esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression Behavior of Rubber-Bentonite Mixture under Different Salinities
## 2 Results and Discussions
### Compression Coefficient Change of MX80 Bentonite/Rubber Mixture
#### 2.1.2 Influence of Salt Solution Concentration on Compression Coefficient  
From the compressibility coefficient curves shown in Figure 3, the effect of salt solution concentration on the compressibility coefficients of the samples under different vertical loads can be observed. In the case of the B10 and B8T2 samples, the compressibility coefficients decreased as the vertical load gradually increased. With an increase in the salt solution concentration, both sample types exhibited similar variations within the range of vertical load from 200 kPa to 400 kPa.  
Figure 3: _Cont._  
For the B6T4 sample, the compressibility coefficient significantly increased when the salt solution concentration reached 0.1 mol/L. With further increases in the salt solution concentration, the compressibility coefficient fluctuated within a relatively stable range, and it increased with the increase in the vertical load. However, as the vertical load continued to increase, the compressibility coefficient started to decrease, indicating that the sample underwent further compression with the increase in vertical load.  
For the B4T6 sample, regardless of the salt solution concentration in the environment, the compressibility coefficient remained relatively close at vertical loads of 200 kPa and 300 kPa. The compressibility coefficient stabilized below 0.40 MPa\({}^{-1}\) at a vertical load of 400 kPa. However, at a vertical load of 400 kPa, the compressibility coefficient significantly decreased, indicating that a significant improvement in sample density only occurs when the rubber content mass ratio exceeds 40%. [PERSON] et al. [28] found that with the increase in rubber fiber content, the compression index (Cc) decreased until the fiber content was 10%. Subsequently, for a 15% fiber content, the Cc value increased slightly. In addition, the presence of salt pore fluid led to a slight reduction in Cc values regardless of fiber content.  
These findings highlight the impact of salt solution concentration and vertical load on the compression coefficients of different sample types, providing valuable insights into their compressibility and compactness characteristics.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Without Encryption** & \multicolumn{2}{c}{**With Encryption**} \\ \cline{3-4}  & & **AES-256** & **XTEA** \\ \hline Occupancy Monitoring System & 357.46 & 382.89 & 378.76 \\ Indoor Environmental & 167.13 & 183.53 & 176.39 \\ Monitoring & 182.02 & 186.43 & 203.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Energy consumption of IoT applications with and without encryption for single data read and transmission. Figure 13: Comparative metrics for different encryption algorithms: (**a**) Execution time; (**b**) Energy consumption. either using dedicated hardware accelerators or light weight algorithms had minimal impact on the overall energy consumption. Overall, while encryption does introduce a small delay and consumes energy, it is justified from the benefits of security and privacy. As more heterogeneous devices are getting connected to the internet for various services, interoperability decreases because of ad-hoc networks, hardware specific solutions and lack of cloud services to cover the extent of IoT applications. When designing an IoT system, developers should consider modularity and extensibility of the system. This paper presents three low power battery powered IoT applications in portable housing for maximum portability with different hardware and cloud services. As seen from the experimental results from Tables 1-3, and Figure 14, it is evident that transmitting data requires most energy so transmission periods should also be minimized. Sensors and microcontrollers that can go into sleep states are preferred as it reduces power consumption and avoids energy wastage during idle periods. Lightweight encryption algorithms in IoT applications provide necessary security and ensures privacy at all layers, all the while introducing a small delay and energy consumption. LiB provides a modular architecture based on which different platforms and heterogeneous components can be integrated to provide a lightweight, highly portable, and secure sensor platform. Overall, while numerous studies have been published on simulation-based work, there is a lack of published literature on real-time measurements of memory, timing, power, and energy in IoT architecture implementation. This research hopes to fill this gap and provide designers a better estimation of these real-time measurements in their respective future implementations [69].
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 3 Results
### Evaluating the RPs for Vine Crop Indicators
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Temperature-Based Agcls**} & \multicolumn{3}{c}{**Precipitation-Based Agcls**} & \multicolumn{1}{c}{**Mixed Agcls**} \\ \cline{3-8}  & & **WI** & **FD** & **BD** & **DDs** & **WR** & **HyI** \\ \hline \multirow{4}{*}{LCCC} & ERA5 & 0.97 & 0.84 & 0.9 & 0.45 & 0.75 & 0.67 \\  & ERA5-Land & 0.95 & 0.82 & 0.94 & 0.49 & 0.75 & 0.64 \\  & SCOPE Climate & 0.95 & 0.91 & 0.94 & 0.61 & 0.58 & 0.57 \\  & FYRE Climate & 0.99 & 0.97 & 0.97 & 0.9 & 0.94 & 0.92 \\  & RFHR & 0.98 & 0.96 & 0.98 & 0.71 & 0.69 & 0.61 \\ \hline \multirow{4}{*}{PBias} & ERA5 & \(-1.71\) & 0.46 & 14.61 & \(-16.59\) & 22.23 & 19.35 \\  & ERA5-Land & \(-3.08\) & 1.36 & 6.27 & \(-15.51\) & 22.35 & 18.98 \\  & SCOPE Climate & \(-4.07\) & 1.2 & 4.871 & \(-6.57\) & 9.74 & 1.9 \\  & FYRE Climate & 2.33 & \(-0.6\) & \(-0.98\) & \(-2.95\) & 4.55 & 3.5 \\  & RFHR & 0.63 & 0.21 & \(-3.37\) & \(-7.43\) & 12.69 & 21.11 \\ \hline \multirow{4}{*}{IQD} & ERA5 & 2.1 & 0.14 & 0.12 & 3.64 & 0.78 & 88.45 \\  & ERA5-Land & 3.55 & 0.36 & 0.02 & 3.34 & 0.69 & 75.63 \\ \cline{1-1}  & SCOPE Climate & 5.09 & 0.18 & 0.13 & 0.8 & 0.64 & 15.52 \\ \cline{1-1}  & FYRE Climate & 2.04 & 0.05 & 0.06 & 0.16 & 0.08 & 8.86 \\ \cline{1-1}  & RFHR & 1.16 & 0.02 & 0.03 & 0.99 & 0.1 & 80.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics between the reference dataset and RPs over the 1996-2012 period across all study sites for each AgcI. Overall, the results highlight the consistent outperformance of FYRE Climate compared to other RPs; a similar performance between ERA5 and ERA5-Land is seen. RFHR shows an overall similar performance compared to ERA5-Land, with an improvement in reproducing HyI values for 60% of the stations. Figure 2: Results for the WI indicator over the 1996–2012 period between the reference dataset and each reanalysis product (RP): (**a**) Lin’s Concordance Correlation Coefficient (LCCC) plots, with the black line representing the 45-degree line (perfect agreement). (**b**) Kernel density estimation (KDE) of percent bias values. (**c**) Cumulative Distribution Function (CDF) of the reference dataset (in black) and each RP. For (**a–c**) the results concern all the studied sites collectively and the linestyles distinguish the RPs. (**d**) LCCC values separately for each study site. Idem for (**e,f**) with percent bias and integrated quadratic distance (IQD) values, respectively. For (**e,f**) the shapes distinguish the RPs.
###### Abstract  
Manganese metallic nanoparticles are attractive materials for various biological and medical applications. In the present study, we synthesized unique Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{\rm x}\)Eu\({}_{\rm x}\)Eu\({}_{\rm x}\)Fe\({}_{\bf 1.8-2x}\)O\({}_{\bf 4}\) (0.01 \(\leq\) x \(\leq\) 0.05) nanoparticles (NPs) by using the hydrothermal approach. The structure and surface morphology of the products were determined by X-ray powder diffraction (XRD), transmission electron and scanning electron microcopies (TEM and SEM), along with energy dispersive X-ray spectroscopy (EDX). We evaluated the impact of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{\rm x}\)Eu\({}_{\rm x}\)Fe\({}_{\bf 1.8-2x}\)O\({}_{\bf 4}\) NPs on both human embryonic stem cells (HEK-293) (normal cells) and human colon carcinoma cells (HCT-116) (cancerous cells). We found that post-48 h of treatment of all products showed a significant decline in the cancer cell population as revealed by microscopically and the (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazololium bromide)tetrazololium (MTT) assay. The inhibitory concentration (IC\({}_{50}\)) values of the products ranged between 0.75 and 2.25 ug/mL. When tested on normal and healthy cells (HEK-293), we found that the treatment of products did not produce any effects on the normal cells, which suggests that all products selectively targeted the cancerous cells. The anti-bacterial properties of the samples were also evaluated by Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) assays, which showed that products also inhibited the bacterial growth.  
Keywords:spinel ferrites; cytotoxicity; antibacterial activity; hydrothermal synthesis +
Footnote †: journal: Journal of chemical and chemical and chemical properties
# Assessment of Frost Depth and Frost Heave in Romania
## 3 Frost Depth Prediction
### USA Standards/Regulations
The freezing index is a measure of the combined duration and magnitude of below-freezing temperatures occurring during a specific freezing season and calculated via the summation in Celsius-degrees-hour (or below-freezing temperatures and subtracting from that the total number of Celsius-degree-hours (or days) above 0 \({}^{\circ}\)C over the same period). The freezing index (FI) is based on the mean daily temperature. Many engineering calculations [9], including, among other factors, the frost penetration depth, are based on the FI. Equation (4) does not separate sandy from clayey soils. \[\text{d}_{\text{frost}}=4.210\ (1.8\ \text{FI})^{0.478} \tag{4}\]  
where d\({}_{\text{frost}}\), = frost depth (cm) and FI = cumulative freezing degree day (\({}^{\circ}\)C \(\times\) day). Based on the simple kriging geostatistical method, Figure 5 presents the FI\({}_{50}\) map of Romania. Table 4 presents the statistically measured FI, the corresponding return period values, and the depth of frost (in cm) for the Arad station. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Investigated** & \multicolumn{3}{c}{**AFI (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**100-, 50-, 20-, and 10-Year Return**} & \multicolumn{3}{c}{**d\({}_{\text{frost}}\), Depth of Frost (in cm)**} \\ \cline{2-10} \multicolumn{1}{c}{**Period**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**St. Dev**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**AFI\({}_{100}\)**} & \multicolumn{1}{c}{**AFI\({}_{50}\)**} & \multicolumn{1}{c}{**AFI\({}_{20}\)**} & \multicolumn{1}{c}{**AFI\({}_{10}\)**} & \multicolumn{1}{c}{**d\({}_{\text{frost},100}\)**} & \multicolumn{1}{c}{**d\({}_{\text{frost},50}\)**} & \multicolumn{1}{c}{**d\({}_{\text{frost},20}\)**} & \multicolumn{1}{c}{**d\({}_{\text{frost},100}\)**} \\ \hline
190  
Figure 4: \(\mathrm{AFI}_{100}\) for Romania using the data from the 1950–2020 period. Figure 5: \(\mathrm{FI}_{50}\) for Romania using the data from the 1950–2020 period.
## 2 The Precursor (P)MCM-22 and MCM-22 Zeolites
Other sources of silicon were used, such as sodium metasilicate, water-glass, and colloidal silica [29]. The use of sodium metasilicate reduced the induction period (less than 12 h) with a crystallized product after 6 days. Colloidal silica and water-glass required induction periods of 2 and 2.5 days, respectively. These differences were attributed to the different dissolution rates of each silicon source. The morphologies of zeolites synthesized with colloidal silica, sodium metasilicate, and water-glass are shown in Figure 3 (images f-h, respectively). The use of silicon alkoxide as tetraethyl orthosilicate (TEOS) for the synthesis of MCM-22 has been reported [27]. The methodology involves a first step of pre-hydrolysis of TEOS catalyzed with a strong acid media (pH ranging from 0.98-1.65), followed by a second step of hydrothermal reaction of the hydrolyzed precursor with HMI and a source of aluminum in a base media with a pH value ranging from 11-12. This allows a shorter crystallization time, which differs from traditional methods where hydrolysis, condensation, and crystallization occur simultaneously in the same basic medium. According to the authors, MCM-22 with a crystallinity of 98% was produced after 3 days at 158 \({}^{\circ}\)C. Figure 3d shows the morphology of the obtained product. Silica from burned rice husks was used to synthesize MCM-22 [30]. X-ray diffraction analysis confirmed that the product has an MWW structure and textural analysis showed a surface area of 384 m\({}^{2}\cdot\)g\({}^{-1}\) and a pore volume of 0.28 cm\({}^{3}\cdot\)g\({}^{-1}\). Microscopic analysis showed different particles with interrupted growths, spherical aggregates, and concentric rings.
## 2 Methodology
### Development of Thermal Models
The common kernel functions include linear kernel, polynomial kernel and Gaussian kernel. \[\text{Inputs}:\ S=\{(x_{1},y_{1}\ ),\ (x_{2},y_{2}\ )\ldots,(x_{n},y_{n}\ )\},\ \text{where}\ x_{i}\in X_{train}\ \text{and}\ y_{i}\in Y_{train}\]  
Objective:  
\[\text{argmin}_{w,\xi_{1},\xi_{2}\ldots\xi_{n}\ \|w\|^{2}}+\sum_{i}\xi_{i} \tag{1}\]  
Subject to:  
\[y_{i}w*x_{i}\geq 1-\xi_{i} \tag{2}\]  
\[\xi_{i}\geq 0 \tag{3}\]  
where \(w\) is a weight vector, C is a penalty parameter controlling how much you want to avoid misclassifying each training example and \(\xi_{i}\) is a slack variable indicating if the sample is misclassified. With larger C, the optimization will select smaller margin of the hyper plane while with smaller C, the optimization will select larger margin of the hyperplane. Unlike SVM, RF is an ensemble learning method that averages the accuracy of a number of decision trees constructed with bagging algorithm at training time. However, random forest adds additional randomness to the model in that it looks for the most important feature among a random subset of features, a random subset of the whole dataset instead of all features, and the whole dataset while splitting the node. Compared to building deep decision trees to train high-dimensional data, owing to randomness, RF builds several smaller trees, thus suffering less from overfitting [31]. As a result, it generally can develop better model than decision trees.
## 3 Results and Discussion
### Thermal Analysis  
Studying the results of the thermal analysis of the metal complexes can expect the number of water molecules, decomposing of the organic molecule in the complexes and metal oxides [31]. The thermogravimetric analysis was measured at room temperature to 800 \({}^{\circ}\)C, Figure 3, and the results were summarized in Table S4.  
Figure 2: EPR spectrum for Cu(II) complex.  
The TG curves of the Mn(II), Ni(II), Cu(II), and Zn(II) complexes showed four decomposition steps while Co(II) complex showed a two-decomposition step; we will discuss them in detail.  
* The Mn(II) curve showed four decomposition steps within the temperature ranges of 38.76-821.43 \({}^{\circ}\)C, with a mass loss of 47.09% (47.59% calc.). The first step includes the loss of hydrochloric molecular with a mass loss of 10.5% (10.43% calc.), the second step involves losing 3 water molecules with a mass loss of 17.61% (17.79% calc.), the rest of the steps include the loss of organic part as gases with a mass loss of 37.09% (36.66% calc.).
* The Co(II) complex shows two decomposition steps within the temperature ranges of 49.07-473.88 \({}^{\circ}\)C, with a mass loss of 84% (84.28% calc.). The first step includes the dehydration of water molecules and loss of the most organic part with a mass loss of 65.5% (66.11% calc.), the second step involved losing the organic interest as a carbon dioxide with a mass loss of 18.5% (18.17% calc.).
* The Ni(II) complex shows four decomposition steps within the temperature ranges of 57.43-800.23 \({}^{\circ}\)C, with a mass loss of 64.96% (64.93% calc.). The first step includes the loss of dehydration of 3 water molecules with a mass loss of 14.25% (13.85% calc.); the rest of the steps include the loss of organic part as gases with a mass loss of 50.71% (51.08% calc.).
* The Cu(II) complex shows four decomposition steps within the temperature ranges of 54.60-752.67 \({}^{\circ}\)C, with a mass loss of 82.64% (81.73% calc.). The first step includes the loss of water molecules and some of the organic parts as gases with a mass loss of 13.41% (12.84% calc.); the rest of the steps include the loss of organic part as gases with a mass loss of 69.23% (68.79% calc.).
* The Zn(II) complex shows four decomposition steps within the temperature ranges of 55.73-693.79 \({}^{\circ}\)C, with a mass loss of 84.54% (85.08% calc.). All four steps involved losing the organic part as gases, with a mass loss of 72.42% (72.5% calc.).  
Figure 3: TG curve of the Mn(II) complex.
## 4 Simulation Analysis  
The simulation analysis was analyzed in a network simulator 3 (NS3) simulator tool for finding the below parameter of the MANET nodes has shown in Table 1. The proposed SAARICS was compared with DMR, SAODV, and SRP. The parameters, such as transmission power range, longevity of node, malicious node, and traffic density of the nodes, were analyzed to strengthen the proposed methodology. As such, 100 to 500 nodes were deployed in the MANET area to find the parameter values in a proper manner.  
\begin{table}
\begin{tabular}{l l} \hline
**Simulator** & **NS3 Simulator** \\ \hline Area & 1000 \(\times\) 1000 m \\ \hline Network Size & 100 to 500 nodes \\ \hline Mobility Model & Random Way Point \\ \hline Traffic Type & FTP \\ \hline Simulation Time & 600 s \\ \hline Standard & IEEE 802.11 G \\ \hline Routing protocol & SAODV, SZRF, DMR \\ \hline \end{tabular}
\end{table}
Table 1: SAARICS Simulation—Parameter Table.  
Figure 5: Tree Scenario Cluster—Single and Multiple Clusters. Inter- and Intra-Cluster Tree Scenario.
## 6 Evaluation Procedure
### Sampling and Participants
#### 6.1.1 Real-User Studies  
In our sample, only some studies were conducted with participants of the addressed target group [29; 30; 38; 46; 47; 48; 55; 61; 64; 67; 70; 72; 85; 103]. Most of these studies were domain-driven research conducted in the consumer sector, the academic/technical field, and the healthcare sector.  
The prevalence of real-user studies in the consumer domain is attributed to the ease of recruiting participants from widely used, mass-market systems. For example, in studies  
Figure 3: XAI evaluation procedure.  
focusing on streaming and social media recommendation algorithms, [PERSON] et al. [71] recruited users of Amazon Prime, [PERSON] et al. [32] engaged Netflix users, and [PERSON] et al. [55] recruited Facebook users. [PERSON] et al. [29] focused on Instagram users impacted by a service ban. Similarly, in studies of driver assistance systems and drawing software, respectively, the target groups of [PERSON] et al. [67] and [PERSON] et al. [48] belonged to mass markets. In summary, the high proportion of studies with real users in this domain can be explained by the widespread adoption of these systems, which facilitates the recruitment of real users. A special case arises when the target group is the general public, such as in studies concerning the perception of fair decision-making [35]. In this context, recruiting \"real users\" is straightforward since everyone belongs to the target population.  
In the academic/technical domain, the reason for the prominence of real-user studies results from the close proximity between researchers and participants. Although the evaluated systems do not represent a mass market, the target group is part of the researchers' social environment. For instance, [PERSON] et al. [56] evaluated e-learning platforms, [PERSON] et al. [58] focused on a science literature recommender, and [PERSON] et al. [30] examined data scientists work practices, where the convenience sampling method presented an easy-to-implement recruiting method. This practice is largely due to the proximity of the domain to the researchers' own field, facilitating easier access to target groups like students, researchers, and technicians. These studies highlight a significant representation of real user studies, but the ease of recruitment is a key factor influencing this trend.  
The situation in the healthcare domain is distinct. In contrast to the previous cases, gaining access to the target group in this domain is not straightforward. Challenges are common in reaching the desired participants, which include medical professionals such as doctors, nurses, paramedics, and emergency service providers. Examples of this include [PERSON] et al. [44] and [PERSON] et al. [85], who involved a medical doctor from a hospital. In contrast to studies using proxy users, these real-user studies often have fewer participants. They prioritize addressing the specific needs, skills, and preferences of the target group, unlike large-scale evaluations that rely on proxy users.
## 2 Related Work
Then we extract frequency characteristics using Wavelet Packet Decomposition (WPD) and spatial characteristics using Common Spatial Pattern (CSP). The obtained characteristics are transmitted to the proposed classifiers: ANN, CNN1, CNN2, merged CNNs, and modified merged CNNs. Each CNN has a different depth. Our CNN-based method reports improved performance for EEG MI data using its frequency and spatial characteristics. We also show that merging the two CNN models and adding LSTM layers in the CNN model does not always give better results.
## 5 Data Preparation and Filtering
### Types of Errors  
#### 5.1.1 Measurement Error  
The measurement error is the error that occurs in the data gathering chain. This could be due to the flaws in the measuring instruments which result in the difference between the real value and the actual data recorded. An example of different measurement errors is shown in Figure 8. Figure 8 shows an example of different RPM values with respect to time for the shaft of a propeller. The significance of the measurement error could be quantified by computing the mean squared error (MSE) given as  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2} \tag{5}\]  
where \(n\) is the number of data points, \(Y_{i}\) the observed values and \(\hat{Y}_{i}\) the predicted/recorded values. The MSE indicates the distance a regression line (black lines given in Figure 8) is to a set of points (observed values) where the square of the bracket terms in Equation (1) is to eliminate any negative signs and to give more weight to larger differences. Therefore, data with smaller MSE imply a higher accuracy in the recorded/predicted data. It is to note that the lower the MSE, the closer it is to determine the optimum fit line.  
Figure 8: Example of deviation between real and measured value for RPM data, showing (**a**) duplicate data time unit 5 and 5.5; (**b**) contradictive data at time unit 5.