esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Heat Transfer Performance and Anti-Fouling Mechanism of Ternary Ni-W-P Coating
## 3 Results
### Heat Transfer Behavior
How to effectively balance the needs of the economy and efficiency should be in conformity with the actual heat transfer conditions of industrial requirements. To avoid the measurement deviation of heat transfer coefficient caused by uneven local fouling deposition, we multi-measured the heat transfer coefficient of the specimen coated with ternary Ni-W-P coating before flow fouling experiment. As shown in Figure 10b, it is noticeable that the thermal conductivity of the specimen coated with ternary Ni-W-P coating decreases from 8.48 W/m-K to 8.19 W/m-K with the up of W content. Specifically, excessive W element deposited on the surface of the substrate with the concentration of sodium tungstate in the plating solution increases, leading to more W element solid dissolved in amorphous Ni unit cell. The phonon vibration was suppressed and the average kinetic energy of heat transport of electrons as hot carriers decreased during thermal conduction simultaneously [32; 45; 46]. The heat transfer process of the coated specimen thus is inhibited as well. In Figure 10c, the thermal conductivity of the coating increases from 8.38 W/m-K to 8.93 W/m-K with the up of the heat source temperature, which is also consistent with the heat transport mechanism [31; 32]. With the increase of temperature, the average velocity of thermal carriers in amorphous Ni[W,P] solid solution increases accordingly. Since the collision process of energy exchange between thermal carriers is more intense and frequent, the heat transfer rate is accelerated and the thermal conductivity of the coating mounts. During this process, the thermal resistance of the un-fouled specimen with ternary Ni-W-P coating decreases from 191,500 Km\({}^{2}\)/W to 179,800 Km\({}^{2}\)/W. This is compatible with the conclusion we analyzed before in Figure 10a.
## 3 Experimental Work
### Comparison of Sample-Charging Rates  
Figure 18 shows a comparison of the sample-charging rates. The reference measurement was a PCM sample that did not contain any components to increase thermal conductivity. The total phase change time is 590 s. The steel twisted matrix curve melted the PCM in a time of 660 s. Compared to the reference sample, this time is 11.86% longer but, at the same time, the total charging time along with the sensible heat is 3.3% shorter after the end of the phase change process. The PCM curve (copper spiral) shows a longer charging time for sensible heat but, at the same time, the time required to go through the phase change is significantly lower at 200 s. Compared to the reference sample, this is a significant acceleration of 66.1%. The total time even with sensible heat is 14.1% shorter, which means that the copper spiral transfers heat more efficiently.  
The PCM (copper matrix) achieves a transition time of 500 s, which means an acceleration of 15.25% compared to the reference sample. Compared to the PCM with a copper spiral, this  
Figure 18: Comparison of sample-charging rates.  
acceleration is not as efficient, but the overall time is significantly different, specifically, by 22.62% compared to the reference sample. It is obvious that the copper matrix can work even more efficiently than the copper spiral. The higher content of the embedded copper element does not always guarantee a faster phase change transition, but due to the acceleration of the overall charging (sensible + latent), this phenomenon can be ignored.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 2 System Model
### Prestige Evaluation System in Spectrum Sensing  
In order to effectively identify the second type of malicious users hidden in the centralized collaborative spectrum sensing system model, the \(\beta\) reputation evaluation system is usually used to analyze the cognitive users' reliability in the data fusion center. Currently, the cognitive user prestige building algorithm based on single channel decision is used and the system model of this algorithm is shown in Figure 2.  
Figure 2: Single channel spectral sensing system model based on \(\beta\)-reputation system.  
Figure 2 shows for the specific channel \(B_{N-1}\), the data fusion center analyzes each cognitive users' perceived results of the channel in the system, before obtaining the confidence model of the cognitive users' perceived results for this channel. This method is more effective for finding the type of malicious users who interfere with a particular channel, although it is difficult to use this to accurately identify malicious users who are highly concealed.  
SSDF attacks can be divided into three forms: false alarm attack, missed detection attack and probability attack in the process of centralized collaborative spectrum sensing [14]. In practice, the malicious cognitive users with the purpose of interfering with the sensing system usually use probabilistic attacks. In order to increase their concealment, malicious cognitive users generally attack with a method that randomly selects the channels. Broadband cognitive wireless communication systems typically divide spectrum resources into several independent sub-channels. In the same perceived interval, each cognitive user's perceived result on the different sub-channel should be correlated. Therefore, it is possible to more accurately distinguish hidden malicious cognitive users by comprehensively assessing the perceived results of cognitive users on multiple sub-channels. We propose a cognitive user reputation-building algorithm based on multi-channel collaboration. The algorithm uses the \(\beta\)-function and the feedback iteration for calculating, before integrating the perceived results of each cognitive user on each sub-channel. We modelled the reliability of cognitive user perceived results as a whole, using the system model of the algorithm shown in Figure 3.  
In this system model, the data fusion center carries out the information fusion according to the plurality of cognitive users' perceived results on each sub-channel in order to calculate the optimal estimate of the occupied state of different sub-channels. After this, we adjust the reputation value of cognitive users by comparing the data provided by each cognitive user with the optimal estimate  
Figure 3: Multi-channel collaborative spectrum sensing system model based on \(\beta\)-reputation system.  
value. After the data accumulates for a certain period of time, the data fusion center can distinguish the hidden malicious cognitive users.
## 3 Results and Discussion  
Analysis of the Results of Physical, Mechanical and Structural Tests of Silicones Used for the HSC Composites Production  
In the first phase of the work, the physico-mechanical parameters of silicones used to produce HSC composites were determined and their chemical structure was compared by performing and analyzing infrared spectroscopy spectrums. Physical-mechanical parameters of silicones used to prepare composites are presented in Table 1.  
The lowest values of parameters, i.e., hardness, elongation at tear, tensile strength, density, and viscosity, are provided by Za 22 Mould silicone ([PERSON], Badia Polesine, Italy). This silicone is also characterized by the shortest lifetime and demolding time, which is an important element in semi-technical and industrial processing. The highest parameters of hardness, tear strength, elongation at tear, and tensile strength are characterized by MM 228 silicone (ACC Silicones, Bridgwater, England). Later on, FT-IR structural tests of silicones used to produce hybrid silicon-ceramic composites were also conducted. The obtained FT-IR spectrums are shown in Figure 3.  
The spectrums obtained for each of the three silicones used to obtain hybrid silicon-ceramic composites are characterized by the occurrence of bands at wavelengths of 660-690 cm\({}^{-1}\), corresponding to bending oscillation of single carbon bonds. The presence of a low intensity band in the wavelength range 761 cm\({}^{-1}\) corresponding to bending vibrations of C-H bonds was also observed. Moreover, the presence of bands at wavelengths of 801 cm\({}^{-1}\) and 865 cm\({}^{-1}\) was observed from deformed pendulous oscillation of CH\({}_{3}\)-Si-CH\({}_{3}\) group and 1021.3 cm\({}^{-1}\) and 1093-1094 cm\({}^{-1}\)--characteristic for asymmetric tensile oscillation of Si-O-Si group. Based on the analysis of the FT-IR spectrums obtained, the presence of bands characteristic for symmetrical and asymmetrical deformation oscillations of Si-CH\({}_{3}\) groups can also be noted, with wavelengths of 1261 cm\({}^{-1}\) and 1411 cm\({}^{-1}\) respectively. The bands in the range of wave numbers 2905 cm\({}^{-1}\) and 2963 cm\({}^{-1}\) characteristic for tensile oscillations of C-H bonds indicate the presence of methyl groups in the silicone molecule. However, no bands in the range of 2160-2170 cm\({}^{-1}\) were recorded, which indicates the absence of -SiH groups in molecules of the compounds used. There were also no differences in the occurrence of individual bands, their displacement towards higher or lower wavelengths or significant changes in intensity of individual bands. The obtained FT-IR spectrums indicate that all silicones used are aliphatic compounds containing methyl groups, which can be classified as polydimethylsiloxane.  
Figure 3: FT-IR spectra of silicone elastomers used to make hybrid silicone-ceramic composites.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 5 Conclusions  
In this paper, we propose the angular circle array MIMO scheme, which systematically enhances the ability to establish and maintain communication links without adding extra following systems, thereby improving channel capacity. Our proposed scheme has less path loss under NLOS links and achieves significant improvements in channel capacity compared to both the planar circle array MIMO communication system and the SISO communication system. Our scheme can be used for ocean observation, submarine volcano early warning, and ocean oil and gas exploration. In the future, we will conduct experimental verification of a real underwater optical communication channel.  
**Author Contributions:** Conceptualization, Z.C. and Y.L.; methodology, Z.C.; software, Y.L.; validation, Z.C. and Y.L.; formal analysis, Y.L.; investigation, Z.C.; resources, Z.C.; data curation, Y.L.; writing--original draft preparation, Z.C.; writing--review and editing, Z.C.; visualization, Y.L.; supervision, X.Y.; project administration, R.Z.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grants 52471368 and 62071385.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 20: Path loss under different communication system settings in a mobile scenario: (**a**) position, (**b**) angle, and (**c**) position + angle.
## 3 Results and Discussion
### Potential Health Hazards Resulting from Bovine Meat Consumption  
The analysis of the bovine meat quality is of great relevance, given the growing global trend referring to the consumption of such foodstuffs [50]. In particular, with reference to national and supranational regulations related to bovine meat, the Council Regulation 2016/52 (Euratom) sets maximum permitted levels of radioactivity for food and feed following a nuclear accident or any other case of radiological emergency [51]. Specifically, it sets threshold levels only for anthropogenic radionuclides in various types of foodstuffs, in the particular scenario reported above. However, since the district investigated in the present study has never been affected by nuclear accidents, the assessment of any potential health risk for the population due to bovine meat consumption was carried out by calculating the effective dose for bovine meat ingestion by adult members of the population (Equation (2)), and by comparing it with the total natural radioactivity value (external + internal) for humans. More in detail, the effective dose was evaluated taking into account, as the average consumption per person, per year, 21 kg, according to the literature [50], and that the \({}^{137}\)Cs specific activity was found to be lower than the MDA in all investigated samples (see Table 2).  
The obtained total effective dose value, due to the \({}^{40}\)K radionuclide, is reported in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sampling Point** & **E\({}_{\textbf{k-40}}\)** \\ \{**(\textbf{\mu}\textbf{Sv}\textbf{ y}^{-1})**\} \\ \hline ID1 & 10.3 \\ ID2 & 10.3 \\ ID3 & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The total effective dose due to sample ingestion by adult members of the population.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Country** & \multicolumn{2}{c}{\({}^{40}\)**K Activity Concentration (Bq kg\({}^{-1}\))**} & **References** \\ \multicolumn{2}{c}{} & **(Bq kg\({}^{-1}\))** & \\ \hline Southern Italy & 78.9–88.2 & Present study \\ \hline Turkey & 99.6 & [46] \\ \hline Egypt & 44.0 & [47] \\ \hline Korea & 90.1 & [48] \\ \hline Nigeria & 265.9 & [49] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of values of \({}^{40}\)K activity concentration (Bq kg\({}^{-1}\)) in bovine meat samples of various countries.  
Worthy of note, the aforementioned doses are several orders of magnitude lower than the value of the total (external + internal) exposure to natural radioactivity for human beings, i.e., 2.4 mSv y\({}^{-1}\)[52], thus, excluding any significant radiological health risk due to the ingestion of the investigated bovine meat.
## 6 DC-Bus Design  
The DC-Bus can be designed in different ways. In general, a \"sandwich busbar\" design is used in applications, because the sandwich busbar design reduces the stray inductance value, which means reducing the additional voltage that will be reflected on the DC-Bus voltage and thus on the IGBTs, as mentioned before. The design's three-dimensional model is illustrated in Figure 15 [16].  
As shown in Figure 15, the \(+\) and \(-\) plates of the busbar are parallel to each other and separated by an insulator. DC-bus capacitors will be used as mounted on these busbar coppers. The cooler and its placement on the IGBTs are shown in Figure 16. As shown in Figure 16, DC-Bus capacitors are placed on the DC busbar located on the appropriate IGBT that provides input parallel to the cooler by forming a parallel group. The desired capacitance value is created in this way [16].  
Figure 14: (**a**) Axial-flow fan; (**b**) Radial-flow fan; (**c**) Cross-flow fan.  
Figure 15: Sandwich busbar design.  
The current resistance is increased by connecting the capacitors in parallel instead of current flowing through the DC bus, interfering with a single capacitor. The surface area and thickness of the DC-bus are designed according to the current to pass. It is manufactured for a current passage of 300 A. If the capacitor's voltage-current values are not at values appropriate to all be connected in parallel, the sandwich busbar can be designed both in series and parallel.
## 1 Introduction
To demonstrate the validity of the proposed method, according to our previous studies [14; 21], three representative DNN-based medical image classifications were considered because of the importance of adversarial attacks on medical machine learning [11], that is, skin cancer classification using skin lesion images [22], diabetic macular edema classification using optical coherence tomography (OCT) images [23], and pneumonia classification using chest X-ray images [23]. Instead of the white-box conditions in [14; 21], we evaluated how much DNN models with several architectures are vulnerable to black-box UAPs for both nontargeted and targeted attacks.
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 3 Proposed MPPT Algorithm and Irradiance Estimator
### Computational Complexity  
The pseudocode of the proposed NMPPT algorithm is given in Algorithm 1. If we assume that the hidden layer has \(M\) neurons, then the NMPPT method will require \(5M+2\) multiplications and \(5M+2\) additions. Moreover, only one multiplication and two additions are needed for implementation of the NI&I estimator, while the NGE requires \(M\) additions and \(M\) multiplications. It is also important to highlight that the computational complexity will be approximately increased \(P\) times if all algorithm's steps are iterated \(P\) times. For example, if \(M=4\) and \(P=2\), then the proposed algorithm will require only 42 additions and 44 multiplications. The model-based algorithm that is used for comparison purposes requires 32 additions and 66 multiplications [29].  
```
1:\(V(0)\), \(\hat{G}(0)\)
2:for each time instant\(k\)
3: Measure \(I_{k},V_{k,1},T_{k}\)
4:for\(i=1,\ldots,P\)do
5:\(\mathbf{u}=\left[\begin{array}{cc}\hat{G}_{k,i}&T_{k}&V_{k,i}\\ \end{array}\right]^{T}\)
6:\(\hat{I}_{k,i}=net(\hat{G}_{k,i},T_{k},V_{k,i})\)
7:\(\frac{\partial V_{i}}{\partial V_{k}}=\hat{I}_{k,i}+V_{k,i}\left(\mathbf{W}_{ 13}^{T}\odot\mathbf{W}_{2}\right)\tanh^{\prime}\left(\mathbf{W}_{1}\mathbf{u} +\mathbf{b}_{1}\right)\)
8:\(V_{k,i+1}=V_{k,i}+\mu\frac{\partial\hat{I}_{k,i}}{\partial V_{k}^{2}}\)
9:\(e_{k,i}=I_{k}-\hat{I}_{k,i}\)
10:\(\hat{G}_{k,i+1}=\hat{G}_{k,i}+\gamma e_{k,i}\)
11:endfor
12:\(\hat{G}_{k+1,1}=\hat{G}_{k,P}\)
```  
**Algorithm 1** The Proposed Algorithm.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 3 Conclusions  
This work has reviewed recent studies on the preparation and application of soft actuators controlled by electrical stimuli, i.e., ionic polymer metal composites, dielectric electroactive polymers, and magnetorheological elastomers. Stimulating these materials with electricity is a very convenient way to get them to work because this power source is the most readily available of all others. In addition, softness, flexibility, and small thickness, thanks to which they are light, and the ease of changing the properties by appropriate selection of polymer (using compounds with different structures and with different amounts) or filler, make soft actuators promising materials for applications in advanced automation and robotic systems. The brief summary of the advantages and disadvantages of all described materials is given in Table 1. The types of elastomer (flexible, soft polymer), as well as types of filler used for obtaining these kinds of materials and their improvements, are presented. Additionally, improvement by modification of other parts of soft actuators as electrodes is shown. In the latter, the modification of Nafion with carbon nanotubes or the use of conductive polymers (e.g., PEDOT) can replace metal electrodes for polymer membranes in IPMC actuators. The other way is electroless plating of the metal electrode on the surface of the ionomer using the chemical reduction of metal cations such as Ag\({}^{+}\) (much cheaper than platinum or palladium) or lately other metals such as Cu or Ga/In. The future aspect of an IPMC is to increase the force versus volume ratio. In DEAP materials, multilayer systems decrease operating voltage and extend the lifetime of the prepared actuator. The improvement of the membrane properties can be realized by the addition of various types of fillers, reinforcements, or plasticizers that can modify mechanical and electrical properties, output force, deformation rate, or displacement of known polymers. Another way to modify these materials is the synthesis of new types of elastomers with polymerization or copolymerization. This is an easy way to prepare membranes with designed properties using e.g., an appropriate selection of monomers and their concentration for synthesis. The future aspect of DEAPs is to decrease the operating voltage and increase the permittivity without losing the stretch ratio. Furthermore, the possibility of developing new actuators with custom shapes seems to be insufficiently used. The most important challenge in MREs is to improve their permeability (so as to improve its possibility for energy storage) without losing flexibility. In this area, the anisotropic character of magnetic material is exploited. Furthermore, in a robotics system like a gripper, it is important to overcome the low response of MREs if the source of the magnetic field is far away. Currently, the most promising method of preparing soft actuators seems to be 3D printing. It offers great possibilities for the design of actuators of various shapes, sizes, and
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 3 Results and Discussion
### Quantitative Analysis with PLS Regression  
To quantify the soy protein contents in the mixtures, four models were developed corresponding to the four different soy products for each of two NIR spectra. The number of latent variables (LV) used in each model was chosen to optimize the model performance and minimize the model errors. The results of \(R_{\mathrm{c}}{}^{2}\), \(R_{\mathrm{cv}}{}^{2}\), RMSEC and RMSECV of different models developed by the dispersive and FT-NIR spectral data were compared in Tables 1 and 2. Overall, the RMSEC are all low (<0.021) and all the predicted results had a high correlation of the calibration data (\(R_{\mathrm{c}}{}^{2}\) = 0.99). As expected, PLS regression with dispersive spectral data for toasted soy grits had the lowest \(R_{\mathrm{cv}}{}^{2}\) value (0.91) and the highest RMSECV value (0.1164). The reason is that toasted soy grits mixtures showed the lowest correlation with the dispersive spectra (Figure 1a). Generally, the FT-NIR system gave better results for the four soy types than the dispersive system by showing a higher \(R_{\mathrm{cv}}{}^{2}\) (0.98-0.99) and lower RMSECV (0.013-0.057). Also, as Figure 1b indicated, the results of toasted soy grits and TVP showed slightly worse values than the other two soy types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soy Types** & **LV** & \(R_{\mathrm{c}}{}^{2}\) & **RMSEC** & \(R_{\mathrm{cv}}{}^{2}\) & **RMSECV** \\ \hline Arconet & 6 & 0.99 & 0.0050 & 0.99 & 0.0135 \\ Toasted & 9 & 0.99 & 0.0035 & 0.91 & 0.1164 \\ Profam & 2 & 0.99 & 0.0204 & 0.98 & 0.0249 \\ TVP & 3 & 0.99 & 0.0130 & 0.99 & 0.0204 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of PLS regression based on dispersive spectra, PLS: partial least squares; RMSEC and RMSECV: root mean square error of calibration and cross-validation; TVP: textured vegetable protein; Toasted: toasted soy grits. LV: latent variables.  
Figure 2: Dispersive NIR and FT-NIR spectra of ground beef (0%) and soy samples (100%) in the region of 1000–2500 nm.  
In order to compare the actual soy contents and predicted soy contents by cross-validated models, Figure 3 reveals the quantitative ability of the two spectra visually. Figure 3a,b presents the correlation diagram between actual soy contents and NIR-predicted soy contents for different types of soy products using models developed by dispersive NIR and FT-NIR spectra, respectively. As shown, Figure 3b presents excellent prediction ability regardless of the soy product. Although the correlation coefficients for the two kinds of NIR models were both close to one, the FT-NIR spectra apparently worked better in the quantification of soy products content in ground beef.