esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modelling the Reactive Power with ANFIS
#### 2.2.1 AnFIS  
A neuro-fuzzy network is a smart hybrid system that integrates the characteristics of the two systems it is made of. As a result, the hybrid system outperforms all neural networks and fuzzy systems. In the development of intelligent systems, fuzzy logic and RNA are naturally complementary methods. Though neural networks are low-level computational systems that excel when dealing with raw data, fuzzy logic is associated with \"high-level\" reasoning and relies on linguistic information collected from human experts [55]. Fuzzy systems, on the other hand, lack the ability to learn and adapt to new situations. However, while neural networks are capable of learning, they are not  
Figure 3: The variation in the active power (**a**) and reactive power (**b**) in the railway power supply substation.  
transparent to the user. Integrated neuro-fuzzy systems combine neural networks' parallel processing and learning capabilities with information representation and the ability of fuzzy systems to provide human-like explanations. As a result, neural networks have become more transparent, and fuzzy systems have gained learning capabilities.  
With Takagi-Sugeno fuzzy systems, ANFIS hybrid neuro-fuzzy systems are functionally similar to adaptive neural networks [51]. In contrast to traditional fuzzy systems, ANFIS neuro-fuzzy systems can adapt throughout the learning process. The fuzzy membership functions can be adapted in this way by using an optimization approach [51].  
Figure 4 depicts the ANFIS architecture. Several references, such as [32; 50; 51], offer information about the learning process in an ANFIS.  
ANFIS is a tool for modelling and forecasting chaotic time series. According to the paper, power samples (active, reactive, or distorted) are samples collected at discrete time intervals using a data acquisition board. After testing, an ANN network is capable of predicting the value of a time series for which it has not been trained. As a result, the values in the time series represented by the reactive power were used to train ANFIS. K samples were used for training, and n-k samples were used for testing and validation from a total of n data taken. MATLAB was used to implement ANFIS. We used ANFIS based on grid partition, ANFIS based on subtractive clustering, and ANFIS based on FCM, all of which are available in MATLAB.  
To make an ANFIS prediction, it must first know the values of the time series up to time t in order to predict the value at time t + p. To predict a future value x (t + p), the typical prediction method is to create a mapping from N samples sampled at d time units: x(t - (N - 1) d,... x (t - d), x (t).  
For example, in the case presented in the paper, a vector with data will be created for N = 5, d = 3, and p = 3 to drive a vector with 5 columns:  
To predict x(t + 3), use [x(t - 12) x(t - 9) x(t - 6) x(t - 3) x(t)].  
The samples of the powers are represented by x in our paper.
# Time Shared Optical Network (TSON): A Programmable Network Edge Solution for Multi-Access Support
## 3 TSON, Present
### Evolution Stage 5: Current TSON Architecture  
Figure 12 presents the current TSON edge node data plane architecture. The ingress TSON edge nodes are responsible for parsing, aggregation, and mapping of any input traffic combination with different bandwidths (in this implementation less than 10 Gbps) into either 10 Gbps TSON output with different granularity or converged 100 Gbps Ethernet output, whereas the egress edge nodes have the reverse functionality. As it can be seen in Figure 12, the architecture is similar to the pervious TSON solution architecture.  
Although this architecture carries out the previous features, it adds more values to the TSON solution. Seven new TSON solution features in the current implementation are as follows:  
* Modularity--a set of intellectual property (IP) cores have been created to ease and shape the TSON system integration.
* Extendibility--the TSON pipelines are designed to be extendable. The only limitation factor is the FPGA chip density and IOs for implementation.
* Optimisation--the TSON pipelines are optimised to reach lower latency for 10 Gb pipelines and reduce the latency by 6.7%.
* 100 Gb aggregation--\(\times\) 10 Gb to 100 Gb aggregation is added to the design.
* Frame format--the TSON frame format is changed. In this scenario, each TSON frame slice carries the incoming client frame format. This means if the client data is Ethernet packets, the TSON frame slices are shaped by Ethernet frames. This feature makes the TSON frames transparent for other packet processing devices, but the time-slices frames are visible for them for processing.
* 1 = number of 10 G IOs, where m is the number of clients and n is the number of lambdas. This feature is another result of the changing TSON frame format.
* Elastic bandwidth allocation mode--this mode is enabled for both 10 Gb and 100 Gb wavelengths.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 5 Limitations  
Our method still has certain limitations. In high-density scenarios, where objects are densely packed, a significant amount of localization errors can occur. Since our method utilizes more historical information, these errors may accumulate, potentially degrading tracking performance. Additionally, compared to the baseline model, our method introduces additional computational complexity. In video sequences without object overlap, the performance improvement over the baseline model is not very pronounced, while the increased computational complexity leads to slower processing speeds. Finally, when objects are completely occluded and exhibit complex non-linear movements, our method struggles to handle such cases effectively. Future improvements could involve incorporating advanced features such as appearance features into the association algorithm to address these challenges.
## 1 Introduction  
In diseases of the oral cavity, such as malignant or benign tumors, extensive trauma, osteoradionecrosis, osteomyelitisitis, or medication-related osteonecrosis of the jaw, segmental mandibuelectomy is often inevitable. Segmental mandibuelectomy results in a mandibular continuity defect which often leads to serious functional and aesthetic complications, therefore, adequate reconstruction of mandibular defects is essential [1; 2; 3]. Since accurate mandibuelectomy and appropriate reconstruction are challenging issues, recently, computer-assisted surgery (CAS) has been introduced and widely used in clinical practice. This technique includes preoperative virtual surgical planning (VSP) based on a patient's images, such as computed tomography (CT) and magnetic resonance imaging (MRI), and preparation of surgical guides by computer-aided design and computer-aided manufacturing (CAD/CAM) technologies and adding materials layer by layer to transfer VSP into the operative field. These three-dimensional (3D) printed surgical guides are used for the resection of the mandible, and to shape the free bone flap [4; 5; 6]. The advantages of CAS are reduction of the operative time and aesthetic and functional results through anatomical reconstructions [7; 8]. Additionally, it has been reported that CAS can more safely and accurately reset the mandibular malignancies with VSP performed using various imaging modalities, such as CT and MRI simultaneously [9].  
Despite a large number of advantages and various osteotomy lines that can be designed, most of the literature on CAS has shown designs of straight mandibular resection lines. Straight mandibular resection lines make the overall surgical procedure easier but more mandibular anatomical structures could be resected even if they are not affected by the disease. These unnecessarily resected anatomical structures are mostly predicted to be the lower teeth, the mandibular inferior borders, and these structures are important for function and aesthetics.  
Therefore, the purpose of this study was to present and evaluate alternative lesion-specific mandibularectomy (tailor-made mandibular resection) methods for preserving the mandibular anatomical structures as compared with the previous virtual surgical plan.
# Numerical Investigation on Handling Stability of a Heavy Tractor Semi-Trailer under Crosswind
## 6 Conclusions  
In the present work, the effect of aerodynamics on the motion of a tractor semi-trailer subjected to crosswinds was analyzed. A coupling method was used to analyze the effects of crosswind on the handling stability of a tractor semi-trailer. In order to obtain the crosswind aerodynamic characteristics, the steady aerodynamics of a tractor semi-trailer were analyzed under various relative inflow angles. To get the mathematical relationship of aerodynamic characteristics under crosswind, a polynomial was used to fit the variation of the aerodynamic coefficient with relative inflow angles. The aerodynamic coefficient functions of the tractor semi-trailer were applied to the aerodynamic module in multi-body dynamic simulation to describe the aerodynamic changes of the vehicle under crosswind. Then, a series of dynamic performance responses including the lateral displacement, the lateral acceleration and the yaw rate were obtained. The dynamic responses, which considered the change of aerodynamics, showed that the crosswind had great influence on the handling stability of the tractor semi-trailer. The handling stability of the tractor semi-trailer with and without a deflector was compared. The dynamic responses indicated that the handling stability of the tractor semi-trailer has been improved a lot with a wind deflector acting on the roof, in which, the lateral acceleration and yaw rate were  
Figure 18: Comparison of lateral displacement, lateral acceleration and yaw rate with or without wind deflector: (**a**) lateral displacement; (**b**) lateral acceleration; (**c**) yaw rate.  
decreased by a maximum of 14.6% and 16.5%, respectively. The deflector cannot only improve the aerodynamic characteristics, but also the handling stability of the vehicle, which offers a guidance to practical engineering applications. Besides that, the crosswind with a long period of non-uniform changes can promote more significant changes; this is worth improving in future studies.  
Project administration, C.S., C.Z. and Y.W.; Resources, Y.Z.; Validation, J.D.; Writing-original draft, Q.Z. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China (Grant No. 51775395), State's Key Project of Research and Development Plan (Grant No. 2018 YFB0105301), the Fundamental Research Funds for the Central Universities (WUT: 2017I18 XZ), Innovative Research Team Development Program of Ministry of Education of China (IRT_17R83), 111 Project (B17034) and the China Scholarship Council (No.201906950068).  
The authors declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 2 Related Work  
Several studies explore the integration of medical devices for IoMT systems from different perspectives. For example, [PERSON] et al., investigated various security vulnerabilities [11] and possible solution for IoMT thread scenarios. A thorough analysis of the advancement and development of IoMT is provided in [12], and the IoMT application along with enabling technologies for different deployment domains is reviewed in [13]. IoMT literature is also examined in [14] for the purpose of developing a smart healthcare system for early diagnosis and spread prevention. Investigation of the IoMT requirements, and challenges with respect to Healthcare 4.0, are also reported in [15]. A three-layer design framework for Healthcare Internet of Things (H-IoT) networks is provided in [16]. Important supporting technologies for IEEE 802.11 wireless real-time communication protocol [17] are also listed, along with the QoS requirements.  
Virtual integration for medical device interoperability is presented in [5]. A medical device integration approach based on the IoT is described in [2]. Common features of the devices and a single protocol for data integration are discussed. In [4], [PERSON] et al. provide a model-driven approach to the design and validation of closed-loop medical device systems. A considerable number of studies, like [18; 19; 20], have explored security and sustainability perspectives in the design of IoMT systems. Different Artificial Intelligence (AI) and Blockchain-based design techniques, for effective communication among medical devices and data analysis, are investigated [21; 22; 23]. [PERSON] et al., in [24], present a multi-mode medical data transmission approach based on the Industrial Internet of Things (IIoT). Design of an IoMT-based healthcare monitoring system is presented in [25], while an ontology-based service for composition and interoperability of medical devices is investigated in [26].  
The related studies described above generally assume a dependable and trustworthy communication network when addressing the safe interoperability and architectural specifications for medical device application platforms and integrated clinical environments. However, our study adopts a more realistic approach for the composition of medical devices, and takes into account the unreliability of the communication networks. Such imperfect networks can further intensify the challenges of security vulnerabilities and network disruptions. Our approach of achieving trusted composition in IoMT systems ensures high assurance in essential functional components while accommodating lower assurance communication infrastructure.
## 3 Results and Discussion
### Effect of Tool Wear on the Entrance and Exit Delamination Factors  
Figure 12 shows the effect of tool wear on the delamination factors. With the increase of the number of holes, the delamination factors both showed a trend of increasing, but the growth rate of the exit delamination factors was significantly higher than that of the entrance delamination factors. Because the worn tool cannot cut the material sufficiently, fibers were increasingly torn at the edges of the holes. Therefore, the value of the delamination factors at the entrance and exit reflected the change of the VB. Combined with Figures 10 and 12, when the feed rate was as low as 30 mm/min, the variation trend of the delamination factors was basically the same as that of the thrust force; however, when the feed rate was as high as 70 mm/min, the thrust force increased less than the delamination factors. Therefore, both of them should be used as the main influencing factors to predict tool wear.  
According to the wear mechanism diagram established by [PERSON] [31], the wear rate gradually increased with increasing spindle speed and feed rate. It was found that the increase in spindle speed and feed rate within the different stages of wear not only led to changes in the tool wear mechanism, but also had a huge impact on the surface quality and delamination factors. Furthermore, the increase in tool wear was not only related to the machining conditions, but also depended on the forces generated during machining [17]. Combining the results of the analysis in Figures 9 and 11, the delamination factor with the axial force should be selected as the input layer of the neural network for the prediction of tool wear.  
The Tool Wear Prediction Model Based on an Improved Circle Chaos Mapping Grey Wolf Algorithm for BP Neural Network
## 2 Materials and Methods
### Study Area  
The Songnen Plain (43\({}^{\circ}\)36\({}^{\prime}\)-49\({}^{\circ}\)26\({}^{\prime}\) N, 121\({}^{\circ}\)21\({}^{\prime}\)-128\({}^{\circ}\)18\({}^{\prime}\) E) is located among the Daxing'an and Xiaoxing'an Mountains, the Changbai Mountain Range, and the Song-Liao Watershed in northeastern China, with a total area of about 187,000 km\({}^{2}\), mainly formed by the alluvial deposits of the Songhua and Nenjiang rivers (Figure 2). The Songnen Plain can be divided into three geometric units: the eastern uplifted area, the western plateau area, and the alluvial plain area. The piedmont plateaus are distributed to the east, north, and west, with elevations ranging from about 180 to 300 m. The alluvial plain area, on the other hand, has elevations ranging from 110 to 180 m, with a flat and expansive topography. The study area is located in a semi-arid and semi-humid transition zone at high latitudes in the eastern part of the Asian continent, with a typical continental monsoon climate characterized by distinct seasons, high temperature and rain in summer, cold and dry climate in winter, and short and rapid temperature changes in the two transitional seasons of spring and autumn [51]. The annual average temperature gradually increases from north to south, and the temperature contour shows a clear dimensional zonal distribution; the annual precipitation is 400-600 mm, gradually decreasing from east to west; the period from June to August is the flood season, with concentrated and extreme rainfall (60%-80% of the annual precipitation) [52]. It serves as an important black soil area, wetland concentrated distribution area, and commercial grain base in China.  
Figure 1: Technology Roadmap for Temporal and Spatial Variations in Net primary productivity (NPP) of Different Land Cover Types in the Songnen Plain and Quantifying Relevant Contributing Factors.
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 2 Principle  
The single-effect AC is shown in Figure 1, with H\({}_{2}\)O-LiBr as the working fluid. The driving heat (point 13, 14) is supplied to the generator, desorbing the refrigerant vapor (point 1) from the solution. Then, the vapor becomes liquid (point 2) in the condenser, and the condensation heat is transferred to the cooling water (point 18, 19). Subsequently, the refrigerant is throttled by the valve and reaches the evaporator (point 4). And then, it evaporates to extract heat from outside (point 15, 16), producing a cooling effect. Finally, the refrigerant vapor arrives in the absorber (point 5) to complete an absorption process.  
In the meanwhile, the strong solution leaving the generator (point 10) passes through the economizer (point 11) and the throttle valve, also reaching the absorber (point 12). In the absorber, the strong solution becomes weak solution (point 7) by absorbing refrigerant vapor, and the cooling water removes the released absorption heat (point 17, 18). Thereafter, the pressure of the weak solution is increased by a pump (point 8), and then its temperature rises in the economizer (point 9). With the weak solution returning to the generator, the next circulation repeats.
## 2 The Evolution from Cloud Computing to MEC
### Fog Computing
#### 2.2.2 Applications  
Fog computing emerges as a practical solution for executing tasks under limitations related to bandwidth and delay. It plays an important role in handling different needs for various applications, such as making homes smarter [34; 35; 36], managing transportation [37; 38], surveillance with face recognition [39], running health-related applications [40], and even helping large industries [41]. However, interoperability is a challenge [42]. To address these issues, fog computing integrates all various tasks and their requirements into a single platform and provides elastic resources to smart home apps. There are numerous applications for vehicular fog computing [43], such as traffic signal scheduling, congestion reduction, hazard sharing, traffic situation data, and so on. The fog nodes are in charge of sending/retrieving data to/from passing cars.  
Smart health is another area addressed by fog computing. Health data include important and confidential information, and management of health information has been an issue of concern. It is possible to accomplish the objective of patient ownership of their own medical information locally using fog computing [42]. Medical information will be kept in a fog node, which may be a smartphone or an intelligent vehicle. When a patient seeks assistance from a clinical laboratory or a physician's office, computations occur in a privacy-preserving mode. Moreover, information is modified locally in the patient-owned fog node [42].  
Figure 3: Three-tier fog computing architecture [33].
## 3 Bond Behavior and Bond Strength
### Influence of FRCC or ECC Characteristics  
The FRCC or ECC characteristics are affected by many factors such as type of fiber, fiber-volume fraction, mix design, and curing age. The bond characteristics results of [PERSON] et al. [22] showed that the bond strength increased with the increase in fiber-volume fraction.  
[PERSON] et al. [27] evaluated the bond performance of GFRP bars embedded in normal-strength sea-water sea-sand ECC (SS-ECC) with PVA fibers and high-strength SS-ECC with PE fiber. A parallel test was also performed on GFRP bars in ECC made with fresh water (FR-ECC). In this study, it is reported that the influence of the saline content enhances the bold performance, and it refines the microstructures of ECC. This paper provided a more comprehensive understanding of the bond performance relationship with chemical adhesion, friction resistance, and mechanical interaction.  
Table 1 provides an overview of the study design and findings reported by various researchers regarding the bond behavior between FRCC and FRP bars. The researchers employed different experimental setups to examine the bond strength. Figure 10 schematically shows the different setups used in the studies. From Table 1, it can be concluded that the bond performance of the FRP with FRCC is affected by variables such as FRP type, diameter of the bar, type of surface of the bar, modulus of elasticity of the bar, fiber type in the FRCC, fiber-volume fraction, bond length, grade of the matrix, environment, and so on.  
Figure 10: Test specimen geometry and test setup: (**a**) direct pullout method [26]; (**b**) RILEM beam method [23].
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments
### Underwater Acoustic Channels
#### 4.1.2 Wuyuan Bay  
Wuyuan Bay is a semi-enclosed shallow bay. The data were collected in a shallow water channel with a depth of approximately 10 m in the bay. As seen in Figure 6b, there is a large bridge near the channel. The passage of vehicles on the bridge causes vibrations, which are transmitted to the sea through the bridge piers. This results in a complex underwater noise environment with significant human-made noise. In Figure 7b, six paths can be observed. The maximum delay of the paths is 11 ms. The transmission loss in Wuyuan Bay is too serious to keep the other transmitting path.