esa-sceva/satcom-chunk-collection huggingface dataset:
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Depth Prediction  
A depth map was used as the connecting medium to realize the matching of CT and video. A depth map describes the spatial geometry information, with each pixel value representing the spatial distance of that pixel from the camera, and it can be applied in virtual reality, 3D reconstruction, and other fields. There have been a lot of related studies on natural images [11; 12; 13; 14; 15; 16; 17] that have studied how to obtain depth information from monocular images, that is, monocular depth prediction. In monocular depth prediction, it can be regarded as a regression problem, just like image segmentation. In this paper, we used the SI-DM dataset obtained from CT to train the depth map prediction model, mainly because we cannot obtain the real ureteroscopic images' depth maps. This step is a fundamental part of solving the depth map of ureteroscope video images through style transfer. For virtual endoscopic images, a depth map can be recovered from CT image series directly. As to real endoscopic images, they are transferred to the virtual endoscopic domain at first, and then the depth maps are generated using the depth prediction model trained from virtual endoscopic images.  
This paper uses an encoder-decoder network structure which was first proposed by [PERSON] et al. [17].
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
## 2 Loop-Back QKD in a Linear Topology  
The Loop-Back QKD protocol provides a versatile framework for quantum key distribution, allowing for different implementations depending on security requirements and network constraints. While the standard protocol follows a direct interaction between [PERSON] and [PERSON], its design naturally extends to multiuser environments, supporting various network architectures.  
This section introduces the Loop-Back QKD protocol in a linear topology, detailing both its standalone implementation and its hybrid integration with BB84. The following section explores a ring topology optimized for multi-node quantum networks. Each approach strikes a balance between efficiency, security, and feasibility, allowing the protocol to adapt to various operational environments. A comparative analysis is provided to emphasize the strengths and trade-offs of each configuration.
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 1 Introduction  
Burrs are formed due to the plastic deformation of materials during machining processes, such as milling and drilling. When they occur in places with poor accessibility, deburring can be very difficult. Burrs reduce the precision of products, hinder assembly operations, and lead to expensive post-processing costs [1]. If burrs on components fall off during operation, it can cause problems during machining and result in downtime.  
Several studies have been performed on deburring tools and processes, such as steel wire brushes, hand tools, electrochemical machining, abrasive ultrasonic machining, abrasive jet machining with air or water, thermal impulse deburring, and suction-based abrasive flow control [2, 3, 4, 5, 6, 7, 8, 9]. Each of these deburring methods has its own merits and disadvantages and can be improved further. For example, deburring microfluidic channels with a stainless steel wire brush might seem a relatively simple process. However, damage to the workpiece beyond the burr zone is inevitable. As for electrochemical machining, difficulties with the burr edge accessibility can be alleviated, but the formation of large burrs, in addition to the difficulties faced in precision control of machining depth and high cost, is a challenge.  
Abrasive flow machining (AFM) has been mainly used for polishing and deburring [10, 11]. [PERSON] classified AFM techniques into one-way, two-way, multi-way, and orbital processes [12]. These AFM processes employ a high-viscosity medium, incurring a high-drive load [13]. This limits its applications to low-speed flow and relatively simple workpiece geometries. Attempts have been made to apply AFM to a low-viscosity abrasive medium for high-speed and complex workpiece geometries [14, 15, 16, 17]. For milling burrs, [PERSON] suggested that an objective function should be applied to the design of flow guides and experimentally verify the deburring performance [18]. The suggested objectivefunction includes the effect of flow intensity near the burr edge in terms of wall shear stress and the effect of streamline curvature near the burr edge, characterizing the flow pathlines brushing through the burrs.  
In this study, the effect of flow speed on deburring near the burr edge was investigated in addition to the local shear stress and streamline curvature suggested by [18]. Based on the preliminary deburring tests on milling specimens of AL6061, an updated objective function is proposed and verified through experiments. Based on this new objective function, the deburring performance on intersecting holes with offset was predicted and confirmed through experiments.
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 2 Related Works
Besides medical research and radiology, machine learning/deep learning techniques have made astounding performance gains in multiple domains. In conclusion, machine learning and deep learning techniques could play a significant role in predicting, classifying, screening, and minimizing the spread of the COVID-19 pandemic [25]. The COVID-DeepNet system is one of the accurate methods used to diagnose infection with COVID-19, and it depends on chest radiography images. DBN algorithms associated with other deep learning architectures, such as CBDN, were trained on the top of pre-processed chest radiography images to reduce overfitting and improve the generalization capabilities of the adopted deep learning approaches [36]. Numerous current applications have focused on using deep learning for diagnosing EEG signals, aiming at the identification and prediction of several challenging brain activities. Due to the wide field of applications and the results of EEG in terms of efficiency and reliability, the information it provides has become a key element in the health sector. From the reviewed works, it was found that EEG data--in combination with processing techniques (Fourier transform (FT), fast Fourier transform (FFT), short-time Fourier transform (STFT), wavelet transform (WT)), and machine learning tools such as support vector machines (SVMs) and neural networks (NN)--achieve an efficiency greater than 90%, thus becoming a competitive tool for solving problems in its field of study. The authors of [37] used an extreme learning machine (ELM) and achieved 95%. EEGs measure brain electrical activity without invasive procedures and have numerous medical applications, one of which is the detection of neurodegenerative diseases. Dementia diseases are rapidly increasing and have become an alarming problem for the health sector. This is a growing challenge for health systems. Various parameters or measurements can be extracted during the processing of EEG signals, such as frequency spectra; time-frequency; values such as entropy and fractal average; and combinations of more than one parameter. During the processing stage, the objective is to extract relevant information that allows the identification of EEG patterns/biomarkers that, during the classification stage, contribute to increasing efficiency [37]. The deep learning algorithms presented can be implemented using Keras [38; 39; 40; 41; 42].
## 3 Simulation Results and Analysis
### Mechanism Analysis of the Interaction between the Previous and Next Drive Coils and the Armuture
In both configurations of the drive coil, the forces acting on the armature by the previous and next drive coils during the two stages of launching are shown in Table 4.  
Figure 12: Current density contour maps of the launching device at different time points: (**a**) at 0.4 ms; and (**b**) at 0.8 ms. The magnitude of the electromagnetic force on the armature by the drive coil is dependent on the distance to this coil [17]. In both configurations of the launching device, the influence of the (n + 1)th-stage coil on the armature during the acceleration stage and the influence of the (n - 1)th-stage coil on the armature during the deceleration stage are not considered in order to simplify the analysis and focus on the more significant factors. As a result, in the PN configuration coil launcher, the previous and next drive coils mainly provide a deceleration effect on the armature, while in the PP configuration coil launcher, the previous and next drive coils mainly provide an acceleration effect on the armature. Therefore, in the launching device with a distance \(D\) = 5 mm between each drive coil, as shown in Figure 8a, the PP configuration armature exit velocity is ultimately better than the PN configuration. However, in the launching device with a distance \(D\) = 15 mm, the increased distance between the previous and next drive coils and the armature weakens the influence of the drive coils on the armature. This weakens the deceleration effect of the PN configuration and the acceleration effect of the PP configuration, resulting in an enhanced velocity for the PN configuration armature and a relatively reduced velocity for the PP configuration armature. As shown in Figure 9a, the simulation results are consistent with the theoretical analysis mentioned above.
## 3 Enabling Technologies and Challenges for Futuristic Vehicular Networks
### Network Softwarization
#### 3.1.2 Software-Defined Networking  
With the unprecedented increase in demand for heterogeneous services in VN, there is a need for a platform that can dynamically adapt the network and service according to the demand request. Software-Defined Networking (SDN) technology can create a more flexible and programmable VN for supporting the critical requirements of the services and applications [39, 40]. The SDN forms a fully programmable wireless network by logically separating the data and control plane, where all control operations are performed through the centralized controller unit. The controller can have a global view of network topology, traffic load, network states, and link failures. In the case of an EC-enabled VN, having different EC layers of heterogeneous hardware having a proper centralized controller assisted by the individual layers local controller can be useful for providing flexible VN services. In the past several studies have shown the importance of SDN-based VNs, where SDN technology is integrated into VN to manage different parts with improved performance in terms of network flexibility, throughput, and flexible deployments of new services and applications. However, various issues need to be handled carefully during the integration of SDN into VN including, possible security attacks over the data plane, the vulnerability of a centralized controller in terms of single-point failures, issues related to the heterogeneous hardware of different EC platforms, various access network technologies, etc. Over the years, researchers have provided numerous solutions for these issues [26, 40, 41, 42, 43]. As an example, in [41], a 5G software-defined vehicular network having integrated SDN technology is proposed with clear separation of data, control, and application planes. In another study [26], the authors proposed a collaborative EC-based software-defined VN with multiple EC platforms. Furthermore, in [40], the authorshave experimented with the different SDN controllers over a complete software-defined vehicular networking on hardware. Recently in [42], the authors have proposed a novel IoV automation and orchestration system using SDN for connected autonomous vehicles. In [43], the authors study different VN architectures and propose a localized intelligence augmented highly reconfigurable software-defined heterogeneous vehicular networking architecture for avoiding single-point failures.
# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 3 Hazard Assessment Results for the Study Area
### Evaluation of Ground Subsidence Hazard in the Study Area
#### 3.2.2 Calculation of Information Value for Single Indicator  
Building upon the classification results of the single hazard evaluation factors, the coordinates (longitude and latitude) of the 34 subsidence points within the study area were imported into the ArcGIS platform. The classification intervals of each evaluation factor for the subsidence points were statistically analyzed using the Extract Values to Points tool. The graded values of the 12 influencing indicators were assigned to each subsidence point based on the numerical values of the corresponding raster cells. The information value for each level of the single factors was subsequently obtained, as illustrated in Table 20.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Influencing Factor** & **Classification Index** & **Reclassification** & **N\({}_{\mathbf{i}}\)** & **S\({}_{\mathbf{i}}\) (m\({}^{2}\))** & **Information Quantity Value (I\({}_{\mathbf{i}}\))** \\ \hline \multirow{4}{*}{Stratigraphic} & Sandy soil & 4 & 1922 & 1,729,800 & 0.224698523 \\  & Loess, shale & 3 & 216,005 & 194,404,500 & 0.204508631 \\  & Clayey soil rock & 2 & 63,457 & 57,111,300 & \(-\)1.096280969 \\  & Hard carbonate rock & 1 & 4839 & 4,355,100 & 0.112548736 \\ \hline \multirow{4}{*}{Quaternary} & \textless{}10 & 3 & 35,799 & 32,219,100 & \(-\)0.523901308 \\  & 10–15 & 2 & 196,691 & 177,021,900 & \(-\)1.129003029 \\  & \textgreater{}15 & 1 & 53,715 & 48,343,500 & 1.321618158 \\ \hline \multirow{4}{*}{Fault layer distribution} & 0–40 m & 5 & 204,612 & 184,150,800 & \(-\)1.57394967 \\  & 40–80 m & 4 & 15,329 & 13,796,100 & 1.017419373 \\  & 80–120 m & 3 & 18,823 & 16,940,700 & 0.812086306 \\  & 120–160 m & 2 & 30,571 & 27,513,900 & 1.579876954 \\  & \textgreater{}160 m & 1 & 16,870 & 15,183,000 & \(-\)0.464665425 \\ \hline \multirow{4}{*}{Subsidence area distribution} & Severe collapse & 4 & 128,522 & 4,649,400 & 2.510626579 \\  & More serious collapse & 3 & 51,93 & 3,413,700 & 2.126420831 \\  & Light collapse & 2 & 77,815 & 36,334,800 & 0.965414422 \\  & No collapse & 1 & 55,489 & 213,210,900 & \(-\)1.027234986 \\ \hline \multirow{4}{*}{Road network density} & 49.38–83.94 & 5 & 23,773 & 21,395,700 & 1.590209273 \\  & 34.56–49.38 & 4 & 29,819 & 26,837,100 & 0.757478178 \\ \cline{1-1}  & 21.39–34.56 & 3 & 46,497 & 41,847,300 & \(-\)0.09222895 \\ \cline{1-1}  & 10.86–21.39 & 2 & 87,516 & 78,764,400 & \(-\)0.501519238 \\ \cline{1-1}  & 0–10.86 & 1 & 98,598 & 88,738,200 & \(-\)2.230186494 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Single-factor information at different levels.
## 3 Results
### Performance of Classifiers with Features Extracted from One Channel at a Time  
Table 7 displays the results of RF- and GBDT-based classification using the combined complexity and power features for the 19 EEG channels. A total of 19 features was considered per run. The best AUC, F-measure, and accuracy of 94%, 89.96%, and 89.51% respectively, were obtained with the RF classifier in the Pz channel.
###### Abstract  
High-frequency vibration error of a moving radar platform easily introduces a non-negligible phase of periodic modulation in radar echoes and greatly degrades terahertz synthetic aperture radar (THz-SAR) image quality. For solving the problem of THz-SAR image-quality degradation, the paper proposes a multi-component high-frequency vibration error estimation and compensation approach based on the short-time Fourier transform (STFT). To improve the robustness of the method against noise effects, STFT is used to extract the instantaneous frequency (IF) of a high-frequency vibration error signal, and the vibration parameters are coarsely obtained by the least square (LS) method. To reduce the influence of the STFT window widths, a method based on the maximum likelihood function (MLF) is developed for determining the optimal window width by a one-dimensional search of the window widths. In the case of high noise, many IF estimation values seriously deviate from the true ones. To avoid the singular values of IF estimation in the LS regression, the random sample consensus (RANSAC) is introduced to improve estimation accuracy. Then, performing the STFT with the optimal window width, the accurate vibration parameters are estimated by LS regression, where the singular values of IF estimation are excluded. Finally, the vibration error is reconstructed to compensate for the non-negligible phase of the platform-induced periodic modulation. The simulation results prove that the error compensation method can meet THz-SAR imaging requirements, even at a low signal-to-noise ratio (SNR).  
high-frequency vibration error; maximum likelihood function (MLF); short-time Fourier transform (STFT); random sample consensus (RANSAC); terahertz synthetic aperture radar (THz-SAR)  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] High-Frequency Vibration Error Compensation Method for Terahertz SAR Imaging Based on Short-Time Fourier Transform. _Appl. Sci._**2021**, _11_, 10862. [[https://doi.org/10.3390/appl112210862](https://doi.org/10.3390/appl112210862)]([https://doi.org/10.3390/appl112210862](https://doi.org/10.3390/appl112210862))  
1
## 5 Conclusions  
The results suggest that we have improved the accuracy of live poultry weighing, and the real-time automated monitoring system developed in this study, paired with a single passage, can accurately monitor the body weight changes and feeding behavior of young chickens. Our research may be useful in poultry breeding or can be used as a verification device for other breeding methods, and it could improve the growth rate of poultry and make the FCR better, which would reduce the time taken in raising poultry and improve the feed utilization rate, shorten the production cycle and reduce the feeding cost. This is beneficial to alleviate the current food crisis facing the world. Our research is also of great significance for poultry weight tracking and feeding behavior monitoring, which would improve the automation level of poultry production, and the timely detection of abnormal behavior in poultry to prevent major economic losses before they occur, bringing economic benefits for poultry farmers.  
Y.P.: Writing--original draft, writing--review and editing, conceptualization, methodology, investigation, data curation, software, formal analysis, validation, Resources. Z.Z.: writing--review and editing, supervision, conceptualization, Resources, funding acquisition. E.L.: investigation, data curation, software, formal analysis, Resources, conceptualization, funding acquisition. X.H.: Data curation, investigation, Methodology, Software. B.Z.: Data curation, investigation, Methodology, Software. B.Z.: Data curation, investigation, Methodology, Software. F.W.: Data curation, Resources, investigation, review. J.G.: investigation, data curation, formal analysis, Resources. Z.L.: investigation, Resources, Validation, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a Start-up Research Project of Maoming Laboratory (2021 TDQD002), the Key Laboratory of Modern Agricultural Intelligent Equipment in South China, Ministry of Agriculture and Rural Affairs, China (HNZJ20209), Special Fund for Rural Revitalization Strategy of Guangdong Province, Improvement of Agricultural Science and Technology Capacity (403-2023-XMZC-0002-90).  
Not applicable.  
We acknowledge Guangzhou Jiaen Technology Co., Ltd. for the materials and facilities.  
The authors declare no conflict of interest.
## 5 Attacks and Countermeasures
### Attacking P2P
#### 5.1.2 Attacks on the P2P Routing Tables  
P2P routing tables have to be updated regularly to provide reliable routes when lookup requests have to be forwarded. Most DHTs update and optimize their routing tables by using entries provided by the routing tables of other nodes in the DHT. Malicious nodes may forge routing tables and provide routing table entries with links to inappropriate or non-existing nodes [22]. Further, corrupted routing updates can increase the fraction of routing table entries that refer to malicious nodes [23]. As most DHTs use metrics like proximity or age to optimize their routing table entries, malicious nodes may exploit these metrics to maximize the impact of the attack. Manipulation of the routing updates in order to increase the number of malicious nodes in a routing table of a benign node also results in a cascading effect. As each malicious node that is added to a routing table will provide routing updates, the fraction of malicious nodes increases as shown in Equation 2[23]. The fraction of malicious routing entries \(f_{table}\) increases whenever a routing update is initiated as each benign node provides entries with a fraction of \(f_{network}\) malicious nodes, but each malicious node provides malicious entries only.  
\[f_{table}=(1-f_{table})*f_{network}+f_{table}*1 \tag{2}\]
## 3 Numerical Model of the Single-Stage FLTD
### Equivalent Model of Toroidal Cores  
This part will introduce a homogenization approach for wound magnetic cores with 25-\(\upmu\)m thick ribbons and determination of electrical conductivity and permeability of the equivalent model.  
Compared to the wound toroidal core, the simulation model with equivalent conductivity and permeability is determined based on the following two requirements [38]: the dissipated power must be equal; the magnetic field distribution outside the core must be the same.  
Due to the skin effect [26], the eddy current flows only on the surface of the magnetic cores. Considering the aforementioned two requirements, resistance and current values between the original wound core model and equivalent solid conductor model should be equal, as shown in Figure 9.  
The orthotropic conductivity and permeability of the homogeneous core are [38]:  
\[\sigma_{x}=\sigma_{\rm r}(b/h)^{2},\ \sigma_{y}=\sigma_{z}=\sigma_{\rm r}, \tag{24}\]  
\[\mu_{x}=\left.1/\left[F/\mu_{0}\mu_{\rm r}+(1-F)/\mu_{0}\right],\ \mu_{y}=\mu_{z}=F\mu_{0}\mu_{\rm r},\right. \tag{25}\]  
where \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\) denote orthotropic conductivity of the solid conductor; \(\mu_{x}\), \(\mu_{y}\), and \(\mu_{z}\) denote orthotropic permeability of the solid conductor; \(\sigma_{\rm r}\) is isotropic conductivity of the magnetic core; \(F\) is lamination filling factor; \(\mu_{0}\) is permeability of free space; \(\mu_{\rm r}\) is relative permeability of the core and it is chosen to be 1000 [6; 37] in simulation.  
Figure 8: Final simplified model of the discharge brick. (**a**) Perspective view, (**b**) cut-away view.  
Figure 7: Schematic of the field-circuit coupling port for a single discharging brick.  
The explicit modeling and full discretization of the magnetic core with a large number of ribbons is impractical, as this would result in a large number of grid elements (e.g., \(\sim\)10 million) and a very small time step size. However, the equivalent model can significantly reduce computational cost and make reasonable approximations. Furthermore, the losses in the toroidal magnetic cores are modeled by a time-dependent resistor [37]:  
\[R_{\text{core}}(t)=\big{(}2S_{0}\sqrt{\mu_{0}\mu_{\text{r}}\rho_{\text{core}}} \big{)}/\big{(}\pi^{1.5}r_{\text{core}}\delta\sqrt{t}\big{)}, \tag{26}\]  
where \(S_{0}\) is the effective cross-sectional area of the magnetic core; \(\rho_{\text{core}}\) is the electrical resistivity of magnetic core; \(r_{\text{core}}\) is the average radius of the toroidal cores; \(\delta\) is the thickness of the ribbon used to fabricate the cores; and \(t\) is the time instant.