esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Performance Metrics  
This work employed four widely utilized evaluation metrics in ML to further assess the classification outcomes. These metrics were employed to evaluate the performance of ML algorithms. The assessment metrics used are as follows: (i) accuracy, (ii) precision, (iii) recall, and (iv) F1-score [1]. The formulations are given in the following Equations (16)-(19):  
\[accuracy=\frac{(TP+TN)}{(\ TP+TN+FP+FN)}\times 100\% \tag{16}\]  
\[precision=\frac{TP}{(\ TP+FP)}\times 100\% \tag{17}\]  
\[recall=\frac{TP}{(\ TP+FN)}\times 100\% \tag{18}\]  
\[F1-score=2\times\frac{precision\times recall}{(\ precision+recall)}\times 100\% \tag{19}\]  
where the abbreviations _TP_, _FP_, _FN_, and _TN_ refer to true positives, false positives, false negatives, and true negatives, respectively. F1-score is often favored over accuracy to address the challenges of model evaluation and selection in scenarios where class distributions are imbalanced, reducing the risk of misleading results. Considering the impact of both false negatives and false positives is an essential step in achieving a comprehensive analysis of error costs in the evaluation process. In this study, the F1-score is emphasized for its ability to provide a comprehensive evaluation that captures all error types, rather than prioritizing a single metric. The F1-score offers a detailed and balanced evaluation by addressing both false negatives and false positives, making it particularly effective for imbalanced datasets. Selecting this metric ensures compatibility with the study's objectives and satisfies its specific evaluation requirements.
## 4 Methodology
### Integration of Fuzzy Logic Modules  
The integration of presented fuzzy logic modules forms a complex decision-making system, named _FuzzyMain_ (Figure 10).  
The outputs _crispDecision_, _crispCriticalility_, _crispExperience_, and _crispEye_ from the modules _FuzzyDecision_, _FuzzyCriticality_, _FuzzyExperience_, and _FuzzyEye_, from _Cascade 1_ of the _FuzzyMain_ component, are normalized values within a 0-1 range, representing the assessed state from low to high criticality or significance. These outputs are then fed as inputs to the next fuzzy logic decision modules _FuzzyDC_ and _FuzzyEE_, forming _Cascade 2_ of the _FuzzyMain_ component. Rules for combining fuzzified values of _crispDecision_ and _crispCriticalility_ pair, and _crispExperience_ and _crispEye_ pair, are identical to those specified in Table 4.  
For instance, a rule states that if both the _crispEye_ and _crispExperience_ variables are fuzzified as _'low'_, the outcome should also be _'low'_, indicating the minimal intervention needed. Conversely, if _crispCriticality_ and the _crispDecision_ variable are fuzzified as _'high'_, then the system outputs a _'high'_ intervention requirement, signaling the need for immediate and decisive action.  
The defuzzified outcomes _crispDC_ and _crispEE_ from modules _FuzzyDC_ and _FuzzyEE_ are being propagated to the final decision module, _FuzzyAll_. This module is also the last cascade, named _Cascade 3_, from the _FuzzyMain_ component.  
There are 25 rules to combine fuzzified values of _crispDC_ and _crispEE_ (_fuzzifiedDC_ and _fuzzifiedEE_) to calculate the value named _fuzzifiedSA_. The rules are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rule** & _fuzzifiedEyeSub1_ & _fuzzifiedEyeSub2_ & _fuzzifiedEyeSub3_ & _fuzzifiedEye_ \\ \hline
001 & L & L & L & VL 1  \\
002 & M & L & L & VL \\
003 & H & L & L & L \\ \hline
004 & L & M & L & VL \\
005 & M & M & L & L \\
006 & H & M & L & M \\ \  
Finally, through the defuzzification phase applied to the value _fuzzifiedSA_, the final _crispSA_ is produced as the output from the _FuzzyMain_ component, representing the predicted situational awareness.  
This approach allows for nuanced processing and decision-making, ensuring that all relevant factors are considered. The system leverages fuzzy logic to analyze and synthesize data from multiple sources, aiming to produce optimal decisions under varied driving conditions.  
By merging outputs from multiple fuzzy modules, this cascaded approach enhances the robustness and adaptability of ADAS. It allows for a more holistic assessment of both the driver's state and environmental conditions, leading to safer and more efficient driving decisions.
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 4 State-of-the-Art Energy Harvesting Methods for Transmission Lines
### Corona Energy Harvesting  
Electric-field and magnetic-field EH are appropriate for AC power systems, but they are not suitable for DC systems due to the lack of an alternating field. Therefore, in DC systems, other strategies are required. Corona energy harvesting overcomes this difficulty.  
Corona is a type of electric discharge that occurs in gas insulation systems under a highly inhomogeneous AC or DC electric field [119; 120]. It is generated at a lower voltage than required for complete breakdown [121]. Due to its ionization effect, the corona generates a space charge area, producing a flow of ions perceived as current pulses. In [122], it was shown that in the case of a single conductor energized at 100 kV-DC, the corona ion flow density is approximately 200 \(\upmu\)A/m\({}^{2}\).  
In [20], an EH approach based on the current induced by corona discharges is proposed based on using the corona current pulses generated by a corona electrode and a simple energy-harvesting circuit, resulting in a stable output of approximately 10 mW in a 22 kV-DC system, which is sufficient for the discontinuous operation of most sensors.  
Figure 7: Basic structure of an RF EH node adapted from [107].  
Figure 8 shows the main characteristics of a corona discharge taken by the authors of this work.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Models  
This section presents the centralized proactive route selection mechanism with the distributed reactive RL model. While the CC establishes more stable routes and backbones, the distributed RL model learns to offload traffic from MC BSs in a collaborative manner.
## 5 Conclusions  
In this paper, an advanced FRT strategy was proposed. To ride through an on-shore fault without operation of a DC chopper or crowbar, control of the DC voltage by unbalancing input and output power and the stored kinetic energy of a wind turbine rotor was implemented. For deloading DFIG and transferring FRT signals to a wind farm, on-shore voltage to off-shore frequency droop was used. Finally, to minimize rotor acceleration, calculation of the maximum absorbable power in the DC link voltage was analyzed. With these coordinated control strategies, an on-shore fault can be ridden through with no DC overvoltage. Moreover, by the DC capacitor storage of energy, the rotor acceleration can be minimized. Through four scenarios, the superiority of the proposed control was verified. According to the voltage dip scale, the proposed control strategy can cope with varying on-shore voltage by calculating the deloading turbine power. By the duration of fault, the effectiveness of the proposed control was clearly shown in the short fault duration, but for longer faults, the output of the turbine deloading strategy and proposed control seemed similar. The main difference between turbine deloading and the proposed control was the minimization of turbine acceleration during the fault. In low power generation case, no turbine acceleration was observed. Especially, the proposed control method can have a significant effect in FRT depending on the ratio of power imbalance versus the absorbable power of the HVDC system and supposed to be utilized for stable operation of highly implemented wind farms.  
Conceptualization, Y.Y. and J.L.; validation, G.J. and M.Y.; data curation. J.L. and M.Y.; investigation, J.L. and Y.Y.; methodology, G.J. and M.Y.; writing, original draft, J.L.; writing, review and editing, G.J. and Y.Y.  
This research received no external funding.  
This work was supported under the framework of the international cooperation program managed by the National Research Foundation of Korea (No. 2017K1A4A3013579) and the Human Resources Program in Energy Technology of the Korean Institute of Energy Technology Evaluation and Planning (KETEP) financial resources granted by the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20174030201540).  
The authors declare no conflict of interest.
## 6 Experiments and Discussion
### Verification of the Proposed Rendering System
When the number of frames reached the threshold value, the overly fragmented or flat point clouds on the left side of the red line in Figure 14b were re-integrated, and the results applied directly to the subsequent shape fitting procedure. The regions on the right side of the red line were kept to serve as the basis for integration following completion of segmentation in the next image. Figure 14: Results of point cloud segmentation in a complex environment. (**a**) Mapping, (**b**) point cloud segmentation. Figure 13: Results of point cloud segmentation using a simple test scene. (**a**) test scene (2D image), (**b**) 3D point clouds obtained using Kinect camera, (**c**) supervoxelization results, (**d**) plane removal, (**e**) point cloud segmentation. We then performed a shape fitting test using the simple scene, the results of which are presented in Figure 15. The results of modeling the scene using multiple frames and object segmentation are presented in Figure 15b. Application of the algorithm in Section 5 to the segmentation results for shape fitting generated the result in Figure 15c. If we place the required images on the flat point clouds and use related virtual objects in place of the shape fitting results, then elements in the real environment can be replaced with virtual ones. Suppose that we want to create a scene from the African plains. The final result might look like that shown in Figure 15d. The original objects can be replaced with objects presenting a similar geometric shape, such as an elephant, a tiger, a giraffe, a monkey, and a zebra. To verify the versatility of the presented methods, we conducted an additional experiment involving a complex scene in which the camera was moved from right to left, the results of which are presented in Figure 16. The proposed scheme has a number of advantages over existing object segmentation results (i.e., using machine-learning or graph-based methods). As shown in Figure 16a-d, our system is able to perform object segmentation during the mapping process (i.e., online object segmentation) and does not require _a priori_ training to deal with objects in actual scenes. The shape-fitting results in Figure 16e demonstrate the effectiveness of the proposed methods in AR applications. Figure 16: _Cont._  
Figure 15: Results of virtual object replacement using simple scene involving segmentation of multiple frames and shape fitting. (**a**) Scene modeling (2D image, side view), (**b**) results of point cloud segmentation using multiple frames (top view), (**c**) shape fitting and plane point cloud (top view with green dots indicating the plane), (**d**) virtual scene.
## 1 Introduction
### Mechanism of Action  
Endermologie is a treatment technique based on mechanical stimulation of the skin and subcutaneous tissues using vacuum-assisted mobilization. The device used in this procedure generates negative pressure, which lifts a skin fold and subjects it to controlled mechanical stimulation through motorized rollers and/or flaps. This process, known as mechano-stimulation, triggers physiological responses within the connective tissue, influencing circulation, lymphatic drainage, and adipocyte and fibroblast activity. The treatment heads used in endermologie are designed to adjust to different treatment areas and therapeutic goals. The device allows for a large panel of treatment heads and the modulation of various parameters, including suction intensity, frequency, and movement direction, enabling customized treatments tailored to individual skin conditions and therapeutic needs. By altering these settings, practitioners can influence tissue mobilization depth; optimize treatment for conditions such as cellulite, skin laxity, fibrosis, post-surgical swelling, pain, and scar remodeling; and enhance both aesthetic and medical outcomes. Depending on the treatment mode, endermologie can be applied using continuous or sequential vacuum stimulation. The continuous vacuum is typically used for deep mobilization, supporting fat metabolism and fibrotic tissue remodeling [4; 5].  
The intermittent or sequential vacuum operates by rhythmically lifting and releasing the skin, which enhances microcirculation, lymphatic flow, and tissue oxygenation, making it particularly effective for reducing swelling, improving skin elasticity, and addressingcellulite. The versatility of endermologie lies in its ability to precisely control the depth and intensity of tissue mobilization, ensuring that treatments are adapted to specific indications, such as body contouring, post-operative rehabilitation, or skin rejuvenation. The parameters can be adjusted manually by the practitioner or set according to pre-programmed treatment protocols, optimizing both safety and efficacy [5; 6].
## 2 Theory
### Losses in Solid Structures  
Losses in solid structures are very important when the sound transmission loss has to be computed at and above the critical frequency. In some cases, increasing the losses can be an effective way to improve the sound transmission loss of a partition [32]. With the word 'losses', one typically indicates the conversion of vibrational energy into other forms, like thermal energy. The total losses are the sum of different contributions: internal losses, describing the inherent damping of the material; radiation losses, due to the coupling between the partition and the surrounding medium, which for lightweight structures are particularly relevant in the coincidence region; and boundary losses, induced by flanking transmission to the adjacent structures and significant in the low frequency range.  
When comparing the predicted sound transmission loss to the experimental curves obtained in transmission suites, the total loss factor \(\eta_{tot}\) must be considered to account for the actual behavior of the partition and measurement conditions. Studies to model the radiation component [33; 34] and generic values of the internal loss factor [35] are available in the literature, but a complete theoretical determination of the loss factor is not possible to the present day. Experimental methods have been introduced for this purpose. By recording the decay of the vibration energy in a certain frequency band as a function of time, the loss factor can be estimated by applying the structural reverberation time technique, by means of Equation (22):  
\[\eta_{tot}=\frac{2.2}{f_{1/3}\times T_{60}}, \tag{22}\]  
where \(f_{1/3}\) is the center-frequency of the one-third octave band of interest and \(T_{60}\) is the time required for the vibration level to decay by 60 dB when the specimen is excited through an external force.  
Figure 5: Measurement of free-free natural frequencies with impedance hammer and accelerometer.
# Multiple Attribute Decision-Making Method Using Linguistic Cubic Hesitant Variables
## 1 Introduction
For instance, when we require five experts to assess the service quality of a hotel from the linguistic term set (LTS) \(Y\) = \(\{y_{0}\) (extremely poor), \(y_{1}\) (very poor), \(y_{2}\) (poor), \(y_{3}\) (slightly poor), \(y_{4}\) (moderate), \(y_{5}\) (goodish), \(y_{6}\) (good), \(y_{7}\) (very good), \(y_{8}\) (extremely good)\(\}\), the interval linguistic value \(\{y_{5},y_{7}\}\) is given by two of the five experts and the HLS \(\{y_{4},\,y_{5},\,y_{6}\}\) is given by three of the five experts under the situation of their uncertainty and hesitancy, and then the hybrid form of both \(\{y_{5},\,y_{7}\}\) (the uncertain linguistic part) and \(\{y_{4},\,y_{5},\,y_{6}\}\) (the hesitant linguistic part) cannot be expressed simultaneously using the aforementioned various linguistic concepts. Clearly, this expression problem requires us to solve the gap using a hybrid linguistic form. Hence, this study presents a new linguistic concept based on the combining form of both an LCV and a hesitant LV, which is called a linguistic cubic hesitant variable (LCHV), the operations and ranking method of LCHVs so as to solve MADM problems under the situation of decision makers' uncertainty and hesitancy. In this study framework, Section 2 presents a LCHV concept to express the hybrid information of both an interval linguistic argument and a hesitant linguistic argument, and then the operational relations and linguistic score function of LCHVs. Section 3 presents the WAA and WGA operators of LCHVs based on the LCMN extension method, which contain the objectivity and suitability of the aggregation operations for LCHVs, and discusses their properties. Next, a MADM approach is proposed based on both the WAA and the WGA operators of LCHVs and the linguistic score function of LCHVs in Section 4. Section 5 applies the proposed MADM approach to the MADM problem regarding an illustrative example in a LCHV setting, and then its decision results show its applicability. Lastly, conclusions and the next study are contained in Section 6.
# T-Splines for Isogeometric Analysis of the Large Deformation of Elastoplastic Kirchhoff-Love Shells
## 3 Formulations for the Large Deformation of Elastoplastic Kirchhoff-Love Shells
### Kinematics
The relationship between in-plane stresses and strains is given as  
\[\mathrm{d}S^{\alpha\beta}=\hat{\mathbb{C}}^{\alpha\beta\gamma\delta}\mathrm{d }E_{\gamma\delta}. \tag{29}\]  
According to the plane stress constraint:  
\[S^{33}=\mathbb{C}^{33\alpha\beta}E_{\alpha\beta}+\mathbb{C}^{333}E_{33}=0, \tag{30}\]  
the material tensor is defined as  
\[\hat{\mathbb{C}}^{\alpha\beta\gamma\delta}=\mathbb{C}^{\alpha\beta\gamma \delta}-\frac{\mathbb{C}^{\alpha\beta 33}\mathbb{C}^{33\gamma\delta}}{\mathbb{C}^{3333}}. \tag{31}\]  
Membrane forces \(\mathbb{N}\) and bending moments \(\mathbb{M}\) are computed by integrating the second Piola-Kirchhoff stress tensor \(\mathbb{S}\) over the thickness:  
\[ \begin{split}\mathbb{N}&=\int_{-h/2}^{h/2}\mathbb{ S}d\xi^{3},\\ \mathbb{M}&=\int_{-h/2}^{h/2}\mathbb{S}^{\alpha}d \ \xi^{3}.\end{split}  \tag{32}\]  
Combining with Equations (29)-(32), the total differentials take the form of  
\[ \begin{split} d\mathbb{N}&=(\int_{-h/2}^{h/2}\hat{ \mathbb{C}}d\xi^{3})d\varepsilon+(\int_{-h/2}^{h/2}\hat{\mathbb{C}}\xi^{3}d\xi^ {3})d\kappa,\\ d\mathbb{M}&=(\int_{-h/2}^{h/2}\hat{\mathbb{C}} \xi^{3}d\xi^{3})d\varepsilon+(\int_{-h/2}^{h/2}\hat{\mathbb{C}}(\xi^{3})^{2}d \xi^{3})d\kappa.\end{split}  \tag{33}\]  
Note that the three-dimensional material models described in Section 3.1 are defined in the local coordinate system. Therefore, a transformation between local Cartesian and curvilinear coordinates is necessary. A Cartesian coordinate associated with the shell midsurface is defined as follows:  
\[\overline{\mathbb{E}}_{1}=\frac{\mathbb{G}_{1}}{\|\mathbb{G}_{1}\|},\quad \overline{\mathbb{E}}_{2}=\frac{\mathbb{G}_{2}-\big{(}\mathbb{G}_{2}\cdot \overline{\mathbb{E}}_{1}\big{)}\overline{\mathbb{E}}_{1}}{\|\mathbb{G}_{2}- \big{(}\mathbb{G}_{2}\cdot\overline{\mathbb{E}}_{1}\big{)}\overline{\mathbb{E }}_{1}\|},\quad\overline{\mathbb{E}}_{3}=\mathbb{G}_{3}. \tag{34}\]\(\mathbf{\bar{E}}_{i}\) is the base vector of local Cartesian coordinates with the upper bar \(\overline{(\cdot)}\) standing for local nature. The deformation gradient \(\mathbf{F}\) for Cartesian coordinates can be given by  
\[ \begin{split}\bar{F}_{ij}&=F_{mn}\left(\mathbf{\bar{E} }_{i}\cdot\mathbf{G}^{m}\right)\left(\mathbf{G}^{n}\cdot\mathbf{\bar{E}}_{j} \right),\\ \mathbf{F}&=\bar{F}_{ij}\mathbf{\bar{E}}_{i}\otimes \mathbf{\bar{E}}_{j}.\end{split}  \tag{35}\]  
Similar transformations can be performed for the strain and the stress tensors.
## 3 Results
### Electrical Impedance Spectroscopy System  
The results of the measurements were analyzed; Figure 7a shows the result of the comparison between the normalized electrical impedances of diabetic patients versus healthy subjects. Significant differences were found for frequencies above 20 kHz (\(p<0.05\)). The graph shows the behavior of the normalized impedance in both study groups: in healthy subjects, it is less than 0.05 for frequencies higher than 20 kHz, while the normalized impedance in diabetic patients is almost double (0.09) compared to healthy subjects.  
Figure 7b shows that another analysis method that was performed to find greater differences is to use the result of dividing the voltage measured at the electrode by the injected voltage. That is, using the data one step before the impedance determination:  
\[Z=\frac{V_{meas}}{I_{inj}}=\frac{V_{meas}}{\frac{\left(V_{ref}-V_{inj}\right) }{K_{out}}}=\frac{V_{meas}}{V_{ref}-V_{inj}}R_{out} \tag{1}\]  
For the measurement of the injected current, the impedance equipment of [PERSON] 2015 [18] uses the assumption of a known and previously characterized floating reference voltage upstream of the output resistor, whose trend is exponentially decreasing but invariant (200 mA to 10 mA). By using this voltage to determine the current, the output values are small and hide variations in the measurements that could be important. For this reason and given that \(V_{ref}\) and \(R_{out}\) are constant, it was decided to study the result of  
Figure 7: (**a**) Comparison of normalized impedance in diabetic patients and healthy subjects. Dotted lines indicate the standard error of the mean (SEM). (**b**) Comparison of the ratio of measured voltage over injected voltage in diabetic patients and healthy subjects. Behavior of the ratio of the measured voltage to the injected voltage in the two study groups: diabetic patients and healthy subjects. The dotted lines are the standard deviation (SD). SEM is not shown as it appears very close to the mean values.  
dividing directly the \(V_{meas}\) by the \(V_{inj}\) of the previous equation. The results, dimensionless but electrical impedance-related data, are shown in Figure 7b, with a statistical significance level of \(p<0.0001\) for most frequencies (some frequencies of \(p<0.001\)), using two-way ANOVA. The behavior of the measured voltage/injected voltage is stable in diabetic patients for all applied frequencies and above 0.2, contrary to healthy subjects where this value drops after 10 kHz for values lower than 0.15.  
With these results of the electrical impedance measurement in this number of subjects, we can conclude that the electrical impedance measurements between patients with diabetes and healthy subjects are different, and this variable will allow us to deduce some change in the structure of the tissues once we have more samples and more specific clinical analysis.