esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model  
Figure 1 shows the architecture of the time-frequency coded cooperative MC-CDMA for an underground coal mine. The network is composed of three layers, _i.e._, the WSNs deployed in the underground coal mine, the wired backbone bus and the monitoring center on the ground [21, 22]. As shown in Figure 1, the WSN consists of sink nodes and different kinds of wireless sensor nodes to form a banded coverage area along mine tunnels which monitor the multimedia information such as video and environment parameters in the underground coal mine. The varied monitoring information is transmitted to the sink node that connects with the wired backbone bus. The ground monitoring center receives the monitoring information through the wired backbone bus to realize comprehensive monitoring for the underground coal mine.  
Figure 1: The system model.  
Assume that the width and height of mine tunnel shown in Figure 1 are 2a and 2b respectively and the coverage distance of each WSN is about \(L\). Sink nodes are located in the central section of the considered WSN. A Cartesian coordinate system is set up with the sink node as reference point [23].
# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 4 Results and Discussion  
### App Design  
For the visualization of the predictions of the classification models and their use in a real environment, the design of a prototype for a mobile application was proposed that allows the visualization and analysis of the data obtained. Figure 9 shows the prototypes so that a user can diagnose a potato leaf from a mobile device and identify whether or not it is infected with late blight. Initially, the user fills out a form with information about the crop from which the sample is being taken, in order to collect data for later reading and analysis. Within the information requested from the user is the type of crop, the phenological stage, the variety, the species, and data on the locality or place where the crop is located, such as temperature, humidity, and average rainfall. From a capture or upload of an image, the application will show the result based on the supervised model integrated in the application.  
Figure 8: Proposed CNN Architecture.  
The application prototypes, in addition to allowing the diagnosis of a sheet, contemplates access to users with an administrator role to visualize the general statistics of the processed data. That is, the administrators will be able to observe graphs that provide them with relevant information on the diagnoses made.  
In the prototype (Figure 10a) of Figure 10, a bar diagram is initially observed, which graphs the number of healthy and infected leaves that are processed per month. In this way, historical results can be compared to determine which are the months where the potato crops are affected mainly by late blight, and thus one is able to generate time series for later analysis to support decision making. In the prototype (Figure 10b), a bar chart is shown where the number of healthy and infected leaves grouped by age can be displayed, information that allows evaluating which of the phenological stages of the crop is more susceptible to being infected with the Phytophthoraha infestans fungus. In the prototypes (Figure 10c) and (Figure 10d) of Figure 10, circular diagrams are observed that allow visualizing the percentage of processed leaves grouped by species and variety. It can be seen that in early stages of crop development the disease does not attack significantly, however, from month 4 onwards the leaves of the crop begin to show symptoms of late blight infection. Finally, in the prototypes (Figure 10e) and (Figure 10f) of Figure 10, the environmental conditions under which the diagnoses of both healthy and infected leaves have been made are visualized. The environmental conditions under which the samples were taken to form the data set correspond to a temperature that ranges between 9 \({}^{\circ}\)C and 12 \({}^{\circ}\)C, a relative humidity of 91%, average monthly rainfall of 885 mm and height above the sea level of 2600 and 3050 to 3115 m.a.s.l.  
Figure 9: Prototype design for diagnostic visualization of a leaf from an image.
# Modeling of Transmission Compliance and Hysteresis Considering Degradation in a Harmonic Drive
## 6 Conclusions  
In this study, we presented a new model based on the mechanical system analogy to explain the transmission compliance and hysteresis behaviors in HDs. By considering the effect of interference and multi-tooth meshing, the proposed model captured the degradation of reducer behavior caused by wear. The hysteresis loss was captured by taking the micro-sliding friction in the tooth engagement area into account. The main sources of flexibility in HD transmissions include the FS cup, the FS teeth, and the WG. The number of meshing teeth was affected by the load torque, which is the main cause of nonlinear stiffness. The interference, WG stiffness, and friction coefficient were the main influencing factors in hysteresis behavior. The method of estimating the parameters of the proposed HD model has been described in detail. Experimental results confirmed the effectiveness of the proposed model.  
Furthermore, the model can capture the degradation of transmission flexibility and hysteresis better than the existing models. Therefore, the model will be useful in understanding the hysteresis mechanism of HDs. There are two characteristics of the behavioral degradation of HD: the degradation of stiffness and enhancement of nonlinear characteristics, and the reduction of hysteresis loss near zero load.  
Funding acquisition--[PERSON], [PERSON]; project administration--[PERSON], [PERSON]; conceptualization--[PERSON], [PERSON]; validation--[PERSON]; formal analysis--[PERSON], [PERSON]; investigation--[PERSON], [PERSON]; data curation--[PERSON], [PERSON]; writing--original draft preparation--[PERSON]; writing--review and editing--[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2018 YFB1304800) and Key Research and Development Program of Guangdong Province (2020B009026002).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 15: The simulation results for the local elastic coefficient.
## 3 Results
### WBM Model
Overall, the WBM performs exceptionally well across key performance metrics, with high accuracy rates and solid probability estimates, making it highly suitable for practical deployment. The combination of high macro and micro accuracy and a reasonably low log loss demonstrates that this WBM is reliable and versatile for various classification tasks.
# Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
## 3 Methodology
### N-BaIoT Dataset  
The N-BaIoT dataset was generated by [PERSON] et al. [42] and consists of data samples with 115 features. The datasets were collected through the port mirroring of IoT devices. The benign data were captured immediately after setting the network to ensure that the data was benign. For two types of packet sizes (only outbound/both outbound and inbound), packet counts, and packet jitters, the times between packet arrival were extracted for each statistical value. A total of 23 features were extracted for each of the 5 time windows (100 ms, 500 ms, 1.5 s, 10 s, and 1 min), for a total of 115 features. We use all of the 115 features in our framework. Table 4 shows the detailed features of the dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Standard Dataset**} & \multicolumn{3}{c}{**Original Dataset**} & \multicolumn{3}{c}{**N-BaIoT**} \\ \hline Reference & [43] & [47] & [44] & [18] & [19] & [20] & [48] & [49] & [50] \\ \hline \multirow{8}{*}{ML} & Random forest & & & & & **O** & \\  & Decision tree & & & & **O** & **O** & \\  & BLSTM & & & **O** & & & \\  & MLP & **O** & & & & & \\  & Naïve Bayes & & **O** & & & & \\  & KNN & & **O** & & & **O** & \\  & LDA & & **O** & & & & \\  & ANN & & **O** & & & & \\  & SVM & & & & & & \\  & OCSVM & & & & & & \\  & IF & & & & & & \\  & LOF & & & & & & **O** \\ \hline \multirow{8}{*}{DL} & DLANN & & **O** & & & & & \\  & LSTM & **O** & **O** & & **O** & & **O** \\ \cline{1-1}  & deep autoencoder & & & & & & **O** \\ \cline{1-1}  & Dense RNN & & & & & & & \\ \cline{1-1}  & RNN & & & & & & \\ \cline{1-1}  & CNN & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset(s) and algorithm(s) used in each reference. The datasets were collected by injecting two types of attacks into various types of IoT devices, as shown in Table 5.  
Each dataset was generated by injecting various Bashlite and Mirai attacks. Bashlite, also known as gafgyt, was written by Lizard Squad in C. This botnet is used for DDoS attacks by infecting Linux-based IoT devices. Various types of flooding attacks are used, such as UDP and TCP attacks. Mirai, which was written by [PERSON], is used for large-scale attacks using IoT devices. Mirai was discovered in August 2016. Since 2016, the botnets have evolved significantly and have become more proficient [51; 52]. Mirai is now available as open source [53]. [PERSON] et al. [54] suggested a framework to identify Mirai and Bashlite C&C servers by combining 4 heuristic algorithms. Table 6 shows the 10 specific attack types of Bashlite and Mirai.
## 4 Discussion  
The characterization of FBGs on a high-scattering MgO-NP fiber, with enhanced backscattering properties, has implications, particularly in terms of sensing and polarization effects, as it can provide an additional layer of complexity in sensing.  
The main difference between the MgO-NP fiber and the other methods for enhancing the backscattering is that the first one can be used, effectively, as a fiber, and thus the FBG is part of the optical circuitry used to implement filtering and cavity effects [4; 10], as well as to create distributed reflectors supported by the random effect of the scattering nanoparticles [23]. In sensing, this is important as the FBG allows \"tagging\" a specific sensing point where the FBG is located, and referencing the remainder of the fiber to the valued measured in this location, enabling solutions that mix optical frequency domain reflectometry of fiber scattering and FBG interrogation [24; 25]. The main application for the MgO-NP fiber is in scattering-level multiplexing, which requires a fiber with high Rayleigh scattering in order to simultaneously detect multiple channels on the OBR device [16; 17]. The addition of FBGs to this sensing system can be used to extend the sensing length of each channel, by using the additional reflectivity of the FBG in addition to the scattering level, compensating for the relative inline high losses of the fiber.  
In addition, the different sensitivity exhibited by the polarizations to thermal effects is a significant effect, as the difference is estimated as 4% with a good degree of confidence (R\({}^{2}>0.99\)). In comparison, this difference is 0.5% for a fiber doped with MgO nanoparticles but having no scattering enhancement, and is \(<\)0.1% for a standard FBG [1].  
Similar results have been obtained with FBGs inscribed on fibers having high birefringence [26; 27] or on polarization-maintaining fibers [8], whereas it is clearly possible to distinguish between the two Bragg wavelengths of slow/fast axis. In this work, however, we do not use a fiber with asymmetric design, but rather the polarization effect happens due to the scattering events occurring in the fiber, which determine the S/P polarizations to have a different thermal coefficient. This effect has been used in birefringent fibers to discriminate strain and temperature by means of detecting the difference  
Figure 8: Polarization analysis of the FBG parameters: the chart reports the FWHM (**left**) and the maximum amplitude (**right**) of the FBG spectra at different temperature values, for the whole FBG and for each polarization independently.  
between the two Bragg wavelengths, which is \(\sim\)0.5 nm in [28]. It is noteworthy that the polarization effect is not obtained by asymmetrical design of the fiber [29], but is routed in the scattering content of the MgO nanoparticles. Overall, the results presented in this work open the possibility to thermally tune the wavelength and polarization of the FBGs inscribed on this fiber, considering also the different bandwidth exhibited by the two polarization states.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 3 Characterization of Heating-Responsive SME
### Influence of Maximum Programming Strain (Room Temperature Programming)  
To investigate the influence of the maximum programming strain on the SME at room temperature, some samples were pre-stretched at room temperature to five maximum tensile strains (\(\varepsilon_{m}\)) (namely, 5%, 10%, 15%, 20% and 25%) and then unloaded. Note that it was found that over 25% straining at room temperature may cause fracture of the sample.  
Typical strain versus stress relationships are presented in Figure 6. As we can see, in the cases of 5% and 10% programming strains, the samples experience mainly elastic deformation. With the increase in maximum programming strain, more quasi-plastic strain is built up, and thus more residual strain is observed after unloading. Figure 7 (black symbols) presents the relationship of \(R_{f}\) versus the maximum tensile programming strain. As we can see, when the maximum programming strain increases from 5% to 10%, \(R_{f}\) increases slightly from 3% to 6.5%. Upon further increasing of \(\varepsilon_{m}\), \(R_{f}\) increases significantly. Over 60% is observed at 25% maximum programming strain.  
After programming, the samples were heated to 180 \({}^{\circ}\)C, which is about 40 \({}^{\circ}\)C higher than the \(T_{g}\) of this material according to the DSC result reported in Figure 3. According to Figure 7 (blue symbols), all samples are virtually able to fully recover their original shape.
## 4 Problem Formulation and Analysis
Equation (16) shows that constraint C1 represents the binary offloading decision, and constraints C2 and C3 ensure that each MD \(m\) can select at most one SBS. Constraint C4 implies that the number of tasks to be offloaded to the MEC server must be less than the total number of subcarriers, and constraints C5 and C6 indicate that the total computing resources assigned to the offloading MDs by each s MEC cannot exceed its maximum computing capability (note that \(f_{mn}=0\) if \(m\
otin\mathcal{M}_{n}\)). Equation (16) is an MINLP problem of NP-hard complexity and difficult to solve optimally in polynomial time [10; 34]. Although global optimization can be used to obtain an optimal solution to this problem, the worst-case exponential computational complexity limits its application to 5G wireless networks due to the massive level of connectivity, heterogeneous quality of service requirements, and highly dynamic wireless channels. In this study, we developed an efficient and low-complexity algorithm capable of obtaining a high-quality solution with guaranteed convergence using PSO.
# Optimization Design and Control of Six-Phase Switched
## 3 Surrogate Modeling Method
### Kriging Model
The relationships between optimization objectives and parameters generated by the Kriging model are presented in Figure 5.  
It can be found in Figure 5 that \(T\) increases significantly with the advance of the turn-on angle and the increase in the conduction angle. The minimum value of \(T_{\text{rip}}\) occurs at the minimum values of the conduction angles. Moreover, \(T_{\text{rip}}\) and \(I^{*}\) do not exhibit a single linear trend relationship between the conduction angles. The comparison between the Kriging model and the FEM is presented in Figure 6. A certain number of design samples are randomly selected. It can be found that the errors between the Kriging model and FEM are acceptable for all three optimization objectives. These errors are explained by two aspects: one is the inherent error when estimating the parameters such as \(\beta\) and \(\sigma^{2}\), and the other is the number of samples from the FEMs. Too few samples will cause a large deviation between the estimated and actual values of these estimated parameters in the Kriging model. Moreover, due to the nonlinear characteristics of SRMs, errors between the estimated and actual values of the objectives may be positive or negative. If the relationship between the objectives and the variables is directly established by FEM without surrogate models, it may require \(10^{5}\) FEMs if the sampling numbers of each variable are set as 10. With the help of the surrogate model, the sampling numbers could be greatly reduced; for instance, in this example, only 1125 (\(5\times 5\times 5\times 3\times 3\), where the numbers represent the sampling numbers of \(\lambda_{\text{hcs}}\), \(\alpha_{\text{Aon}}\), \(\alpha_{\text{Bon}}\), \(P_{\text{dA}}\), and \(P_{\text{dB}}\), respectively. It requires only about 1% samples compared with the method of directly using the FEMs. The computational time of each FEM established in ANSYS/Maxwell is about 5 min, and the computer can run six samples simultaneously. Moreover, the running time of the Kriging model is about 1000 times larger than the time of the Kriging model. Figure 6: Comparison between the Kriging model and FEM of the three objectives. (**a**) \(T\), (**b**) \(T_{\text{rip}}\), and (**c**) \(P\). model is only a few seconds, which can be ignored compared with that of the FEMs. Thus, the total time is about 39 h; therefore, the Kriging model combined with the FEMs can be employed as the alternative for directly using FEMs to reduce the computational burden. After the establishment of the Kriging model, the relationship between the objectives and the parameters can be expressed by it, which provides the foundation for the further optimization process illustrated in Section 4.
## 2 Methodology
### Sample Augmentation and Reduction Methods
#### 2.3.1 KKT Condition Examination for New Samples  
In the optimization framework of support vector regression (SVR), the KKT conditions serve as both sufficient and necessary conditions for convex optimization problems [35]. Examining whether the Lagrange multipliers are zero can determine whether samples are support vectors, thereby identifying under-learned sample information in the SVR model. This provides a basis for sample updates in incremental learning [36].  
For the optimal solution \(a=[a_{1},a_{2},\cdots,a_{i}]\) in the dual problem discussed in Section 3.1, each sample satisfies the KKT conditions for the optimal solution of the optimization problem:  
\[\left\{\begin{array}{l}E=\{i||\theta_{i}|=\!C,|h(x_{i})|>\varepsilon\}\\ S=\{i|0<|\theta_{i}|<C,|h(x_{i})|=\varepsilon\}\\ R=\{i|\theta_{i}=0,|h(x_{i})|<\varepsilon\}\end{array}\right. \tag{5}\]  
Based on the model, the error of the sample coefficient can be defined as \(\theta_{i}=a_{i}-a_{i}^{*}\), and the boundary function is denoted as \(h(x_{i})=f(x_{i})-y_{i}\). In this context, samples in set \(|\theta_{i}|=C\) are distributed outside the \(\varepsilon\)-region, while samples in set \(0<|\theta_{i}|<C\) lie on the boundary of \(\varepsilon\), and samples in set \(|\theta_{i}|=0\) are distributed inside the \(\varepsilon\)-region.  
Figure 3 illustrates the sample set division, where the error support vector is in set \(E\), the margin support vector is in set \(S\), and the support vector is in set \(R\).  
If \(\{x_{new},y_{new}\}\) represents a new sample, its KKT condition can be expressed as either \(|h(x_{new})|>\varepsilon\) or \(|h(x_{new})|<\varepsilon\). When \(|h(x_{new})|>\varepsilon\), it indicates that the new sample does not satisfy the KKT conditions (as shown in Figure 4), suggesting that the original support vectors do not contain this information and that the model's prediction error is significant. Therefore, it is necessary to learn from the new sample. Conversely, when \(|h(x_{new})|<\varepsilon\), it implies that the new sample satisfies the KKT conditions (as shown in Figure 5), indicating that the information is already included in the current support vectors, and thus, the new sample can be discarded.
## 6 Performance Evaluation and Results  
### A Human-In-The-Loop Experiment  
Toward providing realistic results and properly capturing the real preferences of the museum visitors, we have initially performed a detailed research experimentation with actual visitors as participants. Specifically, a detailed questionnaire was circulated to potential visitors [24] with respect to the main goals of the RSVTM framework. The questionnaire consisted of 10 distinct questions. In the first step, it collected some demographic data related to visitors while the main questions aimed to identify the most representative values of the parameters act as input to the RSVTM algorithm. Based on the collected information from the circulated questionnaire and their statistical analysis, we were able to identify the style of the visitor, the minimum and maximum amount of time that the visitor would be willing to invest in a museum tour, the preference of the visitor on the considered recommendations \(R_{A},R_{B},R_{C}\), and the willingness to wait for that type of recommendation, which reflects the associated \"cost\" of the recommendation. As a result, realistic values regarding the previously mentioned parameters were collected and subsequently were used to feed the RSVTM algorithm. The questionnaire was answered by over 150 museum visitors with a balanced population of males and females originating from more than 10 different countries, e.g., Greece, Cyprus, UK, France, India, Thailand, Ukraine, Spain, USA, etc.  
Based on the statistical analysis of the previously mentioned data, the most representative realistic values were determined as input for the RSVTM algorithm. Therefore, the numerical results and the performance evaluation of the RSVTM algorithm presented in the following sections are based on values originating from the previously mentioned experiment. Specifically, the collected data enabled us to determine proportions of visitors according to their styles, their minimum and maximum visiting times, i.e., \(t_{i}^{Min}\) and \(t_{i}^{Max}\), the QoE values offered by each recommendation, i.e., \(Q_{x},x=A,B,C\), and the values of the congestion control parameter, i.e., \(c_{x},x=A,B,C\). Based on the collected data, typical values of the ratio of the four visiting styles within the examined pool of visitors are 28% ant visitors, 24% butterfly visitors, 24% fish visitors, and 24% grasshopper visitors. The visiting time of a visitor ranges from approximately 60 min to 180 min while the visitors' preferences for the three recommendations is given by the following ratio, i.e., \(Q_{A}:Q_{B}:Q_{C}=5:7:9\). Moreover, the visitors expressed their willingness to wait in order to enjoy a specific recommendation and their answers were translated to the ratio of the congestion control parameters, i.e., \(c_{A}:c_{B}:c_{C}=3:6:10\).
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 3 Results and Discussion
### Entire Algorithm Test  
To test for the algorithm working, we used an old \(\upmu\)-CT dataset acquired at the Syrrep beamline of the Elettra synchrotron facility [9, 21]. The CCD resolution of each image is of 600 \(\times\) 300 pixels, and the dataset contains 450 projections of a mouse femur that span a [0, 180\({}^{\circ}\)] angular range, acquired with a parallel-beam setup. To simulate the misalignment, we added a random \(x,y\) jitter in each projection with a standard deviation of 10 pixels, obtaining a severely misaligned dataset, as can be seen in Figure 7 panel a; the use of the alignment algorithm allows correcting for the jitter, producing the aligned dataset in panel b. An off-axis feature seems completely missed in panel (a), but it is simply faint; by using the alignment procedure, it becomes quite evident within the sinogram.