esa-sceva/satcom-chunk-collection huggingface dataset:
_applied sciences_  
_Article_  
Structure and Electrical Properties of AlFe Matrix Composites with Graphene  
[PERSON] 1,*, [PERSON] 1(c), [PERSON] 1(c), [PERSON] 2(c), [PERSON] 2, [PERSON] 1, [PERSON] 1(c) and [PERSON] 1(c)  
Footnote 20: https://www.  
matrix and the accompanying process of the precipitation of metastable aluminides due to the saturation of the structure with deformation vacancies [19].  
The development of efficient methods for the production of metal matrix composites (MMCs) has become a crucial task in material sciences. Composites based on aluminum alloys hold a prominent place among MMCs since they find application in various branches of industry [21; 22]. For example, boride-reinforced composites based on Al-Mg-Si alloys are used as high-conductivity materials [23; 24]. The addition of 3% aluminum boride AlB2 has been shown to strengthen the alloy and rid the Al solid solution of impurities, thereby improving its electrical properties.  
In addition, there is intensive research on the properties of aluminum matrix composites with carbon-containing additives such as nanotubes and fullerenes [25; 26]. A number of papers point out the positive role of graphene as a nanocarbon material with unique properties [26; 27; 28]. However, the wide application of aluminum matrix composites as construction materials is only possible if there are efficient techniques for their production.  
As shown in [29; 30; 31; 32], one such technique is \"in situ\" synthesis in a molten metal matrix under a layer of molten salts. This method has been used to obtain metal matrix composites based on aluminum [29; 30] and its alloys of different compositions (AA-3003 [31], AA-5154 [32]) and doped with 0.10-0.13 wt.% graphene. Aluminum matrix composites have been studied in as-cast coarse-grained and in deformed-via-severe-plastic-deformation ultrafine-grained (grain size \(<\) 1 \(\upmu\)m) states. The study of their structures and properties revealed an effect of microalloying with graphene. Graphene is not an inoculant and does not affect the grain size of as-cast aluminum matrix composites. The hardness of these as-cast composites depends on the chemical composition of the matrix and is 5-10% greater than that of unreinforced Al alloys.  
When an ultrafine-grain structure is formed in composites via high-strain-rate deformation, graphene nanoplatelets break up, bend, and primarily occupy the position along the grain-subgrain boundaries, providing an additional strengthening mechanism due to an interaction with the dislocations. The transition from a coarse-grained structure to an ultrafine-grained structure brings about a sharp increase in the hardness of the composites, namely, by a factor of 1.6 in the composites based on an Al-Mn alloy and by a factor of 2.6 in a AlGr composites [30].  
Follow-up research on \"in situ\" synthesized graphene aluminum matrix composites was carried out using an Al-Fe alloy as a matrix. The aim of this paper is to study the structure formation and electrical and thermal properties of AlFeGn composites in the as-cast state and after deformation via high-pressure torsion.
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 3 ECG Monitoring in a Car
### Capacitive ECG Monitoring
In addition in 2015, the approach of electrode humidification was evaluated by other groups. [PERSON] and [PERSON] [50] described special electrode designs allowing to release some humidity directly at the spot. [PERSON] et al. [51] presented a textile breast belt incorporating a water reservoir used to moisten the electrodes. However, while highly integrated, these concepts are open-loop and provide no definition of the acting local humidity. Hence, in 2017, [PERSON] et al. from RWTH Aachen University disclosed a closed-loop concept for feedback control of local humidity [53]. As storage and release of water vapor in silica gel is dependent on the local water vapour pressure, but is also a function of temperature, we proposed to establish closed-loop temperature control as a method to release water vapor from the gel and hence to enhance SNR. The corresponding car seat demonstrator is shown in Figure 4h. In 2016, Plessey introduced WARDENTM, a six-electrode seat cover including a driven ground electrode built into the main electronic unit located at the opposite site of the backrest [52]. It can be used to easily retrofit car seats for contactless ECG recording and connects to in-vehicle data systems via Bluetooth. In late 2017, a six-electrode design featuring round capacitive electrodes in the backrest was presented to the public by the Belgian research organisation IMEC [54; 65]. The overall design has a similiar look as the design presented in [60]. However, in this specific seat, radar sensors were integrated as well, which allows sensor fusion (see Section 9). To simplify any comparison of designs, Table 2 compares specific features of capacitive ECG electrode placements for car seats as reported in the literature to date.
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 3 Thermodynamic Analysis Model  
### Basic Assumptions and Mathematical Model  
The proposed ST-CAES system is comprised of a conventional CAES and a STC subsystem. The classical Peng-Robinson equations of the state were selected as the property package. The analysis was carried out using Thermoflow software. To simplify the analysis, the following assumptions are made:  
* The system operates in a steady state condition;
* All gases in the system are treated as the ideal gas;
* A constant isentropic efficiency is assumed for the compressors and air turbine;
* The pressure drop of each heat exchanger is 2%, and its heat loss is ignored.
## 5 Construction of Algorithm
### Tabu Search Algorithm  
Considered in this work problem were solved using tabu search algorithm. Next, we will describe each element of the algorithm in more detail.  
**Neighborhood.** In TS algorithm there is neighborhood used, generated by swap and insert moves. Let \(\mathcal{B}\) be a division of \(\mathcal{D}\)-OPT permutation \(\pi\) into blocks. Using the elimination block properties for \(i-\)_moves_ we determine (Corollaries 2-3) a set of moves before and after the block  
\[\mathcal{M}(\pi)=\bigcup_{j\in\mathcal{I}}\;(\mathcal{M}^{i}_{bf}\cup \mathcal{M}^{j}_{af}). \tag{9}\]  
Next:  
1. According to Corollary 2 and 3 z \(\mathcal{M}(\pi)\) we remove some subsets of \(i-\)_moves_ leaving only their representatives.
2. We remove \(s-\)_moves_, whose performance means that one of the conditions of the Theorem 5 is not met Therefore, the neighborhood of permutation \(\pi\) \[\mathcal{N}(\pi)=\{m^{k}_{l}(\pi):\;\;m^{k}_{l}\in\mathcal{M}(\pi)\}.\] (10)  
The procedure for determining the neighborhood has a complexity \(O(n^{2})\).  
**Startup solution.** For each example, in TS algorithm, there were adopted startup solutions determined by the best construction algorithms: SWPT, EDD, COVERT, AU and META algorithms. Their description is presented in Section 5.1.  
**Stop condition.** A stop condition for completing the calculations of both algorithms was the maximum number of iterations. In the process of parallel implementations of TS algorithm was a strategy of many computing processors working in parallel used. The maximum number of iterations for each processor was \(\lceil 1000/p\rceil\), where \(p\) is the number of processors.  
**Tabu List in TS algorithm.** To prevent the formation of cycles too quickly (i.e., a return to the same permutation, after a small number of iterations of the algorithm), some attributes of each move are remembered on the so-called tabu list (abbreviated to \(\mathcal{LT}\)). It is supported on a FIFO queue basis. Making a move \(m^{r}_{j}\in\mathcal{M}(\pi)\) (i.e., generating from \(\pi\in\Phi\) permutation \(\pi^{r}_{j}\)), we write some attributes of this move on the list, i.e., threesome: \((\pi(r),j,\mathcal{F}(\pi^{r}_{j}))\). Let us assume that we are considering the move \(m^{k}_{l}\in\mathcal{M}(\beta)\) generating permutation \(\beta^{k}_{l}\). If on the list \(\mathcal{LT}\) there is a threesome \((r,j,Y)\) such that \(\beta(k)=r,\;l=j\) and \(\mathcal{F}(\beta^{k}_{l})\geq Y\), then this move is eliminated (removed) from the set \(\mathcal{M}(\beta)\).
###### Abstract  
Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures and data processing workflows were specifically developed within this infrastructure and applied retrospectively to a monocentric dataset as a proof of concept. A custom-made 3D-CNN was first trained and tested on an \"unambiguous\" well-balanced data sample, which included strictly normal and highly pathological scans. For the training phase, 90% of the data sample was used (learning set: 80%; validation set: 20%, 5-fold cross validation) and the remaining 10% constituted the test set. Finally, the model was applied to a \"real-life\" test set which included any scans taken. Text mining of the PET reports systematically combined with visual rechecking by an experienced reader served as the standard-of-truth for PET labeling. Results: From 8125 scans, 4963 PETs had processable cross-matched medical reports. For the \"unambiguous\" dataset (1084 PETs), the 3D-CNN's overall results for sensitivity, specificity, positive and negative predictive values and likelihood ratios were 84%, 98%, 98%, 85%, 42.0 and 0.16, respectively (F1 score of 90%). When applied to the \"real-life\" dataset (4963 PETs), the sensitivity, NPV, LR+, LR- and F1 score substantially decreased (61%, 40%, 2.97, 0.49 and 73%, respectively), whereas the specificity and PPV remained high (79% and 90%). Conclusion: An AI-based triage of whole-body FDG PET is promising. Further studies are needed to overcome the challenges presented by the imperfection of real-life PET data.  
FDG PET; artificial intelligence; deep learning; convolutional neural network
## 4 Results
### Pore System after Combustion
#### 4.5.2 Pore Volume and Specific Surface Area  
The PV and SSA of micropore, mesopore, and macropore show great difference between pre-combustion and post-combustion samples (Figure 10 and Table 4). For samples after combustion, the PV of micropore ranges from 0.09 to 0.13 cm\({}^{3}\)/100 g (average = 0.1 cm\({}^{3}\)/100 g) and the PV of mesopore ranges from 0.78 to 1.31 cm\({}^{3}\)/100 g (average = 1.09 cm\({}^{3}\)/100 g). The PV of macropore ranges from 0.32 to 0.87 cm\({}^{3}\)/100 g (average = 0.54 cm\({}^{3}\)/100 g). The SSA of micropore ranges from 3.19 to 4.83 m\({}^{2}\)/g (average = 3.79 m\({}^{2}\)/g), and the SSA of mesopore ranges from 3.47 to 5.63 m\({}^{2}\)/g (average = 4.46 m\({}^{2}\)/g). The SSA of macropore ranges from 0.13 to 0.35 m\({}^{2}\)/g (average = 0.22 m\({}^{2}\)/g) (Table 4). Compared to pre-combustion samples, the average PV of micropore and mesopore were decreased by 77.8% and 14.2% after combustion, respectively, whereas the average PV of macropore was increased by 86.2%. Compared to pre-combustion samples, the average SSAs of micropore and mesopore were decreased by 73.2% and 42.2% after combustion, respectively, whereas the average SSA of macropore was increased by 100%.
## 1 Introduction and Objectives
The aim of this paper was to perform a detailed analysis of absorption systems to upgrade industrial waste heat to appropriate conditions for its transport at long distances using an absorption heat transformer and use it in distribution district heating networks to supply heat or heat and cooling simultaneously to the connected buildings using an absorption heat pump. Absorption technology plays a key role in this system. In this paper, several absorption system configurations were analyzed for both waste heat upgrading, including single effect and double-absorption heat transformers for the \"step-up substation\" (SUS) and single effect and double-lift heat pumps to provide heating or heating and cooling (distribution of heat) as a \"step-down substation\" (SDS). We developed and simulated six complete system configurations using the software Engineering Equation Solver (EES) [20]. A detailed thermodynamic analysis of each configuration was performed, and selected energy performance indicators were used to calculate its global performance for comparison purposes. In Section 2, the considered global configurations for the proposed transport and distribution networks are presented and a description of the absorption units used as SUS and SDS units. Section 3 shows the details of these configurations, their thermodynamic modelling, and the used performance indicators. Finally, Sections 4 and 5 present the results and conclusions, respectively.
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Satellite Data Processing  
Due to differences in the coverage areas, temporal resolution, spatial resolution, storage formats, and units among different satellite products, it was first necessary to use the ArcGIS 10.8 software to clip, convert the format, resample, and convert the scale of the satellite product data. This study created a satellite product dataset in a 0.05\({}^{\circ}\) TIF format that was consistent with the measured data units in the research area. Then, we used the comprehensive software MATLAB R2022a/Python 3.13.2 to batch extract the attribute  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product Name** & **Construction Method** & **Temporal Resolution** & **Spatial Resolution** & **Period** & **Access Address** \\ \hline \multirow{3}{*}{PAR [33; 34; 35]} & Apply different methods & & & & \\  & for MODIS and & Daily & 0.05\({}^{\circ}\) & From 2000 to 2020 & ([[https://www.geodata.cn](https://www.geodata.cn)]([https://www.geodata.cn](https://www.geodata.cn))), \\  & AVHRR inversion. & & & & & accessed on 15 April 2024. \\  & Generate through & & & & & \\  & reconstructing vegetation & & & & & \\  & indices, MODIS 1 SF, GLOA, & & & & & \\  & and other datasets. & & & & & \\  & Generate through & & & & & \\  & Advonscale based on & Daily & 1 km & From 2003 to 2023 & ([[https://www.geodata.cn](https://www.geodata.cn)]([https://www.geodata.cn](https://www.geodata.cn))), \\  & AMSR-E and AMSR-2. & & & & & \\  & Generate through & & & & & \\  & Martingale based on 0.5\({}^{\circ}\) & & Monthly & 1 km & From 1901 to 2021 & \(\text{agformet.2016.11.29}\), \\  & climate material. & & & & & & accessed on 17 April 2024. \\  & VPD [48] & & Generated using machine & & & & \\  & learning methods. & & & & & & accessed on 18 April 2024. \\  & MODIS GPP [49] & MODI/2A2 HGF 6.1 version, & & & & & [[https://pdata.isp.gov/](https://pdata.isp.gov/)]([https://pdata.isp.gov/](https://pdata.isp.gov/)) \\  & from Terra satellite. & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic information on the five environmental factor satellite products.  
values of satellite products corresponding to the observation station and verified their applicability in arid regions.  
Figure 2 shows the Taylor diagram of linear regression fitting between the station observation data of GPP and five environmental factors in different ecosystems and the corresponding station data of their satellite products. The fitting results indicate that the five environmental factor satellite products can capture 65-98% of the changes in environmental factors in different ecosystems, while MODIS GPP can capture 65-82% of the changes in GPP. Therefore, based on the comparison of GPP inversion results in arid regions using these satellite products, the analysis of driving factors and the construction of expression patterns are feasible.  
In addition, because different meteorological variables have different units and amplitudes, the variables in the Taylor diagram shown in Figure 2 were normalized and evaluated on a daily scale.
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Smoking, Alcohol Consumption, Lifestyle, Medications, and Environmental Factors  
Smoking and alcohol consumption, which cause oxidative stress, are also independent risk factors correlated with hair graying [125; 126; 127; 123; 127]. A sedentary lifestyle, which can exacerbate metabolic diseases, affects hair pigmentation, whereas adequate sun exposure and regular exercise can help promote general health and prevent hair graying [22; 65; 123].  
Hair plucking can occur when people shampoo, comb, dye, or perm your hair, and frequent repetition can deplete melanocytes and cause hair graying. Accordingly, repeated hair plucking is a technique to induce hair graying in animal models [11; 59].  
Various medications including certain chemotherapy drugs and antimalarial medications might cause hair graying [18]. Hair loss and graying were observed in patients during the treatment of depressive disorder with mirtazapine which fully recovered 10 weeks after medication discontinuation [128]. On the other hand, some anti-inflammatory drugs rarely promote the repigmentation of gray hair [28; 29], but these cannot be applied for general treatment.  
Hazardous environmental factors, such as X-rays, \(\gamma\)-rays, UV rays, and air pollution, can cause oxidative damage to hair follicle cells and hair filaments [129; 130; 131]. PUVA phototherapy or irradiation with X- or \(\gamma\)-rays can induce oxidative damage to cells and hair graying in animals, providing useful experimental models [96; 97; 101].
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results
### Comparison with Deep Learning Methods  
To assess the classification performance of our network, we use our data set to calculate the OA and Kappa values for the other methods (Table 7). The first three are different DeepLab network versions propose recently by [PERSON] et al., all of which use ResNet-101 as the network backbone, and are generally considered to be methods with high accuracy and robustness. The next two methods are two kinds of OCNet, ResNet-OC, and ASP-OC, which introduce object context pooling (OCP) on the basis of ResNet and ASPP modules respectively. DANet proposes to append two types of attention modules on top of dilated FCN. OCNet and DANet all achieve new state-of-the-art performance in challenging scene pixel level classification.  
As shown in Table 7, the proposed approach shows a consistent performance improvement over the other classification methods. OCNet and DANet do not achieve as good a performance in the classification of ice subsurface targets as they do in scene pixel level classification. From this comparison, note that Deeplabv3+ obtains better classification results, which indicates that ASPP and decoder modules are necessary for the classification of ice subsurface targets. Although Deeplabv3+ and the proposed method both have ASPP and decoder modules in the structure, as compared with Deeplabv3+, the proposed method shows improvements of 0.13% OA on a radar image dataset; that is, the proposed method modified using bilateral filtering, ASPP of removing image level features,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Type** & **OA** & **Kappa** \\ \hline Original image & 97.56 & 95.23 \\ Filtered image & 97.73 & 95.53 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification performance using the original image and the filtered image as the network input.  
Figure 6: (**a**) Original image. (**b**) Lee filtering image. (**c**) Anisotropic diffusion image. (**d**) Bilateral filtering image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter Type** & **Equivalent Number of Looks (ENL)** & **Edge Preserving Index (EPI)** \\ \hline Original images & 8.0418 & 1 \\ Lee filtering & 8.1804 & 0.4740 \\ Anisotropic diffusion & 8.1196 & 0.4204 \\ Bilateral filtering & 8.2257 & 0.4965 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different noise reduction methods.  
changing feature dimensions, and effective decoding of feature fusion can learn more information from radar images.
## 3 Results
### Validating the Accuracy
#### 3.1.2 Three-Dimensional Anisotropic Model  
To further validate the accuracy of this algorithm, we compared its results with those obtained using the edge-based FEM [20] for a 3D conductive generalized anisotropic and magnetic model. As depicted in Figure 3, an anomaly is embedded within an isotropic half-space of 100 \(\Omega\cdot\text{m}\). The dimensions of the anomaly are 1200 m \(\times\) 1200 m \(\times\) 1000 m along the x-, y-, and z-directions, respectively, with a top depth of 250 m. The principal resistivities (\(\rho_{x}\), \(\rho_{y}\), and \(\rho_{z}\)) of this anomaly are 200 \(\Omega\cdot\text{m}\), 50 \(\Omega\cdot\text{m}\), and 100 \(\Omega\cdot\text{m}\), respectively, while Euler's angles (\(\alpha_{S}\), \(\alpha_{D}\) and \(\alpha_{L}\)) are determined using values of 10\({}^{\circ}\), 30\({}^{\circ}\), and 20\({}^{\circ}\), respectively. The relative permeability of this anomaly is 1.5. The frequency used for computation is set at 10 Hz.  
Figure 4 displays the apparent resistivities of the xy-mode and yx-mode. The first and second columns correspond to the apparent resistivities obtained from the \(\mathbf{A}-\phi\) method and the edge-based FEM, respectively. The third column shows the relative error, while the upper and lower rows represent xy-mode and yx-mode, respectively. Both methods exhibit a high level of accuracy, with relative errors less than 3%. Our aim is solely to validate this approach; therefore, we refrain from further analysis of Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequencies (Hz)** & \multirow{2}{*}{\(\rho(\Omega\cdot\text{m})\)} & \multirow{2}{*}{\(\mathbf{Relative}\) Errors \(\mathbf{3D}\)} & \multirow{2}{*}{\(\mathbf{3D}\)} \\  & & & \\ \hline
100 & 240.3 & 240.8 & 0.21 \\
40 & 231.7 & 231.4 & 0.13 \\
20 & 224.1 & 223.5 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(\mathbf{A}-\phi\) results (3D) and analytical solutions (1D): the apparent resistivities.  
Figure 3: One-dimensional magnetic model.
# An Overview of Shared Mobility Operational Models in Europe
## 3 Carsharing
### Effects of Carsharing on Vehicle Ownership  
Based on a study conducted in North America by [14], wherein the effects of carsharing on household vehicle ownership and the total number of vehicles were the main focus of a  
Figure 1: Research methodology.  
before-and-after analytical design. The findings demonstrate that carsharing participants statistically own a significantly lower number of vehicles. In the sample, the average number of cars per household decreased from 0.47 to 0.24 cars. This means that for every carsharing vehicle, 9-13 vehicles should be removed from the road. In a document funded by Horizon 2020, \"Learning from the Stars--The Future of Carsharing in Europe\", research shows that in Switzerland, one carsharing vehicle replaces up to 18 private cars, and in Bremen, one carsharing vehicle replaces up to 16 private cars [14; 15].
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## Appendix A Spectral Indices
### Band Ratios  
Band ratios were also proposed to identify Li pegmatites [62; 63]. These ratios were based on spectral absorption features of Li minerals, such as spodumene and lepidolite (Table A2).