esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Features and Data Preparation
### Variable's Information and Features Analysis  
The gathered information includes vehicle dynamics data such as linear acceleration on \(x\), \(y\), and \(z\) axis, linear speeds on \(x\), \(y\), and \(z\) axis. Moreover, angular velocity and angular acceleration around the \(x\), \(y\), and \(z\) axis are measured.  
The measurements of lateral velocity and acceleration can provide information of movements that are being performed on the \(y\) axis, such as lane changing and other characteristic maneuvers of certain environments, e.g., low-speed collision avoidance and continuous lane changing, both characteristics of high traffic urban environments. Another variable related to the change of the vehicle direction is the angular displacement around the z axis, also known as _heading_. The _heading_ provides information on the vehicle's turning maneuvers in angular magnitudes. Differentiation of the _heading_ variable in time produces a well-known magnitude, the yaw rate. The yaw rate provides information on the vehicle-rotation velocity around its \(z\) axis, and it is a valuable factor in vehicle stability assessment. The _heading_ was not directly used as a feature in this work due to it acting as a \"compass\" that precisely gives away the vehicles' orientation relative to that at the starting position and can be considered data leakage.  
The rotation of the vehicle around its \(x\) axis is referred to as the _rolling_ angle. The rolling angle of the vehicle is affected by vehicle characteristics and by the vehicle's dynamics. The suspension subsystem of the vehicle can influence the _rolling_ ranges during turning maneuvers and, in some cases, the effect of the wind in the laterals of the vehicle can induce rolling if the suspension is not rigid enough. After performing a characterization of the rolling behavior of a vehicle, it is possible to use this variable to obtain useful information of the driving environment.  
Together with the _Heading_ and _Rolling_, there is the _Pitch_ angle, which represents the rotation of the vehicle around its lateral axis (\(y\) axis). This measurement is useful to measure the vehicle's inclination and can be used to monitor the effect of acceleration and braking maneuvers and as an indicator of the road grade.  
The _slip angle_ is a variable that correlates the direction of the movement of a vehicle with the direction of its longitudinal axis. When the vehicle is moving on a straight line, the slip angle is zero, and its absolute value increases as the vehicle takes a curve. The slip angle is greater for higher vehicle speeds and for a smaller instantaneous turning radius.
## 4 Model Results
### Design Code and Empirical Formulas
The critical perimeter (\(b_{0}\)) is calculated differently in BS 8110-97 when compared to Eurocode 2 and ACI 318-19, as it assumes a rectangular shape at a distance of \(1.5\,d\) from each column face. The factor \(k=\left(\frac{400}{d}\right)^{1/4}\), which accounts for the size effect, has a distinct formulation compared to the size effect factors in both Eurocode 2 and ACI 318-19. [PERSON] et al. 's [3] approach for determining the punching shear resistance of RC flat slabs without shear reinforcement offers an alternative method when compared to established design codes such as ACI 318-19, Eurocode 2, and BS 8110-97. [PERSON] et al. 'sapproach shares the same critical perimeter (\(b_{0}\)) as ACI 318-19. The key equations from these authors are as follows:  
\[V_{c}=v_{c}A_{0} \tag{12}\]  
\[v_{c}=0.51f_{c}^{0.41}\rho_{t}^{0.38}k \tag{13}\]  
\[k=\left(\frac{250}{d}\right)^{0.1} \tag{14}\]  
\[A_{o}=4(c+d)d \tag{15}\]  
[PERSON] et al. [4] propose an alternative method for determining the punching shear resistance (\(V_{c}\)) in RC flat slabs without shear reinforcement, which stands apart from other established design codes such as ACI 318-19, Eurocode 2, and BS 8110-97. In this approach, the punching shear resistance is calculated using unique equations and factors that incorporate several common parameters. The key equation in [PERSON] et al. 's approach is  
\[V_{c}=0.127\sqrt[3]{f_{c}}\sqrt{\rho_{t}f_{y}}\left(1+\frac{8d}{b_{0}}\right) \sqrt{1+\frac{125}{d}}b_{o}d \tag{16}\]  
[PERSON] et al. [27] presented a distinct approach for calculating the punching shear strength (\(V_{c}\)) in RC slab-column connections without shear reinforcement. They gathered data from 342 slab-column connections and used multiple linear regression to develop a model in logarithmic space. Their method provides an alternative to established design codes, such as the ones previously referred to. The key equation in [PERSON] et al. 's approach is  
\[V_{n}=92.43\left(f_{c}^{\prime}\right)^{1.21}\left(\frac{1}{\%\rho}\right)^{ 1.47}\left(b_{o}\right)^{0.42}\left(d\right)^{1.35}\left(k\right)^{4.66} \tag{17}\]  
In this method:  
\(f_{c}^{\prime}\) is the concrete compressive strength. \(\rho_{t}\) denotes the reinforcement ratio. \(b_{0}\) (mm) is the critical shear perimeter, as specified in ACI 318-14. \(d\) is the effective depth of the slab. \(k\) is an additional factor calculated as \(k=\sqrt{(n\rho)^{2}+2(n\rho)}-(n\rho)\), with \(n=E_{s}/E_{c}=2\times 10^{5}/4700\sqrt{f_{c}^{\prime}}\).
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 2 Basics of Fiber Bragg Grating (FBG)
### Temperature Compensation  
According to the sensing principle of FBG, strain and temperature changes directly affect the period of the index modulation as well as the effective index of refraction of FBG. It is noted that monitoring of geohazards are usually long-term tasks. Any change in environmental temperature alters the Bragg wavelength and consequently the measured strain data, especially for cases where large daily and seasonal temperature differences are encountered. As a consequence, temperature compensation is an inherent task for a FBG field monitoring system. In this area, two methods are frequently adopted, namely direct and indirect ones [14].  
A standard FBG sensor cannot possibly measure strain and temperature simultaneously. If the temperature variation \(\Delta T\) is captured using thermocouples, resistance thermometer detectors (RTDs) or ROTDR fibers, the strain measurement of an FBG sensor can be temperature compensated using the following equation [15]:  
\[\Delta\varepsilon=\frac{1}{c_{\varepsilon}}\bigg{(}\frac{\Delta\lambda_{B}}{ \lambda_{B}}-c_{T}\Delta T\bigg{)} \tag{5}\]  
Figure 2: Typical FBG interrogation configurations [12]. (**a**) FBG measurement with a broadband source and an OSA; (**b**) FBG measurement with tunable laser and reflected power detection.  
Alternatively, the indirect method of temperature compensation utilizes an additional FBG sensor that is installed in the same sensing region to compensate for the thermal induced strain [16; 17]. The thermal compensation sensor should be free of any mechanical strain, so a loose-buffered optical fiber where the core, cladding and coating can move freely inside the jacket, making this sensor immune to the deformation of soil or rock. Calibration should be made prior to field implementation. The discrimination of the wavelength shifts due to strain and temperature variations, i.e., \(\Delta\lambda_{B}{}^{e}\) and \(\Delta\lambda_{B}{}^{T}\), can be achieved. Consequently, the corrected strain measurement is obtained as follow [17]:  
\[\Delta\varepsilon=\frac{\Delta\lambda_{B}{}^{e}}{c_{e}\lambda_{B}}=\frac{ \Delta\lambda_{B}-\Delta\lambda_{B}{}^{T}}{c_{e}\lambda_{B}} \tag{6}\]
# Disguised Full-Duplex Covert Communications
## 6 Discussion
### Applications  
The considered system model and obtained solution may be used in various practical networks. In the military, a reconnaissance troop may place on the adversary side a counterfeit FD device that periodically reports situations while disguising itself as a typical half-duplex one. Moreover, the authors in [44] demonstrated the feasibility of FD on a low Earth orbit (LEO) satellite, and the future non-terrestrial military network would utilize the disguise tactic proposed in this paper for either defense or offense purposes. For surveillance, an IoT network administrator may exploit a disguised FD node to covertly monitor any suspicious users that misuse the network for prohibitive activities.
## 4 Kinematic and Dynamic Models of the Robotic Platform
### Dynamic Model
\[\eta=K^{+}\cdot\dot{q} \tag{54}\]  
\[K^{+}=\frac{r}{4}\left[ \begin{array}{cccc}1&1&1&1\\ 1&-1&1&-1\\ \frac{1}{l_{1}+l_{2}}&\frac{-1}{l_{1}+l_{2}}&\frac{1}{l_{1}+l_{2}}&\frac{1}{ l_{1}+l_{2}}\end{array} \right] \tag{55}\]  
For the same reason (transform the data from the operational to the robot space), the frame transformation matrices \(R^{R}_{w}(\theta)\) and \(R^{w}_{R}(\theta)\), have to be transformed through \(K^{+}\). In order to calculate the transformation, \(\dot{\theta}\) is figured out in (56). \[\dot{\theta}=\frac{r}{4\cdot(l_{1}+l_{2})}\cdot(\dot{\varphi}_{1}-\dot{ \varphi}_{2}-\dot{\varphi}_{3}+\dot{\varphi}_{4}) \tag{56}\]  
Lastly, to convert the dynamic model to the compact form [22], the final model in the robot space is shown in (57), where \(\bar{M}\) and \(\bar{C}\) are defined in (57) and (59). \[\bar{M}\cdot\ddot{q}+\bar{C}\cdot\dot{q}=\tau_{\varphi} \tag{57}\]  
\[\bar{M}=\left(E^{T}\right)^{-1}\cdot R^{R}_{w}(q)\cdot M_{R}\cdot R^{w}_{R}( q)\cdot K^{+}+M_{\varphi}\cdot E\cdot K^{+} \tag{58}\]  
\[\bar{C}=\left(E^{T}\right)^{-1}\cdot R^{R}_{w}(q)\cdot M_{R}\cdot\dot{R}^{w}_{ R}(q)\cdot K^{+} \tag{59}\]  
In the compact form, \(\bar{M}\) is the inertial matrix, \(\bar{C}\) is the centrifuge and Coriolis matrix, \(g(q)\) is the gravity vector and \(\tau\) is the torque applied by the motors. The content of these matrices is strongly important and relevant in the design of robotic arms, not so much for the platform. Its use is quite relevant in the design of joint controllers. Even so, it is possible to detail some important features about these matrices [22]:  
* The inertial matrix is very important for (a) the dynamic model (related to the kinematic energy), and for (b) the robot controller design (related to stability studies for robotic arms mainly). * The centrifuge and Coriolis matrix are also important to perform stability studies for control systems in robotic arms. * The gravity vector is present in non-designed robots, this is, a robot whose design does not provide gravity torques of compensation. In addition, for robotic platforms, which move in the horizontal plane, this vector does not appear in the dynamic model.
## 5 Experimental Results
### Evaluation Approach and Metrics  
We assess the effectiveness of our classification model through a rigorous evaluation methodology that incorporates k-fold cross-validation and various metrics, including the confusion matrix and classification report. The k-fold cross-validation technique is a crucial part of our methodology as it ensures a robust evaluation of the model's performance. This methodology divides the dataset into k partitions or folds, enabling multiple training rounds and testing on distinct data subsets. We conduct five-fold cross-validation to assess the model's performance, deliberately choosing a value of k as five. Each fold is the testing set, while the remaining folds constitute the training set. We iterate this process five times to ensure that each fold is used as the testing set precisely once. Within each iteration of k-fold cross-validation, the model undergoes training on the training set, which encompasses 80 percent of the available data. Subsequently, we evaluate the model on the corresponding testing set to assess its performance on previously unseen data.  
We employ metrics such as accuracy, F1 score, sensitivity, and specificity to assess the model's performance thoroughly.  
The accuracy metric evaluates the model's prediction accuracy, estimating its overall performance, and is computed as  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{17}\]  
where  
* _TP_ (True Positive) is when the predicted and actual output is true.
* _FP_ (False Positive) is when the predicted output is true but the actual output is false.
* _TN_ (True Negative) is when the predicted output is false and the actual output is also false.
* _FN_ (False Negative) is when the predicted output is false but the actual output is true.  
The F1 score is a statistical measure that combines precision and recall in a balanced manner, calculated as the harmonic mean of the two metrics. It offers a comprehensive assessment of the model's performance by simultaneously considering precision and recall, which proves advantageous in evaluating classification models and is given by  
\[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{18}\]  
and  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{19}\]  
Sensitivity, also known as recall, measures the proportion of actual positives correctly identified by the model and is computed as  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{20}\]  
Specificity measures the proportion of actual negatives correctly identified by the model and is computed as  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{21}\]
# Charging Platform of Chess-Pad Configuration for Unmanned Aerial Vehicle (UAV)
## 2 Materials and Methods  
The authors of this study proposes a solution for charging a UAV systematically without human interference; the charging system addresses the uncertainty of landing issues. This achievement occurs via direct contact with the unique design of the platform's copper plates supplied by a suitable amount of regulated charging power. That regulation depends on the battery's properties. The platform configuration has successively negatively and positively charged plates that supply power to the UAV. A DC adaptor power source was selected to be equivalent to that associated with the drone itself, with extra voltage due to the diodes forward voltage drops. Thus, the voltage of the power source (V\({}_{\text{in}}\)) can be calculated by the following equation:  
\[\text{V}_{\text{in}}=\text{V}_{\text{drone}}+\text{V}_{\text{d}} \tag{1}\]  
where V\({}_{\text{drone}}\) denotes the full charge voltage of the drone and V\({}_{\text{d}}\) denotes the forward voltage drop of the circuit diodes. This work implemented the voltage concurrence concept between source and load; no overcharge is possible because the voltage levels get to an equivalent state at the end of charging, and no current flows after that. Though the charging time is longer than other charging methods, this scheme improves the energy conversion process's efficiency since there is no DC-DC converter.  
Figure 1 shows the block diagram as a concept of the charging system design. Moreover, the charging system consists of two main parts. First, the onboard circuit that includes a polarity modulator circuit that acts as a voltage and signal modulator along with the already existing UAV battery. Second, the ground station consists of conductive materials plates of a proper size for the UAV's skids and a power supply. In this study, the ground platform was made to host the landing circuit connected to an energy source. As shown in Figure 2, the proposed system uses a design of the ground copper platform as a chess pad. The wiring connection method used in this design provides the connectivity of positive and negative polarities for the copper plate, which was selected to overcome the potential landing similarity in voltage polarity and inaccuracy in landing. Two copper plates are connected to the power supply to provide the rest of the platform's copper plates' power. To avoid short-circuiting, the isolation gap between the plats was made to be slightly further away than that of the drone pins' contact area. The charging works directly through the designed platform consisting of 16 copper plates; this enables the energy produced from the power supply to flow through the contact pins into the polarity modulator circuit, which then charges the UAV battery.  
Figure 1: Main system components of the presented charging platform. UAV: unmanned aerial vehicle.
## 3 Results and Discussion
### Calculation of Activation Energy  
The amounts of activation energy for diffusive mass transfer (E\({}_{\rm d}\)) and convective mass transfer (E\({}_{\rm c}\)) were obtained by plotting the diagram of ln De against 1/T and the diagram of ln ln against 1/T. The results of the activation energy in both the Crank and Dinner models with the correction factor (R\({}^{2}\)) for hot air and combined hot air-infrared methods are shown in Table 4. The lowest amounts of activation energy for both the diffusive and convective mass transfer modes were when hot air drying was used separately, and the highest amount of activation energy was also in the HA-IR500 method. In general, the activation energy increased with the involvement of infrared waves. The highest and lowest values of diffusion activation energy were 35.43 (kJ/mol) and 18.43 (kJ/mol), respectively. The lowest activation energy among the combined methods (HA-IR750) was about 34.94% higher than that of the hot air (HA70) method. The amount of diffusion activation energy was different from the results reported by other researchers. For example, values for the convective drying of apples in the range of 17.17-25.97 (kJ/mol) [31], for the convective drying of kiwi 12.19-29.93 (kJ/mol) [32], and the combined hot air-infrared drying of sweet potato in the range of 8.74-34.76 (kJ/mol) [25] were obtained. In this research, the highest and lowest values of convective activation energy were 48.72 (kJ/mol) and 26.41 (kJ/mol), respectively. This parameter in a study for convective drying of Cynara cardunculus was 70.78 kJ/mol [24], and in another study for convective drying of kiwi was in the range of 17.23-20.44 (kJ/mol) [33].
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 5 Improving Energy Efficiency in FL Using Emerging Technologies  
This section provides an in-depth look at the latest technologies designed to enhance wireless system performance. Specifically, we highlight advancements in FL-enabled wireless systems that leverage these emerging technologies to improve energy efficiency.
## 2 System Model
### Path Loss Model  
In the current paper, a two-state path loss model is considered. It is modeled as a shot noise process with a modified power law [27]. We divide path loss into two regions, with \(r<R_{0}\) and \(r\geq R_{0}\). The explicit expression for path loss \(L_{i}\left(r_{i}\right)\) for the link between UE and \(i\)th BS is denoted as  
\[L_{i}(r_{i})=\left\{\begin{array}{cc}K_{i}&r_{i}<R_{0}\\ K_{i}r_{i}^{-\eta}&|&r_{i}\geq R_{0}\end{array}\right. \tag{3}\]  
where \(r_{i}\) is the distance between \(BS_{i}\) and \(MT_{0}\), and \(\eta\) is the path loss exponent assumed constant in the network.  
\(K\) is an independently distributed random variable drawn from a common distribution and independent of \(r\). It accounts for the transmitted power, the channel gain and the antenna gain for the \(i\)th link, defined as \(K_{i}\ =\ P_{rx}H_{i}G_{i}\). The details of \(H_{i}\) and \(G_{i}\) are presented in the following subsections.  
**Remark 1**.: _In the present paper, we adopt this two-state path loss model, which has already included the simpler case by setting \(K(r\ <\ R_{0})\ =\ 0\) used in the authors' previous work [25]. In the region where \(r\ <\ R_{0}\)we approximate the path loss with a constant which can be interpreted as a rough average of the attenuation in the near-field region while for \(r\ >\ R_{0}\), the power law decay model is used._
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 4 Ice Thickness Calculation Using 3D Measurement
According to Figure 17, it will be calculated from the distance \(P=(x_{1},\,y_{1},z_{1})\) to \(q=(x_{2},\,y_{2},z_{2})\) in \(xyz-space\). Then, the calculation formula is shown as follows:  
\[d(P,q)=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}+\left  
\[d(P,q)\ \approx\ 10.05\]  
The three-dimensional calculation for ice thickness is adopted from Formula (29) above. Calculate the distance between the edge of the top line and the edge of the bottom line using Formula (30). After knowing the distance between both sides of the iced line, subtract the distances from the diameter of the lines without icing (60 mm) so that the ice thickness can be found. The formula for measuring ice thickness can be seen below. \[Ice\ thickness=D_{tb}-d_{l}. \tag{30}\]  
where \(D_{tb}\) is the distance between the top and bottom edge lines and \(d_{l}\) is the diameter of the cable without an ice load. Therefore, the ice thickness calculation in this research can be applied to measure line icing in straight or curved positions. Figure 18 shows an illustration of the ice thickness calculation using 3D measurements. According to Figure 18, the gold line shows the line detection results at the top, while the light blue line shows the line detection results at the bottom. The star element labeled A is a key point that will be used for ice thickness measurement in that area, while the other stars indicate key points in the PTLI scene. Based on Figure 18, to calculate the ice thickness in area A on the iced line, first find the farthest points of the iced line in the upper and lower boundary areas from point A to find points p and q. Point p is the farthest point from the upper line, and point q is the farthest from the lower line. If we have points p and q, we can easily find the ice thickness using Formulas (29) and (30).
## 5 Experiment Result and Comparative Study
### Experimental Data for Typical Manufacturing Defects  
Hermetic refrigeration compressors have a complex structure and many excitation sources. The raw vibration signals of six typical hermetic refrigeration compressors manufacturing defects are shown in Figure 5. It can be observed seen that the amplitude of #2 and #4 are 8 g and 0.8 g, respectively. The amplitudes of the other four typical manufacturing defect compressors are similar except for #2 and #5; the time-domain diagram of the vibration signal depicts a sine wave.  
A time-frequency image is a two-dimensional data matrix that carries more information in the time and frequency domains than a one\(-\)dimensional signal. The raw vibration signals are transformed by CWT and HHT, and the time-frequency images are shown in Figures 6 and 7. Notably, the CWT time-frequency image shows more features in the low-frequency part, and the HHT time-frequency image shows more features in the high-frequency part. It can be demonstrated that the features of two time-frequency imagesare complementary. It can be observed that the CWT time-frequency image has obvious frequency bands, while the HHT time-frequency image has apparent aliasing phenomena.  
Figures 6 and 7 show that the magnitude of the energy in the time-frequency image is proportional to the amplitude of the vibration signal. It can be seen that the time-domain diagram and the time-frequency images of some typical manufacturing defect compressors are very similar. The time-frequency characteristics of the two hermetic refrigeration compressors are different in the same type of manufacturing defect. Therefore, it is challenging to diagnose the manufacturing defects of hermetic refrigeration compressors by traditional methods.  
Figure 5: The time-domain diagram. (#0) Normal hermetic refrigeration compressor (#1) Discharge pipe contacting stator; (#2) Crankshaft contacting discharge pipe; (#3) Shedding of support spring; (#4) Incorrect position of silencer; (#5) Foreign body in suction valve.  
Figure 6: The CWT time-frequency image. (#0) Normal hermetic refrigeration compressor (#1) Discharge pipe contacting stator; (#2) Crankshaft contacting discharge pipe; (#3) Shedding of support spring; (#4) Incorrect position of silencer; (#5) Foreign body in suction valve.