esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Knowledge-Defined Networking
## 4 Management Plane
### Network Management Protocols/Interfaces  
In work [249], authors show that networks are hard to manage due to protocols exposing all their internal details, such that a Complexity Oblivious Network Management (CONMan) interface has been proposed, that includes minimal protocol-specific information that reduces the difficulty in configuration management. Similarly, Platform for Automated Operation and Configuration Management (PACMAN) achieves network configuration management using active documents and is designed to be operated by humans, which is prone to errors due to human errors [250]. Therefore, a software-defined management plane has been proposed to automate the task of network management. The OpenFlow Management and Configuration Protocol (OF-CONFIG), which relies on the Network Configuration Protocol (NETCONF) for transport, has been introduced for the management of resources in OpenFlow-enabled switches [251]. OF-CONFIG allows automatic discovery of switches, provides a standard way to configure the switches by installing flow tables and other settings, monitors the status of switches using traffic flow statistics, etc., and updates the switches. However, some have complemented the capabilities of OF-CONFIG/NETCONF while presenting a semantic-based approach that automates the configuration of network devices, where they have formalized the semantics of the switch configuration domain using a Web Ontology Language (OWL) and developed an ontology-based information extraction system for the Command Line Interface (CLI) of network devices [252]. They have further developed a learning algorithm to enable automated interpretation of the command-line interface's configuration capabilities in the network. This framework is highly compatible with the KDN, as the knowledge plane can dynamically create configurations in OWL format and provide them to the network configuration module in the management plane to directly convey the configurations to the switches, where the CLI will automatically extract the configurations from the ontology-based configurations. A framework for automated network management in hybrid KDN and legacy networks known as HybNET, considering the practical non-existence of fully KDN networks, is described in [253]. A protocol for monitoring and configuration of network devices in KDN known as Simple Network Management Protocol (SNMP) has been proposed that can be used as an alternative to OF-CONFIG/NETCONF [254]. SNMP provides a standardized way to remotely manage network devices, where network devicesact as servers that provide information about their status, performance, and usage to the centralized management client.
## 2 Analysis of the Apparent Power of Buchholz  
Figure 1 shows a three-phase system with an unbalanced linear load powered by any sinusoidal voltage source. Considering the values of the voltages expressed in symmetrical components, this system can be divided into a total of three systems: the first caused by the positive-sequence voltage (see Figure 2), the second caused by the negative-sequence voltage (see Figure 3), and the third caused by the zero-sequence voltage (see Figure 4). The relationship between the impedances of the system and the three equivalent systems is given by (1).  
\[\begin{array}{l}\overrightarrow{Z_{a}}=\overrightarrow{Z_{a+}}+ \overrightarrow{Z_{a-}}+\overrightarrow{Z_{a0}}\\ \overrightarrow{Z_{b}}=\overrightarrow{Z_{b+}}+\overrightarrow{Z_{b-}}+ \overrightarrow{Z_{b0}}\\ \overrightarrow{Z_{c}}=\overrightarrow{Z_{c+}}+\overrightarrow{Z_{c-}}+ \overrightarrow{Z_{c0}}\\ \end{array} \tag{1}\]  
Figure 1: Electric system with unbalanced three-phase load and sinusoidal voltage source.  
Figure 3: Equivalent system with negative-sequence voltage.  
Figure 2: Equivalent system with positive-sequence voltage.  
If we express the values of the voltages and currents in symmetrical components, the apparent power of Buchholz [11] of the system shown in Figure 1 is given by:  
\[S_{1}=3\sqrt{\Big{(}V_{+}^{2}+V_{-}^{2}+V_{0}^{2}\Big{)}\Big{(}I_{+}^{2}+I_{-}^{ 2}+I_{0}^{2}\Big{)}} \tag{2}\]  
where  
* \(S_{1}\) is the total apparent power of the system.
* \(V_{+}\), \(V_{-}\), and \(V_{0}\) are the _RMS_ values of positive sequence, negative sequence, and zero-sequence voltages, respectively.
* \(I_{+}\), \(I_{-}\), and \(I_{0}\) are the _RMS_ values of positive sequence, negative sequence, and zero sequence currents, respectively.  
By expanding (2), the following equation is obtained [10]  
\[{S_{1}}^{2}=S_{p+}^{2}+S_{p-}^{2}+S_{p0}^{2} \tag{3}\]  
\[S_{p+}=\sqrt{9\ V_{+}^{2}\Big{(}I_{+}^{2}+I_{-}^{2}+I_{0}^{2}\Big{)}} \tag{4}\]  
\[S_{p-}=\sqrt{9\ V_{-}^{2}\Big{(}I_{+}^{2}+I_{-}^{2}+I_{0}^{2}\Big{)}} \tag{5}\]  
\[S_{p0}=\sqrt{9\ V_{0}^{2}\Big{(}I_{+}^{2}+I_{-}^{2}+I_{0}^{2}\Big{)}} \tag{6}\]  
where  
* In the first addend, \(S_{p+}\) is the apparent power of the system caused by the positive-sequence voltage. Its modulus is given by (4), and its equivalent circuit is shown in Figure 2.
* In the second addend, \(S_{p-}\) is the apparent power of the system caused by the negative-sequence voltage. Its modulus is given by (5), and its equivalent circuit is shown in Figure 3.
* In the third addend, \(S_{p0}\) is the apparent power of the system caused by the zero-sequence voltage. Its modulus is given by (6), and its equivalent circuit is shown in Figure 4.
## 3 Model
### Route Planning Priority  
In order to reduce the optimization complexity, this paper proposes the method of route planning priority in the terminal area. The planning and design method of terminal area route should consider the operation requirements, such as flight procedure construction specification (ICAO Doc8168), flight flow distribution, aircraft type distribution, flight time distribution, flight attributes and etc. In order to make the best use of the airspace resources, reduce the environmental impact and avoid flight conflicts physically, the priority of the route planning should be evaluated from three aspects: operational efficiency, airspace utilization and environmental impact.  
Operational efficiency refers to the influence of each route on the terminal area operation, and can be evaluated by considering the characteristic of the flight attributes, the routes directions and etc.  
Figure 2: The protection zone of terminal area routes.  
Resource utilization refers to the utilization of airspace resources in the terminal area, including time resources and space resources, and can be evaluated by taking the flight flow distribution and rules of the route into considerations.  
Environmental impact refers to the impact of each route on the environment of terminal area and surroundings, including air emission and noise, and can be evaluated by considering aircraft type distribution rule and flight characteristics of the route.  
According to the definition of operational efficiency, airspace utilization and environmental impact, this paper chooses 8 evaluation index of route planning priority: associated airport service level, annual route service sorties, routes operation mode, route time utilization ratio, route space occupancy ratio, route capacity utilization ratio, route air emissions and route noise level. The Analytic Hierarchy Process (AHP) is adopted to establish the route planning in the analysis of the priority ladder class structure mode, as shown in Figure 3. According to the characteristics of airport operation, the priority of route planning in terminal area route network can be determined.
###### Abstract  
Although numerous studies have tried to explain the mechanism of directional hydraulic fracturing in a coal seam, few of them have been conducted on gas migration stimulated by directional hydraulic fracturing during coal mine methane extraction. In this study, a fully coupled multi-scale model to stimulate gas extraction from a coal seam stimulated by directional hydraulic fracturing was developed and calculated by a finite element approach. The model considers gas flow and heat transfer within the hydraulic fractures, the coal matrix, and clear system, and it accounts for coal deformation. The model was verified using gas amount data from the NO.8 coal seam at Fengchun mine, Chongqing, Southwest China. Model simulation results show that slots and hydraulic fracture can expand the area of gas pressure drop and decrease the time needed to complete the extraction. The evolution of hydraulic fracture apertures and permeability in coal seams is greatly influenced by the effective stress and coal matrix deformation. A series of sensitivity analyses were performed to investigate the impacts of key factors on gas extraction time of completion. The study shows that hydraulic fracture aperture and the cleat permeability of coal seams play crucial roles in gas extraction from a coal seam stimulated by directional hydraulic fracturing. In addition, the reasonable arrangement of directional boreholes could improve the gas extraction efficiency. A large coal seam dip angle and high temperature help to enhance coal mine methane extraction from the coal seam.  
directional hydraulic fracturing; coal mine methane extraction; multi-scale model; numerical simulation +
Footnote †: journal: Applied Physics Letters B
## 3 Design of a General Relay-Node Selection on Intersection
### Procedure of the Relay-Node Selection on General Intersection
#### 3.2.2 Branch Phase  
Having been selected as the relay node, Relay\({}_{1}\) becomes responsible for initiating the message delivered to all branches except the branch the message comes from. The selection of relay nodes on these branches proceeds independently and simultaneously. Each selection can use different radio channels in VANET or different radio sources in LTE eV2X to transfer the different packets of Request-To-Broadcast/Clear-To-Broadcasting (RTB/CTB) and use different frequency to carry different black bursts on the different branches. In the communication range of Relay\({}_{1}\), shown as the circle area centered at Relay\({}_{1}\) in Figure 2, the farthest nodes on the branches except Br\({}_{k}\) from Relay\({}_{1}\) are expected to be selected as relay nodes. The relay node in the \(n\)-th branch is referred to as Relay\({}_{\rm B}^{n}\). In the following, for simple expression, the relay node on the branch is denoted as Relay\({}_{\rm B}\).
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 2 Radar Digital Video Coding Depth Division Algorithm Based on Deep Learning  
The SCC encoding process is similar to the HEVC standard. The difference is that HEVC is mainly used to encode natural content video captured by cameras, whereas SCC is chiefly for screen content video generated by computers. Shipborne radar digital video is a form of mixed screen content video in the field of computer vision. Therefore, the SCC coding scheme was taken as the basic platform for compressing the shipborne radar digital video to optimize the coding process and reduce the delay in compression.
## 4 Simulation Evaluation  
We implemented the proposed SC-Sched method on the network simulator ns-2 [15] and compared it against a method WuR-TDMA, which implements part of the functions of the proposed method, but uses different channel-access methods. Furthermore, we simulated ContikiMAC [9] on the network simulator ns-3 [16] for a comparison.
## 1 Introduction
Most works presented ML-based image processing approaches for path loss prediction, but to the best of our knowledge, no such comprehensive review of such existing works was supplied. * Investigation on the use of various hand-crafted feature extraction techniques and their combinations on 2D satellite images in path loss prediction. * Combining CNN features with hand-crafted features to improve the accuracy of path loss prediction. While this has been applied in computer vision tasks, to the best of our knowledge, this has not been applied to the problem of path loss prediction. * Interpretation of model prediction based on Shapley Additive Explanations (SHAP). Most ML-based path loss prediction works do not analyze the features deeply, including the comparison of different types of features.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Cellular
#### 3.2.2 Narrowband-Internet of Things (NB-IoT)  
The NB-IoT is a low-power wide-area network (LPWAN) technology, introduced by the Third Generation Partnership Project (3 GPP) in 2016. It has the advantage of being compatible with the traditional cellular networks and operates at the 200 kHz GSMcarrier. Basically, it operates within a small portion of the existing network and available spectrum and is deployed inside the long-term evolution (LTE) carrier, a pioneer technology towards building the 5G. NB-IoT supports the ultra-low end IoT applications for real-time implementations. It also features more than 10 years of battery life and huge data collection capabilities from massive sensors for achieving a smarter way of living. It aims to support more than 50 k connections per cell [11], with high coverage. Through the NB-IoT, enhanced mobile broadband (eMBB) is achieved, which provides accessibility of mobile broadband services everywhere and anytime with extended coverage. Moreover, the NB-IoT utilizes the observed time difference of arrival (OTDoA); thus, several synchronized base stations (BSs) transmit position reference signals (PRSs) to the receivers. The receivers forward the ToA per transmitting BS, together with the PRS, to a localization server for processing and estimating their location [11]. The OFDM is also supported by the NB-IoT technology; therefore, the CSI estimations can be extracted and used for fingerprinting-based indoor localization. In [46], the inverse fast Fourier transform (IFFT) was used for excluding the multipath interference, together with the fingerprinting technique for identifying the LoS paths, followed by the CSI measurements for the position estimation.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### AI-Based Early Fault Diagnosis
#### 2.2.3 Neural network  
Neural network is an operational model consisted of a large number of nodes or neurons, connected to each other. The BP neural network proposed by [PERSON] and [PERSON] in 1986 is a widely used neural network. A BP neural network is trained by an error propagation algorithm, and is a kind of multilayer feed-forward network.  
The BP neural network consists of an input layer, hidden layer and output layer. The structure is shown in Figure 7. The input signals are moved from the input layer nodes to hidden layer nodes, and then to the output nodes. The input layer nodes are equal to the number of fault feature vectors \(X=(x_{1},x_{2},\cdots,x_{n})\), the number of the output layer nodes is equal to the number of failure mode  
Figure 5: Diagram of the KNN method.  
Figure 6: The optimal hyperplane for a binary classification by SVM.  
vectors \(Y=(y_{1},y_{2},\cdots,y_{n})\). The number of hidden layers is usually a layer. Through the training, the various states of the training sample can be stored in the network in the form of weight values. The weights and threshold value of the network are constantly adjusted by back propagation, and they help make the network error sum of squares minimum [59].  
The advantage of the BP neural network is that the output of each layer node only influences the output of the next layer node, and as long as there enough of the hidden layer and the hidden nodes, a BP neural network can approach arbitrary nonlinear hint relations, and it has great generalization ability.
###### Abstract  
This article examines the application of generative artificial intelligence (GenAI) in visualizing meat products and evaluates its potential for use in the food industry. The study compares AI-generated images with conventional photographs in terms of professional accuracy and visual appeal. In a cited preliminary study, images of ten selected meat dishes were generated and evaluated by food technology professionals through a survey focused on realism and technical adequacy. Following this, comparable photographs were taken, and a second survey gathered feedback from the public on the appeal of both image types. Results revealed that while AI-generated images often lacked accuracy in texture, color, and structure, particularly for complex meat products, they were generally rated as more visually appealing by the public. This indicates that although current GenAI tools are not yet suitable for precise professional representation of meat products, they show strong potential for use in marketing and promotional content, where aesthetic appeal may outweigh technical accuracy. The findings suggest that with further development, AI-generated visuals could become more viable for professional applications in the food industry. In such cases, using accurate photographic references remains essential to ensure credibility and realism in food-related visual communication.  
artificial intelligence; food industry; food photography; meat products; visual presentation Article
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
###### Abstract
The main contributions and findings of this study are as follows:  
(1) The study focuses on the collaborative scheduling of operational and support resources during turnaround operations. Under the constraints of specific areas and limited resources, an optimization scheme for stand allocation and vehicle scheduling is proposed, with results provided within an acceptable time frame for both stand assignments and vehicle utilization. Figure 1: Shenzhen airport stand occupancy situation. (2) A comprehensive analysis of the coupling mechanism between operational and support resources is conducted, and two optimization models are constructed to address the scheduling of stand resources and support vehicle resources, respectively. A two-stage optimization framework is proposed, which integrates ground operational resources with support resources. The stand allocation results are used as inputs for the ground support vehicle scheduling model, enabling a global optimization of airport ground operations. (3) A highly efficient NSGA-II algorithm, enhanced with local search strategies, is designed to solve the models. Experiments based on operational data from Shenzhen Airport demonstrate that the multi-apron, multi-vehicle type airport resource scheduling optimization model significantly improves operational efficiency. The remainder of this paper is organized as follows. Section 2 reviews the current research on the stand allocation problem and ground support vehicle scheduling problem, highlighting the limitations of existing studies. Section 3 constructs a stand allocation model that considers the constraints of support resources, and Section 4 builds a ground support vehicle optimization scheduling model based on the stand allocation results, considering the service patterns and coupling relationships of the vehicles. Section 5 explains the computational process and key steps of the LS-NSGA-II algorithm. Section 6 presents an experimental case study using operational data from Shenzhen Airport, offering an in-depth analysis of the optimization results. Section 7 concludes the paper with a summary of findings and future research directions.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
###### Abstract  
Brain-computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects' brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs.  
brain-computer interface; steady-state visually evoked potentials; personal response; visual stimulation; frequency selection algorithm; discomfort index
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 5 Conclusions  
This study proposes a collision avoidance behavior mining model considering encounter scenarios. By analyzing the curvature of ship trajectories, the model identifies collision avoidance turning points and determines encounter scenarios, thereby providing data support for autonomous ship collision avoidance decision making. The comparative analysis shows that by combining sliding windows and curvature calculations, the model performs excellently in handling external interference, such as wind, waves, currents, and data transmission noise, and it accurately identifies the true collision avoidance turning points of ships. Moreover, through the encounter scenario classification standard established by the model, it can accurately identify encounter scenarios. In a case analysis conducted in the complex waters east of the Yangtze River Estuary, the model demonstrated high recognition accuracy, recognizing a total of 827 instances of ship collision avoidance behavior under different encounter scenarios, which shows that the model can effectively capture collision avoidance behaviors under various encounter scenarios. This provides a reliable data foundation and methodological support for autonomous ship collision avoidance decision making and intelligent waterway traffic management.  
Conceptualization, S.C., C.Z. and Z.W.; methodology, S.C.; software, C.Z. and Z.W; validation, C.Z., L.W., H.G. and W.W.; formal analysis, C.Z.; investigation, W.W. and S.L.; resources, S.L.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, S.C. and C.Z.; visualization, Z.W. and S.L.; supervision, S.C. and W.W.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (Grant No. 52071249).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available upon request due to restrictions due to privacy.  
**Acknowledgments:** We sincerely thank the editor and the reviewers for their kind and helpful comments on this manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.