esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Dry Beach Slope Measurement
#### 3.3.2 Point Cloud Denoising Evaluation Criteria  
In this paper, four evaluation criteria, namely the mean square error (RMSE), outlier detection rate (ODR), recall rate, and accuracy, are used to evaluate the denoising effect [42]. Equation (3) was used to calculate the RMSE, which reflects the distribution of noise points in the denoising results. Equation (4) was used to calculate ODR, which reflects the ability of noise detection. Equation (5) was used to calculate the recall rate, which reflects the ability of feature retention. Equation (6) was used to calculate accuracy, which reflects the recognition ability of noise and non-noise points.  
\[RMSE=\sqrt{\frac{1}{N_{F}}\sum_{i=1}^{N_{F}}\parallel p_{i}-\widetilde{p_{i}} \parallel_{2}^{2}} \tag{3}\]  
where \(p_{i}\) denotes the noise point from the denoised result and \(\widetilde{p_{i}}\) denotes the non-noise point closest to \(p_{i}\).  
\[ODR=\frac{N_{T}}{N_{T}+N_{F}} \tag{4}\]  
where \(N_{T}\) denotes the number of noise removed and \(N_{F}\) represents the number of noises reserved.  
Figure 9: Point cloud profile denoising.  
\[Recall=\frac{N_{\mathrm{T}}}{N_{\mathrm{T}}+R_{F}} \tag{5}\]  
where \(R_{F}\) denotes the number of non-noise points removed.  
\[Accuracy=\frac{N_{\mathrm{T}}+R_{\mathrm{T}}}{N_{\mathrm{T}}+N_{\mathrm{F}}+R_{ \mathrm{T}}+R_{F}} \tag{6}\]  
where \(R_{T}\) denotes the number of non-noise points reserved.  
For ODR, recall, and accuracy, the larger the better, and for RMSE, the smaller the better.
# Applying Deep Learning to Medical Imaging: A Review
## 3 Applications in Medical Imaging
### Image Segmentation  
#### 3.1.1 Techniques and Approaches  
Image segmentation is a critical task in medical imaging, which involves partitioning an image into multiple regions or segments, each representing a specific anatomical structure or region of interest (ROI) [45]. DL has shown exceptional performance in this domain, with CNNs being the most commonly used approach. The U-Net [6] is a popular CNN architecture specifically designed for biomedical image segmentation, which has been applied to various medical imaging modalities such as MRI, CT, and microscopy images [45]. Additionally, multiscale architectures [109], attention mechanisms [110], and 3D CNNs [111] have been proposed to improve segmentation accuracy and efficiency in complex medical imaging tasks. Figure 5 shows the application of DL approaches to liver segmentation.  
Figure 5: Schematic of DL approaches for liver segmentation.
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 3 Methodology
### Deep Reinforcement Learning Structure  
The well-trained CAE is then combined with the DRL algorithm to form the final model. The overall structure of the model is shown in Figure 7. The CAE's encoder network generates a compressed representation of traffic states, which is then fed into the fully connected neural network to approximate the Q-value function as described in [4]. At the  
Figure 5: The structure of the proposed convolutional autoencoder (CAE) for traffic state representation. Figure 6: Training history of the proposed convolutional autoencoder. end of each control cycle, the neural network computes the Q-values of all available actions for the given traffic state representation. The agent then selects the action with the highest Q-value, which is expected to result in the maximum reward. After executing the selected action, a new control cycle begins and the agent continues to learn how to maximize rewards through interaction with the environment. To improve learning efficiency and reduce possible over-estimations, the model incorporates techniques such as the target network [5], dueling network [6], and prioritized experience replay [7]. The proposed DRL training algorithm is detailed in Algorithm 1. **Reward Signal**: The design of the reward signal is critical to the success of the RL-based traffic signal control. The reward signal serves as a guide for the agent to learn the objective of its control actions, which is to enhance the intersection's throughput and minimize vehicle waiting time. The reward provides feedback to the agent, evaluating its past actions. It's important to note that unlike other RL-based applications, the traffic signal control problem has no terminal state. This means that the reward signal must reflect the performance of each action taken by the agent, as there is no terminal reward to learn from. There is no deterministic guideline for selecting the most appropriate performance index, but the selection is crucial, as it guides the agent in learning the desired control objectives. Commonly used performance indices in the field of traffic signal control include travel delay, queue length, and average vehicle speed (as seen in [8; 17; 50]). Some approaches focus on reducing road congestion and only use average waiting time or queue length as the reward signal. However, this could result in the agent learning a strategy that frequently changes the traffic signal, leading to shorter queue time but lower speed. On the other hand, using a shorter cycle length reduces average vehicle speed, leading to vehicles spending more time idling at intersections, which not only increases fuel consumption but also contributes to the reduction in road capacity. Figure 7: Proposed deep reinforcement learning model.
## 2 Materials and Methods
### Transcriptome Sequencing and Analyses  
In order to investigate the genetic differences among populations of \(G\). _pentaphyllum_ distributed in different geographical regions, we collected their wild populations from different areas and cultivated them in a common garden resource nursery under the same temperature, humidity, and soil conditions for over one year. Twelve fresh young leaves from four distinct populations were collected and immediately stored in liquid nitrogen to analyze the expression levels of _GpOSC_ genes (Table S2). Total RNA was extracted usingan ethanol precipitation protocol combined with a CTAB-PBIOZOL reagent, and RNA quality was quantified with NanoDrop 2000 and Agilent 2100 Bioanalyzer instruments. Stranded RNA libraries were constructed for each sample and sequenced on the Illumina NovaSeq6000 platform to generate paired-end reads of 150 bp (PE150). The raw reads were uploaded into NCBI with SRA accession number PRJNA1186939. SOAPnuke [36] software was employed to filter raw reads, resulting in clean reads that were subsequently mapped onto the reference genome of _G. pentaphyllum_ using HISAT2 [37]. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped fragments (FPKM) [38], calculated using the ballgown R package. A heatmap based on log\({}_{10}\)(FPKM+1) data was generated using TBtools version 2.096.
# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 2 Feature Extraction and Registration Based on the C-SURF Algorithm  
### The C-SURF Algorithm  
Feature-based image registration is a core component of image stitching, and the quality of feature point extraction significantly influences feature matching and image stitching. The production of digital maps imposes strict requirements on image quality. Commonly used feature point extraction algorithms, such as SIFT and speeded-up robust features (SURF), encounter challenges in detecting feature points in images that contain dynamic scenes, leading to the potential omission of certain elements.  
The traditional SURF algorithm is an enhancement of the SIFT algorithm, resulting in similar operational processes. Initially, the SURF algorithm performs Hessian matrix analysis, followed by non-maximum suppression, while employing integral images to accelerate feature extraction. In generating feature descriptors, the SURF algorithm computes a 64-dimensional feature vector, which diminishes the accuracy of feature matching compared to the 128-dimensional feature vector utilized in the SIFT algorithm. Consequently, while the SURF algorithm retains the stability, robustness, and rotation invariance of the SIFT algorithm and enhances the real-time performance of feature extraction, it sacrifices the precision of feature registration.  
To address this limitation, this paper proposes an algorithm that combines an improved Canny edge detection algorithm with an enhanced SURF algorithm, referred to as the C-SURF algorithm. The C-SURF algorithm not only improves the quality of image feature extraction but also increases the accuracy of feature matching. The specific implementation steps of the algorithm are as follows:  
1. The source image undergoes high-contrast denoising preprocessing to eliminate noise, ensuring that edge information in the source image is preserved and enhancing the stability of feature extraction and contour information;
2. The improved Canny edge detection algorithm is employed to extract edge information from the source image;
3. An improved SURF feature extraction algorithm based on logarithmic polar coordinates is utilized to perform feature detection on the image obtained in step two, ultimately resulting in a set of features from the image.
## 3 Results  
### Repellency  
Among periwinkle flowers treated with 0.05% solutions of the eight plant extracts in the two-choice repellency (all plant extracts) bioassays, only those treated with nicotine, rotenone, pyrethrins and stemonine harbored significantly fewer WFT at one or more observation times than flowers treated with 0.05% Tween 20 solution (Table 1). Significantly fewer WFT were found on flowers treated with rotenone and pyrethrins at all times. Although the numbers of WFT on stemonine- and nicotine-treated flowers were lower than those on the Tween 20-treated flowers at all times, significant differences were detected only at 3 and 6 h after treatment for stemonine, and 3 and 48 h after treatment for nicotine. The numbers of WFT on cniidium lactone-, celangulin-, matrine- and veratrine-treated flowers were not significantly different from those on the Tween 20-treated flowers at any time after introduction in the two-choice repellency (all plant extract) bioassays. Based on their performance in the two-choice repellency (all plant extracts) tests, nicotine, pyrethrins, rotenone and stemonine were selected for the multiple-choice repellency (plant extracts) bioassays. In the multiple-choice bioassays, flowers treated with the four plant extracts consistently harbored numerically fewer WFT than Tween 20-treated flowers at all observation times, but the repellencies among plant extract treatments were not always significantly greater than the control (Table 2). All selected plant extracts appeared to repel WFT as early as 3 h after treatment, but only the pyrethrins treatment resulted in consistently and significantly lower WFT abundance than the control at all times. The repellency of rotenone and stemonine were slightly lower than that of pyrethrins and
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 4 Throughput Analysis of String and Grid Topology Considering Inter-Network Interference
### Analysis of Grid Network Topology
#### 4.2.1 Two Carrier-Sensing Networks  
As shown in Figure 4, in the investigated topology, Network 1, 3, 7, and 9 are in accord with this case. Since the four networks share the same characteristics, we consider only Network 1 in the following. From (14), when the state of Network 2 is idle, the probability of Network 1 being in the idle state can be expressed as in (18).  
\[U_{1,a} = 1-\frac{\sum\limits_{k\in v(a)\cap v(b)}\left[X_{k}\prod\limits_ {h\in v(k)}(1-\gamma_{h,k})\right]+\prod\limits_{k\in v(a)\cap v(b)}\left[X_{ k}\prod\limits_{h\in v(k)}(1-\gamma_{h,k})\right](1-Y_{1})^{-1}}; \tag{18}\] \[a,b\in v(1),a\
eq b.\]  
We use \(v(i)\) to denote the set of networks within the carrier-sensing of Network \(i\), which is the same as the following, such as \(v(a)\), \(v(b)\), \(v(k)\) and so on. \(\prod\limits_{k\in v(a)\cap v(b)}\left[X_{k}\prod\limits_{h\in v(k)}(1-\gamma_ {h,k})\right](1-Y_{1})^{-1}\) is the overlap transmitting time between Network 1 and 5.  
Network 2 and Network 4 is in the carrier-sensing range of Network 1. From (2), the carrier-sensing airtime of Network 1 can be transformed as (19).  
\[Y_{1} = \sum\limits_{i\in v(1)}X_{i}(1-\gamma_{1,i}) \tag{19}\] \[- \frac{\prod\limits_{i\in v(1)}X_{i}(1-\gamma_{1,i})}{1-\sum \limits_{k\in v(i)}\left[X_{k}\prod\limits_{h\in v(k)}(1-\gamma_{h,k})\right] +\prod\limits_{k\in v(i)}\left[X_{k}\prod\limits_{h\in v(k)}(1-\gamma_{h,k}) \right](1-Y_{1})^{-1}}.\]  
The final member of the right-hand side denotes the overlap transmitting time between Network 2 and 4 when Network 1 and 5 do not transmit.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 4 Results and Discussion
### Deep Learning Integration with BMS  
Data collected from BAn and BMS were used to calculate the discharge capacity of the battery over each cycle. Less capacity loss was observed over the 42 cycles conducted on-ground, as seen in Figure 9a. Figure 9a illustrates the maximum discharge capacity noted in each cycle performed on-ground. The blue line represents the capacity calculated using coulomb counting on-ground [38]. In the initial cycle, the battery capacity experienced a rise due to instability in the early cycles of the battery [51]. From the second cycle onward, the battery capacity gradually decreased over the cycles. At the end of the 20 th cycle, the capacity of the battery was 2.84 Ah. Building upon [PERSON] et al. [50] and [PERSON] et al. [52], the proposed BMS demonstrates the successful integration of CD-Net for real-time battery capacity prediction. Capacity predictions started from the 5 th cycle, at 2.92 Ah, whereas the coulomb counting capacity recorded by the BMS for the 5 th cycle was 2.95 Ah. The capacity showed a gradual drop over the cycles, with predictions from the model remaining consistent with BMS capacity, exhibiting a difference of around 0.030 Ah at the 20 th cycle. This information can be further used to predict the approximate SOH of the battery for that cycle [24]. As observed in Figure 9b, the discharge coulomb counting capacity of the battery during flying tests was more unstable than the on-ground tests. This instability in coulomb counting can be attributed to various factors such as temperature rise, and variable current discharge rates [21] similar variations were noted by [PERSON] et al. [53]. The mean difference between the calculated coulomb counting capacity and the predicted capacity observed in air was 0.046 Ah. Despite fluctuations encountered while collecting the in-air data, due to several factors such as temperature rise and current fluctuations throughout cycling, the CD-Net model was able to predict the upcoming cycle capacity. Predicting upcoming cycle capacity helps in the development of the BMS by establishing a threshold of predicted capacity before the battery reaches its EOL.  
Figure 9: Capacity predictions made by the CD-Net model using data collected from BMS. (**a**) Predictions based on on-ground experiments. The model demonstrates accurate capacity predictions over multiple cycles. (**b**) Predictions while flying on a drone. Capacity becomes unstable after 4 cycles, yet the model still provides improved predictions compared to previous methods.
## 3 Control Algorithm Design
### Rate Filter Design
\begin{bmatrix}\mathbf{\omega}^{b}_{bi}\end{bmatrix} ^{\times}\) is a cross product matrix defined as follows:  
\[ \begin{bmatrix}\mathbf{\omega}^{b}_{bi}\end{bmatrix} ^{\times}= \begin{bmatrix}0&- \omega_{z}&\omega_{y}\\ \omega_{z}&0&-\omega_{x}\\ -\omega_{y}&\omega_{x}&0\end{bmatrix} . \tag{11}\]  
It then follows from (9) and (10) that:  
\[\mathbf{Z}_{Rate} =\mathbf{C}_{\Delta}\mathbf{B}^{b}_{prev}-\mathbf{B}^{b}_{prev} \tag{12}\] \[= \begin{bmatrix}0&\omega_{z}\Delta T&-\omega_{y}\Delta T\\ -\omega_{z}\Delta T&0&\omega_{x}\Delta T\\ \omega_{y}\Delta T&-\omega_{x}\Delta T&0\end{bmatrix}\begin{bmatrix}B^{b}_{ px}\\ B^{b}_{py}\\ B^{b}_{pz}\end{bmatrix} _{\mathbf{y}}.\]  
This is regarded as the measurement equation of the rate filter. Then, the measurement matrix \(\mathbf{H}_{Rate}\in\Re^{3\times 3}\) can be obtained:  
\[\mathbf{H}_{Rate}= \begin{bmatrix}0&-B^{b}_{pz}\Delta T&B^{b}_{py}\Delta T\\ B^{b}_{pz}\Delta T&0&-B^{b}_{px}\Delta T\\ -B^{b}_{py}\Delta T&B^{b}_{px}\Delta T&0\end{bmatrix} . \tag{13}\]  
Finally, the 3-axis angular rates could be estimated according to the general EKF method [30].
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CH & Cultural Heritage \\ BIM & Building Information Modelling \\ HBIM & Heritage Building Information Modelling \\ FEA & Finite Element Analysis \\ FEM & Finite Element Model \\ LiDAR & Light Detection and Ranging \\ SfM & Structure of Motion \\ MVS & Multi-View Stereo \\ SIFT & Scale-Invariant Feature Transform \\ UAV & Unmanned Aerial Vehicle \\ UAS & Unmanned Aircraft System \\ HTOL & Horizontal TakeOff and Landing \\ VTOL & Vertical TakeOff and Landing \\ HALE & High Altitude Long Endurance \\ MALE & Medium Altitude Long Endurance \\ GCP & Ground Control Point \\ CP & Check Point \\ GCS & Ground Control Station \\ GSD & Ground Sample Distance \\ ASL & Above Sea Level \\ AGL & Above Ground Level \\ MB & Motion Blur \\ GNSS & Global Navigation Satellite Systems \\ GPS & Global Positioning System \\ SPP & Single Point Positioning \\ PPP & Precise Point Positioning \\ RTK & Real-Time Kinematic \\ PPK & Post-Processed Kinematic \\ RINEX & Receiver Independent Exchange Format \\ CORS & Continuously Operating Reference Station Services \\ KF & Filter Kalman \\ IMU & Inertial Measurement Unit \\ INS & Inertial Navigation System \\ TC & Tightly Coupled \\ LC & Loosely Coupled \\ EDM & Electronic distance measurement \\ TS & Total Station \\ TOF & Time-of-Flight \\ RMS & Root Mean Square \\ RMSE & Root Mean Square Error \\ IO & Internal Orientation \\ EO & Exterior Orientation \\ DG & Direct Georeferencing \\ RANSAC & RANdom Sample Consensus \\ PnP & Perspective-n-Point \\ DLT & Direct Linear Transformation \\ LM & Levenberg-Marquardt \\ \end{tabular}
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 3 Radar-Assisted Multi-Rsu Cooperative Beam Tracking  
In this paper, we aim to improve beam tracking accuracy by multi-RSU collaboration in a radar-assisted V2I downlink scenario. In this section, we first give the design of the new frame structure and the procedure of beam tracking (Section 3.1). Then we give the specific two steps of beam tracking, which are the derivation of the extended Kalman filter (Section 3.2) and the multi-RSU cooperation procedure (Section 3.3).
# Dirac-Based Quantum Admittance of 2D Nanomaterials at Radio Frequencies
## 5 Conclusions  
Starting from the four-component spinor from the Dirac equation, we derived a rigorous formulation of the quantum admittance for a monolayer 2D material at certain radio frequencies. A quantum-mechanical evaluation was then provided in the case of parabolic and linear dispersion in order to extract the frequency-dependent current density and thus the linear admittance. A subsequent analysis allowed us to extract an RLC-equivalent circuit with frequency-independent elements depending on the bias, temperature, effective mass, Fermi velocity and effective e.m. index of the material, losses and other relevant parameters. The circuit was evaluated while considering both low-field and high-frequency as well as high-field and low-frequency approximations.  
Conceptualization, T.R.; software, L.P., D.M. and G.M.Z.; validation, L.P., D.M. and G.M.Z.; formal analysis, T.R.; data curation, G.M.Z.; writing--original draft preparation, T.R. and G.M.Z.; writing--review and editing, T.R. and G.M.Z.; visualization, D.M. and G.M.Z.; supervision, L.P.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
This project received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 101006963 (GreEnergy).  
Not applicable.  
Figure 3: Comparison of the low-field (solid) and high-field (dashed) approximations for \(Cn^{\prime 2}\) in parabolic and linear dispersions.
###### Abstract  
The adaptive chirp mode decomposition method has a good effect on processing chirp signals. The parameter \(\alpha\) controls the smoothness of the output signal. Too small an \(\alpha\) will cause a smooth output signal. The parameter \(\beta\) controls the instantaneous frequency (IF). If too small a \(\beta\) value is used, the output IF will be very smooth. However, rapidly changing IFs require a relatively large \(\beta\). However, the choice of \(\alpha\),\(\beta\) is artificially set, and there are errors in practical applications. Therefore, it employs the state transition algorithm to adaptively optimize \(\alpha\),\(\beta\) to improve the signal-to-noise ratio (SNR) and resolution of the signal. First, as the species number of the state transition algorithm method is set artificially and has a long running time, this paper proposes a Rastrigin optimization test equation to test the optimization time of different species and determine the number of optimal species; second, the state transition algorithm determined by the number of species is employed to adaptively find the \(\alpha\),\(\beta\) in the adaptive chirp mode decomposition algorithm; finally, the optimized adaptive chirp mode decomposition method is applied to the simulation signal and chirp signal from marine animals to verify the proposed method.  
ACMD; state transition algorithm; chirp signal; SNR +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussions  
For the simulations in this paper, layers contained in the simplified HDI have the material properties shown in Table 1. The dielectric functions \(\varepsilon_{ij}(i\varepsilon_{n})\) shown in Ref. [24] are used and the magnetic permeability \(\mu_{j}\) is equal to 1. The constant \(B\) is taken to be \(10^{-76}\) J\(\cdot\)m\({}^{6}\)[15]. Parameters related with TP and the thickness of layers are \(l\) = 8 \(\mu\)m, \(\theta\) = \(10^{-4}\) rad, \(t_{\text{DLC-disk}}\) = 0.5\(\sim\)3 nm, \(t_{\text{lub}}\) = 0.5\(\sim\)3 nm, \(t_{\text{DLC-slider}}\) = 1\(\sim\)2 nm, \(l_{\text{a}}\) = 2 \(\mu\)m, \(l_{\text{b}}\) = 10 \(\mu\)m, \(l_{\text{c}}\) = 5\(\sim\)13 nm. For the TPC slider, the interaction at the HDI is mainly determined by the interaction between the TP and the disk. Therefore, for the sake of computational efficiency, the calculation domain is set to be \(-1.5l_{\text{a}}\leq x\leq 1.5l_{\text{a}}\) and \(-1.5l_{\text{b}}\leq y\leq 1.5l_{\text{b}}\), and the mesh numbers are \(512\times 512\).
###### Abstract  
To improve the quality of service (QoS) on the internet of medical things, a cognitive radio (CR) protocol based on orthogonal frequency division multiple access (OFDMA) is proposed, named CR-OFDMA. In this protocol, we divide a complete channel into multiple orthogonal subchannels and enhance the subchannel assignment scheme, which achieves QoS improvement under consideration of priority and fairness. Furthermore, we improve spectrum resource utilization by fully utilizing the remaining subchannels, feedback slots, and backoff slots. Then, a two-dimensional Markov model is established to describe the dynamic characteristics of the protocol operation, where the backoff stage and the backoff counter value constitute the system state. By establishing the traffic conservation equations for the system operation, the transmission probability and collision probability are calculated, and expressions of system throughput, channel utilization, and fairness index are derived. Finally, numerical results validate the advantages of CR-OFDMA.  
c 2023 13 7163 10.3390/app1327163