esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Quality Changes in Cement-Lined Water Pipe Networks
## 3 Results
### Analysis of Test Results for Water Flowing through the Setup at the Velocity of 0.1 m/s  
The third series was conducted under dynamic conditions, that is, water flowing through the experimental setup at a velocity of 0.1 m/s. This rate is commonly used in the currently over-dimensioned water supply networks. Water samples were collected after 11 months of using the setup. During sampling, a 13-min long contact of water with the cement-mortar lining was considered. Results of the analysis for this series are provided in Table 5.  
A comparison between the quality of water feeding the setup and water flowing out from the setup shows that even after such short-term contact of water with the cement-mortar lining, under flow-through conditions there occurred an interaction between the water and the cement mortar lining. It should be stressed that changes in water quality proved minor, however they have already provided evidence of penetration of the basic cement components into the water. This increased the values of basic water quality indicators, including in particular turbidity, pH, hardness and dry residue content and decreased carbon dioxide water concentration. Values of indicators determining water stability point out that during contact with cement mortar lining, the water increased its capability to precipitate calcium carbonate.
## 2 Materials and Methods
### L2 Adsorption and Regeneration  
The dynamic adsorption experiments of L2 on rGOAs were performed according to the details reported in literature [36]. The experimental parameters, such as the mass of rGOAs, were set to 0.10 g at 20 \({}^{\circ}\)C and the L2 inlet concentration (\(C_{\text{in}}\)) of 14.62 mg L\({}^{-1}\) at a gas flow rate of 50 mL min\({}^{-1}\). \(C_{\text{out,}t}\) (the L2 concentration in the gas line after adsorbing process) was detected via a calibrated GC-FID on the permeate side. The breakthrough curves by plotting \(C_{\text{out,}t}/C_{\text{in}}\) versus time were used to express the experimental results. The following quantities were adopted to denote the adsorption behavior: (i) the breakthrough time (\(t_{\text{B}}\), min), defined as \(C_{\text{out,}t}/C_{\text{in}}=0.05\), and (ii) the breakthrough adsorption capacity (\(Q_{\text{B}}\), mg g\({}^{-1}\)), representing the adsorption capacity at time \(t_{\text{B}}\). The \(Q_{\text{B}}\) was calculated from Equation (1) [36]:  
\[Q_{\text{B}}=\frac{V_{\text{g}}C_{\text{in}}}{m}\int_{0}^{t_{\text{B}}}\left( 1-\frac{C_{\text{out,}t}}{C_{\text{in}}}\right)\text{d}t \tag{1}\]  
where \(m\) is the mass of adsorbent (g), \(V_{\text{g}}\) is the gas flow rate (L min\({}^{-1}\)), \(C_{\text{in}}\) is the inlet concentration (mg L\({}^{-1}\)), and \(C_{\text{out,}t}\) is the outlet concentration (mg L\({}^{-1}\)) at adsorption time \(t\) (min).  
With \(C_{\text{out,}t}/C_{\text{in}}\approx 1\), the adsorbent bed reached saturation. Next, the used rGOAs were regenerated with nitrogen blowing and heated in a water bath for 30 min at 80 \({}^{\circ}\)C. The five times adsorption-desorption cycles were repeated in the same manner to analyze the regenerative properties of rGOAs.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
###### Abstract  
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models' learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.  
imbalanced data; resampling; normalization; deep neural network; convolutional neural network Article
## 4 Experimental Results
### Experimental Result Evaluation  
#### 4.2.1 Light Intensity Measurement Performance  
The incident light intensity primarily depends on the transmission distance between the LED luminaires on the ceiling and the smartphone image sensor. If the transmission  
Figure 9: Experimental setup: (**a**) VLP installation area; (**b**) test field environment.  
distance from the LEDs to the image sensor increases, then the light intensity will decrease, i.e., the amount of light intensity is inversely proportional to the transmission distance. Furthermore, the data rate will increase if the intensity of light from the LEDs increases. We conducted an experiment in two different approaches for observation of the light intensity measurement performance inside the LED luminaires. In one approach, we used a luxmeter to measure the light intensity from each LED light with respect to the transmission distance, as shown in Figure 10a. In the second approach, we used our experimental smartphone image sensor to measure the light intensity. For this operation, we implemented a simple Android application from [30], which can measure the light intensity well. The implemented Android application flow chart is shown in Figure 10b. To analyze the performance of the image sensor for light intensity measurement capability, we compared the image sensor measurement data with traditional luxmeter measurement data, as shown in Figure 10c. We observed that the measurement data accuracy of luxmeter and smartphone image sensor were similar, and the light intensity decreased with increased transmission distance.
###### Abstract  
The interactions between multiple cracks significantly influence fracture mechanics parameters, necessitating their consideration in crack assessments. Codes such as ASME Section XI, API 579, BS 7910, and British Energy R6 provide guidelines for crack growth and coalescence, taking crack interactions into account. However, these guidelines often employ idealized crack models, which lead to overly conservative assessments. This study proposes a new criterion for multiple crack coalescence, based on the plastic zone size, to better model the growth and merging of natural cracks. This criterion was implemented using the Advanced Iterative Finite Element Method (AI-FEM), an automated crack-growth simulation program utilizing re-meshing. Fatigue crack growth (FCG) simulations using AI-FEM validated the proposed criterion by comparing it with experimental data. Additionally, the AI-FEM results were compared with those obtained through code-based procedures to evaluate the conservatism of current codes. The findings demonstrate that the proposed criterion closely matches experimental results, providing a more realistic simulation of crack growth and reducing the conservatism of existing codes.  
multiple cracks; crack coalescence; finite element analysis; stress intensity factor; fatigue crack growth; flat plate; straight pipe
## 5 Conclusions  
In order to improve tracking performances of the radar position servo system facing dead zone and friction nonlinearities, this paper proposes a DRNN-based linear active disturbance rejection control method. Firstly, the ATD, which can adaptively adjust the speed factor and filter factor according to the change rate of the input signal, is designed in order to manage the contradiction between filtering performance and tracking speed; the results of Case Study 1 show that the ATD has good tracking and filtering performances while the input signal is contaminated by Gaussian noise. What is more, the LADRC effectively removes the excessive value of the control signal by introducing the proposed ATD while the LADRC faces step or square wave signal, as demonstrated in Case Study 5, which can track the change of the input signal immediately, reduce the energy of the controller, and reduce the spike impact on the actuator. Secondly, the LADRC has a self-learning ability by introducing the DRNN to optimize its parameters in LSEF in real time, which can adjust the parameters according to the radar system error and reduce the tracking error compared to the traditional LADRC, as demonstrated in Case Study 2. Moreover, the compound controller has higher tracking accuracy of the radar system compared with the BPNN-LADR, as demonstrated in Case Study 3, since the signals within the hidden layer of the DRNN are the feedback signals from the output layer and have different time delays that can predict the plant model accurately. Thirdly, the strong robustness of the proposed compound controller is verified by adding a constructed function with strong dynamic nonlinearity and Gaussian noise to the radar system with the controller parameters unchanged, as demonstrated in Case Study 4.  
At present, this paper is limited to theoretical analysis. Due to the limitation of experimental conditions, this paper only simulates the proposed control scheme, so it is  
Figure 23: Comparison results of control signal between the LADRC with ATD and LADRC without ATD.  
necessary to further verify this method through experiments. At the same time, because the radar servo system is composed of the azimuth and elevation axis, this paper only studies the azimuth axis, and will study the algorithm of the whole system in the next step.  
Conceptualization, S.C.; Methodology, G.Z.; Software, G.Z.; Validation, S.C.; Investigation, T.Z.; Resources, S.C.; Data curation, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Protection against Shiga Toxins
## 2 Compounds that Protect Cells against Stx
### Hg  
The alkylglycerol 1-O-hexadecyl-_sn_-glycerol (HG, also called chimyl alcohol) is a precursor for ether-linked glycerophospholipids. HG enters the biosynthetic pathway of ether lipids after being phosphorylated by alkylglycerol kinase [168]. Addition of HG was shown to alter the lipidome of HEp-2 cells, with the most notable changes being an increase in the cellular level of ether-linked lipids with 16 carbon atoms at _sn_-1 position, an increase in lysophosphatidylinositol (LPI) and a decrease in all glycosphingolipid classes analyzed, amongst them the Stx receptor Gb3 [50].  
We recently showed that a 24 h preincubation with 20 \(\upmu\)M HG strongly protected HEp-2, HMEC-1 and HBMEC cells against Shiga toxin and Stx2, with an average 30-fold increase of IC50 [50]. There was a moderate reduction in Stx1 binding to HEp-2 cells after HG treatment, presumably due to reduced Gb3 levels, but this decrease was not sufficiently large to account for the strong protection against the toxin. Immunofluorescence confocal microscopy revealed that HG treatment led to an accumulation of Stx1 in the Golgi apparatus after 4 h of toxin challenge, while in control cells, the toxin clearly colocalized with both the ER-marker PDI and the Golgi-marker TGN46 at the same time point, suggesting that HG treatment inhibits Golgi-to-ER transport of Stx [50]. Thus, HG prevents Stx intoxication primarily by interfering with the Golgi-to-ER transport of Stx, but also to some degree by downregulating the Stx receptor Gb3.  
The 1-O-alkylglycerols, including HG, are naturally occurring ether lipids that are present in human cells and body fluids, particularly in hematopoietic organs and in neutrophils and human milk [169].alkylglycerols can also be obtained from the diet, with marine oils, especially shark liver oil, being a good source [169; 170]. Shark liver oil contains 9%-13% HG and has been used in traditional medicine in the Nordic countries for beneficial health effects and for wound healing [169; 170]. Shark liver oil and alkylglycerol supplementation have been reported to mediate several biological effects, including the ability to boost the immune system and alleviate radiation therapy-induced side effects [169; 170]. However, to evaluate the potential of HG as an inhibitor of Stx intoxication, further studies, first in animal models, are required.
## 3 Results  
The majority of measured physical parameters exhibited minimal variation among the study catchments (_i.e._, CV < 0.5; Table 3). Sampled headwater streams were similar in regards to average channel depth (mean = 0.14 \(\pm\) 0.06 m), average channel width (mean = 1.65 \(\pm\) 0.56 m), water temperature (mean = 17.96 \(\pm\) 1.42 \({}^{\circ}\)C), pH (mean = 7.87 \(\pm\) 0.27) and conductivity (mean = 575.84 \(\pm\) 87.74 \(\upmu\)S/cm). However, flow velocity, discharge and canopy cover revealed greater variation among sites (_i.e._, large = CV > 0.5). Flow velocity ranged from \(<\)0.01 m/s to 0.13 m/s, with a mean velocity of 0.05 m/s. Average discharge was 0.02 m\({}^{3}\)/s and ranged from \(<\)0.01 m\({}^{3}\)/s to 0.05 m\({}^{3}\)/s. Canopy cover ranged from a minimum of 6.92% to a maximum of 85.75% cover. Mean canopy coverage was 29.27%. Water quality parameters (_i.e._, TN, NH\({}_{4}\), NO\({}_{2}\)-NO\({}_{3}\), TP, SRP, TDP, turbidity, and TSS) sampled during logger deployment and retrieval generally exhibited large variation among the study catchments. The exception was TN concentrations collected at logger retrieval (CV = 0.33). Furthermore, concentrations of all examined nutrient species and TSS were at least 1.3 fold different between sampling events with samples taken during logger retrieval having a consistently larger mean, median, maximum, and standard deviation among sites.  
A total of 60 BMP structures were identified in our 13 study catchments. This total consisted of 40 MS structures, 18 LAR fences, and 2 EC structures (Table 4). The total number of BMPs per study catchment ranged from 1 to 16 with an average BMP implementation of 4.62 structures per catchment. On average each catchment contained 3.08 MS structures, 1.38 LAR fences, and 0.15 EC structures. Average BMP use was less than one BMP per farm (mean = 0.41). BMP implementation was on average 0.29, 0.09, and 0.02 BMPs per farm for MS, LAR, and EC, respectively. Abundance metrics of LAR and EC structures exhibited larger variation among the study sites, as these BMPs were less common and not found in all catchments. Of the 13 study catchments selected, LAR fences were present in six and EC structures in two. BMP location showed substantial variation in regardsto the flow accumulation area and hydrologic distance from the BMP to the river in both the BMP summary and BMP type groups, with the exception of LAR flow distance to the river (CV = 0.46). In contrast, location metrics involving the hydrologic distance from the BMP to the instream sampling point exhibited minimal variation among the catchments for all BMP metrics except EC (CV = 0.76). The length of LAR adjacent to the stream was variable among catchments and ranged from 63 m to 2846 m (CV = 1.03). Likewise, the percentage of river protected by LAR structures exhibited a large variation among catchments (CV = 1.65). The percentage of RBA surrounding the study streams ranged from 0% to 42% among the catchments with an average of 20%. Combining the proportion of river protected through LAR and RBA, the minimum percentage of protected channel length was 5% and the maximum was 61%.
## 5 The Evolution towards a Global Smart Grid Deployment: Challenges and Perspectives
### Data Communication and WSANs
#### 5.1.3 Security  
The application of conventional communication protocols for the management of electrical grids implies a major revision of the existent protection and confidentiality mechanisms for the transmitted data [90; 91]. Moreover, the use of Internet as information transmission support in the SG is the source of new vulnerabilities for the WSANs that make part of it, such as intrusions and denial of service (DoS) attacks [92; 93]. It is then necessary to apply security mechanisms that must assure at least three main aspects:  
* Privacy, a critical issue that is very frequently attacked. Privacy must be assured not only for related customer information, but also for the commercial transactions related with the operation of the whole electrical grid. Some privacy protection mechanisms are based on standard schemes. In example, EG2 (one recommendation proposed by the Expert Groups of the European Commission) recommends a technique that separates the smart metering data into low-frequencyattributable data (data used for billing) and high-frequency anonymous technical data (data used for demand side management) [69].
* Integrity, whose aim is to avoid illegal modifications of the transmitted data. Authentication mechanisms [94] are traditional solutions in order to ensure a correct identification of the transmitter and the receiver of the message, allowing the detection of possible data corruption.
* Availability, that is, the possibility for authorized users to get access to the network services even if an attack has occurred. Communication among nodes and a central access control system can be an appropriate solution in order to ensure the availability of the network.  
Another interesting security aspect is secrecy; that is, the prevention of passive attacks and unauthorized access to confidential and sensitive customer data, such as the billing or the smart meter information. All these requirements must be combined with early detection methods, in order to eliminate false alarms caused by undesirable communication delays, and to discover any unauthorized manipulation of transmitted information. Security in SG communications can be also increased implementing public key infrastructures (PKIs) and trusted computing methodologies [95]. Concerning the WSANs, some basic requirements for achieving a good level of security are prohibition of unauthorized nodes to access to the network information, implementation of distribution key mechanisms robust enough, and encryption of public information [96]. The security level can also be increased by means of secure routing protocols or secure data aggregation procedures [97].
## 2 Numerical Methods
### Validation and Consistency of the Numerical Simulation  
In order to compare the computational results and experimental data, steady simulations were first conducted from choke to surge at the design speed, to get the full compressor map. Each calculation of the steady solution was converged when the variation of the inlet mass flow rate was less than 0.001 kg/s per 500 iterations and the variation of the total-to-total efficiency was less than 0.2% per 200 iterations. Many other researchers have also used the same convergence criteria for the stall point prediction [32; 36]. To obtain the compressor performance characteristic from choke to stall mass flow rate, the exit static pressure was varied in sufficiently small increment of the back pressure as 500 Pa, allowing the solution to converge at each back pressure. Figure 5 compares the aerodynamic performances of the compressor calculated by the numerical method and the experimental data [29] with respect to the mass flow rate, and shows that both the magnitude and trend of the computational data are well matched in comparison to those of the measurements at various operating points. Figure 5 also confirms that the time-averaged data from the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(k-\varepsilon\) & \(k-\omega\)**SST** & **Exp** \\ \hline Pressure ratio & 4.252 & 3.998 & 3.97 \\ Efficiency (\%) & 86.17 & 83.04 & 83.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Influence of turbulence model on pressure ratio and efficiency of the compressor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Pressure Ratio** & **Efficiency (\%)** & \
\begin{tabular}{c} **Pressure Ratio** \\ **\% Difference** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency** \\ **\% Difference** \\ \end{tabular} \\ \hline Coarse & 4.124 & 84.32 & 3.33\% & 1.48\% \\ Moderate & 3.998 & 83.04 & 0.17\% & 0.06\% \\ Fine & 3.991 & 83.09 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grid independence test.  
unsteady simulations at two different operating points, one near surge and the other at design condition, are also well predicted compared to the experimental data [29] (indicated by the red circle symbols in Figure 5).  
Moreover, Figure 6 compares the time-averaged velocity profile computed by the current URANS approach from hub to shroud with the experimental data measured by laser anemometry [37] at the compressor design point. This figure shows the accuracy of our unsteady simulations in prediction of the transient flow-field. It should be noted that the velocity profile is normalized by the tip velocity of the impeller and it is measured at the exit of the impeller (1.05 \(R_{2}\)). It can be concluded from Figure 6 that the unsteady computational data are in a good agreement with the experimental data.