esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Additively Manufactured Electrically Conductive Structures
### Characterization of Electrically Conductive Composite Materials Additively Manufactured by MEX
In contrast to carbon allotropes, Tan and [PERSON] (2018) [18] developed highly conductive metal composites by filling PA6 (polycaprolactone--6) and HDPE (high density polyethylene) with 5-35 vol % of nickel powder and Sn95 Ag4 Cu1 (tin alloy) in a ratio of 2:3. They achieved conductivity of filaments from up to 31,000 S/m for the PA6 at a filler loading of 30 vol %, although, the conductivity decreased by about 25% as a result of absorbing water due to the hygroscopic effect. In comparison, the HDPE filled metal composite achieves a conductivity up to 23,000 S/m at a filler loading of 35 vol %. The composite materials are processed in a multi-material process by using PLA as the non-conductive part. The tensile strength and Young's modulus of the PA6 filled composite materials increases, whereas the tensile strength of the HDPE filled material at a filler loading higher than 15 vol % decreases. As a result of the extrusion process, the conductivity of the processed material in the additively manufactured parts decrease significantly to 80-1850 S/m (3.3-11.6%) in horizontal direction (X/Y) and to 2-380 S/m in build direction. Based on their experimental results, [PERSON] et al. (2017) [4] developed a rudimentary analytical model for the calculation of the electrical resistance of additively manufactured parts by MEX that supports designing electrical components. In addition, [PERSON] et al. (2017) [23] provided an approach for simulating resulting electrical resistance regarding specific process parameters by utilizing finite elements method. Furthermore, [PERSON] et al. (2018) [3] highlighted the challenge of combining different materials utilizing multi-material MEX as the individual thermal shrinkage results in a distortion of the multi-material part and, thus, the resulting conductivity is influenced. The influence of electrical bonding on the resistivity is not part of the studies mentioned above; however, [PERSON] et al. (2017) [24] postulated the suitability of silver paste. Based on the literature review, this contribution aimed at the investigation of material- and process-specific design opportunities for an adjustment of resistivity and heat radiation capacity of additively manufactured parts. It focused on the comparison of different fillers and process parameter sets, in particular raster angle orientation. From this, both design principles for the integration of electrically conductive structures or heat radiant surfaces and advice for its design are presented.
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 2 Thermal Observations of Active Volcanoes
### Tet-1  
The volcanological community has partially overcome the gap between the available meteorological satellites and its requirements with the small satellite TET-1. TET-1 is the first satellite within the FireBird constellation [30]. This consists of two small satellites that are predominantly dedicated to investigating high temperature events. They were built and are operated by the German Aerospace Center (DLR). The constellation is designed on the basis of the Bi-spectral Infra-Red Detection (BIRD) satellite [31]. TET-1 was launched on 12 June 2012. The second satellite in the constellation, the Bi-spectral Infrared Optical System (BIROS), is scheduled to be launched in spring 2016. Together they will provide one dataset per day. BIROS is still not operational, but its basic characteristics are almost identical to TET-1 (Table 1; for further details on the TET-1 mission see [PERSON] _et al._[30] and [PERSON] _et al._[32]). We have ascertained that FireBird satellites have one important advantage over other satellite instruments--their on-board processing allows repeated acquisition over the same area within a few microseconds, thus preventing data saturation. Another specific characteristic of the FireBird satellites is represented by the staggered line arrays in their pushbroom cameras [33], which results in a step in its spatial sampling half the size of the effective pixel size (Table 1).  
FireBird retrievals can be ordered at DLR [34]. Currently this procedure is semi-automatic and it takes approximately three days from when the data are ordered and the first retrieval. However, the 2014 Stromboli eruption started in the middle of the summer vacatio  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **1 VIS Camera** & **2 IR Cameras** \\ \hline \multirow{3}{*}{Wavelength} & 460–560 nm & 3.4–4.2 \(\mu\)m \\  & 565–725 nm & 8.5–9.3 \(\mu\)m \\  & 790–930 nm & \\ \hline \multirow{2}{*}{Quantization} & \multicolumn{2}{c}{14 bit} \\ \cline{2-3}  & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{procedure was longer at that time, thus the first data were retrieved only three weeks after the eruption started.
# Nanovised Control Flow Attestation
## 1 Introduction  
C-FLAT by [PERSON] et al. [1] is a technique for attesting an application's control flow on an embedded device. C-FLAT is a dynamic analysis tool. It complements static attestation by capturing the program's runtime behavior and verifies the exact sequence of executed instructions, including branches and function returns. It allows the verifier to trace the program's flow control to determine whether the application's control flow was compromised. Combined with static attestation, C-FLAT can precisely attest embedded software execution.  
Originally, C-FLAT design allows attestation for simple systems. However, C-FLAT does not support threads, processes or operating systems. Therefore, most industrial systems are too complex for C-FLAT. Complex systems usually require multi-processing, multi-threading, inheritance, or function pointers, etc. These features are available on a General Purpose Operating System (GPOS); C-FLAT does not support GPOS.  
Furthermore, C-FLAT runs on top of TrustZone. TrustZone programming requires high expertise, as well as access to the boot loader code. Such access is usually available only to the SoM (System On a Module) vendor. Here, we provide a similar but more straightforward approach using our a dedicated Nanovisor [2]. In this paper, we extend C-FLAT and eliminate the following limitations:  
* Single threads--C-FLAT is available only for a single thread.
* Single Process--C-FLAT is available only for a single process.
* Multi-core--C-FLAT is utilized only for a single process.
* TrustZone access--C-FLAT requires access to TrustZone [3, 4], which may not be available in many industrial cases.  
The paper's main contribution is adapting C-FLAT for complex applications in a GPOS (Linux). We demonstrate how our system can detect real exploits, such as SlowLoris, that affect production systems and handles a real test case (CVE-2019-9210).  
We record the control flow path and send continuous sub-sequences to an attestation server. Furthermore, as a result of using Linux, the attestation server may execute locally. Local execution reduces the risk of transmitting the data over the network and allows for better performance.  
Additionally, as we use C-FLAT over a complex general-purpose application, we must carefully approach the performance penalty. For this reason, we offer a novel approach to mitigate it. We run the instrumented section alternately. For example, execute this code section once a second.  
Our contribution provides software-only CFI for ARM for software running in a GPOS. We perform the path monitoring in TEE or HYP mode. We provide a context-sensitive CFI and is available for 32 bit applications and is useful on Android Phones. Our Nanovisor software can run on EL2 (HYP mode) or TrustZone. Since it can track suspicious paths, it helps in detecting DOS attacks.
## 3 Data and Methods
### Atmospheric Data  
For the atmospheric analyses, data from the ECMWF (European Center for Medium-range Weather Forecasts) ERA5 climatological reanalysis dataset were utilized. This dataset provides comprehensive reanalysis from 1940 up to 5 days prior to the current date, assimilating a wide range of observations in the upper atmosphere and near-surface regions. In our study, however, we focused on parameters dating back to 1980 [53]. This dataset is known for its consistent coverage in both space and time and is minimally affected by observational conditions such as cloud cover in satellite observations. Nighttime values were specifically considered due to their reduced susceptibility to local meteorological changes. Specifically, we analyzed the parameters of SKT and OLR, which are typically reported to be influenced by impending earthquakes. Several studies [54; 55; 56] have demonstrated how these parameters are directly affected by the \"thermodynamic channel\" in LAIC models. The ECMWF time series for each atmospheric parameter were collected and pre-processed to apply the Climatological Analysis for Seismic Precursor Identification (CAPRI) algorithm [54; 55]. This algorithm compares daily time series of the current year with a historical dataset spanning forty-two years (1980-2021), within a temporal window preceding the seismic event, in our case of 90 days. An anomaly is identified if the observed value persistently exceeds the mean of the historical series by two standard deviations. However, for the current study, anomalies were defined as values exceeding 1.5 standard deviations, considering that the earthquake magnitude was below 6. Additionally, the geographic area investigated was determined based on the circular earthquake preparation region (or Dobrovolsky area) centered on the epicenter [57], resulting in the selection of a geographical area of 2\({}^{\circ}\) latitude and 2\({}^{\circ}\) longitude.
# Effects of Heavy Rainfall on Shallow Foundations in Bukit Timah Granite in Singapore
## 4 Discussion  
The impact of the rainfall in the BTG soil depends on the rainfall duration, rainfall intensity and initial groundwater conditions. The results obtained from this study were severely limited by the BTG residual soil's hydraulic characteristics and were observed through the numerous similar data points in the 20 mm/h and 32 mm/h rainfall intensity scenarios. A larger difference was observed between the 8 mm/h and 20 mm/h scenarios, where the changes in matric suction are more apparent, and the different trends in the impact of the rainfall intensities can be observed. It is apparent that in a short-duration rainfall scenario (one to two days), the variations in rainfall intensities do not affect the settlements to a large extent.  
High rainfall intensities with short rainfall durations are only important when the groundwater table is low (10 m below ground level). However, despite the high groundwater table in Singapore, floods do occur on a frequent basis during monsoon seasons [4]. It is known that the matric suction greatly affects the hydraulic properties of the soil [35]. In the case of climate change causing extreme weather phenomena, the lowering of the groundwater table due to long periods of non-rainfall [5], coupled with a climate change-amplified downpour, may lead to intense floods in Singapore, along with an increase in settlement for buildings with shallow foundations [36].  
Various environments to generate rigorous rainfall scenarios are needed. Through rainfall infiltration and runoff, water transfer across the atmosphere-soil domain occurs due to evaporation or evapotranspiration, which is affected by rainfall, evaporation, initial water contents, air and soil temperatures, and oxygen concentration [35]. These parameters can be measured in fields under asphalt pavement, aeration salb and turf cover in an open space and can significantly affect the amount of rainwater as a flux boundary condition. In order to consider rainfall-runoff-evaporation, reliable hydrological models such as a Sacramento soil moisture accounting model [37] can be utilized to capture and calibrate the actual flux boundary condition. Therefore, measurements and analyses of weather conditions could be incorporated in future studies of foundation stability under transient conditions.
## 4 Experiments and Results  
In order to demonstrate the efficiency of the proposed approach, we conducted an experiment consisting of two phases. The first phase involved labeling the overall movement of the hands, while the second one involved labeling each finger's position (i.e., the index, middle, ring, and pinky fingers). Furthermore, this section provides more information on our recording settings and our dataset and performance metrics for the SVM, \(k\)NN, naive Bayes, random forest, and XG boosting algorithms.
# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
###### Abstract  
The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17-19.  
CYGNSS; HAND; flood inundation mapping; Sentinel-1A SAR +
Footnote †: journal: Computer Science and Engineering  
1
Footnote 1: _Technical Note_
## 4 Verification with Finite Element Model  
The previous model was verified with a finite element method (FEM) where the geometry and loads were reproduced. The following Figure 9 shows the 3D model geometry. It consists of a prismatic soil cell of soil, reinforced by nine rigid inclusions, and cover by a capping layer.  
Figure 8: Pile deformation parcial relationship (**a**): t-z curves. (**b**) Q-z curves.  
Figure 9: General model views (**a**) Finite Element Method (FEM) for verification: full model view; (**b**) FEM for verification: pile distribution detail view.  
Soil and pile settlement are compared between both models. For the piles, the central one of the FEM was chosen. For the soil, the vertical line passing through the middle point between two piles is compared to the results of the spring model. Figure 10 shows the calculated values from the two methods. It is observed that the results obtained by the two methodologies are in good agreement. Both calculation methods have been run for several geometries and load, getting similar outputs  
Besides deformation, both methodologies give also the mobilized skin friction along the pile shaft. Figure 11 shows the obtained results. Both are also in good agreement, even though the FEM shows greater dispersion at the pile's extremes due to the geometric hardness of the edges.  
Figure 11: Pile and soil skin-friction comparison.  
Figure 10: Pile and soil settlement comparison.  
The performed verification is proof of the correct formulation of the analytical model but also allows for calibrating some of its variables.  
The FEM shows how the uniform load applied on the surface is distributed between the pile and the surrounding soil. The following relationship is finally proposed:  
\[\varnothing=\frac{\rho}{2} \tag{32}\]  
where \"\(\emptyset\)\"is the internal friction angle of the capping layer (Figure 12):  
The transmitted load that reached the soil increases the effective vertical stress and so the skin friction on the pile. So that skin friction at the top of the pile is higher than expected on an isolated pile.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 3 Solar Site Evaluation Using GISs
### Analytic Hierarchy Process (AHP)  
Evaluating the relative importance between different factors and calculating the overall suitability is a controversial and difficult task. Some techniques have been applied to reflect the opinions of experts in order to reasonably calculate and quantify the weight of each of these factors.  
As a method of the expert system, SWARA and WASPAS were integrated with the MCDA method to determine the relative significance of every effective criterion and to evaluate specified alternatives [37]. The AHP is the most widely used and useful systematic expert tool for handling MCDA. In the AHP, hierarchical structures are used to represent a problem and make judgments based on experts to derive priority scales. The overall weights and importance of each input parameter are obtained using a pair-wise comparison matrix.  
There have been a number of studies evaluating appropriate sites for CSP or PV power plant installations using AHP [38; 39; 40; 41; 42; 43; 44]. In the process of AHP, solar radiation, transmission lines, water bodies, slope, land use, the possible electricity generation, and various economic and technical factors can be considered as assessment factors. In most MCDA studies using the AHP, the unsuitable area is first excluded considering the constraints and then the suitability for each analysis unit (pixel or administrative district) is calculated considering the weights determined via the AHP.
## 3 Results and Discussion
### Identification of VOCs and nVOCs  
A total of 62 VOCs was identified in the lyophilized samples of _Trichoderma_ strains and only seven were also present in the control medium (PDB). The VOCs identified in the lyophilized culture filtrates of the two _Trichoderma_ strains and the control medium with a percentage \(>0.01\%\) are shown in Table 4. The identified compounds are classified according to the chemical class into alcohols, aldehydes, acids, ketones, pyrazines, esters, and others. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound (VOCs)** & \multicolumn{2}{c}{**Strains**} \\ \cline{2-3}  & _Trichoderma asperellum_ & _Trichoderma atroviride_ \\ \hline _Alcohols_ & & \\ Ethanol & \(0.58\pm 0.01\) & \(1.02\pm 0.07\) \\
2-methyl-1-propanol & \(0.70\pm 0.01\) & \(0.68\pm 0.05\) \\
3-methyl-1-butanol & \(4.47\pm 0.13\) & \(5.29\pm 0.23\) \\
3-methyl acetate-1-butanol & \(0.35\pm 0.01\) & ND \\
2-methyl acetate-1-butanol & \(0.18\pm 0.01\) & ND \\ Benzyl alcohol & \(0.35\pm 0.05\) & ND \\ Phenylethyl Alcohol & \(19.06\pm 0.08\) & ND \\
1-phenyl-1-2-propanol & ND * & \(0.15\pm 0\) \\
2-(4-methoxyphenyl)ethanol & \(0.71\pm 0.03\) & ND \\ \hline _Aldehydes_ & & \\ Benzaldehyde & \(0.13\pm 0\) & \(0.20\pm 0\) \\ Benzeneacetaldehyde & \(0.06\pm 0\) & \(0.91\pm 0.04\) \\
3-methyl-benzaldehyde & \(0.30\pm 0.01\) & ND \\ Nonanal & ND & \(0.11\pm 0\) \\ \hline _Acids_ & & \\
1-methyl-6-oxopyridine-3-carboxylic acid & ND & \(0.20\pm 0.01\) \\ \hline _Ketones_ & & \\
2,3-butadiene & ND & \(1.25\pm 0.04\) \\
3-ethyl-2-cyclopenten-1-one & \(0.41\pm 0.03\) & \(0.87\pm 0.08\) \\ Phenylacetone & ND & \(0.23\pm 0.02\) \\
5,6,6-trimethyl-3-4-undecadiene-2,10-dione & \(0.27\pm 0.02\) & \(1.55\pm 0.02\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Volatile organic compounds (VOCs) produced in the culture filtrate of _Trichoderma asperellum_ IMI393899 and _T. atroviride_ TS identified by GC-MS. Results are expressed as mean relative abundance percentages (as obtained by dividing the area of each peak by the total area of the chromatogram peaks) \(\pm\) standard deviation. Both culture filtres presented high levels of alcohols and pyrazines compared to the control. Among the compounds most identified in _T. asperellum_ IMI 393899 culture filtrate were 3-methyl-1-butanol (4.47%), phenylethyl alcohol (19.06%), tetramethyl pyrazine (4.81%), 6-pentyl-alpha-pyrone (42.66%), \(\alpha\)-Zingiberene (5.86%), and \(\alpha\)-Sesquiphellandrene (6.60%), while in the culture filtrate of _T. atrovide_ TS were 3-methylbutanol (5.29%), tetramethylpyrazine (56.79%), and 1-Hydroxy-2.4-di-tert-butylbenzene (6.17%). The compound 6-pentyl-alpha-pyrone (6-PP) is an unsaturated lactone with a strong coconut-like aroma, which was first characterized by [PERSON] (1972) [29], and identified to be one of the bioactive compounds of several species of _Trichoderma_, such as _T. viride_[29], _T. harzianum_[30; 31], and _T. atrovide_[32; 33]. The biological effects of 6-PP are the reduction of the production of the mycotoxin deoxynivalenol by _F. granimearum_[34] and the fusaric acid by _F. moniliform_[35]; moreover,
## 2 Heat Storage Classification
### Enthalpy of Reaction  
For the reaction given in Equation (2), \(dn_{\rm H_{2}O}\), \(dn_{A}\) and \(dn_{AvH_{2}O}\) are respectively the mole variation of water, solid \(A\) (i.e., dehydrated salt) and solid \(AvH_{2}O\) (i.e., hydrated salt). Then, the degree of advancement of the reaction \(\xi\) is given by:  
\[d\xi=\frac{dn_{\rm H_{2}O}}{\
u}=dn_{A}=dn_{AvH_{2}O} \tag{10}\]  
The variation of enthalpy can be written under the following form:  
\[dH=\left(\frac{\partial H}{\partial P}\right)_{T,\xi}\times dP+\left(\frac{ \partial H}{\partial T}\right)_{P,\xi}\times dT+\Delta_{r}H\times d\xi \tag{11}\]  
where \(\Delta_{r}H\) is the enthalpy of reaction at constant temperature and pressure. Of course, for a transformation at constant pressure and temperature (Constant temperature or neglected variation of enthalpy due to temperature change), the variation of enthalpy is given by \(\Delta H=\Delta_{r}H\times\xi=Q_{P}\)  
The standard enthalpy of reaction (denoted \(\Delta_{r}H^{0}\)) is the enthalpy change that occurs in a system when one mole of matter is transformed by a chemical reaction under standard conditions, i.e., a temperature of 273.15 K and a pressure of \(100,000\) Pa. The standard enthalpy of reaction can be measured or computed using the standard enthalpy of formation of the reactants and products.  
The enthalpy of reaction for a temperature \(T\) is related to the standard enthalpy of reaction via:  
\[\Delta_{r}H^{0}{}_{T}=\Delta_{r}H^{0}{}_{273.15}+\int\limits_{273.15}^{T} \left(C_{AvH_{2}O}-C_{A}-\
u\times C_{H_{2}O}\right)\times dT \tag{12}\]where \(C\) is the specific heat of \(Av\)H\({}_{2}\)O, \(A\) or H\({}_{2}\)O. It is worth mentioning that usually, the quantity \(\int\limits_{273.15}^{T}\left(C_{AvH_{2}O}-C_{A}-\
u\times C_{H_{2}O}\right) \times dt\) is small compared to \(\Delta_{r}H^{0}{}_{273.15}\).  
Moreover, the chemical sorption reaction being monovariant, the equilibrium is given by the Clausius-Clapeyron relation:  
\[\ln\left(\frac{P_{e}}{P_{0}}\right)=-\frac{\Delta_{r}H^{0}}{\
u\times R\times T _{e}}+\frac{\Delta_{r}S^{0}}{\
u\times R} \tag{13}\]  
where \(P_{e}\) is the equilibrium water vapor pressure [Pa] and \(T_{e}\) the equilibrium temperature [K].