esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### Microstructures  
The microstructures of Ag\({}_{1-x}\)Sb\({}_{1+x}\)Te\({}_{2+x}\) alloys can be divided into three groups (Figure 4). Slowly cooled samples with the highest concentration of Sb, \(x\) = 0.4 and \(x\) = 0.3 (Figure 4b and Figure S3), show an evident Widmansatten-like microstructure [23; 25; 32]. The specific pattern is caused by crystallization of elongated grains of Sb\({}_{2}\)Te\({}_{3}\) and Sb\({}_{2}\)Te in matrix of the \(\beta\) phase (Figure 4b, Figures S2a and S3). Shorter time of solidification inhibits straightforward crystallization  
Figure 3: Lattice parameters of the \(\beta\) phase as a function of nominal composition.  
of Sb-Te phases. Thus, the Widmanstatten-like pattern is not formed after quenching (Figure 4a and Figure S3a,c). Chemical compositions of the \(\beta\) phase after different cooling processes are comparable (Table 1).  
A second group of alloys, \(x\) = 0.19 and \(x\) = 0.17, is characterized by a near-single or single-phase microstructure (Figure 4c,d and Figure S2b,c). A small amount of Sb-Te precipitations result in barely visible Widmanstatten-like pattern in alloys after slow cooling (Figure 4d, Figures S2b and S4b). An increase of cooling rate inhibits the precipitation of the secondary phases (\(x\) = 0.19, Figures S2c and S4a). Quenched \(x\) = 0.17 sample contains vestigial inclusions of Ag\({}_{2}\)Te, undetectable by PXRD (Figure 4c and Figure S4c).  
A third type of microstructure: lamellar, pearlite-like precipitations of Ag\({}_{2}\)Te in the \(\beta\) phase matrix was observed in slowly cooled alloys with the highest content of Ag (\(x\) = 0; 0.1; Figure 4f and Figure S4f). The cooling rate strongly affects the morphology of those grains, which are much thinner  
Figure 4: Optical polarized light (**b–d**) and bright field (**a**,**e**,**f**) micrographs of: (**a**,**b**) \(x\) = 0.4, (**c**,**d**) \(x\) = 0.17 and (**e**,**f**) \(x\) = 0.1 alloys; quenched samples—(**a**,**c**,**e**) and slowly cooled samples—(**b**,**d**,**f**). Chemical compositions of phases are given according to EDXS analysis.  
after quenching (on the level of 1 \(\upmu\)m, Figure 4e and Figure S4e). The chemical composition of the matrix in the analyzed alloys is almost the same, suggesting this to be the limiting composition of \(\beta\)-phase-region. Additional annealing during the SPS procedure and LFA measurement does not influence the chemical composition of the matrices; they remain practically unchanged (Table 1). However, a slight increase of the impurity concentration and modification of the microstructure is visible as the result of the slow decomposition of the \(\beta\) phase. Detailed discussion of the chemical compositions is given in supplementary information (Section 3).
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Baseline/Geometric/Spatial Decorrelation (\(\delta_{B}\))
In addition to range single bandwidth, the other most influential factor that impacts the blind angle range is the perpendicular baseline. Figure 8 demonstrates the frequency shift plot with NISAR S-band parameters at varying perpendicular baselines (i.e., \(-\)500 m, \(-\)1000 m, \(-\)5000 m, and \(-\)10,000 m). When the perpendicular baseline increases, so does the blind angle range, and the frequency shift curve becomes more rounded. When comparing the \(-\)500 m (Figure 8a) and \(-\)10,000 m (Figure 8d) scenarios, the frequency shift remains very close to zero until the local terrain slope approaches the incidence angle when the perpendicular baseline is \(-\)500 m. In contrast, the frequency shift rapidly increases as the slope increases when the perpendicular baseline is \(-\)10,000 m. This shows that baseline correlation is more likely to reduce under longer perpendicular baselines. Therefore, coming back to Figure 7, Sentinel-1 and NISAR L-band are two SAR system designs that are less likely to experience geometric decorrelation due to their narrower perpendicular baselines. We note that other systems can have wider blind angles when their baselines are higher than the values set for the derivation of this figure (i.e., Table 10). The third factor that influences the blind angle is wavelength. Large wavelengths can obtain narrower blind angles, while short wavelengths lead to wider blind angles. This is the main reason why TerraSAR-X shows a noticeably larger range of blind angles despite its significantly larger single-range bandwidth. The range of blind angles in subplot (e) is about incidence angle \(\pm\)3\({}^{\circ}\), and in subplot (f), it is about incidence angle \(\pm\)2\({}^{\circ}\).
# Use of Indoor Location Technologies in Healthcare Contexts: A Scoping Review
## 3 Results  
The search strategy identified 1718 documents, which were systematically reviewed, leading to the inclusion of 83 articles. Figure 1 shows the PRISMA diagram for the search process, complemented by Supplementary Table S2, which summarises the articles included in the literature review.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Inclusion** & **Exclusion** \\ \hline \multirow{7}{*}{Population (P)} & Documents focused on & \\  & indoor location & Documents unrelated to \\  & technologies in healthcare & healthcare, outdoor \\  & settings that apply the & location technologies, or \\  & technology to track & those falling outside the \\  & patients, staff, materials, & predefined scope \\  & or equipment & \\ \hline \multirow{7}{*}{Concept (C)} & Documents exploring & \\  & the application, & \\  & implementation, & \\  & challenges, or benefits of & \\  & indoor RTLS in healthcare, & \\  & adopting the RTLS concept & \\  & as proposed by [PERSON] & \\  & [PERSON] et al. [14] & \\ \hline \multirow{7}{*}{Context (C)} & Studies on contextual & \\  & factors impacting the & \\  & adoption and effectiveness & \\  & of indoor location & \\  & technologies in healthcare, & \\  & for both patients and & \\  & materials, using any & \\  & research methods & \\ \hline \multirow{7}{*}{Sources and Study Designs} & Peer-reviewed articles, & \\  & conference papers, reports, & \\  & and grey literature & \\  & discussing indoor location & \\  & technologies in healthcare; & \\  & qualitative and & \\  & quantitative studies & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria.  
While the inclusion of MeSH terms expanded the search parameters, it also yielded a substantial number of articles, many of which fell outside the scope of the study. The main reasons for exclusion were studies focused on outdoor location tracking, articles referring to patient monitoring or identification without attributes of time and location (e.g., remote monitoring of chronic patients), and articles that did not present detailed characteristics of the technology used in the full text.
## 6 Conclusions  
This study comprehensively analyses metal contamination in stream sediments draining into Keban Dam Lake, utilising multiple pollution indices, including the CF, EF, PLI, and Igeo. The results revealed that metals such as Cu, Zn, and Cr are notably enriched in certain downstream areas influenced by urban and industrial activities. These levels correspond to very high contamination factors (CF > 6) and enrichment factors (EF > 10), indicating strong anthropogenic influence. In contrast, metals such as lead (Pb), thallium (TI), thorium (Th), and uranium (U) remained below ecological risk thresholds (e.g., TEL), reflecting minimal contamination and suggesting a dominantly natural origin. The pollution load index (PLI) showed that while most sampling locations are unpolluted (PLI < 1), some urban-impacted zones exhibited moderate pollution (PLI up to 1.55), highlighting localised contamination.  
Multivariate and cluster analyses identified one major pollution hotspot, located in the middle to lower reaches of the stream, where high concentrations of Cu, Zn, and Cr are observed. This region, characterized by residential, industrial, and roadside activity, likely contributes significantly to sediment pollution through surface runoff, waste discharge, and possible legacy effects of former mining activities.  
Strong correlations among Cu-Zn and Ni-Cu indicated shared anthropogenic sources, while the geochemical behaviour of Th and U suggested a natural origin linked to local granitic and plutonic rock units. Negative correlations between some metals reflected differences in geochemical mobility and environmental conditions, such as redox states.  
These findings are crucial for developing effective pollution control measures and provide a basis for further monitoring of sediment quality in the Keban Dam Lake region. Continued research is necessary to track changes over time and assess the long-term impacts of pollution on the local aquatic ecosystems and human health.  
This work was funded by the Firat University Scientific Research Projects Coordination Unit (FUBAP) of Firat University, Elazg/Turkey, for its financial support (No MF 24.106 and MF 25.50).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 5 Implementation and Evaluation
### Communication Overhead  
Our solution introduces the communication overhead for retrieving DID documents. In the considered scenario, every time an edge node receives an advertisement, it requests the network to retrieve two DID documents: the DID document that corresponds to \"smart-city:iot\", and the DID document that corresponds to the building owner. It is reminded, however, that the underlying ICN architecture allows edge nodes to construct multicast delivery trees; hence, multiple requests concerning the same item can be satisfied using a single multicast delivery. However, this requires that the requests arrive almost simultaneously to the edge node. Since we know that an advertisement will trigger requests for DID documents, we evaluate a strategy based on which an edge node adds some delay before responding to a DID document request, hoping that during that period, more requests concerning the same DID will arrive. In order to evaluate the impact of this strategy, we consider that only two buildings, one in each side of the backbone link, advertise \(Resource_{URL}\)s (every 10 s), and we measure the number of DID documents that are transferred through the backbone link in one minute. We consider three strategies: edge nodes add no delay, edge nodes add 2 ms delay, and edge nodes add 4 ms delay. The obtained results can be seen in Figure 4. As it can be observed, by adding 4 ms delay, all requests for DID documents arrive at edge nodes; therefore, they can all be satisfied by a single multicast transmission. As a result, only two DID documents are transferred over the backbone link.  
Similarly, in a system that uses the solution of [36], an advertisement triggers two requests: a request for retrieving the certificate that corresponds to \"smart-city.iot\" and a request for retrieving the certificate of the building. Therefore, both solutions result in the same communication overhead. Additionally, the delay-based mechanism can also be applied for the solution of [36].
## 3 Comprehensive Evaluation of Configuration
### Fuzzy Comprehensive Evaluation  
The weighted average operator is employed to conduct a comprehensive evaluation of all relevant factors. This approach establishes a fuzzy comprehensive evaluation model  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(L\) & \(K\) & \(D\) & \(KIC\) & \(\eta\) \\ \hline \(L\) & 1 & 1/3 & 1/3 & 1/3 & 1/3 \\ \(K\) & 3 & 1 & 1/3 & 1/3 & 1/3 \\ \(D\) & 3 & 3 & 1 & 1/3 & 1/3 \\ \(KIC\) & 3 & 3 & 3 & 1 & 1/3 \\ \(\eta\) & 3 & 3 & 3 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Configuration performance indicator correlations.  
Figure 9: Normalization indicator weights for each configuration.  
specifically for the configuration of the multi-wing deployment mechanism. The model is represented in Equation (15).  
\[b_{j}=\sum_{i=1}^{m}\left(a_{i}r_{ij}\right)\ \ (\mathrm{j}=1,2,\ \cdots,\mathrm{n}) \tag{15}\]  
where \(i\) is the no. \(i\) indicator, \(j\) is the no. \(j\) scheme, \(m\) is the total number of indicators, \(n\) is the total number of schemes, \(a\) is the weight of the indicator, and \(r\) is the degree of membership.  
From Figure 8, the weight matrix is as follows:  
\[A=\begin{bmatrix}0.07&0.107&0.166&0.257&0.4\end{bmatrix}\]  
From Figure 8, the evaluation matrix is as follows:  
\[R_{5\times 17}=\begin{bmatrix}1.013&1.033&1.033&\cdots&1.013&1.013&1.076&1.076 \\ 0.94&0.891&1.036&\cdots&0.949&1.348&1.091&0.892\\ 0.912&0.912&1.824&\cdots&1.824&1.824&0.912&0.912\\ 0.963&0.907&1.101&\cdots&0.963&1.284&1.101&0.963\\ 0.996&0.989&1.009&\cdots&1.002&1.023&1.009&0.996\end{bmatrix}\]  
In the comprehensive evaluation and optimization process, the comprehensive evaluation result matrix \(B\) is obtained by multiplying the weight matrix with the evaluation matrix \(R\) of each indicator.  
\[B=A\cdot R=\begin{bmatrix}0.969&0.948&1.173&\cdots&1.124&1.257&1.030&0.968 \end{bmatrix}\]  
The results are ranked by drawing on the comprehensive evaluation outcomes, as detailed in Figure 10.  
According to the principle of maximizing membership degree, configuration (o) demonstrates superior overall performance, followed by configuration (c), while configuration (h) exhibits relatively poor performance.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 2 Related Work
### Recent Advancements in Predictive Modelling  
Recent advancements in predictive modelling have significantly contributed to environmental monitoring and mental health fields. Among these, two of the most notable developments are the application of cognitive computing to predict the flow status of flexible rectifiers and the predictive modeling of flexible electrohydrodynamic (EHD) pumps using Kolmogorov-Arnold Networks (KAN).  
The first advancement leverages cognitive models to enhance the accuracy of stress prediction systems by integrating environmental and physiological data. [PERSON] et al. [15] explore this methodology, demonstrating its potential in adaptive stress detection systems.  
The second advancement involves the use of KAN, which employs learnable spline-based activation functions to model complex nonlinear relationships. This approach offers improved predictive accuracy over traditional models. [PERSON] et al. [16] highlight KAN's effectiveness in capturing intricate interactions between environmental factors and individual responses, which can be applied to refine stress detection models for better precision in predicting mental health outcomes.  
These advancements underscore the growing role of intelligent, flexible systems in enhancing prediction accuracy within uncertain and dynamic environments, with promising applications in mental health prediction and stress detection.
## 4 Data and Results
### Improvement of Digitalization Benefit in Cost Performance  
Some respondents refused to indicate their financial information due to confidentiality concerns. Hence, only 27 cases' cost performance data were collected. Table 9 shows the designated cost saving via the adoption of additional technologies besides the traditional ones, and the actual cost saving generated with a higher rate of technology utilization.
# The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process
## 3 Energy Efficiency Analysis
### Average Achievable Rate
: _The average achievable rate of the typical user associated with the PBS is expressed as:_  
\[\tau_{p}^{PHP}=\int_{0}^{\infty}\frac{1}{z}(1-e^{-z})\frac{1}{H\big{(}z/\hat{G }_{p}\big{)}}dz \tag{27}\]  
**Proof. ** When the deployment of the PBS is modeled by a PPP, the average achievable rate \(\tau_{p}^{PPP}\) can be expressed as:  
\[ \begin{array}{c}\tau_{p}^{PPP}=\int_{0}^{\infty}f_{p}(r_{0})\int_{0}^{\infty }\frac{1-e^{-z}}{z}E[e^{-zI_{p}}]dzdr_{0}\\ =\int_{0}^{\infty}f_{p}(r_{0})\int_{0}^{\infty}\frac{1-e^{-z}}{z}L_{I_{p}^{PPP} }(z)dzdr_{0}\end{array}  \tag{28}\]  
and its coverage probability can be obtained as:  
\[P_{C}^{PPP}(z)=P\Big{(}SIR_{p}^{PPP}>z\Big{)}=E_{I_{p}^{PPP}}[e^{-zI_{P}^{PPP} }]=L_{I_{p}^{PPP}}(z) \tag{29}\]  
The relationship between \(P_{C}\) and \(\tau\) is given in [14]:  
\[\tau=\int_{0}^{\infty}\frac{P_{C}(z)}{1+z}dz \tag{30}\]  
According to Formula (30), the expression of \(\tau_{p}^{PPP}\) can also be written as:  
\[\tau_{p}^{PPP}=\int_{0}^{\infty}\frac{P_{c}^{PPP}(z)}{1+z}dz=\int_{0}^{\infty }\frac{L_{I_{p}^{PPP}}(z)}{1+z}dz \tag{31}\]  
According to the effective SIR gain [51], we obtain:  
\[P_{C\_p}^{PHP}(z)=P_{C\_p}^{PPP}(z/\hat{G}_{p})=P(h_{p0}>zI_{p}^{PPP}/\hat{G}_ {p})=L_{I_{p}^{PPP}/\hat{G}_{p}}(z) \tag{32}\]  
where \(\hat{G}_{p}=\frac{1}{1+\omega}(G_{p}-1)+1\), \(\omega=\frac{\lambda_{p}}{\lambda_{m}}\Big{(}\frac{\mu_{p}}{\mu_{m}}\Big{)}^{\delta}\), and \(\delta=2/\alpha\). Similar to Formula (31), the expression of \(\tau_{p}^{PHP}\) can be written as:  
\[\tau_{p}^{PHP}=\int_{0}^{\infty}\frac{P_{C\_p}^{PHP}(z)}{1+z}dz=\int_{0}^{ \infty}\frac{L_{I_{p}^{PPP}/\hat{G}_{p}}(z)}{1+z}dz \tag{33}\]By comparing Formulas (28) and (31), the expression of \(\tau_{p}^{PHP}\) can also be written as:  
\[\tau_{p}^{PHP}=\int_{0}^{\infty}f_{p}(r_{0})\int_{0}^{\infty}\frac{1-e^{-z}}{z}L _{p}^{PPP}/\xi_{p}(z)dzdr_{0} \tag{34}\]  
where:  
\[f_{p}(r_{0})=2\pi r_{0}(\lambda_{p}+\lambda_{m}(\mu_{m}/\mu_{p})^{2/\alpha}) \exp(-\lambda_{p}\pi r_{0}^{2}-\lambda_{m}\pi(\mu_{m}/\mu_{p})^{2/\alpha}\tau_ {0}^{2}) \tag{35}\]  
\[L_{p}^{PPP}/\xi_{p}(z)=E[e^{-\frac{\tau}{\xi_{p}}l_{p}^{PPP}}]=\exp(-\pi\lambda _{m}r_{0}^{2}(\frac{\mu_{m}}{\xi_{p}})^{2/\alpha}(H(\frac{z}{\xi_{p}})-1)\cdot \exp(-\pi\lambda_{p}r_{0}^{2}(H(\frac{z}{\xi_{m}})-1) \tag{36}\]  
By substituting Formulas (35) and (36) into Formula (34), we can obtain the expression of \(\tau_{p}^{PHP}\). \(\square\)
## 4 Implementation and Performance Analysis of CAES-Based Image Encryption
### Information Entropy Analysis  
Image information entropy (IE) represents the aggregation feature of the distribution of the image pixel values [28]. The image IE was calculated using the following formula:  
\[H=-\sum\limits_{i=1}^{255}p_{i}\log p_{i} \tag{11}\]  
where \(p_{i}\) is the frequency of each greyscale. For a grayscale image, the pixel had a data field of \([0,255]\), and the maximum value of IE will be 8. The IE test report with the grayscale of image 1 in Figure 10 is given in Figure 12. From Figure 12, the IE value of CAES-ECB is the closest to the ideal value of 8. Therefore, it can be concluded that the encrypted image with CAES-ECB possessed the true random signal property, and the proposed algorithm had the ability to resist entropy attacks.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Based Localization Techniques
#### 2.2.1. Lateration  
The unknown position of the receiver node is estimated by using different range measurements, such as RSS, ToA, and TDoA, from multiple transmitting nodes with known locations. More specifically, a theoretical circle is first formed according to the estimated range from each known transmitting node, indicating the possible positions of the receiver. For a single transmitting node, as shown in Figure 4a, the receiving node can be located anywhere on the circle circumference. By adding another transmitting node, as shown in Figure 4b, then the receiving node can be located on any of the two intersection points formed. Finally, by adding a third transmitting node, as shown in Figure 4c, it eliminates the ambiguity to a unique point, which is the intersection of the three circles. The minimum requirement is three non-collinear transmitting nodes for trilateration (2D space) and four for multilateration (3D space). In error-free environments, these theoretical circles or spheres intersect at a single point, indicating the spatial position of the receiving node. However, in real-world applications, the circles do not necessarily meet at a single point, due to noise, and so a possible region is formed, instead, from which a single point is selected as the best guess using the least squares methods.  
The circumference of the circle or sphere for the 2D or 3D space, respectively, are commonly estimated using the Euclidean distance. For the trilateration, three quadratic equations are formed:  
\[(x-x_{A})^{2}+(y-y_{A})^{2}+(z-z_{A})^{2}=d_{1}^{2} \tag{10}\]  
\[(x-x_{B})^{2}+(y-y_{B})^{2}+(z-z_{B})^{2}=d_{2}^{2} \tag{11}\]  
\[(x-x_{C})^{2}+(y-y_{C})^{2}+(z-z_{C})^{2}=d_{3}^{2} \tag{12}\]  
where \((x_{A},y_{A},z_{A})\), \((x_{B},y_{B},z_{B})\), and \((x_{C},y_{C},z_{C})\) are the known coordinates of the transmitting nodes, \((d_{1},d_{2},d_{3})\) are the estimated distances between the receiving and transmitting nodes, and \((x,y,z)\) are the unknown receiver's coordinates.
## 4 MOFs as Smart Carriers and Delivery Systems of Sensitive Volatile Compounds
### Zr-MOFs
Their high stability, tunable pore sizes, and ability to enhance the efficacy of bioactive compounds make them valuable materials for developing advanced antimicrobial and antioxidant systems in agrifood applications. Addressing challenges related to pore size effects and structural integrity will further enhance their potential in various industries.
## 2 Literature Review
### Public Datasets  
The existing publicly available datasets about the merging behavior are NGSIM, INTERACTION [10], AUTOMATUM [11], and AD4 CHE [12]. Although these datasets are invaluable for data-driven driving behavior analysis, they have certain shortcomings. Mainly captured by a static camera, NGSIM data contains only 9206 trajectories, of which about 3% are truck trajectories. The INTERACTION dataset lacks the trajectories of heavy vehicles. In addition, the NGSIM and INTERACTION datasets are missing precise map reference. The HD map is one of the important technologies for autonomous driving, providing a large amount of more precise vehicle spatial-temporal data. The AD4 CHE dataset provides geo-referenced data but lacks the trajectory of vans [12].  
Despite the valuable insights these datasets provide into traffic flow and driving behavior, their limitations--such as insufficient sample sizes, the absence of specific traffic scenarios, and low traffic density in particular situations--hamper comprehensive research on vehicle interactions during merging. In contrast, the exiD dataset [13], recently released by the German automotive engineering research team at RWTH Aachen University, excels at depicting dense, dynamic traffic interactions. It supplies HD maps in both Opendrive and lanelet2 formats, significantly enriching the resources available for studying complex driving behaviors. Recording the movements of 69,172 road users, including cars, trucks, and vans, the exiD dataset offers a comprehensive view of vehicle merging interactions, positioning itself as an invaluable tool for this research.