esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Related Literature and Applications
### Review of Existing Market Solutions for Controlling Smart Device Use  
Building on the theoretical framework outlined in the previous section, this part of the paper explores how intervention strategies have been operationalised in commercial tools designed to regulate smart device use. Products developed by mobile carriers, device manufacturers, and software developers offer a range of parental control applications, many of which align with the intervention categories identified in prior research.  
In this study, current market solutions are classified into three categories:  
1. Parent-centred device management applications;
2. AI-driven automatic monitoring systems;
3. Child-centred self-regulation applications.  
For each category, representative examples are analysed with respect to their core features, strengths, and limitations. A summary of these tools and their defining characteristics is provided in Table 1. All tools were reviewed as of November 2024, and specific version information was not recorded due to limited access to historical version data. This analysis assesses the extent to which commercial solutions reflect principles from developmental theory while also identifying persistent UX shortcomings--particularly in supporting the formation of autonomous digital habits among preschool-aged children.
## 3 Results and Discussion
### Impact of Seeding Concentration  
The seed suspension was prepared by dispersing SSZ-13 crystals in ethanol (EtOH) at different concentrations and to prevent agglomeration subjected to ultrasonic treatment for 30 min. Generally, as the concentration of the seed suspension increases, the thickness of the formed membrane layer also increases (due to the nucleation site) [33]. The optimal seed concentration ensures the formation of a homogeneous and dense zeolite layer by creating sufficient nucleation sites throughout the substrate. However, high nucleation sites can lead to agglomeration, resulting in a thicker and potentially less uniform layer. Conversely, a lower concentration of seed crystals may result in incomplete coverage and create a non-uniform layer that cannot adequately cover the entire surface. It is necessary for the membranes to have minimum thickness, because by maintaining selectivity  
Figure 1: The cross-section view from three main layers of an asymmetric SSZ-13 membrane (**a**). Results of gas-tightness for SSZ-13 membranes with intermediate layer pore sizes of 100 nm, 200 nm, 800 nm, and 3 \(\upmu\)m (**b**).  
the permeance increases and the possibility of defects or cracks in later steps, e.g., the calcination operation, is reduced. Based on Table S1, membranes with a seed suspension concentration less than 0.25 wt% and above 1 wt% showed N\({}_{2}\) permeances higher than 7 \(\times\) 10\({}^{-9}\) [mol/(m\({}^{2}\)sPa)] during the tightness test. After calcination, single gas permeation was measured on the membranes with seed concentrations of 0.25, 0.5, 0.75, and 1 wt%, as shown in Figure 2a. It can be seen with the increase in seed concentration from 0.25 to 1 wt%, the gas permeance reduced, due to the formation of a more uniform and denser layer and, as a result, permselectivity of H\({}_{2}\)/SF\({}_{6}\), CO\({}_{2}\)/N\({}_{2}\), and CO\({}_{2}\)/CH\({}_{4}\) increased to 95, 14, and 57, respectively.  
Figure 2: Effect of pre-hydrothermal reaction parameters including seed concentration (**a**), suspension liquid and sonication (**b**), Si and Al sources (**c**), Si/Al ratio (**d**), OH/Si ratio (**e**), H\({}_{2}\)O/Si ratio (**f**), TMAdaOH/Si ratio (**g**), and ageing time (**h**) on single gas permeation measurement of SSZ-13 membranes.
## Appendix B
### Approach to 2D Wind Vector Identification for MISTiC Airborne Observations  
The basic approach to observing the wind employed for MISTiC is the geometrical method first developed for Motion Vector Winds following the introduction of GEO multispectral imagers. The image field is examined for features in the cloud or moisture field that have been moved from one location to another during the time interval, and identifies track-points on these features to allow a quantitative measure of the change. This vector position difference is computed using image data, together with attitude and ephemeris data. In MISTiC Airborne, a feature position is determined from the GPS coordinates of the sweep nadir point, that angular position within the image field, GPS altitude, optics focal length, and FPA pixel size. The wind speed observed is just the magnitude of this vector difference. For the operational GEO AMVs, where thousands of AMVs are derived from a sequence of full-disk images, the manual identification of track-points procedure is too time-intensive, has been replaced by a spatial correlation computation method-but that is not needed, given the relatively much smaller number of image-pairs observed in a MISTiC airborne flight. Moreover, the image pixel-level spatial resolution available from MISTiC airborne is very high (50 m at nadir), compared to that available to GEO MET imagers, simplifying the correct identification of a geometric feature, a wind tracer in multiple positions. Finally, the low altitude of the MISTiC observations and wide angular field introduce substantial geometric distortion of the shape of tracer features as well as a truncation of the feature by the very limited spatial field of view. These are aspects of the images that a human image interpreter can reasonably overcome, but which would pose great difficulties to current automated cross-correlation algorithm. The other critical aspect of the AMV observation is the accurate assignment of height.
## 2 Main Concepts
### Multi-Frame Reconstruction
#### 2.2.1 Burst  
Burst mode, or continuous mode, involves capturing a rapid sequence of successive images. This technique is particularly useful in low-light conditions, when building high dynamic range images, and when capturing fast-moving scenes.  
In the context of HDR reconstruction using deep learning techniques, burst mode can significantly improve image quality by reducing noise and increasing dynamic range. This is achieved by capturing multiple frames with constant exposures and using advanced alignment and noise reduction algorithms [52; 53; 54].
## 3 Numerical Simulation
### Numerical Model Properties  
The dimensions of the models used for numerical simulations were similar to the prototype scale performed at the experiments (Figure 2). To increase the accuracy and reduce calculation time, the selected maximum size of hexahedral-dominant mesh for soil media, foundations, and lumped masses was 0.5 m with a concerning frequency of below 25 Hz ([PERSON] 2013; [PERSON] and [PERSON] 2008). The Mohr-Coulomb soil criterion, in accordance with the hysteretic damping model, was implemented to simulate the nonlinear behavior of soil. The parameters required for the Mohr-Coulomb model of the two soils, such as friction angle (\(\varphi\)) and cohesion (c), were determined through experiments on soil specimens at a similar relative density (D\({}_{\text{r}}\)) to the soil in ESB container, such as tri-axial test. The model properties, which are depth-dependent, such as shear modulus (G) and bulk modulus (K), were calculated from V\({}_{\text{s}}\) profiles as functions of the model depth (Figure 1). The Hardin-Drnevich hyperbolic model fitting to RC test results on the same soils was adopted to simulate degradation of soil shear modulus and increase in damping with the shear strain [28]. A Rayleigh damping was applied to be the minimum damping (D\({}_{\text{min}}\)) at a small strain (the damping at \(10^{-3}\) shear strain obtained from RC tests) for all the soil elements with a frequency range from 0.5 Hz to 10 Hz.  
The foundation and top mass of structures were simulated using solid elements. The shell elements (ShellSELs) were implemented to model the walls of structures. The thickness of shell elements was taken to be equal to the thickness of the structural walls at the prototype scale, which was 0.28 m for S1, and 0.18 m for S2 and S3 structures. A  
Figure 2: FLAC3D model with the S1 structure at (**a**) L2 profile case and (**b**) soil and structural elements.  
[PERSON]'s ratio of 0.33 was used for aluminum materials, and values of Young's modulus for the structural material were defined by calibrating the value of the fixed-base frequency of each structure using the linear frequency solver in ABAQUS software [29]. To model the interaction between the foundation and soil elements, the interface elements were adopted as systems of linear spring and slider (Figure 2). The Mohr-Coulomb failure criterion was embedded for the interface shear strength with friction coefficient taken as 0.57 following [PERSON] and [PERSON] [26]. Values of normal (\(k_{n}\)) and shear stiffness (\(k_{n}\)) of interface elements, which control relative movement of the interface, were defined following the recommendation of [PERSON] [28] with a reduction reported by [PERSON] and [PERSON] [25] because of the reduction in soil stiffness during the earthquake. The free-field boundary was applied for the sides of the model to minimize reflected waves from these boundaries. The quiet boundary was used for the base, and the input motion was applied as shear stress estimated from the velocity of input motion.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 2 Tracking Scheme of Direct-State Kalman Filter
### Steady-State Analysis  
In the steady-state region, the Kalman gains \(\mathbf{K}\) converge to a steady-state value given a constant \(q\) and \(R\). The solution of the DARE presents the relation between \(\mathbf{K}\), \(q\) and \(R\). The expression of the DARE is [36; 37]:  
\[\mathbf{P}_{\mathrm{ss}}=\mathbf{A}\:\mathbf{P}_{\mathrm{ss}}\:\mathbf{A}^{ \mathrm{T}}-\mathbf{A}\:\mathbf{P}_{\mathrm{ss}}\:\mathbf{H}^{\mathrm{T}}( \mathbf{H}\:\mathbf{P}_{\mathrm{ss}}\:\mathbf{H}^{\mathrm{T}}+R)^{-1}\mathbf{ H}\:\mathbf{P}_{\mathrm{ss}}\:\mathbf{A}^{\mathrm{T}}+\mathbf{Q} \tag{31}\]  
where \(\mathbf{P}_{\mathrm{ss}}\) is the steady-state convergence of the error covariance matrix \(\mathbf{P}\). The following is assumed to facilitate DARE's solution [19]:  
\[R\gg\mathbf{H}\:\mathbf{P}_{\mathrm{ss}}\:\mathbf{H}^{\mathrm{T}} \tag{32}\]  
The resulting \(\mathbf{P}_{\mathrm{ss}}\) for a third-order DSKF is symmetric and defined as:  
\[\mathbf{P}_{\mathrm{ss}}=\begin{bmatrix}2q^{1/6}R^{5/6}\tau_{\mathrm{int}}&2q^ {1/3}R^{2/3}\tau_{\mathrm{int}}&q^{1/2}R^{1/2}\tau_{\mathrm{int}}\\ \mathrm{sym.}&3q^{1/2}R^{1/2}\tau_{\mathrm{int}}&2q^{2/3}R^{1/3}\tau_{\mathrm{ int}}\\ \mathrm{sym.}&\mathrm{sym.}&2q^{5/6}R^{1/6}\tau_{\mathrm{int}}.\end{bmatrix} \tag{33}\]Applying Equations (32) and (33) into Equation (21), the steady-state Kalman gains \(\mathbf{K}_{\text{ss}}\) are represented as:  
\[\mathbf{K}_{\text{ss}}=\mathbf{P}_{\text{ss}}\,\mathbf{H}^{\text{T}}\,R^{-1}= \left[2(q/R)^{1/6}\tau_{\text{int}}\quad 2(q/R)^{1/3}\tau_{\text{int}}\quad(q/R)^{1/2} \tau_{\text{int}}\right]^{\text{T}} \tag{34}\]
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 3 Ray Tracing
### Typical Characteristic Parameters
The model training process is shown in Algorithm 1, where Reconstruction loss and Classification loss and Kullback-Leibler loss are the loss functions. ```
Input: Pseudolite Observation dataset: \(X_{1}=\left[X_{1}^{(1)},X_{1}^{(2)},\ldots,X_{1}^{(n)}\right]\), Ray Tracing simulation dataset \(X_{2}=\left[X_{2}^{(1)},X_{2}^{(2)},\ldots,X_{2}^{(n)}\right]\), \(n\) is the number of pseudolites. Location label: \(y\). Output: Representation: \(z\) and parameter: \(\phi\); \(\theta\), Classification model: \(Model_{classifier}\{(z,y)\}\)
1: Initialization Parameters: Number of neurons for all layers; The number of iterations (epochs);
2: while \(\{\phi,\theta\}\) not converged do
3: \(D\gets getMinibatch()\)
4: \(\mu_{\theta},\theta_{\theta}\gets x,y\);
5: Sampling \(\varepsilon\);
6: Sampling from the posterior \(z\gets q_{\phi}(z|x,y)\) using the flowing

Reparameterization trick: \(z=\mu_{\theta}+\sigma_{\theta}\cdot\varepsilon\);
7: Calculate the gradient of the variational lower bound \(L\) (Reconstruction loss and Classification loss and Kullback-Leibler loss);
8: Minimize \(L\);
9: end while
10: while Classification model Training do
11: Fit \(\forall\{x,y\}\in D\) train Classifier \(Model_{classifier}\{(z,y)\}\)
12: end while
```  
**Algorithm 1**Model Training
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## Appendix A Simple and Cumulative Moving Average  
In statistical analysis, if \(x\) is a random process for which a time series of \(n\) measurements is available, a Simple Moving Average (SMA) of \(x\) at the \(k\)th epoch, denoted by \(\left\langle x\right\rangle_{k}\), is the unweighted mean of the previous \(n\) data:  
\[\left\langle x\right\rangle_{k}=\frac{1}{n}\sum_{l=k-n+1}^{k}x(l)=\frac{x(k)+x( k-1)+\cdots+x(k-n+1)}{n}. \tag{1}\]  
In a Cumulative Moving Average (CMA), the mean value is computed based on the average of all of the data up until the current epoch on a recursive basis:  
\[\left\langle x\right\rangle_{k}=\frac{1}{n}x(k)+\frac{n-1}{n}\left\langle x \right\rangle_{k-1}. \tag{2}\]  
In Equations (1) and (2), if \(n>k\), \(n\) is replaced by \(k\)[42].
## 2 Continuous-Time Recurrent Neural Network (CTRNN)
### MEMS Analogy  
As stated before and shown in Figure 1b, the MEMS dynamics can be engineered to match the equation of a continuous-time recurrent neuron [6; 7; 8]. Thus, eventually, the MEMS can perform the CTRNN calculation without the need for a computer. In this mapping, the time constant \(\tau\) in the CTRNN corresponds to the mechanical time constant of MEMS resonators, governed by the mass-to-damping coefficient ratio. Synaptic weights can be mapped to coupling stiffness values or transconductance gains in MEMS electro-mechanical systems, while biases relate to baseline actuation voltages or equilibrium positions. However, practical limitations must be considered. MEMS devices are affected by process variability, temperature-dependent drift, and mechanical noise, which may introduce deviations from ideal continuous-time behavior. Despite these challenges, recent work in analog computing shows promise in mitigating such effects through calibration and robust design strategies.
## 4 Measurement Uncertainty Evaluation
### Measurement Uncertainty for the Contact Method  
The metrological characterization described in Section 3.2 supported the determination of the standard measurement uncertainty related to the contact dimensional measurement which was posteriorly used to calculate the strain standard uncertainty (by the application of Equation (16)) and, in a last stage, the modulus of elasticity and the Poisson ratio. Figure 15 shows the measurement estimates and standard uncertainties obtained for the tested masonry specimens, taking into account the specimen's cross-section area and maximum load force measurement uncertainties described in Appendix C.  
A standard uncertainty comprised between 0.18 and 0.78 GPa was obtained for the modulus of elasticity, excluding a 1.9 GPa dispersion value obtained for the specimen with ID M0M12. In this specimen, significant differences were obtained between strain measurements in the frontal and the rear specimen surfaces.  
The results related to the Poisson ratio show a standard uncertainty between 0.10 and 0.32, making this measurement quite inaccurate, since this variation has a similar dimension to the expected measurement interval.
# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 1 Introduction  
The administrative authorities of many advanced countries face the challenge of ensuring equal or at least equivalent provision of public services and goods in all administrative areas (access to food, health, education, public transport, etc.). However, due to dynamic processes, national or regional administrations deal with manifold problems, such as low birth rate [1], dynamic inflow or outflow of population groups [2], limited or ageing (human/structural/technical) resources for the operation of infrastructures and services. In addition, the role of ageing societies and the associated changes in needs and demands should not be underestimated. The latter is especially true for most of the developed countries of the G-20 in the near future [3]. The even or equitable distribution of public services/infrastructure places various demands on planners. They must have an overview of the supply situation of current and future infrastructure components (food markets, healthcare locations, educational facilities, public transport, etc.), as well as the current and future demand potential of the population. However, for planning purposes this alone is not enough, as it is important to evaluate the situation in small-scale spatial units and to be able to respond with planning actions. The reaction could be an infrastructuredecision or measure of a recommendatory nature for decision makers. In order to fulfil the requirements roughly outlined in this introduction, a wide range of data, complex models, technical analysis, and planning tools (in the form of a spatial decision support system, short SDSS) in combination with a well-structured user interface are required.  
Overall, high demands are placed on the access to data, but also on the methodological and computational models with which the analysis is realised, as well as on the process of user guidance. This paper proposes for an integrated planning tool with multicriteria--spatial decision support system (MC-SDSS) functionality (term based on [4]) for public services, which is fully usable as an online tool for a defined group of planners in the federal state of Lower Saxony/Germany.  
This paper is linked to key concepts in the context of services of general interest and SDSS (Section 2). In doing so, we focus on the situation of previous web-based SDSS examinations and explain the background and objectives of this study. This lays the foundations for explaining the prerequisites for our application development, describing use cases and presenting our analysis concept and methods (Section 3). We then present our implementation with the illustration of our user interface and provide a look at an exemplary results map (Section 4). Our work concludes with a summary (Section 5) and an outlook (Section 6).
# Underlying Factors and Strategies for Organizational BIM Capabilities: The Case of Iran
## 3 Research Methodology  
### Survey Development  
In this study, data were collected through a questionnaire survey. Questionnaire surveys have often been used in research to obtain opinions from experts in the construction management area. To develop the questionnaire survey, a systematic literature review (SLR) was conducted to review the existing literature. The objective of the SLR is to identify a list of capability factors and strategies for OBIMC from prior works. The SLR method is divided into two steps. In the first step, the database of Scopus was searched using the 'title/abstract/keyword'. The terms used were 'building information modelling' OR 'building information modeling' AND 'capability' OR 'capabilities'. Based on the search terms, 205 articles were retrieved. Afterward, titles, keywords, and abstracts of the 205 articles were examined, and unrelated articles were removed. Finally, 26 articles were chosen for further investigation. To obtain any additional strategies or capability factors missing from the existing body of knowledge, semi-structured interviews with fifteen BIM professionals were performed. To validate the output of the interview, a thorough synopsis of the interview's results was sent to the participants after each session. Based on the data from SLR and interviews, a survey was developed. Capability factors and strategies with similar meanings were merged. Finally, a list of nineteen capability factors and fourteen strategies was established. Table 1 presents strategies for improving OBIMC. On the contrary, Table 2 shows the capability factors affecting OBIMC derived from the SLR and interviews. Three professors in the construction management field participate in the pilot test. The pilot test was conducted to eliminate vague questions and phrases and to ensure the proper use of technical language. The questionnaire survey was divided into two sections. The first section of the survey includes questions related to the respondent and organization backgrounds. This section is crucial to evaluate the reliability of the respondents. The second section consists of the capability factors and enhancement strategies. Respondents were required to rate the criticality of the strategies on a five-point Likert scale with 1 being not critical, 2 being less critical, 3 being neutral, 4 being critical, and 5 being extremely critical. The third section consists of a list of capability factors affecting the OBIMC. Respondents were required to rank the criticality of the capability factors on a five-point Likert scale with 1 being not critical, 2 being less critical, 3 being neutral, 4 being critical, and 5 being extremely critical. Researchers have used the five-point Likert scale in the construction management field because of its ability to give clear results [79]. Finally, at the end of the questionnaire survey, spaces are given to respondents to add and rank the criticality of any additional strategies and capability factors on a five-point Likert scale.
## 4 Methods (ASMAG Algorithm)
### Data Pre-Processing  
#### 4.1.1 Import of Metadata and Landsat Image Sub-Setting  
ASMAG first imports all metadata (given in the *MTL.txt file) for each Landsat scene (sun elevation, sun azimuth, acquisition date) and each spectral band (bias, gain, pixel size). These numbers are used for the subsequent topographic normalization. Afterwards, all Landsat bands of a scene are cropped to the extent of the study region.
## 4 Simulation Results and Discussion
At about 10 s, the follower has caught up with the leader. Unlike the controller proposed in this paper, it is extremely difficult to avoid a huge input signal and a drastic change of input signal. Figure 4:1 Relative distance under different controllers (the expected distance is 5 m, the minimum communication distance is 10 m and the maximum collision avoidance range is 1 m.  
Figure 4:2 Formation process of the 1 st USV under different controllers, (blue solid line represents the position of USV 0). By using the modified barrier Lyapunov function, the connectivity preservation and the collision avoidance are achieved. By using ESO and the proposed controller, the followers can track the leader accurately. By using an auxiliary variable, the input saturation is solved. Positive definitions of matrix \(K_{i01}\), \(K_{i02}\) and \(K_{i03}\) make sure that convergence of estimation error is achieved. By adjusting \(u_{0}\), the track error can be made arbitrarily small. Both controllers are designed based on Assumption 2, and \(\eta_{0}\), \(u_{0}\), \(v_{0}\), \(r_{0}\) are bounded, meaning that the formation control problem is solved only when the follower starts in a certain neighborhood of the leader. In [29], the path following the control problem of USVs was investigated and the system could provide global asymptotic stability. How to design a controller which could makes the system have global asymptotic stability is an interesting challenge. Both controllers are designed without considering the saturation rate of the actuator, meaning that the conclusion is relatively radical. How to design a controller with the rate saturation factor for formation control will be considered in future work. Figure 4:3 Detailed view of Figure 4:4  
Figure 4:5 Control input under different controllers.