esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Kinematic Analysis  
### Establishment of Coordinate System  
Because the tracks on both sides of the driving mechanism of the prototype are symmetrical to each other on the left and right sides, the center of symmetry \(O_{1}\) is used as the coordinate system of the vehicle body when creating the kinematic coordinate system of the prototype, and the coordinate system of the vehicle body is a coordinate system that changes with the motion of the prototype, as shown in Figure 9a, with the geodetic coordinate system [18; 19] of \(O\). The kinematical equations of the prototype are established by using the RPY attitude descriptive method. The vehicle body rolls around the X-axis at a traverse angle of \(\theta\), deflects around the Y-axis at a deflection angle of \(\beta\), and pitches around the Z-axis at a pitch angle of \(\omega\), and the prototype realizes steering around the Y-axis to travel when there is a speed difference between the left and right driving wheels.  
The Euler angle is chosen to represent the change in spatial position of the prototype, and the prototype is set to rotate around the X-axis by \(\theta\), around the Y-axis by \(\omega\), and around the Z-axis by \(\beta\). Therefore, the rotation matrix can be represented by the transformation matrix RPY \((\beta,\ \omega,\ \theta)\). It can be derived by the transformation:  
\[\begin{split} RPY(\beta,\omega,\theta)&=Rot(z, \beta)Rot(y,\omega)Rot(x,\theta)\\ &=\begin{bmatrix}\cos\theta\cos\omega&\cos\theta\sin\omega\sin \beta-\sin\theta\cos\beta&\cos\theta\sin\omega\cos\beta+\sin\theta\sin\beta&0\\ \sin\theta\cos\omega&\sin\theta\sin\omega\sin\beta+\cos\theta\cos\beta&\sin \theta\sin\omega\cos\beta-\cos\theta\sin\beta&0\\ -\sin\omega&\cos\omega\sin\beta&\cos\omega\cos\beta&0\\ 0&0&0&1\end{bmatrix}\end{split} \tag{22}\]  
Figure 8: Prototype driving over the groove: (**a**) front support wheel over groove; (**b**) rear support wheel over groove.  
Figure 9: Prototype kinematic coordinate system: (**a**) prototype kinematic coordinate system; (**b**) prototype planar kinematic model.  
When the prototype's plane movement in the body coordinate system, relative to the geodetic coordinate system, is equivalent to rotation around the \(Z\)-axis and an angle of \(\beta\), it results in the prototype's body coordinate system relative to the geodetic coordinate system of the transformation matrix, shown in Equation (23):  
\[T=\begin{bmatrix}\cos\beta&-\sin\beta&0\\ \sin\omega&\cos\omega&0\\ 0&0&1\end{bmatrix} \tag{23}\]
## 5 Instrument Used in Milan and the Specific Mapping Approach Solution Applied to the Case Study
### Resolution and \"Density\" of the Images  
As mentioned, the Heron(r) mobile mapping system allows images to be acquired at a frequency of 24 Hz and 4K resolution, rather than images at 5K resolution on-demand. For the Milan project, the mode of photographic acquisition for each type of asset has been defined. Recall that the project's goal is to enable its technicians to navigate within the survey through a simple photo-to-photo approach, allowing even three-dimensional measurements to be made by operating directly on the spherical photos. In the technical specifications, therefore, it was detailed which items required 4K photographic acquisition and which items required a minimum of 5K resolution. This was done to minimize the weight of the survey files. To avoid splitting in image views, it is possible to filter the number of spherical images, defining a minimum spatial distance below which spherical views are not displayed, thus preventing excessive spatial overlap between adjacent spherical views. For Heron(r) instrumentation, this function is available in the Reconstructor(r) software--Version 4.4.2. (Figure 18), employed to export the spherical views associated with the point cloud.  
Therefore, for the surveys in Milan, a 5K high resolution photo acquisition was defined, with a relative distance between spherical image acquisitions of no less than 5 m. In cases where, on the contrary, images were acquired automatically and in low resolution by the system, during post-processing, the images closer than 5 m were removed, since 4K continuous resolution captures too many images. Thus, the goal was to manage data acquisition limited to the needs of the project, as it is necessary to consider that the management and storage of computer-heavy data is a major source of cost, both in terms of slowing down and burdening data processing and in terms of storing and saving such data over time.
## 4 Experimental Results
### Analysis of Convolutional Neural Network Model Results  
This study conducted a comprehensive analysis of the one-dimensional magnetic leakage signals of 18 types of internal and external broken wires in steel wire ropes with diameters of 20 mm, 22 mm, and 24 mm. The signals were transformed into two-dimensional time-frequency maps using continuous wavelet transform (CWT), resulting in a total of 1800 image sample data of size 227 \(\times\) 227. The network training utilized 75% of the sample data, while the remaining 25% was used for testing. The convolutional neural network was trained with an iteration epoch number of 5, and the solving environment was set to CPU. The network structure consisted of three convolutional layers, two max pooling layers with a step size of 2, and an output layer for classifying the 18 types of broken wire faults. After each convolution, a normalization layer and ReLU activation function were applied. The classification results were visualized using a confusion matrix, which is shown in Figure 13. The convolutional neural network achieves a final accuracy of 88.44% with an error rate of 11.56%, with particular shortcomings observed in the recognition performance of the third and fourth broken wire features. Notably, the overall recognition accuracy of the convolutional neural network was superior to that of traditional BP neural networks and support vector machines.  
Figure 12: The confusion matrix based on SVM.
## 4 Discussion
#### Landfill Analysis in Italy  
In many European countries, landfill corresponds to the last stage before the potential of C&D waste is lost. Regarding Italy, the disposal, which the legislation identifies as residual in the waste management hierarchy, consists in the final treatment and storage of waste and scraps that cannot be further exploited. Typically, the disposal can be identified  
Figure 10: C&D waste material prepared to be recycled (tonnes). Source: Own elaboration from ISPRA data.  
Figure 9: Options for waste management Source: Own elaboration.  
with the deposit in landfills, even if the legislation includes in this definition all the preliminary treatment processes that are carried out on the waste in order to minimise the final impacts. Waste is only allowed to landfill if it complies with the admissibility criteria of the corresponding landfill category as established by the decree D.Lgs 13 January 2003, n. 36. To ascertain the admissibility of waste in landfills, some sampling and analysis methods are used. Taking into account that landfills for hazardous waste have a higher level of environmental protection than those for non-hazardous waste, and that the latter has a higher level of environmental protection than those for inert waste, the delivery of waste that meets the criteria for admission to any category of landfill in landfills with a higher level of protection is allowed. According to the last data collected by Eurostat [14], the mineral waste from construction and demolition in the country in 2021 has reached a value of 2.2% over the total production. The quantity of C&D waste disposed in the landfill had a value of 37 million tonnes in 2014, which decreased to 18 million tonnes in 2018. Through this data it can be observed the tendency of the waste destined to landfill that is in continuous decrease.
## 4 Results
### Thermal Simulation Verifications of the Study Area  
1. Surface temperature verification  
Surface temperature plays a critical role in thermal environment simulations. In this study, we conducted a comparison between the simulation results of pavements made of different materials and relevant historical measured data. At 14:00, the surface temperature of grassland in the current simulation scheme ranged approximately from 31.38 to 48.12 \({}^{\circ}\)C, with an average of 35.27 \({}^{\circ}\)C. This finding aligns closely with the average surface temperature of grassland recorded by [PERSON] at 14:00 during summer in Taiwan, which was 36 \({}^{\circ}\)C. Similarly, the surface temperature of concrete in the current scheme ranged from about 42.67 to 50.84 \({}^{\circ}\)C, averaging at 47.35 \({}^{\circ}\)C. This result mirrors the average concrete surface temperature of 45 \({}^{\circ}\)C measured by some researchers in Taiwan during the summer. In conclusion, the simulation results for surface temperature are consistent with the trends observed in the relevant literature, indicating a certain level of reliability in our simulations.  
2. Air temperature verification  
In the current simulation scenario, the air temperature at 13:00 ranges from 34.35 to 37.52 \({}^{\circ}\)C, with an average of 35.22 \({}^{\circ}\)C. Data from the Taichung Weather Station indicate that the air temperature at noon in July 2022 falls between 33.6 and 34.7 \({}^{\circ}\)C, averaging about 34.2 \({}^{\circ}\)C (averaging across sunny days with no rain in the preceding days). Similarly, environmental monitoring data from Tunghai University Smart Campus reveal that the air temperature at noon in July 2022 varies between 33.8 and 35.2 \({}^{\circ}\)C, averaging around 34.4 \({}^{\circ}\)C (averaging across sunny days with no rain in the preceding days). The simulated air temperature for each scheme closely aligns with the actual measured values, indicating that the simulation results provide a reliable reference point.  
3. Relative humidity verification  
In the current simulation settings, the relative humidity at 13:00 ranges from 52.07% to 68.59%, with an average of 62.67%. According to data from the Taichung Weather Station, the relative humidity at noon in July 2022 falls between 54% and 78%, averaging about 62% (averaging across sunny days with no rain in the preceding days). Similarly, based on environmental monitoring data from Tunghai University Smart Campus, the relative humidity at noon in July 2022 ranges from 49% to 59.3%, with an average of about 54.23% (averaging across sunny days with no rain in the preceding days). Overall, the simulated humidity for each scenario closely matches the measured values, indicating a reliable level of reference in the simulation results.
# Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
###### Abstract  
When a wireless sensor node's wireless communication fails after being deployed in an inaccessible area, the lost node cannot be repaired through a debugging interaction that relies on that communication. Visible light communication (VLC) as a supplement of radio wave communication can improve the transmission security at the physical layer due to its unidirectional propagation characteristic. Therefore, we implemented a VLC-based hybrid communication debugging system (HCDS) based on VLC using smartphone and sensor node. For the system's downlink, the smartphone is taken as the VLC gateway and sends the debugging codes to the sensor node by the flashlight. To improve the transmission efficiency of the downlink, we also propose a new coding method for source coding and channel coding, respectively. For the source coding, we analyze the binary instructions and compress the operands using bitmask techniques. The average compression rate of the binary structure reaches 84.11%. For the channel coding, we optimize dual-header pulse interval (DH-PIM) and propose overlapped DH-PIM (ODH-PIM) by introducing a flashlight half-on state. The flashlight half-on state can improve the representation capability of individual symbols. For the uplink of HCDS, we use the onboard LED of the sensor node to transmit feedback debugging information to the smartphone. At the same time, we design a novel encoding format of DH-PIM to optimize uplink transmission. Experimental results show that the optimized uplink transmission time and BER are reduced by 10.71% and 22%, compared with the original DH-PIM.  
sensor nodes; debugging; visible light communication; instruction; modulation 2021  
21 2021
# Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves
## 2 Methods
### Pulse Wave Model for Blood Pressure Estimation
#### 2.1.2 Features Extraction from Decomposed Waves Based on Pulse Wave  
First, the PPG pulse waves were fitted with Equation (1) and with Equation (2). For curve fitting, after data preprocessing and individual pulse wave separation, we used Matlab's lsqcurvefit to solve the nonlinear curve approximation by the least-squares method. The fitting of each pulse wave to three sets of Equations, such as Equations (1) or (2), gave 3 sets of parameters. First, the time difference between the peak of the first wave and the peak of the second wave of the three decomposed waves, \(\Delta\text{t}_{0,1}\), and the time difference between the peak of the first wave and the peak of the third wave, \(\Delta\text{t}_{0,2}\), were obtained as features corresponding to PTT. Next, the amplitude 'a' of the waves was used as a feature. The pulse wave amplitude was obtained from each of the three decomposition waves. Finally, the half-width at half-maximum (HWHM) of the waves was also used as a feature. The reason for using the half-width at half-maximum is that it is known that pulse  
Figure 2: PPG pulse decomposition into three constituent waves and extracted features.  
wave strain correlates with blood pressure, and changes in pulse wave strain are thought to affect the half-width [9]. The half-width at half-maximum was also obtained from each of the three decomposition waves. In this study, we investigated the possibility of estimating blood pressure using a total of eight features: the time difference \(\Delta\)t\({}_{0,1}\), \(\Delta\)t\({}_{0,2}\) from the three decomposed waveforms, the amplitudes a\({}_{0}\), a\({}_{1}\), and a\({}_{2}\) of the three waveforms, and the half-width at half-maximums HWHM\({}_{0}\), HWHM\({}_{1}\), and HWHM\({}_{2}\) of the three waveforms.
## 3 Results and Discussions
The structural shadow effects were investigated for 0\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\) solar panel positions with respect to the simulator. The results are presented in Table 2.  
A performance comparison with different types of recent UHF antennas is presented in Table 3. Most antennas suffer from different limitations, like larger size, occupied solar panel space and design complexity. Therefore, the proposed antenna facilitates a substantial trade-off between antenna performances and the size of 1U nanosatellite communication constraints. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Solar Panel Rotation (Deg)** & **Solar Panel Output Power (W)** & **Effective Power (\%)** \\ \hline Normal Solar panel & 0\({}^{\circ}\) & 0.926 & 100 \\ Solar power integrated with & 0\({}^{\circ}\) & 0.885 & 95.57 \\ Antenna structure & 45\({}^{\circ}\) & 0.663 & 71.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Solar panel output power investigation of the proposed antenna. Figure 6: Solar power output investigation with antenna structure integration.
## 2 Results
### Validation of 2D Sample Results  
To verify an appropriate algorithmic and computational implementation of the 2D sample models, a 3D cellular environment for a specific region was built from sample #1, see Figure 8.  
The trajectory of the first particle that achieves the first deposition with a malignant gland and the last particle that reaches the greatest depth and deposition with the malignant gland was determined, see Figure 9.  
Figure 8: Behavior of liposomes in the interstitial space of the histological sample by means of a 3D simulation. Healthy regions are shown in blue and cancerous regions in fuchsia.  
Figure 9: Trajectory of liposome deposition with malignant cell regions in colorectal cancer histology sample #1 using 3D simulation. Healthy regions are shown in blue and cancerous regions in fuchsia.  
As a result of the particle-gland coupling, a vectorized deposition efficiency with a value close to 1% was obtained. It is important to highlight that this value corresponds to a fraction of the tumor environment in 3D.  
The first particle reached a maximum velocity of \(3.20\times 10^{-8}\frac{\mathrm{m}}{\mathrm{s}}\) and a depth of \(1.78\times 10^{-4}\) m (Figure 10a), while the last particle to engage with the malignant cell region reached a maximum velocity of \(3.50\times 10^{-8}\frac{\mathrm{m}}{\mathrm{s}}\) and a depth of \(4.23\times 10^{-4}\) m (Figure 10b).
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 5 Detailed Analysis of Predictability Performance and Model Validation
### Forecasting Model  
#### 5.1.1 TCN  
Temporal convolutional network (TCN) is a model architecture developed based on convolutional neural networks (CNNs). The TCN model incorporates causal convolution, which ensures the temporal order of the input sequence, and dilated convolution, which expands the receptive field. Additionally, the residual connection block enhances the model's prediction accuracy. To mitigate overfitting, dropout is integrated into the model. The TCN architecture consists of causal convolution, dilated convolution, and residual connections, as illustrated in Figure 13.
## 5 Numeric Integration
### Choosing a Double-exponential Transform  
To proceed, the double-exponential transformation must be specified. All \(\phi\) considered by [PERSON] and [PERSON] [39] have the form  
\[\phi(x)=\frac{x}{1-\exp{(-\eta(x))}} \tag{51}\]  
Inserting this in Equation (47), the sine term can be recast to make \(b_{k}\) robust for large \(k\):  
\[b_{k}=\phi^{\prime}(k-\
u)(-1)^{k}\sin{\left(\frac{\pi(k-\
u)}{\mathrm{e}^{\eta (k-\
u)}-1}\right)} \tag{52}\]  
Next, the function \(\eta\) shall be chosen. It must fulfill the conditions  
\[\eta(x\rightarrow-\infty)\rightarrow-\infty\text{ exponentially} \tag{53}\]  
\[\eta(0)=0 \tag{54}\]  
\[\eta(x\rightarrow+\infty)\rightarrow\infty\text{ exponentially} \tag{55}\]  
Condition (54) guarantees that numerator and denominator of Equation (51) have a zero at the same location \(x=0\). This singularity is removable; the values  
\[\phi(0)=\frac{1}{\eta^{\prime}(0)},\quad\phi^{\prime}(0)=\frac{1}{2}\left(1- \frac{\eta^{\prime\prime}(0)}{\eta^{\prime}(0)^{2}}\right) \tag{56}\]  
are needed to compute \(a_{0}\) and \(b_{0}\) for \(\
u=0\). Originally, [PERSON] and [PERSON] [38] had proposed  
\[\eta_{\mathrm{OM}}(k):=2p\text{ sinh}(hk) \tag{57}\]  
with \(p=3\) or \(p=\pi\). The parameter \(h\) controls the mesh width in \(t\); it will be determined below in Equation (65). In a later study, [PERSON] and [PERSON] suggested a more complicated function \(\eta(x)\) that copes better with singularities near the real axis [39]. Since our kernel \(g(t)\) has no such singularities, we stay with the simple form Equation (57), extending it however by a linear term that decelerates the exponential asymptote at equal \(\eta^{\prime}(0)\):  
\[\eta(k):=2p\text{ sinh}(hk)+2 qhk \tag{58}\]  
Given the poor signal-to-noise ratio \(\delta/\epsilon\), it was not possible to find one parameterization for the entire \(\beta,\omega\) domain not covered by series expansions. Therefore, distinct sets of \(a_{k}\) and \(b_{k}\) are precomputed for five \(\beta\) ranges, using the parameter set shown in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \(\mathbf{\beta}\) & \(\mathbf{p}\) & \(\mathbf{q}\) \\ \hline
0.1...0.15 & 1.8 & 0.2 \\
0.15...0.25 & 1.6 & 0.4 \\
0.25...1.0 & 1.4 & 0.6 \\
1.0...1.75 & 1.0 & 0.2 \\
1.75...1.95 & 0.75 & 0.2 \\
1.95...2 & 0.15 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hand-optimized parameters \(p\), \(q\) for different \(\beta\) ranges, for use in the kernel \(\eta\) (58) of the double-exponential transform \(\phi\) (51).
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 6 Numerical Results and Performance Analysis
### Evaluation Performance of Core Network
In the NLOS channel, in the case that 2 \(\times\) 2 MIMO was employed, the throughput was increased considerably, overcoming the maximum capacity of the 16-QAM in the LOS scenario with 18 dB SNR.
## 2 Problem Formulation
### Objectives
#### 2.1.5 Gearbox Efficiency  
To analyze the performance of the planetary gearbox, various metrics can be employed, such as efficiency, torque, and speed ratios. The efficiency of the gearbox is defined as the ratio of output power to input power, and it is a crucial factor in determining the overall performance of the system. To calculate the efficiency of the single-gear pair in contact, we can use the model [4], which takes into account the sliding and rolling friction. Therefore, the total efficiency of a gear pair in contact can be calculated by integrating the instantaneous efficiency along the active line of action as follows:  
\[\eta_{gb}^{H}=\frac{1}{l_{a}}\int_{A}^{D}\eta_{i}(\xi)d\xi, \tag{13}\]  
where the instantaneous efficiency is determined using the numerical procedure described in [4] and calculated as  
\[\eta_{i}(\xi)=\frac{T_{2}}{T_{1}}\frac{1}{u_{gb}^{H}}, \tag{14}\]  
where \(T_{1}\) and \(T_{2}\) are the unknown torques acting on the pinion and driven gears, respectively, and \(u_{gb}^{H}\) is the relative gear ratio of the considered gear pair, \(l_{a}=\overline{A_{2}E_{2}}\) is the active length of the path of contact, \(\xi\) represents the coordinate in the coordinate system fixed with a line of action (LOA). Using the same procedure, the efficiency \(\eta_{ag}^{H}\) between the central and planet gears can be calculated.  
Since the planetary gearbox in Figure 1 has multiple interactions between gears in the system, in calculating the total efficiency of the complete gearbox, we start with the analysis of power flow. From the problem statement and Figure 1, it follows that the power is inputted from the central sun gear and exits from the gearbox through the shaft, which is connected to the planet carrier, while the central internal gear is stationary. Therefore, the total efficiency is  
\[\eta_{atH}^{b}=\frac{P_{out}}{P_{in}}=1-\frac{P_{loss}}{P_{in}}, \tag{15}\]  
where \(P_{out}\) is the output power captured at the exit shaft, and \(P_{in}\) is the input power at the sun gear, while \(P_{loss}\) is the total power loss, which can be calculated from the analysis of the relative efficiency \(\eta_{ab}^{H}\), according to the expression  
\[P_{loss}=\left(1-\eta_{ab}^{H}\right)T_{a}\omega_{a,rel}, \tag{16}\]  
where the \(\omega_{a,rel}\) is the relative angular speed of the central gear. Substituting (16) into the (15) and taking into consideration that the angular speed ratio is  
\[\frac{\omega_{a,rel}}{\omega_{a}}=\frac{\omega_{a}-\omega_{H}}{\omega_{a}}=1- \frac{\omega_{H}}{\omega_{a}}=1-\frac{1}{u_{aH}^{b}}=\frac{u_{ab}^{H}}{u_{ab} ^{H}-1}, \tag{17}\]  
where \(\omega_{a}\) is the absolute angular speed of the central gear, \(\omega_{H}\) is the absolute angular speed of the gear carrier, and the relationship in overall gear ratio \(u_{aH}^{b}\) is \(u_{aH}^{b}=1-u_{ab}^{H}\). Finally, we obtain the absolute efficiency of the considered planetary gearbox, which can be taken as \(f_{5}\) as follows:  
\[\eta_{aH}^{b}=\frac{1-\eta_{ag}^{H}\eta_{gb}^{H}u_{ab}^{H}}{1-u_{ab}^{H}}. \tag{18}\]