esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Wastewater streams management is one of the most relevant issues of any large human settlement [1]. The final residue from every wastewater stream treatment is represented by several forms of sludge [2]. Over the last decade, the amount of sludge has increased continuously and significantly due to the rapid population growth of cities. The disposal of sewage sludge is a hard task due to its high composition variability and government regulations [3]. Traditionally, sewage sludge has been used in agriculture as fertilizers [4, 5] and for landscape applications [6, 7]. Nonetheless, direct use of sewage sludge involves several issues due to heavy metal accumulation [8]. This is caused by both residue accumulation during water use and treatments with coagulation and flocculation agents. The most widely used coagulation and flocculation agents are iron and aluminum sulphate [9, 10], which are retained in the sludge. The removal of these metals is quite challenging [11, 12], expensive and not economically profitable.  
Nonetheless, several studies have used the treated sludge without removing metals for catalytic applications. Recently, thermally hydrolyzed sludge was used as an efficient catalyst for intra- and inter-molecular esterification processes [13, 14]. Sludge catalytic activity could be magnified by pyrolytic processes that increase the inorganic content through a radical degradation of organic matter. Pyroized sludge was used as a Fenton catalyst for water purification [15] and for the realization of energy storage systems (see, e.g., [16]). Furthermore, sludge derived biochar was used as an adsorbent for wastewater purification [17], promoting a virtuous circular reuse of wastewater streams.  
In the field of composites (see, e.g., [18, 19, 20]), sludge derived biochar has been used for the production of concrete- [18] and poly(ethylene)-based composites [21] to enhance the polymer hosts'mechanical and thermal properties. The electrical properties of biochar derived from vegetation were reported in [22; 23; 24]. Accordingly, in this study, we report the electrical and morphological characterization of sludge derived biochar. DC characterization of thermally annealed biochar and non-thermally annealed biochar was performed, and it was found that the thermally annealed biochar had better electrical performance. Consequently, composite samples of thermally annealed biochar were fabricated, and microwave characterization in a rectangular waveguide was performed. Values of the shielding effectiveness (SE) were extracted from the measured transmission loss.
# CT Scanning of Structural Characteristics of Glacial Till in Moxi River Basin, Sichuan Province
## 1 Introduction
Among them, the study of meso-structure and microstructure and fabric are the basis of the subsequent two aspects of research, especially the modeling basis of numerical simulation [13]. Therefore, the quantitative description of meso-structure and micro-structure characteristics plays an important role in the study of the engineering characteristics of glacial till [14; 15]. Research results in recent years have shown that the application of CT scanning technology and SEM technology for quantitative research on the meso-structure and micro-structure characteristics of glacial till have been favored by the majority of investigations [16; 17]. For this reason, the intact glacial till in the Moxi River Basin in the south of Kangding City, Tibetan Autonomous Prefecture of Garze, Sichuan Province is taken as the research object, and its meso-structure and micro-structure characteristics are quantitatively studied using CT scanning technology and SEM. The research results can a more scientific basis for highway, railway and hydro-power station construction and disaster prevention and mitigation in the alpine mountainous area.
## 4 Results and Discussion
### Performance of Asphalt Binders
#### 4.2.4 BBR Test  
The values of asphalt binders' S and m are displayed in Figure 13 at three temperatures. The results showed similar trends for all four binders. The S and m increased and decreased, respectively, as the temperature decreased. Besides, compared with short-term aging, the S increased after long-term aging, while the m decreased.  
In Figure 13a, the modified binders' S values were lower than the control binder under the same freeze-thaw cycle conditions, especially at \(-24\)\({}^{\circ}\)C, and the S decreased gradually with the increase of the modifier content. This indicates that the low-temperature flexibility and resistance to deformation of the control binder reduced more significantly than the modified binders as the temperature decreased, especially at low temperatures (below \(-20\)\({}^{\circ}\)C), and the impact was more remarkable as the modifier dosage increased.  
From Figure 13b, the modified binders' m values were generally higher than the control binder under the same freezing and thawing temperature, and the values' gap was more evident at \(-18\)\({}^{\circ}\)C. Overall, the greater the modifier content, the greater the m value. These results suggest that the modified binders' stress relaxation and low-temperature cracking performance were better than the control binder as the pavement temperature decreased, and the effect gradually became more obvious as the modifier content increased.  
Figure 12: Master curves of asphalt binders evaluated at temperatures ranging from 12 to \(-12\)\({}^{\circ}\)C: (a) \(|\)G*\(|\); (b) \(\delta\).  
Taking the above analyses into consideration, the BBR test results further confirmed that the addition of CaCO\({}_{3}\) NFs improved the low-temperature cracking performance of the asphalt binder. In detail, the low-temperature crack resistance performance related to NFs addition was ranked as: 5% \(>\) 3% \(>\) 1% \(>\) 0%.
## 3 RNAi Traits for Rootworm Control
### Transgenic dsRNA Events  
Identification of gene targets that generated a robust RNAi response in WCR, and most importantly larval mortality, was the first step in developing insect-specific dsRNA as a management tactic. Incorporation of dsRNA molecules into transgenic maize events was the most viable option to ensure both stability of dsRNA and activity against WCR larvae in field environments. Deployment of transgenic maize hybrids with WCR-active dsRNA in field studies allowed researchers to understand the impact of the RNAi response on WCR survival and feeding injury and evaluate potential non-target effects in the field.  
RNA molecules directed at target genes are carefully selected to avoid putative siRNA sequences with homology to transcripts from non-target organisms or the crop of interest, in this case, maize. Bioinformatic workflows are used to focus on regions of target transcripts predicted to generate a high abundance of siRNA. In WCR, dsRNA needs to be at least 60 bp in length to trigger gene knockdown effectively, and a length of 200-400 bases has shown to be an adequate size to generate a robust RNAi response in WCR. Midgut uptake of long hairpin dsRNA, but not siRNA 21-mers, is efficient in WCR [31]. A broad genome-wide uptake of endogenous plant dsRNA and transgenic dsRNA and subsequent processing of long dsRNA into 21-nucleotide (nt) siRNA by WCR has been demonstrated [142]. When developing RNAi traits, it is a routine practice to express inverted repeats homologous to the mRNA target from a strong constitutive promoter. Inverted repeats are separated by a non-homologous neutral stuffer sequence or intron. Such dsRNA expression constructs are exemplified by those described in [PERSON] et al. [55], [PERSON] et al. [143], and [PERSON] et al. [129]. Transgenic maize events are selected when they contain only one or two expression construct copies to avoid silencing of the insert by the plant and simplify the event molecular characterization. Selected events for advancement are ultimately based on root protection against WCR feeding using a root damage rating system (0-3 node-injury scale) [144]. Further characterization of selected events examines the amount of dsRNA produced. This work is essential for determining non-target organism exposure levels in the field. dsRNA levels expressed in maize events are most efficiently quantified using QuantiGene RNA technology, a nucleic acid hybridization assay that can be run on crude maize tissue lysates without the need for extensive RNA purification as is required for reverse-transcription polymerase chain reaction quantitation [143]. Greenhouse and field-based testing of transgenic plants expressing _DvSnf7_ indicated that by itself this RNAi-based trait MOA did not provide a level of protection comparable to a commercial trait, Cry3 Bb1. However,combining both MOAs enhanced root protection and resulted in a significant reduction in WCR adult emergence, which may delay resistance development to both traits [32; 145].
## 2 Materials and Methods
### Data Processing and Bioinformatics Analysis
#### 2.5.2 Metatranscriptomics  
The overall bioinformatics strategy included the following steps: (i) Trim and clean-up of the sequencing reads using the trim galore wrapper with default parameters, except for the -length 40 and the -fastqc options, so as to remove adaptors and low-quality sequences; (ii) stitching of the pair-end reads using the Fast Length Adjustment of SHort reads (FLASH) with default options except for -M 210; (iii) extraction of the open reading frames (ORFs). HMMER2 GO [34] was used to extract ORFs and assign to Pfam (Pfam Version 31) using the default hummer2 go pipeline; (iv) search for similarities in the NCBI protein Non-Redundant (NR) database. From the NR database, downloaded on 17/11/2017, all the eukaryotic and bacteria protein sequences were extracted and used to create two distinct formatted databases for diamond blast [35]. Diamond blast (version 8.22) was used (parameters -outfmt 100 -p 20 -k 2 -e 10e-10) to find similarities of all the ORFs from the three samples (Day16, Day57, and Day84), in the entire NR, as well as separately in the bacteria and eukaryotic protein database. A total of 3,064,452 ORFs were used for diamond blastp queries from the three samples; (v) Gene Ontologies (GO) were assigned using the MEtaGenome Analyzer (MEGAN, community version 6.10.8) [36]. All analyses were implemented on a Linux-based HPC cluster assigning one node with 32 cores and 256 GB RAM.
## 4 Discussion
### The Sensitivity of the Parameters  
In order to characterize the influence of every structural parameter on the oscillation of the swash plate, the local relative sensitivity, based on the differential method, is given in Figure 11. We can see that the volume \(V_{0}\) of the outlet chamber, the stiffness \(k_{v}\) of the control valve spring, the action area of the actuator piston, and offset distance \(l_{a}\) of the actuator piston are the four key parameters that have a large effect on the swash plate dynamic response. Reducing the values of the four parameters results in a significant increase in the amplitude of the swash plate oscillation and may even cause resonance. Hence, these four values need to be further increased to the appropriate sizes. On the other hand, the diameter \(d_{v}\) of the control valve spool and the dead volume \(V_{3}\) of the actuator piston chamber also have a large effect on the swash plate dynamic response. A reasonable decrease in the values of the two parameters can further reduce the oscillation amplitude of the swash plate.  
Figure 11: The parametric sensitivity on the oscillation of the variable displacement mechanism.  
Figure 10: The influence of the action area of the chamber pressure. (**a**) Diameter of the actuator piston (\(d_{a}\)) and (**b**) diameter of the control valve spool (\(d_{v}\)).
# A Monopole UWB Antenna for WIFI 7/Bluetooth and Satellite Communication
## 3 Parametric Study  
To examine the effect of different parameters of an antenna on its performance, a systematic study is carried out. The goal of this study is to identify the fabrication tolerance and, more importantly, to pinpoint the effects of antenna parameters on the bandwidth. We studied four parameters, i.e., width W\({}_{2}\) of the middle rectangle, length L\({}_{9}\) of the ground, base side of the triangle W\({}_{3}\), and distance S from the L shape to the mid-point. The first two parameters are chosen because they lay the framework of the antenna structure, while the latter two parameters correspond to the evolutionary design process of the antenna.  
Figure 4a demonstrates the effect of width W\({}_{2}\) of the short-sleeve-shaped radiator on the bandwidth. It can be seen that at W\({}_{2}\) = 6 mm, four bands are generated, but only some of them correspond to the required bands. When W\({}_{2}\) is further widened, the covered bands tend to be the same, and for a better matching effect, W\({}_{2}\) = 7 mm is finally chosen. As shown in Figure 4b, the length of the ground L\({}_{9}\) has a great influence on the high-frequency band, and when L\({}_{9}\) = 11.6 mm, the S\({}_{11}\) in the high-frequency band of 10-12.5 GHz is higher than \(-\)10 dB, and the matching effect is not very good. Although there are more resonance points at L\({}_{9}\) = 13.6 mm, L\({}_{9}\) = 12.6 mm has a better matching effect, thus indicating a better choice.  
Additionally, Figure 5a shows the effect of width W\({}_{3}\) of cropping angle on the antenna bandwidth. When W\({}_{3}\) changes from 1.4 mm to 2.4 mm, the return loss goes higher than the standard requirement of \(-\)10 dB at some high frequencies, and the bandwidth in-creases by 2.3 GHz. However, as the width continues to increase, the matching performance degrades. As displayed in Figure 5b, when the L-shaped patch moves away from the feed line, S increases, causing the bandwidth of the antenna to deteriorate significantly. Therefore, the final dimensions are selected as W\({}_{3}\) = 2.4 mm and S = 2 mm.  
Figure 4: The effects of tuning W\({}_{2}\) and L\({}_{9}\) on antenna performance: (**a**) W\({}_{2}\), (**b**) L\({}_{9}\).  
Figure 5: The effects of tuning W\({}_{3}\) and S on the impedance\(-\)matching performance: (**a**) W\({}_{3}\), (**b**) S.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 2 Preparation and Performance Analysis of High-Viscoelastic Modified Asphalt
### Preparation Process of High-Viscoelastic Modified Asphalt  
In this article, high-viscoelastic modified asphalt was prepared using the high-speed shear + mixing method [30]. To investigate the optimal content of the high-viscoelastic modifier in asphalt, the study set the modifier content at 0%, 3%, 6%, 9%, and 12%. All high-viscoelastic modified asphalt samples were prepared using the same method to minimize errors introduced by test variables [26; 31; 32]. The specific preparation process was as follows:  
1. Heat the SBS-modified bitumen in an oven at 175 \({}^{\circ}\)C until it melts.
2. Heat the asphalt to 170-180 \({}^{\circ}\)C, add the high-viscoelastic modifier and co-solvent separately, and activate the high-speed shear for dispersion. The shear time is 45 min at a rotational speed of 4000 rad/min.
3. After shearing, add the stabilizer and stir at 180 \({}^{\circ}\)C for 30 min to obtain the high-viscoelastic modified bitumen.
# Link Prediction with Hypergraphs via Network Embedding
## 3 Experiments
### Compared Methods  
In this paper, we compared the proposed HNE with three categories of baselines: similarity-based methods-CN [10], Jaccard coefficient [11], random-walk-based methods-Katz [12] and RWR [13], and deep-learning-based methods-Node2 vec [32], GCN [38]. The existence probabilities of links were evaluated by the similarity between two nodes. Common neighbors (CN) is a link prediction method that is based on evaluating the overlap or similarity of two nodes by obtaining the number of common neighbors in a graph. The Jaccard coefficient is defined as the ratio of the common neighbor size of node \(i\) and node \(j\) to the size of all their neighbors. Katz centrality is an approach for summing all paths of nodes \(i\) and \(j\), where the weight of paths decays exponentially according to their length, to evaluate how closely two nodes are related in the graph. Random walk with restart (RWR) provides a kind of random walk where node \(i\) moves to its neighbor with probability \(c\) or it jumps to the original node with probability \(1-c\). We set \(c=0.2\) in this paper. Node2 vec learns a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. GCN is a classical graph neural network to learn the representation of nodes in graphs by convolutional networks. For the deep-learning-based methods, we set the embedding dimension as 64, and for all methods, we randomly ran them 10 times and reported the average results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & \(n\) & \(m_{0}\) & \(m\) & **Density** & \(\langle k\rangle\) & \(\langle d_{H}(v)\rangle\) & \(\langle\delta(e)\rangle\) & \(\langle\delta_{H}(e)\rangle\) \\ \hline SUEP & 906 & 24,362 & 19,530 & 0.0297 & 27 & 29 & 47 & 1.3 \\ \hline SHOU & 2680 & 222,126 & 64,958 & 0.0309 & 81 & 41 & 108 & 1.7 \\ \hline SUFE & 1720 & 148,188 & 35,727 & 0.0501 & 86 & 33 & 62 & 1.6 \\ \hline USST & 2733 & 230,597 & 54,437 & 0.0308 & 84 & 36 & 93 & 1.8 \\ \hline SISU & 3089 & 478,953 & 72,100 & 0.0502 & 155 & 46 & 142 & 2 \\ \hline SHNU & 3557 & 263,305 & 93,996 & 0.0208 & 74 & 43 & 120 & 1.6 \\ \hline TJU & 6150 & 988,516 & 131,199 & 0.0261 & 161 & 42 & 134 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structural properties of the seven hypergraphs.  
The training set data selected in this experiment were obtained by random sampling. In order to more comprehensively evaluate the accuracy and validity of the experimental results, in this paper, we used a weighted average processing to consider a training sample, the \(n\) classification problem was decomposed into two classification problems, and then the prediction results of the model were evaluated. Four evaluation indicators, AUC, precision, recall and F1-score were evaluated in the experimental results of the model to ensure the reliability and validity of the HNE method.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 5 Numerical Experiments with Practical Sound Fields
### Sound Propagation Problem in a Rectangular Room Including Acoustic Diffusers
#### 5.2.1 Results and Discussion  
First, a comparison of sound propagation among the reference solution, the proposed explicit TD-FEM, and the standard implicit TD-FEM are portrayed in Figure 9, where the results at \(t=16\), 32, and 64 ms are presented. The sound propagation properties of proposed explicit TD-FEM agree very well with the reference solution. It shows an isotropic and less dispersive sound propagation.  
Figure 8: (**a**) Analyzed sound field including acoustic diffuser with a source point and 27 receivers located on a grid of 1.2 m \(\times\) 1.2 m. (**b**) Details of installed acoustic diffuser for one period. (**c**) Details of discretization of the room around the acoustic diffuser.  
In contrast, the standard implicit TD-FEM shows a dispersive and an anisotropic sound propagation, where the sound speed in axial directions is faster than those in oblique directions. This result in sound interference occurs at an earlier time, as in the result at 32 ms. Additionally, the wavefront becomes indistinct at later than 64 ms.  
Figure 10 presents comparisons of sound pressure waveforms at a receiver (\(x\), \(y\)) = (5.3, 3.0) among the reference solution, the proposed explicit TD-FEM, and the standard implicit TD-FEM. The proposed explicit TD-FEM shows much better agreement with the reference solution, but it includes slight difference in amplitude. This agreement confirms that the proposed explicit TD-FEM has superior performance for dealing with discretization of sound fields including complex geometries. However, the standard implicit TD-FEM shows marked discrepancy from the reference solution because of the large dispersion error. As described, the effect appears as an increase of sound speed. The effects are visible even in the direct sound. Moreover, they accumulate with time. Additionally, one can observe that the standard implicit TD-FEM cannot capture reflected waves with high amplitude accurately at around \(t\) = 0.06 and 0.07 s. More quantitatively, regarding spatial averaged relative errors, the proposed explicit TD-FEM has one-third lower relative error of 0.316% compared to the error 1.031% in standard implicit TD-FEM. In addition, the proposed explicit TD-FEM has other benefits for computational time. The proposed method can compute 4.55 times faster with the total number of MVP of 24,000 in the proposed method and 109,109 in the standard method.  
Figure 9: Comparison of sound propagations at \(t\) = 16, 32, and 64 ms among (**a**) Reference, (**b**) the proposed explicit TD-FEM and (**c**) the standard implicit TD-FEM.
## 4 Experimental Results  
### Test Description  
The main equipment involved in the tests is shown in Figure 12. A V2G charger has been used as a HUT and it has been connected to the EVE through a CHAdeMO connection. The bottom box of the emulator is the AC/DC converter and the upper box is the DC/DC converter. Both of them are connected to the EBox, which is also connected to the grid analyzer (Circutor CVM-MINI) via RS-485 and to the HMI through TCP/IP. The transformer is also inside an enclosure for safety reasons.  
The simplified sequence of the complete test is shown in Figure 13, where it is explained how the system works, and the main processes with their interactions that are running, which are needed to emulate the behaviour of an EV. First, the user has to start the EVE system through the HMI and wait until the DC/DC system is ready to initiate the charge/discharge operation in order to test the HUT accordingly. Then, the user has to plug-in the cable and begin the operation needed to launch the charging/discharging process of the HUT. The user can change the voltage and/or power manually at any time or load a script with the profile of EV charging/discharging, indicating every 0.5 s the required voltage and power via HMI. The test will be finished whenever: the user stops it via HMI, the EVE battery model determines that it has finished the charging/discharging process, or there is any error during the operation.