esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosorption of Pb(II) Using Coffee Pulp as a Sustainable Alternative for Wastewater Treatment
## 3 Results and Discussion
### Lignocellulosic Content of the CP  
Table 1 presents the results of the content of cellulose and lignin in the CP.  
Table 1 shows the results obtained in the research on the quantification of lignin and cellulose in the CP. As it can be seen, the value of cellulose is higher than that of lignin, and these are within the bibliographic reports presented in the literature. Likewise, this parameter was performed in order to quantify the presence of lignin and cellulose in CP because these compounds are located on the outside of the cell wall of these wastes [39] (p. 5). These compounds have functional groups such as hydroxyl, methoxy and carbonyl [40]. These groups are characterized by interacting with the pollutant, allowing to carry out the removal of Pb\({}^{2+}\) ions in wastewater, under optimal conditions (temperature, particle size, amount of biomass, pH, pHpzc, agitation time, initial concentration and volume).  
Similarly, this is in agreement with [PERSON] et al. [29], in relation to the elucidation of the functional groups of coffee pulp using the analytical technique of infrared spectroscopy and the relationship between acidic and basic groups, where the former prevail over the latter. It is important to mention that coffee pulp contains a third polysaccharide known as hemicellulose, and the research carried out by [PERSON] and [PERSON] [23] reported a value of 2.3% for this compound. Likewise, [PERSON] and [PERSON] [41] exposed a value of 39.10 \(\pm\) 1.94 g of CP per dry matter. Finally, [PERSON] et al. [15] reported a range of 5-10% for this compound.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Value of This** & & **Bibliographic** & \\  & **Research** & **Method Used** & **Report and Reference** & **Interval** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Lignin \\ (\%m/m) \\ \end{tabular} } & \multirow{4}{*}{19.25 \(\pm\) 0.16} & ANSI/ASTM & 25-26 [34] & \\  & & D1106-56 & 23.8–17.4 [35] & \\ \cline{1-1} \cline{3-5}  & & [33] & 5.21 [36] & \\ \cline{1-1} \cline{3-5}  & & & 9.0–22.0 [37] & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Cellulose \\ (\%m/m) \\ \end{tabular} } & \multirow{4}{*}{29.93 \(\pm\) 0.21} & ANSI/ASTM & 23–24.5 [34] & \\ \cline{1-1} \cline{3-5}  & & D1103-60 & 18–13 [35] & \\ \cline{1-1} \cline{3-5}  & & [38] & 57.9 [36] & \\ \cline{1-1} \cline{3-5}  & & & 16.0–25.88 [37] & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lignin and cellulose content in CP.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 5 Design Guidelines
### Step 1--Analysis of the Target Application and the Available Solutions  
A good understanding of the target application is critical to effectively extract the functional (interfaces, communication protocols, operating modes, etc.), electrical (voltage levels, expected power consumption, etc.), mechanical (die and package dimensions, bond-pads dimension and position, bonding method, etc.), environmental (temperature range, performance under radiation, etc.), and reliability (lifetime, SER, etc.), requirements of the new radenv-ADC. Particularly, radiation and reliability requirements can be estimated using space environments software, models, and tools, such as CREME [80; 81], FASTRAD [82], GEANT4 [83; 84], OMERE [85], etc. Furthermore, previous solutions (i.e., data collected in Table 1) and/or cutting-edge technologies can inspire particular requirements and other design features (such as the manufacturing technology and the ADC architecture).  
For novel applications, new radenv-ADCs are developed to fulfill key requirements that available radenv-ADCs cannot fulfill. On the other hand, for mature applications, new developments usually enhance important parameters that were considered secondary in early stages of the application's life cycle. A good example illustrating both cases is the power consumption of radenv-ADCs targeting digital down-conversion of L-band signals; this requirement has become more demanding over time to offer more competitive solutions. It is hence critical to identify in Step 1 which parameters should be prioritized above others, considering that the preferences could have changed over time for mature applications. This approach helps solving possible trade-off conflicts in later steps.  
The recent industrial trend of compact-size satellite-fleets in LEO with reduced lifetime (5 to 7 years) relaxes the radiation requirements of radenv-ADCs targeting related applications. In fact, TID is smaller for shorter missions, and the exposure to solar particle events in LEO is reduced compared to higher orbits. Collaterally, this approach reduces power consumption and shielding requirements, and is hence consistent with the size and weight reduction of the overall satellite.  
The most important outcomes of Step 1 are the requirements specification, and the preliminary options of manufacturing technologies and ADC architectures that are suitable for the new design.
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 2 LDPC Code in the IEEE802.16e Standard  
The LDPC code in the IEEE802.16e standard [5] is obtained by the quasi-cyclic constructive method, affording high coding performance, low computational complexity, and easy hardware implementation [6; 7; 8; 9]. The LDPC codes with the same code rate but different lengths have consistently structured check matrices that extend the basic check matrix via a square matrix [4]. The basic check matrix of the LDPC code is denoted as \(H_{b}\) of dimension \(m_{b}\times n_{b}\). The element in \(H_{b}\) corresponds to the submatrix of the check matrix \(H\) (\(H\) could be obtained only by updating and extending the elements in \(H_{b}\)). The column number of \(H_{b}\) is fixed to 24, i.e., \(n_{b}=24\). For the standard LDPC code with a 2/3 code rate, the row number of \(H_{b}\) is eight, i.e., \(m_{b}=8\). The basic check matrix \(H_{b}\) of the LDPC code in the standard form is:  
\[H_{b}=\begin{bmatrix}H_{b1}&H_{b2}\end{bmatrix} \tag{1}\]  
In Equation (1), \(H_{b1}\) size is \(m_{b}\times k_{b}(k_{b}=n_{b}-m_{b})\) and \(H_{b2}\) is \(m_{b}\times m_{b}\)[8].  
In submatrices \(H_{b1}\) and \(H_{b2}\) of \(H_{b}\), the element values are \(-1\) or a non-negative integer. In the updating and extension of \(H_{b}\) towards \(H\), the elements with a \(-1\) value are replaced by \(z\times z\) zero matrices, and the elements with non-negative integer values are replaced by the square matrix after the cyclic shift of a \(z\times z\) unit matrix [8]. \(z\) is the extension factor \(z=n/n_{b}\), \(n\) denotes the length of the LDPC code, and \(n_{b}\) is the column length of \(H_{b}\). The submatrix \(H_{b2}\) has the following structural characteristics:  
In the first column of \(H_{b2}\), the values of \(h(1)\), \(h(r)\), and \(h(m_{b})\) are non-negative integers, \(h(1)=h(m_{b})\), and \(r\in[2,m_{b}-1]\). Except for the first row, tail row, and row \(r\), the element values at the other positions are \(-1\). Apart from the first column, the other parts constitute a quasi-double-diagonal structure (Equation (2)). The basic check matrix of the LDPC code with a code rate of 2/3 in the standard form is displayed in Equation (3).  
\[H_{b2}=\begin{bmatrix}\mathbf{h}(1)&0&&&&\\ -1&0&0&&&&\\ \vdots&&0&0&&&-1&\\ -1&&0&\ddots&&\\ \mathbf{h}(\mathbf{r})&&&&\ddots&\ddots&&\\ -1&&&&\ddots&0&\\ \vdots&&-1&&&0&0\\ -1&&&&0&0\\ h(m_{\mathbf{b}})&&&&0\\ \end{bmatrix} \tag{2}\]  
After \(p\) is obtained, and since the input \(u\) is known, we calculate \(c\) from Equation (12), thereby completing the coding operation.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 5 Conclusions  
In this work, a high-efficiency clustering routing protocol is proposed for AUV-assisted underwater sensor networks. In this paper, the computation of the threshold value of the cluster head selection phase is improved based on the traditional LEACH algorithm by introducing the residual node energy and the node degree influence factor to adjust the probability of cluster head node selection. Sensor nodes are selected to join the cluster based on the residual energy of the CH. In addition, this paper also improves the intercluster communication strategy based on the residual energy and depth of the CH. This paper introduces an AUV as a dynamic relay node to improve network stability. By analyzing the simulation results, this paper finds that HECRA exhibits a longer network lifetime, higher residual energy of nodes, and a higher packet transmission success rate in different network environments, which indicates that the improvements in cluster head selection and data transmission in this paper effectively improve the network lifetime and throughput rate. Nevertheless, in the next step, this paper needs to address the issue of rational path planning for AUVs as relay nodes to better improve network efficiency through better transit operation routes.  
Conceptualization, Y.S. and X.X.; methodology, Y.S. and X.X.; software, X.X. and B.W.; validation, K.H., B.W. and X.X.; formal analysis, B.W.; investigation, X.X. and H.C.; resources, K.H.; data curation, X.X.; writing--original draft preparation, X.X. and H.C.; writing--review and editing, Y.S., K.H. and B.W.; visualization, X.X.; supervision, K.H.; project administration, K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese National Natural Science Foundation, grant number 61902273, and the Chunhui Cooperation Program of the Ministry of Education HZKY20220590-202200265.  
Not applicable.  
The data used to support the findings of the study can be obtained from the corresponding author upon request.  
The authors declare there are no conflicts of interest.
## 5 Analysis and Evaluation  
To investigate the performance of ENRS, we focus on five major aspects: (i) name resolution latency, (ii) hit ratio of the system, (iii) throughput, (v) offloading traffic of the name resolution by ENRS from global NRS, and (vi) the control overhead.  
Our evaluation offers a comparison with DONA [9], KNN-DNRS [44,45] and Random-NRS. Random-NRS randomly selects K resolvers to register and query names, and we set K=1 in Random-NRS for fairness of comparison. DNRS adopts the KNN clustering algorithm to partition the network to classify the subscribers or publishers among NRS and distribute the name bindings into different NRSs by considering the time taken to publish the names in the NRS. The time to publish a name in the NRS is calculated by T\({}_{\text{a}}\)-T\({}_{\text{b}}\), where T\({}_{\text{a}}\) represents the time taken for NRS to receive the message of name binding and T\({}_{\text{b}}\) represents the time taken for the publisher to send the name binding.  
The experiment environment was created in Python 3.7 running on Ubuntu 14.04 LTS with 32 GB RAM. We conducted the simulation in 15 rounds for each graph with the LHP algorithm and chose different numbers of nodes to query different scales of EIDs. In the simulation, the nodes and EIDs were randomly selected, the latency demand distribution for EIDs was uninformed based on T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\), and the query requests to be forwarded to each HM level were equiprobable. KNN-DNRS and Random-NRS were run under the same conditions of resolver distribution, the same numbers of query nodes, and the same numbers of EIDs to look-up. The settings of the experiment are shown in Table 3. We got the results and analyzed the performance of ENRS as follows.
## 4 Conclusions  
This paper presents procedures for generating surface roughness prediction models in a hybrid manufacturing process using MRA and an ANN. After the FDM 3D printing process, the part was machined with a milling operation in order to enhance the surface quality, varying the spindle speed, feed rate, and depth of cut 48 times. After the analysis, the following can be concluded:  
* ANN and MRA modeling can effectively predict surface roughness, with the ANN outperforming MRA. Models with more input parameters (48 in this case) produce better predictions than those with fewer parameters (27 in previous research).
* The ANN model that used the RuLU activation function gave better predictions than the model with the tanh activation function.
* ANN models with smaller numbers of neurons and hidden layers give better predictions for a small number of training parameters due to a complicated network of neurons. The ANN network itself has too many parameters in relation to the number of training examples.
* The best measured surface roughness was 1.958 \(\upmu\)m. In the case of MRA, this was measured at 2.372 \(\upmu\)m and calculated at 2.14 \(\upmu\)m, while with the ANN, it was measured at 2.058 \(\upmu\)m and calculated at 2.056 \(\upmu\)m.  
Figure 11: Comparison of predicted and measured results of \(R_{a}\) for RA-2 and RA-3 MRA models.  
Figure 12: Accuracy values (R\({}^{2}\)) of ANN and MRA models.  
* All ANN models predict a high surface quality at a speed of 3000 or 1000 rev/min, a feed rate of 400 mm/min, and a cutting depth of 0.3 mm.
* The highest degree of correlation (determination coefficient) R\({}^{2}\) = 0.9771 was achieved by the ANN 5-S-R model, while MRA methods gave a smaller correlation.  
ANNs are a powerful tool with complicated networks of neurons. So, for a more accurate prediction of the quality of the surface and a more accurate model, it is necessary to increase the number of input parameters. This would unify the research results so far and involve creating the largest database possible.  
Conceptualization, S.D. and D.L.; methodology, S.D.; software, B.C.; validation, M.M., D.L. and M.I.; formal analysis, B.S.; investigation, S.D.; resources, M.M.; data curation, M.I.; writing--original draft preparation, M.I.; writing--review and editing, A.A.; visualization, M.P.; supervision, D.L.; project administration, M.P.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 6 Experimental Verification
### Fault Diagnosis  
The data used in the test includes not only normal operating data but also various brake system fault data, and the data set used to train the model is extracted from the historical database monitored  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No** & **Fault Types** \\ \hline F1 & Brake shoe clearance is too small \\ F2 & Brake disc overheating \\ F3 & Idle motion time is too long \\ F4 & Emergency brake fault \\ F5 & Residual pressure is too large \\ F6 & Disc spring fault \\ F7 & Normal \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fault types.  
by the hoist brake system. The decision tree algorithm constructs the model by mining the hidden fault law from the historical monitoring data of the hoist brake system and extracting the diagnostic rules that can provide the basis for the hoist fault diagnosis.  
The redundant data is removed by the above-mentioned SPSS analysis and pretreatment of the data and the relevant feature data is retained for the diagnosis of the above-mentioned fault. Fault analysis test data mainly includes X1-brake shoe clearance, X2-hydraulic station residual pressure, X3-the contact area between disc and brake shoe, X4-brake disc swing offset, and other characteristic attributes. Simulate every fault and collect test data. Some data are displayed in the Table 6. Take K = 4 (number of conditional attributes), use SPSS to perform K-means clustering discretization processing. The results will be displayed in Table 7.  
After importing the Table 7 data set into the K_C4.5 algorithm, the decision tree is generated via Python. The results are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Condition Attributes**} & \multicolumn{2}{c}{**Decision Attribute**} \\ \hline
**X1** & **X2** & **X3** & **X4** & **F** \\ \hline
0.86 & 0.43 & 85 & 0.34 & F1 \\
2.26 & 0.48 & 55 & 0.15 & F2 \\
0.79 & 0.37 & 86 & 0.27 & F1 \\
1.35 & 0.54 & 85 & 0.31 & F5 \\
2.36 & 0.35 & 89 & 0.25 & F3 \\
1.37 & 0.42 & 76 & 0.85 & F6 \\
1.28 & 0.21 & 91 & 0.37 & F4 \\
2.25 & 0.47 & 57 & 0.16 & F2 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Partial fault data set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Condition Attributes**} & \multicolumn{2}{c}{**Decision Attribute**} \\ \hline
**X1** & **X2** & **X3** & **X4** & **F** \\ \hline
2 & 1 & 2 & 1 & F1 \\
3 & 1 & 1 & 1 & F2 \\
2 & 1 & 2 & 1 & F1 \\
1 & 3 & 2 & 1 & F5 \\
3 & 1 & 2 & 1 & F3 \\
1 & 1 & 2 & 2 & F6 \\
1 & 2 & 2 & 1 & F4 \\
3 & 1 & 1 & 1 & F2 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Data discretization.  
After using the Python program to get the classification model of the K_C4.5 decision tree, generate diagnostic rules. Table 8 shows the results.
# An Overview of Fog Computing and Edge Computing Security and Privacy Issues
## 6 Fog and Edge Computing Attacks Countermeasures
### Countermeasures for Attacks in Fog Computing
Fog computing network enables users to access the fog services from the fog infrastructure if the user is well authenticated from the system first in order to be a part of the network processing infrastructure. In case of an unsecure authentication scheme, the entire cloud, fog nodes, and user's device can be caused harm by network attackers, which is one of the significant security concerns. * In research work [45], author proposes a secure mutual authentication scheme for fog computing, that enables authentication of any fog user with the fog nodes mutually in a fog network. The author of [46] proposes a multi-tier authentication technique that allows secure Login in fog computing. * Reduces failure of single point. - Allows network transaction with highly secure encryption algorithm. - Capable of tracking node status efficiently. - Immutable technology. Blockchain technology can prevent various malicious attack in fog network including man-in-the-middle attack, DoS attack and data tampering [47; 48]. In Table 4, we have summarized the countermeasure used for securing and preserving the privacy of fog computing environment. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Countermeasures** & **Brief Description** \\ \hline Efficient Encryption Techniques & With efficient encryption techniques, the attackers will be unable to decode the complex encryption algorithms. the authors of [49] discussed the topic of encryption and how to apply it to fog environment. \\ \hline Decoy Technique & It is a security technique that is used to authenticate the data of a user present in the computing network by replacing the original information with the fake one which is then provided to the attackers. The authors of [50] suggested a method that deal with decoy techniques with the help of user profiling. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Security attacks and privacy-preserving countermeasures.
## 2 Materials and Methods  
### Spacecraft Design  
The external and internal layout of the developed small spacecraft follows the logic of the CubeSat standard [33]; the spacecraft has the shape of a parallelepiped (Figure 1). The volume of the spacecraft is 16U and the cross-section of the spacecraft corresponds to the 12U CubeSat standard size of \(226\times 226\) mm. Four parallel ribs form the guide rails for the spacecraft to exit the transport and launch pads. The satellite is divided into two parts: the Fourier spectrometer unit and the service system unit. Each of the compartments is installed on two detachable frames with special connecting frames. The main technical characteristics of the device are given in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Lifetime 1  & years & no less than 3 \\ Dimensions & mm & \(226\times 226\times 454\) (16U CubeSat) \\ Weight & kg & 23 \\ Orbit parameters & km & from 500 to 600 km, SSO \\ Daily power (average orbital) & W & 10 \\ \multicolumn{3}{c}{Triaxial: flywheels with} \\ \multicolumn{3}{c}{Altitude control system} & unloading by magnetic coils \\ Orientation error (3\(\sigma\)) on all axes & deg & no more than 0.1 \\ Stabilization error (3\(\sigma\)) on all axes & deg/s & no more than 0.01 \\ Propulsion system & & electric ablative pulse \\ Payload & FTIR spectrometer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spacecraft technical characteristics.  
Figure 1: Spacecraft internal design.
## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 from CXR Images using Ensemble of Deep Learning Models
Transfer learning was used for model parameter initialization, followed by deep feature extraction using three pre-trained deep convolutional neural network models, namely, AlexNet, GoogLeNet, and ResNet. The ensemble classifier EDL-COVID was obtained via relative majority voting of the aforementioned individual classifiers. With an average accuracy of 99.1%, precision of 99.1%, and recall of 99.6%, the ensemble classifier was able to outperform the results of the individual classifiers. Taken together, the results of these previous studies strongly indicate that deep learning models built using multiple learning algorithms and, in particular, with ensemble learning classifiers, can benefit from improved performance. Nevertheless, it is worth mentioning that these studies have some limitations, not least because building ensemble models based on a single modality (in this case, mainly CT over CXR) leads to clinical challenges because, practically speaking, it is not preferable to expose the patient to CT radiation, and CXR imaging is not sufficient for the diagnosis of COVID-19 without any additional data [48].
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 3 Secrecy Performance Analysis  
In the following three subsections, the SOP, the probability of SPSC, and the ESC over the M-distributed fading channel will be discussed, respectively.