esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 4 Experiments
According to Equation (17), \(P_{\text{c}}\) is about 90.31 W. The voltage of capacitors in circuit II is higher than that in circuit I because of the boost effect of the inductors. As shown in Figure 11, when the capacitors discharge to the load in series, the charging current decreases. These charging currents in the isolating inductors also flow into the resistive load and the capacitors. Therefore, the voltage in capacitors increases and the energy efficiency also increases. The input power is about 108.03 W. \(\eta_{\text{c}}\) and \(\eta\) are calculated as 83.60%, 94.24% according to Equation (16). Compared with circuit I, the capacitor charging efficiency and energy efficiency of circuit II are increased by 24.89% and 23.32%, respectively. The calculation formula is shown in Equation (22). \[\eta_{\text{increase}}=\frac{\eta_{\text{II}}-\eta_{\text{I}}}{\eta_{\text{I}}} \times 100\%. \tag{22}\]  
Figure 9: The waveform of the voltage on the energy-storage capacitors \(U_{\text{c}}\) and the resonant current \(i_{\text{r}}\) (circuit I). Figure 8.0 The voltage waveform of the load (circuit II): (**a**) discharge waveform with a repeatable frequency of 50 kHz; (**b**) enlarged view of load voltage in a different frequency.
# Identifying Misbehaving Greedy Nodes in IoT Networks
## 5 Simulation and Results
### Greedy Node Identification Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Node** & **Packets Sent** & **Collision** & **Packets** & **Transmit** & **Transmit** & **Power** & **Radio on (\%)** & **Radio Tx (\%)** \\ \hline S 1 & 39,761 & 29,578 & 8229 & 0.279 & 0.526 & 1.711 & 2.68 & 0.86 \\ \hline S 2 & 13,825 & 10,203 & 2582 & 0.057 & 0.107 & 1.058 & 1.37 & 0.37 \\ \hline S 3 & 4697 & 2816 & 384 & 0.184 & 0.347 & 1.228 & 2.14 & 0.81 \\ \hline S 4 & 7299 & 3952 & 627 & 0.099 & 0.186 & 1.298 & 3.08 & 1.33 \\ \hline S 5 & 2203 & 1911 & 371 & 0.1 & 0.189 & 1.274 & 1.65 & 0.46 \\ \hline S 6 & 4895 & 2707 & 853 & 0.192 & 0.362 & 1.464 & 2.64 & 1.01 \\ \hline S 7 & 1599 & 2251 & 748 & 0.124 & 0.234 & 1.494 & 2 & 0.43 \\ \hline S 8 & 3280 & 2507 & 926 & 0.347 & 0.653 & 1.881 & 2.75 & 0.96 \\ \hline S 9 & 1463 & 2366 & 1215 & 0.113 & 0.213 & 1.578 & 2.31 & 0.43 \\ \hline S 10 & 6631 & 3547 & 999 & 0.265 & 0.498 & 1.673 & 288 & 1.07 \\ \hline
**Greedy** & **27,136** & **9775** & **359** & **1.759** & **3.312** & **3.666** & **8** & **5.63** \\ \hline S 11 & 2501 & 2275 & 396 & 0.055 & 0.103 & 1.189 & 165 & 0.41 \\ \hline S 12 & 3600 & 2141 & 643 & 0.176 & 0.3  
Table 5 shows that although the nodes S1 and S2 send more packets than the greedy node, they were not considered by our algorithm to be a selfish node. Although the parameter number of packets sent and collision rate of the two nodes exceeds their appropriate thresholds, the number of packets received by S1 and S2 is respectively equal to 8229 and 2582, while the threshold has been set at 460.387298, knowing that the values considered as abnormal must be below this threshold. This condition is sufficient for considered them as legitimate nodes. The malicious node is the only one that was considered to be greedy since it is the only one that meets all the criteria. In short, on 55 legitimate nodes there was no false alarm, and in both networks the algorithm correctly detects the greedy node. This proves the efficiency and the precision of the proposed algorithm.
## 4 Conclusions  
In conclusion, the results suggest that there are predominantly ion pairs existing in the solutions. These ion pairs establish hydrogen bonds with the alcohol molecules via the oxygen atoms of the TIO  
Figure 5: Decomposed FTIR spectra in the OH stretching region for five selected mixtures.  
anion. This configuration was unexpected as previously studied mixtures of short-chain alkyl alcohols and imidazolium-based ionic liquids showed clear signs of ion pair dissociation. In addition, our data illustrate the strong links between the molecular scale studied by spectroscopy and the macroscopic behavior of the solution. Clear correlations between spectroscopic signatures and the thermodynamics derived from VLE data were observed. Therefore, combining vibrational spectroscopy with the analysis of the phase equilibrium is a powerful approach to unravel these structure-property relationships.  
The observed behavior of stable ion pairs existing in the mixture and their solvation by alcohol molecules may be a general phenomenon in 1,3-dialkylimidazolium TfO IL-alcohol mixtures. However, further and systematic investigations are necessary to confirm this. Molecular dynamics simulations will be helpful to obtain additional insights. However, we should also note that the behavior is strongly dependent on the nature of the cation-anion combination and the solvent. For example, a previous Raman study of an imidazolium ethylsulfate IL in mixtures with methanol and ethanol [8] has shown that ion pair dissociation takes place in these solutions. Further evidence of the tendency of ion pair dissociation may also be provided by conductivity measurements in the future. For instance, [PERSON] et al. [33] conducted such experiments in mixtures of imidazolium-based ILs with highly symmetric anions and organic solvents to derive association constants. The behavior they found, however, cannot be directly compared with our results due to the systematic difference in molecular structure. Therefore, finding universal rules and structure-property relationships for ILs and their mixtures with organic solvents remains a topic that requires further theoretical and experimental research.  
Conceptualization, J.K. and B.R.; methodology, J.K.; sample preparation and FTIR experiments, M.S.; Raman experiments, C.C.R.; data analysis, J.K.; data interpretation, all authors; writing--Original draft preparation, J.K.; writing--Review and editing, all authors. All authors have read and agreed to the published version of the manuscript. Part of this work was funded by the Deutsche Forschungsgemeinschaft (DFG) through grant KI1396/4-1.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 6 Conclusions  
In this paper, we presented an enhanced parallel acquisition algorithm that builds up the principles of PIC and coherent accumulation. Additionally, a signal hard reconstruction algorithm based on the acquisition result is also introduced. By combining the aforementioned algorithms, we effectively minimize the impact of MAI on multi-user spread spectrum communication systems. Compared to the traditional correlation-based algorithm, the improved algorithm significantly enhanced \(P_{cd}\) and raised BER by 1 dB, which meets the demand for satellite communication. Through extensive simulation and hardware testing, it is shown that our simulation, analysis, and experimental results are consistent. Specifically, we observed that \(P_{cd}\) can achieve 0.95 when ISR equals 20 dB caused by MAI. In summary, the MAI in multi-user DS-CDMA systems has an unavoidable impact and becomes more severe with an increase in the number of users. The algorithm proposed in this paper effectively eliminates MAI by reconstructing and parallel eliminating interference signals within the received signal, which enables the successful acquisition and reception of individual signals, making the algorithm applicable in various satellite communication systems.  
\begin{table}
\begin{tabular}{c c c} \hline
**Identifier** & **Parameter** & **Value** \\ \hline
1 & Modulation & BPSK \\
2 & Shaping filter & Raised cosine filter \\
3 & Spread spectrum ratio & 2048 \\
4 & Oversample rate & 20 \\
5 & Symbol rate & 800 sps \\
6 & Chip rate & 1.6384 Mcps \\ \hline \end{tabular}
\end{table}
Table 2: System parameters.  
Figure 13: BER performance of the proposed acquisition receiver.  
**Author Contributions:** Conceptualization and methodology, H.L., X.D. and K.Y.; Software, H.L. and Y.J.; Validation, H.L. and D.Z.; Writing--original draft preparation, H.L., Y.J. and D.Z.; writing--review and editing, X.D. and K.Y.; Supervision, X.D. and K.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Key Research and Development Program of China under Grant 2023 YFC3305904, in part by the National Natural Scientific Foundation of China under Grants 62301049.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Flexible Time-Triggered Sampling
### Sampling Period Adaptation Algorithm  
As mentioned above, this paper uses feedback control theory to determine a new sampling period. Generally speaking, many control algorithms/techniques can be used in this context. In particular, the PID (proportional-integral-derivative) control algorithm, which is the most popular controller in practical control applications, is employed in this paper. Some reasons for the use of PID are explained as follows. Firstly, as a combination of three components, i.e., the proportional, integral and derivative components, the PID control algorithm has proved very effective in most control applications. Secondly, a PID controller can perform well even when the system model is unavailable, which is the case for many practical systems as well as the system considered in this paper, given that the controller coefficients are well tuned. Thirdly, the PID control algorithm is very simple, thereby inducing only a small computational overhead. This makes it easy to meet the requirements stemming from the limitations on the data processing capacities of smart sensors.  
From a control perspective, the purpose of adjusting sampling period is to maintain the deadline miss ratio at a desired level. Let \(\rho_{r}\) and \(\rho(j)\) be the desired and measured deadline miss ratio, respectively, where \(j\) corresponds to the \(j\)-th invocation of this algorithm. The sampling period is computed by:  
\[\begin{array}{c}\Delta h(j)=K_{P}(e(j)-e(j-1))+K_{I}e(j)+K_{D}(e(j)-2e(j-1)+ e(j-2))\\ h(j)=\min\left\{h(j-1)-\Delta h(j),\ h_{max}\right\}\end{array} \tag{1}\]  
where \(K_{P}\), \(K_{I}\), and \(K_{D}\) are the proportional, integral, and derivative coefficients, respectively, \(e(j)\) is the deadline miss ratio control error, and \(h_{max}\) is the maximum allowable sampling period. Due to the unavailability of a mathematical model that describes explicitly the relationship between deadline miss ratio and sampling period, the coefficients \(K_{P}\), \(K_{I}\), and \(K_{D}\) in (1) will be determined based on simulations in this paper. In general cases, \(e(j)\) can be simply calculated as \(e(j)=\rho_{r}\) - \(\rho(j)\). However, due to the inherent non-determinism of wireless communication, the measured deadline miss ratio may vary randomly from one invocation interval to another, even in the same network condition. To reduce the effect of this uncertainty as well as measurement noise, a low-pass filter is used in this paper when calculating \(e(j)\), as given by:  
\[e(j)=\rho_{r}-(\lambda\rho(j)+(1-\lambda)\rho(j-1)) \tag{2}\]  
where \(\lambda\) is a forgetting factor that satisfies 0\(<\)\(\lambda\)\(\leq\)1. A \(\lambda\) close to 0 gives a smooth but slow estimate of the actual deadline miss ratio. The general case without the low-pass filter can be viewed as a special case of (2) where \(\lambda\) is set to 1.
## 2 Microgrid Structure and Dispatching Strategy
### Expression Construction
#### 2.3.2 Constraint Condition  
In order to ensure the safe and stable operation of the grid system, the power balance should be regarded as the basic constraint condition, and the output power and SOC of flexible devices should be limited to ensure the normal operation of equipment.  
a. Power balance  
In any period of time, all output, load, flexible device charging and discharging, and interaction powers between the microgrid system and the main grid should be kept in balance, and the constraint equation is given as follows:  
\[P_{xy}(t)+P_{xy}(t)+P_{grid}(t)+P_{x}(t)+P_{EV}(t)=P_{load}(t) \tag{10}\]  
where \(P_{x}\) is the charging and discharging power of the battery, and the total load is expressed as \(P_{load}\).  
b. Wind and photovoltaic generation  
Due to the limitations of the equipment parameters, the output power of wind and photovoltaic generation is within a certain range. Meanwhile, in order to ensure the normal operation of the system when the power required surges, it is necessary to add operating reserves. In detail, 30% of power generated by wind and photovoltaic power generation is stored, and the rest is the maximum power supplied to the system. Furthermore, the change of power in each time period should also be constrained in a certain range. Therefore, the relevant constraints are set as follows:  
\[\left\{\begin{array}{c}0\leq P_{xy}(t)\leq 0.7P_{wf}(t)\\ 0\leq P_{yy}(t)\leq 0.7P_{tyf}(t)\\ R_{w-down}\delta t\leq\delta P_{w}\leq R_{w-up}\delta t\\ R_{v-down}\delta t\leq\delta P_{v}\leq R_{v-up}\delta t\end{array}\right. \tag{11}\]  
where the adjustable power amplitude of wind and photovoltaic generation in a certain period is represented as \(\delta P_{w}\) and \(\delta P_{v}\), respectively. The downward climbing rate of wind and photovoltaic generation during adjustment is expressed as \(R_{w-down}\) and \(R_{v-down}\), respectively. Meanwhile, \(R_{w-up}\) and \(R_{v-up}\) give the upward climbing rate for wind and photovoltaic generation during adjustment, respectively. The adjustment time is represented by \(\delta t\).  
c. Flexible generation  
Batteries are not only constrained by power, their safety performance and service life are affected by their SOC, and the operation of an EV is also affected by its SOC. Specific conditions are simplified as follows:  
\[\left\{\begin{array}{c}SOC_{emit}\leq SOC_{e}(t)\leq SOC_{emax}\\ SOC_{xmin}\leq SOC_{x}(t)\leq SOC_{xmax}\\ 0\leq P_{bes,I}(t)\leq P_{bes,max}\end{array}\right. \tag{12}\]  
\(SOC_{emit}\), \(SOC_{xmin}\), \(SOC_{emax}\), and \(SOC_{xmax}\) are the upper and lower limits of their SOC.  
In addition, to ensure the safe and stable operation of batteries, the charging and discharging rates should be controlled within a certain range. The specific constraints are shown as follows:  
\[\delta SOC_{x}\leq 0.2 \tag{13}\]  
where the adjustable range of a battery's SOC in each period is expressed as \(\delta SOC_{x}\).
## 4 Performance Evaluation
### Breast Cancer Classification Using Extracted Deep Features with TTCNN  
The proposed TTCNN classification approach extracts deep CNN features. The results are acquired by the feature extraction from the optimal layers of every deep CNN model. We selected the best features for all deep CNN frameworks by determining the best feature extraction layer that can deliver the finest performance on TTCNN. To do this, we extract features from different layers, assess the performance of each deep CNN and identify the best layer. Table 6 illustrates the best performance results for deep feature extraction from the optimal layers.  
The TTCNN classifier from optimal layers features are also analyzed using the evaluation metrics. Table 7 illustrates the classification result with features from the optimal layers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Layer** & **Optimal Layer** \\ \hline InceptionResNet-V2 & 813 & ‘activation\_203’ \\ \hline Inception-V3 & 313 & ‘predictions’ \\ \hline VGG-16 & 33 & ‘fc6’ \\ \hline VGG-19 & 39 & ‘fc6’ \\ \hline GoogLeNet & 140 & ‘pool-7x7\_s1’ \\ \hline ResNet-18 & 64 & ‘res5b\_branch2b’ \\ \hline ResNet-50 & 169 & ‘activation\_48\_relu’ \\ \hline ResNet-101 & 342 & ‘res5c’ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Best performance from optimal layers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sensitivity (\%)** & **PPV (\%)** & **Accuracy (\%)** & **Error rate (\%)** \\ \hline InceptionResNet-V2 & 82.69 & 85.53 & 81.74 & 18.26 \\ \hline Inception-V3 & 85.43 & 87.69 & 84.03 & 15.97 \\ \hline VGG-16 & 76.98 & 77.09 & 81.41 & 18.59 \\ \hline VGG-19 & 77.83 & 81.51 & 84.38 & 15.62 \\ \hline GoogLeNet & 82.37 & **90.04** & **88.54** & **11.46** \\ \hline ResNet-18 & 87.25 & 83.02 & 87.68 & 12.32 \\ \hline ResNet-50 & 83.38 & 87.31 & 82.87 & 18.58 \\ \hline ResNet-101 & **89.51** & 87.64 & 88.12 & 11.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Best performance from optimal layers. Best values are emphasized in bold.  
From the above Table 7, it can be perceived that the GoogLeNet provides the best performance than the other deep CNN models with an accuracy rate of 88.54% and an error rate of 11.46%. Although ResNet-101 is also very close to GoogLeNet with an accuracy rate of 88.12% and an error rate of 11.88%. ResNet-18 also provides very decent performance with an accuracy rate of 87.68% and an error rate of 12.32%. Among all the models, VGG-16 comes last with an accuracy rate of 81.41% and an error rate of 18.59%.
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
## 6 Conclusions  
These days, opportunistic routing in UWSNs has drawn a lot of attention from researchers. OR has been shown to be more effective than the conventional routing strategy for wireless networks because it makes use of the broadcast nature of wireless networks. A number of factors have an impact on the performance and effectiveness of the UWSNs, including a shortage of resources (restricted battery power and memory), the harsh underwater environment, and a weak communication channel. The void area problem employing an OR approach is one of the significant concerns and research challenges in UWSNs. We have investigated in this paper the existing OR protocols proposed to address this problem.  
First, we discussed the aspects of routing protocols for UWSNs covering the main challenges facing researchers when designing routing protocols, the concept of the void area problem in UWSNs, and reasons for this problem. OR and its key elements, including OR construction blocks and OR classification have been introduced. Second, the state-of-the-art void avoiding protocols that use the OR technique were investigated in depth.
# A Preliminary Study on the Use of Xylit as Filter Material for Domestic Wastewater Treatment
## 2 Materials and Methods
### Measurements of the Wastewater Quality Indicators  
The samples of inlet and outlet wastewater (seminatural greywater or septic tank effluent) were collected and analysed usually once a week. The following parameters were analysed: chemical oxygen demand (COD), determined by using the bichromatic method (the oxidation of organic compounds using chromosulfuric acid, determination as chromate) using a Merck Spectroquant(r) NOVA 60A spectrophotometer; five-day biochemical oxygen demand (BOD\({}_{5}\)), performed with the use of the respirometric method (Oxitop, WTW), (ammonium nitrogen (N-NH\({}_{4}\), the indophenol method) and total phosphorus (P\({}_{\text{tot}}\)), analysed using a Merck Spectroquant(r) NOVA 60A spectrophotometer; totalsuspended solids (TSS) using the dry weight method with filtration through paper filters. The detection of the total phosphorus and ammonium nitrogen (NNH\({}_{\text{NH}}\)) concentrations was performed using spectrophotometer (Merck,Darmstadt, Germany) kits (Spectroquant kits Nos. 14752 and 14773, respectively). Determination of wastewater quality indicators, defined as dissolved organic and nutrient compounds (COD, BOD\({}_{5}\), NNH\({}_{\text{NH}}\), P\({}_{\text{tot}}\), TSS) was performed for samples filtered through paper filters of 2.5 \(\upmu\)m pore size. The values of the wastewater quality indicators were determined in accordance with the standards (for COD: PN-ISO 6060 [14], for P\({}_{\text{tot}}\): PN-EN ISO 6878 [15], for TSS: PN-EN 872 [16]).  
The pH value is not limited in Poland for on-site wastewater treatment plants treating domestic wastewater up to 5.0 m\({}^{3}\)/d and disposed into the soil being the property of the user. In view of this fact, only a few pH measurements were taken and the average values were 8.29 \(\pm\) 0.03 and 8.36 \(\pm\) 0.05 for inflowing and outflowing seminatural greywater, respectively.
## 4 HRWS Imaging Processing
### SAR Range Model
Using an intelligent optimization algorithm for a certain population is the most intuitive way to solve the minimax problem above, that is, multiple independent sampling points of \(\beta_{0}\) and \(\beta_{1}\) are first randomly selected from population \(X_{\beta}\), and the approximate solution of each sampling sequence can be solved as \(\widetilde{\theta}_{s}=\arg\max_{\theta\in Y_{\theta}}\Gamma(\hat{\beta}_{s}, \theta)\). Then, function value \(\Gamma(\hat{\beta}_{s},\widetilde{\theta}_{s})\) corresponding to \(\hat{\beta}_{s}\) is updated, and the approximate solution is solved as \((\widetilde{\beta}_{0},\widetilde{\beta}_{1})=\hat{\beta}_{s^{*}},s^{*}=\arg \min_{s}\Gamma(\widetilde{\beta}_{s},\widetilde{\theta}_{s})\). However, a global optimization algorithm solely used will result in high computational complexity to obtain \(\widetilde{\theta}_{s}\). Therefore, to further get the value of \(\hat{\beta}\) more efficiently, this paper presents the two-population coevolutionary algorithm based on the meta-heuristic random searching algorithm [26; 27]. As shown in Figure 5, the independent individuals belonging to the global population are first sampled partly to form a new limited population, which is beneficial to reduce the complexity of the minimax optimization problem. Furthermore, the Particle Swarm Optimization (PSO) algorithm is utilized to generate a new population due to its advantages such as the less parameters setting, fast convergence and simple operation [28]. Each particle in the population contains the values of the current position, the best historical position and the velocity. In the search space, the single particle constantly moves according to the best historical position of itself and others particles; then, the optimal population belonging to the next generation is selected through the fitness evaluation [29; 30]. After a specified number of iterations, the estimates of \(\beta_{0}\) and \(\beta_{1}\) are further derived from the returned optimal individual. Based on the analysis above, the approximate model of the SAR slant range can be obtained from the derived fitting coefficients using the two-population coevolutionary algorithm. Then, the azimuth phase errors introduced by the approximate slant range model are calculated for different imaging scenes, and the simulation results are shown in Figure 6.  
According to Figure 6, the azimuth phase error curves are directly shown as the characteristics of the sine function, and the maximum error is less than \(\pi/4\). Thus, the imaging precision in azimuth can be well satisfied with the approximate polynomial f  
Figure 5: The flowchart of solving fitting coefficients using the two-population coevolutionary algorithm.