esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
Due to the losses, the RMS value for \(I_{2}\) was slightly inferior to the expected one, about 256 mA, and the RMS voltage over \(R_{L}\) was 3.3 V (Figure 19d). Subsequently, the voltage stresses over the capacitances \(C_{1}\) and \(C_{2}\) can be noticed in Figure 19e,f, respectively. The expected RMS voltages for the respective primary and secondary capacitances were close to 16.4 V and 9.4 V.  
Figure 18: Class-E circuit analysis. (**a**) DC source input current. (**b**) \(L_{e}\) and \(C_{e}\) current. (**c**) \(C_{m}\) current. (**d**) \(C_{e}\) voltage. (**e**) \(C_{s}\) and switch drain–source voltage. Figure 20 shows the active power drained in different parts of the circuit. In Figure 20a, the DC source input power is presented. As can be noticed, its value corresponded to 1.0 W. Figure 20b shows the power delivered to the PS WPT stage, which resulted in being about 0.91 W. Finally, the output power is depicted in Figure 20c, reaching 0.86 W. Based on the obtained values, it is possible to conclude that the Class-E part operated with an efficiency of 91%; the PS WPT part reached about 94%; the overall efficiency was near 86%. It is worth mentioning the Class-E efficiency was sufficiently good in comparison with the other current source circuits, and the PS WPT efficiency was close to that expected in the preliminary results presented in Table 4. In summary, the overall expected efficiency was significantly satisfactory for this range of power. Based on the comprehensive analysis of the simulation results, we can assertively conclude that the system's overall performance met, if not exceeded, the anticipated standards set forth for this project. The recorded values of the currents and voltages across the inductances and capacitances played a pivotal role in the system's behavior. It is imperative to understand that these measurements are not just mere numerical results; they offer valuable insights that are instrumental for the judicious selection of components. By ensuring the components align with these values, we can guarantee optimal performance and reliability, fortifying the system's robustness against potential anomalies or discrepancies in real-world applications. Figure 19: PS WPT circuit analysis. (**a**) Primary coil current. (**b**) \(C_{1}\) current. (**c**) Secondary current. (**d**) Load voltage. (**e**) \(C_{1}\) voltage. (**f**) \(C_{2}\) voltage.
## 2 Dynamic Modeling and Problem Formulation
### Oil Stirring Viscos Loss Modeling  
Due to high pressure in the deep sea, deep-sea electric oil-filled joint actuator will suffer from oil stirring viscosos loss, output shaft dynamic seal loss, and core loss. Since the cause and change rules of output shaft dynamic seal loss and core loss are very complicated, there are no strict expressions at present describing these two losses, and we only focus on the modeling of oil stirring viscosos loss.  
Many researchers have studied the cause and change rules of the oil stirring viscosos loss. The impact of oil type and motor structure on the oil stirring viscosos loss was studied in [7]. The influence of temperature and pressure on the change rules of the oil stirring viscosos loss was observed in [8]. In [9], the impact of pressure on the oil stirring viscosos loss was researched. As the motor structure and oil type are not changed after the oil-filled joint actuator molding, we adopted the change rules summarized by reference [8] to calculate the oil stirring viscosos loss in this paper.  
According to reference [8], the oil stirring viscosos loss of an oil-filled motor is a combination of the flank viscosos loss and the disk viscosos loss, which is expressed as  
\[T=T_{v1}+T_{v2}=\frac{\pi Ld^{3}\mu\omega}{4\delta}+\frac{\pi\big{(}d_{2}^{4}- d_{1}^{4}\big{)}\mu\omega}{32h}=k\mu\omega \tag{6}\]  
\[\mu=\mu_{0}\exp(\alpha P) \tag{7}\]  
where \(k=\frac{\pi Ld^{3}}{4\delta}+\frac{\pi(d_{2}^{4}-d_{1}^{4})}{32h}\) is a constant related to the motor geometry; \(d\) is the diameter of the rotor, \(d_{1}\) is the diameter of the rotor end face, \(d_{2}\) is the diameter of the end face of the motor housing, and \(h\) is the distance between the end face of the rotor and the end face of the motor housing; \(\omega\) is the angular velocity, \(P\) is oil pressure, \(\mu\) is the dynamic viscosity, \(\mu_{0}\) is the dynamic viscosity at given temperature \(T_{0}\) and pressure \(P_{0}\), and \(\alpha\) is the viscos-pressure coefficient.  
In general, the viscosity of oil is related to pressure and temperature, increasing with pressure and decreasing with temperature, which is a complicated changing rule. Therefore, researchers usually obtained the corresponding values of \(\mu_{0}\) and \(\alpha\) by experiments corresponding to the typical working conditions.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
###### Abstract  
In order to ensure the safety of transmission lines, the use of unmanned aerial vehicle (UAV) images for automatic object detection has important application prospects, such as the detection of birds' nests. The traditional bird's nest detection methods mainly include the study of morphological characteristics of the bird's nest. These methods have poor applicability and low accuracy. In this work, we propose a deep learning-based birds' nests automatic detection framework--region of interest (ROI) mining faster region-based convolutional neural networks (RCNN). First, the prior dimensions of anchors are obtained by using k-means clustering to improve the accuracy of coordinate boxes generation. Second, in order to balance the number of foreground and background samples in the training process, the focal loss function is introduced in the region proposal network (RPN) classification stage. Finally, the ROI mining module is added to solve the class imbalance problem in the classification stage, combined with the characteristics of difficult-to-classify bird's nest samples in the UAV images. After parameter optimization and experimental verification, the deep learning-based bird's nest automatic detection framework proposed in this work achieves high detection accuracy. In addition, the mean average precision (mAP) and formula 1 (F1) score of the proposed method are higher than the original faster RCNN and cascade RCNN. Our comparative analysis verifies the effectiveness of the proposed method.  
transmission line; bird's nest detection; convolutional neural network; deep learning +
Footnote †: journal: Applied Sciences
###### Abstract  
Non-Kolmogorov turbulence has been widely observed in free-space optical communication links and should be used to evaluate the system performance. We calculated the wavefront residual variance in the condition of the non-Kolmogorov turbulence model and deduced the mathematical expression of the probability density function (PDF) for the coupling efficiency (CE) of a single-mode fiber (SMF). The PDF was simulated, and the results showed its robustness and rationality. Moreover, an experiment was set up to verify the PDF with experimental distribution. The correlation coefficients are above 0.95 in all cases, which means the statistical model of the CE fitted the experimental distribution well.  
free-space optical communication; non-Kolmogorov turbulence; probability density function; coupling efficiency
## 3 Results and Discussion
### Experiments Using the Multi-Object Tracking Algorithm
This advancement not only contributes to the field of Multi-Object Tracking by addressing a well-known limitation, but also sets a new standard for robustness and reliability in varied settings. Table 4 demonstrates the effectiveness of individual improvements to both the detector and the tracker, with the enhancements to the tracking strategy, in particular, resulting in a significant improvement in the algorithm's tracking performance. The improvements across various metrics are notable. Specifically, the proposed algorithm increased by nearly six percentage points in the MOTA index, nearly seven percentage points in the IDF1 index, and reduced by 40% in IDSWs. Upon analyzing the principles behind the improvements, it can be seen that the modified YOLOX_s detector has enhanced the algorithm's ability to recognize small-scale objects and targets in dimly lit environments. Meanwhile, the improved DeepSORT  
Figure 10: Tracking results of proposed algorithm indoors. Figure 9: Tracking results of ByteTrack indoors. algorithm provides a solution to the issue of abrupt changes in target positions at boundary areas in panoramic scenes. The combination of these two methods has resulted in an even better performance overall. These quantitative metrics underscore the success of modifications in enhancing the algorithm's overall tracking accuracy and reliability. The increase in MOTA highlights the proposed algorithm's enhanced ability to accurately track multiple objects without losing their identities. The improvement in the IDF1 score reflects better identity matching over time, indicating that the proposed algorithm is more effective at consistently tracking individual targets across frames. Lastly, the significant reduction in ID switches (IDSWs) points to the proposed algorithm's improved capability to correctly maintain target identities without erroneously swapping them, which is crucial for applications requiring precise individual tracking over extended periods or through complex scenes.
## 3 Experimental Results and Discussion
### Experimental Platform Setup  
The robotic system included a 6-DOF industrial robot (KUKA KR16 R1610), a servo screw unfastening tool (AND ENGINGEERING, ACT-64 XX) for unfastening \(M\)10 screws, and a monocular camera (HIKVISION, MV-CE200-10 GM), whose the internal parameters were as follows: the focus was 25 mm, \(S_{x}\) was 2.4 \(\upmu\)m, \(S_{y}\) was 2.4 \(\upmu\)m, and resolution was 5472 \(\times\) 3648). The repetitive positioning accuracy of the robot is \(\pm\)0.04 mm (ISO 9283) [35]. The accuracy has little influence on screw-tool engagement. Therefore, in this work, the positioning accuracy of the robot is not taken into account during the engagement accuracy analysis.  
A hexagonal screw removal platform was constructed, as shown in Figure 21. The workpiece to be disassembled denoted a T-shaped plate with seven \(M\)10 screw holes and a size of \(700\times 450\times 300\) mm. Three screws were located on the horizontal plane with a spacing of 50 mm. The other screws were located on the vertical plane with a spacing of 50 mm. The three screws were set up, and distances from their bottoms to the workpiece surface were 15 mm (#1), 10 mm (#2), and 5 mm (#3).  
In the proposed strategy, during the camera pose adjustment step, the characteristic circle was used to determine the camera orientation and position relative to a screw, which provided the necessary information for the pose adjustment whose purpose was to improve the screw image quality. This step aimed to increase the calculation accuracy of the screw pose. Therefore, the number of planes determined the number of characteristic circles needed for the pose adjustment. Namely, the proposed system only needed to have one single characteristic circle for each of the surfaces. Therefore, in the experimental platform, characteristic circles were located on the horizontal and vertical planes of the workpiece and were generally far away from obstacle surfaces of the workpiece to avoid collisions during the rotational and translational motions.  
The screw-unfastening system used the Visual Studio Platform, and the program was written in the C# programming language.
## 3 Materials and Methods
### MLP Classifier
#### 3.3.3 End-to-End Method  
To mitigate the need for exhaustive feature extraction on the wearable device, we also propose a deep convolutional neural network model that enables feature learning directly from the short-term ECG recordings. In this workflow, the ECG signal recorded by the device is homomorphically encrypted on the device and sent to the cloud for analysis and classification. To speed up the computations in deep-learning models, the data are normalized to zero mean and unit variance. Moreover, since the length of ECG recordings in the database ranges from 9 to 61s, and to maximize the detection accuracy for atrial fibrillation, the maximum length is considered for analysis. Therefore, ECG recordings shorter than 61 s are zero-padded. No further transformations (e.g., denoising) are per  
Figure 2: Workflow of the proposed privacy-preserving feature-based ECG classification method.  
formed in the preprocessing part (data padding and normalization are performed on the device). However, these operations can also be performed on the cloud.  
The proposed workflow is outlined in Figure 3. The deep learning-based classifier has two main components: the CNN-based feature extractor and the MLP-based classifier. The network takes as input a 61-second encrypted ECG recording with a sampling rate of 300 Hz. Hence, the encrypted ECG signal goes through three stages of convolution, non-linearity, and down-sampling, through which relevant features are extracted. One-dimensional convolutional layers are used to learn local features (i.e., feature maps) from the time sequence data, while average pooling layers are used to reduce the dimension of the data. By stacking a series of such operations, the network is able to provide a hierarchy of features. The initial feature maps are obtained by convolving the encrypted ECG signal with 32 filters of kernel size \(55\times 1\). The resulting feature maps are passed to a tanh non-linearity function, and their complexity is reduced by a factor of 10 through the pooling operation. The down-sampled feature maps are further used as input to the next block of computation, which generates a set of 32 new feature maps by convolving the input feature maps with 32 filters of kernel size \(25\times 1\). Again, their resolution is reduced by a factor of 5 by the pooling operation. The last convolution block uses 32 filters of kernel size \(10\times 1\), tanh activation, and a pooling size of 5. The feature extraction process ends when the data reaches the classifier. Consequently, the resulting features are fed into an MLP-based network that classifies the ECG recording into one of the four classes. The MLP-based classifier consists of a fully connected hidden layer with 32 units and tanh activation function, followed by a fully connected output layer with four units and a softmax function.  
The encrypted parameters of the network are optimized over 100 epochs to minimize categorical cross-entropy loss. Training is performed with batches of 32 data samples using SGD and a learning rate of 0.01.
## 6 Results and Discussion
### Descriptive Statistics of the Experimental Dataset Variables
#### 6.1.3 Water Content vs. Density  
* A moderate negative correlation (r \(\approx\) -0.65) indicates that samples with higher moisture content generally exhibit lower dry density (lower-left cluster on the scatter plot), as expected when porosity increases
## 3. Mathematic Model of CDR on the Ground and Water Surface
### Dynamic Model of CDR on the Ground and the Water Surface
\(X_{u}\), \(X_{|u|u}\), \(Y_{u}\), \(Y_{|v|v}\), \(N_{\omega}\), \(N_{|\omega|\omega}\) are hydrodynamic damping parameters. According to Assumption 5, we do not discuss the conversion of motor torque to traction and rotation torque. The traction force and rotation torque of the CDR in both water and ground environments are expressed as \(F_{u}\) and \(T_{r}\). By comparing Formula (9) with Formula (11), the dynamic models of the robot on the ground and the water surface are similar except for the changes of robot inertia parameters and the drag force caused by different working environments. The dynamics equations of the CDR can be expressed uniformly as:  
\[\left\{ \begin{array}{l}\dot{u}=k_{1}v\omega+f_{u}+\frac{F_{u}}{m_{1}}+\frac {d_{u}}{m_{1}}\\ \dot{v}=-k_{2}u\omega+f_{v}+\frac{d_{v}}{m_{2}}\\ \dot{\omega}=k_{3}uv+f_{r}+\frac{T_{r}}{m_{3}}+\frac{d_{r}}{m_{3}}\end{array} \right. \tag{12}\]  
When the robot moves on the water surface, dynamic parameters of the CDR can be expressed as: \(m_{1}=m-X_{\dot{u}}\), \(m_{2}=m-Y_{\dot{v}}\), \(m_{3}=I-N_{r}\), \(k_{1}=m_{22}/m_{11}\), \(k_{2}=m_{11}/m_{22}\), \(k_{3}=(m_{11}-m_{22})/m_{33}\), \(f_{u}=-\frac{X_{u}}{m_{11}}u-\frac{X_{u|u|}}{m_{11}}|u|\), \(f_{v}=-\frac{Y_{u}}{m_{22}}v-\frac{Y_{u|v|}}{m_{22}}|v|\), \(f_{\omega}=-\frac{N_{u|v|}}{m_{33}}\omega-\frac{N_{u|v|}}{m_{33}}|\omega|\omega|\). \(f_{u}\), \(f_{v}\), \(f_{\omega}\) are water force. When the robot moves on the ground, the dynamic parameters are \(m_{1}=m\), \(m_{2}=m\), \(m_{3}=I\), \(k_{1}=1\), \(k_{2}=1\), \(k_{3}=1\). \(f_{u}=0\), \(f_{v}=0\), \(f_{r}=0\). \(f_{i}(i=u,v,r)\) are the air force of the environment. \(d_{i}(i=u,v,r)\) are unknown and time-varying disturbances.
## 4 Biosensor Application of Ramified or Dendritic Electrodeposited Structures  
Dendritic structures that can form during crystallization from solution or electrodeposition form on the basis of fast growth along one axis, which represents an energetically favorable crystallographic face. The dendritic structures that often emerge under conditions of a stronger driving force for deposition such as a more negative potential may require the addition of a structure-directing agent that favorably adsorbs on a particular crystal face. In the case of gold surfaces, amine-containing compounds would be frequent choices. Other observed morphologies have been classified as 'fractal', which is meant to imply morphological self-similarity on multiple length scales [65]. The rapid growth and advancement of an interface can lead to interfacial instability [66], and hence, the emergence of ramified or irregular morphologies such as those referred to as 'flower-like'. Electrodeposition has been modeled theoretically [67], and has been the subject of computer simulations [68]. Ramified or dendritic nanostructures provide an increased surface-to-volume ratio when particles of the same volume of material are compared. The surfaces of such nanostructures will have regions or directions of greater curvature that may allow larger molecules to pack in a less crowded manner, making terminal functional groups or binding sites more accessible. For example, the deflection angle between neighboring oligonucleotides has been calculated to be greater for small particles of greater curvature [69]. Ramified or dendritic nanostructures will project further out into solution, such that biomolecules may be more effectively presented for interaction with their binding partners or substrates. Dendritic structures present a different distribution of crystal faces, and in the case of gold, the Au (111) face is favored along the growth axis, and present in the structure to a greater extent than for spherical gold nanoparticles [70].
# Temperature Fluctuation Attenuation of Circulating Cooling Water Using Dynamic Thermal Filtering
## 4 Experiment and Discussion  
### Experimental Setup  
The effectiveness of the proposed DTFM method is experimentally verified. The schematic diagram and photo of the experimental setup are shown in Figures 4 and 5 respectively.  
Figure 4: Schematic diagram of experimental setup.  
Figure 3: Theoretical temperature fluctuation attenuation ratio of the proposed DTFM attenuator.  
Two CCW machines (STTP5000-1/10 of SMEE) are used to produce the streams of CCW with different temperatures. Each CCW machine is capable of supplying CCW with a maximum flow rate of 55 L/min and temperature stability of \(\pm\)0.01 \({}^{\circ}\)C. The two streams of CCW are alternately pumped into the DTFM attenuator through the control of a pair of electromagnetic three-way valves. In this way, a stream of CCW with square temperature waveform is produced, serving as the standard test signal for the DTFM attenuator. The amplitude of the square waveform depends on the difference in temperature of the two CCW streams from the two CCW machines, and the frequency of the square waveform depends on the switching frequency of the three-way valves.  
Two temperature sensors (5641 of Fluke) with an accuracy of \(\pm\)0.001 \({}^{\circ}\)C are used to monitor the temperature at point A and B of the DTFM attenuator. A flowmeter (FD-Q10C of Keyence) with the repeatability of \(\pm\)0.5% in the full measuring range of 0-40 L/min is used to measure the CCW flow rate. A control module (homemade with STM32F407 as CPU) is used for valve control and data acquisition. A PC is used as the human machine interface.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 5 Simulation Results and Analysis
### Processing Delay Analysis  
In this section, we indicate the group delay of our method. Figure 11 shows the desired sound pressure and the reconstructed sound pressure of a signal containing three frequency components of \([600,850,1300]\) Hz at the point \([-0.13,0.07,0.02]\) m. We can obtain from Figure 11 that the processing delay of the system is \(1046-774=272\) samples, which equals to \(L+\lceil R_{Q}F_{s}/c\rceil\). The \(L\) samples of the delay is from the group delay of the proposed filter \(\rho_{n}(\
u,R_{Q})\), while \(R_{Q}F_{s}/c\) is the delay introducing by the Legendre function within filter \(p_{n}(\
u,r)\) to reconstruct the sound pressure at a point with the time domain spherical harmonic coefficients. Comparing to a conventional frequency domain scenario with 512 frame-size and 75% of overlap Short Time Fourier transformation, which refers to a 2048 samples of delay [43], our proposed method can significantly reduce the processing delay.  
Comparing to one of the start-of-art frequency domain spherical harmonic filter designs [44], which states a 75 ms delay with a 900 sample long filter, our method can achieve a 972 samples (20.25 ms with 48k Hz sampling frequency) delay with the same length of filter. Meanwhile, as our method is processed in time domain, there is nothing to stop us from doing a sample by sample signal processing instead of frame based signal processing. This sample based processing considerably extends the application of spherical harmonic analysis.