esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Models  
Figure 1 gives an example of the schematic diagram of the mobile charging WRSN model, including one sink node (SN), charging station (CS), MC and a set of \(N\) rechargeable randomly deployed nodes \(S=\{s_{1},s_{2},\ldots,s_{N}\}\). The positions of the nodes \((x_{s_{j}},y_{s_{j}})\) are fixed and known \(1\leq i\leq N\). CS is responsible for replenishing energy for MC. SN collects the real-time information of all nodes and formulates the mobile charging sequence. According to the established charging scheduling sequence, MC starts from the CS, moves near to the node for charging one by one according to the schduling decision, then returns to the depar  
ture when all nodes have been charged. We assume that the network deployment scenarios are barrier-free, and accessible. Symbols in this paper are summarized in Nomenclature.  
**Definition 1**.: _Charging time step, CTS: the time step when MC is ready to charge or has finishing charging._  
_The following describes the model assumptions and conventions from three aspects: nodes, MC, and network sensing coverage._  
**Assumption 1**.: _Sensor Node: Each node has the same function and can predict their own power consumption and residual energy according to their data acquisition frequency and data transmission rate [12], which is expressed as \(V_{cs}=\{v_{cs}^{1},v_{cs^{\prime}}^{2},\ldots,v_{cs}^{N}\}\) and \(E_{r}=\{e_{r}^{1},e_{r}^{2},\ldots,e_{r}^{N}\}\). In this paper, nodes will stop working if they run out of energy and resume working after being charged._  
At the m CTS, the real-time residual energy of the WRSNs is expressed as:  
\[E_{r}(m)=E_{r}(m-1)-V_{cs}t_{w}(m-1), \tag{1}\]  
where \(t_{w}(m)\) is the total working time of MC, including the MC moving time, \(t_{l}(m-1)\), and charging time, \(t_{c}(m-1)\), at the (m-1) CTS, which is represented by \(t_{w}(m)=t_{l}(m-1)+t_{c}(m-1)\). Assume that \(\Phi\) is the MC sequence. At the m CTS, \(s_{\Phi(m)}\) is selected to charge, and we know that \(t_{l}(m)=d(m)/v_{m}\) and \(t_{c}(m)=e_{m}-e_{r}^{\Phi(m)}(m)/v_{c}\). We can see that \(d(m)\) is the Euclidean distance between \(s_{\Phi(m-1)}\) and \(s_{\Phi(m)}\), and \(D(m)\) is the set of distances between all nodes.
## 5 Conclusions  
In this paper, a new temperature sensor clustering method is derived for the selection of sensor locations. An indicator comprehensively considering both the Euclidean distance and the correlation coefficient was proposed to reflect the difference between them. Then, the indicator was expressed as the form of distance matrix, which was used in hierarchical clustering. A genetic algorithm (GA) was used to optimize the parameters in clustering, and the fitness function of the algorithm was also rebuilt. After the optimization by GA, the proper parameters for clustering were derived, as well as the final selection of the temperature sensors. Finally, the effectiveness of the method was verified and compared to some other temperature sensor selection method through an experiment on a heavy floor-type milling machine tool. Conclusions can be drawn according to the experiment results:  
The RMSE of the method is 0.0118 mm at 1000 rpm and 0.0200 mm at near-real working conditions. The maximum deviation of the method is 0.0227 mm at 1000 rpm and 0.0434 mm at the near-real conditions. The accuracy is better than all other comparison groups and also meets the demand of manufacturing. It can also be found that all the groups appeared overestimating at a much lower rotation speed, while the method in this paper can better control this phenomenon. These above conclusions proved the effectiveness of the method to be used in the thermal error modeling of the machine tools.  
Additionally, it can be found that, on the basis of a proper temperature sensor selection, a simple model can also guarantee an accurate and stable prediction of thermal errors. Actually, a much more  
Figure 10: Comparison of the accuracy.  
complex error model would lead to more instability, influencing its application in engineering. This conclusion emphasizes the importance of temperature sensor selection for thermal error modeling.  
Future work will increase the initial number of temperature sensors for the method, adjusting the structure of the combination of the Euclidian distance and correlation coefficient (not just linear superposition), and applying the method to another machine tool.  
This work was supported by the National Natural Science Foundation of China (No. 51675292), and the National Science and Technology Major Project of China (No. 2015 ZX04001002 & No. 2016 ZX04004004).  
**Author Contributions:** [PERSON] and [PERSON] proposed the method; [PERSON], [PERSON], and [PERSON] performed the experiment; [PERSON], [PERSON], and [PERSON] analyzed the experimental data; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology
### Fine-Tuning Phase  
The SEI is a classification task. Therefore, when the SSL phase is over, the encoder network that has learned universal feature representations from unlabeled radiation source signal data can be used as a feature extraction network. In the proposed framework, adversarial perturbations are injected into the output space of the encoder in order to enhance the robustness to the feature distribution shift. Finally, a linear classifier is added to adapt to different downstream classification tasks.  
Specifically, after the SSL phase, freeze parameters of the encoder \(f(\cdot,\theta)\) as the feature extraction network, where \(\theta\) is the frozen parameters. Then, VAT is introduced in the feature space. The specific process is as follows: first of all, sample random noise \(d\sim\mathcal{N}(0,\xi^{2}I)\), where \(\xi=10^{-6}\) for initialization. Then, for the feature vector \(\mathbf{h}=f(\mathbf{x};\theta)\in\mathbb{R}^{256}\) output by the frozen encoder, the adversarial direction is generated through iterative gradient ascent:  
\[\mathbf{r}_{adv}=\underset{\|\mathbf{r}\|_{2}\leq\varepsilon}{argmax}\,D_{KL} (p(y|\mathbf{h})\|p(y|\mathbf{h}+\mathbf{r})) \tag{15}\]  
where \(\varepsilon\) controls perturbation magnitude, \(p(y|\mathbf{h})\) denotes original prediction distribution, \(p(y|\mathbf{h}+\mathbf{r})\) denotes perturbed prediction distribution, \"\(\|\)\" denotes the KL divergence between two probability distributions, and KL divergence measures the difference in classification distribution before and after feature perturbation. Then calculate the VAT loss:  
\[\mathcal{L}_{VAT}=\mathbb{E}_{x}[D_{KL}(p(y|\mathbf{h})\|p(y|\mathbf{h}+ \mathbf{r}_{adv}))] \tag{16}\]  
Finally, a lightweight network \(q_{\psi}\) is appended to adapt features for downstream tasks:  
\[\mathbf{h}_{enh}=Dropout(GeLU(LayerNorm(\mathbf{W}_{f}\mathbf{h}+\mathbf{b}_{f }))) \tag{17}\]  
where \(\mathbf{W}_{f}\), \(\mathbf{b}_{f}\), and \(\mathbf{h}\) is the frozen encoder output. Furthermore, a linear layer \(c_{\omega}\) is used as a classifier to map the enhanced features to the class logic.  
The total loss combines cross-entropy and VAT regularization:  
\[\mathcal{L}_{CE}=\frac{1}{N}\sum_{i=1}^{N}-\log p(y_{i}|\mathbf{x}_{i}) \tag{18}\]  
\[\mathcal{L}_{total}=\mathcal{L}_{CE}+\lambda\mathcal{L}_{VAT} \tag{19}\]  
where \(\lambda\) is a weighting factor that determines the intensity of regularization, \(y\) denotes the ground-truth label.
## 3 Results and Discussion
### Surface Roughness  
Our surface roughness measurements indicate that the toy figure samples produced by plastic injection molding consistently exhibit lower Ra values and smoother surfaces compared with the 3D-printed samples. As detailed in Table 4, the ABS sample produced via plastic injection molding demonstrated the smoothest surface, with an Ra value of 0.16. In contrast, the ABS 3D-printed samples had a surface roughness of 1.43 Ra. It is important to note that the toy part was tested in its as-printed condition, without any post-processing or surface enhancement. We hypothesize that the significant difference in surface roughness, even for samples from the same material group, is primarily due to the inherent characteristics of the production methods.  
When comparing the FDM-produced samples with the injection-molded design in this study, the PLA 3D-printed samples had a surface roughness of 3.05 Ra. This indicates that PLA exhibits higher surface roughness than ABS FDM samples. A detailed analysis of the SEM images presented in Figure 8 corroborates this finding, revealing that PLA samples exhibit significantly more pronounced internal structure cavities in comparison with ABS.
## 5 Conclusions  
This paper presents a modeling method of the BPNN-MDSG digital twin ATF compressor system optimized by a BP neural network. The key parameters \(H\) and \(F\) are obtained from the dimensionless performance curve of the actual compressor. The compressor digital twin modeling obtained by BPNN-MDSG can simulate the characteristics of surge conditions with high precision and in real time, as well as simulate the full working conditions.  
Taking the ATF compressor system as an example, one steady state condition and two unsteady states are simulated and verified by BPNN-MDSG digital twin modeling in virtual space. The average flow error is less than 3%, and the average pressure error is less than 1%. The average flow error under dynamic conditions is less than 5%, and the average pressure error is less than 3%. The experimental results show that the real-time simulation of the BPNN-MDSG method is reliable.  
In the virtual space obtained by BPNN-MDSG modeling, the full-condition simulation and anti-surge characteristics of the ATF compressor system are studied. Taking the starting condition as an example, the operation characteristics of the anti-surge valve are discussed. The characteristics of surge induced by different conditions are obtained. Through the simulation and characteristic prediction of all conditions in virtual space, the full-condition anti-surge operation map of the ATF system is obtained. The digital twin modeling and virtual simulation provide guidance for the safe and energy-efficient operation of the ATF system.  
Conceptualization: X.W. and X.C.; methodology, Y.S.; software, Y.S.; validation, Y.S., X.C. and X.W.; formal analysis, Y.S.; investigation, X.C.; resources, X.W.; data curation, X.W.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S. and X.C.; visualization, Y.S.; supervision, X.C.; project administration, X.C.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Stable Support Research Project of AECC Sichuan Gas Turbine Establishment, digital technology for the operation of large-scale pipeline network equipment based on multi-information fusion (No. CJCZ-0302-05).  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy restrictions.  
Author [PERSON] is affiliated with AECC Sichuan Gas Turbine Establishment but confirms that no commercial interests influenced this study. His role as a PhD student at Xi'an Jiaotong University further supports the academic nature of his contribution. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Coating Materials with Embedded Antibiotics and Other Antibofilm Agents
### Biofilm Control by Inhibitors or Modulators of Bacterial Signaling Processes
#### 5.5.2 Antibiofilm Coatings Are Interfering with c-di-GMP Signaling
[72] took advantage of the unique adhesive ability of PDA to nicely use a PDA-coated glass substrate as a versatile scaffold for subsequent surface functionalization by grafting of low-fouling polymer (PEGylation) and introduction of _N_-diazeniumdiolate moieties as NO precursors. PDA is rich in quinoide and indoline species (Scheme 6). The grafting of the hydrophilic low-fouling polymer is achieved through Michael addition of quinone functionalities with amino or thiol groups or by Schiff base formation, while the secondary amine indoline groups form _N_-diazeniumdiolates on purging with NO gas. The NO release rate is tunable by controlling the thickness of the PDA film. The antibiofilm activity of the NO releasing coatings was tested using  
Figure 6: The chemical structure of dibutylhexyldiamine diazeniumdiolate. Gram-negative _P. aeruginosa_ (i.e., wild-type PAO1 and multidrug-resistant PA37) and Gram-positive _S. aureus_ (ATCC 29213). Biofilm development on _N_-diazeniumdiolates functionalized substrates was inhibited by 96 and 70% after exposure to bacterial culture solution for 24 and 36 h, respectively. The authors ascribe the excellent outcome to synergic combination of the antifouling properties imparted by pegylation and antibacterial activity of NO. The killing efficiencies of this coatings against surface attached PAO1, PA37, and _S. aureus_ bacteria were, 99.9, 97, and 99%, respectively. The other enzymes involved in controlling of c-di-GMP intracellular levels, namely DGCs, also represent a potentially promising target for antibiofilm drugs to be exploited. Based on an in silico pharmacophore screen study, [PERSON] et al. [73] were the first scientists to identify four low molecular mass lead compounds acting as inhibitors of DGC enzymes. All four DGC inhibitors (LP 3134, LP 3145, LP 1062, LP 4010) prevented biofilm formation by _P. aeruginosa_ and _A. baumannii_ in a continuous-flow system and dispersed _P. aeruginosa_ preformed biofilms. Only LP 3134 was able to disperse preformed _A. baumannii_ biofilms. The authors also conducted studies to determine the impact of the four lead compounds onto the first step in biofilm development, namely the primary adhesion of bacteria to a surface. They found out that only LP 3134 effectively inhibits _P. aeruginosa_ adherence to a surface, while none of the compounds could impede _A. baumannii_ adherence. Growing _P. aeruginosa_ as a biofilm on 14-French urethral catheters in the presence and absence of LP 3134 and LP 3145, the authors proved the ability of both compounds to reduce biofilm formation on catheters surface. When cytotoxicity to eukaryotic cells was assessed, only LP 3134 and LP 4010 showed to lack of toxicity on human keratinocytes. Based on all the above results, the authors draw the conclusion that LP 3134 is the most promising as a drug candidate DSCs inhibitor.
## 3 Results and Discussions
### Surface Characterization  
Infrared spectroscopy is the main instrumental technique used to illustrate the functional groups on AC [45]. The FTIR spectra of Raw-RGL and AC-RGL adsorbents are shown in Figure 4. The spectrum of Raw-RGL shows six major functional groups with a strong, broad vibration at 3287 cm\({}^{-1}\) due to R-O-H vibrations [46]. The peaks at 2924 and 2842 cm\({}^{-1}\) are due to aliphatic groups indicated by the vibration of C-H stretching [38]. The absorbance peak between 1731 and 1623 cm\({}^{-1}\) is attributed to the vibration of carbonyl (-C=O) in the Raw-RGL [47]. The other peak at 1025 cm\({}^{-1}\) is characteristic of C-OH vibration [48]. A different pattern is observed on AC-RGL whereby the peak at 1558 cm\({}^{-1}\) is associated with stretching vibrations of -C=O for primary and secondary amides, usually between 1630 and 1520 cm\({}^{-1}\)[47]. A peak at 1318 cm\({}^{-1}\) indicates -O-C-O- vibrations [47]. AC-RGL have fewer peaks because most frequencies or functional groups are absent compared with Raw-RGL. The disappearance of the peaks in AC-RGL might be ascribed to the loss of volatile matter at carbonization at 600 \({}^{\circ}\)C.
## 2 Materials and Methods
### Simulation Setup for Dielectric Heating  
The computational model is developed to analyze the interaction between electromagnetic waves and biological tissue, represented by a fish model. A horn antenna is used to emit microwave energy at 900 W feed power, operating at a frequency of 2.45 GHz, and is strategically positioned to optimize energy delivery to the fish sample. As illustrated in Figure 3, the distance between the antenna and the fish is a key parameter that significantly affects electromagnetic field distribution and the resulting dielectric heating patterns. The model considers four distances, 3 cm, 6 cm, 9 cm, and 12 cm, to evaluate the effect of energy absorption at varying proximities.  
In CST Studio Suite, the simulation setup involves defining the horn antenna geometry and the fish tissue model. The antenna is designed with precise aperture size and flare angle to ensure focused microwave energy delivery. The fish model consists of three distinct layers--scales, skin, and muscle--each assigned specific dielectric properties, including the dielectric constant and loss factor, which are crucial for simulating realistic electromagnetic interactions. The model accounts for the different thicknesses of each layer, with the scales  
Figure 3: Simulation setup for dielectric heating using a horn antenna.  
at 1 mm, the skin at 0.5 mm, and the muscle at 18.5 mm, as shown in the figure. To simulate an open environment, Perfectly Matched Layers (PML) are applied as boundary conditions to eliminate reflections at the domain edges. The horn antenna is excited using a waveguide feed in the TE10 mode, ensuring a well-controlled electromagnetic wave emission. A fine computational mesh is generated around both the fish model and the antenna, especially in regions with high field intensity variations, to achieve accurate results.
## 4 Tuning Parameters and Results Assessment
### Parameter Optimization and Accuracy Assessment
#### 4.2.3 The DT Algorithm  
DT is one of the most useful and simple ML algorithms for classification and regression [36]. However, irrelevant attributes within the training data can result in overfitting. This problem can be resolved using the CV method [37]. To optimize the accuracy of DT, the maximum depth of the tree (max-depth) needs to be attained. In the next step, we analyzed the optimal values for the two cases of classification and regression. The max-depth values were manually chosen using the precision score and CV score shown in Figures 17 and 18.  
Figure 16: The mean score in the center area of: (**a**) positioning accuracy and (**b**) CV.  
* Classification Function  
As can be seen in Figure 17, the mean precision score and CV score gradually improve as the max-depth value increases. However, both these scores are nearly stable when max-depth \(\geq\) 8. To avoid time-consuming computer processing, the smallest possible value is chosen. In this case, max-depth = 8 was the best choice.  
* Regression Function  
In Figure 18a, the mean and standard deviation of the positioning error were inversely proportional to the max-depth of the tree when the max-depth was in the range of 2 to 8. Then the errors remained almost unchanged as we continued to increase the max-depth. As mentioned earlier, higher values of max-depth result in longer execution times. We therefore chose max-depth = 8 for the whole floor. Similarly, we chose max-depth = 6 for the center area (Figure 18b), and max-depth = 8 for the edge area (Figure 18c).
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
## 3 MIMO Performance of the Proposed Antenna
\[H=\sqrt{\rho_{scale,RX}}\;H_{ild}\sqrt{\rho_{scale,TX}} \tag{10}\]  
where \(H_{ild}\) is a 4 \(\times\) 4 matrix whose elemental values are independent and identically distributed complex Gaussian random variables, \(\rho_{scale,TX}\) is the calculated 4 \(\times\) 4 matrix at the transmitter end with the consideration of null ECC and 100 percent efficiency, and \(\rho_{scale,RX}\) is the calculated 4 \(\times\) 4 matrix by employing measured ECC and efficiency as explained in [51]. Further the \(\rho_{scale,RX}\) is calculated using Equation (11). \[\rho_{scale,RX}=\sqrt{n_{total}}\rho_{RX}\sqrt{n_{total}} \tag{11}\]  
The variables used in Equation (11) are \(\rho_{RX}\) and \(n_{total}\), which represents ECC and total efficiencies of receiving antennas. In order to estimate the ergodic channel capacity C, Equation (12) [52; 53] is used. \[C=E\bigg{\{}log_{2}\bigg{[}det\bigg{(}I+\frac{SNR}{n_{T}}HH^{H}\bigg{)}\bigg{]} \bigg{\}} \tag{12}\]  
Here, the variables in Equation (12), \(H^{H}\), SNR, \(n_{T}\), E and I, characterize Hermitian transpose, signal-to-noise ratio, number of antennas at the transmitter side, expectation in regard to different channel realizations, and 4 \(\times\) 4 identity matrix, respectively. In this article, the ideal value of CC is computed using an average of more than 10,000 Rayleigh fading realizations with a 10 dB/20 dB SNR, null ECCs, and full efficiency of antennas. The ideal CC for 4 \(\times\) 4 MIMO and 2 \(\times\) 2 MIMO are 22.2 bps/Hz and 11.3 bps/Hz at SNR = 20 dB, respectively; whereas, for 4 \(\times\) 4 MIMO and 2 \(\times\) 2 MIMO, the ideal CC is 10.94 bps/Hz and 5.53 bps/Hz at SNR = 10 dB, respectively. The CC should be at least 60% of the ideal value for a MIMO antenna to work well. The simulated CC over the operating frequency bands OFB1, OFB2, and OFB3 is 4.53-8.95 bps/Hz, 7.46-8.70 bps/Hz, and 7.20-8.18 bps/Hz, respectively, at SNR = 10 dB, whereas the CC at SNR = 20 dB is 12.60-19.57 bps/Hz, 17.46-19.21 bps/Hz, and 17.20-18.56 bps/Hz, respectively. The measured CC over the operating frequency bands OFB1, OFB2, and OFB3 is 4.35-8.62 bps/Hz, 6.89-8.68 bps/Hz, 7.02-8.01 bps/Hz, respectively, at SNR = 10 dB; whereas, the CC at SNR = 20 dB is 12.13-19.13 bps/Hz, 16.55-19.18 bps/Hz, and 16.92-18.44 bps/Hz, respectively. The channel capacity versus frequency plot is depicted in Figure 10b.