esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 2 Deep Small-World Neural Network (DSWNN)
### Fine-Tuning of the DSWNN Parameters  
After the layer-wise pre-training and add-edge small-world transformation, the DSWNN model will be fine-tuned by using BP algorithm. BP is a classical method commonly used for supervised learning to improve the representation of data features and optimize the parameters of hidden layers in the fine-tuning. In this process, the initial parameters of the DSWNN model are composed of the pre-training parameters (weights and bias) and the edge-weights obtained by the small-world transformation. Because the fine-tuning only completes the local search based on these superior initial parameters, the convergence time of the optimization is significantly shortened in this process. After fine-tuning, the globally optimized parameters of the DSWNN model are obtained.  
In this paper, the _tanh_ function is regarded as the activation function for hidden layers, and the Softmax as the activation function for the top classifier layer. Moreover, the Cross Entropy [28] shown in Equation (13) is chosen as the cost function \(C\) to measure error.  
\[C=-\frac{1}{k}\sum_{j=1}^{k}[t_{k}\log(a_{k})+(1-t_{k})\log(1-a_{k})] \tag{13}\]  
where, \(k\) is the number of neurons in the output layer, \(t\) is the expected output.  
To summarize, the training process of the DSWNN model includes three steps.  
1. Pre-training: In the unsupervised case, each RBM of DSWNN is trained one by one to mine the feature information of the unlabeled input data.
2. Small-world transformation: Lock the weight values obtained by pre-training, add new edges according to the probability \(p\), and randomly assign the weight values to the newly added edges.  
3. Fine-tuning: Add a classification layer in the last layer of DSWNN to receive the output eigenvector from the last RBM. Train the entity DSWNN model with labeled data in the supervised case and the network global weights are adjusted by the BP algorithm.  
Figure 6 gives the three training processes of the DSWNN model. The process of RBM training can be regarded as the initialization of the weight parameters for the DSWNN model, which overcomes the shortcomings of the network easily falling into local optimum and long training time due to the random initialization of the weight parameters. For the detection of WT abnormality, a large number of unlabeled SCADA data can be used for pre-training, and a small number of labeled data can be used for fine-tuning. In this way, the trained DSWNN is more optimized than the deep auto-encoder network or BP network alone.
## 2 Results and Discussion
### MazEF  
The MazEF (ma-ze means \"what is it?\" in Hebrew [61]) family is among the most well characterized TA families [2]. However, the role of the MazF toxin has been debated whether it is bacteriostatic [62; 17; 63] or bactericidal, where the cell reaches a \"point of no return\" and undergoes the PCD [64; 15; 65; 66]. [PERSON] et al. demonstrated that the PCD is mediated by a quorum-sensing signal molecule, a pentapeptide known as the extracellular death factor (EDF) [67], representing another example on the integration of TA modules into the cellular signaling pathways mentioned above. The MazEF TA family includes different TA composites, since MazF may pair with antitoxins other than MazE, a phenomenon described as a \"mix and match\" between toxin and antitoxin superfamilies [68]. For instance, MazF may pair with antitoxins harboring the RHH or AbrB (AidB regulator domain) domains. The TA family of these different composites is also classified as MazEF [69] (for details, the reader is also referred to [2; 22; 70]). Bacterial species may have more than one pair of MazEF homologs [71]. This is the case with the _Geobacillus_ strains analyzed in this study, as discussed below.
## Materials and Methods  
### Soil Sampling and Microbial Isolation  
For bacterial isolation, 50 mL of standard glycerol (Vetec, Rio de Janeiro, RJ, Brazil) and crude glycerol (kindly provided by Cesbra Quimica S/A, Volta Redonda, RJ, Brazil) (65% purity) were spilled onto the conserved (native) Cerrado soil in two different sampling points at the Embrapa Cenargon Experimental Station Fazenda Sucupira (Brasilia, Federal District, Brazil): _Amaranthus_ ssp. point (S: 15\({}^{\circ}\)54\({}^{\prime}\)46.3\({}^{\prime\prime}\) W: 048\({}^{\circ}\)02\({}^{\prime}\)2.1\({}^{\prime\prime}\)) and dam point (S: 15\({}^{\circ}\)55\({}^{\prime}\)03 0.80\({}^{\prime\prime}\) W: 048\({}^{\circ}\)01\({}^{\prime}\)19.7\({}^{\prime\prime}\)). In each location, the spill point of each glycerin was separated by approximately 1 m. Samples of the glycerin-enriched soils (collected from the uppermost layer; 0-10 cm deep) were collected after two months, placed in a sterile bag, and stored at 25 \({}^{\circ}\)C until bacterial isolation.  
The screening of microorganisms capable of metabolizing commercial and crude glycerol was performed using tryptic soy agar (TSA, KASVI) culture medium, containing 4% each of glycerol and Benomyl fungicide solution (50 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)) to inhibit the fungal contaminants. The bacterial isolation was performed through sequential dilution and plating steps on the agar medium. The isolates were purified by successive restreak, and pure cultures were maintained at \(-\)80 \({}^{\circ}\)C in glycerol (20%), used as a cryoprotectant agent.  
Eighteen bacterial strains isolated from soil enrichment with pure and crude glycerin were analyzed to determine the secreted metabolites, focusing on lactic acid. All the bacterial strains were submitted to fermentative tests, and only the one with the highest efficiency in the production of lactic acid was identified and reported in the present work.
## 3 Experimental Verification  
### Detection System  
To validate the feasibility and accuracy of the proposed algorithm in this paper, we conducted a coaxiality simulation experiment. According to the experimental requirements, a coaxiality detection system was designed, as shown in Figure 15. The detection system mainly consisted of an electric rotary platform, an industrial camera, a lens, an LED ring light source, a stepper motor driver and controller, and a computer, among other components.  
The industrial camera used in the experiment was the Hikvision MV-CU120-10 GC color CMOS array camera (Industrial Camera and Lens Sourcing with Changchun, China), with a resolution of 3036 pixels \(\times\) 4024 pixels. The lens was the Hikvision MVL-KF5028M-12 MP, which is a high-definition, ultra-low distortion lens. The light source was an LED ring light with a diameter of 100 mm, and its brightness was adjustable.  
The electric rotating platform drove the measured part to perform rotational motion, and the driver sent pulse signals to the stepper motor, which was set to rotate 9\({}^{\circ}\), pause for 0.5 s, and repeat 40 times. A total of 40 images were collected.
## 2 System Model  
### Signal Model  
In an underwater channel, the signal received by the communication receiver is the sum of multiple proportional copies of the transmitted signal, each with a different delay and attenuation. Assuming that the difference in the angle of incidence reached by the multiple sound lines generated by the multipath effect is small, each path has the same Doppler factor \(\mu_{p}\), \(N_{p}\) is the number of paths, and \(A_{p}\) and \(\tau_{p}\) are the attenuation coefficient and delay of the \(p\) th path; then, the time-domain shock response of the time-varying coherent multipath channel can be expressed as  
\[h\big{(}\tau_{p},t\big{)}=\sum_{p=1}^{N_{p}}A_{p}(t)\delta\big{[}t-\tau_{p}(t) \big{]}=\sum_{p=1}^{N_{p}}A_{p}(t)\delta\big{[}\big{(}1+\mu_{p}\big{)}t-\tau_{p} \big{]} \tag{1}\]  
where the Doppler factor \(\mu_{p}\approx v_{p}/v_{c}\), and \(v_{p}\) and \(v_{c}\) are the relative velocity and underwater acoustic propagation velocity, respectively. The Doppler factor is on the order of \(10^{-3}\) or \(10^{-4}\)[32].  
This paper uses binary phase shift keying (BPSK) modulation, and the SC signal transmitted in the passband can be expressed as  
\[s(t)=Re\Bigg{\{}\sum_{k=0}^{K-1}x_{k}g(t-kT_{0})e^{j2\pi f,t}\Bigg{\}},t\in[0,KT_{0}] \tag{2}\]where \(Re\{\cdot\}\) is the real part, \(x_{k}\) is the \(k\) th symbol modulated by the BPSK (the total number of symbols is \(K\)), \(g(t)\) is the raised cosine pulse waveform, \(f_{c}\) is the carrier frequency, and \(T_{0}\) is the duration of a symbol. After undergoing an underwater acoustic channel, the SC signal is affected by the time-varying coherent multipath channel and ambient noise. Therefore, the received signal \(\widetilde{r}(t)\) can be expressed as  
\[\widetilde{r}(t)=h(\tau_{p},t)*s(t)+\widetilde{w}(t)=\sum\limits_{p=1}^{N_{p} }A_{p}(t)s\big{[}\big{(}1+\mu_{p}\big{)}t-\tau_{p}\big{]}+\widetilde{w}(t) \tag{3}\]  
where \(\widetilde{w}(t)\) is the additive ambient background noise. The received signal is down-regulated, and a low-pass filter (LPF) is used to obtain the baseband SC received signal, which is expressed as  
\[\begin{array}{l}r(t)=LPF\big{[}\widetilde{r}(t)e^{-j2\pi f_{c}t}\big{]}\\ \approx\sum\limits_{p=1}^{N_{p}}\bigg{\{}A_{p}(t)e^{j2\pi f_{c}(\mu_{p}t-\tau_{ p})}\sum\limits_{k=0}^{K-1}x_{k}g\big{[}\big{(}1+\mu_{p}\big{)}t-kT_{0}-\tau_{p} \big{]}\bigg{\}}\\ \quad\quad+w(t)\end{array} \tag{4}\]  
Therefore, the signal in the SC communication system is modeled as a multipath time-varying underwater acoustic channel affected by the Doppler Effect and environmental noise. In this paper, the transmission signal consists of a BPSK modulation signal.
## 3 Results and Discussion
### Effect of Compression Direction on Overall Performance of Inlet
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Inlet** & \(\phi\) & \(\sigma_{inlet}\) \\ \hline Normal inlet & 0.818 & 0.543 \\ Inverted inlet & 0.886 & 0.481 \\ Difference (\%) & 8.24\% & \(-\)11.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance comparison. Figure 7: Comparison of normal and inverted inlet layout. Furthermore, the inlet layout significantly influences the flow structure at the throat. To investigate the differences in flow characteristics between the normal and inverted inlet layouts, we meticulously plotted the flow field Mach number contours for a comparative analysis, as shown in Figure 9. In Figure 9,b, the left side shows a normal inlet layout, and the right side shows an inverted intake layout. The analysis reveals that in the throat region, the mainstream area of the normal inlet layout is broader, while the low-speed region near its lower surface is relatively smaller, resulting in a more uniform overall velocity distribution. However, from the total pressure contours, it is observed that although the mainstream total pressure absolute value is higher in the normal inlet layout, its mainstream position is relatively lower compared to the outlet center. Notably, in the nonmainstream region of the normal inlet layout, the interaction between the lip shock and the boundary layer intensifies, leading to significant flow distortion and energy loss. This phenomenon is particularly severe in the corner region.
# On the Effect of Friction on Tibiofemoral Joint Kinematics
## 6 Damage Formulation
### Cross-Shear Ratio  
Under the sliding motion of a metallic part on ultra-high molecular weight polyethylene, the polymeric chains align in the direction of the maximum frictional work according to the experimental findings [15; 94], which is the so-called principal molecular orientation (PMO) [12; 15]. The cross-shear ratio can be quantified as the quotient of the frictional work done perpendicular to the PMO direction (\(W_{\text{CS}}^{f}\)) to the total frictional work (\(W_{\text{T}}^{f}\)) as is formulated below [95; 96; 97].  
\[\Im=\frac{W_{\text{CS}}^{f}}{W_{\text{T}}^{f}} \tag{40}\]  
The frictional work can be calculated based on \(W^{f}=\sum\limits_{i=1}^{n}\mathbf{F}_{\mu}(\mathbf{v}_{t},\sigma,\ t_{i})\cdot \Delta\mathbf{s}(t_{i})\) where \(\Delta\mathbf{s}(t_{i})\) is the incremental sliding distance that can be computed by \(\Delta\mathbf{s}(t_{i})=\mathbf{u}_{t}^{k_{j}}(t_{i})\Delta t_{i}\) at each element. The direction (\(\varphi_{f}\)) of incremental motion along the contact point trajectory is calculated to determine the vector of the incremental sliding distance, \(\Delta\mathbf{s}\) (\(t_{i}\)). Moreover, considering a test PMO direction (axis) with an angle \(\varphi\), frictional force and incremental sliding distance components parallel to and perpendicular to that axis can be calculated for each increment [12; 95; 96]. Assuming that the friction coefficient does not vary during a cycle, the cross-shear can be formulated as follows.  
\[\Im=\frac{\sum\
olimits_{i=1}^{n}\|\mathbf{F}_{\mu}(\mathbf{v}_{t},\sigma,\ t_{i})\|\|\Delta\mathbf{s}(t_{i})\|\sin^{2} \left(\varphi_{f}(t_{i})-\varphi\right)}{\sum\mathbf{F}_{\mu}(\mathbf{v}_{t},\sigma,\ t_{i})\cdot\Delta\mathbf{s}(t_{i})} \tag{41}\]  
The dynamic model developed in the previous sections obtains the sliding distance and contact force with time, which allows calculating Equation (41) for each angle \(\varphi\), from 0 to \(\pi\), until the principal molecular orientation, \(\varphi_{PMO}\), is reached where the cross-shear ratio is minimized.
## 1 Introduction
To achieve more reasonable resource allocation, it is also possible to refine each state parameter by adding multiple Active states [6]. Upon analyzing existing congestion control schemes, this research introduces a hybrid congestion control scheme named the HPR-DCC for V2X communications by integrating the DCC framework and rate-power-based control mechanism. The hybrid approach leverages the complementary strengths of TPC and TRC, mitigating the limitations of each technique. TPC helps to maintain reliable communication by adjusting the transmission power based on channel conditions, while TRC regulates the data transmission rate to prevent congestion and ensure fair resource allocation. By combining the advantages of the TPC with the TRC, the HPR-DCC scheme offers enhanced performance in terms of congestion mitigation, reliable communication, and fair resource allocation. Furthermore, the HPR-DCC enhances adaptability to dynamic network conditions by dynamically switching between TPC and TRC based on real-time feedback and network metrics. Thus, the TPC and TRC hybrid control DCC algorithm provides a robust and efficient solution  
Figure 1: The generic outline of the reactive approach. for congestion control in wireless networks, addressing the challenges of varying channel conditions and network congestion while optimizing resource utilization. The primary contributions of this paper are twofold:  
* Proposing a novel method that combines the strengths of both TPC and TRC to achieve efficient and reliable communication;
* Evaluating the performance of the proposed scheme through simulations, comparing it to existing congestion control schemes and demonstrating its effectiveness in alleviating congestion across various traffic situations. This paper offers valuable insights into designing efficient and reliable congestion control schemes for 5G-NR-V2X, which are essential for implementing future intelligent transportation systems. And the rest is organized into following sections: Section 2, provides an overview of congestion control in 5G-NR-V2X sidelink communications, explores transmission power and rate control schemes in wireless networks, and introduces the HPR-DCC algorithm. Section 3 presents the system model and assumptions, elaborates on the proposed algorithm's design, and analyzes the control scheme's stability. Section 4 outlines the simulation setup and scenarios, defines performance metrics and evaluation criteria, offers experimental results and analysis, and compares the proposed scheme to alternative congestion control strategies. Finally, it recapitulates the principal contributions and outcomes, discusses practical implications and applications, and recommends directions for further research.
## 4 Results and Discussion
### An Hour-Ahead Forecast Results  
In the hour-ahead DNN model, 5 out of 25 inputs (\(E_{h}\), \(I_{SW}\), \(\mathcal{C}_{n}\), \(\mathcal{C}_{c}\), \(\mathcal{R}\)) are employed in the prediction stage, and each polynomial equation were comprehensively shown in Table 3. Recall that each variable used in this section was defined in Section 3.1 and in Table 2 in detail.  
The final equation of the hour-ahead DNN model is summarized in Equation (2),  
\[\hat{E}=-161.54+6.78N_{1}-5.78N_{11}+2.86N_{1}^{2}\times 10^{-4}+1.46N_{11}^{2} \times 10^{-4}+1.40N_{1}N_{11}\times 10^{-4}, \tag{2}\]  
where \(N\) terms stand for the neurons that express unique quadratic functions (see Table 3). In the hour-ahead GEP model, 4 out of 25 inputs (\(E_{h}\), \(I_{SW}\), \(C_{n}\), and \(m_{y}\)) are utilized as shown in Equation (3) and visualized as an expression tree in Figure 7.  
\[\hat{E}=\sqrt{C_{n}}+(E_{h}\times 0.986)+I_{SW}\times(m_{y}\times 25.858+2)-52.097 \tag{3}\]  
In Table 4, the hour-ahead model performance results of the proposed methods are presented in terms of R\({}^{2}\), nMAE, nRMSE, and run time for out-of-sample meticulously. According to Table 4, the hour-ahead DNN model outperforms the corresponding GEP model by any means. In comparison with the GEP model, the DNN model shows 8.27% and 82.83% better performances with respect to nRMSE and computational time.
## 4 Empirical Analysis  
### Fuzzy TOPSIS Analysis for Evaluation Criteria  
The fuzzy scale in Table 5 was applied based on the absolute evaluation results to develop the TFN for the evaluation criteria of fuzzy TOPSIS. The geometric average fuzzy weight was derived by triangular fuzzification for each evaluation criterion of 48 effective samples, and TFN values were calculated by Equations (1)-(4). Based on the above, the minimum eigenvector value for defuzzification, that is, the normalized weight, and the final rank by evaluation criterion were identified (Table 6).  
The analysis showed that the TFN was (0.10, 0.19, 0.36) for crime prevention effectiveness, (0.04, 0.11, 0.29) for policy conformity, (0.04, 0.10, 0.28) for competitiveness with existing services, (0.06, 0.15, 0.34) for growth in the service sector, (0.05, 0.13, 0.32) for economic viability, (0.07, 0.16, 0.35) for field applicability, and (0.07, 0.16, 0.35) for sustainability. In terms of ranks, crime prevention effectiveness had the highest weight of 0.24, followed by sustainability (0.17), field applicability (0.16), and growth in the service sector (0.14). The results likely reflected the evaluators' opinions about the practical realization, field operation, and service sustainability of the technologies. Notably, economic viability ranked just fifth, indicating that the evaluators did not mind economic viability when adopting new technologies, such as intelligent CPTED.
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 4 Discussion  
The considered direct digital synthesis chaotic communication system demonstrates the possibility of data transmission over a physical channel using SCM modulation. In all of our experiments, SCM overwhelmed PM in secrecy, while its noise resistivity (BER) was similar.  
The used equipment has a low cost and ease of programming as its main advantages, but its computational performance may be described as non-satisfactory. In our experiments, we achieved a rather low data transfer rate: the best result was 150 milliseconds per bit, which gives us a transfer rate of approximately 6 bits/s. Apparently, other reported chaotic communication systems based on Arduino have comparable characteristics. Switching the hardware platform may help to overcome this issue.  
Meanwhile, it is more correct to evaluate the transfer rate not in bits but in bands. Note that our system uses the binary alphabet. Extending the alphabet to \(2^{n}\) characters would increase the data rate by \(2^{n-1}\) times (but can reduce noise immunity). In particular, with a 256-character alphabet, a transfer rate of 0.8 kbps would be achievable, which can already be practically useful for compressed voice transmission [33] and IoT devices. As an example, the article [34] describes the application of the LoRa protocol, which has applications for solving problems in the field of IoT. In some cases, the bitrate of such a protocol can reach approximately 5-10 bits per second, which roughly corresponds to the results obtained in this study. In study [35], the authors describe a novel paradigm called low-power wide-area networking (LP-WAN) for industrial IoT services. Using this technique, a maximum data rate of 100 bps can be achieved by transmitting messages with a maximum payload length of 12 bytes. Using such a low bitrate permits covering ranges of up to 10 km and beyond with a very low transmission power, which saves energy at edge nodes. Another solution in the field of Arduino-based solutions, which can be  
Figure 10: QRMA vs. SNR estimation.  
applied in IoT, is described in the study [36]. In this article, the authors describe visible light communication (VLC) with light-emitting diodes as transmitters and receivers that enable low-bit rate wireless ad hoc networking. LED-to-LED VLC ad hoc networks with VLC devices communicating with each other over free-space optical links typically achieve a throughput of less than 1 Kbit per second at distances of no more than a few meters.
## 3 Results and Discussion
### Application Analysis of Superhydrophobic HNTs
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**W/\%**} & \multicolumn{3}{c}{**Heating**} & \multicolumn{3}{c}{**Cooling**} \\ \cline{3-8}  & & **Onset** & **Peak T/\({}^{o}\)C** & **Latent** & **Onset** & **Peak T/\({}^{o}\)C** & **Latent** \\  & & **T/\({}^{o}\)C** & & **Heat/J g\({}^{-1}\)** & **T/\({}^{o}\)C** & & **Heat/J g\({}^{-1}\)** \\ \hline SA & 100 & 52.81 & 56.62 & 184.30 & 52.96 & 51.15 & 177.90 \\ SA/P-HNTs & 46.2 & 51.03 & 54.52 & 68.12 & 54.12 & 52.45 & 60.75 \\ SA/S-HNTs & 58.6 & 51.52 & 54.82 & 88.42 & 54.43 & 52.15 & 79.37 \\ SA/S-HNTs & 300 cycles & 58.0 & 51.31 & 54.74 & 85.23 & 54.02 & 52.20 & 75.61 \\ \hline \hline \end{tabular}  
* \({}^{1}\) W represents the mass fraction of SA in the as-prepared form-stable composites. S-HNTs, superhydrophobic HNTs.  
\end{table} 
Table 2: Thermal properties of stearic acid (SA) and the related form-stable composite phase change materials (PCM) composites \({}^{1}\). Figure 9: Differential scanning calorimeter (DSC) curves for heating (**a**) and cooling; (**b**) of stearic acid (SA) and form-stable composite phase change materials (PCM).