esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Improvement in AD Performance through Bioaugmentation, Comparison with Recent Research
The rate constant increased from 0.071 h\({}^{-1}\) to 0.087 h\({}^{-1}\) as doses of the bioaugmenting mixture were increased, as compared to values of 0.066 h\({}^{-1}\) and 0.069 h\({}^{-1}\) obtained with sewage sludge alone. Next-generation sequencing revealed that _Cytoplaspa_ sp. predominated among bacteria in digesters and that the hydrogenotrophic methanogen _Metlunoculleus_ sp. was the most abundant genus among archaea [60]. It was also reported that bioaugmentation with propionate-degrading enrichment culture can improve the digestion performance of high C/N ratio feedstock without co-digestion with nitrogen-rich substrate [61]. Bioaugmentation with aero-tolerant propionate enrichment culture, dominated by _Spirochaetaceae_ (30%), _Thermovirga_ (12%), and _Methanosaeta_ (65%), was successful in improving by 11 \(\pm\) 3% of CH\({}_{4}\) production in quasi steady state performing anaerobic digesters. CH\({}_{4}\) production increase after bioaugmentation was correlated to increased relative abundance of _Methanosaeta_ and _Methanospirillum_ originating from the bioaugment culture [62]. [PERSON] et al. [47] applied an adapted consortium for the intensification of methanogenesis under high VFAs load. Using the consortium shortened the retention time while the process stability was maintained. It was found that the ratio between active methanogens and syntrophs was one of the key aspects that determined the rate of methanogenesis and the stability of the process [47]. [PERSON] et al. showed that active syntrophic interactions promoted increased biogas yield, since they provided for a more complete utilization of the waste substrates [64]. In this study, under thermophilic conditions, both cumulative methane production and methane production rates were significantly (more than 3.5 and 6.2 times, respectively) improved through bioaugmentation with syntrophic butyrate-degrading consortium, which is very competitive with other works (Table 3). Our study revealed the thermophilic butyrate-oxidizing consortia as a bioaugmented culture could be a potential strategy to alleviate the high organic load and VFA stress of AD.
## 7 Open Research Challenges in UWSNs
### Heterogeneity in UWSNs  
Adequate integration and communication among underwater vehicles and sensor nodes are essential to support a wide range of emerging applications. The homogeneous sensor network is unfavorable for many applications.  
1. Common Standard and Interface  
It becomes a crucial challenge for the cooperation of heterogeneous nodes, and optical and acoustic modems in an operational setting because of the lack of a common standard and interface to support communication and message exchange.  
2. Sensor Heterogeneity  
Besides the different type of assets, sensor heterogeneity that enables cooperation of both mobile and static nodes in implementing specific applications also faces another challenge. The challenge arises in nodes communication holes to maximize data transmission.  
3. Complex Acoustic Environment  
Additionally, the problems of heterogeneous underwater sensors emerge from the complex acoustic environment, particularly in shallow water. The detection of acoustic signals is becoming increasingly difficult with the existence of multiple control techniques from heterogeneous nodes as well as the challenges from the unique features of UWSNs themselves.
## 3 Results and Discussion  
### Welding Results  
Fifty samples of each geometry (weld beads, coatings and cubes) were produced. It was ensured that a large random variation would provide a good data base for the artificial neural networks. For this reason, approximately the same ratio was generated from each category (good, medium, poor). Figure 3 exemplifies one sample from each geometry and category.  
Quality characteristics are different for individual applications and geometries. In practice, additively manufactured components are often compared with cast components. In this case, a visual inspection according to DIN EN 13018 [30] was used for quality assessment. This includes surface defects such as cracks, shrinkage cavities, gas pores or non-metallic inclusions, as well as a dimensional inspection. Typical defects in the DED process are pores, fine holes, poor bonding, spatter and microcracks in a layer. Due to the multilayer structure, these can lead to deviations in the shape of the entire structure, as well as to a change in the melt bath properties.  
Categorization is one way of combining various target variables. The machine operator often decides, based on experience, if the welding result is sufficient or not. This procedure was used to evaluate the results of this work. The categories were selected in consultation with practice users. To ensure objective validation, the categorization was performed independently by three different operators. In the case of results that could not be classified unambiguously, a consensus was reached. The aim here was to reflect practice as closely as possible.
# Research on Clock Synchronization of Data Acquisition Based on NoC
## 2 The General Scenario  
### Data Acquisition Based on NoC  
In NoC-based data acquisition, ADCs are mapped to the nodes on the network resources [17], while the router clock is used as the ADC sampling clock. Under the control of the routers, TI-ADCs perform parallel data acquisition with a sampling rate fs and resolution of m bits. Thus, the total sampling rate is increased to n \(\times\) fs, while the resolution remains m bits to achieve the effect of a high sampling rate. Firstly, the data pass through the analog front end and into the ADC. Secondly, under the control of the ADC resource network interface, the data are encapsulated and transmitted to the on-chip network. Thirdly, under the router's forwarding, the data are transmitted to the destination node's high-speed transmission resource network interface. Finally, the data are output through the optical port.  
In the proposed scheme, according to the global-asynchronous local-synchronous (GALS) characteristics of the clock distribution on the NoC, the asynchronous router clock is used as the ADC's sampling clock in Figure 1. To ensure high-precision TI-ADCs, an improved White Rabbit protocol is adopted in this paper to implement high-precision synchronization of the ADC's sampling clock.
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 3 Disease Classification Models of Xception Fused by the SE Module
### SE_miniXception Network Structure  
In order to simplify the model structure, reduce the model parameters, improve the training and recognition speed, and meet the memory requirement of the model used in the mobile platform, we carry out an experiment to compress SE_Xception. The SE module is lightweight and hard to compress again, so we compress the depth and width of SE_Xception. Xception architecture has 36 convolution layers. These 36 convolution layers are divided into 14 blocks, most of them connect with residual. The stack of depth-wise separable convolution layers makes it easy to modify the network structure.  
The number of channels in classic CNN models has been carefully designed and fine-tuned through a large number of experiments. Therefore, the number of channels in SE_Xception can also be increased or decreased according to the model performance on the ATLDD dataset. Based on the above theories, a compression experiment is carried out on SE_Xception by condensing and pruning the network structure.  
After compression experiment, the selected lightweight network structure is illustrated in Figure 5. The compression scheme is as follows: compress the width of SE_Xception to 0.25-times those before, then delete 6 depth-wise separable convolution blocks from the middle flow and the deep flow. The lightweight network is named as SE_miniXception, as shown in Figure 5. With relatively high classification accuracy, the model reduced the number of parameters from 23.85 to 0.61 M, so that the parameter compression ratio reached 39:1, which laid a foundation for the SE_miniXception network to be embedded in the mobile platform.
## 3 Model Development  
### S-GBS Models  
Solar-assisted geothermal borehole systems (S-GBS) were modeled by using the MATLAB R2021b programming language. The code followed closely with the analytical methods developed previously in Section 2 and summarized in Figure 3. In particular, the computation of a S-GBS was shown as follows:  
1. Prescribe geometry, thermophysical properties, and time-dependent heat flux boundary of S-GBS;
2. For a single borehole case, compute the exact solution, as expressed in Equation (10), to a 1-D radial transient cylindrical heat conduction problem subjected to a time-dependent heat flux boundary; and
3. For the multiple boreholes case, compute the thermal superposition solution based on Equation (11) after step (ii). The scenarios of double and 3-by-3 boreholes at the center line were given in Equations (13) and (14), respectively.  
The geometrical and thermophysical parameters required in the model were listed in Table 1, consistent with the values in [39]. On the other hand, the time-dependent boundary for the S-GHS was typically seen as a sinusoidal function varying from 20 (W/m) to \(-10\) (W/m) over a year period. For one unit depth, this boundary was graphed by the red solid line in Figure 4 and it was expressed as  
\[q=-k\frac{\partial T}{\partial r}=q_{0}f(t)=A_{0}+\sum_{n=1}^{2}\bigg{(}A_{n} \cos(nwt)+B_{n}\sin(nwt)\bigg{)}, \tag{15}\]  
where \(q_{0}=1\) and the constants in the Fourier series were: \(A_{0}=3.574,\ A_{1}=14.95,\ B_{1}=-1.389,\ A_{2}=1.478,\ B_{2}=-0.2771,\ w=1.96 \times 10^{-7}\). Note that all the variables are in SI units for consistency.
## 4 Research Efforts
### Platform Solutions  
There have been some efforts to investigate using the current general commercial Cloud platforms for e-health services. In addition, there have been other efforts to propose new Cloud platforms that are specifically designed for e-Health services. [PERSON] [57] provided an insightful discussion of advantages and disadvantages of using commercial platforms and Clouds (such as Windows Azure, Google Apps) by healthcare organizations for their e-Health Cloud applications. For example the benefits of fast and low cost implementations are a tradeoff with the security risks and outage issues of the Cloud providers.  
On the other hand, specialized e-Health Cloud platforms can provide a set of common services specifically needed for developing and operating different e-health applications. In one example, [PERSON] _et al._[58] presented the Data Capture and Auto Identification Reference (DACAR). DACAR aims to develop, implement and disseminate a novel secure platform in the Cloud for capturing, storing and consuming data within a healthcare domain. By using a single point of contact, the DACAR platform promises to provide solutions for the challenges of e-Health Cloud services. This is done through a rule based information security policy syntax and data buckets hosted by cost effective and scalable cloud infrastructures. These services include integration, large scale deployment, security and confidentiality of medical data. The DACAR platform was also used to develop EWS software [56] and it is the only platform so far that is designed specifically for e-health services.  
In another specialized e-Health Cloud platform example, researchers developed an approach for a cloud platform called CyberHealth for Aggregation, Research, and Evaluation (CARE) [59]. This platform was mainly proposed to enable data integration, filtering, and processing for data mining in e-health. They identified a need for an infrastructure for data integration and languages, algorithms, and tools to analyze medical information to discover new medical patterns. With this approach, heterogeneous data from different sources can be integrated, processed and analyzed to improve health understanding and medical treatment effectiveness.  
Generally, general cloud platforms can be more open to integration with other applications, while specialized e-health platforms can provide better customized environments for implementing, integrating, and operating e-health applications.
# Arabidopsis Plastid-RNA Polymerase RPOTp Is Involved in Abiotic Stress Tolerance
## 2 Results
### ABA Down-Regulates the Expression of NEP and Genes Encoding PEP Subunits  
ABA plays a pivotal role in the response of plants to different abiotic stresses [41]. Hence, we decided to investigate if the expression of NEP genes could also be affected by ABA. To this end, we determined by qRT-PCR the steady-state levels of the transcripts of the _RPOTp_, _RPOTmp_ and _RPOTm_ genes in 10 DAS Col-0 seedlings grown in the presence or absence of 1.5 \(\upmu\)M ABA. We included in this analysis the aforementioned nuclear genes _mTERF5_ and _mTERF9_, as well as plastid genes _rpoA_ and _rpoB_, as representatives of genes essentially transcribed by NEP. We selected 1.5 \(\upmu\)M ABA because this concentration slows and decreases Col-0 cotyledon greening, but to a lesser extent than 3 \(\upmu\)M ABA (Figure 2d,f), and it provided a sufficient number of seedlings to study the ABA effects at 10 DAS.  
To confirm the effectiveness of the employed phytohormone concentration, we first analyzed the expression of the ABA-induced gene _RD29A_, as we did for NaCl stress (see above). In response to 1.5 \(\upmu\)M ABA, _RD29A_ was significantly up-regulated (\(32.7\pm 9.0\); \(p=4.11\times 10^{-4}\)), which was consistent with the induction of _RD29A_ mRNA by ABA [30], and besides, it confirmed the effectiveness of our treatment. We found that ABA repressed the expression of all the studied genes, although this down-regulation was statistically significant for _RPOTp_ (\(0.62\pm 0.21\); \(p=0.001\)), _mTERF5_ (\(0.59\pm 0.29\); \(p=0.001\)), _mTERF9_ (\(0.44\pm 0.22\); \(p=2.42\times 10^{-6}\)), _rpoA_ (\(0.52\pm 0.18\); \(p=4.113\times 10^{-4}\)) and _rpoB_ (\(0.57\pm 0.23\); \(p=4.113\times 10^{-4}\)), but not for _RPOTm_ (\(0.85\pm 0.32\); \(p=0.223\)) or _RPOTmp_ (\(0.86\pm 0.42\); \(p=0.223\)).
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Hardware for the Data Monitoring System  
The first subject is the printed circuit board (PCB) design because is the basis of the production. For the development of this Section the main program used for the designs is \"Eagle CAD\" [38]. When we started there is not supposed to be a limit of size but it is recommendable to minimise it in order to offer a good design. Furthermore, the free version of \"Eagle CAD\" only allows a certain maximum size of (56 \(\times\) 36 \(\times\) 23 cm). During the design, a prototype had been created in order to solve possible troubles during the construction and the working life. The final solution is presented in this Section.
# Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning
## 3 Materials and Methods  
Most of the abovementioned methods were based either on standard Machine Learning/Deep Learning approaches, or on Word2 Vec/AraVec approaches. This statement was also confirmed by [PERSON] [27] in his literature review. The main issue with these methods is that, when they try to represent a word, they tend to miss on its context. For example, a word like \"\(\underline{v}\)\" will be represented in the same way every time regardless of its context. The thing here is that the same word, depending on context, can either mean \"Morocco\", \"west\", or \"sunset\".  
As it will be further developed in this section, we tried to build our hate speech detection model while taking context into consideration. This prompted us to explore the possibilities offered by BERT [4]-based models. These models can provide more insight on context by scouting a particular word's successors and predecessors, rather than relying just on its successors, as it was the case with the previous methods.  
Based on the usual classification performance indicators, i.e., Accuracy, Precision, Recall, F1-Score, and Confusion Matrix, we will compare different BERT-based approaches and see which can provide the best results. The approaches we will compare are the following:  
* Translating the comments into English and using the classical BERT [4];
* Using the Multilingual BERT (mBERT) [4] on the translated comments;
* Using mBERT on the original comments in Arabic;
* Using AraBERT [30], a BERT-based model trained on Arabic text, on the original comments in Arabic.
## 4 The Proposed Method
### Spatio-Temporal Hypergraph Modeling of Human Skeleton Correlation
#### 4.2.1 Physically Connected Joint Spatio-Temporal Hypergraph  
For the physically connected joint (PCJ) spatio-temporal hypergraph, the inherent topology of the human joints is used as a basis to divide them into different regions, including the head, trunk, left hand (_Lhand_), right hand (_Rhand_), left leg (_Lleg_), and right leg (_Rleg_) in six regions \(\mathbf{\mathcal{V}}_{area}\), \(area=\{head,trunk,Lhand,Rhand,Lleg,Rleg\}\). \(\mathbf{\mathcal{V}}_{area}\) represents the set of all joints belonging to a certain area, and the joints of the human in consecutive frames of the same area are connected by a hyperedge to construct a spatio-temporal hypergraph as a fixed information representation of the human body structure, as shown in Figure 4a.  
The physically connected joint spatio-temporal hypergraph is denoted as \(\mathbf{\mathcal{G}}_{PCJ}=(\mathbf{\mathcal{V}}_{PCJ},\mathbf{\mathcal{E}}_{PCJ},\mathbf{W}_ {PCJ})\), where \(\mathbf{\mathcal{V}}_{PCJ}\) denotes the set of vertices of this hypergraph, \(\mathbf{\mathcal{E}}_{PCJ}\) denotes the set of hyperedges of this hypergraph, and \(\mathbf{W}_{PCJ}\) denotes the diagonal matrix of hyperedge weights in this hypergraph. Specifically, \(\mathbf{\mathcal{V}}_{PCJ}\) is the set of all joints \(v_{t}^{(i)}\) (the \(i\)-th joint in the image of frame \(t\)) in all frames; the elements in \(\mathbf{\mathcal{E}}_{PCJ}\) are noted as \(e_{area}=\{\forall v_{t}^{(i)}|v_{t}^{(i)}\in\mathbf{\mathcal{V}}_{area}\}\), \(\mathbf{\mathcal{E}}_{PCJ}=\{e_{area}|area=head,trunk,Lhand,Rhand,Lleg,Rleg\}\). The incidence matrix \(\mathbf{H}_{PCJ}\) of the spatio-temporal hypergraph of physically connected joints is constructed based on the association between \(\mathbf{\mathcal{V}}_{PCJ}\) and \(\mathbf{\mathcal{E}}_{PCJ}\), and the hyperedge degree \(\delta(e_{area})\) and vertex degree \(d(v_{t}^{(i)})\) are calculated using Equations (2) and (3), following which the diagonal matrices \(\mathbf{D}_{e_{area}}\) and \(\mathbf{D}_{v_{t}^{(i)}}\) of the hyperedge degree and vertex degree are obtained. In order to optimize the network using higher-order information, the incidence matrix \(\mathbf{H}_{PCJ}\) is transformed by Laplace to generate the Laplace matrix \(\mathbf{G}_{PCJ}\), which is calculated as  
\[\mathbf{G}_{PCJ}=\mathbf{D}_{v_{t}^{(i)}}^{-1/2}\mathbf{H}_{PCJ}\mathbf{W}_{PCJ}\mathbf{D}_{e_{area }}^{-1}(\mathbf{H}_{PCJ}^{\top})\mathbf{D}_{v_{t}^{(i)}}^{-1/2} \tag{7}\]  
Figure 4: Visualization of different hypergraph constructions: (**a**) the construction of a physically connected joint spatio-temporal hypergraph; (**b**) the construction of a non-physically connected joint spatio-temporal hypergraph; (**c**) the construction of a non-physically connected bone spatio-temporal hypergraph.
## 4 Results and Discussion
Additionally, the low packet loss ratio of 1.5% demonstrates the reliability of the blockchain-based IDS in maintaining data integrity during transmission. The algorithm's outstanding accuracy of 98.02%, along with precision and recall values of 96.40% and 97.53%, respectively, highlights its effectiveness in identifying positive cases. In fact, the results provide further evidence for the reason for the low detection rate and authentication time. In summary, these metrics together provide a comprehensive evaluation of the proposed method, highlighting its proficiency in decision-making, authentication, network efficiency, and maintaining data integrity. Table 3 provides the performance metrics of the proposed methodology.
# Fast 3D Semantic Mapping in Road Scenes +
###### Abstract  
Fast 3D reconstruction with semantic information in road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a fast 3D semantic mapping system based on the monocular vision by fusion of localization, mapping, and scene parsing. From visual sequences, it can estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping. Our system consists of three modules: a parallel visual Simultaneous Localization And Mapping (SLAM) and semantic segmentation module, an incrementally semantic transfer from 2D image to 3D point cloud, and a global optimization based on Conditional Random Field (CRF). It is a heuristic approach that improves the accuracy of the 3D semantic labeling in light of the spatial consistency on each step of 3D reconstruction. In our framework, there is no need to make semantic inference on each frame of sequence, since the 3D point cloud data with semantic information is corresponding to sparse reference frames. It saves on the computational cost and allows our mapping system to perform online. We evaluate the system on road scenes, e.g., KITTI, and observe a significant speed-up in the inference stage by labeling on the 3D point cloud.  
3D semantic mapping; incrementally probabilistic fusion; CRF regularization; road scenes
## 2 Proposed Method and Environment
### Proposed Method
#### 2.2.2 MFCCs (Mel Frequency Cepstral Coefficients)  
MFCC is a conversion algorithm used mainly in speech recognition. This is one of the methods for extracting the features from sound signals, and the procedure for feature extraction consists of the following six steps [38; 39], as shown in Figure 7:  
Figure 5: Original signals of each target class: Normal, Misalignment, Rubbing, and Unbalance (**Left**); Examples of Short-Time Fourier Transform (STFT) conversion of Normal, Misalignment, Rubbing, and Unbalance raw signals (**Right**).  
Figure 6: Examples of the changes in STFT spectrograms.  
* Frame the signal into short frames.
* For each frame, calculate the periodogram estimate of the power spectrum.
* Apply the mel filterbank to the power spectra, and sum the energy in each filter.
* Take the logarithm of all filterbank energies.
* Take the Discrete Cosine Transform (DCT) of the log filterbank energies.
* Keep DCT coefficients 2-13, and discard the rest.