esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards the Implementation Process of Accessible Virtual
## 3 Technological Accessibility Requirements in Higher Education Institutions
### Discussion Results  
In order to elaborate a thematic synthesis, the [PERSON] et al. [47] method was followed. This consists of defining relevant terms based on the main topic of the research, and finding their connections with other extracted data that represent a relevant topic in the systematic review of the literature.  
In Figure 3, a thematic map can be seen in which concepts associated with technological accessibility have been identified. Each concept contains different elements derived from the studies obtained from the investigation.  
Based on white literature (Appendix A) and grey literature (Appendix B) studies, specific concepts were identified as barriers that represent obstacles to the implementation of accessible virtual campuses. Some of these barriers include the following:  
* **Architectural barriers**. Manifested by the absence of accessible classrooms, accessibility signage and access ramps, among others.
* **Pedagogical and human resources barriers**. Defined by the lack of sensitivity training, employee training, and for educators in accessibility themes.
* **Attitude barriers**. Encompassed by discrimination towards people with disabilities on the part of the campus community, disregard for norms and regulations, as well as cultural aspects or beliefs.  
Additionally, strategies focused on attending to the accessibility requirements identified include policy making; implementation of technological accessibility courses; personnel training; specialized learning materials; and educational resources made available by ICT.  
As far as laws and regulations that support the creation of accessible technology strategies are concerned, there exist organizations with objectives to enforce norms, such as governmental bodies and legislation passed within each country. Among the countries identified that have passed disability laws and acts, the United States of America leads  
Figure 3: Thematic map of association of terms in response to the research questions [47].  
as the primary promoter in the application of accessibility norms within its institutions, followed by nations such as Spain, Mexico, Colombia, Chile, Argentina, Ecuador, Brazil, and the U.S territory of Puerto Rico.  
With reference to web accessibility guidelines, adherence to the standards by the W3C such as WCAG and WAI-ARIA stand out, as they contain principles and recommendations for digital content creation in websites, interface design, and the application of methodologies for the development of accessible software.
## 5 Plans for the Future  
We continue to upgrade SACLA not only to enhance the performance and availability of XFEL but also to establish a technological foundation for the future SPring-8-II, our upgrade of the existing SPring-8 facility. The unique capability to operate multiple FEL beamlines allows us to offer exciting research opportunities with new experimental schemes. For example, BL1 and BL2/BL3 can be synchronized to provide FEL pulses with a finely controlled time interval because both the SCSS+ and SACLA linacs use a common trigger source. This synchronized operation will enable pump and probe experiments with both soft and hard X-ray FELs.  
Pulse-by-pulse switching is presently realized in a scheme that distributes laser pulses equally over multiple beamlines. In this scheme, an equivalent number of pulses is delivered to each beamline. However, depending on the combination of experiments at BL2 and BL3, one beamline may require more pulses than the other. A flexible control system enabling an arbitrary pulse distribution, e.g., 40 pulses/s at BL2 and 20 at BL3, is under development to improve the utilization efficiency.  
On the other hand, we plan to use the SACLA linacs as an injector for SPring-8-II. Although the dynamic aperture of SPring-8-II is much narrower than the current one, the small emittance beams from the SACLA linac can maintain excellent injection efficiency with high stability. Another advantage of this scheme is that we can save massive amounts of electricity by stopping operations of the existing SPring-8 injector composed of the 8 GeV booster synchrotron and 1 GeV linac. However, this scenario of sharing the SACLA linac between SACLA and SPring-8-II requires two major developments: one is to change the bunch length in a pulse-by-pulse manner to keep the small beam emittance at the injection point of the ring; the other is to enable \"on-demand beam injection\" that accepts injection requests from the ring for the top-up operation. The first test of beam injection from the SACLA linac to the current storage ring is scheduled for FY2018.  
The authors are grateful to all of the SACLA/SPring-8 staff who have participated in the construction and operation of SACLA. All authors wrote the paper on behalf of the SACLA team.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### 3.3. Bread Characterization
#### 3.3.4. Bread Crump Porosity and Elasticity  
The breads obtained were characterized by high porosity values (Table 4). Higher porosity was found in bread prepared from WF-Rp mixtures compared to the control (87.75 \(\pm\) 1.06%), except for the porosity of bread with an addition of 0.5% Rp (87.50 \(\pm\) 0.71%), which was slightly lower. The porosity values increased by 1.0% and 1.5% with the addition of Rp, then slightly decreased following the same trend as the height and the volume of bread. The differences were significant (\(p\) < 0.05). Besides dough stabilization and gluten network strengthening [26; 27], ascorbic acid used in breadmaking contributed to obtaining bread with a larger amount of smaller and evenly distributed pores, a larger volume [28], and a better porosity. Therefore, the addition of Rp provided the dough with a certain amount of ascorbic acid, which determined the increase in bread height, volume, and porosity.  
The elasticity of the bread crumb it is property to return to its original shape after the action of a pressing force, and it depends on the quality and quantity of gluten in the flour and the freshness of the product [5]. It was determined a few hours after cooling and is reported as an average of triplicates \(\pm\) SD (Table 4). Breach crumb elasticity showed a slight decrease for all samples compared to the control (93.3 \(\pm\) 0.58%), varying from 91.70 \(\pm\) 0.58% (0.5% Rp) to 88.50 \(\pm\) 0.50% (2.5% Rp) with significant differences (\(p<\) 0.05). The decrease of bread crumb elasticity with the increase in the Rp addition could be associated with the slight decrease of gluten content in WF-Rp mixtures, as was discussed before (Section 3.1, Table 1). The crumb elasticity of bread with the AA addition was lower (92.30 \(\pm\) 0.58%) but close to the control as expected, since its gluten content was equal to that of the control bread.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## Results
### ANOVA Test Results  
Generally, growth traits and sETR calculated using four methods showed significant differences between treatments but no difference between genotypes (Table 2). This suggests that the sETR measurements were effective in identifying the differences in plant growth between control and Diuron-treated groups. It should be noted that although an interaction effect was identified for \(\Delta LMF\) and sETR calculated using the HSI methods, control groups still showed higher values than Diuron-treated groups irrespective of genotype, indicating the validity of main effect (treatment) significance for those traits. RGR was the only trait showing a significant difference between genotypes.  
For each cultivar, growth traits and sETR calculated using four methods had statistically higher values for control groups than Diuron-treated groups (Figure 7). Growth traits (e.g., CGR, NAR, RGR, and \(\Delta LMF\)) were positive for control groups, indicating normal plant growth, whereas those traits were close to zero or even negative, indicating plant loss of mass and leaf area in Diuron-treated plots. The same patterns were observed for sETR calculated using four methods. An exception was \(\Delta LMF\), which represents dry matter partitioning to leaf area. Both cultivars showed negative \(\Delta LMF\) for control and Diuron-treated groups, meaning both cultivars distributed a smaller fraction of total dry matter to leaves at the later sampling times irrespective of treatment. The Pima cultivar exhibited a greater decline in LMF for the Diuron-treated group than the control group, likely reflecting the damage caused by Diuron treatment and resulting defoliation that led to leaf mass fraction reduction. In contrast, the Upland cultivar showed no difference of \(\Delta LMF\) between treatments. This is likely because the total growth was negatively affected to a comparable extent as leaf mass.  
Figure 6: Coefficient of determination (\(R^{2}\)) between the effective quantum yield of PSII (\(\phi_{PSII}\)) values derived from PAM and HSI data: (**a–c**) are for the Pima cultivar and (**d–f**) are for the Upland cultivar. \(\phi_{PAM}\) indicates values calculated using PAM data. \(\phi_{HSIL}\), \(\phi_{HSIQ}\), and \(\phi_{HSIA}\) indicate values derived from hyperspectral data using the maximal fluorescence value estimated by the linear, quadratic, and average models. It is noteworthy that to avoid potential effects due to outliers, all \(R^{2}\) values were obtained from least-squares linear regression analyses with robust option (“bisquare”) in MATLAB.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 3 Problem Statement  
**System architecture (Figure 1).** Our protocol consists of three computing parties: \(P_{0}\), \(P_{1}\), and \(P_{2}\). It follows an offline/online paradigm, offloading part of the computational and communication burden to a setup phase to improve efficiency during the online phase. Moreover, in the setup phase, the data owner (DO) distributes secret shares of the dataset to the computing parties. At the beginning of the online phase, users obtain their RSS vectors through APs and secretly share them among the three computing parties.  
**Threat model and design goals.** Our protocol assumes a non-colluding computing server setting, allowing at most one server to be corrupted. It is designed to achieve accuracy, privacy, and efficiency. In terms of privacy, the primary requirements are that the dataset remains hidden from both the computing parties and users, while users' query indexes and query results are kept confidential from the computing parties and the data owner.  
**Problem Formalization.** The user first measures and obtains their location representation RSSs data via APs, then securely sends the RSSs data to computational servers. The computational servers, based on the RSS data of RPs obtained from the data owner during the offline phase, first calculate the k nearest RPs to the user. Then, the average of the coordinates of these k RPs is computed to determine the user's location coordinates.
###### Abstract  
Soil instability is a major hazard facing the mining industry in its role of supplying the indispensable mineral resources that our societal challenges require. Aiming to demonstrate the monitoring potential of radar satellites in the mining sector, we analyze the deformation field in the Riotinto mine, Spain. We propose a new method for combining ascending and descending results into a common dataset that provides better resolution. We project the LOS measurements resulting from both geometries to a common reference system without applying any type of geometric restriction. As a projection system, we use the vertical direction in flat areas and the slope in steep topographies. We then identify and remove outliers and artifacts from the joint dataset to finally obtain a deformation map that combines the two acquisition perspectives. The results in the Atalaya pit are consistent with GNSS measurements. The movements observed in the rock dumps were unknown before this study. We demonstrate the great potential of the Sentinel-1 satellite as a complementary tool for monitoring systems in mining environments and we call for its use to be standardized to guarantee a safe and sustainable supply of mineral resources necessary for a just technological transition.  
InSAR; Sentinel-1; Coherent Pixel Technique; ground stability; mine monitoring; sustainable mining Article  
[PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Radar Interferometry as a Monitoring Tool for an Active Mining Area Using Sentinel-1 C-Band Data, Case Study of Riotinto Mine
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 3 Results
### Results: Functional Gait Assessments
#### 3.3.1 Timed \"Up and Go\" Test--Risk of Falling  
In the TUG, eight subjects (73%) showed an absolute decrease in the required time when walking at the follow-up assessment with vibrotactile stimulation, while three subjects (27%; p# 2, 4 and 9) required more time (see Figure 6). Out of those eight subjects, three subjects (27%; p# 1, 7 and 11) exhibited a relative decrease of up to 10%, two subjects (18%; p# 3 and 6) showed a relative decrease between 10% and 20% and in three subjects (27%; p# 5, 8 and 10), the required time decreased by over 20%. The maximum relative decrease was -32% (p# 5), and the maximum relative increase was 7.1% (p# 4).  
Figure 5: The boxplots illustrate the comparison of the cohort’s performance at baseline and after the intervention during the follow-up for the Timed Up and Go (TUG) (\(n=11\)), Four-Square Step Test (FSST) (\(n=11\)), 10 Meter Walk Test (10 MWT) (\(n=10\)), and 2 Minute Walk Test (2 MWT) (\(n=10\)) using boxplots. The results of the Wilcoxon signed-rank test indicate that only the TUG demonstrated a significant change, implying an improvement in the cohort’s performance following the intervention (\(p=0.042\) *). The comparison with the MDC and MCID reference values shows that the cohort exhibits an intervention-related change only in the FSST. Outliers are marked with ‘x’ and statistical significance is indicated by *’.  
The correlation analyses showed a weak, non-significant negative correlation between individual performance (\(\Delta\) t\({}_{1}\)\(-\)t\({}_{0}\)) and both the VTFS usage time (r = 0.126, \(p\) = 0.713) and the time since amputation (r = 0.142, \(p\) = 0.678).  
A comparison of the medians for the entire cohort (\(n\) = 11) before and after the intervention indicates a significant change in performance in the TUG (see Figure 5, \(p\) = 0.042 *; medians (IQR): 11.23 s (7.35 s) vs. 10.84 s (4.84 s)). The reduction in the median time required to perform the test for the entire cohort from the initial to the follow-up measurement was 0.39 s.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 7 Experimental Results
### Accuracy  
We measured the accuracy of our method using feature extraction and the Seq2 Seq model. Additionally, we measured the accuracy of the following models: decision tree (DT), naive Bayes (NB), neural network (NN), multilayer perceptron (MLP), random forest (RF), and gradient boost (GB) [26]. The decision tree and random forest methods showed the highest accuracy. In our experiment, the accuracy of the decision tree and random forest models was 92.64%, whereas that of our method was 99.97%, as shown in Figure 13. When we used the feature extraction method and Seq2 Seq model, the accuracy of our method increased by 7.33%.  
The confusion matrix for the proposed method is presented in Table 4, and most attacks were classified correctly.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Predicted**} \\ \cline{2-7}  & & **Normal** & **BF** & **DoS** & **Flood** & **SlowTTe** & **Malformed** \\ \hline \multirow{4}{*}{Actual} & Normal & 4419 & - & - & - & - & - \\ \cline{2-7}  & BF & - & 606 & - & - & - & - \\ \cline{2-7}  & DoS & - & - & 18,653 & - & - & - \\ \cline{2-7}  & Flood & - & - & - & 62 & - & - \\ \cline{2-7}  & SlowTe & - & - & - & - & 408 & - \\ \cline{2-7}  & Malformed & 1 & - & 4 & - & - & 735 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of our method.  
Figure 13: Accuracy comparison with other ML methods.
# Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix
## 2 Theory of Grout Transport in Dual-Porosity Media  
In this analysis, the permeability medium is assumed to be homogeneous and isotropic, and the grout and air are incompressible and immiscible. Grout is the wetting phase and air is the nonwetting phase. The governing equations in fracture system are:  
\[-C_{p}\frac{\partial}{\partial t}\big{(}p_{a}-p_{g}\big{)}+\
abla.\left[-\frac {k_{\text{int}}k_{r,g}}{\mu_{g}}\big{(}\
abla p_{g}+\rho_{g}g\
abla D\big{)} \right]=0 \tag{1}\]  
\[C_{p}\frac{\partial}{\partial t}\big{(}p_{a}-p_{g}\big{)}+\
abla.\left[-\frac {k_{\text{int}}k_{r,a}}{\mu_{a}}(\
abla p_{a}+\rho_{g}g\
abla D)\right]=0 \tag{2}\]  
where \(C_{p}\) is the specific capacity; \(p\) is pressure (Pa); \(t\) is time; \(k_{\text{int}}\) is the intrinsic permeability of the porous medium (mm\({}^{2}\)); \(k_{r}\) is the relative permeability; \(\mu\) is the fluid's dynamic viscosity (Pa\(\cdot\)s); \(\rho\) is the fluid density (kg/m\({}^{3}\)); \(g\) is acceleration of gravity; \(D\) is the coordinate of vertical elevation (m); and the subscripts \(g\) and a indicate grout and air, respectively.  
Figure 1: Argillaceous roadway site with different support. (a) U-shaped steel support in Zhuxianzhuang Mine; (b) truss in Liuhai Mine; (c) cable and grout in Liuhai Mine. U-shaped steel support and truss cannot effectively restrain the deformation of surrounding rock mass.  
The pressure differential in the interface between grout and air is the capillary force.  
\[p_{c}=p_{a}-p_{g} \tag{3}\]  
Imbibition capacity between in fracture and matrix system is  
\[q_{a}+q_{g}=0 \tag{4}\]  
The equation of \(q_{g}\) can be measured by experiment, generally close to  
\[q_{g}(t)=R\Big{(}\alpha-\beta e^{-\lambda t}\Big{)} \tag{5}\]  
where \(R\) is the ultimate grouting capacity per unit rock under imbibition effect; \(\alpha\) and \(\beta\) are parameters indicating the imbibition intensity.
## 3 Results and Discussion
The sample ML2:2:2 exhibited the highest lightness value (83.253), while MD4 GA2 had the lowest L value (73.107), making it the darkest encapsulate. The significant differences identified by [PERSON]'s HSD test (Table 3) suggest that the carrier choice significantly impacts the lightness of the encapsulates. Obtained encapsulates were lighter than those obtained with spray drying and MD, GA, and sodium casenate as carriers by [PERSON] et al. [33]. This could be justified by high temperatures applied during spray drying and possible non-enzymatic browning (caramelization of polysaccharide carriers), which was avoided by low temperatures during freeze-drying in the present study. The \"a\" parameter indicates the red/green value, with positive values towards red and negative towards green. Sample MD4 GA2 had higher \(a\) and \(b\) values, which means that those encapsulated tend to be red and yellow, which could also indicate lower encapsulation efficiency where more of the core material remains non-encapsulated (on the surface of encapsulates). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Moisture Content (\%)** & **A\({}_{\textbf{w}}\) Value** & **WAI (g/g)** & **WSI (\%)** \\ \hline MD6 & 2.410 \(\pm\) 0.029 cde & 0.170 \(\pm\) 0.002 c & 0.884 \(\pm\) 0.042 f & 77.233 \(\pm\) 0.042 c \\ GA6 & 3.615 \(\pm\) 0.281 ab & 0.203 \(\pm\) 0.006 b & 0.354 \(\pm\) 0.003 s & 88.530 \(\pm\) 0.003 b \\ CMC6 & 4.027 \(\pm\) 0.000 a & 0.207 \(\pm\) 0.012 b & 11.316 \(\pm\) 0.104 a & 7.113 \(\pm\) 0.104 f \\ MD2 GA2 CMC2 & 1.936 \(\pm\) 0.012 de & 0.110 \(\pm\) 0.001 e & 6.580 \(\pm\) 0.06 c & 30.152 \(\pm\) 0.06 e \\ MD4 GA2 & 3.474 \(\pm\) 0.009 ab & 0.240 \(\pm\) 0.001 a & 1.302 \(\pm\) 0.012 e & 60.535 \(\pm\) 0.012 d \\ MD4 CMC2 & 2.532 \(\pm\) 0.032 cd & 0.180 \(\pm\) 0.010 c & 5.371 \(\pm\) 0.049 d & 32.093 \(\pm\) 0.049 e \\ GA4 CMC2 & 2.036 \(\pm\) 0.016 d & 0.180 \(\pm\) 0.001 c & 8.107 \(\pm\) 0.074 b & 8.169 \(\pm\) 0.074 f \\ MD2 GA4 & 2.695 \(\pm\) 0.064 c & 0.193 \(\pm\) 0.006 b & 0.363 \(\pm\) 0.003 s & 98.257 \(\pm\) 0.003 a \\ MD2 CMC4 & 3.394 \(\pm\) 0.189 b & 0.203 \(\pm\) 0.006 b & 11.282 \(\pm\) 0.102 a & 8.935 \(\pm\) 0.102 f \\ GA2 CMC4 & 1.854 \(\pm\) 0.057 e & 0.140 \(\pm\) 0.001 d & 11.499 \(\pm\) 0.100 a & 6.267 \(\pm\) 0.100 f \\ \hline \hline \end{tabular} Values are expressed as mean \(\pm\) standard deviation where different letters within the same column represent significant differences at \(p\) \(<\) 0.05. MD—Matlodektrin, GA—Gum Arabic, CMC—Carboxymethylcellulose, A\({}_{\textbf{w}}\)—water activity, WAI—water absorption index, WSI—water solubility index.  
\end{table} 
Table 2: The physical properties of prepared citrus byproduct encapsulates.
## 3 Materials and Methods  
### Data Preparation  
The Sentinel-1 data products used were previously curated within Google Earth Engine and therefore have undergone essential pre-processing, including thermal noise removal, radiometric calibration, terrain correction, and conversion to log-scaled decibels (dB). The Analysis-Ready-Data framework for Sentinel-1A backscatter was then applied using the GEE Python API [28], which includes multi-temporal speckle filtering, border noise correction, and radiometric terrain normalisation. Gray Level Co-occurrence Matrix (GLCM) texture analysis was performed to generate an additional 16 bands, complementing the SAR data with spatial and textural information. This approach is widely used in land-cover classification [29], as it enhances the feature set for pixel-based classification models.  
To capture temporal feature variability, we composited each year of data with three statistical methods: median, minimum, and maximum. This resulted in a total of 54 input features, with 18 for each statistical composite, two from VV/VH backscatter intensity bands and the remaining 16 from GLCM analysis.  
Training data were acquired through manual inspection of the Airbus high-resolution 30 cm base maps ([[https://intelligence.airbus.com/imagery/reference-layers/basemap/](https://intelligence.airbus.com/imagery/reference-layers/basemap/)]([https://intelligence.airbus.com/imagery/reference-layers/basemap/](https://intelligence.airbus.com/imagery/reference-layers/basemap/)), Accessed: 19 January 2025). The most recent image available in the region, dated 31 December 2022, was used to outline labelled polygons to extract training pixels from the 2023 composite. Creating labelled polygons on static imagery and applying them to temporally composited test data can introduce class confusion if pixels within the training polygons undergo change during the composite period. To minimise this effect, mixed forest training pixels were selected in the preserved Orashi National Forest away from any mapped pipelines. Standard mangrove pixels were outlined to the East of the study region, where no spills have been reported. The identified bare regions are stable over the composite period as the transition of contaminated land to another class over a year is considered an unphysical transition in the context of forestry. A minimum of 65,000 training pixels were exported per class. A visual key for the classes--Mixed Forest, Standard Mangroves, and Bare Land--is depicted in Figure 2.  
Figure 2: A visual representation of the classes, depicting Mixed Forest, Standard Mangroves, and Bare Land from left to right, acquired from Airbus 30 cm basemaps.