esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Motor Topology and Simulation Analysis
### Simulation Analysis for No-Load Condition  
In order to verify the MAS effect and the flux aggregation effect of the proposed model, the simulation analyses of the two models were carried out under no-load conditions. Figure 4 shows the flux line distribution of the conventional model and the proposedmodel under no-load condition. After comparison, it is found that the permanent magnet flux linkage \(\psi_{pm}\) of the proposed model is shifted and forms an angle of \(\alpha\) with the _d-axis_. The proposed model utilizes a permanent magnet at the center of the rotor single-pole to achieve the magnetic field deflection inside the rotor, shielded from flux by a connected flux barrier on the right side of the rotor single-pole. By comparing Figure 4a,b, it is found that the inner flux line density of the rotor of the proposed model is lower, while the flux line density in the stator is higher, which is consistent with the properties of the Halbach array. The proposed model incorporates the permanent magnet arrangement of the Halbach array, which makes the permanent-magnet-assisted excitation more effective. The higher density of flux lines in region A of Figure 4b for the proposed model indicates that the proposed model has a better flux aggregation effect.  
Figure 5 demonstrates the comparison of the no-load air-gap flux density between the conventional model and the proposed model. According to Figure 5a, it can be seen that the air-gap flux density magnitude of the proposed model is higher than those of the conventional model, and its air-gap flux density RMS is improved by 32.43% compared with the conventional model. Based on the air-gap flux density waveforms, it can be seen that the proposed model produces a certain offset angle compared to the conventional model. According to Figure 5b, due to the asymmetric structure of the proposed model, the higher harmonics in the harmonic decomposition of the air-gap flux density are larger compared to the conventional model, and there are obvious distortions. The no-load back-EMF waveforms and harmonic decomposition of the two models are shown in Figure 6. Consistent with the air-gap flux density waveforms, the magnitude of the back-EMF waveforms of the proposed model is higher compared to the conventional model, and there is a certain offset. From Figure 6b, it can be seen that the high harmonics of the back EMF of the proposed model are still larger than those of the conventional model, and distortions also appear.  
Figure 4: Flux line distribution plots for both models under no-load conditions. (**a**) Conventional model. (**b**) Proposed model.
# Fast Frequency Acquisition and Phase Locking of Nonplanar Ring Oscillators
## 4 Experimental Results
Main causes include uncertainty of the initial frequency shift direction, laser frequency drift during the acquisition process, and a shortened frequency shift step in the last few control cycles. The dashed line in Figure 6 is the linear fitting line with a slope of 2.03 ms/MHz. The slope corresponds to the ratio of time of one frequency control cycle, 6 ms, to the maximum frequency shift step in one cycle, 3 MHz. Compared to the frequency acquisition time achieved via the frequency sweeping technique based on laser crystal temperature turning, which was longer than 10 s [2], the acquisition time of this work was 1-2 orders of magnitude shorter.
## 5 Conclusions  
This study presented an innovative integrated thermal protection structure based on optimization methods. By minimizing equivalent thermal conductivity and elastic strain energy of the core area, a topology optimization method was established and a brand new core configuration for unit-cell core was derived. Three-dimensional four-cell ITPS structures were then built on the basis of this new configuration. In order to control the buckling phenomenon induced by thermo-mechanical coupling loads, size and shape optimization was conducted. Three critical design factors, the maximum bottom surface temperature, the maximum top surface deformation and the smallest critical load factor, were studied in detail for both initial and optimized designs. Both optimized ITPSs show better thermal and structural performance without mass cost. The maximum bottom surface temperature drops from 416 K to 338 K and 351 K, with a reduction of 18.8% and 15.6%, respectively. Thermal short effect was alleviated due to the better usage of insulation materials. The maximum displacement of the top surface slightly increased from 9.27 mm to 9.43 mm and 10.47 mm, respectively. However, the buckling strength was greatly enhanced. The critical load factors for the initial design were lower than 1, while for both optimal designs were greater than 1. This result demonstrates that the optimized structures are more reliable under severe thermal conditions. The load-bearing capacity under aerodynamic pressure is improved due to better core stiffness. The weight of optimized designs remains almost the same as the initial design. The existence of material with medium density indicates the possibility of altering the material of upper webs, which could drastically decrease the weight. Additionally, it should be noted that the webs were very briefly optimized in this study, and it is still far from the best layout for 3D structures. For the purpose of reducing weight while maintaining the thermo-mechanical performance of ITPSs, more attention should be paid to the detailed optimization model of the webs in the progress of our further work. In the present work, the topology optimization method was validated to be an effective approach for ITPS design. Conceptualization, Q.X., S.L. and Y.M. ; methodology, Q.X. and S.L. ; formal analysis, Q.X. ; writing--original draft preparation, Q.X. ; writing--review and editing, S.L. and Y.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**  
TPS thermal protection system  
ITPS integrated thermal protection system  
TFS top face sheet  
BFS bottom face sheet  
\(t_{T}\) thickness of TFS  
\(t_{B}\) thickness of BFS  
\(t_{w}\) thickness of web  
\(L\) half-width of the cell  
\(H\) height of the panel  
\(\theta\) inclination of the web  
\(m\) mass  
\(T\) temperature  
\(t\) time
## 4 Discussion  
The numerical study performed in this article confirms the concept illustrated by previous investigations. The results showed the pattern in which a decrease in the energy dissipation capacity of steel braces under cyclic loading is connected with the increase in brace slenderness and width/diameter-to-thickness ratios. A large series of parametric experiments were undertaken to determine the effect of compressive-tensile resistance ratios, on a broad range of slenderness ratios and width/diameter ratios, of both square and circular shaped braces. This study provides a comprehensive outline of the collection of various slenderness ratios and width/diameter-to-thickness ratios that should be chosen for the use of these steel braces in concentric bracing frames.  
As shown by the results, certain compressive resistance is retained after the braces undergo global buckling. The degraded compressive resistance is withstood through the establishment of plastic hinges at the middle and end sections of the braces, and the resistance steadily reduces with the loading cycles of large amplitudes. [PERSON] (1986) gathered the test results of various specimens with symmetrical and unsymmetrical loading protocols, and proposed the predictive curves for the compressive resistances after the occurrence of brace buckling. Figure 14 displays the normalized compressive resistances of the braces of the current FEA study, in conjunction with the predictive curves of [PERSON] (1986) and [PERSON] _et al._ (2018), at loading displacements of 2\(\Delta\)y and 4\(\Delta\)y against a non-dimensional slenderness ratio \(\overline{\lambda}\), which is defined as:  
\[\overline{\lambda}=\sqrt{AF_{y}/N_{cr}} \tag{4}\]  
where \(A\) is the cross sectional of brace section, \(F_{y}\) is the yield strength of the adopted material, and \(N_{cr}\) is the elastic-critical buckling load. The compressive resistances of the modeled braces degrade with the increase in slenderness ratio and show good agreement with the predictive curve of [PERSON] (1986) at 2\(\Delta\)y, and have deviations at 4\(\Delta\)y. However, the AISC Cottes _et al._ (2018) prediction remained over-conservative in both cases.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### [PERSON] et al. in [15] proposed the S-LEARN MAC protocol for Cognitive Radio Network (CRN), which can be applied to WSNs. In the mechanism, each node makes a schedule for coordinating the energy harvesting activity and the data transmission activity. The schedule is based on the energy detection information obtained from the Primary User (PU) as the owner of the frequency and from the other nodes in the network, through the use of four counters. The schedule is dynamic and it has a self-adaptation property.  
The S-LEARN MAC protocol is validated through simulation. The result showed a significant performance improvement in throughput of the network compared to the random method and the modified CSMA method for energy harvesting. The S-LEARN MAC weakness is that it takes a long time for the system to have enough knowledge to improve the network performance. Thus, it also takes a long time when the system has to adapt to the changes in the environment.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 2 The Establishment of the Cable Icing Data Set
### Experiment Preparation  
#### 2.1.1 Experimental Equipment  
1. The step-in laboratory had constant humidity and temperature; the model was RH-60 (Andersen Instrument Equipment Co., Ltd., Wuhan, China), with an external size of 10 m \(\times\) 5 m \(\times\) 2.8 m and a temperature range of \(-\)60. The height of the rainfall device (Andersen Instrument Equipment Co., Ltd., Wuhan, China) was 2400 mm, the upper width was 2000 mm, and the three spray devices were evenly distributed. The spray flow range was 0-3 L/min, and the water temperature control range was 0.5-2 \({}^{\circ}\)C.
2. The camera fixed bracket (Made by a 3D printer Andersen Instrument Equipment Co., Ltd., Wuhan, China) simulated the perspective of the stay cable deicing robot, as shown in Figure 1. The size was 21 cm \(\times\) 5 cm \(\times\) 10 cm (diameter \(\times\) thickness \(\times\) length). The two cameras were connected by a 10 cm pole and the horizontal angle between the camera and the cable was set to 30\({}^{\circ}\). Through two cameras, the degree of icing on one side of the stay cable can be judged separately, and the diversity of the data set can be increased.  
3. A practical engineering stay cable sheath with a diameter of 0.2 m, a wall thickness of 0.005 m, and a length of 2 m was used, and the sheath was smooth and hydrophobic.
4. Other equipment used in this experiment included a meteorological instrument (Beijing Sun Technology Co., Ltd., Wuhan, Chain), a cable-stayed bracket with adjustable  
Figure 1: Camera fixed bracket.  
angle, a fan, two Hikvision network cameras (DS-IPC-B12V2-1) (HIKVISION Co., Ltd., Zhejiang, Chain), and an anemometer, as shown in Figure 2 (stay cable ice experimental equipment).
## 4 Description of the System
### Incorporation of New Components
#### 4.2.3 Communication System
The structure of the data packets exchanged between the OBC and a user mobile device coincides with the structure of the previously described packets, with the exception that the packets do not have SRC-ADD and DST-ADD fields because the connection is the UDP. The flow control also matches the previously described flow control. In the event that the connection between the OBC and the mobile device of a passenger is lost, e.g., because the device loses battery power, then the OBC node communicates this situation to the infrastructure with the objective of alerting the driver who can supervise the trip of the passenger with special needs. The performance of the communication system has been studied in two different scenarios. The first scenario corresponds to services that do not involve mobile user terminals; an example of this type of service is the first use case described below in Section 5.1 (Accessibility surveillance system at stops). The second scenario corresponds to services in which users use their mobile terminals to access the service; an example of this type of service is the second use case described below in Section 5.2 (Route assistant for travelers with special needs). The results of these analyses are presented in each of these sections.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
###### Abstract  
Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.  
Keywords:wireless communications; channel modeling; path loss; propagation characteristics; machine learning; neural network; random forest; regression; 5G; 6G +
Footnote †: journal: _Sensors_
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 6 Open Research IIoT Application Domains, Fog Computing as an Enabler
### Software Defined Networking (SDN)  
SDN is an evolving concept to remotely control the entire network from a centralized location. Distributed networks can be integrated with SDN architecture for more granularity of control in the network. Since edge computing is done near the edge devices, there is a high probability that the edge devices are mobile due to the exponential evolution of smartphones and other handheld devices. Similarly, the ever-changing requirements of resources for each user brings a certain degree of dynamism in the network which needs to be catered to ensure efficient operation. Since fog is a new concept and it cannot completely replace the existing cloud architecture, both fog and cloud work hand in hand for smooth operation of the network. The interplay between fog networking and cloud computing is always there when both work in an integrated fashion. The four-layer architecture was studied for end-to-end delay, energy consumption and packet loss ratio in [115]. The authors formulate mixed integer programming (MIP) problem of minimizing the energy consumption under data rate, bandwidth and delay threshold constraints. A new routing protocol was proposed for VANET applications using SDN and fog computing, named as Energy Efficient Multicast routing protocol (EMSFV). SDN controller, OpenFlow switches and fog computing works under two algorithms,priority based scheduling algorithm for classifying the traffic message type (emergency or safety applications) and a classification algorithm to schedule the requests.
## 2 Astronomical Parameters  
### Transit-Spectroscopy Method  
Transit spectroscopy is a widely applied method for spectral detection to obtain the atmospheric environmental characteristics of exoplanets. It has relatively low contrast demand (10-100 ppm) and an adequate number of available samples, which is conducive to studying warm and hot exoplanets with a short period and nearby orbit [7]. Among five complementary methods for transiting planets [8], primary and secondary transits (eclipse) have great significance for studying the atmospheric composition of exoplanets; they were selected as the detection methods in this paper.  
The detection principle of the primary transit-spectroscopy method is shown in Figure 1. When a planet passes in front of its host star, the latter is partially blocked, and its luminous flux is reduced by a few percentage points. The reduction in percentage corresponds to the ratio of the projected area of the planet and star (transit depth), and the radius of planet can be inferred from the flux measurement. When starlight transmits through the atmosphere on a planetary terminator, additional specific wavelength absorption occurs corresponding to the signatures of molecular species in the atmosphere, which varies the transit depth at different wavelengths. Light curves at different wavelengths are marked in red, green, and blue in Figure 1a. The light curve at each wavelength has a characteristic transit depth in the periods of duration (\(T_{14}\)), ingress (\(T_{1}\)-\(T_{2}\)) and egress (\(T_{3}\)-\(T_{4}\)) [9]. Secondary transit (eclipse) spectroscopy has a similar principle. When a planet orbits behind the host star, the planet is occulted, resulting in a drop in flux. Since the planet flux including the contribution of planetary atmosphere is removed, an eclipse light curve is produced [10]. Thus, information of planetary-atmosphere composition can be inferred from the spectral characteristics.
## 7 Datasets
### Server Monitoring  
Time series anomaly detection datasets within the domain of server monitoring are primarily used to monitor the performance metrics of server systems to promptly identify performance bottlenecks, system failures, or security issues. For instance, SMD dataset [105] gathers a variety of server performance indicators, such as CPU usage, memory consumption, disk I/O, etc., for anomaly detection and performance analysis. Exathlon dataset [102] may collect performance data of servers under high load conditions to detect performance anomalies.  
Large companies need to monitor various metrics (such as page views and revenue) of their applications and services in real time. At Microsoft, a time-series anomaly detection service has been developed to help customers continuously monitor time-series data and promptly alert potential incidents. [PERSON] et al. [122] proposed a novel algorithm based on Spectral Residual and CNN, which is the first attempt to apply the SR model, originally from the visual saliency detection domain, to time-series anomaly detection. The approach aims to provide an accurate, efficient, and general solution. [PERSON] et al. [123] proposed a segment-based time series anomaly detection method for monitoring machining processes. This approach detects anomalies by using subprocess-specific thresholds, enabling more precise identification and localization of abnormal behaviors in the machining process.
## 2 Results and Discussion
### Synthesis and Characterization of Polymer and Polymer Hybrid Films
On the other hand, as observed in Figure 7b, the L2 formulation contained ZnO in the form of stars, homogeneously distributed in the polymer matrix, while the L3 fracture (Figure 7c) evidenced cube-like forms of ZnO doped with SnO\({}_{2}\) incorporated into the polymer matrix. The composition and spatial atoms distribution into the composite films based on L1, L2, and L3 formulations were surveyed by EDX spectroscopy and elemental dot mapping of the fractured films, which allowed us to asses the main atoms scattered in the samples (Figure 8). Thus, the EDX spectrum of the L1 formulation was characterized by the presence of three peaks that corresponded to C, O, and N atoms (Figure 8a), in good agreement with the composition of the photopolymerized film without nanoparticles. For the L2 sample, besides the peaks for C, N, and O, some peaks characteristic for Zn atoms were clearly evidenced (Figure 8b), demonstrating that ZnO particles were successfully incorporated into the polymeric film. Moreover, the bright spots in the mapping image of Zn atoms indicated a uniform distribution of the atoms in the film. The EDX analysis of the L3 fractured film, illustrated in Figure 8c, denoted the presence of clear peaks characteristic for C, O, N, Zn, and Sn, a clear proof for the incorporation of ZnO-SnO\({}_{2}\) particles into the film during photopolymerization. Also, the uniform distribution of zinc and tin atoms inside the L3 photocrosslinked film could be remarked. The EDX analysis allowed the estimation of the content of Zn atoms to be as 0.88 wt.% in the L2 film, while in the L3 film, the content of the Zn and Sn atoms was found to be 0.96 wt.% and 0.92 wt.%, respectively. Figure 6: Transmittance spectra of L1–L3 thin films. Figure 7: SEM images of polymeric films (in fracture) for L1 without particles (**a**), L2 with ZnO (**b**) and L3 with ZnO-SnO\({}_{2}\) (**c**).