esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Optimization Design of Diesel Engine System Based on the GO Method
## 2 Diesel Engine Reliability Modeling and Distribution Based on the GO Method
### Establishment of the GO Diagram Model for Diesel Engine System  
#### 2.2.1 Selection of Diesel Engine System Unit Operator  
The system operators of the diesel engine are listed in Table 1. The operator types are regulated as Type 1 for the two-state unit, Type 5 for the signal generator, Type 6 for the signal-enabled element, Type 13 for multiple input and multiple output unit, Type 24B for multiple closed-loop feedback structures, Type 10 for the AND gate, Type 18A and Type 20 for operator standby correlation structure, and Type 23 signal integrated gate. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Unit** & **Operator Number** & **Operator Type** & \(R_{i,old}\) & \(R_{i,\text{min}}\) & \(R_{i,\text{max}}\) & \(f_{i}\) \\ \hline Power supply & 1 & 5 & 0.999880 & 0.999 & 1 & 0.6 \\ Operating signal 1 & 2 & 5 & 1.00000 & & & \\ Power switch & 3 & 6 & 0.999850 & 0.999 & 1 & 0.6 \\ Gas cylinder & 4 & 5 & 0.999750 & 0.999 & 1 & 0.7 \\ Operating signal 2 & 5 & 5 & 1.000000 & & & \\ Cylinder valve & 6 & 6 & 0.999850 & 0.999 & 1 & 0.6 \\ Operating signal 3 & 7 & 5 & 1.000000 & & & \\ Air starting switch & 8 & 6 & 0.999850 & 0.999 & 1 & 0.6 \\ Air distributor & 9 & 1 & 0.999500 & 0.999 & 1 & 0.75 \\ Air duct & 10 & 1 & 0.999400 & 0.999 & 1 & 0.6 \\ Air valve & 11 & 1 & 0.999850 & 0.999 & 1 & 0.6 \\ Piston & 12 & 1 & 0.998900 & 0.99 & 1 & 0.75 \\ Connecting rod & 13 & 1 & 0.999450 & 0.999 & 1 & 0.7 \\ NOT gate & \(14,50,56\) & 20 & & & & \\ Electric starting switch & 15 & 6 & 0.999850 & 0.999 & 1 & 0.6 \\ Starting motor & 16 & 1 & 0.999750 & 0.999 & 1 & 0.6 \\ Flywheel & 17 & 1 & 0.999500 & 0.999 & 1 & 0.6 \\ Standby gate & \(18,52,58\) & 18A & & & & \\ Crankshaft & 19 & 1 & 0.999450 & 0.999 & 1 & 0.7 \\ Crankshaft drive gear & 20 & 1 & 0.999750 & 0.999 & 1 & 0.6 \\ Generator transmission gear & 21 & 1 & 0.999750 & 0.999 & 1 & 0.6 \\ Generator gear & 22 & 1 & 0.999750 & 0.999 & 1 & 0.6 \\ \hline \end{tabular}
\end{table} 
Table 1: Diesel engine system unit operators, reliability data and parameters.
# Simulation and Catastrophe Detection of Spontaneous Combustion Processes in Suffide Ores
## 1 Introduction
Most scholars focus only on the temperature changes of sulfide ores during dynamic, spontaneous combustion, studying the generation and variation of temperature. However, the concentration of SO2 gas monitored during the entire unsteady process of spontaneous combustion in sulfide ore heaps, like the time series of ore temperature, contains rich information about spontaneous combustion. Therefore, it is worth exploring the implicit details to reveal the hazardous characteristics of the complete spontaneous combustion process and achieve early warning of ore spontaneous combustion. Considering this, the study conducted small-scale simulation experiments of the whole unsteady process of spontaneous combustion of sulfide ore heap through a custom experimental setup indoors, and the trends of temperature and SO2 gas concentration at each measurement point of the heap were investigated to find out the distribution of high-temperature regions in sulfide ore heap and the migration pattern of high-temperature areas over time. The wavelet transform and fuzzy integrated evaluation methods were also used to extract the ore spontaneous combustion information from the measured ore temperature and SO2 gas concentration data series. Finally, catastrophe detection and kinetic feature parameter extraction methods are used to detect the hidden catastrophe points in the whole unsteady process of the spontaneous combustion of sulfide ore heaps, providing some insight into the implementation of sulfide ore spontaneous combustion warning.
## 4 Result and Analysis
### The Calibration and Validation of Parameters
The comparison diagram of runoff simulation in Figure 4 demonstrates a strong correlation between the simulated and observed values, showing that the model generally satisfies accuracy requirements. Figure 4 illustrates that R\({}^{2}\) and NS attain values of 0.75 and 0.77, respectively, indicating a substantial concordance between the simulated and actual observed values, thereby satisfying the accuracy standards typically regarded as acceptable by the academic community. Numerous factors contribute to the discrepancies between the measured and simulated values, including the uneven distribution of meteorological stations within the study area, as well as meteorological components, such as precipitation, temperature, and wind speed, which are critical inputs for runoff simulation. The unequal distribution of meteorological stations will necessitate the reliance on interpolation for certain regional data, leading to discrepancies from actual values. The research area exhibits a broad and complicated array of soil types, and the assessment of parameters like infiltration capacity and porosity is frequently approximated using empirical estimates. Furthermore, fluctuations in vegetation growth will impact the discrepancy between the simulated and measured runoff values in the region, constituting an additional influencing factor. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Encoding** & **Parameter Name** & **Parameter Meaning** & **Optimal Parameter** \\ \hline
1 & R\_CN2.mgt & SCS runoff curve value & 0.134 \\
2 & V\_ALPHA\_BF.gw & Base flow alpha factor (1/day) & 0.105 \\
3 & V\_GW\_DELAY.gw & Groundwater delay time (h) & 82.5 \\
4 & V\_GWQMN.gw & Level threshold of shallow aquifers when groundwater & 1.27 \\
5 & V\_CANMX.hru & Maximum canopy water storage (mm) & 71.5 \\
6 & V\_ESCO.hru & Soil evaporation compensation coefficient & 0.525 \\
7 & V\_CH\_K2.rte & Effective permeability coefficient (mm/h) & 482.5 \\
8 & V\_REVAPMN.gw & Shallow groundwater evaporation depth threshold (mm) & 57.5 \\
9 & R\_SOL\_K.sol & Soil saturated water conductivity (mm/h) & 0.099 \\
10 & R\_SOL\_AWC\_sol & Surface water availability (mm) & 0.645 \\
11 & V\_RCHRG\_DP.gw & Permeability coefficient of deep aquifer & 0.275 \\
12 & R\_SOL\_ALB.sol & Moist soil albedo & 0.23875 \\
13 & V\_ALPHA\_BNK.rte & Base flow regression constant & 0.705 \\
14 & V\_SSUBBSN.hru & Average slope length (m) & 69.5 \\
15 & V\_HRU\_SLP.hru & Average slope (m/m) & 0.565 \\
16 & V\_SFTMP:bsn & Average air temperature on snowfall days (\({}^{\circ}\)C) & 16.6 \\
17 & V\_SMTMP:bsn & Average temperature on snowfall days (\({}^{\circ}\)C) & 19.0 \\
18 & V\_SMFMX.bsn & Snowmelt factor & 3.7 \\
19 & V\_THMP:bsn & Temperature lag coefficient of snow cover & 0.585 \\
20 & V\_SMCOCOVMX.bsn & Snow depth threshold/cm & 172.5 \\
21 & V\_TLAPS.sub & Temperature lapse rate (\({}^{\circ}\)C/km) & 7.3 \\
22 & V\_EPCO.hru & Plant absorption compensation coefficient & 0.515 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The optimal value of the parameter.
## 2 Related Work
### Adaptive Bitrate  
To enhance the user's QoE, the adaptive bitrate algorithms should be taken into account, which can dynamically select different video bitrates for the next video segment playback according to either the network status [13; 14] or buffer conditions [15; 16]. For the network state-based methods, [PERSON] et al. proposed a novel rate-adaptive algorithm for adaptive HTTP streaming based on segment fetch time (SFT) to detect bandwidth changes [17], and further proposed the ratio of expected SFT as a new rate-adaptive metric to quickly detect network congestion and idle network capacity [18]. For the buffer-based methods, [PERSON] et al. proposed an approach that began by using only the buffer, and then asked when capacity estimation was needed [15]. BOLA was devised by using Lyapunov optimization techniques to minimize rebuffering and maximize video quality [16]. However, both of the above two methods are highly dependent on the assumption of prediction accuracy, which can be hardly competent in the sophisticated environment and heterogeneous user demand. In [19], the authors proposed a method based on a reinforcement learning algorithm to select the bitrates of the region of interest adaptively for panoramic videos. In [20], in order to optimize the QoE, the authors proposed a novel ABR algorithm considering the user preference based on short trajectory segments.
## 4 SDR Implementation of FM-ACSK System Prototype
### Hamming Encoder  
To understand how the employment of simple FEC affects the performance of FM-ACSK systems, simulation of FM-ACSK system baseband models (without SDR) has been carried out. Comparison of results from Simulink models with (1023, 1013) and (7, 4) Hamming FEC with different detectors is shown in Figure 11.  
Figure 10: Implementation of the symbol timing (ST) unit.  
Figure 9: Signal at the input of decision-making unit.  
As it can be seen, the system with a (7, 4) Hamming code provides better performance than the system with a (1023, 1013) Hamming code at SNR \(>-11\) dB using SD. In the case of a (7, 4) Hamming code, the system with ED has an improvement in performance over the system using SD. System employing a (1023, 1013) Hamming code and ED has a slight performance improvement at SNR \(<\)\(-8\) dB, but has a visible performance threshold at BER \(\approx 4.7\times 10^{-2}\).
## 5 FPGA Implementation
### FPGA Implementation of Receiver  
The receiver system is to implement the carrier synchronization loop and the code synchronization loop. The FPGA top-level design of the DSSS receiver is shown in Figure 34. From this figure, we can see that FPGA receiver contains AD Ctrl, SDRrec Carrytongbu, Capture, Track and Txuart.  
AD Ctrl is the driver module of the A/D chip. The main function of AD Ctrl is to control the A/D chip, transform the received analog signal into a digital signal, and then load the digital signal to the FPGA. SDRrec Carrytongbu is the carrier synchronization module. Its main function is to load the programmed carrier codes to the FPGA platform. CAPTURE and TRACK are the code acquisition module and code tracking module, respectively. The function of CAPTURE and TRACK are to load the programmed code synchronization module to the FPGA platform. TXUART is the serial port driver module and its function is to transmit the restored original baseband signal to the PC through the serial port. The transmitted information can then be analyzed using PC software.
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
## 4 Experimental Results
### Discussion  
This study illustrates the throughput improvements achieved by the NUDIF framework across clusters of varying sizes. However, it is important to note that the NUDIF framework is specifically designed for batch inference optimization, focusing on balancing the processing time at each stage while maximizing the throughput for each phase. While this approach enhances overall throughput, it does not achieve optimal latency for individual inference tasks. Additionally, the mixed-integer nonlinear programming (MINLP) problem, by its nature, is NP-hard, and the solving time increases exponentially with the scale of the problem. Therefore, in large-scale clusters with a significant number of devices, it is essential to adopt multi-level optimization strategies, such as decomposition methods (including Benders decomposition and Lagrangian relaxation), heuristic and metaheuristic algorithms (such as genetic algorithms and simulated annealing), and machine-learning-based techniques. These approaches can effectively reduce computational complexity and enhance efficiency while minimizing the loss of optimality, thereby providing more viable solutions for optimizing distributed inference tasks in expansive edge computing environments.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Charge Detrapping
#### 3.4.3 Effect on \(V_{t}\) Distribution after Cycling  
From the previous results, it is clear that charge detrapping leads to both an average charge loss and an enlargement of the \(V_{T}\) distribution, due to the randomness of the detrapping process. A comparative analysis of the impact of charge detrapping on the width of the \(V_{T}\) distribution after cycling and bake was reported in [239] and shown in Figure 28, normalized to the ISPP step \(V_{s}\). The two bottom-most contributions are related to the time-0 placement of the \(V_{T}\) distribution--namely, the ISPP step \(V_{s}\) and the fluctuation in the number of injected electrons per step [158; 159; 240]--and are not discussed here. The remaining part is made up of a native RTN contribution plus the additional terms induced by cycling. Note that RTN is not dependent on the \(V_{T}\) level while detrapping increases with such level, as previously discussed, becoming a non-negligible error source for the higher \(V_{T}\) levels. A similar breakdown of the distribution width was presented in [241], without accounting for detrapping. Other studies also identified charge detrapping as the dominant error source in highly-cycled NAND samples [242].  
To overcome this limitation, several solutions have been proposed, including a dummy read [243] or a moving reference [244] scheme and a _self-healing_ approach [245], where a built-in heater in the proximity of the tunnel oxide is used to accelerate damage recovery and prolong the endurance. The insertion of specific block healing cycles during run time was shown to increase lifetime and performance of NAND devices [246], suggesting that distributed healing schemes could be devised to maximize their efficacy while reducing the energy dissipation of such cycles [247].
## 4 Results
### Discussion
#### 4.2.4 Comparison with 4G LTE Reference Network  
In this section, we compare the levels of EMF exposure due to a 4G LTE BS and a massive MIMO 5G one in terms of the electric field. A 4G LTE BS is equipped with a three sector antennas each transmitting at 43 dBm, with 10 dBi gain. The massive MIMO 5G BS antennas consist of multiple antenna elements with a total input power of 43 dBm. Each antenna element has 0 dBi gain. For a fair comparison, we optimize the power levels of the antennas and the positions of the BSs for both technologies.  
The comparison between the cumulative distribution function (CDF) of the 5G downlink EMF exposure in terms of DL electric fields obtained with scenario 2 based on the tri-objective optimization and the CDF of the 4G reference scenario (optimized for exposure as well) is presented in Figure 11.  
The latter shows that the DL EMF exposure level of the massive MIMO 5G network is always lower than the 4G one at the same probability. The 4G DL EMF exposure is 5 times higher compared to the DL EMF exposure of the massive MIMO 5G network (389.45 mV/m for the 4G reference network  
Figure 11: Comparision of the cumulative distribution function (CDF) of the downlink exposure (scenario 2): 4G vs. 5G (\(E4G\) is the DL electric field due to a 4G BS, while \(E5G\) refers to the DL electric field due to a 5G BS).  
Figure 10: Pareto front for the bi-objective optimization (224 users and various BS antenna elements: 16, 32, 64, 128).  
versus 82.8 mV/m for the 5G network, Table 3). This is mainly due to the fact that the 4G BS transmit all the times at maximum optimized power, while in massive MIMO 5G networks, the BS transmit power is lower due to the multiple antennas that are equipped. This reduces the power per antenna element. Moreover, the use of the TDD and spatial duty cycles significantly reduces the BS transmit power. This power reduction at the BS level is compensated by the beamforming gain to help maintain an acceptable radio link with the users. Furthermore, both the 4G (389.5 mV/m) and 5G DL (82.8 mV/m) level of EMF exposure still remains significantly lower than the limits imposed the ICNIRP guidelines (61 V/m [29]). This has been achieved thanks to the control of the power levels and the placements of the BSs.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Proba-3  
The Proba-3 [16] program, proposed by the ESA and scheduled for launch in 2023, consists of a tracking satellite (200 kg Occulter dwarf satellite) and target satellite (340 kg Coronagraph satellite). The purpose of the mission is to demonstrate techniques for high-precision spacecraft formation flying. Coronagraph's and Occulter's GNC system comprises four reaction wheels, three three-axis gyros, a three-headed star tracker, six solar sensors, and two GPS receivers [17].  
The Proba-3's first task will be to verify the GPS and radio data to perform relative position measurement, including both rough measurement and precise measurement. The second task is to create a 100-150 m satellite formation at the apogee to survey the artificial solar eclipse. The relative state measurement used by Proba-3 includes the relative tangential coarse measurement between satellites (coarse lateral sensor, CLS), GPS longitudinal relative position measurement, and longitudinal fine measurement (Fine Lateral and Longitudinal Sensor, FLLS), as shown in Table 4. The formation distance between satellites ranges from 25 m to 250 m, and the formation is only maintained at the apogee. According to the accuracy requirements of the task, RF (radio frequency) sensors, GPS, and optical measurement systems will be used to achieve state measurement.
## 3 Results
### Intestinal First-Pass Metabolism  
Caco-2 cell homogenate was used to assess the hydrolysis of TU. The hydrolysis-time profile of TU in cell homogenate is shown in Figure 3. The in vitro half-life (\(t_{\text{1/2 inv}}\)) for the hydrolysis of TU was 109.28 \(\pm\) 21.42 min. The value for the calculation of \(Cl_{\text{int}}\) form \(t_{\text{1/2 inv}}\) (Equation (1)) was 0.0066 \(\pm\) 0.001 mL/min/mg protein. Subsequently, \(Cl_{\text{int}}\) was corrected by \(f_{\text{u,mic}}\) (0.781, Equation (2)) to give \(Cl_{\text{u,int}}\) value of 0.0084 \(\pm\) 0.001 mL/min/mg protein. Scaling of \(Cl_{\text{u,int}}\) to physiological factors for the total protein weight of enterocytes in adult human (122.3 g [32]) resulted in a gut intrinsic clearance (\(Cl_{\text{G,int}}\)) of \(61.94\)\(\pm\) 12.07 L/h.  
Figure 3: The hydrolysis–time profile of testosterone undecanoate (TU) in Caco-2 cell homogenate. Values are expressed as mean \(\pm\) SD (\(n=4\)).  
Figure 2: The hydrolysis–time profiles of testosterone undecanoate (TU) in human hepatic microsomes in the presence of nicotinamide adenine dinucleotide phosphate tetrasodium salt hydrate (NADPH, open symbol) and without NADPH (closed symbol). Values are expressed as mean \(\pm\) SD (\(n=3\)).  
The effective intestinal permeability (\(P_{\rm eff}\)) of TU was \(5.67\times 10^{-4}\) cm/s, as predicted by GastroPlus(r) software. Permeability across enterocytes (\(C\)\(I_{\rm perm}\)) was measured based on Equation (7) to yield 13.47 L/h. These values were applied in Equation (6) of the \"Q gut\" model to estimate the fraction of TU that would escape intestinal first-pass metabolism (\(F_{\rm G}\)), which was \(0.114\pm 0.02\). Therefore, it was estimated that only 11% of the absorbable fraction of TU could escape intestinal metabolism.  
As presented in Figure 4, it is predicted that a significant fraction of TU could escape hepatic metabolism compared with intestinal metabolism.
# An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
## 3 Results and Discussion
### Point Cluster Properties and Geomorphological Regionalization
At the same time, their results have a good correspondence with geomorphological divisions, which can serve for geomorphological classification and mapping. In general, the DEM we use has limited elevation accuracy and a large grid cell size. Research on  
Figure 14: Spatial distribution of the HDPS and SDI: (**a**) spatial distribution of the HDPS in Shaanxi Province; (**b**) spatial distribution of the SDI in Shaanxi Province. the areas with less relief requires high-precision DEM data, such as LiDAR point cloud data. Therefore, under the condition of limited DEM accuracy, the ability to describe the landform of point clusters in the flat area is relatively weak, and further research is needed. It is worth noting that terrain representation and various terrain-based geological analyses are significantly scale-dependent [60]. The scales of terrain analysis include geographic scale, observational scale, structural scale and analytical scale, all of which have scale effects [61; 62]. For terrain feature extraction, the most significant is the analytical scale, i.e., what scale is used to extract the terrain features. To address this issue, the LLNV method was adopted to determine the optimal analysis scale in this paper. As for the scale, the effects caused by the geographic scale (i.e., the geographic extent of the study area) and the observational scale (i.e., the spatial resolution and height difference accuracy of the DEM data) need to be further researched using more data. On the other hand, this paper used morphological and runoff simulation methods to obtain integrated terrain feature-point clusters; according to [PERSON]'s classification of geomorphologic features, the result is a feature point at the small-scale landform with an area about \(10^{-3}\) to \(10^{-1}\) km\({}^{2}\)[63; 64]. Further study is required for when the scale becomes larger, such as medium-scale tectonic units.
## 3 Application in Interventional Surgical Robot Training  
Next, the sensor prototype was further validated by applying it to analyze the hand motions made while manipulating the master interface of an interventional robot. The robotic system, shown in Figure 6a, was used for catheterization of tools such as a guidewire during intravascular interventions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classification Methods**} & \multicolumn{5}{c}{**Recognition Accuracy on Test Data (\%)**} \\ \cline{2-6}  & **PUSH** & **PULL** & **RTCL** & **RTCC** & **Aggregate** \\ \hline LSTM + Dense & 81.21 & 88.55 & 89.94 & 93.97 & 87.38 \\ SVM & 78.66 & 83.81 & 89.21 & 91.13 & 84.82 \\ MLP & 82.78 & 83.81 & 90.19 & 94.23 & 87.37 \\ DT & 84.04 & 75.41 & 87.18 & 89.88 & 84.52 \\ KNN & 85.33 & 80.70 & 90.58 & 92.08 & 87.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recognition rate of different classification methods.  
Figure 6: Application of the piezoresistive tactile sensor on intravascular intervention robot; (**a**) positioning of two tactile sensors on index and thumb fingers; (**b,c**) relative resistance change during push and rotate operations, respectively.