esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 2 Spatial Perception Correntropy Matrix
### Spatial Perception of Window
The cosine distance between the target pixel and the surrounding pixel can be expressed as:  
\[cos(\textbf{p}_{1},\textbf{p}_{i})=\frac{<\textbf{p}_{1},\textbf{p}_{i}>}{\| \textbf{p}_{1}\|_{2}\cdot\|\textbf{p}_{i}\|_{2}},i=1,2,\ldots,L^{2} \tag{1}\]  
where \(\langle\cdot\rangle\) and \(\|\cdot\|_{2}\) represent the inner product and \(L^{2}\)-norm. Through this formula, the window \(\textbf{W}_{t}\) with the largest average value of the first \(S\) surrounding pixels is obtained. The pixel-averaged cosine distance \(\textbf{CW}_{t}\) of the window \(\textbf{W}_{t}\) is expressed as:  
\[\textbf{CW}_{t}=\frac{1}{S}\sum_{i=1}^{S}cos(\textbf{p}_{1},\textbf{p}_{i}),t =1,2,\ldots,9 \tag{2}\]  
After selecting the optimal window \(\textbf{W}_{t}\), the correntropy matrix is constructed by the first \(K-1\) most similar pixel vectors in \(\textbf{W}_{t}\).
## 3 Experimental System  
In order to validate the proposed methods, a ventilation duct system with side-wall cavities is built up, which is shown in Figure 3. A centrifugal fan is used to generate the air flow, which is equipped with mufflers at its inlet and outlet, respectively. The wind speed can be adjusted from 6.0 to 35.0 m/s. The geometry of the experimental duct system is presented in Figure 3a and the dimensions are listed as follows: 80 \(\times\) 80 mm for the overall cross-section and 12 mm for the wall thickness; 870 mm for the distances between the test microphones and the cavity; \(l=60\) mm for the cavity width; the cavity depth \(H\) is variable.  
To reduce the flow-induced resonance noise, the ANC system is established at the cavity position of the experimental system, as shown in Figure 4. A loudspeaker is used as the secondary source, which is installed at the bottom of the cavity. An electret condenser microphone (ECM) is used as the error microphone, which is placed near the leading edge of the cavity. The resonance frequency of the flow-induced noise is first estimated. The noise power spectrum inside the duct is tested, and the frequency corresponding to the peak value is taken as the estimation. The frequency resolution of the noise power spectrum is 1.25 Hz, which is also the upper bound of the frequency estimation error. The estimated frequency is used by the controller to generate the tonal reference signal. Then, a secondary path modelling process is conducted, in which a tonal stimulus with  
Figure 3: The experimental system: (**a**) the schematic diagram; (**b**) the experimental setup.  
the estimated frequency is used. The least mean square (LMS) algorithm is utilized to calculate the model of the secondary path model. The sampling frequency of the controller is 16k Hz. The orders of the control filter and the secondary model are both set to 150. The noise reduction performance in the duct is evaluated by comparing the acoustic energy of the narrowband resonance noise at the error microphone, test microphone I and test microphone II before and after the control system is switched on.
###### Abstract  
This paper presents a control strategy that secures both precision and manipulation sensitivity of remote center motion with direct teaching for a surgical assistant robot. Remote center motion is an essential function of conventional laparoscopic surgery, and the most intuitive way a surgeon manipulates a robot is through direct teaching. The surgical assistant robot must maintain the position of the insertion port in three-dimensional space during the four-degree-of-freedom motions such as pan, tilt, spin, and forward/backward. In addition, the robot should move smoothly when controlling it with the hands during the surgery. In this study, a six-degree-of-freedom collaborative robot performs the cone-shaped trajectory with pan and tilt motion of an end-effector keeping the position of the remote center. Instead of the bulky mechanically constrained remote center motion mechanism, a conventional collaborative robot is used to mimic the wrist movement of a scrub nurse. A force/torque sensor that is attached between the robot and end-effector estimates the surgeon's intention. A direct teaching control strategy based on position control is applied to guarantee precise remote center position maintenance performance. A motion generation algorithm is designed to generate motion by utilizing a force/torque sensor value. The parameters of the motion generation algorithm are optimized so that the robot can be operated with uniform sensitivity in all directions. The precision of remote center motion and the torque required for direct teaching are analyzed through pan and tilt motion experiments.  
surgical assistant robot; remote center motion; direct teaching; impedance control 2021  
15 April 2021  
Accepted: 6 May 2021  
Subject: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 2 Experimental and Theoretical Tools
### Diagnostic Techniques
#### 2.3.1 Schlieren Imaging  
The schlieren technique is based on the fact that when a light ray travels through a medium with refractive index gradients, it suffers a deflection due to the refraction phenomenon [25]. Accordingly, any variations of refractive index such as those produced by density variations at the injection of fuel can be recorded as different gray levels in an image. Consequently, this technique allows to observe the local density variations that the fuel air mixing, auto ignition or flame development provoke. In this  
Figure 1: Optical arrangement for the first set up (**top**) and second setup (**bottom**).  
experiments, a high-speed single-pass schlieren imaging configuration was implemented to visualize the spray and flame boundaries at any operating conditions, with an optical arrangement similar as that described in [26].  
On the illumination side, light from a xenon lamp is driven with a liquid light guide into an iris diaphragm, to generate a point light source at the focal length plane of a parabolic mirror (f = 610 mm, D = 150 mm) so that the measurement area is illuminated with a collimated beam. In addition, to avoid interference and restrict the spectrum of the xenon lamp in the DBI and NL images commented later, a BG18 band pass filter was used. On the other side of the chamber, a spherical lens (f = 750 mm) was placed very close to optical access. This lens focusses the light onto the Fourier plane where an iris diaphragm with a cut-off diameter of around 3 mm was located. Additionally, another BG18 band pass filter was placed just before the Photron Fastcam SA-5 camera (Photron, San Diego, CA, USA) to minimize undesired light from sources other than the schlieren illumination lamp. The camera was equipped with a Carl Zeiss Makro-Planar T 100 mm f/2 ZF2 camera lens (Carl Zeiss, Aalen, Germany). Images were recorded at 25 kfps. The shutter time was 4.24 us and it was kept constant throughout all the experiments. The resolution was 800 \(\times\) 320 pixel with a total magnification of 6.9 pixel/mm. 20 injection cycles per test were recorded in all cases. The schlieren images have been used to describe the spray tip penetration under nonreactive, as well as the penetration under reactive conditions and the ignition delay. Image segmentation from the background has been performed by using the standard methodology developed by ECN [20].  
In this paper, ignition delay (ID) is obtained from schlieren images as the time at which the derivative of the accumulated pixel intensity within the spray boundaries is 50% of range between the minimum and maximum ones. This criterion was defined and explained in [27].
# Real-Time Compensation for Thermal Errors of the Milling Machine
## 5 Conclusions  
This study aimed to develop a compensation system for controlling the thermal induced positioning error of a CNC milling machine. The compensation system based on the data-driven thermal model was implemented on a milling machine and then verified through the machining test. According to the results, some conclusions can be drawn as follows:  
1. Selection of the locations for sensing the temperature fields of the machine tool is very important for establishing the correct thermal model. We first used 14 temperature sensors to examine the real temperature fields around the machine, from which the four major sensing points were selected.
2. Through the proposed thermal compensation system, the displacement variations on the \(x\)- and \(y\)-axes and the position error at the tool center can be controlled within 20 \(\upmu\)m.
3. Application of the thermal compensation system in milling operation examinations of the machined surface reveal that the surface accuracy can be controlled within \(-\)14 \(\upmu\)m (minimum) and 1 \(\upmu\)m (maximum) under the activation of the compensation system. Compared with the machined surface without thermal compensation, the overall efficiency is increased by 33%. The feasibility of the compensation system was successfully demonstrated in application in the milling operation.  
Figure 14: Machined surface (**a**) without compensation (**b**) with compensation.  
Finally, the proposed compensation system was developed for a three-axis milling machine. For application on four- or five-axis machine tools, the thermal model should be established following the proposed method. However, this would be a hard task since there are more heat sources in the rotary table and swivel head to be examined, which may cause the displacement errors of the rotational axis. This will be part of our future studies and experiments on multiple-axis machine tools.  
The authors would like to acknowledge the support of Quaser Machine Tools Inc. The assistance with the experiment from the Precision Machinery Research & Development Center (PMC) in Taiwan is also highly appreciated.  
[PERSON] contributed to the organization of the research work, including the experimental configuration, compensation module and manuscript preparation. [PERSON] and [PERSON] contributed to the experiment measurements and data analysis. [PERSON] and [PERSON] conducted the experimental work of the machining systems.  
The authors declare no conflict of interest.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 3 Theory and Calculations
### Experimental Validation  
#### 3.5.1 Test Rig Description  
In order to validate the proposed procedure, it was decided to compare the PFM with the standard ratio method (RM), on a real water system of enough complexity to highlight the specificities of the two procedures. The implemented circuit consisted of a significant number of terminals (eight in total) distributed two-by-two over four branches derived from a main collector, as shown in Figure 11.  
The adopted configuration made it possible to verify the balancing procedures applied firstly at the terminal level (in pairs) and then at the branch level, combining the manifold-based architecture and four double-user sub-systems. Flow measurement at terminals was conducted using ultrasonic probes embedded in electronically controlled Belimo EP015R flow valves, differential pressure measurement across the users was acquired via Thermokon mod. DPL1V differential pressure probes, while central flow measurement at the pump was carried out using Frank M350 rotameters. A detailed description of the circuit, the type of control and the measuring instruments used can be found in Appendix D. Figure 12 shows a picture of the realized test rig and Table 1 shows the main information about the measurement equipment used.  
Figure 11: Configuration of the system to be balanced.
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 2 Materials and Methods
### Factor Graph Inference Algorithm for Integrated Navigation  
The factor graph model converts the solution of the maximum posterior estimation for integrated navigation state variables into factor graph inference. The inference algorithm employed for the factor graph is the sum-product algorithm [33], widely applied in practical engineering for precise or approximate reasoning, with documented success [34]. In light of the imperative for robust real-time performance in navigation systems, incremental inference technology [35] is employed. This approach is driven by the continuous expansion of the integrated navigation factor graph model, evolving dynamically over time.
## Materials and Methods
### Robot Motion Control  
#### 2.5.1 Software Architecture  
The developed system architecture is depicted in Figure 8. In the proposed robotic surgical system, the control scheme consisted of two levels: one was a low-level joint motion controller, and the other was a high-level robot controller. At the low level, an industrial robot controller (RC8, DENSO Corporation, Aichi, Japan) controlled the manipulator joint positions, monitored arm singularities, and limited the joint velocities and workspace for safety motion. Motor drivers (ESCON, Maxon Motor, Sachseln, Switzerland) in the forceps controller box controlled the multi-DOF forceps joint positions. The high-level controller provided the target robot pose based on the input from the interface devices. The proposed control system was implemented on a 2.4 GHz Core i7 CONTEC computer running Linux (Ubuntu 16.04, Canonical) with real-time patches (RT-PREEMPT) and the Robot Operating System (ROS) framework on top of it. For the communication between the high-level controller and the robot manipulator, the b-CAP protocol was implemented with the C++ libraries provided by the robot manufacturer. The interface devices were connected to 16 bit data acquisition (DAQ) systems (PCI-1227U-AE, Advantech, USA), and the forceps low-level controller was connected to analog input/output boards (PCI-1216U-AE, Advantech, USA) and four axis encoder boards (PCI-1284-AE, Advantech, USA). The control loop ran at a rate of 500 Hz in synchronous mode with the RC8. In each cycle, the current robot pose was updated, and then, the target robot pose was computed and sent to the RC8 and the forceps controller box. In order to provide a modular and scalable architecture, communication with the interface devices was implemented as ROS nodes in the middleware level.  
Figure 7: **(a)** The neutral posture of the surgeon’s hands having a 45\({}^{\circ}\) offset to provide comfortable operation. **(b)** Serial-link mechanism workspace.  
In addition, virtual constraints (virtual RCM and workspace virtual walls) were implemented to enhance the operational safety by preventing damage to the surrounding tissues when accessing areas with high risks.
## 2 Related Work
### Communication between Devices with Mechanical Signals  
In a previous study, an acoustic-based input technique was proposed to turn a wide variety of everyday objects, such as walls and tables, which can transmit sounds, into interaction surfaces [18]. This type of interaction is advantageous in that it can be seamlessly integrated into the regular environments. Another recent work has demonstrated the feasibility of mechanical vibration for inter-device communication purposes [19]. They achieved short-range wireless communication between two mobile devices by utilizing the built-in vibrating motor for actuation and an accelerometer for sensing. Compared to other types of near-field wireless communication methods, the proposed system has advantages in terms of scalability (_i.e.,_ application to objects without radio functionalities).
## 4 Feature Extraction of RC Valve Fault
### Fault Characteristics Analysis of Excitation Signal
#### 4.1.2 Excitation Signals EEMD and Selection of IMFs  
The side frequency component of the current signal is the primary carrier for the failure characteristics of the RC valve. The power supply component and its higher harmonic will weaken the features of the side frequency component, which should be eliminated as much as possible. In this paper, EEMD is used to decompose the excitation signal to extract the side frequency component and other related components. After EEMD decomposition, 16 IMFs were obtained from each group of data and the spectrum of 16 IMFs was analyzed. The IMFs spectrum is shown in Figure 9, taking 6 mm leakage fault as an example.  
In Figure 9, IMF1 is the original current signal and IMF7 is very similar to the original current signal, including a strong power component and higher harmonic component, which is not conducive to the analysis of fault characteristic. IMF8 contains a significant sideband component, and 33.79 Hz is a single-side frequency component generated by RC operation and fault impact. Additionally, there are components that are related to the rotation frequency in IMF9, and 18.43 Hz is the rotation frequency related component that is generated by the operation frequency of the RC.  
Therefore, IMF8 and IMF9 of each group of IMFs were extracted for energy spectrum analysis, and the analysis results are shown in Figure 10. This figure shows that the IMF8 and IMF9 of each set of data all contain single side frequency components and rotation frequency related components. Moreover, IMF8 and IMF9 have no obvious harmonic components. Hence, the IMF8 and IMF9 of the current signal was used to reconstruct the excitation signal.  
Figure 9: Intrinsic mode functions (IMFs) spectrum of 6 mm leakage fault data.
## 4 Discussion
For the presented case study, 1556 images were used for aerial photogrammetry, 1555 images were used for aerial thermography and photogrammetry, 1114 images were used for terrestrial photogrammetry, and 34 images were used for ground-level thermography and several UV fluorescence images during the night. These image sets resulted in a 3D model in FBX file format of 43 MB, with 11 4K colour textures for the mesh parts (summing up to 41 MB) and 11 4K normal map textures (summing up to 103 MB). For imaging data,  
Figure 12: Data flow chart from acquisition through the processing steps, finally resulting in repository files that the user can access and view in the web application. 15 texture files were generated and processed from the three sensors involved (summing up to 108 MB). A total of 296 MB of data were used for the whole case study. These numbers can be optimized, but even so, this is a reasonable data weight for online usage.
## 3 Automated Diagnosis of COVID-19
### Pixel-Level Diagnosis: Segmentation-Based Models
#### 3.2.4 Evaluation  
The most commonly used evaluation metrics of the models related to segmentation are the accuracy and Area Under Curve (AUC). The average accuracy and AUC of diagnosing models based on X-ray scanning are 98.74% and 99, and the average accuracy and AUC of CT scanning-based models are 91.68% and 95.37. In fact, compared with X-ray, CT can more deeply examine the lesions in a certain position of an organ. However, here, the models based on X-ray scanning achieve a better performance. We think that there are a couple of reasons for this phenomenon, which are as followed. First, COVID-19 diagnosing models based on segmentation are far fewer than COVID-19 diagnosing models based on classification, and there are fewer models based on X-ray scanning, which makes the result relatively singular. Second, X-ray images are easier to collect than CT images. The average training set size of X-ray-based models is much larger than that of CT-based models, where the former is 3843 and the latter is only 2723.  
The performance of existing models related to segmentation is relatively high (average accuracy of 92.97% and average AUC of 95.89). In addition, in the models we have collected, the average COVID-19 images of test sets are 507, and the average total images of test sets are 829, where the average COVID-19/total ratio of test sets is 0.611:1. This ratio is still relatively balanced. Only when the ratio is more balanced can we train a more optimized model. Among them, by comparing MiniSeg with state-of-the-art image segmentation methods, [PERSON] et al. (2020) proved that MiniSeg not only achieves the best performance but also has a high efficiency, where MiniSeg has an accuracy of 99.15%. [PERSON] et al. (2020) proposed a \"3D Unet++--ResNet-50\" combined model, which achieved the best Area Under the Curve (AUC) of 0.991 among four combined models mentioned in the text. In addition to the accuracy and AUC, sensitivity and specificity are also good evaluation metrics. Sensitivity/specificity measured the fraction of positives/negatives that were correctly identified as positive/negative, so sensitivity/specificity was also known as the true positive/negative rate.
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 6 Discussion  
### Traditional Tracking Method  
The multiple signal classification (MUSIC) algorithm is a method based on matrix feature space decomposition. First, we obtain the covariance matrix estimate \(\hat{R}_{x}\) of the output signal based on \(N\) received signal vectors:  
\[\hat{R}_{x}=\frac{1}{K}\sum_{1}^{K}x_{k}x_{k}^{H}\,, \tag{32}\]  
where \(K\) is the signal sample size, \(x_{k}\) is the signal sample, and \(H\) computes the transpose of the matrix. Then, eigenvalue decomposition is performed on the covariance matrix obtained above:  
\[\hat{R}_{x} =\hat{U}\Sigma\hat{U}^{H} \tag{33}\] \[=\hat{U}_{S}\Sigma_{S}\hat{U}_{S}^{H}+\hat{U}_{N}\Sigma_{N}\hat{U }_{N}^{H}\]  
where \(\hat{U}_{S}\) is the subspace spanned by the eigenvectors corresponding to the large eigenvalues (i.e., the signal subspace), \(\hat{U}_{N}\) is the subspace spanned by the eigenvectors corresponding to the small eigenvalues (i.e., the noise subspace), and \(\Sigma=\text{diag}\big{(}\sigma_{1}^{2},\dots,\sigma_{N}^{2}\big{)}\) is the eigenvalue matrix. According to the order of the eigenvalues, the eigenvalues equal to the number of signals M and the corresponding eigenvectors are regarded as the signal part space, and the remaining eigenvalues and eigenvectors are regarded as the noise part space, and the noise matrix is obtained. Because the array direction vector is orthogonal to the noise subspace, the orientation of the target will appear minimal in the signal incident direction. Finally, the spatial spectral function \(P_{MUSIC}(\theta)\) is sorted out, and the maximum spectral peak is searched:  
\[P_{MUSIC}(\theta)=\frac{1}{\mathbf{a}^{H}(\theta)\hat{\mathbf{U}}_{N}\hat{ \mathbf{U}}_{N}^{H}\mathbf{a}(\theta)}\,, \tag{34}\]  
where \(\mathbf{a}(\theta)\) is the guiding value of the spatial array.  
The MUSIC algorithm is calculated under a number of known signal sources, which is impossible in practical application, and the number of sources can only be estimated based on the observed data. This method of source estimation based on the distribution of eigenvalues of array covariance matrix is perfect in theory, at least for independent sources and partially related sources, but in fact, due to the limited data length, the source number can only be determined by subjective judgment to a large extent. This is also the biggest drawback of the MUSIC algorithm.