esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Application Challenges
#### 5.1.4 Coverage Issue  
Sensor coverage refers to how long the measuring physical environment can be observed by the CPS sensing device. For small-scale applications such as indoor localizations [107; 108; 109; 110], the coverage of the sensing devices may be considered insignificant. However, in water distribution network applications, which are large-scale, the coverage of sensing devices and communication technologies used is another major challenge. Since different communication technologies have different coverage areas, short-range communication technologies may need more devices to cover the same region  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Operation** & **Sensor Module** & **Mode** & **Measured Current (mA)** \\ \hline \multirow{3}{*}{Communications} & \multirow{3}{*}{Radio} & Receiving & 21.8 \\ \cline{3-4}  & & Transmitting (0 dB) & 21.7 \\ \cline{3-4}  & & Transmitting (\(-\)25 dB) & 12.1 \\ \hline \multirow{3}{*}{Data processing} & \multirow{3}{*}{Microcontroller} & CPU active & 2.33 \\ \cline{3-4}  & & CPU disable & 0.0018 \\ \hline \multirow{3}{*}{Data storage} & \multirow{3}{*}{Internal flash memory} & Erase & 1.35 \\ \cline{3-4}  & & Write & 0.9–1.34 \\ \cline{3-4}  & & Read & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Trend of energy consumption for a typical TelosB sensing platform.  
Figure 10: Energy consumption profile of a typical sensing device.  
covered by conventional long-range communication technologies. Therefore, coverage is one of the key challenges in the deployment of a CPS in the water supply system applications.
## 3 Results and Discussion
### Analytical Performance and Application of the HFM-Protected-MI-MSPE of Triazines in Soil Samples
The limits of detection were calculated as three times the signal of the background noise obtained in the analysis of non-spiked samples at the retention times of the corresponding analytes, and are also included in Table 1. The obtained values varied within a 1.2 and 3.7 ng g\({}^{-1}\) concentration range, which is low enough to prove the suitability of the developed method for routine laboratory analysis of selected triazines at real trace concentration levels in soil samples from different origin. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Murcia**} & \multicolumn{3}{c}{**Valencia**} & \multicolumn{3}{c}{**Madrid**} \\ \cline{2-10}  & **\%R** & **RSD (\%) 1** & **LOD 2** & **\%R** & **RSD (\%) 1** & **LOD 2** & **\%R** & **RSD (\%) 1** & **LOD 2** \\ \hline SIM & 77.3 & 9.2 & 3.7 & 72.8 & 9.0 & 3.4 & 76.1 & 8.9 & 2.9 \\ CYA & 85.9 & 8.9 & 1.9 & 94.6 & 7.1 & 1.5 & 101.1 & 6.4 & 1.3 \\ ATR & 102.3 & 10.0 & 1.5 & 82.5 & 9.5 & 1.4 & 85.8 & 5.8 & 1.2 \\ PPZ & 93.7 & 9.5 & 3.6 & 86.7 & 9.0 & 3.6 & 92.4 & 7.3 & 3.4 \\ TER & 99.2 & 10.7 & 1.6 & 96.2 & 7.4 & 1.2 & 84.3 & 8.2 & 1.3 \\ \hline \end{tabular} \({}^{1}\) = 5, 2 ng g\({}^{-1}\).  
\end{table} 
Table 1: Analytical performance and application of HFM-protected-MI-MSPE for the determination of triazines in soil sample from different origin. Figure 4: Comparison of chromatograms obtained in the analysis of soil samples from Albufera: (**a**) Spiked crude soil sample extract (10 ng g\({}^{-1}\)); (**b**) Spiked soil sample extract (10 ng g\({}^{-1}\)) after performing HFM-protected-MI-MSPE; (**c**) non-spiked soil sample extract after performing HFM-protected-MI-MSPE. Peak assignment: 1. SIM; 2. CYA; 3. ATZ; 4. PPZ; 5. TER.
## 2 Related Work
### Spatial Reconstruction and Feature Separation Methods  
Spatial reconstruction approaches aim to address feature redundancy through separation and reorganization strategies. The Spatial and Channel Reconstruction Convolution (SCConv) demonstrates effectiveness in handling spatial and channel redundancy through separation-reconstruction strategies, utilizing dedicated units for spatial and channel processing. Coordinate Attention (CA) performs cross-dimensional fusion embedding of spatial channels, showing excellent performance in feature interaction across different dimensional spaces. Meanwhile, lightweight network architectures such as Residual Network (ResNet) and Densely Connected Convolutional Network (DenseNet) employ residual connections and dense connectivity, respectively, to achieve computational efficiency through improved connection methods, yet these approaches show limited effectiveness in handling complex spatial relationships and feature separation requirements characteristic of industrial detection scenarios.
## 4 Results and Discussion  
### Intelligent Equipment Management System  
Automated methods are used for vibration signal sensing, data transmission, data preprocessing, model training, and retraining. The results are visually presented through dashboards. The system utilizes AI degradation level values to detect anomalies and sends warning notifications for timely handling by managers and onsite personnel. A comparison between this system and traditional methods is presented in Table 4.  
Figure 15: Model quantization: conversion from float32 to int8.
# Microbial Life on the Surface of Microlgistics in Natural Waters
## 6 Factors Influencing the Microbial Composition of Plastisphere
Amount of bacteria from order _Burkholteriales_ was twice higher on plastics, meanwhile the amount of bacteria from genera of _Actinobacteria_ and _Cytophagin_ was much higher on non-plastic materials. Analysis of the presence of pathogenic bacteria in polyethylene terephthalate (PET), polyethylene (PE), and polypropylene (PP) microplastics-associated communities showed their higher on PET, especially potentially pathogenic members of _Vibrio, Tenacibaculum_, and _Escherichia_[101]. Transportation and release of microplastics with ballast water may be a serious threat posed by potential pathogens occurring in MPs plastisphere. Firstly, ships' ballast water could contain bacterial species which are alien or opportunistic pathogens and, in the case of uncontrolled discharge of such water in coastal areas, undesirable microorganisms could be released in the local environment. It was reported that ballast water contained from 0.7 to 39.5% of eubacteria, from 0 to 2.5% of enterobacteria, from 0.2 to 35.8% of _Vibrio_ spp., and from 0 to 2.5% of _Escherichia coli_ from total number of bacterial cells [92]. So, during transportation in the presence of microplastic particles these bacteria could become part of the community of the plastisphere, and their number may increase significantly. Secondly, ballast water transports microplastics with biofilms across different world areas, and incidences of bacterial infections may occur in the recipient country [93]. The hydrophobicity of the substrate surface had a great influence on community structure. The ability of bacterial cells to attach to plastic depends on its structure, level of roughness, and the presence of hydrophilic groups on the structure of the polymer surface [63; 102; 103]. It was found the positive correlation between hydrophobicity of polymethacrylates and bacterial adhesion [98]. Evaluation of the influence of size (diameter 3 mm and 100 \(\upmu\)m) and shape (spherical or irregular) polyethylene and polylactide acid microplastics submerged in sea water for 2 months showed the absence of differences in abundance and diversity in microbial communities, but had an effect on bacterial activity. The higher activity was detected in the biofilm with size 100 \(\upmu\)m [104]. It was suggested that bacterial colonization may depend on such intrinsic plastic properties such as pigment content [99].
# Synthesis of Zeolitic Material with High Cation Exchange Capacity from Paper Sludge Ash Using EDTA
## 4 Conclusions  
Zeolitic materials with high CEC, including zeolite-A crystals, can be synthesized from PSA with the addition of EDTA because of the inhibition of Ca interference in zeolite synthesis via Ca-masking by EDTA. The following conclusions can be drawn from the results:  
1. EDTA can extract Ca from the solution and inhibit Ca interference during zeolite synthesis by Ca masking.
2. Hydroxyodalite, zeolite-P, and zeolite-A can be synthesized from PSA using an alkali reaction with EDTA, and the main zeolite phase can be adjusted by the addition of EDTA.
3. A product with a high CEC, which has a high zeolite-A content, can be obtained.  
Therefore, a chelating agent can inhibit Ca interference in zeolite synthesis by Ca-masking, and a product with a high zeolite-A content can be obtained from PSA using an alkali reaction with EDTA.  
Figure 11: Proposed mechanisms for the zeolite reaction with the addition of 1 M EDTA at 80 \({}^{\circ}\)C.  
Funding: This research received no external funding.
## 4 BER Computational Strategies for Decision Unit--Background Tutorial
### Monte Carlo (MC) Method Simulation  
The MCS approach is predominantly used for BER estimation in communication systems [90; 91]. This estimation approach is implemented by passing N data symbols through a model, which reflects the influencing features of the underlying digital communication system by counting the error numbers that take place at the receiver. The simulation run includes noise sources, pseudo-random data, and device models, which process the digital communication signal. In conclusion, the MC simulation processes a number of symbols, and eventually, the BER is estimated.  
Let us assume that we have a standard baseband signal model representation, as shown in Equation (2), and the decision unit is using the Bernoulli decision function \(I(a_{k})\) expressed as follows:  
\[I(a_{k})=\left\{\begin{array}{ll}1\;if\;d_{k}\;\
eq\;a_{k}\\ 0\;otherwise\end{array}\right. \tag{4}\]  
where \(a_{k}\in\{0,1\}\) as defined in Section 3.4, and the \" sign refers to the assembled average of the variable. Accordingly, the BER can be indicated in terms of the probability of error \(p_{e}\) as follows:  
\[p_{e}=P(\hat{a_{k}}\;\
eq\;a_{k})=P[\;I(a_{k})=1]=E[I(a_{k})], \tag{5}\]  
where E[.] is the expectation operator, \(P(\hat{a_{k}}\;\
eq\;a_{k})\) is the probability that the instant value of \(a_{k}\) does not equal its average \(\hat{a_{k}}\). If we take into consideration the entire stream of symbols in Equation (2b), then the BER is estimated by utilising the ensemble average of \(p_{e}\):  
\[\hat{p_{e}}=\frac{1}{K}\sum_{k=1}^{K}I(a_{k}) \tag{6}\]where \(K\) is the maximum number of symbols (the bit stream size) in Equation (2b). Equation (7) helps to determine the estimation error, and its variance will be given as follows:  
\[\varepsilon=p_{e}-\hat{p_{e}}=\frac{1}{K}\sum_{k=1}^{K}\left(p_{e}-I(a_{k})\right) \tag{7}\]  
This means that the variance of \(\varepsilon\) can be expressed as:  
\[\sigma_{\varepsilon}^{2}=\frac{p_{e}(1-p_{e})}{K}\] (8a) Hence, we can write the normalised estimation error as follows: \[\sigma_{n}=\frac{\sigma_{\varepsilon}}{p_{e}}=\sqrt{\frac{(1-p_{e})}{K.p_{e}}}\] (8b) For a small BER, Equation (8b) can be simplified to \[\sigma_{n}\cong\sqrt{\frac{1}{K.p_{e}}} \tag{8c}\]  
Here, \(\sigma_{n}\) is an indicator for the target accuracy we must aim at. Consequently, we can determine the required \(K\) for a target performance as given below:  
\[K\cong\frac{1}{\sigma_{n}^{2}p_{e}} \tag{8d}\]  
Equation (8d) indicates that a small BER value requires a large, simulated signal bit stream. For example, to configure a system with a BER of \(10^{-6}\), we require no less than \(10^{8}\) bits in the signal stream. This numerical limitation ensures that the MC simulation trial size will satisfy the central limit theorem. This operational limitation means the decision unit will take a long time to estimate a trusted value of BER. Accordingly, MC simulation is impractical for a baud rate larger than 100 MBit. It is worth mentioning that in our discussion, we assumed that the bit errors were independent.
###### Abstract  
In recent years, the production of plasma-treated water (PTW) by low-temperature low-pressure glow plasma (LPGP) has been increasingly gaining in popularity. LPGP-treated water changes its physical and physiological properties compared to standard distilled water. In this study, a non-conventional lipolytic yeast species _Yarrowia lipolytica_ was cultivated in culture media based on Nantes plasma water with heightened singlet oxygen content (Nantes PW) or in water treated with low-temperature, low-pressure glow plasma while in contact with air (PWTA) or nitrogen (PWTN). The research aimed to assess the influence of culture conditions on castor oil biotransformation to gamma-decalactone (GDL) and other secondary metabolites in media based on nanowater. The Nantes plasma water-based medium attained the highest concentration of gamma-decalactone (4.81 \(\pm\) 0.51 g/L at 144 h of culture), maximum biomass concentration and biomass yield from the substrate. The amplified activity of lipases in the nanowater-based medium, in comparison to the control medium, is encouraging from the perspective of GDL biosynthesis, relying on the biotransformation of ribonucleic acid, which is the primary component of castor oil. Although lipid hydrolysis was enhanced, this step seemed not crucial for GDL concentration. Interestingly, the study validates the significance of oxygen in \(\beta\)-oxidation enzymes and its role in the bioconversion of ribonucleic acid to GDL and other lactones. Specifically, media with higher oxygen content (WPTA) and Nantes plasma water resulted in remarkably high concentrations of four lactones: gamma-decalactone, 3-hydroxy-gamma-decalactone, dec-2-en-4-olide and dec-3-en-4-olide.  
Keywords:glow plasma; plasma-treated water; gamma-decalactones; lipases; \(\beta\)-oxidation; _Yarrowia lipolytica_
## 3 Study on Surface Integrity of Milled Workpiece Surface
### Microscopic Topography of Milled Workpiece Surface
#### 3.1.4 Effect of Tool Wear on the Microscopic Topography of the Milled Workpiece  
The high chemical activity and low thermal conductivity of titanium alloys make them extremely susceptible to tool wear during the cutting process. In turn, the worn tool can affect the integrity of the milled surface. To investigate the form and extent of the effect of  
Figure 12: The surface topography of the machined workpiece at different feeds per tooth.  
Figure 13: The surface topography of the machined workpiece at different lubrication amounts.  
tool wear on the milled surface of titanium alloys, a worn milling cutter with VB = 0.1 mm is used to milling titanium alloy TC21 under MQL condition. The surface topography of the machined workpiece is shown in Figure 14. According to Figure 14, it can be seen that the scraping effect between the workpiece and the worn tool causes scratches on the machined surface and makes the surface rough. Since the weakened plastic deformation ability of the workpiece under MQL condition, when the worn tool scrapes against the feeding tool marks, it will squeeze the residue material of the feeding tool marks and make it come off in blocks or flakes, which leaves pits.  
Effect of Milling Parameters and Lubrication Parameters on the Surface Roughness of the Milled Workpiece  
The stability, contact stiffness, corrosion resistance, fatigue strength, and wear resistance of machine parts are closely related to the machined surface roughness. During the milling process, feeding tool marks, scratches, plastic deformation, and secondary material deposition affect the machined surface roughness [29; 30]. According to the study in the previous section, the use of different lubrication methods can also have a significant effect on the milling surface roughness. The feeding tool marks have the greatest effect on the machined surface roughness for the MQL environment. In contrast, under the high-pressure air-cooling and dry milling conditions, the surface roughness is greatly affected by the banding bulges on the workpiece surface caused by the plastic deformation and by the secondary material deposition resulting from the chemical reaction of the TC21 surface. According to the ISO 25178 standards, surface roughness is described by Sa and Sq parameters.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements
### Achievements
#### 2.2.5 The RINA Domain  
We have successfully deployed RINA (the other technology domain) in an emulated network scenario consisting of a service provider network on top of an infrastructure provider network. The overall scenario included 10 nodes spanning from end-users to DC servers. We accomplished the following two tests:  
1. The evaluation of the RINA QoS support by injecting synthetic traffic flows reproducing diverse network applications and load conditions and measuring the perceived QoS metrics. This test proved the ability of RINA to effectively deliver packets with the required QoS between distributed applications in a multi-layer packet network.
2. The demonstration of a real High Definition (HD) video streaming in highly congested network scenarios, with perfect users' Quality of Experience (QoE).
## 2 Background and Related Work
Afterward, the Random Forest AI algorithm is used to differentiate between the user's movement and the imposter's movement. Furthermore, the paper proposed by [22] used deep Learning-based techniques for user authentication reinforcement processes. In this work, the authors collected user activities by designing a custom application. Afterward, these data are processed and several features are extracted. These features are used to train three different AI algorithms: Support Vector Machine, Multi-Layer Perceptron, and a Deep Learning approach. Finally, these algorithms successfully performed the user authentication process and detected the imposter in the system. From the recent papers published that considered detecting cheating while performing an online exam is the one proposed by [PERSON] et al. [23]; the author collected the mouse dynamics of students performing an online midterm exam. They used the mouse dynamics and the Moodle plugin to detect cheaters. Their proposed system succeeded in detecting 94% of students cheating in online midterm exams. Furthermore, papers published considering the online examination using mouse dynamic is the one proposed by [24]. This paper helps the proctoring of the online exam to detect cheating using both the recorded video of the exam and the mouse dynamic of the student. They proved with the experimental results the effectiveness and efficiency of their algorithm for a fair assessment of online exams. This new proposed paper is concerned with developing a cheating module to detect whether the students cheat or not while conducting online laboratory experimentation exams from mouse dynamics. The main contribution of this paper is providing a new fair assessment module in the LLS. This module is used to detect cheating students while performing online experiments and laboratory exams. Moreover, the proposed cheating system used only mouse dynamics, which requires no extra cost, and used AI algorithms to achieve high precision and accuracy. So, the classification between cheating and non-cheating students enhanced, and the degree of separation of students increased. Furthermore, the experiments are performed on different types of online exams. These exams may include static questions like MCQ, True and False questions, or may contain Virtual Remote Lab (VRL) based questions. The VRL questions can be added to the system, which allows the user to remotely access a computer that exists in the laboratory using the Myrtille technology [25] from the LLS. This computer includes a VM that has the required simulator software installed on it. Therefore, the instructor can add any type of virtual simulator software within the experiment, which will be explained in detail in Section 3. The paper is organized as follows, Section 1 gives an introduction to the paper, and Section 2 includes the literature review. Furthermore, the new LLS intelligent, fair assessment module is described in Section 3. The experiments are carried out on several students performing exams and experiments to detect cheating or not, as presented in Section 4. Finally, the conclusion is presented in Section 5.
## 4 Results and Discussion
### Model Verification
#### 4.1.3 Computational Efficiency  
When computing the analytical and numerical models, we find that the analytical computational efficiency (including time and power) is significantly less than the numerical counterpart in all borehole configurations. For the yearly simulation of the geothermal borehole system, the analytical solution (via MATLAB R2021b) takes approximately 6.74 s, 8.46 s, and 9.05 s for the single borehole, double boreholes, and 3-by-3 boreholes, respectively. This analytical algorithm is implemented in a workstation (laptop) of 4 cores, 8 logical processors, and 8 GB of RAM. In contrast, the numerical model built by COMSOL needs 171 s, 262 s, and 507 s in each borehole configuration, respectively, for the same yearly simulation, which is computed under a high performance computing (HPC) workstation using parallel computing with 32 CPUs and 2 GB memory per CPU (64 GB in total). The computational comparison between the analytical and numerical models is demonstrated in Figure 8. It is rather clear to see that our analytical model requires 4\(\sim\)8 times less of the computational power (regarding both CPUs and total memory) and 24\(\sim\)55 times less the computational time than the conventional numerical approach.  
Figure 5: Temperature distribution over the \(x\)-coordinate (i.e., the line \(y=0\)) for a single borehole over four seasons: (**a**) Spring (\(t=3\) (mon)); (**b**) Summer (\(t=6\) (mon)); (**c**) Fall (\(t=9\) (mon)); and (**d**) Winter (\(t=12\) (mon)). The analytical solution is given by the red solid line and the numerical solution is given by the blue dashed line.  
Figure 6: Temperature distribution over the \(x\)-coordinate (i.e., the line \(y=0\)) for double boreholes over four seasons: (**a**) Spring (\(t=3\) (mon)); (**b**) Summer (\(t=6\) (mon)); (**c**) Fall (\(t=9\) (mon)); and (**d**) Winter (\(t=12\) (mon)). The analytical solution is given by the red solid line and the numerical solution is given by the blue dashed line.  
Figure 7: Temperature distribution over the \(x\)-coordinate (i.e., the line \(y=0\)) for 3-by-3 boreholes over four seasons: (**a**) Spring (\(t=3\) (mon)); (**b**) Summer (\(t=6\) (mon)); (**c**) Fall (\(t=9\) (mon)); and (**d**) Winter (\(t=12\) (mon)). The analytical solution is given by the red solid line and the numerical solution is given by the blue dashed line.  
Another finding is that the computational cost of the numerical model is significantly greater in going from the single to multiple boreholes, yet the analytical solution is not. This is credited to the fact that the analytical solution is done by the linear interpolation in the multiple borehole systems after the exact solution to a single borehole problem is completed. The 2-D numerical model, on the other hand, requires more mesh elements as the number of boreholes increases, and the mesh and time independency studies are needed extensively for each case.