esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ML-Based Routing Algorithms in WSNs
### Discussions  
The comparisons of different ML-based routing algorithms are shown in Table 4, and the metrics, such as routing type, overhead, delay, QoS, scalability, and energy efficiency are considered.  
As shown in Table 4, in order to implement energy-efficient routing in WSNs, ML-based routing schemes adopt different learning algorithms, including kernel linear regression, SOM, Q-learning, reinforcement learning (RL), convolutional neural network (CNN), deep neural network (DNN), and hybrid learning algorithms, which achieve different performances. For example, ESDFM [64] utilizes a kernel linear regression scheme to learn information, which causes a low overhead in WSNs. Q-learning-based QLRR-WA can achieve both low delay and low overhead, which provides a potentially good way to optimize power consumption in WSNs. RL can achieve both low delay and low overhead, which provides a potentially good way to optimize power consumption in WSNs. CNN and DNN have high overhead but good scalability and low delay. Meanwhile, EL based on hybrid learning algorithms has better scalability in WSNs.  
ML-based routing algorithms achieve energy efficiency by reducing redundant data, optimizing paths, or optimizing protocols. Most schemes optimize routing paths to implement power saving [65; 66; 67; 68; 69; 70; 71; 72]. ESDFM [64] reduces redundant data to save power, which results in high delay. Based on the hybrid ML, EL optimizes the routing protocols to balance the energy consumption and overhead [73]. On one hand, the complex learning procedure incurs additional overhead due to massive calculations. On the other hand, the forwarding consumption of routes and the packet transmission are optimized due to the ML-based prediction and optimal routing, which prolongs the overall lifetime of WSNs to some extent. It is a challenging issue to design energy-efficient ML models to balance the tradeoff between the overhead of training models and the energy saving of the optimal routing.
## 5 Virtual Surgical Planning in Orthognathic and Oral Maxillofacial Surgery  
VSP has become an invaluable tool in the field of orthognathic and oral maxillofacial surgery. The application begins with the acquisition of patient-specific imaging data, typically through CT or CBCT scans, which are then converted into 3D digital models (Figure 4). These models can be manipulated to simulate various surgical outcomes and to design patient-specific surgical guides [6]. It allows the surgical team to visualize complex anatomical structures, understand the spatial relationships better, and perform virtual osteotomies and repositioning, thus fine-tuning the surgical plan [21].  
Several case studies have underscored the effectiveness of VSP. In a study conducted by [PERSON] et al. [58], the use of VSP in orthognathic surgery resulted in a significant reduction in operative time and an improved postoperative outcome in terms of facial symmetry and patient satisfaction. Another study by [PERSON] et al. [59] demonstrated improved accuracy in executing the surgical plan using VSP, resulting in better postoperative occlusal outcomes. These cases underscore the increasing role of VSP as an integral part of surgical planning in orthognathic and oral maxillofacial surgery.  
In order to achieve precise mandibular osteotomy through an intraoral approach, the utilization of a 3D-printed surgical guide has proven effective [60]. Despite limited visibility during the surgery, the surgeon can accurately identify the osteotomy site with the assistance of the surgical guide [61]. This guide is prepared preoperatively by adapting it to a 3D digital model [62]. Numerous variations of 3D-printed surgical guides or templates have been successfully employed in mandibular contouring surgeries, demonstrating their suitability for surgical purposes [61]. When performing a mandibular osteotomy, adherence to the computer-assisted simulation planning (CASP) is crucial, ensuring proper transfer of the preoperative surgical template [63].  
Traditional mandibular contouring surgeries rely on 2D images for planning purposes [64]. Unfortunately, the imprecise nature of these surgical plans has led to nerve-related complications and residual asymmetry in approximately 10% of patients [64]. Evaluating the success of mandibular outcomes has typically involved a simple comparison of pre- and postoperative clinical photographs [65]. The use of CASP and a 3D-printedsurgical guide in mandibular contouring surgery offers advantages over traditional planning techniques relying on 2D images [66]. By incorporating CASP and the surgical guide, operating time can be reduced and surgical accuracy increased [66].
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Testbeds for 6G Technologies
#### 6.2.6 Optical Wireless Communication (OWC) Testbeds  
OWC offers a direct path for future 6G systems. Eindhoven University demonstrated a 40 Gbps user data rate in a multi-user LiFi scenario [254]. Fudan University showcased a LiFi testbed for a classroom, demonstrating real-time handover, multi-user access, and mobility support [255]. Mitsubishi Electric demonstrated a point-to-point free-space optics (FSO) system transmitting 14 Tb/s over 220 m [256]. The German Aerospace Centre (DLR) achieved 13.16 Tb/s over 10.45 km with commercial coherent fiber optic transceivers [257]. Optical camera communication (OCC) utilizes embedded cameras, with [PERSON] et al. developing a testbed using direct current optical orthogonal frequency division multiplexing (DCO-OFDM) [258].
## 5 Mapping
### Keyframe Management  
Our algorithm was based on sparse direct image alignment of the current frame to the last frame and the current to the last keyframe. As the camera moved, the content of the current frame changed constantly, and the keypoints that were tracked from the last keyframe were significantly reduced, so it was necessary to add the keyframe to the map in time to guarantee the stability of visual odometry. Each frame was regarded as a candidate keyframe after image alignment, and whether the current frame was added to the map as a new keyframe was determined by applying several criteria:  
1. Enough time \(t\) (e.g., 1 s) had passed since the last keyframe was added;
2. The number of keypoints in the last keyframe that were observed in the current frame was reduced by 30%.
3. The distance or rotation of the current frame to the last keyframe exceeded the set threshold.  
The first case applied to situations in which the camera was moving at a lower speed, and the latest information from the environment was added to the map to cope with dynamic factors in the environment. The second and third rules dealt with the camera moving at a faster speed by expanding the map in time to prevent the loss of localization.  
It could be seen from the above that the conditions for adding keyframes were loose, so many keyframes were added to the map over time. However, because of limited memory capacity and computing power, redundancy and low quality keyframes must be culled. For example, when the camera was stationary, a large number of keyframes with almost the same appearance was added to the map, which was detrimental to the optimization process, resulting in the degradation of the optimization problem. If the number of keypoints of the common view in the two keyframes exceeded 90%, the keyframe added first was removed.  
The above keyframe management strategy that added a keyframe to the map loosely and culled it from the map strictly was very effective. The loose keyframe selection strategy could expand the map in time, provide more landmark points for the map, and improve the stability of camera pose tracking. The strict culling mechanism of redundant keyframes ensured that the optimization did not degenerate while ensuring the system's computational efficiency and low memory usage.
## 4 Case Study  
### Data Description  
The Lanxin railway line is an electrified normal-speed railway from Lanzhou to Sinkiang with a maximum permissible speed of 160 km/h and a total length of 2423 km. It is an important part of the railway network in Northwest China. Based on dividing the line into multiple track segments with a unit length of 200 m, 2171 track segments of the downlink line within the jurisdiction of the Jiayquian Section of Lanzhou Railway Bureau (mileage range K548+000 to K982+400) were selected and their tamping maintenance cycle analyzed using the proposed method, as shown in Figure 3. The track geometry of the Lanxin Railway is inspected at least twice a month using a track-inspection vehicle. A fixed-period-based preventive maintenance strategy was employed for the tamping activities, with the interval typically set to 1-2 years. We collected track longitudinal-level inspection data with 108,608 records from April 2015 to November 2018 for the selected 2171 track segments and tamping maintenance records. By associating the longitudinal-level inspection data with the tamping maintenance record data based on mileage and time information, the longitudinal-level inspection dataset of each track segment can be divided into multiple data subsets; that is, the inspection data within one tamping maintenance cycle is a data subset. The subset with the largest sample size from the data was used for the model construction.
###### Abstract  
Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.  
MMW fuzes; rain attenuation; scattering effects; Monte Carlo simulation +
Footnote †: journal: Applied Sciences
## 3 Numerical Uncertainty Analysis
### Generation of the Training and Test Data Sets  
The training data can be generated by solving an Initial Value Problem (IVP). Given the governing Equation (7) and an initial state \(x(0)=[q(0),\dot{q}(0)]^{T}\), the signals of acceleration \(\ddot{q}(t)\), velocity \(\dot{q}(t)\), and displacement \(q(t)\) can be calculated simultaneously with a differential equation solver, such as ODE45 from Matlab [44].  
The noise in a real measurement was simulated by adding the normal distributed random value \(S_{i}(t)\) into the generated signals \(\ddot{q}(t)\), \(\dot{q}(t)\), and \(q(t)\), as shown in the following equation:  
\[S_{1}(t) \sim\mathcal{N}(0,(\epsilon_{N}\max(q(t)))^{2}), \tag{8}\] \[S_{2}(t) \sim\mathcal{N}(0,(\epsilon_{N}\max(\dot{q}(t)))^{2}),\] (9) \[S_{3}(t) \sim\mathcal{N}(0,(\epsilon_{N}\max(\ddot{q}(t)))^{2}). \tag{10}\]  
The noise factor \(\epsilon_{N}\) regulates the intensity of a noise vector \(S(t)\). In Table 1, five levels of noise are selected. The intensities of these five noise levels can also be interpreted with a Signal-to-Noise Ratio (SNR) as \(\{\infty,40.1,34.1,30.5,28.0\}\) dB.  
For training purposes, time series with the initial state \(x(0)\) are generated. In the training data set, 10,000 examples of numerical oscillators were randomly generated for each noise level. Thus, the training data set contained 50,000 examples. Furthermore, two different initial states, \(x_{1}^{\star}(0)\) and \(x_{2}^{\star}(0)\), were used to generate two test data sets. Similar to the training data set, each test data set also had 50,000 examples. The values of these initial states are shown in Table 1.  
In order to test the effect of overfitting in the polynomial model, the test initial states were selected to bring varied amplitudes of vibrations into consideration. As shown in Figure 2, compared with \(x(0)\), the vibrations that started with the initial states \(x_{1}^{\star}(0)\) and \(x_{2}^{\star}(0)\) have approximately doubled and grown 20-fold in amplitude according to velocity signals.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(m\) in kg & \(\mathcal{N}(5,0.05^{2})\) \\ \(d\) in kg/s & \(\mathcal{N}(5,0.5^{2})\) \\ \(k\) in kg/s\({}^{2}\) & \(\mathcal{N}(2000,66.7^{2})\) \\ \(\epsilon_{N}\) & \(\{0,0.0025,0.005,0.0075,0.01\}\) \\ \(x(0)\) & \([0;1]\) \\ \(x_{1}^{\star}(0)\) & \([0;1;0]\) \\ \(x_{2}^{\star}(0)\) & \([1;0]\) \\ \hline \end{tabular}
\end{table}
Table 1: Oscillator parameters in numerical uncertainty analysis.
## 3 Results and Discussion
### PPI Network Construction and Analysis  
The 234 putative protein targets were uploaded to the STRING database for network analysis and the 223 protein targets with overall confidence scores \(>\) 0.4 ([PERSON] et al., 2019) were used to construct the PPI network which consisted of 223 nodes and 2308 edges with a median DC of 26 (Figure 4). The protein targets with the higher values of DC were INS (105), ALB (104), AKT1 (97), TNF (76), PTGS2 (72), EGFR (72), CXCL8 (64), CAT (64), BDNF (59), and ESR1 (58), respectively, indicating their important roles in the PPI network.  
Figure 2: The chemical structures of twelve key cannabinoids and their biosynthesis pathways [25; 40].  
information processing, and two diseases had pathways related to organismal systems and metabolism, respectively.  
According to the documented beneficial and adverse effects of _Cannabis_ use in humans (Table 1), the 65 disease entries were divided into three groups (Table S5): Group I (beneficial) consisted of 32 diseases that were reported to be benefited from the use of _Cannabis_[41; 42]; Group II (adverse) included six diseases that were reported to be induced by the use of _Cannabis_[43]; and Group III (unknown) had 27 diseases that had not been reported to be affected by the use of _Cannabis_.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Measurement Uncertainties Assessment Procedure
#### 5.6.2 Applying the GUM to the Calibration of \(\mathbf{V_{\text{LOS}}}\)  
The GUM is applied to the calibration relation between the lidar-measured \(V_{LOS}\) and \(V_{ref}\). The measurement model defining the estimated measurand is:  
\[y=a\cdot V_{ref} \tag{7}\]  
where \(a\) is the gain of the forced linear regression on the binned calibration data. Using the law of propagation of uncertainties for uncorrelated input quantities (Equation (6)), we obtain:  
\[u_{c,y}=\sqrt{a^{2}u_{c,V_{ref}}^{2}+V_{ref}^{2}u_{a}^{2}} \tag{8}\]  
The uncertainty \(u_{a}\) is taken as the half-width of the 68% confidence interval (CI) on \(a\) (equivalent to a coverage factor \(k=1\) for normally distributed uncertainties). The method to estimate \(u_{a}\) is based on QR-decomposition and T-tests. The order of magnitude of \(u_{a}\) is \(10^{-3}[-]\). For LOS 0 of 5B, \(u_{a,5B,los_{0}}=7.1\times 10^{-4}\). For ZDM, \(u_{a,ZDM}=1.1\times 10^{-3}\). Considering this measurement model,once measuring stand-alone, the best estimate of the lidar-measured \(V_{LOS}\) is defined by the reciprocal of the calibration relation:  
\[V_{LOS,be}=\frac{V_{LOS}}{a} \tag{9}\]  
and its corresponding calibration uncertainty is \(u_{c,y}\).
## 1 Introduction
### Current Approaches in Exposure Estiamation  
The assessment of the exposure is often realized either by in-situ measurements [8] or system-level simulations [6]. For 5G specifically, in-situ measurements are generally unconvincing in studying the exposure of a 5G system due to the variability of the transmission beams. The beam serving a specific area or MT changes in a period much smaller than the sweep time of most commercially available measuring equipment which means that during the time the equipment is measuring the whole band, the beam would have already changed. Moreover, the high dependence of the received power on the channel and the MT distribution make the analysis of the measurements especially challenging. Thus, it is why recent studies suggest the extrapolation of the received power from a single constant beam, ignoring the system usage and variability. The difficulty of generalizing measurements to study the exposure make theoretical studies and simulations more attractive. These studies allow freedom in assuming the network to emulate different scenarios. While system simulations are straightforward in design, they are very time and resource heavy, and they do not give direct insight on the different system parameters, therefore analytical analysis becomes more attractive to consider. Analytical estimations of the exposure have been previously considered, e.g., [5] although with a simplified antenna beamforming and system design.  
An area in analytical modeling that was widely used in the domain of cellular networks is stochastic geometry. The assumption that the BSs are distributed following a point process gives this approach a relatively accurate basis and provides the mathematical tools necessary for the modeling of the system. Obtaining the closed-form expression will also give a direct way to study different parameters affecting the exposure, hence we chose it as the basis of our study. In this study, we also use the active antenna model developed by 3 GPP [9; 10], which assumes a uniform rectangular array antenna, for communication in bands above 6 GHz. As for the channel model, we use the NYUSIM statistical mmWave channel simulator [11], which is backed by extensive measurements in the mmWave band, and was shown to be better suited for above 6 GHz frequencies [12]. Multiple large-scale channel measurements have been conducted in the mmWave band over the last few years [13; 14; 15; 16], and multiple studies compared the different channel models used by the different standardization bodies as in [12; 17].
## 3 Results and Discussion
### Molecular Dynamics Simulation of the Interaction Between Lipid Molecules and Ethanol Molecules  
To clarify the influence mechanism of lipid molecules on the release of aroma compounds in 53%ABV solution, a molecular dynamics simulation system was constructed. Since too many molecules will bring excessive computational load and cause the system to crash, heptanoic acid, ethyl hexanoate, and ethyl lactate were selected as representative compounds. Ethyl lactate is hydrophilic (\(\log p=-0.19\)) and shows unique release behaviors in the interaction with saliva [16], and the dynamic drinking process of baijiu under the effect of peanut [2; 3; 21]. Here, its release was promoted by peanut lipid in water but inhibited in 53% ABV solution. Ethyl hexanoate is highly concentrated in baijiu. Its release intensity was significantly inhibited by peanut lipid in water, but not in the 53% ABV solution. Heptanoic acid, a main acid in baijiu, had its release intensity notably reduced by peanut lipid in the water solution, and the inhibitory impact of peanut lipid on it was further augmented in 53% ABV solution. The 53% ABV system and the 53% ABV + peanut lipid system were established, respectively, and an equivalent amount of compound molecules was added. As can be observed from the molecular motion trajectory (Figure 3), in the 53% ABV system, the ethanol molecules showed an aggregating tendency, forming ethanol clusters, with the compound molecules encapsulated within these clusters. This led to an increase in the solubility of the compounds and a consequent reduction in their release intensity. In the 53% ABV-peanut lipid system, the peanut lipid molecules were present in an aggregated state, and both the ethanol and all the baijiu aroma compound molecules aggregated around the peanut lipid molecules, whether they were lipophilic or hydrophilic. In the case of lipophilic compounds, the binding ability of the lipid molecules with the ethanol molecules was greater than that with the compounds. Ethanol and lipid had an antagonistic effect on the release intensity of the aroma compounds. For the hydrophilic compounds, the
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 2 SAR Image Denoising Algorithm Based on Self-Learning Strategy DnCNN
### Data Augmentation  
To address the issue of a small number of training samples, data augmentation methods are adopted to expand the training sample set, thereby constructing a target dataset suitable for SAR imaging recognition. This article uses the following three data augmentation methods to expand the training dataset of ISAR images of large aerial targets.  
(1) Target displacement: In SAR imaging results, the grayscale values of the target are concentrated in the middle position of the image. However, under ideal conditions, the target may shift in various directions, causing the target data to deviate from the central area of the image. In this situation, if the random displacement process of the image can be simulated, the generated image can be regarded as an enhancement of the original image.  
(2) Image rotation: During the process of SAR image acquisition, target rotation is also possible, and there are 360 ways to enhance images by measuring rotation by 1\({}^{\circ}\). Therefore, rotating the original image by multiple angles is also a way to expand the training set.  
(3) Image denoising: Due to the SAR imaging mechanism and possible interference in the system, the obtained image is highly likely to have noise interference, manifested as numerous and dense white spots on the image. Therefore, adding noise with different variances to the original image is also a reasonable and effective image enhancement method. For example, noise with different means and levels of variance can be used to implement a data augmentation scheme with added noise.