esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Immobilized Systems in Microbiological Degradation of EDCs
(2020) described the immobilization of horseradish peroxidase on Fe\({}_{3}\)O\({}_{4}\) nanoparticles for the removal of bisphenol-A and 17\(\alpha\)-ethinylestradiol. In that case, removal of both the EDCs used was slightly higher in the immobilized system than in the free enzyme solution, however, the authors advised that enzyme immobilized on iron (II, III) oxide could be easily magnetically separated from the solution and reused by contrast to free peroxidase, which is its greatest advantage. After the fifth cycle of using the immobilized enzyme, the removal efficiencies of BPA and 17\(\alpha\)-ethinylestradiol were 56% and 48%, respectively. The level of the removal rate is still notable and could be relevant in potential use in the industry, however, it still requires more research. Laccase from _T. versicolor_ immobilized on novel polyamide/polyethylenimine (PA/PEI) nanofibers also shows great reusable properties ([PERSON] et al., 2020) with 100% initial activity after five cycles of 2,2'-azino-bis(3-ethylbenzothazoline-6-sulfonic acid (ABTS) oxidation. Removal efficiency against triclosan, 17\(\alpha\)-ethinylestradiol and bisphenol-A was 73.6, 47.3 and 27.9%, respectively. It should be noted that EDCs removal in wastewater effluent was slightly decreased. Whereas, the storage stability of immobilized lactose after 30 days of storage retained 52% of its initial activity. Despite an almost 50% decrease of immobilized system activity during storage, [PERSON] et al. (2020) shows great potential to reuse the PA/PEI carrier in continuous cycles of oxidation. It makes PA/PEI polymer potentially useful in EDCs and other pollutant removal. As can be seen, reusability is one of the most important advantages of the immobilization process. Using polystyrene-divinyl benzene (PS-co-DVB) microspheres with poly(glycidyl methacrylate) chains as the enzyme carrier allows the reuse of the enzyme in 10 degradation cycles while retaining its initial activity of about 53% for bisphenol A and 67% for Congo Red dye (2020). Modification of nanoparticle and microparticle materials, such as PS-co-DVB except significant surface areas, gives the possibilities to surface modifications by functionalized groups such as epoxy, hydroxyl, amine, thiol, carboxyl, chloride, bromide or cyclic carbonate. It opens many possibilities to change, modify and optimize immobilization of enzymes and whole cells (2020). Summarize of immobilization matrix/technologies in EDCs biodegradation in Table 3 is presented. The use of natural carriers, such as luffa fibres or biopolymers such as chitosan, is still a widely developed issue. Simultaneously, researchers attempt to modify these carriers by introducing new functional groups or combining techniques such as cross-linkage of chitosan. On the other hand, synthetic carriers, nanoparticles and metals usage increases. Moreover, development of the properties are still the subject of research.
## 5 Path Following Control Design
### 3 DOFALOS Guidance Law  
Based on the path following error model, LOS guidance law is established to convert the desired path to reference heading angle. The look-ahead distance \(\Delta\) is taken on the \(x_{SF}\)-axis, as indicated by the line TC between the target point \(T\left(x_{T},y_{T}\right)\) and the virtual point \(C\left(x_{C},y_{C}\right)\), as shown in Figure 4.  
In fact, the path following can be divided into two parts according to the cross-track error, the convergence stage (\(y_{e}\
eq 0\)) and the guidance stage (\(y_{e}=0\)), as shown in Figure 5. The convergence stage is mainly to pursue the rapidity and smoothness. The guidance stage is mainly to resist the influence of drift force. In the paper, a 3 DOFALOS guidance law is proposed.  
Figure 4: Path following model based on LOS.  
Figure 3: Block diagram of the controller system.  
In particular, time-varying look-ahead distance is derived to perform a good convergence performance. Proportional guidance law is designed to perform a good anti-interference with respect to the drift angle.
# Effect of Hydrophobic Silica Nanochannel Structure on the Running Speed of a Colloidal Damper
## 5 Conclusions  
In this paper, based on fractal theory, a fractal seepage model reflecting the relationship between the pore structure parameters of porous hydrophobic silica gel nanochannels in CD damped media and changes in the CD operating speed was established. The results of the CD velocity test were compared and verified, and the following conclusions were obtained:  
1. The primary particles of the hydrophobic silica gel had fractal distribution characteristics. The fractal dimension \(D_{f}\) of the pore area of the hydrophobic silica gel increased with an increase in the number of grafted molecules and decreased with increasing pore size, while the fractal dimension \(D_{t}\) of tortuosity decreased with an increase in the number of grafted molecules and increased with increasing pore size.
2. The number of grafted molecules and pore size of the CD hydrophobic silica gel surface functional groups affect the seepage velocity of water in nanochannels. Under the initial conditions of no residual water in the channels, there were more grafted molecules with smaller pore size and a higher seepage velocity, under constant pressure difference.
3. The fractal percolation model can effectively characterize the trend of CD velocity change during the first loading, which has great guiding value for the design of CDs for shock absorption. It should be noted that the theoretical average velocity value at the first loading was still less than the experimental value, and the difference was between 11.3% and 24.6%; after continuous loading, \(D_{f}\) and \(D_{t}\) changed because of the retention of a small amount of water in hydrophobic silica nanochannels, leading to the failure of fractal characterization.  
Conceptualization, G.C. and Z.L.; methodology, G.C.; software, Z.L.; data curation, Z.L.; writing--original draft preparation, G.C.; writing--review and editing, Z.L.; visualization, Z.L.; project administration, G.C.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51405036, the scientific research fund of the Hunan provincial education department, grant number 15K008, and the Changsha Municipal Natural Science Foundation, grant number kq2014099.  
Not applicable.  
The authors declare that there are no conflict of interests regarding the publication of this paper.
# Compensation Observer-Based Adaptive Output Feedback Control for Multi-Agent Systems
## 1 Introduction
Commonly referenced models include the dead zone hysteresis model, the Prandtl-Ishlinskii (PI) hysteresis model, and the Bouc-Wen hysteresis model. This study discusses the processing of hysteresis signals using the Prandtl-Ishlinskii model. The specific work content is as follows:  
1. This study introduces a modified compensating observer scheme. By incorporating this compensatory mechanism, the proposed method effectively reduces tracking errors and confines them to a small region near zero while ensuring stability, compared to traditional control schemes. 2. Unlike most output feedback systems, the system investigated in this study involves unknown parameters, nonlinear coupling, and hysteresis interference, and the proposed compensating observer has more practical significance. The proposed scheme in this study demonstrates superior performance in handling nonlinear multi-agent systems through numerical simulations and case studies while maintaining low computational complexity. This provides new insights and methods for controlling nonlinear multi-agent systems and lays a foundation for future research directions. By comparing with existing mainstream control strategies, this study not only highlights the advantages of the new method but also reveals its potential limitations in practical applications, thereby providing valuable references for further optimization and improvement. The remainder of this paper is organized as follows. Section 2 outlines the relevant background information, problem models, assumptions, and lemmas necessary for understanding the subsequent analysis. In Section 3, we propose a decentralized adaptive output feedback backstepping approach that incorporates a high-gain observer. Section 4 provides a comprehensive stability analysis, which substantiates the stability and tracking capabilities of the system. Finally, Section 5 corroborates the effectiveness of our proposed design through simulation results based on an example presented earlier in the paper.
## 5 Routing in 3D Sensor Network with Internal Holes
### 3D Sensor Networks with One Internal Hole  
As we have shown in the previous section, VHM can be directly applied on a solid 3D sensor network without inner holes to produce virtual coordinates for supporting greedy routing.  
If there is one hole inside (see Figures 1a and 6d,e for examples), the boundary condition becomes different. Two boundary surfaces will be detected, one outside and the other inside. The same spherical harmonic mapping algorithm is applied to map them to two unit spheres, respectively. Then, the boundary nodes perform simple local calculations to align the inner sphere to the outer sphere. Specifically, the nodes on the inner boundary scale their coordinates to reduce the radius of the inner sphere to \(r^{\prime}\), which is constant, less than one. Next, a node on the outer boundary with its virtual coordinates most close to (0,0,1) finds its closest node on the inner boundary based on the UTC mesh. The two nodes and the center of the spheres (_i.e._, (0,0,0)) form an angle, denoted as \(\theta_{0}\) (which is calculated according to virtual coordinates). \(\theta_{0}\) is broadcast to all nodes on the inner boundary, which subsequently apply a rotation matrix with angle \(\theta_{0}\) on their virtual coordinates. Therefore, the inner sphere is aligned with the outer sphere with respect to this pair of nodes. Then, another node on the outer boundary with its virtual coordinates most close to (0,1,0) repeats the above process to initiate the second round of rotation. After two rotations, the inner sphere and the outer sphere are approximately aligned, setting the spherical boundary conditions. Finally, the volumetric harmonic map introduced in Section 4 is applied to produce 3D virtual coordinates for each internal node in the network. Examples of such mapping results are illustrated in Figures 1d and 6n,o. Greedy routing is always successful based on the virtual coordinates created by the proposed mapping scheme. An example is given in Figure 1f, where a data packet is delivered from \(S\) to \(D\). Node \(S\) first identifies a sequence of faces, \(\Phi\), that intersects with the line segment between \(S\) and \(D\). If the next face is reachable according to local information, the packet is forwarded accordingly by face-based greedy routing. When the packet fails to find the next face toward Node \(D\), it must arrive at a boundary, which has been mapped to a sphere. Thus, node-based greedy routing is applied to move the packet across the void. Whenever \(D\) becomes reachable, face-based greedy routing is applied again. The above process continues, until the packet reaches its destination.
## 3 Results and Discussions
### Experimental Validation  
The responses of eight representatives of DE optimal combinations were acquired during the experiments, such that a minimum of five replicates (forgings) in each combination were tested and averaged. A good correlation was witnessed between the experimental values and DE optimal responses.
## 2 Materials and Methods
### Methods
#### 2.2.4 Questionnaire Validation and Preliminary Results  
Because the success of any marketing research depends overwhelmingly on the quality of the questionnaire in its design, two criteria were taken into account: ensuring the management of the information necessary to substantiate the decisions and the characteristics of the respondents. The model obtained from the design process was analyzed by a team of specialists who have solid knowledge, are involved in IoT projects and support services. After eliminating the double, confusing or unnecessary questions, its pretesting was performed in all aspects, on a restricted sample of respondents, the best data operators and respecting the proposed methodology. The results of the pretesting of the questionnaire were analyzed very carefully, bringing the necessary improvements. After collection, the data were entered into the SPSS application to verify the correctness of the information provided by the respondents but also to eliminate the errors. Interactive data validation involves validating the questionnaire as soon as corrections have been made. Preliminary results (215 respondents in the previous period) for the latent variables--Attitude Towards IoT (CA = 0.839, AVE = 0.682), Perceived Autonomy (0.752, AVE = 0.673, Perceived Privacy (CA = 0.746, AVE = 0.639), Social Influence (CA = 0.773, AVE = 0.697), Intrinsic Motivation (CA = 0.783, AVE = 0.814)-- indicated measures Cronbach's alpha (CA) greater than 0.6 and Average Variance Extracted (AVE) greater than 0.5 [97]
# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
###### Abstract  
The building materials of Cultural Heritage monuments are subjected to continuous degradation throughout the years, mainly due to their exposure to harsh and unexpected weather phenomena related to Climate Change. The specific climatic conditions at their vicinity, especially when there are local peculiarities such as onshore breeze, are of crucial importance for studying the deterioration rate and the identification of proper mitigation actions. Generalized models that are based on climate data can provide an insight on the deterioration but fail to offer a deeper understanding of this phenomenon. To this end, in the context of the EU-funded HYPERION project a distributed smart sensor network will be deployed at the Cultural Heritage monuments in four study areas as the solution to this problem. The developed system, which is demonstrated in this paper, includes smart IoT devices, called Smart Tags, designed to provide environmental measurements close to monuments, a middle-ware to facilitate the communication and a visualization platform where the collected information is presented. Last but not least, special focus is given to the device's NB-IoT connectivity and its power efficiency by conducting various tests and extract useful conclusions.  
IoT network; micro-climate sensors; smart sensors; cultural heritage; monument deterioration; environmental monitoring; IoT visualization Article
## 3 Materials and Method
### Machine and Deep Learning Techniques
Additionally, in a study with 531 patients to predict the likelihood of severe pneumonia after kidney transplantation, ML models such as SVM, LR, and RF were used, with RF showing excellent performance with 97% specificity [37]. Numerous studies have incorporated the Random Forest algorithm in this context [23; 24; 25; 26; 31; 32; 33; 34; 35]. Some studies have identified SVM as the most suitable model for predicting graft functionality [38]. Other frequently used algorithms in the literature include SVM [23; 24; 25; 31; 32; 34], DT [23; 24; 25; 29; 39], LR [23; 24; 25; 29; 39], KNN [24; 34; 36], RNN [35; 40], NB [34; 41], and LSTM networks [35; 42]. ANNs (artificial neural networks) can be divided into four main types: single-layer perceptrons, multilayer perceptrons (MLPs), feed-forward neural networks, and feedback neural networks. A single-layer perceptron consists of only one input layer and one output layer. It is restricted to linearly separable problems and has a simple structure. The MLP, on the other hand, is a widely used model designed for learning nonlinear patterns. It consists of an input layer, one or more hidden layers, and an output layer. MLPs allow complex mappings between inputs and outputs. Feed-forward neural networks, as shown in Figure 6, have a unidirectional flow of information. Input data, such as in a multilayer perceptron, are passed through the hidden layers and finally to the output layer. In contrast, feedback neural networks have a bidirectional flow of information between the hidden and output layers. These networks have dynamic memory and can retain information from previous steps, as an output may serve as input in subsequent iterations [43]. In this study, both machine learning algorithms (SVM, NB, KNN, LR, RF, DT) and deep learning algorithms (RNN and LSTM) were utilized.
## 2 State of the Art
### Impact of Repeaters on the Network Performance--Theoretical Background  
A BPL repeater extends the coverage of the access part of the network (last mile). The repeater could be a particular slave modem, e.g., Smart Meter, which is used as customer premises equipment (CPE). A designated modem only for repeating also must be used, which reduces the network cost because the modem is more expensive than repeater. However, repeater utilization increases the delay and reduces the throughput. Because the Head-End (HE) modem master and repeaters (REP) use the same medium, their transmissions have to be multiplexed. Based on technical solutions of particular vendors and costs considerations, the Time Division Multiplex (TDM) was considered.  
The delay, D, caused by TDM for a repeater could be written in the following form:  
\[D=(1+R)\cdot T_{TS}, \tag{1}\]  
where \(T_{TS}\) is the time slot (TS) in which any packet transmission has to occur at the beginning of a TS, and the packet arrives to its destination in this same TS (see Figure 2). \(R\) is the number of repeaters between communication nodes (e.g., master modem in transformer substation and slave modem in Smart Meter).
# An SQP Algorithm for Structural Topology Optimization Based on Majorization-Minimization Method
## 4 Numerical Examples
### MBB Beam  
The MBB beam is a typical benchmark example used in topology optimization. In accordance with [24], the design domain, the boundary conditions, and the external load for the MBB beam are shown in Figure 2. The design domain was discretized with \(120\times 40\) square elements, and the volume fraction \(f\) was set to 0.5. The optimized designs obtained using the OC, IPC, and QPOC methods are illustrated in Figure 3.  
An excellent solution algorithm is mainly reflected in the optimal solution, convergence rate, and calculation cost. The advantages and disadvantages of the method proposed in this paper was compared with the OC method from these aspects.
## 4 Results
### Comparing Models
#### 4.4.3. Applicability of Models Trained on DS2 (COVID-19 Dataset) to DS1 (Climate Change Dataset)
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**DS2 Models Tested on Dataset DS2**} & \multicolumn{6}{c}{**DS2 Models Tested on Dataset DS1**} \\ \cline{2-10}  & & **LR** & **SVM** & **NB** & **RF** & **LR** & **SVM** & **NB** & **RF** \\ \cline{2-10}  & **LIWC** & 74.62 & 73.32 & 74.58 & 69.98 & 66.99 & 65.47 & 65.57 & 64.02 \\ \hline \multirow{3}{*}{**1,1**} & **n-grams** & 67.69 & 70.76 & 69.62 & 66.09 & 75.00 & 72.74 & 76.15 & 70.11 \\ \cline{2-10}  & **n-grams +** & **175.05** & 73.37 & 72.55 & 71.68 & 72.66 & 73.79 & 76.18 & 74.97 \\ \hline \multirow{3}{*}{**1,2**} & **n-grams** & 69.22 & 66.59 & 73.86 & 63.95 & 78.40 & 78.79 & 80.68 & 72.29 \\ \cline{2-10}  & **n-grams +** & 73.79 & 74.64 & **76.39** & 71.67 & 73.79 & 78.73 & 80.71 & 73.43 \\ \hline \multirow{3}{*}{**2,2**} & **n-grams** & 69.17 & 72.05 & 70.04 & 67.45 & 78.77 & 78.40 & 79.17 & 71.94 \\ \cline{2-10}  & **n-grams +** & 73.80 & **76.34** & 70.87 & **72.97** & 71.54 & 74.91 & 77.28 & 75.71 \\ \hline \multirow{3}{*}{**1,3**} & **n-grams** & 70.49 & 65.69 & 71.29 & 64.84 & 79.90 & 81.05 & 81.44 & 72.71 \\ \cline{2-10}  & **n-grams +** & 74.64 & 74.64 & 73.82 & 71.25 & 74.57 & 74.94 & 79.96 & 76.11 \\ \hline \multirow{3}{*}{**2,3**} & **n-grams** & 71.67 & 68.71 & 70.91 & 70.43 & 76.88 & 76.54 & 77.66 & 70.80 \\ \cline{2-10}  & **n-grams +** & 74.66 & 74.64 & 69.20 & 72.93 & 71.54 & 73.76 & 79.93 & **76.88** \\ \hline \multirow{3}{*}{**3,3**} & **n-grams** & 59.33 & 58.89 & 59.73 & 57.59 & 69.36 & 69.34 & 70.50 & 64.00 \\ \cline{2-10}  & **n-grams +** & 73.77 & 72.48 & 65.20 & 69.98 & 71.54 & 71.21 & 71.27 & 73.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance comparison of models trained on DS2 (COVID-19 dataset) and tested on DS2 and DS1 (Climate change dataset). Figure 8: Models trained on DS2 (COVID-19 dataset) and tested on DS1 (climate dataset).
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 1 Introduction
### Identification of Software Security Vulnerabilities  
Being able to identify and fix the CWEs early in the software development phase, before a product-specific CVE is discovered and published, is critical to achieving application resilience to cyber-attacks. On the other hand, cyber criminals have no intention to declare the identified vulnerabilities, as their exploitation will not be possible anymore, so reducing the CWE number in the source code by the programmers and security analysts remains the best and more economical solution to protect the applications against malicious actors.  
Despite the lack of well-documented benchmarks regarding the number of weaknesses accepted by security professionals in business-critical or not critical applications, there are analyses such as the one run by CAST Research Labs [1] showing that the average density of well-known security flaws (only 35 CWEs checked) for NET and Java EE applications is between 30 and 100 per 10k lines of codes (LOC), with outliers, meaning \"insecurely developed applications\", providing more than 350 CWEs per 10k lines of code. If we consider the entire palette of CWEs managed by MITRE (418 CWEs for Software Development), there is a good assumption to consider that the density of weaknesses in software applications is higher than 300 CWEs per 10k LOC.  
As manual detection of technical security flaws in source code requires a combination of highly skilled professionals in software security and heavy routine-based activities at the same time, which is always hard to find, there is a strong need for solutions being able to scan source code automatically and identify code-level security vulnerabilities early in the software development phase. Currently there are technology providers and open communities which maintain Static Analysis Security Testing (SAST) solutions. These technologies are able to browse line by line the source code written in the most popular programming languages and development frameworks and identify the technical vulnerabilities. Depending on the maturity of the solution, the weaknesses may be mapped automatically to various standards (e.g., Top 10 OWASP, Open Web Application Security Project, Top 10 Security Weaknesses: [[https://owasp.org/Top10/](https://owasp.org/Top10/)]([https://owasp.org/Top10/](https://owasp.org/Top10/)), last accessed on 20 August 2022) and regulations (e.g., GDPR, General Data Protection Regulation (EU) 2016/679), and the mature commercial solutions are able to understand up to 20-30 programming languages.