esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result Analysis
### Experimental Results
The evaluation metrics are shown in Table 4, and the prediction curves are shown in Figure 9. Both the trends and amplitudes were well-predicted. When combined with key technologies such as traffic classification, load balancing, and traffic scheduling, health monitoring of the LEO constellation can be achieved, ensuring the quality of service for users. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**Internal Low Earth Orbit Constellation Service Simulation System**} \\ \hline
**DLS-Traffic Prediction** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline One day & \(1.18\times 10^{-5}\) & 0.0034 & 0.0026 & — \\ \hline One week & \(8.48\times 10^{-6}\) & 0.0029 & 0.0022 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics for the long-term and short-term prediction on the dataset of the internal LEO constellation business simulation system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**Abilene**} \\ \hline
**Modules** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline GRU & 0.0003 & 0.0168 & 0.0117 & 9.1156 \\ \hline BiLSTM & 0.0007 & 0.0257 & 0.0176 & 12.5431 \\ \hline GCN & 0.0125 & 0.1120 & 0.0876 & 110.5250 \\ \hline Data separation & 0.0009 & 0.0298 & 0.0220 & 17.1589 \\ \hline Attention & 0.0030 & 0.0548 & 0.0413 & 35.5827 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics for the long-term prediction of each model on the Abilene dataset after removing different modules in the ablation experiment.
## 2 Overview of the Study Area
### Investigation of Landslides  
In the present study, we have compiled a comprehensive catalog of landslide points, conducting a statistical analysis of historical small-scale landslides, which may facilitate the investigation into the interrelationships between past landslide occurrences and various environmental factors [24]. According to the scope and objectives of the study, monitoring data of landslides during a heavy rainfall event in Yiyang City, Hunan Province, from July to August 2016 were collected from the Global Disaster Data Platform, along with 550 historical landslide records specific to Yiyang City, Hunan Province. As depicted in Figure 3, landslides in the study area are characterized by small scale, large quantity, and wide distribution.
## 3 Analysis and Results  
### Research Area and Data  
This study focused on expressways, leveraging digital tachograph (DTG) data from trucks in conjunction with expressway node-link data to derive microsegment speed information. Truck DTG data were used to collect precise vehicle location and speed data, effectively capturing driving patterns representative of both autonomous and typical vehicles. While DTG data are generally available from taxis, buses, and trucks, taxis and buses were excluded from this study due to driving behaviors that differ significantly from those in typical traffic flows. Specifically, taxis and city buses operate predominantly in urban settings and frequently make unpredictable stops for passenger boarding, alighting, or waiting. Intercity buses may also exhibit distinct driving patterns from general traffic due to the use of dedicated bus lanes.  
In contrast, trucks primarily travel in the right lane, or the slow lane, on highways, operating at relatively lower speeds than passenger cars, which creates a difference from the general traffic flow. Consequently, outlier removal was conducted to minimize this disparity. However, given the current limitations in acquiring operational data for autonomous vehicles and car-sharing services, truck DTG data present the most viable alternative for simulating general vehicle patterns on expressways. Therefore, this study used truck DTG data based on their availability and their alignment with typical driving behaviors.  
The collected dataset, illustrated in Figure 6, comprised 2775 vehicle data points gathered from expressways in Gyeonggi Province, South Korea, for April 2018. To isolate expressway-specific data, the study used the standard node-link structure provided by an intelligent transportation system. The analysis included 951 nodes and 2133 links across expressways in the Gyeonggi region, providing a comprehensive basis for detailed speed and location assessments.  
Figure 6: Research area.
# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 3 Modeling of the SDM Problem Based on the Hypernetwork Model
### Hypernetwork Model  
In CMfg systems, a complex network of relationships emerges between a vast array of manufacturing services and demands. The matching hypernetwork model employed in this paper defines manufacturing services and tasks as nodes in different layers and describes the relationships among nodes across these layers using hyperedges [34]. This model comprises the following components:  
1. Two types of nodes: * Service nodes (\(S\)): Each node in the service network denotes a service provider. * Task nodes (\(T\)): Each node in the task network denotes a task requester.
2. Three types of edges: * Service edges (\(E^{S}\)): Connections between service nodes \(S_{i}\) and \(S_{i^{\prime}}\). * Task edges (\(E^{T}\)): Connections between task nodes \(T_{j}\) and \(T_{j^{\prime}}\). * Service-task hyperedges (\(E^{S\_T}\)): Connections between service node \(S_{i}\) and task node \(T_{j}\).  
The matching network model, consisting of two types of nodes and three types of edges, can be expressed as _Matching_Net = \(<\)V(\(S\), \(T\))_, _E_(\(E^{S}\), \(E^{T}\), \(E^{S\_T}\))\(>\). The _Matching_Net_ includes _S_Net_, _T_Net_, and \(E^{S\_T}\), as shown in Figure 2.  
Figure 2: Matching_Net.
## 3 Verification of Automated Preliminary Design Methodology
### Mean Line Design
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Unit** & **T-100 (Original)** & **Verification** & **T-100 re (Optimization)** \\ \hline Loading Coefficient & [-] & 0.91 & 0.91 & 0.90 \\ Flow Coefficient & [-] & - & 0.22 & 0.21 \\ Rotational Speed & [r/min] & 106,588 & 106,588 & 114,185 \\
**Rotor** & & & & \\ Inlet Radius & [mm] & 58.2 & 57.3 & 54.1 \\ Inlet Blade Height & [mm] & 6.35 & 6.35 & 6.84 \\ Inlet absolute Flow Angle & [\({}^{\circ}\)] & 76.8 & 76.6 & 77.0 \\ Inlet relative Flow Angle & [\({}^{\circ}\)] & \(-31.8\) & \(-22.5\) & \(-27.0\) \\ Axial Clearance & [mm] & 0.25 & 0.25 & 0.3 \\ Radial Clearance & [mm] & 0.22 & 0.22 & 0.3 \\ Backface Clearance & [mm] & 0.46 & 0.46 & 0.3 \\ Number of Rotor Blades & [-] & 16 & 16 & 14 \\ Outlet Hub Radius & [mm] & 15.2 & 15.2 & 14.2 \\ Outlet Shroud Radius & [mm] & 36.8 & 36.3 & 37.5 \\ Outlet Blade Height & [mm] & 21.6 & 21.1 & 23.4 \\ Outlet Absolute Flow Angle & [\({}^{\circ}\)] & \(-0.03\) & 0 & 0 \\ Outlet Relative Flow Angle & [\({}^{\circ}\)] & \(-57.4\) & \(-64.2\) & \(-66.7\) \\ Chord Length & [mm] & 45.7 & 40.9 & 40.5 \\ Trailing Edge Thickness & [mm] & 0.76 & 0.50 & 0.43 \\
**Nozzle** & & & & \\ Inlet Radius & [mm] & 74.0 & 74.0 & 69.0 \\ Blade Height & [mm] & 6.35 & 6.35 & 6.84 \\ Outlet Radius & [mm] & 63.5 & 63.5 & 59.1 \\ Outlet Absolute Flow Angle & [\({}^{\circ}\)] & 77.8 & 77.8 & 77.0 \\ Chord Length & [mm] & 22.9 & 22.9 & 20.0 \\ Trailing Edge Thickness & [mm] & 0.51 & 0.51 & 0.36 \\ Number of Nozzle Vanes & [-] & 19 & 19 & 17 \\ Nozzle Loss & [kJ/kg], [\%] & - & 11.63, 19.88 & 10.81, 19.64 \\ Incidence Loss & [kJ/kg], [\%] & - & 0.19, 0.33 & 0.03, 0.06 \\ Passage Loss & [kJ/kg], [\%] & - & 26.06, 44.55 & 25.66, 46.62 \\ Tip Clearance Leakage Loss & [kJ/kg], [\%] & - & 8.88, 15.18 & 7.67, 13.93 \\ Exit Energy Loss & [kJ/kg], [\%] & - & 9.64, 16.48 & 8.89, 16.15 \\ Windage Loss & [kJ/kg], [\%] & - & 2.09, 3.58 & 1.98, 3.60 \\ Total-to-static Efficiency & [\%] & 86.4 & 86.4 & 87.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Range of design space.
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 7 Conclusions  
Anomaly analysis in highly heterogeneous and dynamic environments, such as the smart city, requires sophisticated mechanisms. Research carried out to date has shown that machine learning-based techniques perform very well in these type of scenarios. However, in this paper, we have shown that putting this into practice is not easy at all.  
There exists no black-box solution based on a multipurpose detection algorithm that is capable of performing a good anomaly detection regardless of the specific smart city environment. Quite the opposite, data analysts will face many difficulties and challenges, among which we have highlighted the following in this paper: choosing the right machine learning technique, analysing all variables through a single model or several of them, tackling with attacks or malfunctions that go unnoticed, obtaining anomaly-free data and defining the right aggregation time window.  
In general, our work brings to light that analysts must be highly skilled both in data analysis and network administration and security because they must know how networks are supposed to behave. Therefore, multidisciplinary teams are necessary. Dealing with this from the very beginning, additionally, is of utmost importance (even though this is rarely the case) because otherwise it is difficult to obtain anomaly-free data and, without it, modelling the normal data behaviour is not possible.  
Additionally, our work also reveals that there is still much work to do in the area of developing tools that promote and simplify the use of different types of anomaly detection techniques in real scenarios.  
Finally, in our work, we have used two specific well-known machine learning algorithms, but it is worth noting that the encountered difficulties mentioned in our discussion would have been the same using other techniques. The stated difficulties or challenges are not motivated by any particular algorithm feature but instead to the fundamental way machine learning algorithms work.  
Conceptualization, V.G.-F., C.G. and H.R.-P.; Investigation, V.G.-F., C.G. and H.R.-P.; Software, V.G.-F.; Writing--Original Draft, V.G.-F. and C.G.; Writing--Review & Editing, H.R.-P.  
This work is partially funded by the Ministry of Economy and Competitiveness through the project SMARTGLACIS (ITN2014-57364-C2-2-R).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Materials and Methods
### Mathematical Description of the Method
In the adopted method, the parametric Equation (16) with parameter \(t\in\langle 0;2\pi\rangle\) was used to determine the coordinates of the points \(E_{i}\) located on the perimeter of the ellipse. \[E_{i}=\left[ \begin{array}{c}x_{i}^{E}\\ y_{i}^{E}\end{array} \right]=\left[ \begin{array}{c}a\cos(t)\\ b\sin(t)\end{array} \right] \tag{16}\]  
where \(E_{i}\)--point located on the perimeter of the ellipse, \(x_{i}^{E}\)--horizontal position of the ellipse's envelope point, \(y_{i}^{E}\)--horizontal position of the ellipse's envelope point, \(a\)--length of the ellipse's long axis, \(b\)--length of the ellipse's short axis, and \(t\)--parameter of the parametric equation of the ellipse. Based on the known values of horizontal coordinates \(x^{E}_{i}\) and vertical coordinates \(y^{E}_{i}\) of the ellipse, taking into account the dimensions of the semiaxes a and b, one should then rotate the ellipse by the angle of inclination of the directional line \(\alpha\) and its shift by the coordinates of the position of the center of the ellipse \(x_{\text{COG}}\) and \(y_{\text{COG}}\) (17). These mathematical operations result in the calculation of coordinates for the perimeter of the ellipse \(E_{i}^{\prime}\) rotated in accordance with the angle of inclination of the trendline of analyzed points, and shifted relative to the beginning of the coordinate system, so that it covers 95.4% of all measured points. \[E_{i}^{\prime}=R(\alpha)E_{i}+COG=\left[ \begin{array}{cc}\cos(\alpha)&-\sin( \alpha)\\ \sin(\alpha)&\cos(\alpha)\end{array} \right]\left[ \begin{array}{c}a\cos(t) \\ b\sin(t)\end{array} \right]+\left[ \begin{array}{c}x_{\text{COG}}\\ y_{\text{COG}}\end{array} \right]=\left[ \begin{array}{c}[a\cos(t)\cos( \alpha)-b\sin(t)\sin(\alpha)]+x_{\text{COG}}\\ [a\cos(t)\sin(\alpha)+b\sin(t)\cos(\alpha)]+y_{\text{COG}}\end{array} \right] \tag{17}\]  
where \(E_{i}^{\prime}\)--point located on the perimeter of the ellipse by the angle \(\alpha\), \(E_{i}\)--point located on the perimeter of the ellipse, \(R(\alpha)\)--rotation matrix, \(\alpha\)--the angle of inclination of the ellipse, \(COG\)--midpoint of the ellipse and the set of analyzed points \(P_{i}\), \(a\)--length of the longer semiaxis of the ellipse, \(b\)--length of the shorter semiaxis of the ellipse, \(t\)--parameter of the parametric equation of the ellipse, \(x_{\text{COG}}\)--mean position on the horizontal axis of the analyzed set of points, and \(y_{\text{COG}}\)--mean position on the vertical axis of the analyzed set of points.
## 1 Introduction
The actuator is realized with a rotational driving mechanism devised on the basis of the operating principle of wobble-stepping motors. In designing the actuator, the analysis result of the parametric effect is utilized, and driving characterization and working performance verification are experimentally carried out with a fabricated functional prototype. With the experimental results, differentiating characteristics related to the operating principle of the proposed actuator and the thermomechanical behavior of SMA elements are investigated and discussed. [PERSON] et al. [15] designed an SMA spring design method for a given range of stiffness. Based on the physical and mechanical properties of titanium nickelide, the relationship between spring shear strain rate and output stress was quantitatively investigated. However, the above studies mainly focus on the characteristics and application areas of a single MRF or SMA, while they are less involved in the combined use of MRF and SMA. To address the problem of magnetorheological fluid performance degradation at high temperatures, [PERSON] et al. [16] proposed an SMA and multi-arc surface MR composite transmission device. The SMA spring adds torque by pushing the friction rings, allowing the MR transmission to maintain a smooth transmission over the entire operating temperature range. The magnetic saturation of MRF limits its transmission torque so that it cannot be continuously increased, and the transmission torque based on shear mode design is relatively small, thereby limiting the application and development of MRF in the transmission field. Furthermore, the SMA devices mentioned above generally lack active control of the output force. In response to such problems, this article proposes an electrothermal SMA spring-driven variable thickness MRF composite transmission device. The heat generated by the copper-core polyurethane enameled wire spirally wound on the SMA spring causes the spring to be heated and elongated, which drives the squeeze disc to push the MRF axially, changing the thickness of the working gap and finally achieving the purpose of squeezing the MRF. The shear yield stress of the MRF is significantly increased by the squeeze strengthening effect, thus significantly increasing the torque of the transmission.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 3 Input Data
### Soil Texture Data  
The soil texture type for each one-degree pixel was determined by the sand, silt, and clay fractions. The soil texture data were downloaded from the Harmonized World Soil Database (HWSD) ([[http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4](http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4)]([http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4](http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4)), accessed on 3 July 2021). The spatial resolution of the original HWSD dataset is 0.00833 degrees (~1 km) and they were resampled into grids of 0.004167\({}^{\circ}\) (~-500 m) to match the spatial resolution of the global LC map. The soil texture type of each grid cell was determined by the USDA soil texture triangle and the sand, silt, and clay fractions.  
For global ET simulation at 1\({}^{\circ}\) resolution, the soil texture map at 500 m resolution was aggregated to obtain the dominant soil texture types for the 9 PFTs (Section 3.2). The soil texture types were used to determine the soil hydrological and thermal parameter values such as saturated hydraulic conductivity, porosity, field capacity, wilting point, and soil  
Figure 3: A global LAI map on 4–11 July 2015 at 8 km resolution is an example.  
thermal conductivity, which would influence the estimation of soil evaporation through the ground surface temperature and soil moisture.
## 4 Discussion  
### Diurnal and Seasonal Variations of Latent and Sensible Heat Fluxes  
During the course of one day, the latent heat flux became positive at about 6:00, that is, there was a sharp increase in the amount of heat energy dissipated by evaporation. By 13:00, the latent heat flux reached its maximum value, which was due to the rapid increase in latent heat flux values during the day because of the fast temperature rise, strong turbulence and strong ground evaporation and plant transpiration. After 13:00, although the air temperature was still high, the latent heat flux had a sharp decrease. [PERSON] [36] suggested that one of the reasons for this was that the stomata on the leaf surfaces of the trees closed after the high temperature at 14:00, and transpiration decreased. The latent heat flux only became negative after 20:00, when evapotranspiration stopped and water vapor released heat to the ground through condensation. At night, due to low temperature, weakened turbulence, reduced evaporation and basic cessation of plant transpiration, the absolute value of latent heat flux was smaller and less variable. As for the sensible heat flux from the daily process, the minimum value appeared in the night and was negative, while the maximum value generally appeared from 12:00 to 14:00. This was due to the sensible heat flux of energy coming from the radiant energy, the radiation being the largest before and after noon, the presence of the forest canopy layer increasing the turbulence of the exchange. At night, the forest canopy absorbed heat from the atmosphere by turbulent exchange as the long-wave radiation from the canopy layer lost heat.  
The latent heat flux and sensible heat flux of the forest in the study area had obvious seasonal variations [34]. In the growing season, the latent heat flux was larger than the sensible heat flux and dominated, and the forest evapotranspiration dissipated a large  
Figure 11: Regression analysis of estimated and MODIS NPP.  
amount of latent heat flux. The latent heat flux reached a maximum in June, mainly because the forest growth was in the most vigorous stage, the LAI had reached the maximum value, and there was sufficient rainfall. The leaves began to fall off in September, and the latent heat flux showed a gradual decreasing tendency. In the non-growing season, most of the energy was allocated to the sensible heat flux, which was higher than the latent heat flux, when the evapotranspiration of the forest was small [37].
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 1 Introduction
Aimed at providing the longest network lifetime, the authors of [24] proposed a modified integrated greedy method and a heuristic and greedy combinational method to find the base station location. In [25], location of BSs and distributed clustering by cluster heads are jointly optimized by the LEACH-C protocol to improve the energy consumption. The rest of the paper is organized as follows. Section 2 introduces the system model and the main notation. In Section 3, we derive the optimized region of GW locations which meet the requirements on capture and IC for both pairs of SNs in conflict. In Section 4, two algorithms, the weight bipartite graph (WBG) algorithm and the pixels with gray levels (PGL) algorithm, are presented. The simulations of average contention for different GW placements are illustrated in Section 5 for different network topologies. Finally, Section 6 concludes the paper.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 5 Phased Array Modelling  
This section describes how to model a large phased array using SystemVue from the patch antenna electromagnetic results using Figure 12 workflow.  
Once the antennas were designed and EM simulated using PathWave ADS, a file with the radiation pattern using a python script is obtained. EMPro was used to import, compile and run a far field simulation from the file with the geometric information of the antenna. The results were exported to an User-Defined Antenna (.UAN) file containing the gain pattern, separated in a theta and phi component, with the associated phase information. This file was imported into SystemVue to simulate a large phased array. For each LEO constellation, the number of radiating elements were calculated. For transmission, this number has been calculated as a function of its EIRP, and for reception, as a function of the required gain.  
Figure 11: (**a**) \(S_{11}\) for the reception patch antenna (**b**) 3-D reception antenna radiation pattern (**c**) 2-D reception antenna radiation pattern.  
Figure 12: Workflow to model a large phased array using SystemVue from the patch antenna electromagnetic simulation.  
Figure 10: Reception antenna layout diagram.
## 3 Results and Discussion
### Quantification of Enhancement Factor  
The parameters in Table 1 are brought into Equation (1) to find that the critical buckling pressure value deviates significantly. The sensitivity of different parameters to the critical buckling pressure of the liner was analyzed in Section 3.2. Figures 8, 10 and 13 show the deviates of critical buckling pressure values between the calculation results and simulation results, which indicate that the enhancement factor \(K\) is significantly relevant with the values of \(DR\), \(a/R\) and \(q\). \(DR\), \(a/R\) and \(q\) are used as variables and a polynomial fit is  
Figure 11: The pressure–displacement curves of FIPP liner with different \(a/R\) under (**a**) \(q=0\); (**b**) \(q=5\).  
Figure 12: The stress distribution of (**a**) Sample B-2; (**b**) Sample C-6 under the critical buckling pressure.  
Figure 13: Comparing the influence of increasing \(a/R\) for critical buckling pressure under different \(q\).  
performed based on the numerical results to obtain a prediction model for enhancement factor \(K\) as in Equation (2). For simplification of Equation (2), \(a/R\) is replaced by \(\Delta\), as shown in Equation (3).  
\[K_{\Delta}=37.94 DR^{1/2}\Delta^{3/2}-170.9\Delta^{1/2}q^{1/2}-0.000069 DR^{2}+115 61\Delta^{2}+0.0964q^{2}+0.1068q\\ -36.522 DR^{1/2}-44831\Delta^{1/2}+162.57q^{1/2}+33287 \tag{2}\]  
\[\Delta=a/R \tag{3}\]  
The comparison between the results calculated by Equation (1) and the result calculated after using the enhancement factor \(K_{\Delta}\) is shown in Figure 14 and after revising the enhancement factor \(K\), the calculation results are more accurate and fit better with the numerical results and the average difference rate between predicted results and numerical results was reduced from 23.43% to 5.52%.