esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aims to identify the most suitable pre-reinforcement support measures to prevent TBM jamming when passing through the fractured zone of a gully fault. Given the high likelihood of jamming in such areas, the research focuses on selecting the most effective support system by considering factors such as surrounding rock stability, strata displacement, support structure stress, and cost-effectiveness. Theoretical analysis is employed to predict TBM jamming risks, based on design data, a 10 m gully unit and fractured rock mass were established at 75 m in the excavation direction with assigned parameters. Support effects of pipe curtains, grouting, anchors, and arch supports were analyzed under four conditions: chemical grouting, conduit installation, advanced pipe grouting, and double-layer pipe grouting. On-site verification reveals that TBM jamming occurs when the resisting torque on the cutter exceeds the maximum torque the cutter can generate. For the gully fault, pre-reinforcement measures are essential to stabilize the surrounding rock. Among the different methods, surface drilling reinforcement is the most effective. It significantly improves the surrounding rock's stability, reducing the plastic zone's depth by approximately 52.3% compared to the advanced pipe shed method. The axial force on the anchors decreases by 77.9-83.8%, arch stress is reduced by 68.9-90.8%, and tunnel deformation is minimized by 2.13-50.78%, all of which contribute to enhancing the safety of the initial support structure. On-site coring results, TBM boring parameters, and deformation monitoring data confirm that the surface drilling pre-reinforcement method outperforms the grouting pre-reinforcement for the pipe shed, ensuring the safe excavation of TBM in the gully fault conditions. These findings provide valuable insights for TBM tunnel construction in similar geological environments.  
TBM tunnel; fault fracture zone; cutter head jamming mechanism; grouting pre-reinforcement +
Footnote †: journal: Applied Sciences
# Revisiting the Meandering Instability During Step-Flow Epitaxy
## 1 Introduction  
For molecular-beam epitaxy (MBE), it is well accepted that the thin-film epitaxial growth on a vicinal surface via step flow at a sufficient high temperature constitutes an ideal growth regime [1; 2]. The overview and discussion of the crystal growth can be found in References [2; 3; 4; 5; 6]. In the step-flow regime, the temperature, while below the surface roughening temperature, is sufficiently high to make adatom diffusion fast and, concomitantly, island nucleation improbable. Thus, the sole mechanism for growth or sublimation is the attachment of adatoms to, or their detachment from, the pre-existing steps. Specifically, during the growth on a surface consisting of terraces separated by steps, the deposited atoms diffuse across the terraces and attach to the step, diffuse along the edge and eventually incorporate into the crystalline bulk (Figure 1). Figure 1: Schematic view of the steps separated by broad terraces with mean width \(l\) on which the deposited adatoms diffuse across the terraces and attach to the step, diffuse along the edge and eventually incorporate into the crystalline bulk. Therefore, it is important to study the morphological instability of the terrace edge. Reference [7] showed a scanning tunneling microscopy image of a Si(001) surface which consists of broad terraces separated by steps of atomic height. The experimental and theoretical efforts have been worked on the one-dimensional structures, such as quantum wires, as the result of morphological instability during the epitaxy of self-assembling materials [5]. A step-flow model for the heteroepitaxy of a generic, strained, substitutional, binary alloy has been studied [8]. References [9; 10; 11] have investigated modeling epitaxial growth by kinetic Monte Carlo simulations. Some applications and experiments concerning epitaxy in thin films have been developed [12; 13; 14]. In 1951, [PERSON] (BCF) [15] developed a model for the growth of crystals and described the crystal growth as a regular flow of edges. In the presence of the Ehrlich-Schwoebel barrier, the meandering of the periodic monatomic steps separated by broad terraces has been discussed in Reference [16]. The classical BCF model, however, does not consider the energetic contribution of the adjacent terraces to the step chemical potential and hence cannot explain some phenomena observed experimentally [5]. Therefore, the classical BCF framework was extended by considering this energetic contribution in Reference [17] which developed a thermodynamically consistent continuum theory for the step-flow epitaxy of single-component crystalline films and studied the step-bunching instabilities of one-dimensional step-flow epitaxy. Since the general theory is two-dimensional, it is appropriate to investigate the step-meandering instabilities of a two-dimensional single step separated by broad terraces. We discuss the details in Section 2.
## 2 Background
### Sparse Storage Schemes
#### 2.1.1 Coordinate Storage (COO)  
The coordinate (COO) [57] format is a simple sparse storage scheme. Three arrays, _row_, _col_, and _val_ are used to store the row indices, column indices, and values of the nonzero elements in the matrix. Figure 2 illustrates the COO representation of the example matrix \(A\). Each GPU thread will compute the product of a non-zero with the associated element in vector \(x\). Further reduction is required to sum the partial products to achieve the final results. The reduction procedure creates an overhead that slows down the SpMV.
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
## 1 Introduction
Due to the high complexity in DSA, we develop a dueling deep Q-network approach, which has the ability to determine the best action for each state in the large dynamic unknown environments. Compared with MARL and MADRL, the simulation results show that the proposed DMRL model can obtain a higher average reward and a lower average collision. It means our method can effectively achieve the dynamic cooperative spectrum sensing, and significantly reduce the chance of conflict between PUs and SUs.
## 4 Simulation Results
The suggested method also has a substantial performance advantage over the methods presented in [42; 43]. This is due to the fact that the suggested method compensates for IQDs, while the aforementioned methods do not. Moreover, the suggested estimation and recognition iterative approach accomplishes a BER performance that is comparable to that of the ideal system. Figure 5: Bit error rate of the proposed approach as a function of the SNR. Figure 6: Probability of false recognition comparison. The blue line represents the performance without correcting IQD, red line shows the performance of the algorithm reported in [42], green line describes the performance of the algorithm reported in [43], black line shows the performance of the proposed algorithm, and brown line indicates the benchmark performance. Figure 8: Bit error rate comparison. The blue line represents the performance without correcting IQD, red line shows the performance of the algorithm reported in [42], green line describes the performance of the algorithm reported in [43], black line shows the performance of the proposed algorithm, and brown line indicates the benchmark performance. Figures 9-11 depict the effectiveness of the advocated technique with respect to various modulation kinds. We also show the corresponding benchmarks and BER performance in the ideal situations. Two points are worth mentioning. The first point is that there is a decline in performance when higher-order modulation types are adopted. This is due to the fact that higher-order modulation formats result in less accurate soft information representations from the error control decoder, hence reducing the maximum possible performance. The second observation is that the performance of the suggested method is within 1 dB of the relevant benchmarks. This proves that the suggested receiver architecture is beneficial. Figure 7: Mean square estimation error comparison. The blue line represents the performance without correcting IQD, red line shows the performance of the algorithm reported in [42], green line describes the performance of the algorithm reported in [43], black line shows the performance of the proposed algorithm, and brown line indicates the benchmark performance. Figure 11: Bit error rate for different modulation types. Figure 10: Mean square estimation error for different modulation types. Figure 9: Probability of false recognition for different modulation types.
## 3 Dark Current Measurement Based on Focal-Plane Temperature and Dark Current Correction Experiment
### Satellite-Borne LWIR QWIP Detection System
#### 3.1.2 Other Subsystem Components  
The following describes the subsystem components in the order of target infrared signal transmission. First, the LWIR light emitted from the black body passes through the telescope and is divided by the plane grating. The infrared light, divided into 36 spectral  
\begin{table}
\begin{tabular}{c c} \hline
**Detector Parameters** & **Parameter Values** \\ \hline Spectral range & 9.01–10.45 \(\upmu\)m \\ Array size & 320 \(\times\) 64 \\ Peak wavelength/um & 9.7 \\ Pitch/um & 60 \\ Quantum efficiency & 2.87\(\%\) \\ Blind pixel rate & 0.88\(\%\) \\ Nonuniformity & 5.90\(\%\) \\ Dynamic range & 71 dB \\ Cooling temperature/K & 41 \\ Frame rate/Hz & 17.5 \\ \hline \end{tabular}
\end{table}
Table 1: The QWIP’s primary parameters.  
Figure 4: Physical view of QWIP focal-plane device.  
Figure 3: Schematic diagram of the structure of the satellite-borne LWIR QWIP detection system.  
bands, enters the detector window and arrives at the pixels, where photons interact with electrons to produce a current signal, which is collected and processed by a DI-type readout circuit to output an analog signal. Then, the information acquisition circuit follows, amplifies, and low-pass filters the analog signal from the detector, and then enters the ADC with 12 quantization bits to generate the infrared digital signal and transmits this to the FPGA; finally, the FPGA transfers the processed digital signal to the information processor through TLK2711 to store it as image data.  
The information acquisition circuit has a temperature measurement module, which is an important design to achieve the objectives of this study. The QWIP has a built-in temperature measurement diode in series with a constant-current source, which allows the real-time temperature of the focal-plane to be measured by the voltage value output from the temperature pin. The selected ADC AD976 has a high accuracy with 16-bit quantization and a quantization accuracy of up to 3 mV. The optics, circuitry, and QWIP components are all within the cooling area affected by the thermal control system, and the cooling temperature of the thermal control is below 200 K, which greatly reduces the effect of the system's own background radiation [29]. The two-dimensional mechanism is used to adjust the angle between the imaging system and the target and was not in working condition in this study.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 2 System Model
### NOBC Phase  
In this phase, the received signals from the near node pair and far node pair are detected, decoded to obtain the PLNC signals using maximum likelihood (ML) detection. Thus, by simply multiplying near node detected signals and far node detected signals, the PLNC signals can be computed as [30]  
\[\hat{x}_{r_{1}}(t)\;=\;\hat{x}_{1}(t)\times\hat{x}_{2}(t);\quad\hat{x}_{r_{2}}( t)\;=\;\hat{x}_{3}(t)\times\hat{x}_{4}(t), \tag{3}\]  
where \(\hat{x}_{m}(t),\;m\in(1,2,3,4)\) are detected signals at relay. Then, these signals are added with different power levels \(\alpha_{n}\) where \(n\in(1,2)\) using NOMA scheme and it can be written as  
\[\hat{x}_{r_{3}}(t)\;=\;\alpha_{1}\sqrt{P_{r}}\hat{x}_{r_{1}}(t)\;+\;\alpha_{2 }\sqrt{P_{r}}\hat{x}_{r_{2}}(t), \tag{4}\]  
where \(P_{r}\) is the AP transmit power, which is chosen such that it will be equal to the node transmit power. The power allocation coefficient \(\alpha_{n},\;n\in(1,2)\) satisfy the condition \(\alpha_{1}+\alpha_{2}\) = 1 and for convenient we used the notations \(\alpha_{1}\sqrt{P_{r}}\;=\;p_{1}\), and \(\alpha_{2}\sqrt{P_{r}}\;=\;p_{2}\) in rest of the paper.  
In downlink, the AP transmits \(\hat{x}_{r3}(t)\) to all the four nodes simultaneously over NOBC link. The time domain signal \(z_{n_{1}}(t)\) and \(z_{n_{2}}(t)\) received by the near node pair can be written as  
\[z_{n_{1}}(t)\;=\;\hat{x}_{r_{3}}(t)g_{1}(t)+n_{n_{1}}(t), \tag{5}\]  
\[z_{n_{2}}(t)\;=\;\hat{x}_{r_{3}}(t)g_{2}(t)+n_{n_{2}}(t), \tag{6}\]  
where \(g_{1}(t)\) and \(g_{2}(t)\) are the channel coefficients between the AP to the near node 1 and the AP to the near node 2 and modeled as an \(ind\) random variable which follows the exponential distribution with mean zero and variances \(\eta_{n_{1}}=d_{1}^{-\alpha}\) and \(\eta_{n_{2}}=d_{2}^{-\alpha}\) respectively, where \(d_{1},d_{2}\) are distances between near nodes and AP [18], \(n_{n_{1}}(t)\) and \(n_{n_{2}}(t)\) are the \(iid\) Gaussian noise. Similarly, the received signals \(z_{f_{1}}(t)\) and \(z_{f_{2}}(t)\) by the far node pair are written as  
\[z_{f_{1}}(t)\;=\;\hat{x}_{r_{3}}(t)g_{3}(t)+\beta e_{3}(t)b_{3}(t)\hat{x}_{r_{ 3}}(t)x_{b}(t)+n_{f_{1}}(t), \tag{7}\]  
\[z_{f_{2}}(t)\;=\;\hat{x}_{r_{3}}(t)g_{4}(t)+\beta e_{4}(t)b_{4}(t)\hat{x}_{r_{ 3}}(t)x_{b}(t)+n_{f_{2}}(t), \tag{8}\]  
where \(\beta\) is the reflection coefficient used to normalize \(x_{b}(t)\)[26] which is backscatter own message and its average value is \(\mathbb{E}(|x_{b}(t)|^{2})\) = 1, \(g_{3}(t)\) and \(g_{4}(t)\) are the channel coefficients between the AP to the far node 1 and the AP to the far node 2 with mean zero and variances \(\eta_{f_{1}}=d_{3}^{-\alpha}\) and \(\eta_{f_{2}}=d_{4}^{-\alpha}\) respectively, where \(d_{3},d_{4}\) are distances between far nodes and AP, \(e_{3}(t)\), \(b_{3}(t)\), \(e_{4}(t)\) and \(b_{4}(t)\) are ambient backscattering channel coefficients, \(n_{f_{1}}\) and \(n_{f_{2}}(t)\) are the Gaussian noise.
## 3 Results and Discussions
### In Vitro Digestion
It contains 8% fewer phenolic compounds than the aronia bar enriched with powder before digestion, which had 1207.22 \(\pm\) 0.56 \(\upmu\)g/g. The aronia bar with powder has a modest amount of hydroxybenzoic acid, hihydroybenzoic acid, and protocatechuic acid. After digestion in the intestinal phase, phenolic compounds increased significantly in the aronia samples by 755.74% (10,337.12 \(\pm\) 1.49 \(\upmu\)g/g). This suggests that digestion can release phenolic compounds from aronia powder and contribute to better absorption in the body. The aronia by-product had a phenolic compound content of (16,792.24 \(\pm\) 3.78 \(\upmu\)g/g), but their quantity was much higher after digestion in the intestinal phase of the lyophilized powder (44,373.46 \(\pm\) 3.21 \(\upmu\)g/g). Also, [PERSON] et al. obtained a total of 8566-9693 mg GAE/g of polyphenols, concluding the benefits of the powder [30]. The difference between freeze-dried aronia before and after digestion is only \(-\)4.79%, indicating good absorption by the body even after digestion. This suggests that aronia is a significant source of phenolic compounds and that digestion can enhance the benefits of these compounds for health. Anthocyanins were not detectable in the aronia bar before digestion. After digestion in the intestinal phase, anthocyanins were released in the aronia samples. Specifically, cyanidin diglucoside, delphinidin glucoside, cyanidin glucoside, and cyanidin were detected in significant quantities. This suggests that digestion can release anthocyanins from aronia powder, which can benefit their absorption in the body. In conclusion, adding aronia powder to cereal bars is an effective way to provide anthocyanins after digestion and increase the content of phenolic compounds, therefore offering antioxidant and health benefits. Overall, these results suggest that consuming products enriched with aronia powder can contribute to the intake of beneficial nutritional and antioxidant compounds for health, while digestion can play a significant role in releasing these compounds in the body.
## 4 Experiment and Result Analysis
### Differential Displacement Stability Experiment  
In this experiment, M1 and M2 were fixed at the same distance from PBS to minimize other uncontrollable influences on the measurement results. The experiment lasted for 100 min; during the experiment, the two interferometers MI and RI recorded the displacement simultaneously. The experimental results are shown in Figure 4. In Figure 4a, the red line and blue line represent the measurement results of MI and RI, respectively. The yellow line is the measurement results of DPMI obtained by the difference between the measurement results of MI and RI. The measurement results of MI and RI drift with the environmental change such as the changes in air temperature, air pressure, and humidity, as shown in Figure 4b,c, during 100 min. The maximum measurement drifts in MI and RI were, respectively, 902.9 nm and 892.1 nm during the experiment. Additionally, in the subplots, the changes in measured displacement by MI and RI were, respectively, 61.1 nm and 60.7 nm between 80 and 90 min. However, the trends in the measurements of MI and RI were in good agreement. Due to the good consistency, there was no significant change trend in the measurements of DPMI. The maximum measurement drift of DPMI was 16.3 nm during the experiment, and the change in measured displacement by DPMI was 3.6 nm between 80 and 90 min, respectively. The experimental results verified that the designed DPMI can effectively reduce the influence of environmental disturbance.
## 4 Experimental Setup
### Performance Assessment Methods and Testing Parameters  
ML models necessitate evaluation through a range of criteria as reliance on a singular metric is inadequate to ensure reliable outcomes. Conventional metrics encompass accuracy, recall, precision, and F-1 score, while in scenarios featuring an imbalanced dataset, the average precision score assumes significance as an indicator of model performance concerning minority dataset. Therefore, we include these metrics in our testing parameters.  
The present study aims to identify the optimal set of features through the utilization of a GA-driven optimization technique complemented by local optimization. This selection process not only enhances performance metrics, including accuracy and recall, but also mitigates inference time by significantly reducing the number of features employed. Since each feature extraction operation incurs a computational cost, a reduction in feature usage entails a corresponding reduction in cost. The experimental phase incorporates a carefully monitor of cost fluctuations in relation to the chosen feature list, thereby integrating these changes into the evaluation of performance results.
## 2 Experimental Methods
### Testing Program and Procedure  
#### 2.3.1 Fatigue Test Equipment  
The constant amplitude fatigue test of the M24 twisted-shear high-strength bolt connection was carried out in the civil engineering laboratory of Taiyuan University of Technology with a room temperature atmospheric environment. The test equipment included an MTS Landmark 370.50 hydraulic servo fatigue test machine. The device has high load accuracy and the dynamic load capability of 500 kN; the load frequency can reach 100 Hz at most; the loading displacement measurement accuracy reaches 0.00028 mm.
## 2 System Model
### Improved DSP  
It is concluded from the proposed model that FWM effects can be mitigated by use of advance duo-binary modulation scheme for a WDM-based LHOCS and further improvement in the performance can be achieved with a IDSP receiver rather than using conventional dispersion compensation fiber (CDCF) for dispersion compensation. The IDSP based receiver is presented in Figure 3, which describes that two cascaded least mean square (LMS) and recursive lease square (RLS) transversal filtered are installed, including 7 and 11 taps, respectively. This integration approach of LMS and RLS present excellent performance and fidelity against the error signals. Moreover, LMS and RLS filters offer faster convergence and smaller error, in results the time convergence of the system is enhanced against variable channel impairments. The number of taps are optimized for the proposed system, for the minimum least square error (LSE) and rapid convergence time. Blind adaptive equalization is applied in order to avoid the used of training sequence and complexity of the proposed LHOCS, using constant modulus algorithm (CMA). The effect of FWM nonlinearities inside LHOCS are multiplicative in nature, thus, for detecting the received bits an extended Kalman filter [41] is installed instead of matched filter. It uses a likelihood threshold (LTR) method, aiming to adjust receiver orientation, and updates the threshold after each iteration.
# Impact of Combine Harvester Technological Operations on Global Warming Potential
## 2 Data and Methods
### Calculation Methodology for Greenhouse Gases and Global Warming Potential  
For the GHG and GWP calculations, this study converted the fuel consumption data from the telematics database from 1 year\({}^{-1}\) and 1 h\({}^{-1}\) to kg year\({}^{-1}\) and kg h\({}^{-1}\), respectively. A diesel fuel volume-to-mass conversion factor of 0.832 kg l\({}^{-1}\)[30; 31; 32] was then employed.  
The GHG emissions in the telematics data analysis were not measured directly but were assessed using the methodology outlined in Chapter 1.A.4 of the EMEP/EEA's Air Pollutant Emission Inventory Guidebook [33]:  
\[E_{ pollutant}=FC_{fuel\ type}\times EF_{ pollutant}, \tag{1}\]  
where \(E_{ pollutant}\) denotes the emission of a specified GHG (CO\({}_{2}\), N\({}_{2}\)O, and CH\({}_{4}\)), \(FC_{fuel\ type}\) denotes the fuel consumption, and \(EF_{ pollutant}\) denotes the emission factor of the pollutant (g t\({}^{-1}\) or kg t\({}^{-1}\)) of the consumed diesel fuel (Table 1).  
The GHG impact on GWP was estimated using Equation (2) [34; 35; 36; 37; 12]  
\[\text{GWP}=\text{E}_{\text{CO2}}+25\times\text{E}_{\text{CH4}}+298\times\text {E}_{\text{N2O}}. \tag{2}\]  
The GWP in CO\({}_{2}\) equivalents is such that 1 kg of CO\({}_{2}\) equates to 1 kg of CO\({}_{2\text{eq}}\), 1 kg of CH\({}_{4}\) equals 25 kg of CO\({}_{2\text{eq}}\), and 1 kg of N\({}_{2}\)O corresponds to 298 kg of CO\({}_{2\text{eq}}\).