esa-sceva/satcom-chunk-collection huggingface dataset:
# Pressure-Driven Nitrogen Flow in Divergent Microchannels with Isothermal Walls
## 4 Model Validation and Verification  
The Poiseuille argon flow in a microchannel with a uniform cross-section was considered to verify the reliability of the present model. The microchannel length was \(15\,\upmu\)m and its height was \(1\,\upmu\)m. Gaseous argon entered the channel with a constant temperature of \(300\,\text{K}\), and the inlet-to-outlet pressure ratio \(\Pi\) was set to 3. The channel walls were assumed to be fully diffuse (i.e., \(\alpha=1\)), and their temperature was set to \(300\,\text{K}\). Four different cases with different Knudsen numbers were studied, which cover the slip and transition rarefaction regimes. For each case, about \(1.5\times 10^{6}\) DSMC simulator particles were utilised. The predicted pressure and Mach number (Ma) distributions along the microchannel centreline are shown in Figure 2. The Mach number is defined as follows:  
\[\text{Ma}=\frac{\|\vec{V}\|}{\sqrt{\gamma RT}}, \tag{2}\]  
where \(\vec{V}\) is the fluid velocity vector, \(\gamma\) is the ratio of specific heat of nitrogen at a constant pressure to its specific heat at a constant volume, \(R\) is the specific gas constant, and \(T\) is the temperature. The results are in reasonable agreement with the data reported by [PERSON] et al. [64]. Further comparison of the results obtained from the present model with experimental, analytical, and numerical data was performed for gaseous nitrogen flow in divergent microchannels, and can be found in [31; 38; 39].  
Figure 3 shows the influence of grid size, number of simulator particles per cell (PPC), and time-step size on the results obtained from the present DSMC simulations. For this study, nitrogen flow in a microchannel with uniform cross-section (\(\phi=1\)), \(\text{Kn}_{\text{i}}=0.1\) (transition rarefaction regime), and the inlet-to-outlet pressure ratio \(\Pi\)  
To determine an appropriate time-step size \(\Delta t\) within which the molecular movement and collision are distinguishable, the following correlations were employed:  
\[\Delta t_{\rm c}=\frac{0.2\lambda}{\sqrt{\frac{2\kappa_{\rm b}T}{m_{\rm p}}}}, \tag{3}\]  
\[\Delta t_{\rm t}=\frac{0.5\Delta x}{\sqrt{\frac{2\kappa_{\rm b}T}{m_{\rm p}}}}, \tag{4}\]  
where \(\Delta t_{\rm c}\) is the mean collision time, \(\Delta t_{\rm t}\) is the mean transit time, and \(\kappa_{\rm b}\) is the Boltzmann constant. The time-step size \(\Delta t\) was defined as a fraction of the minimum value of \(\Delta t_{\rm c}\) and \(\Delta t_{\rm t}\) (i.e., \(\Delta t=\zeta\cdot\min(\Delta t_{\rm c},\Delta t_{\rm t})\) and \(\zeta\leq 1\)). For the range of parameters studied in the present work, the results are practically insensitive to the grid size, number of simulator particles per cell, and time-step size.  
Figure 2: Comparison of the numerical results obtained from the present model (solid lines) with the data reported by [PERSON] et al. [64] (symbols) for pressure-driven rarefied argon flow in a microchannel with a uniform cross-section. Profiles of (**a**) pressure and (**b**) Mach number along the microchannel centreline.
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 3 Experimental Analysis
### Accuracy Evaluation of Observation Data  
There are many methods to characterize the frequency stability of atomic clocks. In this paper, Allan variance is used to describe frequency stability [51]. The overlapping Allan deviation of the four groups is shown in Figure 1. In the figure, the blue circle and green square are groups A and B, respectively, representing the frequency stability of the BDS. The pink triangle is group C, representing the frequency stability of the GPS. The red diamond is group D, representing the frequency stability of the Galileo system. The dotted line shows the overlapping Allan deviation of the time deviation for each group. The Allan deviation of each group with a sampling interval of 86,400 s is given in Table 1.  
In Figure 1 and Table 1, C1 is obtained from G19-G08, and G08 is a cesium clock. Its short-term frequency stability is the worst, but its long-term frequency stability is good. When the sampling interval is less than one day, it can be seen that the three times deviation in group D has the best frequency stability, followed by group A, group B, and group C. It can be seen that Galileo's onboard clock is better than BDS, and BDS is better than GPS. In addition, when the average interval is 1000 s, except in group C, the frequency stability of satellite clocks is mainly around \(4\times 10^{-14}\) to \(1\times 10^{-13}\). When the average interval is 10,000 s, the frequency stability can reach about \(2\times 10^{-14}\) to \(1\times 10^{-13}\). The frequency stability of C2 and C3 in group C was close to that of the other groups, while C1 was around \(2\times 10^{-13}\). When the average interval is 10,000 s, the frequency stability of other clocks basically reaches the minimum value except for C1, all of which are in the order of \(1\times 10^{-15}\). As the average time interval increases, the frequency stability of other satellite clocks progressively deteriorates, whereas the frequency stability of C1 continues to improve, reaching the level of \(1\times 10^{-15}\). As mentioned above, hydrogen clocks have good short-term stability, while cesium clocks have good long-term stability.  
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **Time Deviation** & **Allan Deviation (\(\tau\) = 1 Day)** \\ \hline  & A1 & 7.9560 \(\times\)\(10^{-15}\) \\ A & A2 & 6.9398 \(\times\)\(10^{-15}\) \\  & A3 & 7.1120 \(\times\)\(10^{-15}\) \\  & B1 & 1.1840 \(\times\)\(10^{-14}\) \\ B & B2 & 1.5846 \(\times\)\(10^{-14}\) \\  & B3 & 1.2103 \(\times\)\(10^{-14}\) \\  & C1 & 6.6612 \(\times\)\(10^{-14}\) \\ C & C2 & 1.0659 \(\times\)\(10^{-14}\) \\  & C3 & 1.1856 \(\times\)\(10^{-14}\) \\  & D1 & 6.0926 \(\times\)\(10^{-15}\) \\ D & D2 & 6.4042 \(\times\)\(10^{-15}\) \\  & D3 & 6.9688 \(\times\)\(10^{-15}\) \\ \hline \end{tabular}
\end{table}
Table 1: Allan deviation of each group. The average interval is 86,400 s.  
Figure 1: Allan deviations of observation data.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 4 Results
### Step 3: Criticality Analysis on SPEC (Example for STR)
Finally the O index is obtained applying dedicated IF-THEN rules (for the sake of simplicity, an extract of the relevant combinations for the proposed example is reported in Table 14, out of total 150 IF-THEN combinations). For S index, the assigned score is equal to 5/5 for all cases, which have the potential to compromise the entire mission (cf. Table 4, \"mission is totally compromised\"). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Failure Probability**} & **Condition Probability** & **Condition Probability** & **Occurrence (O)** \\  & **(Sub-System)** & **(Part)** & **Occurrence (O)** \\ \hline
5 & 4 & 2 & 4 \\
5 & 4 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Excerpt of IF-THEN rules for the Occurrence (O) index. Figure 6: FMECA logical loop for STR. For the D index, it has been necessary to preliminarily assign falsifiability and observability coefficients. Following respectively Tables 6 and 7, for STR-3.2 the assigned value are respectively 2/5 (because both, independently, STR subsystem and mechanical interface between the launcher payload adapter and CubeSat may produce this observable effect), and 5/5 (because the only source of information about this failure is the interface itself, being the satellite turned off during the launch phase). For STR-3.3-THM-1, the falsifiability coefficient is equal to 2/5 (because both, independently, the STR subsystem, or PLD extension mechanism may produce damages to the radiation shield if compromised under vibration); and the observability coefficient is 4/5 (in this case, if the radiation shield is damaged, several other subsystems may experience consequential effects, but only one at a time can confirm the specific fail, i.e., overheating in the EPS, SEU on the OBD, noise in the CCD of the PLD). For STR-3.3-PLD-6, the falsifiability coefficient is 2/5 (either in this case two independent subsystem might produce the same observable effect: because if the PLD extending mechanism results blocked, the concrete result would be the almost total impossibility in gathering images from the telescope, and that might the same effect of a failure in the EPS subsystem, other subsystems data may help in the traceability of the source); and the Observability coefficient is 4/5 (apart from the subsystem failing, the ADC shall provide data of the variations in attitude during the extension of the telescope, if these values are different from the expected ones, then the source of the failure is find). Consequently, following the IF-THEN rules conceptually introduced in Section 3.3.3, the respective D indexes are 5/5 4/5 4/5 (with a similar process as the one illustrated in Table 14). Table 15 summarises the results of the analysis for the exemplar cases, providing a summary of indexes and coefficients.
# Research on Control of Intelligent Vehicle Human-Simulated Steering System Based on HSIC
## 1 Introduction
HSIC has been successfully applied to the MR semi-active suspension system [11; 12] and the combustion process of gas heating furnace [13]. Intelligent vehicle automatic steering system was a nonlinear time-varying complex system. There were many uncertain factors, and the difficulty of control strategy design increased. In view of the fact that the HSIC algorithm can better simulate the excellent control characteristics unique to human control behavior, and the HSIC has been successfully applied to some difficult objects in the industrial field, this provides a relatively novel idea for the design of intelligent vehicle automatic steering system controller. Therefore, subjects who were skilled in driving were selected as the objects of learning and simulation. Moreover, the driving trajectory data of skilled drivers under different vehicle models, vehicle speeds, and steering conditions were collected. In order to do human-simulated control, the driving trajectory of skilled drivers was simulated. The segmental polynomial expression of the above-mentioned driving trajectory was solved under the two typical steering conditions of normal right-steering and U-turn, and the _hp_ adaptive pseudo-spectral method was used to solve the connection problem of the vehicle segmental driving trajectory. Meanwhile, a novel EPS steering system was proposed. The steering system dynamics model and motor model were established. In the Simulink/Carsim joint simulation environment, the intelligent vehicle human-simulated steering system control model based on HSIC was established to carry out the simulation analysis. In order to further verify the validity and reliability of the human-simulated intelligent control law, an intelligent vehicle steering system test bench with a steering resistance torque simulation device was built, and the dSPACE rapid prototyping platform was used as the actual control of the intelligent vehicle steering system platform. The device constitutes a control system bench test platform to comprehensively analyze the system control performance. The structure of the paper is as follows. Section 2 presents polynomial fitting of vehicle driving trajectory of skilled driver, and the _hp_ adaptive pseudo-spectral method is used to solve the connection problem of the vehicle segmental driving trajectory. Design and performance simulation of intelligent vehicle steering controller based on human-simulated intelligence theory is shown in Section 3. In Section 4, the rapid control prototype test of human-simulated steering system will be highlighted. Finally, the conclusions are given in Section 5.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 5 Conclusions  
The rapid environmental changes in cold land regions have profound impacts on ecosystems, geomorphology, animal habitats, and human lives. Remote sensing is the most valuable and indispensable technique for large-scale monitoring of such changes, accurately quantifying both transient anomalies and longer-term trends, while providing observational benchmarks to test earth system model projections. Calibrated long-term observations from these sensors have produced relatively precise satellite data records documenting significant changes in landscape FT states, snow extent and depth, glacier mass and movement, water body dynamics, and lake ice [309] and vegetation phenology. Earlier onset of snow melt [310], soil thaw, and lake ice break-up, longer potential growing seasons, and expanding lakes were identified in both the northern high latitudes and TP; and continuous ice loss has been observed in both Greenland and Antarctica. Despite these achievements, pressing issues such as melting permafrost, disappearing glaciers, shrinking ice cover, and structural and functional changes in ecosystems require more timely and accurate interpretations from remote sensing observations. Multi-sensor data fusion approaches show promise in overcoming the drawbacks of single sensor observations, while strengthening capabilities for monitoring and detecting environmental changes in cold regions. Next generation satellite missions including SWOT, NISAR, and BIOMASS; emerging techniques such as micro-satellites and data mining; and coordinated research activities [311; 312; 313] will enable cold land mapping with unprecedented sampling frequency, spatial resolution, and accuracy. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/11/16/1952/s1](http://www.mdpi.com/2072-4292/11/16/1952/s1)]([http://www.mdpi.com/2072-4292/11/16/1952/s1](http://www.mdpi.com/2072-4292/11/16/1952/s1)), Table S1. Overview of satellite missions/sensors for cold land remote sensing and summarized in the review, Table S2. Vertical accuracy of DEMs from different sensors and methods over glacier surface. **Author Contributions:** All authors contributed equally to the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. **Acronym list**
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 2 Related Works
### Deep-Learning-Based Signal Detection
(2018); [PERSON] et al. (2018), but a more effective approach involves lightweight and optimized modifications to the network itself [PERSON] et al. (2018).
## 3 Results and Discussion
### Kinetics of Dye Sorption on MES and MMS  
The time needed to reach the sorption equilibrium is within the 120-150 min range for both dyes (RB5 and RY84) and sorbents (MES and MMS) (Figure 4). Similar sorption times have also been reported in studies investigating RB5 sorption on carbonized carob tree biomass (120 min) [33] and loquat seed shells (150 min) [34], as well as RY84 sorption on wool [35] (180 min). The rate of dye sorption on MES and MMS was the fastest at the start of the process. After just 10 min, the fraction of dyes sorbed is 54% to 61% of q\({}_{\text{e}}\) for MES and 46% to 51% for MMS q\({}_{\text{e}}\) (q\({}_{\text{e}}\)--the amount of dye sorbed at equilibrium state). The faster sorption at higher initial concentrations of RB5/RY84 probably resulted from the higher likelihood of dye molecules colliding with sorbent active sites, resulting in faster saturation of the sorption centers and termination of the process. Figure 4: Trends in dye levels throughout the sorption process: (**a**) RB5 on MES, (**b**) RY84 on MES, (**c**) RB5 on MMS, and (**d**) RY84 on MMS. Pseudo-first-order model and pseudo-second-order model. In each experimental group, the pseudo-second-order model shows the greatest fit to the data (higher values of R\({}^{2}\) and smaller values of Chi-square--\(\chi^{2}\)) (Table 1, Figure 4), which is typical of organic dye sorption on biosorbents [36; 37; 38]. Initial dye levels have a significant effect on the k\({}_{2}\) and q\({}_{e}\) values derived from the pseudo-second-order model (Table 1). This may suggest that RB5 and RY84 have relatively low affinity to the sorbent active sites and that the sorbent efficiency is high only at high dye concentrations. The similar k\({}_{2}\) values for experimental groups with the same initial dye levels (Table 1) suggest that RB5 and RY84 follow similar mechanisms of sorption on MES and MMS. The data on dye sorption kinetics are also modeled using the intraparticle diffusion model (Table 2, Figure 5). The analysis of the resultant constants shows that the process unfolded in two primary sorption phases, characterized by different sorption rates and durations. In the first phase, the dye molecules most likely diffused from the solution onto the surface of the sorbent and bound to the most accessible active sites. This phase is characterized by high sorption rates in a short amount of time. Once most of the sorbent active sites are saturated, the second phase commences, in which the dyes mainly occupy hard-to-reach sorption sites in the deeper layers of the sorbent. Due to the lower accessibility to remaining sorption sites and the increased competition between dyes, phase 2 is longer than the first, with lower sorption intensity (Table 2, Figure 5).
## 7 Results and Discussion: Mean Torque  
### DE: Comparison of Slot/Pole Combinations for PMSMs and MPMSMs  
In Table 18, the mean torque for different DE magnitudes is presented. Both the modular and monolithic machines are evaluated considering different slot/pole combinations. From Table 18, it can be seen that the mean torque is not affected by DE regardless of the slot/pole combination, which was expected from the back-emf results: since the flux linkage is not considerably penalized by eccentricity, the mean torque should not be decreased either.  
It could be noted that the 18-slot, 12-pole modular machine develops significantly lower mean torque than the monolithic counterpart. This can be explained by the same analysis carried out for the radial forces in Section 4.2 (see Figure 5): the absence of ferromagnetic material between stator modules penalizes the flux linkage in machines having a high value of \(q\).
## 3 Monitoring of C and Water Cycling in Terrestrial Ecosystems
### Satellite Monitoring  
Satellite-borne remote sensing offers unique opportunities to parameterize land surface characteristics over large spatial extents at variable spatial and temporal resolutions. For example, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides a global dataset every 1-2 days with 36 bands. The spatial resolution of MODIS (pixel size at nadir) is 250 m for channels 1 and 2 (0.6-0.9 \(\mu\)m), 500 m for channels 3 to 7 (0.4-2.1 \(\mu\)m) and 1,000 m for channels 8 to 36 (0.4-14.4 \(\mu\)m), respectively. Data from the satellite-borne MODIS are currently used in the calculation of global weekly GPP at 1 km spatial resolution [10]. Other sensors, the Landsat Thematic Mapper sensors carried onboard the Landsat series of satellites, acquire images at a 30 m spatial resolution with a 16 day interval.  
In general, water evapotranspiration from ecosystems into the atmosphere will reduce the land surface temperature (\(T_{a}\)). Reduction in soil moisture will decrease plant transpiration and evaporation from soil and plant surfaces. Reduction in ET will increase \(T_{a}\). \(T_{a}\) can be derived from remotely-sensed thermal-infrared (TIR) band (8-14 microns) from various operational satellites. Based on the relationship between \(T_{a}\) and ET, remotely sensed \(T_{a}\) has been used to estimate regional ET [118-121]. The existing thermal imaging sensors provide adequate coverage of thermal dynamics that are useful for operational monitoring applications of ET. For example, thermal images at 15 minutes intervals and at a spatial resolution of 5 kilometers can be obtained from the NOAA Geostationary Operational Environmental Satellites (GOES), and TIR data at a fine spatial resolution (60 m or 120 m) with a much longer time interval (16 days) have been provided by the Thematic Mapper (TM) and ETM+ instruments on Landsat 5 and Landsat 7.  
ET, the largest component of water loss from ecosystems, plays an important role in affecting soil moisture, vegetation productivity, C cycle, and water budgets in terrestrial ecosystems [122-124]. [PERSON] _et al._[12] provided a comprehensive review of remote sensing methods for assessing ET and soil moisture content across different scales and [PERSON] _et al._[11] reviewed satellite-based algorithms for estimating ET and land surface temperatures at local, regional and continental scales, with particular emphasis on studies published since the early 1990s.  
In addition, as [PERSON] and [PERSON] [13] reviewed, satellite-based instruments can also provide information about CO\({}_{2}\) concentration in the atmosphere, but no current satellite-borne instrument comes close to providing the accuracy, precision, and continuity required to determine regional CO\({}_{2}\) concentrations and local fluxes. Future satellites, including the Greenhouse Gases Observing Satellite (GOSAT) [125], are expected to provide more accurate CO\({}_{2}\) measurements than do today's satellites [13,126].
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 2 Related Works
### Anomaly Location  
After using anomaly detection algorithms to detect anomalies in multidimensional monitoring metrics at a certain time, we need to quickly and accurately find the specific dimension that causes these anomalies, that is, anomaly location. Association rule mining [20] and decision trees [21] are two mature anomaly location methods in intelligent O& M. Association rule mining is a rule-based machine learning algorithm that aims to discern strong rules present in a dataset using a number of metrics. When association rules are generated, they can be used to assist with the correlation analysis of events (errors and alerts). Decision trees belong to supervised learning in machine learning and represent mappings between object attributes and object values. Each node in the tree represents a judgment condition for object attributes, with branches representing objects that meet the conditions of the nodes and the leaf nodes of the tree representing the judgment results to which the objects belong. Tsinghua University and Baidu Inc. proposed a root cause analysis algorithm called HotSpot in 2018 [22], which uses Monte Carlo tree search to transform root cause analysis into a large spatial search problem with a heuristic search algorithm to locate the root causes that cause anomalies. [PERSON] et al. proposed a multidimensional monitoring metric for the number of problem reports, called the iDice root cause analysis algorithm [23], which reduces the workload of O&M staff by reducing the combinations of attribute values under each dimension through three pruning strategies.  
In addition, [PERSON] et al. proposed the Adtributer algorithm [24] for the KPI of the advertising revenue of a website. This algorithm uses the concepts of explanatory power and surprise to identify the set of the most likely root causes of anomalies after detecting anomalies in the KPI. Pinpoint [25] uses cluster analysis to diagnose the root causes of large dynamic Internet services. Argus [26] uses a hierarchical data structure for aggregation and performance metrics for groups of users with the same attributes and attempts to locate users with performance faults.