esa-sceva/satcom-chunk-collection huggingface dataset:
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment
### Impacts on Soil Structure and Composition
The soil CEC increases after biochar addition due to the enhancement of the oxidation of aromatic carbon [90]. The increase in CEC helps in improving nutrients retention in the soil and in mitigating leaching process, which increases the fertility of the soil [91]. The physico-chemical properties of soils in humid areas of Asia varied significantly following biochar addition, where pH value increased from 3.9 to 5.1, CEC from 7.41 to 10.8 cmol/kg, base cation percentage from 6.4 to 26, and bulk density decreased from 1.4 to 1.1 mg/m\({}^{3}\)[92]. In a study performed by [PERSON] et al. [93], authors recorded an increase in the key indicators of soil quality by about 33% for soil organic carbon, 6% for pH, 10% for total nitrogen and 43% for N-use efficiency. Application of biochar also promoted the formation of macro aggregates in soils, which decreased the soil erosion potential [92]. Soils behave differently following biochar addition. Despite the good number of studies reporting the positive impact of biochar addition on fertility of tropical and subtropical soils and plant growth and yield [86; 94], other investigations showed that the improvements in crop production were only observed in highly degraded and nutrient-poor soils [95], while richer soils showed less tendency to take benefits from biochar addition. Nevertheless, soil amendment with biochar is still an effective method for C sequestration in soils [73]. In addition to biochar properties, soil type and composition and the method of biochar application, i.e., single application of biochar or combined application with compost, are essential factors that define the effects of biochar on soil agronomic properties. Despite the favorable effect of biochar on soil properties, some noticeable unfavorable changes in soil structure and composition following the addition of biochar have also been reported. [PERSON] et al. [96] applied oak-derived biochar pyrolyzed at 650 \({}^{\circ}\)C to silt loam soil in a field experiment of 16 weeks and observed that the resistance to water penetration in soil was lowered by 10-18%. Authors reported that short-term application of biochar could not lead to a significant effect on the physico-chemical properties of soils, whereas greater changes are mainly related to biochar ageing. Moreover, biochar in certain cases may contain some potential contaminants such as Zn, Cu, Cd, Pb and Cr when prepared from toxic solid wastes [97]; this can lead to undesirable effects on soil composition and soil microbial activity.
## 3 Materials and Methods  
### Preprocessing  
The pre-processing stage is crucial for improving image quality during network training. This stage involves several steps designed to enhance image quality and feature extraction for the segmentation task. First, windowing is applied within a specified Hounsfield Unit (HU) range of \(-200\) to \(200\). This technique eliminates irrelevant tissues by focusing on the HU values typically associated with liver tissue. Furthermore, voxel values are normalized by scaling them from \(-1\) to \(1\), enhancing the network's training. Moreover, data augmentation techniques, including random flips and rotations, enhance the dataset size. The raw image provides intricate spatial information, whereas the SWT-processed image presents a multi-resolution viewpoint, as illustrated in Figure 1.  
The lightweight CNN model enhances liver images through five layers. The first two layers, each with 32 filters, capture basic details such as edges and textures, which help improve the overall structure and appearance of the liver in the image. The middle two layers, with 64 filters, refine these details, making the textures and shapes clearer and more defined. Finally, the last layer, with 128 filters, sharpens finer details, ensuring that the enhanced image clearly shows the liver and tumor's important features and boundaries. This step-by-step enhancement improves the quality of the liver image. The CNN model is represented in Figure 2.  
A second pre-processing method is applied to the liver area to remove noise. This method incorporates a two-channel denoising approach using convolutional neural networks (CNNs) with the original and stationary wavelet transformed (SWT) images, which is well suited for noise removal and enhancement. This paper presents an image-denoising approach integrating a CNN with raw images and SWT representations. The input image is fed into the lightweight CNN to obtain a denoised image. The CNN preserves fine and sharp details in the time domain and retains spatial details free of noise. The same input image is then processed in the frequency domain using the same network, where the noise is removed. Finally, both results are combined to produce an image with fine details without noise distortion. Therefore, the method can enhance the image quality by considering noise information in spatial and frequency domains.
# Analysis and Applications of GlobeLand30: A Review
## 5 Discussion and Outlook
### Continuous Updating  
GlobeLand30 has users from more than 120 countries on five continents. As such, GlobeLand30 has been identified as a fundamental geospatial dataset by a number of international organizations, such as UN Committee of Experts on Global Geospatial Information Management (UN-GGIM) [89], UNEP [12], and Global Observation for Forest Cover and Land Dynamics (GOFC/GOLD) [90]. This has promoted data sharing in the field of geo-sciences and Earth observation, and stimulated the development of finer-resolution global land cover data products in the world [13; 90]. However, a number of new requirements have been put forward by the users, such as more thematic classes, longer time series, and higher spatial resolution. For instance, GlobeLand30-2010 has ten major land cover classes, but more classes may be asked by certain applications, such as Ecosystem Accounting [91] and integrated biodiversity monitoring [12]. A total of 15 classes will be offered by the 2015 version of Gloebland30 which is under development. Since one single GLC dataset will never be perfectly suited for all applications either in terms of the legend or the accuracy, developing a new philosophy for generating land cover products by designing an operational system that can meet varied user requirements is important [92].  
Time-series land cover data products are essential for understanding land cover dynamics, identifying its trends, as well as assessing social, economic, and environmental impacts. Although GlobeLand30 has only two base-line years (2000/2010), several other GLC maps with more epoch series have been developed. One is the CCI-LC product that covers three-epoch series (2008-2012, 2003-2007, 1998-2002) [87; 93]. The resolution of CCI-LC data product is relatively coarse (300 m) and may not reveal certain land cover and its changes. The other time-series GLC map is the annual global tree cover maps that are produced by [PERSON] et al. (2013) with a high temporal resolution (annual forest cover change from 2000 to 2012), but is limited only to the percent tree cover/tree cover change. The development and provision of time-series 30 m and full class GLC maps is a significant challenge. This will be implemented by combining the mainstream change detection-based approach with the rapidly-increasing crowdsourced information [94; 95].  
Specific tools and systems need to be developed to support \"faster\" and operational GLC data product generation and updating, such as discovering useful alternative and ancillary information from the deep web [96; 97], service relation-driven detection [98; 99], trust of volunteered data [100], integration of crowdsourcing information [101]. At present, GEO is examining the possibility to develop a so-called \"Data Cube\" with the flexible classification concept, which will facilitate the on-demand extraction of land cover classes with deep learning and other data mining algorithms [92]. Another direction is to provide a web-based tool in which can design their own legends online and generate the desired land cover product [102; 103].
## 4 Conclusions  
In summary, we have calculated the electronic, magnetic, half-metallic, structural stability and the mechanical properties of the new designed EQH compounds FeRhCrSi and FePdCrSi by using the first-principles method. The conclusions are listed as below:  
1. FeRhCrSi and FePdCrSi compounds are two new ferrimagnetic HMs with a wide half-metallic band gap of 0.336 eV and 0.177 eV, respectively.
2. The half-metallicity of the FeRhCrSi and FePdCrSi compounds are very robust to the hydrostatic strain or tetragonal distortion. Especially for FeRhCrSi, the half-metallicity can be kept in a wide lattice constant range (5.28 A-5.85 A) under hydrostatic strain and a c/a ratios range (0.98-1.08) under tetragonal distortion, respectively.
3. The total magnetic moment of FeRhCrSi and FePdCrSi compounds are 3 \(\mu_{\text{B}}\) and 4 \(\mu_{\text{B}}\), respectively, which obey the Slater-Pauling rule: M\({}_{\text{t}}\) = Z\({}_{4}\)-24. The main contributor of the total magnetic moments are both Cr atom for FeRhCrSi and FePdCrSi.
4. The large negative values of the calculated formation energy and cohesion energy show the direct evidence of the chemical and thermal stability for FeRhCrSi and FePdCrSi compounds. This indicates that they are likely to be synthesized in the experiment.
5. The elastic constants and the various moduli indicate the mechanical stability of FeRhCrSi and FePdCrSi compounds.  
[PERSON] designed the project. [PERSON] and [PERSON] performed the calculations and prepared the manuscript. All authors analyzed the data and discussed the results.  
This work was funded by Natural Science Foundation of Tianjin City (No.16)CYBJC17200, 17 JCQNJC02800), and National Nature Science Foundations of China (No. 51701138).  
The authors declare no conflicts of interest.
# Synchronizing Many Filesystems in Near Linear Time
## 3 Filesystem Synchronization  
The synchronization paradigm used in this paper follows the traditional one described in, for example, [17] and depicted in Figure 1. At the beginning of the synchronization cycle, each replica stores an identical copy of the same filesystem \(\Phi\). Due to local modifications, the replicas diverge, and after some time, the filesystem in the \(i\)-th replica changes to \(\Phi_{i}\). At a certain moment, the replicas call for synchronization by sending information extracted by the _update detector_ (run locally) to a central server, which hosts the reconciliation algorithm. After the server has received all update information, it determines what the common synchronized filesystem \(\Psi\) will be. Then, it sends instructions to the replicas separately telling them how to transform their local filesystem to the synchronized one. Finally, each replica executes the received instructions, which transforms their local copy to the synchronized state \(\Psi\), optionally informing the user about some (or all) of the conflicts and how they have been resolved.
## 5 Future Work
### Improvement of Processing Procedures
Future work could focus on improving rainfall mapping techniques by considering the unique topology and measurement characteristics of CMLs [179; 180]. In addition, since the proposed CML processing procedures are usually applicable to different regions, open-source algorithms are not directly usable. While dedicated rainfall observations can be used to calibrate the algorithms to specific climates and CML networks [192], calibrating algorithms using only CML data remains a challenge in regions where rain sensors are not already available. Investigating low-cost but efficient calibration methods in these regions (such as utilizing other opportunistic rain sensors) may be a future direction of research.
# A Novel 1-D CCANet for ECG Classification
## 3 Proposed Method
### Neural Feature Extraction  
To exploit the correlation between two ECG leads, we use a one-dimensional variant of the canonical correlation analysis network (CCANet). First introduced in the field of image recognition by [PERSON] et al. [33], CCANet has been employed in two-view image recognition tasks. Recently, CCANets, which are intrinsically two-dimensional, have been successfully employed in the signal processing field for the classification of two and three lead heartbeats [20]. A CCANet is usually composed of two cascaded convolutional layers and an output layer: (1) in the convolutional layers, the CCA technique is used to extract dual-lead filter banks; (2) in the output layer, the features extracted from the second convolutional layer are mapped into the final feature vector [20].  
In this paper, with the aim of increasing performance, we design a new 1-D canonical correlation analysis network that is composed of four 1-D convolutional layers and an output layer. Contrary to CCANet, the filters are found by combining a CCA with a singular value decomposition (SVD), and features are extracted after each layer. The use of 1-D convolutions instead of 2-D permits to limit computational cost, thus allowing to increase the number of layers from two to four and, consequently, to increase performance.  
The processing pipeline is shown in Figure 3. The input of the proposed 1-D CCANet-SVD is the concatenation of autoregressive features, spectrogram features, and the original normalized heartbeat, resulting in the following vector \(x_{h,\text{cat}}=[x_{h,\text{ar}}\ x_{h,\text{spec}}\ x_{h,\text{norm}}]\in \mathbb{R}^{m}\), \(h=1,2\). The 1-D CCANet-SVD is trained with \(N\) two-lead heartbeats and then used as neural feature extractor in combination with a linear SVM for heartbeat classification. The network is trained separately for the MIT-BIH and INCART databases.
## 3 Results
### Risk Calculator to Measure the Probability of PCa-Free Survival Observations for 5 Years  
In this study, a model was constructed to predict the probability of survival in patients without PCa. From the Cox proportional-hazard model, it showed each variable's HR (hazard ratio) and CI (confidence intervals). STK showed the highest HR (1.444), followed by ETC (1.041), PA_VD (1.028), AGE (1.026), HTDZ (1.013), BMI (1.009), HTN (1.007), and SMOKE_DRT (1.006) (Table 3). Based on seven input variables, this risk calculator can measure the probabilities of PCa-free survival over 5 years (Figure 4): STK, HTDZ, ETC, age, PA_VD, BMI, and SMOKE_DRT (Table 4). STK, HTDZ, and ETC are variables that explain past medications or treatments for stroke, heart disease, and other diseases, including cancer. AGE, PA_VD, BMI, and SMOKE_DRT were used as numerical variables. As the other variables in Table 1 had no effect on the risk calculator, seven variables were used as prediction inputs. The output of the risk calculator revealed that as the total score increased, the likelihood of being PCa-free over a 5-year period decreased. Figure 4: Risk calculator, Cox proportional-hazard regression analysis: probabilities of 5-year overall survival (Prob of 5-year OS) without prostate cancer (PCa). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Hazard Ratios**} & \multicolumn{2}{c}{**Confidence Intervals**} \\  & **(Exp(Coef))** & **(Lower 0.95–Upper 0.95)** \\ \hline HTN & 1.007 & 0.8499 & 1.194 \\ STK & 1.444 & 0.7145 & 2.917 \\ HTDZ & 1.013 & 0.6643 & 1.544 \\ ETC & 1.041 & 0.7872 & 1.375 \\ AGE & 1.026 & 1.0146 & 1.038 \\ PA\_VD & 1.028 & 0.983 & 1.075 \\ BMI & 1.009 & 0.9822 & 1.036 \\ SMOKE\_DRT & 1.006 & 0.9992 & 1.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hazard ratios and confidence intervals of Cox proportional-hazard regression. The C-index of the risk calculator was close to 0.6 (0.573). Typically, in survival analyses, the C-index ranges from 0.6 to 0.7. Considering that we used a large public dataset that presents difficulties in creating a predictive model, the current predictive model results can be said to have a high prediction level. Although this study's result was close to 0.6, an additional validation method is required to evaluate the results of the risk calculator. For additional validation, the risk calculator was calibrated using predicted and observed outputs (Figure 5). Calibration explains the correctness of the predictions based on real observations. The cross-dotted line indicates the ideal prediction results. The prediction of the risk calculator is represented by black dots with vertical bars reflecting 95% confidence intervals. The predictions of the risk calculators closely matched the ideal outcome.
## 3 Results and Discussions
### Characterization of the Plastic Pyrolysis Char after Activation
As observed in Table 2, the concentration of metals in the solids was in the ppb range, except for Ti, Zn, Ba, and Sb, which raised until the ppm level, which is consistent with what has also been reported in the characterization of plastics [51, 52] and pyrolyzed plastics chars [8]. The presence of a relevant amount of Ti is expected since TiO\({}_{2}\) is overwhelmingly used as a white pigment [53]. ZnO and Sb\({}_{2}\)O\({}_{3}\) are also frequently used as multifunctional additives, not only due to their pigmentation properties but also as flame retardants or due to their fungicide abilities [52]. Barium sulfate is frequently added as an inert filler to increase the stiffness and hardness of plastics [52]. If the amount of Ti, Zn, Ba, and Sb are compared among the samples, a raising tendency in terms of concentration tendency of the metals after stabilization and chemical activation in which temperature is raised to 760 \({}^{\circ}\)C can be seen due to the slight mass loss during the chemical activation, keeping the metallic content in the final solid. Figure 3: FTIR spectra of the char from plastic pyrolysis before and after activation.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 3 Methods  
### Lasso  
Least Absolute Shrinkage and Selection Operator (LASSO) is a regression analysis method that was first introduced by [PERSON] [14] in 1996. It minimizes the sum of squared errors, with a bound on the sum of the absolute values of the coefficients. The loss function of LASSO can be described as Equation (4).  
\[\hat{\beta}=\underset{\beta\in R^{P}}{\text{argmin}}\{\|Y-X\beta\|^{2}+\lambda \|\beta\|_{1}\} \tag{4}\]  
Here, \(X\) is the standardized variable, \(Y\) is the response vector, \(\beta\) is the coefficient vector, \(\lambda\) is penalty parameter.  
Due to high prediction accuracy and interpretability of the model based on LASSO, LASSO is often used to predict regression coefficients between miRNAs and mRNAs [15]. Let \(t\in\{1,2,\ldots,T\}\) be a sample, \(y_{i,t}(i\in\{1,2,\ldots,N\})\) and \(z_{k,t}(k\in\{1,2,\ldots,M\})\) be expression profiles of mRNA or miRNA in sample \(t\), we can change the form of LASSO regularized linear regression as Equation (5) shows.  
\[y_{i,t}\approx\beta_{i,\rho}+\sum\limits_{k}\beta_{i,k}(z_{k,t}c_{i,k}) \tag{5}\]  
Here, \(\beta_{i,0}\) is the bias, \(c_{i,k}\) is the number of target sites in mRNA \(i\) for miRNA k, \(\beta_{i,k}\) is the fitted linear coefficient and is constrained to Equation (6).  
\[\sum\limits_{k}\big{|}\beta_{i,k}\big{|}\leq\lambda \tag{6}\]  
Only when \(c_{i,k}>0\), miRNA \(k\) can regulate mRNA \(i\). By this method, we can build a more trusted miRNA-mRNA regulatory network.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 4 The Proposed Solutions
### CRT Map Construction and Message Exchanges  
A novel mechanism to collect and share traffic information with a low overhead is proposed in this section. It is different from some other geographic routing protocols, such as GyTAR and TFOR, adopting the infrastructure free traffic information system (IFITS) to calculate traffic density. The basic idea of the proposed mechanism is, if you want to know information of a certain place, the best way is to ask a person who has just come from that place. The information is the most updated and the cost is the lowest. A vehicle driving on a road segment observes how many cars are on the same road. As soon as the vehicle reaches an edge of the road, traffic density is then derived. This information is then shared at junctions. We describe the details as follows. A flowchart of CRT Map construction and message exchanges for each vehicle is shown in Figure 4.
## 4 Hazards Analysis
### Preliminary Flood Hazard Assessment  
According to the National Environment Agency [27], no officially recorded flood events causing damage exist for Lefkas Island. However, local news sources report flooding incidents in the Vasiliki area on 16 November 2017, 6 May 2018, and 18 September 2020, indicating that localized events do occur.  
To provide a preliminary assessment of flood hazard on the island, we combined two spatial parameters in a GIS environment. The first is morphological slope, categorized as above or below 2\({}^{\circ}\), based on the principle that flatter areas are more prone to surface water accumulation due to reduced flow velocity. The second is geological formations, specifically the presence of recent deposits such as alluvial and fluvial sediments, which typically accumulate in flood-prone areas.  
These criteria were used to assign hazard levels: areas with both low slopes and recent deposits were given the highest score (2); areas lacking one or both of these conditions received a lower score (0); and transitional areas--where recent deposits exist on steeper slopes--were assigned an intermediate value (1).  
The resulting Flood Hazard Map (Figure 9) shows that, overall, flood susceptibility is low across most of the island. However, localized medium- to high-hazard zones appear in four key areas: Lefkada city (north), Vasiliki (south), Nidri (east), and Karia (central). These areas align with the island's main residential and tourist centers, where population density and infrastructure development increase vulnerability. Although the hazard may not be extreme in physical terms, the exposure of people and assets elevates the level of risk, underlining the need for focused flood mitigation and land-use planning in these regions.