esa-sceva/satcom-chunk-collection huggingface dataset:
# Digestate Management and Processing Practices: A Review
###### Abstract  
The implementation of sustainable agro-energy systems that integrate crop, livestock, and bioenergy production is attracting increasing interest from farmers. Livestock produces large amounts of animal manure which can serve as organic fertilizer for crops and pasture growth. However, the nutrients contained in manure can adversely affect air, water, and soil quality and pose a public health risk if not handled properly. Existing manure management practices vary widely on a global scale. Researchers are striving to identify appropriate manure management practices with the aim of environmental protection. Anaerobic digestion of manure and subsequent digestate (DG) processing technologies have been proposed to stabilize manure so that it can be safely used for land applications. DG, which represents digested substrate removed from the anaerobic reactor after recovery of biogas, is a rich source of N, P, K, and S, various micronutrients, and organic matter, the addition of which to the soil can stimulate soil microbial biomass metabolic activities thus improving soil ecosystem function. However, the optimal fertilization properties of DG can be lost if it is neither fully stabilized nor contains biodegradable materials. To overcome these problems, various processing technologies can be used to convert DG into value-added by-products. Composting has been proposed as one such preferred post-treatment that can convert DG into mature, stable, safe, humans- and nutrient-rich compost. Other processing technologies such as thermal drying, gasification, hydrothermal carbonization, pyrolysis, membrane filtration, struite precipitation, ammonia stripping, and evaporation have also been proposed for DG processing and nutrient recovery from DG. The objective of this review paper was to provide an overview of the current state of the art in DG management regulations and practices and to provide an update on the various processes that have been developed to meet DG stabilization requirements, with a focus on composting as one of the preferred solutions.  
Keywords:composing; digestate management; fertilization; organic fertilizer; waste vaporization +
Footnote †: journal: Applied
## Appendix A  
For BPSK modulation, the average BER of the SD link can be approximated as the asymptotic form [44] of  
\[P_{\text{B,SD}}\approx\frac{1}{4\bar{\gamma}_{\text{sd}}} \tag{11}\]  
where \(\bar{\gamma}_{\text{sd}}\) indicates the average SNR of the SD link and \(P_{B,\text{SD}}\) is  
\[P_{B,\text{SD}}=\int_{0}^{\infty}Q\Big{(}\sqrt{2x}\Big{)}\frac{1}{\bar{\gamma}_ {\text{sd}}}\exp\left(-\frac{x}{\bar{\gamma}_{\text{sd}}}\right)dx. \tag{12}\]  
In order to obtain (11) from (12), the approximation \(Q\Big{(}\sqrt{2x}\Big{)}\approx\exp(-4x)\) is applied, leading to  
\[P_{B,\text{SD}}\approx\int_{0}^{\infty}e^{-4x}\frac{1}{\bar{\gamma}_{\text{sd }}}e^{-x/\bar{\gamma}_{\text{sd}}}dx=\frac{1}{4\bar{\gamma}_{\text{sd}}+1}< \frac{1}{4\bar{\gamma}_{\text{sd}}}. \tag{13}\]  
It should be noted that this asymptotic form is valid for \(\bar{\gamma}_{\text{sd}}\gg 1\), i.e., under the high SNR approximation [44].  
With the same argument, using \(M_{\gamma_{\text{sd}}}^{\text{App}}(\text{s})\) of (15), \(P_{B,\text{id}}\) in (17) can be asymptotically written and bounded by  
\[\begin{split} P_{B,\text{id}}&\approx\int_{y=0}^{ \infty}\left[\int_{x=0}^{\infty}\frac{1}{\bar{\gamma}_{\text{sr}}}e^{-\frac{xy }{\bar{\gamma}_{\text{sr}}}4}e^{-\frac{x}{\bar{\gamma}_{\text{sr}}}}dx\right] \frac{1}{\bar{\beta}_{\text{rd}}}e^{-\frac{y}{\bar{\gamma}_{\text{rd}}}}dy\\ &=\left.M_{\gamma_{\text{id}}}(\text{s})\right|_{\text{s}=4}\\ &<M_{\gamma_{\text{rd}}}^{\text{App}}(\text{4})=\frac{1}{4\bar{ \gamma}_{\text{sr}}+1}+\frac{4\bar{\gamma}_{\text{sr}}}{4\bar{\gamma}_{\text{ sr}}+1}\frac{\ln\Big{(}\bar{\gamma}_{\text{rd}}\Omega_{\text{sr}}\frac{4\bar{ \gamma}_{\text{sr}}+1}{\bar{\gamma}_{\text{sr}}}+1\Big{)}}{\bar{\gamma}_{\text{ rd}}\Omega_{\text{sr}}\frac{4\bar{\gamma}_{\text{sr}}+1}{\bar{\gamma}_{\text{sr}}}}. \end{split} \tag{14}\]  
It is seen from (14) that when the SR link SNR is low, i.e., \(\bar{\gamma}_{\text{sr}}\to 0\), the BER of SWIPT relay system converges 1, because the second term is nullified. Otherwise when the SR link SNR is strong enough, i.e., \(\bar{\gamma}_{\text{sr}}\gg 1\), the BER depends on the second term which is written in this regime as\[\underbrace{P_{\text{B,id}}}_{\gamma_{\text{sr}}\gg 1}\rightarrow\frac{1}{4 \gamma_{\text{rd}}\Omega_{\text{sr}}/\ln(4\gamma_{\text{rd}}\Omega_{\text{sr}}+1)} \tag{10}\]  
Consequently, the BER expression of the SWIPT relaying systems in (11) can be asymptotically expressed as \(P_{\text{B,id}}^{\text{Asym}}\) of (19).
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization
### MoM Antennas and GA
This concept avoids a large number of repetitive computations and therefore reduces the computation time for tuning the optimized structure considerably. MSMoM was implemented for two-dimensional structures and successfully applied to the classical patch antenna design [205], and then improved in [206] where the same results were obtained as with the classical MoM, but with a significant reduction in the time spent. Later, the same authors managed to significantly reduce the computation time compared to a direct MoM/GA implementation. This was achieved by excluding the EM simulation from the iterative optimization cycle [207].
# Landslide Displacement Prediction Method Based on GA-Elman Model
## 2 Method
### GA-Elman Displacement Prediction Model
#### 2.1.3 Workflow of GA-Elman Model  
The workflow of the GA-Elman neural network model is mainly as follows (Figure 2):  
(1) Determine the topology of Elman neural network, mainly including the number of hidden layer neurons and other network topology parameters.  
(2) Elman neural network initialization and initial weights and thresholds. GA encodes the initial weights and thresholds of the Elman network into an initial population composed of several chromosomes.  
(3) Selection of fitness function. The key optimization goal of this research is the error between the output value of the neural network and the expected value, and the reciprocal of the sum of the squares of the actual output value and the expected value error is taken as the fitness function. The fitness function is calculated as shown in Equation (2).  
\[f(i)=\frac{1}{\sum_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}} \tag{2}\]  
where \(n\) is the number of network output nodes, \(\hat{y_{i}}\) is the actual output value of the ith node of the Elman neural network and \(y_{i}\) is the expected value of the ith node.  
(4) Perform selection, crossover and mutation operations. The main purpose is to calculate the best fitness individual and eliminate the worst fitness individual. After repeated iterative optimization, the best neuron weights and thresholds of Elman neural network are obtained.  
(5) The optimal weights and thresholds optimized by GA are passed to the Elman neural network. Elman network is trained and updated to calculate the error observation of neural network.  
(6) Check whether the constraints are met and use the test set to verify the prediction effect.
## 4 Results
### Factor Importance  
In the study of debris flow susceptibility modeling in the Songpan region, feature importance for RF and XGBoost was calculated using their respective built-in methods. For RF, this involved Gini importance based on the mean decrease in impurity, while XGBoost utilized gain importance to reflect split contributions. Given that SVM does not directly provide feature importance metrics, SHAP (SHapley Additive exPlanations) values were employed for this model. SHAP quantifies feature importance using game theory principles to determine each feature's contribution to model predictions.  
Due to the differing measurement scales and principles across algorithms, all raw importance values were normalized to the [0, 1] interval using Min-Max scaling. The comprehensive importance ranking presented in Figure 8 is derived from the arithmetic  
Figure 7: Songpan debris flow susceptibility maps.  
mean of the normalized values across the three models (RF, XGBoost, and SVM-SHAP). This approach ensures cross-model comparability and enables an integrated prioritization of features.  
The results indicate that elevation, earthquake kernel density, population density, and distance to roads significantly influence debris flow susceptibility in the study area, which is consistent with findings from previous studies [85; 86].
## 2 Related Literature  
The traditional access-control systems [14; 15; 16; 17] are the main access-control schemes. The Discretionary Access Control (DAC) model [14] allows object owners to limit access to their objects or the information contained in them depending on the identities of users or membership of particular groups. The mandatory access control (MAC) model [15] protects against direct and indirect information leaks, but perfect information privacy cannot be ensured. As the DAC model is typically less safe than the MAC model, it is utilized in settings where a high level of security is not necessary [18]. The idea underlying the role-based access control (RBAC) model [19] is that \"a subject's duty is more essential than the subject's identity.\" In the attribute-based access control (ABAC) model [17], the attributes can be defined or used in a variety of ways. However, in cloud computing, it might be difficult to reach a consensus on what kind of qualities should be used and how many traits should be used when making access decisions [20].  
The authors of [21] introduced CoRBAC. It is considered an RBAC system. It focuses on introducing access control to cloud computing. The domain model and role model of distributed RBAC are carried over. It can combine the distributed authentication services provided by the CoRBAC, in order to give the \(BiC\) the ability to issue certificates. CoRBAC has some significant drawbacks. The authors in [22] introduced adaptive extensible access control markup language (XACML) access policies or heterogeneous distributed Internet of Things (IoT) environments. The authors in [23] employed blockchain technology to introduce a privacy-preserving access-control scheme for securely sharing electronic health records. Another notarization-based authorization model was proposed by [PERSON] and [PERSON] [24].  
[PERSON] and [PERSON] introduced the LW scheme [10] which decentralizes the attribute-based encryption. This scheme still depends on categorizing users into the limited number of roles that can be used to access the encrypted data. [PERSON] et al. [11] introduced the LCHWY scheme. In this scheme, the individual authorities are able to decrypt ciphertexts. The authors in [12] introduced the MAACS scheme. In this scheme, the decryption overhead at the client side has been eliminated but the revocation process in MAACS is only based on the attributes of the user. It does not consider revoking the user based on his behavior. The authors in [13] introduced the MD-AC scheme. This scheme places importance on the central authority in managing huge computations which makes it a single point of bottleneck and attack.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 3 Materials and Method
### _Tph_  
Due to the significant variation in object sizes within remote sensing images, including numerous extremely small instances, experimental results have shown that YOLOv8's original three detection heads do not adequately address the challenges presented by remote sensing imagery. As a result, we added an additional prediction head specifically designed for detecting tiny objects. When combined with the other three prediction heads, this approach enables us to capture relevant information about small targets more effectively while also detecting objects at different scales, thus improving overall detection performance.  
We replaced the original detection head with a transformer prediction head to capture more global and contextual information. The structure of the Vision Transformer is depicted in Figure 6. It consists of two main blocks: a multi-head attention block and a feedforward neural network (MLP). The LayerNorm layer aids in better network convergence and prevents overfitting. Multi-head attention allows the current node to focus not only on  
Figure 5: The architecture of SPPFE.  
the current pixel but also on the semantic context. While the additional prediction head introduces a considerable amount of computational and memory overhead, it has improved the performance of tiny-object detection.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 3 Results
### Planet Fusion Comparison with PlanetScope  
We found a high agreement (r\({}^{2}\geq 0.90\)) for the same-day surface area pairwise comparisons between Planet Fusion and PlanetScope for all size classes (Figure 5). MAPE decreased as observations increased in size, and the highest MAPE values were found for synthetic, mixed, and real for all size classes (Figure 5). The number of pairwise comparisons for real was higher than mixed and synthetic, to a large extent (\(\sim\)60%). This finding is somewhat expected, as the Planet Fusion algorithm uses PlanetScope images as an input to generate daily Planet Fusion imagery.  
Figure 5: Same-day pairwise comparisons between PlanetScope and Planet Fusion real, mixed, and synthetic for multiple observations in time and for all OFRs divided into three size classes (0.1–5 ha, 5–10 ha, and 10–30 ha). Brighter colors indicate higher point density.
# Efficient QC-LDPC Encoder for 5G New Radio
## 2 5G NR QC-LDPC Codes
### 5G NR QC-LDPC Characteristics
- For BG2: \(k_{b}=10\) if \(K>640\); \(k_{b}=9\) if \(560<K\leq 640\); \(k_{b}=8\) if \(192<K\leq 560\); and \(k_{b}=6\) elsewhere. **Step 2:**: Determine \(Z\) by selecting the minimum \(Z\) value in Table 2, such that \(k_{b}\times Z\geq K\). **Step 3:**: After the lifting size \(Z\) is determined, the corresponding shift coefficient matrix is then selected from Table 1 [Set 1, Set 2,..., Set 8] according to set \(Z\). **Step 4:**: Calculate the shifting coefficient value \(P_{i,j}\) by the modular \(Z\) operation, as discussed in Equation (4). **Step 5:**: Replace each entry in the final exponent matrix with the corresponding circulant permutation matrix or zero matrix of size \(Z\times Z\). The QC-LDPC code construction is completed and a parity check matrix \(H\) of size \(m_{b}Z\times n_{b}Z\) is obtained. In 5G QC-LDPC codes, shortening and puncturing is carried out to obtain the desired information lengths and rate adaption. Figure 2 presents an illustration of the encoding process of these codes
## 2 Datasets and Methodology  
The violent eruption of a submarine volcano and tsunami on Ahapai Island (HTHH) (Figure 1a), Tonga, on 15 January 2022, has attracted widespread international attention. In this paper, we investigate the response characteristics of this volcanic eruption to environmental factors in the lower atmosphere region using a priori data such as ERA5 reanalysis data, water vapor data from GNSS inversion and surface temperature data from Landsat inversion for the Tonga Islands region.  
The surface temperature data are Landsat OLI/TIRS sensor data released by the United States Geological Survey (USGS) and cropped from April 2019 to April 2022 at a spatial resolution of 30 m. The atmospheric circulation, precipitation, water vapor flux, and water vapor flux dispersion data used in the experiment are from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The climate state used for the experiment is from 1981 to 2010. The GNSS data used in the experiment are provided by an international collaboration established by the International Association of Geodesy (IAG), which provides GNSS observations from various tracking stations and various products of the IGS. Data from 11 regional IGS stations (Figure 1b) worldwide and 237 IGS stations (Figure 1c) in Tonga were selected from January 2020 to March 2022 for the GNSS/MET net inversion of water vapor using GAMIT/GlobK software (version 10.71).  
The surface temperature inversion is mainly based on the surface thermal radiation observed via the thermal infrared sensor of the satellite. Then, the atmospheric influence is subtracted from it to obtain the surface thermal radiation intensity, and finally, the surface temperature is obtained by converting the thermal radiation intensity. PWV is obtained based on the basic theory of the ground-based GNSS inversion of atmospheric precipitable water, i.e., the inversion of PWV via the PWV and ZWD relationship equation. The water  
Figure 1: (**a**) Tonga location distribution, the red triangle is the HITHH volcano. (**b**) Distribution of 11 IGS sites in Tonga region. (**c**) Global distribution of 237 IGS sites; the blue triangle is the location of the HTHH volcano, and the red circles are the locations of IGS sites. The black box is the range of (**b**) graph.  
vapor flux and water vapor flux divergence are calculated from meteorological elements such as air density, specific humidity, and wind speed.
## 3 CNQF Architecture
### Resource Management Subsystem (RMS)  
The two key elements that make up the RMS are the Resource Brokers (RBs) and the Resource Controllers (RCs). The distributed instances of resource brokers which could be Wireless Access (WARB), Fixed Access (FARB) or Core Network (CNRB), perform policy-based resource management roles depending on the segment of the converged transport plane the RB oversees (see Figure 1). For instance, in a wireless access network, a WARB will administer admission control and bandwidth management policies that affect end-users present in that access domain. CNRB is responsible for resource management in a core network but would also interface with various WARBs and FARBs (if present) to allow interchange of resource availability status thus enabling end-to-end (global) resource management in the converged network. The RBs act as PDPs and communicate with one or more RCs which are located at various PEPs (_i.e._, routers and gateways). The policy decisions in the RBs invoke _policy actions_ which translate to a set of configuration and control functionsundertaken by the RCs. In our previous work we demonstrated CNQF-based open loop configuration of routers for DiffServ operation with the RC functionality under RB command [35]. The policy-driven fuzzy-based resource control strategy described and evaluated in Section 6 of this paper is implemented within the Resource Broker as part of the RMS functionality (for controlling the edge network devices where admission control is administered).
# Releasing the Full Potential of Cannabis through Biotechnology
## 2 The Advances of Cannabis Tissue Culture  
The increase in shoot regeneration rate is critical to establish an efficient transgenic system. Since existing studies have implied its recalcitrant nature to regeneration, combinations of different plant hormones were tested with various explants in Cannabis, such as shoot tips, axillary bud, leaf, and cotyledon [40; 42]. Leaf is the most widely used one in Cannabis tissue culture among all the explants reported [43]. Compared with other cash crops, Cannabis has low regeneration efficiency of tissue culture and wide variation among cultivars. [PERSON] et al. (2021) performed a regeneration assay for one hundred Cannabis varieties and observed significant differences among all the varieties estimated [44]. [PERSON] et al. (2016) demonstrated the highest shoot regeneration rate induced from callus axillary buds using thidiazuron [43]. [PERSON] et al. (2016) obtained the highest shoot induction rate in the cotyledon [45]. [PERSON] and [PERSON] (2019) developed a photoautotrophic system, and a 97.5% rooting rate was obtained from regenerated shoots [46]. Nevertheless, Cannabis regenerations are still limited to specific varieties. Results of previous investigations showed that the process of plantlet regeneration requires a detailed evaluation of genotype response to medium, and even the same combination of plant hormones can result in different recovery rates among varieties [45; 46]. To overcome the bottleneck imposed by tissue culture-based regeneration, [PERSON] et al. (2021) developed a transgenic method through coexpressing developmental regulators (DRs) and genome editing (GE) components, which simplified tissue culture by reprogramming somatic cells into meristens, thus enabling direct regeneration of shoots from callus [44]. The overexpression of DRs, such as _ZmWUS2_ and _OsGRF4_, showed positive influences on shoot organogenesis in several plant species recalcitrant to regeneration [47; 48; 49; 50]. To increase the regeneration efficiency, [PERSON] et al. (2021) cloned the homologous genes of five DRs in Cannabis, which were reported to be effective in several monocot or dicot species and delivered these DRs into hypocotyls [44]. The average regeneration efficiency for the _CsGRF3-CsGF1_ chimera demonstrated a twofold improvement compared to the control. This alteration of DR gene expression facilitated the acquisition of regenerated clones and opened the door to transformed Cannabis.
## 1 Introduction
Thus, considering the field irregularity concerns of FGILs, in this research, Autodesk Inventor(r) was used to model the geometric variants of stranded conductors. These conductor models were then analyzed in COMSOL Multiphysics(r) regarding electrostatic and dielectric aspects along with the development of the geometrically and electrostatically optimized FGIL model. Considering the performed optimized design, an 11 kV scaled-down model of a 132 kV FGIL was also fabricated in order to practically investigate the electrostatic and dielectric stresses in the FGIL through a high-voltage experimental setup. Simulation and experimental investigations revealed that SCF-coated stranded conductor significantly minimized the field irregularity of the FGIL and improved its dielectric breakdown characteristics.
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Mathematical Models
#### 2.1.4 Total Atmospheric Weather Attenuation  
Figure 4 illustrates the attenuation arising from different weather conditions along the path of propagation.  
Total attenuation \(A_{\text{total}}\) along the path of propagation due to atmospheric weather conditions is calculated by summing each individual attenuation due to gases, clouds and fog, and rain.  
\[A_{total}=\gamma_{g}+\gamma_{c}+\gamma_{r} \tag{6}\]