esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Smart Angel Project
### The Caregivers' Point of View  
The Smart Angel system foresees a situation in which the person with intellectual disability is followed by a caregiver or tutor who helps and monitors the person's everyday life. Often caregivers tend to limit the person's freedom because they foresee more problems than advantages. They are worried that the person may get lost or have any trouble and therefore do not let him move independently. Furthermore, they feel the responsibility that if anything bad should happen it would be their fault.  
Having the possibility to use a monitoring system, will reassure the caregivers and the families who will be able to know the position of the person at any time, get in touch with him as needed, and receive an immediate help request if anything should go wrong.  
Furthermore, the availability of a large set of data to be analysed from everyday monitoring activities allows having a better insight of the person's real level of independence. It often happens that the final user actually knows wider areas in town and that he is able to move around much more than what their caregivers think. Therefore, the level of independence that is given to each end user can be tailored to his real skills, making it higher if possible or, on the other hand, planning for specific intervention in case specific recurring problems should arise. Any deviation from standard can also be traced, which is important because it may be a sign that some kind of problem has arisen and correcting actions may be needed.  
Caregivers can access the data in the cloud system and all its functions from any place via Internet; therefore, their presence is not linked to their being physically in the caring centre. Furthermore, outside official monitoring hours, family members may be involved in monitoring and support activities.  
Finally, since data is continuously tracked by the system, the central database will become an informative resource to allow the health professionals to have a deeper knowledge of each single person on the one side and, on the other, to have statistical information about the disabilities. This will enable the social group to give better help by having a deeper understanding of the problems people with intellectual disabilities have to face and therefore giving them the tools needed to be as socially active as possible.
# New Advances, Challenges, and Illustrations in Applied Geochemistry
## 3 Environmental Geochemistry
### Elemental Bioaccumulation  
Elemental bioaccumulation refers to transfer of elements from soils to crops or the crop uptake of elements from soils, which is measured by conducting a geochemical survey on the soils and the crops grown. The bioconcentration factor (BCF) is often used to quantitatively calculate the accumulation of heavy metals in plant tissues relative to the environmental soils [22].  
[PERSON] et al. (contribution 7) studied the bioaccumulation of arsenic in the soil-rice system, using paired soil-rice samples from karst regions in Guangxi, China. They indicate that concentrations of As in the rice are inconsistent with the total concentrations of As in the soils, and the low bioavailability of As by rice in the karstic paddy soil was attributed to its residual form being present in Fe-Mn oxides. This low bioavailability of heavy metals can explain the inconsistent assessment results for soils and their crops (contribution 5).  
Although inconsistent assessments and uncorrelated concentrations between soils and crops are widespread findings, many studies aiming to predict heavy metal concentrations in crops based on their concentrations in soils have been carried out. [PERSON] et al. (contribution 8) established a prediction model for zinc concentration in rice from farmland soil, using 371 paired rice-soil samples from the Pear River Delta and Heyuan regions in Guangdong, China, and machine learning models. [PERSON] et al. [23] also presented a prediction model for cadmium concentrations in paired rice-soil samples, using paddy soil samples from Guangxi Province, China, and machine learning algorithms. Besides rice, heavy metal contamination levels in other crops, such as maize [24] and peanut [25], have also been predicted on the contamination levels of the soil in which they were grown. However, these prediction models are only pilot studies and primarily data-driven, and knowledge-based models have not been well developed.
## 1 Introduction
The effective coefficients are computed for two different geometrical configurations of cells: square and hexagonal. The FEM implementation is proposed by using 4-node and 8-node quadrilaterals. The main contribution of this research consists of the generalization of the SAFEM proposed in refs. [3; 6] to the field of thermoelastic composites with perfect contact. Additionally, a computational tool for calculating the effective thermoelastic properties of composite materials from the properties of their constituents was created. Finally, the effective properties of composite materials based on elliptical cross-section fibers with different orientations, were estimated. Following this introduction, the reminder of the paper is organized as follows: the statement of the basic equations of thermoelasticity and the fundamental steps of the AHM are presented in Section 2. In Section 3, linear and quadratic finite element formulations are developed. Meshes are constructed to discretize the regions corresponding to the quarter of the periodic cell, and FEM formulation is applied to two reference local problems. Numerical results are shown in Section 4. Effective thermoelastic properties are obtained for different fiber volume fractions. In order to validate the numerical results, comparisons with the semi-analytical expressions reported in ref. [8] are performed. The results are compared with those reported in ref. [23] and they are also shown in the form of figures. Section 5 shows the effective properties for an elliptical cross-section fiber embedded into a square cell distribution. Distributions of the fiber's cross section with different orientations are also studied. In Section 6, some details considered during the implementation of the program are explained. Finally, the conclusions are presented in Section 7.
# Development of Track Support Stiffness Measurement and Evaluation System for Slab Tracks
## 2 Overview of the Track Support Stiffness Evaluation System
### Performance Requirements of Track Support Stiffness Measurement System  
The track support stiffness measurement system in this study is a TSS tester (Samlim Engineering, Co. Ltd., Seoul, Korea) used to measure the performance and durability of elastic materials for the slab track that can evaluate the state of track support stiffness by using the load-displacement curve at desired measuring points. The items that can be measured by using the track support stiffness measurement system are static and dynamic wheel loads and the displacement of rails or sleepers. Therefore, the static spring stiffness test is possible, using the static load and the displacement of the serviced track, and the dynamic spring stiffness test and dynamic characteristics analysis are possible using the dynamic load. In addition, the track support stiffness of the track structure can be evaluated by measuring the displacement of the track according to the static and dynamic loads. The core function of the TSS tester in this study is the loading function of the actuator. Therefore, in this study, the possibility of deriving the load-displacement curve for the tracks that are commonly used in the  
Figure 11: Evaluation of track support stiffness.  
Figure 10: Example of the test result for the load–displacement curve.  
field by using the TSS tester, load control capability, and dynamic excitation capability of the test equipment is evaluated.
## 4 Results and Discussion
### Temperature of the ESC  
The correlation between changes in the temperature of the ESC and particle contamination during the etching process was analyzed. The ESC had a fixed voltage of 2500 V, and its temperature was set as 20, 40, 60, and 80 \({}^{\circ}\)C in the experiments. Four temperature cycles were tested in each experiment, and no plasma was produced. For each temperature condition, two wafers were used, as displayed in Figure 14. Figure 15a reveals that relatively few particles were produced under temperature changes of less than 20 \({}^{\circ}\)C. If the temperature change was higher than 20 \({}^{\circ}\)C, edge particles were generated on the wafer. These particles are attributed to the scratches on the back side of the wafer caused by thermal expansion during the ESC heating cycle. The particles were attracted to the wafer surface because of the positive potential of the ESC and finally adhered to the edge of the wafer, as depicted in Figure 15b. The aforementioned thermal expansion can be expressed as follows:  
\[L=L_{0}(1+a\cdot\Delta T) \tag{8}\]  
where \(L\) is the length after expansion, \(L_{0}\) is the length before expansion, \(\Delta T\) is the temperature difference, and \(a\) is the coefficient of thermal expansion (2.6 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) for silicon). The related frictional force is expressed as follows:  
\[\text{F}=\mu\times w \tag{9}\]  
where \(\mu\) is the coefficient of friction, and \(w\) is the normal force (=\(m\text{g}+\text{F}\), where \(m\) is the weight of the silicon wafer, and g is the chucking force). Thermal expansion and contraction of the wafer results in friction between the wafer and the surface of the ESC. The effects of temperature on the ESC were analyzed, and temperature was found to be a key factor affecting the particle count. The particle map presented in Figure 16 reveals that decreases in temperature resulted in increases in the particle count. This result is attributed to particles on the backside of the wafer traveling to the front side when the temperature was reduced.  
Figure 14: ESC heating cycle for the particle deposition test.  
Figure 16: Particle deposition under increases and decreases in the temperature of the ESC.  
Figure 15: Particle deposition under various temperature changes for the ESC. (**a**) Particle distribution results under different temperature changes for the ESC. (**b**) Thermal expansion of the wafer because of the heating of the ESC, which resulted in scratches on the backside of the wafer.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 5 Simulation Results  
The latest work [32] estimates the cost of a given path in the network based on five criteria: adaptive network packet size, accurate packet count, overall required time interval, QoS (Quality of Service) link capacity (bandwidth), and shortest path in terms of hops. This paper also takes into account QoS. In this section, we construct a simulation testbed for FANETs based on the NS-3 network simulation platform and evaluate the performance of OLSR, AODV, DSDV, B.A.T. Mobile, PARRoT, and QEHLR. The evaluation metrics include packet delivery ratio, network throughput, average end-to-end delay, and average jitter. The definitions of these metrics are as follows.
## 2 Literature Review
### Historical Concept of Intelligent Reflecting Surface Technology  
A summary of the historical perspective can be observed in Table 1 and Figure 2. Intelligent walls (IW), based on frequency selective surfaces (FSS), can alter the propagation direction, environment and performance of the electromagnetic (EM) waves through switching [48; 49]. Subsequently, 2D metamaterials were designed to be an alternative to FSS for operation at various frequencies [33]. In [33], the authors outlined several use cases of 2D metasurfaces and discussed the ability of wave guides to trap and guide EM energy between two metasurfaces as well as the ability of terahertz devices to control metasurfaces to benefit operation at terahertz frequency. The benefits and applications in wireless communication of 2D metasurfaces, which occupy less physical space and experience lower loss when compared with three-dimensional (3D) surfaces, were also highlighted along with the operation at different frequency bands. Tunable metasurface-based spatial microwave modulators (SMM) were implemented by placing the SMM on the walls for maximizing the power or range of the transmission signal [50]. More recently, coding metamaterials (MM)s with the ability to manipulate the EM properties, replacing the phases 0 and \(\pi\) with a binary 0 and 1 [51] was introduced. This further helped in implementing the software program defined metamaterials as mentioned in [52]. Furthermore, programmable metasurface based on a PIN diode was introduced in [53] and references therein. Finally, in 2016 reconfigurable reflect arrays with passive elements were introduced, [54] and a large intelligent surface was proposed as a concept for beyond mMIMO [55; 56; 57]. In [58],  
Figure 1: Outline of the covered topics.  
the authors discussed backscatter principles and communication and reflective relay and introduced a large intelligent surface antenna (LISA). A software controlled hype-surface for IoT devices and for non-line-of-sight (NLoS) indoor communication was presented in [59; 60]. Intelligent reflecting surface-based phase shifting of reflecting elements to steer the EM direction was proposed in [61].
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
## 2 Methods
### Conjugate Gradient Method  
The conjugate gradient method (CG) is usually used to solve Equation (6). The solving process can be understood as the problem of solving linear equations \(\mathbf{A}\cdot\mathbf{x}=\mathbf{b}\) using CG given the least-squares solution for the overdetermined problem and the minimum norm solution for the underdetermined problem. The steps of CG are given as follows, assuming four vector sequences \(\tau_{k}\), \(\overline{\tau_{k}}\), \(\mathrm{p_{k}}\), \(\overline{\mathrm{p_{k}}}\), \(k\), \(k\) = 1,2..., and \(\mathrm{p_{1}}=\tau_{1}\), \(\overline{\mathrm{p_{1}}}=\overline{\mathrm{r_{1}}}\):  
\[\alpha_{k}=\frac{\tau_{k}\cdot\overline{\tau_{k}}}{\mathrm{p_{k}}\cdot\mathrm{ A}\cdot\overline{\mathrm{p_{k}}}} \tag{7}\]  
\[\mathrm{r_{k+1}}=\mathrm{r_{k}}-\alpha_{k}\mathrm{A}\cdot\mathrm{p_{k}} \tag{8}\]  
\[\overline{\mathrm{r}}_{k+1}=\overline{\mathrm{r}}_{k}-\alpha_{k}\mathrm{A} \cdot\overline{\mathrm{p}}_{k} \tag{9}\]  
\[\beta=\frac{\tau_{k+1}\tau_{k+1}}{\tau_{k}\cdot\overline{\mathrm{r}}_{k}} \tag{10}\]  
\[\mathrm{p_{k+1}}=\tau_{k+1}+\beta_{k}\mathrm{p_{k}} \tag{11}\]  
\[\overline{\mathrm{p}}_{k+1}=\overline{\mathrm{p}}_{k+1}+\beta_{k}\overline{ \mathrm{p}}_{k} \tag{12}\]  
The large amount of computation is an inherent problem in solving the Radon transform in the time domain. The operator \(\mathbf{L}\) and \(\mathbf{L}^{T}\) is the large sparse matrix. Each iteration requires the multiplication of the large sparse matrices \(\mathbf{L}\) and \(\mathbf{L}^{T}\) with vectors, which requires significant calculation.
# Reuse of Spent Coffee Grounds: Alternative Applications, Challenges, and Prospects--A Review
## 6 SCGs as an Additive to Increase the Calorific Value of Biomass  
Another example of the reuse of SCGs could be their use as an additive to raise the heat of combustion value of biological materials. [PERSON] and [PERSON] (2016) analyzed the effect of adding SCGs to tea grounds, pine wood, and wheat straw. They determined the heat of combustion of individual substrates and then prepared their mixtures with SCGs in the proportions shown in Table 5. The results obtained by [PERSON] and [PERSON] (2016) show that the calorific value of individual substrates increases with increasing the content of SCGs in the mixtures [42]. SCGs have a high calorific value, and, for this reason, their combustion can be one of the better methods for their utilization. The combustion of coffee waste and the use of this process in practice involves the requirement for a constant supply of large quantities of SCGs. This may pose a challenge due to the lack of continuous supply, which is particularly problematic in regions where the coffee industry is less developed. Limited availability of raw material can lead to interruptions in energy production or other industrial processes that use SCGs. Furthermore, collecting, transporting, and storing large quantities of coffee waste require organized logistics and appropriate infrastructure, which entail additional costs and operational challenges. For this technology to become practical, it is necessary to address issues related to the availability of raw materials and ensure economic feasibility. In the future, the development of efficient methods for collecting and  
\begin{table}
\begin{tabular}{c c c c} \hline
**Form of SCGs** & **Application in MFC** & **Results** & **References** \\ \hline Washed and dried SCGs (rinsed with & Anodic region & Power density—60.6 mW/m\({}^{2}\) & [36] \\ deionized water, dried at 105 °C) & Anodic region & Power density—3927 mW/m\({}^{2}\) & [37] \\ Carbonized SCGs & SCGs as a fuel cell & Power density—87.2 mW/m\({}^{2}\) & [38] \\ Used coffee wastewater & Substrate for the MFC & Power density—300–900 mW/m\({}^{2}\) & [39] \\ Used coffee wastewater & Carbonate fuel cells immersed in coffee & Power density—25 mW/m\({}^{2}\) & [40] \\  & leachate—bioanode & Power density—264 mW/m\({}^{2}\) & [41] \\ \hline \end{tabular}
\end{table}
Table 4: Utilization of different forms of SCGs in MFCs.  
processing SCGs, along with sustainable logistical solutions, may improve raw material availability and reduce costs.
## 4 Conclusions  
This study systematically, experimentally, and analytically investigated the influence of fly ash on chloride diffusion, chloride bonding capacity, and the microstructure of normal concrete in a water soaking environment. The main findings are summarized as follows.  
1. The addition of fly ash reduced free chloride diffusion, increased the chloride binding capacity, and improved the concrete compactness, which could further improve the durability of concrete.
2. Two individual zones of chloride deposition, namely the descending zone (0-15 mm) and the gentle zone (15-40 mm), were identified through the measurement of chloride content. The chloride concentration of samples at the 0-7.5 mm zone reduced by 54.5%, 65.9%, and 69.4%, respectively, for PCFA0, PCFA15 and PCFA30. Fly ash addition indeed decreased the free chloride concentration.
3. In natural immersion, the chloride ion diffusion coefficients of concrete with 15% and 30% fly ash replacements for cement were reduced by 26.1% and 37.9%, respectively, compared to concrete without any fly ash addition, which indicated that the more fly ash was added in the concrete, the lower the chloride diffusion coefficient.
4. Both chemical ion exchange and physical adsorption processes contributed to the reduction of free chloride ion concentrations. The free chloride ion content had a linear relation with the total amount of chloride ions. An equation was proposed to predict the binding capacity of chloride. Concretes with 0%, 15%, and 30% replacement for cement had binding capacity indexes of 0.1426, 0.1882, and 0.2134, respectively, which indicated that the chloride binding capacity was improved by fly ash. In this case, fly ash reduced the free chloride ion concentration, and if this concentration was less than the critical value, the rebar corrosion could be prevented, thus resulting in a longer service life of reinforced concrete. Moreover, a future study is expected to differentiate between chemical and physical adsorption process.
5. Fly ash particles and hydration products (filling and pozzolanic effect) led to the densification of microstructures. SEM results showed that the volume of pores less than 100 nm obviously increased for concrete with 15% fly ash replacement for cement, the volume of pores sized from 4.5 to 50 nm, and 50 to 100 nm pronouncedly increased, while the volume of pores larger than 100 nm decreased significantly.  
Conceptualization, J.L. ([PERSON]); investigation, J.L. ([PERSON]); writing--original draft preparation, Z.H.; validation, J.Z.; writing--review and editing, W.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
The financial support from Key-Area Research and Development Program of Guangdong Province (2019B111107002), Natural Science Foundation of China (No. 51708360 and 51978407), Shenzhen City Science and Technology Project (No. JCYJ[20180305124008155, JCYJ[20170302143133880], JCYJ[20180305124106675]), and Shenzhen International Cooperation Research Project (No. GJHZ20180928155602083) are gratefully acknowledged.  
The authors declare no conflict of interest.
## 5 Discussion
### Limitations  
Unfortunately, there are still limitations corresponding to SWA achieving good estimation performance. One limitation arose when the ratio of CBF amplitude was more than two times larger than that of SBF, obtained in Section 4.3. Even though our proposed method is based on off-line ShortPCA GLM, whose estimation is robust even under larger CBF ratio conditions (see further details in the simulation section of the supplementary files in [14]), the result we obtained was not robust. It was possibly due to the standard design matrix which has a similar increase in activated channels with rt-ShortPCA GLM. Both of these design matrices but not the simple design matrix contained temporal and dispersion derivatives (CHM\({}^{(1)}\) and CHM\({}^{(2)}\), respectively). Those elements represent individual differences of width and latency [19], even though those are regardless of our simulation settings, no differences during the subject CBFs. Conversely, those elements will possibly work under subject-dependent differences. However, an applicable method for both conditions is desired, thus further studies should enhance the present performance in such situations.  
Next, fixed-length SWA-based least squares showed slow convergence for the weight estimation [34]. In particular, it arose owing to the difference in large amplitudes between trials. At the start of the current trial, samples in the sliding-window were mostly occupied by data from the previous trial. In such a case, \(\hat{\beta}\) was affected by previous CBF activities, leading to a false estimation. \(\hat{\beta}\) would gradually converge to the true value with the transition of \(k\). To overcome this weakness, an enhanced, faster convergence method, for example, by variable forgetting factors or variable-length sliding window analysis, should be utilized [34; 35; 36; 37].  
Finally, the proposed method does not account for the serial correlation that exists in the residual error term of the GLM equation, Equation (1). Due to the high sample rate of fNIRS, the signals have serially correlated errors which may lead to an inflated t-value calculated by sample-wise t-statistics. In this way, the brain activation may be overestimated. To overcome this negative effect, further studies should implement correction methods, for example, precoloring or an autoregressive model in real-time [38; 39; 40].