esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Physicochemical Properties of Soils Affected by Biochar
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Raw** & **Production** & **C** & **N** & **C:N** & **P** & **K** & **Literature** \\
**Material** & (\({}^{\circ}\)C) & **pH** & **g kg\({}^{-1}\)** & **g kg\({}^{-1}\)** & **Ratio** & **g kg\({}^{-1}\)** & **g kg\({}^{-1}\)** & **Literature** \\ \hline Green waste & 450 & 6.2 & 680 & 1.7 & 400 & 0.2 & 1 & [PERSON] et al., 2007 [45] \\ \hline Poultry litter & 450 & 9.9 & 380 & 20 & 19 & 25 & 22 & [PERSON] et al., 2007 [45] \\ \hline Residue (_Zea mays_) & 350 & & 675 & 9.3 & 73 & & 10.4 & [PERSON] and [PERSON], 2009 [46] \\ \hline Residue (_Zea mays_) & 600 & & 790 & 9.2 & 86 & & 6.7 & [PERSON] and [PERSON], 2009 [46] \\ \hline Penaut shell (_Arachis hypogaea_) & 400 & & 499 & 11 & 45 & 0.6 & 6.2 & [PERSON] et al., 2009 [47] \\ \hline Rice husk & 370–550 & 8.5 & 470 & 5.9 & 80 & 1.03 & 7.9 & [PERSON] et al., 2014 [48] \\ \hline Wood (_Quercus lex_) & 400 & 9.9 & 676 & 5.3 & 128 & & 3.2 & [PERSON] et al., 2014 [49] \\ \hline Pine wood (_Pinus spp._) & \(\sim\)480 & 8.4 & 532 & 3.7 & 143 & & 9.4 & [PERSON] et al., 2015 [50] \\ \hline Wood (_Quercus spp._) & 400 & 6.9 & 427 & 3.3 & 130 & 0.6 & 3.8 & [PERSON] et al., 2015 [51] \\ \hline Wood (_Quercus spp._) & 600 & 9.5 & 455 & 4.1 & 111 & 0.6 & 4.4 & [PERSON] et al., 2015 [51] \\ \hline \end{tabular}
\end{table} 
Table 3: Chemical constituents of biochar produced from various feedstock sources under different production temperatures (based on [PERSON] et al., 2021 [44]). trients to plants stem from the following factors: (1) readily soluble nutrients brought into the soil [15] and nutrients from the mineralization of the labile fraction [13; 14; 15]; (2) reduced leaching due to biochar's high cation exchange capacity [54; 55]; (3) the reduction of ammonia volatilization and lower N loss due to the removal of N\({}_{2}\) and N\({}_{2}\)O emissions from denitrification [56; 57]; and (4) N, P, and S retention due to increased biological activity [58; 59]. It is important to note that some of the above processes are inseparable from soil biological functions. Adding biochar creates a darker soil surface, which increases the soil temperature, thus increasing the microbial biomass and activity values, which is also a positive factor [58]. The effects on the improvement of light absorption capacity and the increase of soil temperature can be efficiently utilized in the agricultural practices of boreal countries. Biochar usage can thus extend, for example, the northern border of wheat and maize cultivation [59].
## 4 Interdisciplinary Applications
### Biomedical Systems  
**Medical Image Reconstruction (ReconPINN).** ReconPINN revolutionizes MRI reconstruction by embedding Bloch equation constraints into its loss function:  
\[\frac{dM}{dt}=\gamma M\times B-\frac{M_{z}}{T_{1}}+\frac{M_{0}-M_{z}}{T_{1}} \tag{11}\]where \(\frac{dM}{dt}\) represents the time derivative of the magnetization vector \(M\), which describes the change in the magnetization over time. The term \(\gamma\) denotes the gyromagnetic ratio, which is a constant that relates the magnetic moment of a particle to its angular momentum. \(M\times B\) represents the cross product of the magnetization vector \(M\) and the magnetic field \(B\), which is responsible for the precessional motion of the magnetization vector in the presence of a magnetic field. \(M_{z}\) refers to the component of the magnetization along the \(z\)-axis, while \(T_{1}\) is the spin-lattice relaxation time, which characterizes the time it takes for the magnetization to return to its equilibrium value after being disturbed. \(M_{0}\) is the equilibrium magnetization, representing the magnetization when the system is in a steady state with no external perturbations.  
While suppressing artifacts through rotating coordinate system regularization. Evaluated on multi-center 7T MRI datasets, ReconPINN improves structural similarity (SSIM) to 0.92 \(\pm\) 0.04, outperforming compressed sensing (SSIM: 0.75) and reducing tumor boundary localization errors to 0.7 mm [15]. Open-source implementations (e.g., DeepXDE [11]) further enhance reproducibility, enabling rapid clinical adoption.  
**Personalized Medicine (CardioPINN).** CardioPINN integrates patient-specific CTA data with reduced-order Navier-Stokes projections, enabling real-time coronary artery stenosis assessment with \(<\)8% error. In a multicenter trial, its predictions of fractional flow reserve (FFRct) correlate strongly with catheter measurements (\(r=0.89\), \(p<0.001\)), achieving an AUC of 0.93 for atrial fibrillation ablation targeting [17].
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 3 Design of Framework
### Feature Extraction and Fusion  
In this study, we extract ed features from the preprocessed traffic using three pretrained models. In order to extract spatial information, we use two CNN models, AlexNet and VGG16, and LSTM architecture is used to learn temporal features. The top 100 deep features for each neural network architecture were chosen after the collected deep features were downscaled using the minimum redundancy maximum relevance (mRMR) algorithm. The mRMR feature selection method produces the features, which are then combined and input into the classifier.
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
## 1 Introduction  
Among high-efficiency solar cells, interests in low-cost, high-efficiency Si solar cells such as bifacial solar cells are increasing. Photovoltaics (PVs) on restricted land should be installed on buildings or on the ground [1]. The power would be correlated with the location installed for the applications [2] and environmental temperature [3], thus Building-Integrated Photovoltaics (BIPVs) and Floating Photovoltaics (FPVs) are becoming more common these days. Unlike conventional solar modules, which use the solar radiation transmitted through the front of the existing Si photovoltaic module, it generates electricity using the solar radiation transmitted through the front and the back [4]. The bifacial module has an advantage that it generates power from the incidence irradiance from both the front and the rear sides of the module. However, there are limitations in predicting and measuring accurate front and rear outputs leading to total power. Recent research indicates that the output of the bifacial module is boosted up to 50% over the single-sided module depending on the backside environment [5]. The power output of the bifacial module increases by about 20% when it is installed at the optimum height and no blockage for the direct sun array shining on the area beneath the module [6]. A study on output change according to the height and installation angle [7] would also be critical issue. Prediction of bifacial module output could be basically obtained from one-diode model [8]. Performance gain could be used for the comparison of operation for the bifacial module [9]. Annual change in power generation would also be measured with differentinstallation [10; 11]. Measurements are underway to analyze the power generation of the module depending on the rear side status of the module such as rooftop design environments [12; 13; 14; 15]. Rear side power would be obtained using the modeling of output power from the measurement of the I-V curve (current-voltage curve) [16].  
Therefore, it is expected that the use of the bifacial module will be frequent, so it is important to predict the power output of the bifacial module.  
In the paper, a basic prediction model is introduced to calculate both the output of front and back sides of bifacial module. The total amount of output power is calculated and measured by estimating the amount of back side output using the amount of output power of bifacial module under Standard Test Conditions (_STC_) condition. The experiments were then measured for the elements of various bifacial PV output on an outdoor test site. The experimental data then allow investigation on multiple factors that influence on the bifacial PV performance, including ground albedo, angle, direction and the position of the module and the date and time to be measured.  
The front and rear side of the output power are then estimated to be added to compare with the experimental data within an error rate range.
## 3 Results
### Identification of EGFR and HER-2 in Cells Isolated from Asctic Fluid  
To identify whether cells from astic fluid were epithelial in origin, we used an array of well-known protein markers, previously described in the association with ovarian cancer cell lines. Before investigating the protein markers in astic fluid-derived cells, we tested the specificity of the antibodies for selective proteins of interest in two well established ovarian cancer cell lines (Figure 3). Figure 3A demonstrates the immunological specificity of anti-EGFR and anti-HER-2 antibodies in ovarian cancer cell lines. OVCAR-5 showed a high level of EGFR, but a low level of HER-2 proteins. The SKOV-3 cell line showed a highly positive expression of both EGFR and HER-2. We next used this protocol to determine the expression of these two proteins in astic fluid-derived cells. Figure 3B demonstrates the three different staining patterns that we categorise based on the staining of EGFR and HER-2. Low expression was depicted by a lack of plasma membrane staining. Moderate expression showed some plasma membrane-associated receptors, and high expression had strong plasma membrane-associated receptor staining (Figure 3B).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patient ID** & **Ages** & **Subtypes** & **Stages** & **Treatment** \\ \hline
1 & 59 & HGS & IIIC & Car/Pac \\
2 & 74 & HGS & IIC & N-C \\
3 & 70 & HGS & IIIC & Car/Pac \\
4 & 67 & HGS & IV & Car/Pac/Gem \\
5 & 74 & HGS & IIIC & N-C \\
6 & 73 & HGS & IIIC & N-C \\
7 & 50 & HGS & IV & N-C \\
8 & 75 & LGS & IIC & N-C \\
9 & 61 & HGS & IIIC & N-C \\
10 & 58 & HGS & IIIA & N-C \\
11 & 74 & HGS & IIC & Car/Pac/Gem \\
12 & 50 & HGS & IIIC & N-C \\
13 & 81 & HGS & IIIC & Car/Pac/Gem \\
14 & 59 & HGS & IIIC & Car/Pac \\
15 & 63 & HGS & IIIC & Car/Pac \\
16 & 53 & HGS & IIIC & N-C \\
17 & 56 & HGS & IIIC & Car/Taxol \\
18 & 59 & HGS & IC & N-C \\
19 & 67 & HGS & IIIC & N-C \\
20 & 67 & HGS & IIIC & N-C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical characteristics of 20 ovarian cancer patients from a gynaecological ward, Christchurch’s Women Hospital, Christchurch, New Zealand. Of 20 patients, 19 were high grade serous (HGS) subtypes and one was a low grade serous (LGS) subtype. At the time of collecting astic fluids, 12 patients were newly diagnosed without having received any chemotherapy (N-C). Four patients displayed carboplatin-paclitaxel (Car/Pac) resistant tumours. One patient was carboplatin-taxol (Car/Taxol) resistant. Three patients displayed carboplatin-paclitaxel-gemcitabine (Car/Pac/Gem) resistant tumours.
## 3 Verification of the Analytical Solution
### Compared with the Existing Analytical Model  
A pure diffusion analytical model proposed by [PERSON] et al. [30] is selected to verify the proposed solution. The values of parameters used for comparison are the same as those in Section 3.1. The breakthrough curves of the CGCW under different leakages are shown in Figure 3. It can be concluded that the difference between the results predicted by the pure diffusion model and the proposed model is only 1.01% when there is no leakage (\(v_{2}\) = 0). This demonstrates the accuracy of the proposed solution. Additionally, it can be found that the leakage can pose significantly effects on the CGCW performance. For example, the contaminant concentrations for \(v_{2}\) = 10\({}^{-9}\) m/s can be 7.43 and 1.79 times larger than that for \(v_{2}\) = 10\({}^{-10}\) m/s and \(v_{2}\) = 5 \(\times\) 10\({}^{-10}\) m/s when \(t\) = 50 years, respectively. It indicates that the effects of leakage caused by the GMB holes should be taken into consideration. Additionally, the proposed solution can offer more accurate suggestions for the design of the CGCW.  
Figure 2: Comparison with the results predicted by COMSOL.
## 3 Results and Discussion
### Implication for the Contaminated Site Management
However, the values determined considering also the particle size distribution (hence using Equation (10)) were found to be generally lower than the values of previously published works. This difference can be explained through considering the fact that in these studies, the bioaccessibile fraction was estimated by referring to the total content of the soil. The cleanup goals (CSR) for each soil sample were calculated using Equations (7) and (8), accounting for the bioaccessibility estimated using the two proposed approaches, i.e., either by Equation (9) (BA\({}_{1}\)) or Equation (10) (BA\({}_{2}\)). The obtained values are reported in Figure 5 for each soil sample and compared with the cleanup goal of each contaminant assuming 100% bioaccessibility. The latter value was obviously the same for all samples, since it depended only on the target risk (THI = 1 or TR = 10\({}^{-6}\)), on the exposure parameters, and on the toxicity of the contaminant. For all contaminants and for any BA considered, the CSR were calculated for both the carcinogenic and non-carcinogenic effects, and the lower value was taken as representative CSR for each sample. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Bioaccessibility** & **Method** & **References** \\ \hline  & **(\%)** & & \\ \hline [PERSON] et al., 2015 & 61–79 & RIVM & [35] \\ [PERSON] et al., 2012 & 46–70 & SBET & [36] \\ [PERSON] et al., 2011 & 50–100 & SBRC & [37] \\ [PERSON] et al., 2008 & 75–85 & PBET & [38] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies on the bioaccessibility of Pb in shooting ranges. Clearly, the cleanup goals had an opposite behavior with respect to the one reported for bioaccessibility, meaning that a lower bioaccessibility corresponded to a higher cleanup goal. Let us consider the case of arsenic for sample A2--looking at Figure 5, we can observe that the CSR was higher when the estimated bioaccessibility, shown in Figure 5, was lower. In this case, BA was calculated by starting from a bioaccessible concentration equal to the quantification limit, and therefore represents a maximum estimate, i.e., the effective BA could be lower. Hence, the corresponding CSR represents a minimum estimate, and the effective CSR could be higher. Similarly to what was observed for the bioaccessibility values, the cleanup goals proved to be significantly influenced by the approach adopted for evaluating the bioaccessible fraction. The bioaccessibility method did not notably affect the results, although the cleanup goals estimated adopting the SBET proved to be in most cases higher than those estimated on the basis of the UBM. Clearly, as expected, accounting for bioaccessibility led to a remarkable increase of the cleanup goals, up to 100-fold using BA\({}_{1}\) and up to 1000-fold using BA\({}_{2}\).
# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 2 Related Works
In this case, the Seq2 seq model presents the best results for short-term forecasting, also in accordance with the results obtained in the present work. A generic Seq2 seq with a specific attention mechanism is proposed in [49] for multivariate time-series forecasting. **Deep learning models:** Using the same dataset proposed for this work, [16] presents an ANN model that works on a 24 h day-ahead forecasting of electric loads previously aggregated into clusters by consumption patterns. The patterns are obtained with a self-organizing map (SOM) followed by a k-means clustering algorithm. The work in [50] introduces a deep learning architecture based on an ensemble of convolutional blocks acting on segregated subsets of the input data. The model is applied for day-ahead forecasting of individual residential loads with data obtained from a smart metering electricity customer behavior trial (CBTs) in Ireland. The work focuses on achieving low training time and high accuracy, the proposed model being the best in both aspects with an MAE of 0.3469. The authors in [51] present a simple MLP regressor with a sophisticated selection of features based on previous consumptions and weather data. The work in [52] introduces a combined model of CNN and LSTM layers for STLF. A comparison between recurrent neural networks (LSTM) and support vector regression (SVR) is provided in [53], showing that the bidirectional LSTM model outperforms both the unidirectional LSTM and the SVR models.
## 3 Results and Discussion
### X-Ray Diffraction Results  
Results of the XRD analysis for CuCrFeMnNi HEA coatings along with the as-cast HEAs used as targets for magnetron sputtering are shown in Figure 5 (for 5 wt. % Cu HEAs) and Figure 6 (for 10 wt. % Cu HEAs). XRD patterns of as-cast alloys reveal the presence of two phases, identified as the main FCC and secondary BCC structures. This corresponds with the findings from SEM analysis of the bulk alloys shown in Figures 2 and 3. Coupling it with the EDS analysis, one may conclude that as-cast HEAs consist of a dendritic BCC phase and interdendritic FCC phase. The higher intensity ratio of the FCC phase in the HEA-5 Cu sample may suggest its higher fraction in this sample compared to the HEA-10 Cu alloy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Coating Thickness (\(\mu\)m)** \\ \hline I8-6-C1 & 2 \\ I8-6-C2 & 1.8 \\ I8-6-C3 & 1.51 \\ I8-7-C1 & 1.78 \\ I8-7-C2 & 1.68 \\ I8-7-C3 & 1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coating thickness measured from cross section SEM analysis.  
Figure 4: Cross section SEM image of HEA coating (I8-7-C1) on silicon substrate.  
Both figures with XRD patterns of as-deposited coatings demonstrate low-intensity broad peaks with a maximum around 2 Theta = 43.5 deg, which can potentially be attributed to the (111) plane of a cubic structure, indicating a consistent phase across all samples. In the case of HEA-5%Cu magnetron sputtering, a slight shift to lower 2 Theta values is observed with an increase in magnetron power from 200 to 250 and 300 W (Figure 5). Across all samples, no other XRD pattern evolutions were observed with an increase in magnetron sputtering power.  
Overall, such broad peaks on the XRD patterns of complex compositional alloys can indicate an amorphous structure, a low degree of crystallinity, or nanocrystalline structure, likely due to high cooling rates during deposition. Peak broadening may be also caused by sever lattice distortion, commonly occurring in HEAs.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency
### Metal or Non-Metal Ion Doping  
The introduction of impurity in the forbidden band by ion doping is one of the most common and effective methods for improving semiconductor performance. As early as 1982, [PERSON] [275] mixed chromium ions (C\({}^{5+}\)) into TiO\({}_{2}\) to realize visible light cracking water. On one hand, ion doping can increase the concentration of semiconductor carriers, but, on the other hand, it is possible to form ion traps to capture electrons and holes and reduce the recombination of electron-hole pairs. Ion doping causes the red shift of the TiO\({}_{2}\) photocatalyst, and the photo response of the photocatalyst can be extended to the visible region. According to the types of doped ions, ion doping can be classified into metal ion doping, non-metal ion doping, and metal/non-metal ion simultaneous doping.
# Reversing File Access Control Using Disk Forensics on Low-Level Flash Memory
## 2 General Background
### Filesystem  
A filesystem takes OS instructions for file creation/deletion and settings changes and translates them to read/write instructions to deliver to the drive the OS is stored on [8]. Filesystems are responsible for almost every binary representation of data coming from the OS. Typically, filesystems store data in the forms of either files or nodes, depending on what they represent. Other forms of data storage exist, but there must be some method of finding any single section of data relatively quickly due to OS demands. The objects represented include file authors, related groups, tracking metadata such as size and access permissions, directories and subdirectories, etc. Since the filesystem must store all of this data, anything that can access and interpret the data on the filesystem can read the drive as though it were the OS. We leverage this possibility to recover access control data.  
**EXT file systems.** EXTended File System (EXT) was made for Linux in the 1990s and is still used as its primary filesystem in the modern age. The system has gone through several iterations, i.e., EXT2 [9], EXT3 [10], and EXT4 [11]. It is based around a series of records for boot/basic controls, then uses a series of \"inodes\" to manage data. These inodes store everything from access control schemes to file size to creation time. In general, any non-direct data relating to a file is held in the inodes. These inodes are stored according to a series of groups on the drive. The inodes for a single group are stored contiguously, but the groups are not stored back-to-back. File data is stored across the drive, with inodes containing pointers to it. All of the data relating to these groups (but not the files they contain) is stored in a \"superblock\", a set of data regarding the filesystem stored at the beginning of the drive. Figure 1 demonstrates the structure of an EXT filesystem.  
Notice that the superblock is stored at the beginning of the partition, followed by a series of \"block group descriptors\", followed by data intermixed with \"inode groups\". The superblock stores a wide variety of data related to the filesystem, including the sizes and numbers of various data structures, version data, and which inodes are allocated. Immediately following the superblock is a set of \"block group descriptors\". These dictate data revolving around inode groups and their locations. This stores the number of inodes, the start location of the set of inodes for the group, and the number of directories the group contains [9,12].  
In 1999, EXT3 was introduced in order to add support for a technique called journaling. This is a method of tracking writes to a drive which have not yet been committed. This is the main included feature of EXT3 [10]. This version of the filesystem is still used for legacy applications and can be found on some modern systems, giving us a more up-to-date filesystem to test on.
#### Article
###### Abstract  
Micro energy harvesters (MEH) based on microelectromechanical systems (MEMS) are rapidly developing, providing a green and virtually infinite energy source. The electrostatic vibratory power generator outputs electric power when it vibrates, motivating us to apply it to vibrating civil infrastructures excited by ambient and daily traffic loadings. In this study, an innovative monitoring system utilizing MEH devices was proposed for detecting slab damage and pier scours for bridge structures. Its performance was numerically investigated with finite element models, where the damage in slabs was modeled with a reduced Young's modulus and scours with fixed boundaries of inclined depth. It was shown that the powers generated at each MEH varied as the target structure's modal frequency shifted and amplitude changed by damage or scour. A power generation index was proposed to identify slab damage and a reference-free method was introduced to detect uneven pier scours. Utilizing an electrostatic vibration-based MEH (MEMS vibrational power generator), this pioneering study showed that MEMS vibrational power generators can work as sensors for an infrastructure structural health monitoring system.  
MEMS; Micro Energy Harvester; structural health monitoring; vibration; frequency; amplitude; pier; scouring Article