esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 4 Experimental Results
### Simulated Image
This extreme imbalance leads to a lower overall accuracy of the boundary, such as FCM (56.79%). On the contrary, MSHLICM sacrificed UA, resulting in a significant improvement in PA, and the OA reaches 78.15%. VT-HMRF-FCM and the proposed algorithm effectively maintained the balance between UA and PA. Both of UA and PA reach around 90%. The OA of the proposed algorithm reaches 91.74%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{5}{c}{**Classes**} \\ \cline{3-6}  & & **I** & **II** & **III** & **IV** & **V** \\ \hline \multirow{3}{*}{FCM} & UA (\%) & 96.23 & 65.67 & 75.63 & 76.06 & 99.41 \\  & PA (\%) & 62.38 & 79.98 & 67.03 & 95.98 & 99.34 \\  & & \multicolumn{5}{c}{OA = 82.71, Kappa = 0.78} \\ \hline \multirow{3}{*}{FCM\_S} & UA (\%) & 99.09 & 95.94 & 96.02 & 92.16 & 99.91 \\  & PA (\%) & 95.60 & 96.23 & 96.29 & 99.11 & 96.74 \\  & & \multicolumn{5}{c}{OA = 96.71, Kappa = 0.96} \\ \hline \multirow{3}{*}{FGFCM} & UA (\%) & 100 & 49.18 & 72.35 & 88.30 & 99.95 \\  & PA (\%) & 39.61 & 60.44 & 93.08 & 99.21 & 95.89 \\  & & \multicolumn{5}{c}{OA = 80.32, Kappa = 0.75} \\ \hline \multirow{3}{*}{RFLICM} & UA (\%) & 99.69 & 79.94 & 95.22 & 86.85 & 100 \\  & PA (\%) & 77.47 & 95.55 & 89.86 & 99.18 & 97.07 \\  & & \multicolumn{5}{c}{OA = 92.36, Kappa = 0.90} \\ \hline \multirow{3}{*}{GKWFLICM} & UA (\%) & 75.18 & 99.83 & 99.94 & 98.82 & 97.44 \\  & PA (\%) & 95.63 & 65.86 & 99.63 & 99.67 & 99.91 \\  & & \multicolumn{5}{c}{OA = 93.50, Kappa = 0.92} \\ \hline \multirow{3}{*}{MSHLICM} & UA (\%) & 100 & 95.66 & 92.83 & 97.59 & 96.03 \\  & PA (\%) & 91.34 & 97.24 & 100 & 95.88 & 96.17 \\  & & \multicolumn{5}{c}{OA = 96.20, Kappa = 0.95} \\ \hline \multirow{3}{*}{VT-HMRF-FCM} & UA (\%) & 96.20 & 97.62 & 96.42 & 99.66 & 99.77 \\  & PA (\%) & 99.13 & 98.13 & 99.94 & 96.87 & 96.85 \\  & & \multicolumn{5}{c}{OA = 98.07, Kappa = 0.97} \\ \hline \multirow{3}{*}{Proposed FVT-FCM} & UA (\%) & 99.86 & 96.65 & 99.18 & 98.33 & 98.78 \\  & PA (\%) & 98.01 & 99.22 & 96.94 & 99.01 & 99.52 \\ \cline{1-1}  & & \multicolumn{5}{c}{OA = 98.60, Kappa = 0.98} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation of simulated image classification results.
# A Numerical Gear Rolling Test Method for Face-Hobbed Hypoid Gears
## 4 Numerical Gear Rolling Test Method
### Calculation of Contact Ellipse and Contact Path  
Step 1: As indicated in Figure 11, \(P\) is the meshing point and \(M\) is the rotary projection of the tooth surface. A cylindrical surface is established with point \(P\) as the center, \(r\) as the radius, and the normal vector as the axis of rotation. The generatrices of the cylindrical surface are intercepted by two meshing tooth surfaces to extract a series of intercepted generatrices. When the length of the intercepted generatrices does not reach the amount of elastic deformation (0.00635 mm according to [PERSON]'s experience [21]), the two endpoints corresponding to the shortest intercepted generatrices appear on the long axis of the instant contact ellipse, and the two endpoints corresponding to the longest intercepted generatrices appear on the short axis of the instant contact ellipse. When the length of the intercepted generatrices is equal to the amount of elastic deformation, the two endpoints of the intercepted generatrices appear on the boundary of the instant contact ellipse.  
Step 2: The radius \(r\) increases from 0 until the length of all intercepted generatrices is greater than the elastic deformation. A series of points on the long and short axes, and the boundary of the instant contact ellipse can be obtained. When the longest intercepted generatrix has not reached the amount of elastic deformation, there are two points in each contact ellipse corresponding to the shortest intercepted generatrices that appear on the long axis, indicated by points \(e\) and \(f\) in Figure 12a, and two points corresponding to the longest intercepted generatrix appear on the short axis, indicated by points \(m\) and \(n\). When the longest intercepted generatrix is greater than the elastic deformation, there are three conditions because the actual tooth surface contact ellipse is a non-standard ellipse (\(a\), \(b\), and \(c\) in Figure 12b). In Figure 12b, the points \(a\), \(b\), \(c\), and \(d\) correspond to the length of the intercepted generatrices equal to the amount of elastic deformation appearing on the boundary of the instant contact ellipse, \(e\) and \(f\) correspond to the shortest intercepted generatrices appearing on the long axis, and n corresponds to the longest intercepted generatrix appearing on the short axis. These points are projected onto the rotary projection  
Figure 11: Cylindrical intercepted meshing surface.  
surface \(M\), and the boundary of the instant contact ellipse and long axis can be obtained for meshing point \(P\).  
Step 3: The contact pattern and contact path of the entire tooth surface can be calculated by repeating these steps at each contact position in the meshing cycle.  
Unlike the traditional method, contact pattern and transmission error can be obtained without quadratic approximation of the surface and curvature derivation, so the results are closer to the actual meshing conditions of the gear.
## 3 Proposed Method
### Surface Electromyography
#### 3.1.2 EMG Signal Processing  
In Figure 2a, the original EMG signal is depicted, characterized by a high-frequency signal with a 500 Hz bandwidth. A smoothing technique was used to convert the original EMG into a low-frequency signal in real time, thereby offsetting noise and enhancing signal smoothness (Figure 2b). The acquired original EMG signals were then transmitted to Unity through serial communication, where they underwent real-time smoothing processing as a preprocessing step utilizing a moving average filter. Algorithm 1 is the pseudocode implemented in Unity for the moving average filter.  
```
1:EMG = ParseLong(stream.ReadLine0)
2:total --= readings[readIndex]
3:readings[readIndex] = EMG
4:total += readings[readIndex]
5:readIndex = readIndex ++
6:ifreadIndex >= numReadings then
7:readIndex = 0
8:endif
9:Smoothed = total/numReadings
```  
**Algorithm 1** Pseudo code for moving average filter algorithm.  
In Figure 3, signal acquisition from the attached sensor, signal smoothing processing, and signal recording are shown as performed in real time. During the calibration phase of the experiment, participants were asked to contract the FDP with maximum effort \(k\) times (in this experiment, 10), and the highest EMG value measured at each of the \(i\) contraction times was set as the maximum voluntary contraction (MVC) value for the \(i\)-th time, as shown in Equation (1).  
\[\text{MVC}_{i}=\text{max}\big{(}\text{EMG}_{j}\big{)},\ (i=1,\,2,\,...,\,k), (j=1,\,2,\,...,\,n-1,\,n). \tag{1}\]  
Figure 2: SEMG Signals: (**a**) raw EMG, (**b**) smoothed EMG.  
As in Equation (2), the mean MVC was calculated based on the average of the six MVC values, excluding the first two and the last two from \(k\) MVCs.  
\[\text{meanMVC}=\frac{1}{(k-4)}\sum_{i=k-7}^{k-2}\text{MVC}_{i}. \tag{2}\]  
As in Equation (3), the normalized EMG values were derived through min-max normalization based on the mean MVC value and the real-time input of smoothed EMG values [12]. After data preprocessing, the real-time input of normalized EMG values was utilized to trigger events according to the grip strength of the objects.  
\[\text{NormalizedEMG}_{j}(\%)=\frac{\text{fEMG}_{j}-\text{min}(\text{EMG}_{j })}{\text{meanMVC}-\text{min}\big{(}\text{EMG}_{j}\big{)}}\times 100(\%) \tag{3}\]
## 3 The CART-Based Methods
### Model Evaluation Index  
To evaluate the performance of the CART integrated model, this study uses three different indicators, namely, mean absolute error (\(MAE\)), average root mean square error (\(RMSE\)), and coefficient of determination (\(R^{2}\)). \(MAE\), \(RMSE\), and \(R^{2}\) are defined as follows:\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|f(x_{i})-y_{i}\right| \tag{4}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(f(x_{i})-y_{i})^{2}} \tag{5}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-f(x_{i}))^{2}}{\sum_{i=1}^{n}\left(\overline {y}-\overline{f}(x_{i})\right)^{2}} \tag{6}\]  
where \(y_{i}\) is the true value of the sample; \(f(x_{i})\) is the predicted value of the model; \(n\) is the number of prediction samples; \(\overline{y}\) is the average of the true values of the sample; \(\overline{f}(x_{i})\) is the average of the predicted values.  
As indicators of regression accuracy, the \(RMSE\) and \(MAE\) are indicator types that represent the difference between model output and actual value. If the values of these indicators are particularly small, it means that the predicted output matches the recorded output well. The \(R^{2}\) represents the proportion of variance that has been explained by the independent variables in the model. When the \(R^{2}\) value is larger, it means that there is a strong correlation between the model output and the actual value. In the actual model, we expect lower \(RMSE\) and \(MAE\) and higher \(R^{2}\).
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 2 Materials and Methods
### Hardware Architecture
#### 2.3.1 Communications Architecture  
The communications architecture of the RPSC has been designed to guarantee efficient integration between its components. The correct allocation of ports and communication protocols minimises delays in critical processes, optimising the stability and security of the system. The interconnection of the elements was organised according to their operational needs, prioritising those processes that require minimum latency, such as navigation and odometry, over others that are less critical (Figure 4).  
The RPSC motors are connected in parallel to the MDD10A controller, Cytron Technologies, Penang, Malaysia, enabling efficient power distribution without compromising the system's differential control. To optimize the connection, the motors on the right-hand side share a positive and negative terminal with channel A of the driver. In contrast, the motors on the left-hand side share a positive and negative terminal with channel B.  
The Pixhawk 2.4.8 generates the PWM speed control signals sent via the AUX OUT pins. The motor direction signals are transmitted via the MAIN OUT pins. The left and right front motor encoders are also directly connected to the Pixhawk's AUX OUT pins, accurately estimating the robot's displacement.  
The RPSC's navigation system relies heavily on the Here3+ positioning module. This module is connected to the Pixhawk via the CAN interface, enabling stable and low-latency data transmission.  
Figure 4: Pixhawk and Raspberry Pi-based autonomous vehicle subsystem interconnection diagram.
## 3 Results and Discussion
### Total Phenolic Content
Additionally, the vaporization of water at boiling point, by increasing the temperature, affects the ability of extraction of some phenolic compounds [32]. All these explanations could justify the above results and the fact that greater total phenolic content was demonstrated at higher temperatures, but not at boiling temperature. Concerning the different values of the two herbs, the obtained results are in accordance with previous ones, which demonstrated higher total phenolic content of sage infusion in comparison with chamomile infusion [25]. As a result, the extraction of total phenolic compounds from aqueous extracts of herbs was significantly affected by the temperature of extraction, but also by the species of plant material. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{4}{c}{**Herb**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Chamomile**} & \multicolumn{2}{c}{**Sage**} \\ \hline \multicolumn{5}{c}{Total phenolic content expressed as mg of gallic acid equivalents} \\  & \multicolumn{2}{c}{(GAE)/mL of extract} \\ \hline  & Median & Range & Median & Range \\ \hline
25 \({}^{\circ}\)C & Nd\({}^{1}\) & Nd\({}^{1}\) & 0.222 & 0.019 \\
80 \({}^{\circ}\)C & 0.165 & 0.007 & 0.207 & 0.018 \\
100 \({}^{\circ}\)C & 0.041 & 0.006 & 0.079 & 0.005 \\ \hline \multicolumn{5}{c}{Antioxidant activity expressed as ug Trolox equivalents (TE)/} \\  & \multicolumn{2}{c}{mL of extract} \\ \hline  & Median & Range & Median & Range \\ \hline
25 \({}^{\circ}\)C & Nd\({}^{1}\) & Nd\({}^{1}\) & 6.39 & 0.331 \\
80 \({}^{\circ}\)C & 4.62 & 0.616 & 11.9 & 1.80 \\
100 \({}^{\circ}\)C & 2.53 & 0.022 & 5.84 & 0.022 \\ \hline \hline \end{tabular} \({}^{1}\) Nd: Not detected.  
\end{table} 
Table 1: Total phenolic content and antioxidant activity of chamomile and sage aqueous extracts obtained at different temperatures.
## 4 Results and Discussion
### Validation of Video-Extensometry Technic for Use in Testing  
Video-extensometry techniques are very useful for materials that show cracking during the evolution of testing. They allow to obtain maps of strains, to observe the generation of cracks and to assess their opening during the test, without physical contact with the specimen.  
Figure 9 shows the load-displacement curves obtained by the LVDTs extensometers and by video-extensometry in the tensile tests. The values obtained by the video-extensometry match with  
Figure 8: Nucleation and growth of two symmetric cracks in the notched cross section under tensile testing (from upper to bottom photos starting and growth of the cracks).  
Figure 7: (**a**) Linear variable differential transformer (LVDT) placed in the specimen surface for assessing the crack mouth opening displacement (CMOD). (**b**) Evolution of the relative position of the white dots placed on both sides of the notch (upper and bottom part) obtained by the video-extensometry.  
the values obtained by traditional LVDTs. Figure 9a shows the load-displacement curves for SCCP6 concrete, Figure 9b for VCCP6 and Figure 9c for VCLP6.
## 4 Conclusions  
The asphalt mix performance test results indicate that mixes containing low amounts of HealRoad capsules (0.44% and 0.64%) have as good and better mix performance as the control mix in terms of strength, water sensitivity and rutting. Unfortunately, mixes containing a low amount of HealRoad capsules, 0-1.45%, do not conduct inductive energy and therefore do not initiate healing. This is due to the poor conductivity properties of iron powder and the high dispersion of the HealRoad capsules in the mix. However, increasing the HealRoad capsule content of the mix to >5% demonstrates increased temperatures when test specimens are subjected to the induction heating. The results show that mixtures containing 20% of HealRoad capsules can reach a temperature >80 \({}^{\circ}\)C and can recover up to 35% of its initial ITS. However, mixes without HealRoad capsules recovered 32.5% of their initial strength, without increased temperature. This indicates that although HealRoad capsules are capable of heating the mix and efficiently initiate self-healing, they do not assist the mix in recovering its material strength. Furthermore, increasing the HealRoad capsule content in the mix above 5% gradually decreases the mix strength, whereas the mix containing 20% of HealRoad capsules has a 55% lower initial ITS in comparison to the control mix, i.e., mix without capsules. These results show that HealRoad is not an efficient asphalt damage repair system. The results of our initial investigation [3], where HealRoad capsules were tested in pure bitumen and bitumen mortar mixes, indicated that HealRoad capsules may be suitable for use in asphalt mixtures with high bitumen and low aggregate content. Further studies will therefore focus on testing HealRoad capsules as a healing system in asphalt plug joint mixes. To enhance the conductivity of HealRoad capsules, the pure iron powder (Fe) will be replaced with magnetite (Fe3O4) as a conductive material. [PERSON] et al. [4; 21] have demonstrated that alginate capsules containing magnetite as a conductive material have good conductive properties. The assumption is that a more conductive material can improve energy conduction and perhaps also the healing efficiency of the system. Despite weak healing performance, HealRoad capsules may still have the potential to improve asphalt mix performance, paving the way for further improvement and development of the self-healing systems for asphalt pavement mixes.
# Preparation of Co-Ni Alloy Coating with Stable Composition by Jet-Electrodeposition
## 4 Conclusions  
Co-Ni alloy coatings were prepared by jet electrodeposition on brass substrate under different Co\({}^{2+}\)/Ni\({}^{2+}\) ratio contents (M = 2:1, 1:1, 1:2 and 1:3) and solution flow rates (from 2.0 to 4.5 L/min). The influence of Co-Ni content and flow rate of the plating solution on the coating morphology and mechanical properties was investigated. Following are the main conclusions of the present studies  
1. Increasing the flow rate of the plating solution reduces the concentration polarization of the cathode, so that the Co content in the coating increases accordingly. The range of increase is affected by the ratio of the main salt concentration of Co and Ni. If the ratios of Co\({}^{2+}\)/Ni\({}^{2+}\) in the plating solution are 2:1, 1:1, 1:2, and 1:3, the change ranges of Co content are 0.63%, 1.67%, 6.75%, and 8.54%, respectively, when the flow rate changes from 2 to 4.5 L/min.
2. The surface morphology of the coating is determined by the flow rate of the plating solution. When plated at a higher flow rate, the coatings have a higher Co content, with a smaller grain size and a denser structure. The phase structure of the coating is mainly determined by the content of Co in the coating. The flow rate of the plating solution changes the Co content in the coating, so does the phase structure of the coating. When the Co content is more than 80%, the coating is a single HCP structure. When the Co content is less than 80%, the coating has a structure with both HCP and FCC coexisting.
3. The mechanical properties of the coating are mainly determined by the grain size. The coating with higher Co content has a smaller grain size, higher hardness, and stronger wear resistance. The increase of the flow rate increases the Co content in the coating, reducing the grain size of the coating, increasing the hardness of the coating, and decreasing the wear volume.  
Figure 11: Wear volume of the coatings.  
* (4) The flow rate plays a significant role in the performance of Co-Ni coatings by jet electrodeposition. High-quality flow-independent Co-Ni coatings can be obtained when Co\({}^{2+}\):Ni\({}^{2+}\) ratio in the solution is 2:1.  
Writing--Original draft, Q.Z.; Supervision, J.T.; Resources, L.M.; Methodology. F.X.; Investigation, H.Z.; Data Curation, Z.Y.  
This publication was funded by a grant from the Major State Key Research and Development Program of China (No. 2018 YFB407401).  
The authors declare no conflict of interest.
# Threat Modelling and Beyond-Novel Approaches to Cyber Secure the Smart Energy System
## 4 Demonstration Use Case
### Smart Grid and Black-Out Attack Use Case Components  
_Power Electronic Device (PED)_ consists of an Intelligent Local Energy Manager (ILEM), which is responsible for the management of the device components, a Power Conversion System and a Battery Management System with hybrid energy storage features, based on predefined voltage support actions configured by the operator.  
_Phasor Measurement Unit (PMU)_ is a metering and control device for measuring phasor data (voltage and current synchronization), voltage and current waveforms.  
_Power Quality Monitor (PQM)_ is a lightweight metering device with a measurement and control unit and enables the calculation of all power quality parameters required according to EN 50160.  
Figure 2: Smart energy system cyber security use case architecture.  
Wide Area Monitoring System (WAMS) collects processes and monitors geo- referenced spatial temporal field data coming from distributed PMUs and PQMs located both on low voltage and on the medium voltage side.  
The _Remote Terminal Unit (RTU)_ provides communications features to field devices to exchange telemetry data and control messages with the capability of processing data.  
Distribution Management System (DMS) provides sophisticated functionality for advanced monitoring and control of the distribution grid to the DSO, which is important to handle increased customer generation and energy storage facilities as well as the management of demand response capabilities.  
Supervisory Control and Data Acquisition System (SCADA) is in charge of real-time monitoring and control of the distribution grid and manages diverse remote control systems via a telecommunication network and provides data analytics and data storage functionality.  
Enterprize Service Bus (ESB) is a middleware that enables the messaging and routing capabilities as well as the integration of distributed services and applications.  
Data Concentrator Unit (DCU) is a unit responsible for the gathering of measurement data from multiple metering devices installed at the substation.  
Meter Data Management System (MDMS) stores large quantities of smart meter data performs their validation and analytical processing.  
Meter Data Collector (MDC) collects and manages the measurement data received by the data-concentrating units (DCUs) which is then forwarded to MDMS.  
Photovoltaic system (PV) is a standalone solar PV with a power conditioning unit, a DC/AC converter and which is connected via the gateway to delivers data.  
Switchgear (SwG) is a motorized switchgear which enables coupling and decoupling of the grid sector.