esa-sceva/satcom-chunk-collection huggingface dataset:
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 5 Conclusions  
We introduce Siamese Convolutional Neural Networks (SCNNs) in this paper, which make full use of depth information and use a late fusion strategy to fuse the rgb stream with the depth stream to solve the loop closure detection task in RGB-D slam. We used SCNN-based features from a pre-trained model for scene classification. The experiment results on the public dataset show that SCNN-based image descriptors perform better than CNN and hand-crafted descriptors. In addition, the SCNN-based image descriptors can be applied to RGB-D slam in loop closure detection. Our study can be applied to most indoor scenes. However, for the special task of loop closure detection, an SCNN model suitable for outdoor scenes needs to be trained.  
All named authors initially contributed a significant part to the paper. The experimental model is built by [PERSON] ([PERSON]) and [PERSON] Analyses were carried out by [PERSON] ([PERSON]) and [PERSON] The organization of data was led by [PERSON] ([PERSON]) and [PERSON] ([PERSON]). The descriptions of text use were assisted by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **BoVW** & **GIST** & **CNN** & & **SCNN** \\ \hline Time(s) & 1.517 & 0.524 & \begin{tabular}{c} CPU \\ 0.142 \\ \end{tabular} & \begin{tabular}{c} GPU \\ 0.021 \\ \end{tabular} & \
\begin{tabular}{c} CPU \\ 0.208 \\ \end{tabular} &
\begin{tabular}{c} GPU \\ 0.029 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average computational time for different approaches.  
**Funding:** This research was funded by the National First-Class Discipline Program of Light Industry Technology and Engineering, grant number LITE2018-17. And it was also funded by the Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, grant number 2017070503B026-A01.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** \"New College Dataset\" at [[https://ori-drs.github.io/newer-college-dataset/](https://ori-drs.github.io/newer-college-dataset/)]([https://ori-drs.github.io/newer-college-dataset/](https://ori-drs.github.io/newer-college-dataset/)); \"NYU dataset\" at [[https://cs.nyu.edu/~silberman/datasets/](https://cs.nyu.edu/~silberman/datasets/)]([https://cs.nyu.edu/~silberman/datasets/](https://cs.nyu.edu/~silberman/datasets/)).  
**Acknowledgments:** The work was supported by National First-Class Discipline Program of Light Industry Technology and Engineering (LITE2018-17). And it was also sponsored by the Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University (2017070503B026-A01).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 4 Experiment Studies and Result Analysis
### Experiment Setup  
The experiments are performed with the UR5 robot (Universal Robots, Denmark), which is a collaborative robot used for various circumstances. Optoforce (Hungary-based) 6 Axis F/T force sensor (\(HEX-70-XE\)) is attached to the end effector of the robot to measure the interaction force. A \(3D\) printer probe with a spherical indentation tip (radius 5 mm, weight 200 g--close to the weight of handled surgical tools) is designed and fixed with the force sensor to perform the palpation task. The soft tissue is made by silicone (Poisson's ratio \(v=0.5\)), in which three hard inclusions (stiffer than the phantom tissue and can be considered as a tumor in the experiments) are embedded at different depths under the surface of the tissue (5 mm, 8 mm and 10 mm respectively). The robot and force sensor are running under the Robot Operating System (ROS) with the \(ur\_modern\_driver\)[40] for low-level control of the robot, and the Cartesian position of the robot is planned using the _moveit_ package. The experiments are conducted to palpate the soft tissue with four different setups: \(A\): No hard inclusion (tumor); \(B\): Hard inclusion (tumor) with 5 mm depth; \(C\): Hard inclusion (tumor) with 8 mm depth; and \(D\): Hard inclusion (tumor) with 10 mm depth (the depths denote the distance between the hard inclusion place and the surface of the soft tissue). The palpation task is performed along the \(Z\) axis of the robot, and the surface of the tissue is determined by various contact experiments, thus the indentation depth can be calculated from the Cartesian position of the robot. The whole experiment setup is shown in Figure 5.
## 3 Results
### Performance Indicators Comparison Between Match Outcomes
The median counter-press was 8.0 (IQR = 4.0) for losses, mean = 9.00, 95% CI [7.94, 10.06], SD = 3.08; 10.0 (IQR = 4.25) for draws, mean = 10.23, 95% CI [8.97, 11.49], SD = 3.12; and 11.0 (IQR = 4.0) for wins, mean = 11.29, 95% CI [10.10, 12.47], SD = 3.45. Regarding set-piece related activities (Figure 8), total set plays showed a marginal effect of match outcome, F(2, 93) = 3.09, \(p\leq 0.050\), \(\eta^{2}=0.062\), but pairwise comparisons revealed no significant differences between losing (M = 30.49, 95% CI [28.20, 32.77], SD = 6.65), drawing (M = 34.31, 95% CI [31.39, 37.23], SD = 7.23), and winning teams (M = 33.97, 95% CI [31.59, 36.35], SD = 6.93). Free kicks did not differ significantly across match outcomes, with means of 12.60 (95% CI [11.14, 14.06], SD = 4.26), 12.62 (95% CI [10.75, 14.49], SD = 4.63), and 13.83 (95% CI [12.32, 15.33], SD = 4.38) for losing, drawing, and winning teams, respectively. Similarly, penalties showed no significant differences, with means near zero for all groups (Lose: M = 0.09, 95% CI [\(-\)0.01, 0.18], SD = 0.28; Draw: M = 0.19, 95% CI [0.03, 0.36], SD = 0.40; Win: M = 0.20, 95% CI [0.04, 0.36], SD = 0.47). Corners differed significantly, \(\chi^{2}(2)=6.97\), \(p=0.031\), \(\epsilon^{2}=0.073\), where draws recorded more corners (Mdn = 4.5, M = 5.77, 95% CI [4.13, 7.41], SD = 4.06) compared to losses (Mdn = 3.0, M = 3.69, 95% CI [2.82, 4.55], SD = 2.52), with a significant pairwise Wilcoxon test (W = 3.39, \(p=0.043\)). Winning teams had a median of five corners (M = 4.94, 95% CI [3.98, 5.90], SD = 2.80), not differing significantly from draws or losses.
# Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography
###### Abstract  
Recently, some facilities have utilized the dual-energy subtraction (DES) technique for chest radiography to increase pulmonary lesion detectability. However, the availability of the technique is limited to certain facilities, in addition to other limitations, such as increased noise in high-energy images and motion artifacts with the one-shot and two-shot methods, respectively. The aim of this study was to develop artificial intelligence-based DES (AI-DES) technology for chest radiography to overcome these limitations. Using a trained pix2 pix model on clinically acquired chest radiograph pairs, we successfully converted 130 kV images into virtual 60 kV images that closely resemble the real images. The averaged peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between virtual and real 60 kV images were 33.8 dB and 0.984, respectively. We also achieved the production of soft-tissue- and bone-enhanced images using a weighted image subtraction process with the virtual 60 kV images. The soft-tissue-enhanced images exhibited sufficient bone suppression, particularly within lung fields. Although the bone-enhanced images contained artifacts on and around the lower thoracic and lumbar spines, superior sharpness and noise characteristics were presented. The main contribution of our development is its ability to provide selectively enhanced images for specific tissues using only high-energy images obtained via routine chest radiography. This suggests the potential to improve the detectability of pulmonary lesions while addressing challenges associated with the existing DES technique. However, further improvements are necessary to improve the image quality.  
Keywords:dual-energy subtraction; chest radiography; artificial intelligence; deep learning; pix2 pix sciences +
Footnote †: journal: Medical Imaging
# Nanomaterials for Healthcare Biosensing Applications
## 2 Carbon Allotrope-Based Nanomaterial Applications in Healthcare Biosensing
### Graphene and Graphene Derivatives
succeeded in detecting H\({}_{2}\)O\({}_{2}\) from living breast cancer cells using physisorption of Au-Pd bimetallic nanocubes (AuPdNCs) and RGO on GCE [101]. Due to the high electroactive surface area and exceptional electrical conductivity, the sensor displayed a low detection limit of 4 nM and a broad linear response in the range of 0.005 uM-3.5 mM. Diao and co-workers monitored propofol, an intravenous anaesthetic agent, in the presence of horseradish peroxidase (HRP) and H\({}_{2}\)O\({}_{2}\) using fluorescent GQDs derived from the carbonisation of citric acid [39]. The optical sensor determined propofol in a range of 5.34-89.07 mg L\({}^{-1}\) with an LOD of 0.5 mg L\({}^{-1}\).
## 3 Results and Discussion
### Feature Extraction and Selection
#### 3.2.3 Maximum Gradient Value  
Maximum gradient value (MGV): The response curve of the gas sensor had a maximum response value. Thus, the slope of the line segment between the maximum value and the initial value could be used to express the signal change speed in the initial stage.  
\[MGV=\frac{x_{\text{imax}}-x_{0}}{\text{t}} \tag{5}\]  
where \(x_{imax}\) is the maximum response value that corresponds to a sample; \(x_{0}\) is the initial response value of a sample; and \(t\) is the response time that corresponds to the maximum response value.
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 3 Pose Regression Methods
### Pose Regression Model
The target values of our model are the positions of the six joints, \(\mathbf{p}_{4}\), \(\mathbf{p}_{5}\), \(\mathbf{p}_{6}\), \(\mathbf{p}_{7}\), \(\mathbf{p}_{8}\), and \(\mathbf{p}_{9}\), which are the left elbow, the right elbow, left shoulder, right shoulder, spine, and pelvis positions, respectively. In order to mitigate the issue of overfitting, we employ two strategies: dropout and dataset augmentation. We apply a dropout solution to the first convolution layer and the first fully connected layer. Therefore, a neuron stops working at 0.7 probability--a 30% probability of dropping the neuron from the network. Furthermore, after acquiring the captured data, we re-generate the in-between pose data by applying linear interpolation of the position and orientation values of the joints. Finally, we use the mean squared error (MSE) loss function and obtain a continuous numerical output. Equation (2) defines how we calculate the MSE in our model, where \(n\) is the number of iterations (150 for the target joint) in one epoch, and \(R_{t}\) and \(\hat{R}_{t}\) are the ground truth and predicted values, respectively. Here, \(k\) is the counter for training steps for a specific target joint (from 0 to \(n\)). We train the network with five epochs and the Adam optimizer [41] with a learning rate of 0.001. \[\text{MSE}=\frac{\sum_{k=0}^{n}\left(R_{k}-\hat{R}_{k}\right)^{2}}{n}, \tag{2}\]
## 2 Materials and Methods
### Feature Extraction  
Compared to other available feature extraction techniques, an entropy-based method is more suitable for describing nonlinear and unstable dynamic EEG signals. Sample entropy (SampEn) is a new measure of time series complexity proposed by [PERSON] and [PERSON] [22]. Entropy is originally a thermodynamic concept that is a measure of the degree of chaos (disorder) in a thermodynamic system. After the establishment of information theory, the concept and theory of entropy have been developed. As a nonlinear dynamic parameter for measuring the incidence of new information in time series, entropy has been applied in many scientific fields [23]. Sample entropy is an improvement of the approximate entropy algorithm. Sample entropy is intended to reduce the error of approximate entropy. The sample entropy method is similar to the current approximate entropy method but with better precision [24]. Compared with approximate entropy, sample entropy has two major advantages: first, sample entropy does not include the comparison of its own data segments, and the calculation of the sample entropy does not depend on the data length; second, compared to approximate entropy, sample entropy has better consistency. Therefore, in the present work, we quantify the complexity of the EEG signal with the sample entropy method. The sample entropy is calculated as follows:  
1. For a time series consisting of \(N\) data \(x(1)\), \(x(2)\),..., \(x(N)\), reconstruct m-dimensional vector \(X_{m}(1)\), \(X_{m}(2)\),..., \(X_{m}(N-m+1)\), where \(X_{m}(i)=[x(i),x(i+1),\)..., \(x(i+m-1)]\).
2. Define the distance d \([X_{m}(i),\)\(X_{m}(j)]\) between vectors \(X_{m}(i)\) and \(X_{m}(j)\) as the absolute value of the maximum difference between the two elements. \[d[X_{m}(i),X_{m}(j)]=\max_{k=0,\cdots m-1}(|x(i+k)-x(j+k)|)\] (1)  
Figure 1: The names and positions of the electrodes.  
3. For a given \(X_{m}(i)\), count the number of \(j\) (\(1\leq j\leq N-m,j\
eq i\)) where the distance between \(X_{m}(i)\) and \(X_{m}(j)\) is less than or equal to \(r\), and record it as \(B_{i}\). For \(1\leq i\leq N-m\), define: \[B_{i}^{m}(r)=\left(N-m-1\right)^{-1}B_{i}\] (2) 4. Define \(B_{m}(r)\) as: \[B^{m}(r)=\left(N-m\right)^{-1}\sum_{i=1}^{N-m}B_{i}^{m}(r)\] (3) 5. Increase the number of dimensions to \(m+1\), calculate the number of distances between \(X_{m+1}(i)\) and \(X_{m+1}(j)\) that are less than or equal to r, and record it as \(A_{i}\). \(A_{i}^{m}(r)\) is defined as: \[A_{i}^{m}\Big{(}r)=\left(N-m-1\right)^{-1}A_{i}\] (4) 6. Define \(A^{m}(r)\) as: \[A^{m}(r)=\left(N-m\right)^{-1}\sum_{i=1}^{N-m}A_{i}^{m}(r)\] (5) In this way, \(B_{m}(r)\) is the probability of two sequences matching m points under the similarity tolerance \(r\), and \(A_{m}(r)\) is the probability of two sequences matching \(m+1\) points. The sample entropy is defined as: \[\text{SampEn}(m,r,N)=-\ln[\frac{A_{m}(r)}{B_{m}(r)}]\] (6)
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
## 5 Proposed Method  
Various filters can be used for removing the jitter noise. In this manuscript, Kalman filter is used as an optimal estimator and is designed accordingly. It is a recursive filter, which tracks the state of a linear dynamic system that contains noise, and is based on measurements made over time. More accurate estimation results can be obtained than by using only measurements at that moment. The Kalman filter, which recursively processes input data including noise, can predict optimal current state statistically [36; 37; 38; 39]. The application of the Kalman filter to the SFF system is shown in Figure 5.  
The system is defined by the position of each image frame in a 2D image sequence. The system state is changed by the jitter noise, which is the measurement noise, in the microscope. The optimal estimate of the system state is obtained by removing the jitter noise through the Kalman filter.  
The entire Kalman filter algorithm can be divided into two parts: prediction and update. The prediction refers to the prediction of the current state, and update means that a more accurate  
Figure 5: Application of Kalman filter to SFF system.  
prediction can be made through the values from the present state to the observed measurement. The prediction of the state and its variance is represented as follows:  
\[S=T\times S+C\times U \tag{17}\]  
\[V=T\times V\times T^{\prime}+N_{p} \tag{18}\]  
where, \(S\) is \"estimate of the system state\", \(T\) is \"transition coefficient of the state\", \(U\) is \"input\"; \(C\) is \"control coefficient of the input\", \(V\) is \"variance of the state estimate\", and \(N_{p}\) is \"variance of the process noise\". In the SFF system, \(S\) is represented as the position of each image frame in the 2D image sequence estimated by the Kalman filter, and \(C\), \(U\), and \(N_{p}\) are all set as 0, since there is no control input in the SFF system, and only jitter noise, as the measurement noise, is considered in this manuscript. Next, the computation of the Kalman gain is given for updating the predicted state as follows:  
\[G=V\times A^{\prime}\times inv(A\times V\times A^{\prime}+N_{m}) \tag{19}\]  
where, \(G\) is \"Kalman gain\", \(A\) is \"observation coefficient\", and \(N_{m}\) is \"variance of the measurement noise\". In an SFF system, \(N_{m}\) is defined as the variance of the previously modeled jitter noise. Finally, the update of the predicted state on the basis of the observed measurement is provided by:  
\[S=S+G\times(O-A\times S) \tag{20}\]  
\[V=V-G\times A\times V \tag{21}\]  
where, \(O\)
## 2 Materials and Methods
### Methods
#### 2.2.2 Cross-Well Seismic Computed Tomography Method  
Cross-well seismic computed tomography is mainly based on the difference in p-wave velocity between rock and soil mass and karst caves to realize the imaging of underground karst areas. The detection process needs to transmit multiple artificial seismic waves from different depths of one borehole, set up multiple detectors in another borehole to receive the seismic waves and obtain the first arrival time of the seismic waves on each geophone, then iteratively invert the wave velocity distribution between the two boreholes, and draw karst caves according to velocity.  
The cross-well seismic computed tomography method adopts the cross-hole seismic system developed by Geotomographie Company in Bad Salzdetfurth, Germany, and the high-performance seismometer of Seismic Source in the United States. The IPG1005 high-voltage energy storage transmitter of the in-hole launching system has a working voltage of 5 KV and an output energy of 1000 joules. The spark source SBS42P emits a P-wave source with a frequency of 5 KHz into the hole. The receiving device is the BHC-3 hydrophone chain, the standard pitch is 1 m, the hydrophone is equipped with a preamplifier, the maximum amplification can be 20 dB, the channel is 24, and the resonance frequency is 20 KHz. The borehole depth used was 5 m below the tunnel floor. During data collection, the number of shot points was adjusted according to the borehole depth and groundwater level, and data superposition was collected six times at each point to reduce background noise interference.  
The equipment used and the on-site acquisition process are shown in Figure 4B.  
After bad track elimination, initial extraction and inversion iteration of the collected seismic wave data are carried out using relevant software, the obtained wave velocity image is interpreted according to the in situ wave velocity test results, the interface of the rock and soil layer is identified, and the location, size, and shape of the concealed karst cave are delineated.
## 4 Experiment Results and Discussion
### Attitude Stabilization of Transition Mode  
Attitude stabilization is a key function during the transition mode. The transition mode requires the attitude to be stabilized to \(\xi_{d}=[0,0,0]^{T}\), which reduces the hydrodynamic torque as well as disturbance to the depth subsystem, making depth control more stable.  
During the transition mode, the rotors tilt to produce a forward force, and as the speed of the vehicle increases, the hydrofoil produces lift force to balance the weight of the vehicle in the water. The hydrofoil also produces torque, which is treated as a disturbance to the attitude stabilization.  
To verify the effect of the proposed disturbance-rejection control in transition mode, two experiments were carried out for the rotor tilt with the the LESO on and with the LESO off. The tilt ran for 10 s, tilting from \(0^{\circ}\) to \(60^{\circ}\), performed over the 10\(\sim\)20 s segment of the experiment. Figure 11 shows the effect of attitude stabilization on transition mode. Figure 12 shows control torque \(\tau\). Figure 13 shows estimation of disturbance \(d_{e}\).  
From Figure 11, we can see that the attitude-stabilization error with the LESO on is \(\pm\)0.005, which is much smaller than with the LESO off, which is \(\pm\)0.02. With the help of the LESO, the disturbance torque is compensated, and the attitude is stabilized to \([0,0,0]^{T}\) during transition mode. From Figure 12, we can also see that the input torque vibrated much more violently than when LESO is off, which costs more energy. Figure 13 shows the estimated disturbance torques during transition mode; this is obviously different from the attitude-stabilization and attitude-tracking modes. It is not a constant or harmonic disturbance, and cannot be measured. This experiment shows that the proposed control scheme can compensate for this kind of un  
Figure 10: Disturbance estimation with the LESO on using attitude tracking.  
Figure 11: Attitude-stabilization effect with the LESO on and off in transition mode.  
Figure 12: Control torque with the LESO on and off in transition mode.  
Overall, for most situations, the proposed controller and disturbance observer performs well in attitude-tracking tasks. However, harmonic disturbance cannot be estimated without phase delay, even if it is insignificant. Moreover, input torque vibration costs significant amounts of energy.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 2 Experimental Part
### Synthesis of Zn-SnO\({}_{2}\) Nanoparticles  
A modified sol-gel process [29] was successfully applied to prepare Zn(1% and 5%)-doped SnO\({}_{2}\) NPs. Firstly, 10 mmol of tin (IV) chloride (SnCl\({}_{4}\cdot\)5H\({}_{2}\)O) was dissolved in 50 mL of distilled water with continuous stirring to obtain a transparent solution. Then, varying concentrations (1 and 5 mol%) of zinc acetate dehydrate (Zn(CH\({}_{3}\)COO)\({}_{2}\cdot\)2H\({}_{2}\)O) was added dropwise under continuous stirring for 30 min for homogeneous dispersion. Subsequently, a total of 30 mL of 100% ethanol was added dropwise to the mixed solution with continuous stirring. Furthermore, 20 mmol of polyvinyl alcohol (PVA) was added dropwise to act as a stabilizing agent on a hotplate at 80 \({}^{\circ}\)C for 4 h for gel formation. The produced gel was further dried in an oven at 70 \({}^{\circ}\)C for 12 h. The dried gel was carefully crushed and annealed at 500 \({}^{\circ}\)C for 3 h. Pure SnO\({}_{2}\) NPs were generated at the identical protocol without the addition of zinc acetate. The main steps of Zn-doped SnO\({}_{2}\) NPs synthesis are provided in Scheme 1.