWI (Ruelzheimer, Germany) and Fluka Analytical (Steinheim, Switzerland) respectively.  
_S. cerevisiae_ Safale S-04, hereafter referred to as SF-04, was provided by the Femrentis Division of S. I. Lesaffre (Lille, France). _Saccharomyces cerevisiae var. boulardii_ CECT 1474 was purchased from the Spanish Type Culture Collection (CECT, Valencia, Spain). \(Sc\) and \(Sc_{b}\) were suspended in distilled water to be grown in Sabouraud Glucose Agar with Chloramphenicol (SGAC, Sigma-Aldrich, Steinheim, Switzerland) solid medium plates at their optimal culture temperatures, 27 and 37 \({}^{\circ}\)C, respectively, for isolation. After that, \(Sc\) and \(Sc_{b}\) were grown in yeast peptone dextrose (YPD) broth (Sigma-Aldrich, Steinheim, Switzerland) with an orbital shaker-incubator for 48 h also at 27 and 37 \({}^{\circ}\)C, respectively. Finally, 1-mL aliquots of pure cultures were subsequently frozen at \(-80\)\({}^{\circ}\)C.
## 5 Illustrative Example
### Scenario I  
The purpose of Scenario I is to evaluate the closed-loop system stability and its closed-loop performance for a given initial condition with three different constant values of the control input saturation. To do this, we simulate the closed-loop response from an initial state \(x(0)=[0.42,0.04]^{T}\) and \(\theta(t)=1-\mathrm{e}^{-t}\). Finally, fixing the frozen values of \(\phi_{1}=\phi_{2}=0\), \(\phi_{1}=\phi_{2}=0.5\) and \(\phi_{1}=\phi_{2}=1\), thus obtaining instantaneous saturation limits values \(\gamma_{1}=15\) and \(\gamma_{2}=7.5\), \(\gamma_{1}=12.75\) and \(\gamma_{2}=6.37\) and \(\gamma_{1}=10\) and \(\gamma_{2}=5\), respectively.  
As shown in Figure 2, the closed-loop system stability is guaranteed for all the values of \(\gamma_{1}\) and \(\gamma_{2}\) that were mentioned. Moreover, note that the system's response that was evaluated with the scheduling parameters \(\phi_{1}=\phi_{2}=0\), corresponds to the maximum allowed limit values of \(\gamma_{1}\) and \(\gamma_{2}\), obtaining the fastest system response and showing that the designed LPV state-feedback controller is able to adjust the system's performance depending on the different values taken by \(\gamma\).  
Figure 3 shows the instantaneous values of the saturation limit of \(u_{1}\) and \(u_{2}\) for the three frozen values of \(\phi_{1}\) and \(\phi_{2}\) and the evolution of the control signals. For illustrative purposes, since the signal \(u_{1}\) takes only negative values during the system's response, only the lower bound of the saturation is plotted. As a variation of the saturation limit occurs, the input signal changes as a result of the adaptability capacity of the designed controller. For example, the interval of linearity of the control signal \(u_{1}\) corresponds to \([-15,15]\) when \(\phi_{1}=\phi_{2}=0\) and to \([-10,10]\) when \(\phi_{1}=\phi_{2}=1\). Note that if  
Figure 2: Scenario I: closed-loop system response.  
the controller gain corresponding to \(\phi_{1}=\phi_{2}=0\) had been used for the case in which \(\phi_{1}=\phi_{2}=1\), then saturation would have occurred.  
Figure 4 shows the evolution of the Lyapunov function \(V(x)\) for the three frozen values of \(\phi_{1}\) and \(\phi_{2}\), which correspond to guaranteed decay rates of 10, 5.5 and 1 respectively. It can be seen that the largest decay rate corresponds to the fastest closed-loop system response, whose saturation scheduling parameters are \(\phi_{1}=\phi_{2}=0\) and \(\alpha(\phi)=10\). Also, all the functions are under the unit value, hence it is guaranteed by design that none of the control inputs saturates, as already shown in Figure 3.