esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 3 Typical Case: Integrated Demonstration of Xinqiao Iron Mine  
### Overview of Xinqiao Mine  
Xinqiao Mine has a proven geological reserve of 170 million tons and an industrial reserve of 110 million tons, covering an area of 3.529 km\({}^{2}\). The current mining scale is 1.5 Mt per year (900,000 tons from open-pit mining and 600,000 tons from underground mining). All the elevations mentioned in this paper, including those at \(-\)106 m, \(-\)156 m, and others, are relative to the reference horizon (horizon 0). The geological resource reserve in the open-pit to underground transition zone below the \(-\)156 m level between lines 1 and 21 amounts to 48.057 million tons. The open-pit to underground mining transition at Xinqiao Mine involves the area below the \(-\)156 m level and to the east of line 21, with the primary extraction target being the eastern wing of ore body No. 1. The design follows a downward staged mining sequence. The ore bodies below \(-\)156 m are classified by thickness as follows: Class A (thickness \(<\) 5 m, 4.4%), Class B (thickness 5-15 m, 11.8%), Class C (thickness 15-50 m, 79.4%), and Class D (thickness \(>\) 50 m, 4.4%). As illustrated in Figure 5, the three-dimensional geological model of Xinqiao Mine shows the classification of ore body thicknesses and their spatial distribution, providing a solid foundation for the design of the subsequent underground mining plan.  
In terms of capacity transition, the mine currently operates at a total capacity of 1.5 Mt per year, with future plans to increase this to 1.8 million tons per annum. Given that the remaining service life of the western underground mining area is similar to the eastern open-pit to underground transition phase, consideration must be given to the capacity transition after the western mining phase concludes. The second phase of the open-pit to underground transition is scheduled for implementation in 2028. Upon completion of the western mining phase, the additional 900,000 tons per year capacity from the second phase will be achieved by constructing a blind inclined shaft or a blind vertical shaft in a  
Figure 5: Three-dimensional geological model of Xinqiao Mine. Source: Figure 5 adapted from [[[http://www.xqmcl.com/](http://www.xqmcl.com/)]([http://www.xqmcl.com/](http://www.xqmcl.com/))] (accessed on 8 July 2025).  
suitable location in the western area, with ore transported to the surface via the main shaft to maintain the total capacity of 1.8 million tons per year. The capacity transition diagram for Xinqiao Mine is presented in Figure 6.
## 3 Path Loss Models
### FI Model [46]  
In this model, the intercept parameter can vary based on the environment or specific scenario to present a more accurate estimation of the path loss [47]. Traditional path loss models assume a fixed intercept value for a particular environment. However, in real-world scenarios, environmental factors can significantly affect the path loss, such as building density, terrain, foliage, and other obstructions. A floating intercept model takes these factors into account by allowing the intercept value to adjust accordingly.  
The FI model is presented in (17) as follows [47; 48]:  
\[P_{L}^{FI}(f,d)[dB]=\alpha+10.\beta^{FI}\text{log}_{10}(d)+X_{\sigma}^{FI}, \quad d>1\ m, \tag{17}\]  
where \(P_{L}^{FI}(f,d)\), \(f\), \(d\), \(\alpha\), \(\beta\), and \(X_{\sigma}^{FI}\) represent the FI path loss in dB; frequency in GHz; separation distance between the transmitter and receiver; the floating intercept in dB; the slope of the line, which represents the path loss' dependence on distance; and the randomly distributed variable having a Gaussian distribution and a zero mean.  
\[\overline{PL}(d)[dB]=\alpha+10\beta\text{log}_{10}(d) \tag{18}\]  
The tilt is represented by the floating intercept in the path loss model of (19) [47], measured in dB.  
\[\alpha=\overline{PL}(d)[dB]+10\beta\text{log}_{10}(d) \tag{19}\]  
The slope of the line, \(\beta\), is given as in (20) below:  
\[\beta=\frac{\sum_{i}^{n}\left(d_{i}-\overline{d}\right)x\big{(}PL_{i}- \overline{PL}\big{)}}{\sum_{i}^{n}\left(d_{i}-\overline{d}\right)^{2}}, \tag{20}\]  
The path loss models' standard deviation can be determined, using (21), as follows:  
\[\sigma(dB)=\sqrt{\frac{\big{(}PL_{i}-\overline{PL}\big{)}^{2}}{N}}. \tag{21}\]
## 3 Rigid Model Pressure Measurement Test  
### Test Equipment  
The rigid model pressure measurement test was carried out in the XNJD-3 wind tunnel of Southwest Jiaotong University. The wind tunnel is the largest boundary layer wind tunnel in the field of wind engineering in the world. The size of the test section is 36 m (length) \(\times\) 22.5 m (width) \(\times\) 4.5 m (height). The huge test section size enables pressure testing of the large-scale large-span roof models. The wind speed of the wind tunnel in the vacant state can be adjusted from 1.0 to 16.5 m/s, and the turbulent intensity of the incoming flow is less than 1%, and the average airflow deflection angles in both the longitudinal and vertical directions are less than 1\({}^{\circ}\)[39; 40; 41].  
The turbulent wind field characteristics were collected using the TFI Cobra three-dimensional fluctuating anemometer (Figure 1a). Its measuring linear frequency range is 0-2000 Hz, the measurable wind speed range is 2-100 m/s, and the allowable error of measuring wind speed is \(\pm\)0.1 m/s. Wind pressure measurement adopted Scanivalve DSM4000 electronic pressure scanning valve system (Figure 1b). The system converts pressure signals into electrical signals, which are amplified and sent back to the pressure acquisition system, and then the pressure values of each circuit are collected and stored by the computer. The system can realize the synchronous acquisition of dynamic pressure signals of several measuring points, the measuring range is 0-\(\pm\)2000 Pa, and the measuring accuracy is \(\pm\)0.08% of the full scale [42; 43].
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Communication-Efficient Federated Learning
The reliability of FL is defined as the probability that it will be able to reach a certain value of loss in training. Since the devices that actually participate in each training phase are a subset of the total number of devices, the devices that communicate the parameters may change each step. This has the power to impact convergence time and loss. These performance metrics are strongly influenced by the wireless communication factors: in particular, the spectral resources allocated in each device determine the signal to interference-plus-noise ratio (SINR), the data rate, and the probability that the parameter transmits includes errors. As a result, they have an impact on training \(T_{T}\), \(E_{T}\), and reliability. The number of updates to the gradient descent method that may be made for each learning step is determined by the computational capacity. As a result, it has impacts on the amount of time and energy spent on local training. In the meantime, decreasing the number of SGD updates will increase the loss in training and the number of steps required for convergence. SINR, data rate, and the chance to include mistakes in parameter transfer are all determined by transmit power and wireless channel. As a result, increasing the transmission power of each device reduces losses, \(T_{T}\), \(N_{T}\), and dependability while increasing \(E_{T}\). In FL, learning losses and \(N_{T}\) decrease as the number of participants increases, whereas \(T_{T}\) and reliability improve. Training losses, reliability, and the overall number of training steps may decrease as the amount of the FL parameters trained by each device grows. However, the amount of energy and time spent training the model will rise.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 qlIBC  
Three-Phase Interleaved Boost Converter  
AC  
Alternate Current  
Al-Caps  
Aluminum Electrolytic Capacitor  
ANFIS  
Adaptive Neuro-Fuzzy Inference System  
ANN  
Artificial Neural Network  
ASD  
Adjustable Speed Drive  
BDF  
Bandpass Filter  
BTB  
Back-to-Back  
C  
Capacitance  
CCM  
Continuous Conduction Mode  
CF  
Crest Factor  
CWT  
Continuous Wavelet Transform  
DB  
Data Based  
DC  
Direct Current  
DCM  
Discontinuous Conduction Mode  
DFT  
Discrete Fourier Transform  
DL  
Deep Learning  
DWT  
Discrete Wavelet Transform  
EDA  
Exploratory Data Analysis  
EE  
Energy Entropy  
EMD  
Empirical Mode Decomposition  
EMI  
Electromagnetic Interference  
ESR  
Equivalent Series Resistance  
EV  
Electrical Vehicles  
FS
Fisher Score  
HCNN  
Hybrid Convolutional Neural Network  
HHT  
Hilbert-Huang Transform  
HS  
Hope size  
Implantable Medical Devices  
IoT  
Internet of Things  
IPM  
Information Processing Module  
KNN  
K-Nearest Neighbors  
LDB  
Linear Decision Boundary  
LinR  
Linear Regression  
LR  
Logistic Regression  
MB  
Model Based  
MI  
Mutual Information  
ML  
Machine Learning  
NB  
Naive Bayes  
NLDB  
Non-linear Decision Boundary  
OFF-FDTs  
Offline Fault Diagnosis Techniques  
ON-FDTs  
Online Fault Diagnosis Techniques  
PCs
Power Converters
# Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis
## 2 Results  
Ferric Reducing Antioxidant Power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) Calibration Curves  
This study aimed to compare the suitability and efficiency of the DPPH and FRAP assays when measuring total antioxidant activity in hops and cannabis extracts procured from differently processed biomass, including a pre-freezing step followed by freeze-drying or MAHD. The DPPH and FRAP colorimetric assays are universal tools that are currently used for assessing nonenzymatic antioxidants present in plants [15; 52]. The DPPH assay measures the radical scavenging activity of most phenolic compounds such as flavonoids and tannins [37; 53]. The FRAP assay is a measure of the transition metal ion chelating activity of antioxidants such as ascorbic acid, uric acid and polyphenic compounds such as catechins under acidic conditions [37; 54]. Bleaching of the DPPH solution from violet to pale yellow increases with an increase of antioxidant activity in each sample (Figure 1A). This assay is based on the reduction of the free radical DPPH to DPPH-H. The FRAP assay uses the reduction of ferric ions (Fe\({}^{3+}\)) to ferrous ions (Fe\({}^{2+}\)) as the signal and measures the change in absorbance at 593 nm owing to the formation of a blue colored Fe\({}^{2+}\)-tripyridtriazine compound from the colorless oxidized Fe\({}^{3+}\) form by the action of electron-donating antioxidants (Figure 1B).  
The percentage of radical scavenging capacity for different Trolox concentrations used for DPPH and FRAP assay calibration curves is shown in Figure 2. Figure 2 shows inhibition values using 0.005 to 2.5 mM Trolox concentrations. Preliminary data exhibited a flattening of the graph between 2.5 and 10 mM Trolox concentration. This can be attributed to the almost complete quenching of DPPH and FRAP by Trolox, which does not affect the absorbance values. As such, sample dilution is necessary to dilute samples to within the measurable range (0.005 to 2.5 mM Trolox concentrations). A similar curve flattening observation was made by [PERSON] et al. (2010) and [PERSON] et al. (2009), where the absorbance of Trolox did not change at concentrations of 200-1000 \(\upmu\)mol L\({}^{-1}\) and 0.15-0.2 mM, respectively. Calibration graphs (Figure 2) used to quantify the antioxidant capacities of hops and cannabis in this study are linear, in the range 0.005 to 2.5 mM for Trolox, with strong correlation coefficients (R\({}^{2}\)) of 0.996 and 0.982 for DPPH and FRAP, respectively.
## 2 Materials and Methods
### Hydrogen Embrittlement (HE) Evaluation Under Simulated Hydrogen Storage Conditions
#### 2.3.3 Tensile Testing and Hydrogen Embrittlement Evaluation Under Different Conditions  
Uniaxial tensile tests were conducted using a universal testing machine at a crosshead speed of 5 mm/min under the four predefined testing conditions for all 316L stainless steel material processing conditions (as-built AM, HT1, HT2, and as-rolled). An extensometer was used to accurately measure elongation during testing. Specimens were prepared following the ASTM E8 standard [24], and the average results from at least two specimens per condition were recorded.  
Figure 4 illustrates the tensile test setup for liquid nitrogen exposure, ensuring the cryogenic temperature required for Testing Conditions 3 and 4.  
The susceptibility to hydrogen embrittlement (HE) was evaluated based on the loss of total elongation at failure caused by hydrogen charging. This was quantified using the Hydrogen Embrittlement Index (HEI), as described below.  
Figure 4: Tensile testing of 316L stainless steel specimens in a liquid nitrogen environment under testing conditions 3 and 4.  
2.3.4 Impact Testing, Hydrogen Embrittlement Evaluation, and Fractography Under Different Conditions  
Charpy impact tests were conducted on AM 316L stainless steel specimens (as-built, HT1, and HT2) under the four predefined testing conditions. Specimens were prepared in accordance with the ASTM E23 standard [25], and the average results from at least two specimens per condition were recorded.  
The susceptibility to hydrogen embrittlement (HE) was assessed based on the loss of impact toughness due to hydrogen charging, quantified using the hydrogen embrittlement factor (HEF), as detailed below.  
To further investigate the effects of hydrogen embrittlement, fracture surfaces from selected impact test conditions were analyzed using scanning electron microscopy (SEM) to examine the fracture characteristics under different processing and testing conditions.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 4 The Predictable Time of Wind Speed Series
### Autocorrelation Analysis of Wind Speed Series Based on MIC  
The MIC was used to measure the autocorrelation of the selected wind speed series. There are 36 months of wind speed series. Taking a month's wind speed series {\(v(t)\), \(t\) = 1, 2,..., \(N\)} as an example, the MIC of {\(v(t)\), \(t\) = 1, 2,..., \(N\)} and {\(v(t)\), \(t\) = 1, 2,..., \(N\)} was firstly calculated. Then, the MIC of {\(v(t)\), \(t\) = 1, 2,..., \(N-1\)} and {\(v(t)\), \(t\) = 2, 3,..., \(N\)} was calculated. Continuing this step until the MIC of {\(v(t)\), \(t\) = 1, 2,..., \(N-\tau\)} and {\(v(t)\), \(t\) = \(\tau+1\), \(\tau+2\),..., \(N\)} was calculated. In the above example, \(N\) represents the number of wind speed data; \(\tau\) represents the number of delay time (unit is 10 min) and this paper took \(\tau\) as 300. After calculating the auto-correlated MIC of all wind speed series, the variation law of the MIC with the delay time is shown in Figure 4.  
It can be seen from Figure 4 that as the delay time increases, the auto-correlated MIC of the wind speed series gradually decreases and finally reaches a steady trend. With the decrease of MIC, the correlation between future and historical wind speed series also decreases. At this time, the forecasting error will gradually increase when forecasting the future wind speed based on the historical wind speed. Therefore, in order to ensure the forecasting accuracy of the direct multistep forecasting, a fixed MIC value can be determined, and the delay time corresp  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Location** & \begin{tabular}{c} **Local Terrain** \\ **Condition** \\ \end{tabular} & \
\begin{tabular}{c} **Selected Time of Wind** \\ **Speed Series** \\ \end{tabular} &
\begin{tabular}{c} **Height of Wind Speed** \\ **Measurement (m)** \\ \end{tabular} \\ \hline
1 & Hunan province & Mountainous area & From April 2014 to March 2015 & 80 \\
2 & Henan province & Plain area & From June 2016 to May 2017 & 120 \\
3 & Zhejiang province & Coastal area & From August 2011 to July 2012 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of wind speed series from three anemometer towers.  
Figure 4: The variation law of the MIC with the delay time: (**a**) wind speed series of anemometer tower 1; (**b**) wind speed series of anemometer tower 2; (**c**) wind speed series of anemometer tower 3.  
can be used as the predictable time of the wind speed series. According to the calculation results of the three anemometer towers in Figure 4, the variation law of MIC with delay time tends to be gentle when the MIC drops to 0.2. Therefore, we can take the corresponding delay time (or correlation length) as the predictable time of the wind speed series when the MIC is equal to 0.2. The calculation results are shown in Table 2.
## 2 Synthesis and Characterization
### Characterization
#### 2.2.2 Solvent Uptake  
The quantification of solvent uptake such as heptane, methanol, and toluene by the polymeric resins provide an indirect measurement of the accessible pore-volume and confirms the estimates obtained from porosimetry [4, 34, 41] methods. The measurement of pore-volume in the solid state using porosimetry utilizes various backfill gases in which fixed inaccessible pores are evaluated using BET analysis. In contrast, the uptake of solvent enables access to restricted pores through morphological changes (_i.e._ swelling) by filling the fixed pores, expanding pores through uncoiling of framework units, and swelling of highly crosslinked domains.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 5 Conclusions and Future Works  
In the proposed work, we introduced three modes for D2D communication to prevail. D2D communication in Mode 1 occurred at a frequency of 2 GHz, which was a direct communication using cellular uplink resources. In Mode 2, D2D communication took place at a carrier frequency of 28 GHz. If the radius of coverage exceeded a certain threshold, it switched to Mode 3. The D2D communication in Mode 3 took place through a DF relay. The bitwise binary XOR operation was executed to encode the message received at the relay node from the D2D transmitter at a carrier frequency of 28 GHz using the uplink Rician fading channel. The radius of coverage was derived for the switching of modes. The coverage probability was also derived to demonstrate the efficacy of the communication system. In addition, the DRS method was proposed for the optimal selection of DF relays based on the higher sum SINR, lower distance, and higher channel gain of the instantaneous SINR of the D2D communication. The simulation results also validated the efficiency of the proposed method. The EE for the DF relay scheme was compared with the AF relay scheme for better understanding and showing the effectiveness of the DF relay over the AF relay. Nevertheless, there is scope for improvement in the coverage area of the D2D users by considering the unknown CSI, which would make the problem statement more realistic in nature. The future works may also consider the dynamics of the interference introduced by the interacting users by applying machine learning and game theory methods.  
S.S.S. modeled the system for D2D communication in a 5G mmWave cellular network using an uplink channel through the assistance of a two-way DF relay protocol, implemented the case study, and analyzed the performance of the system under the supervision of [PERSON] and [PERSON]. The manuscript was drafted by [PERSON] and was revised and proofread by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable, this study did not report any data.  
The authors declare no conflict of interest.
## 5 Numerical Simulation and Results Discussion  
### Numerical Model and Grid Meshing  
The profiling constrained flow passage for titanium alloy complex surface of artificial joint is composed of multi-segment constrained modules and workpiece surface, and the numerical model is shown as Figure 7. Firstly, a three-dimensional mechanical structure model is set up, and then it is imported into a finite element model. The radius of circular arc of upper surface from left to right are 10 mm, 32 mm, and 10 mm, respectively. The mesh dividing is completed and essential boundary condition is configured, in which the element is Tet/Hybrid, the type is TGrid, the interval size is 1, and the number of cells is 324,041. The mesh dividing schematic of the profiling constrained flow passage is shown in Figure 7b. For the essential boundary condition setting, the inlet is VELOCITY_INLET, the outlet is OUTFLOW, and other sides is WALL.  
Figure 7: _Cont._  
This paper selects 3D single-precision steady implicitly pressure solver, and the solution is obtained by the SIMPLEC algorithm of pressure-linked equations. In this paper, the solid particle phase is silicon carbide particle, the density is 3170, the mean diameter of abrasive particle is 0.05 mm, and the volume fraction is 0.1; the fluid phase is distilled water. According to computed Reynolds number, the abrasive flow in the flow passage can form the turbulent state, and the turbulence intensity is 9.8%. Based on the features of flow passage, the solving conditions are configured for the Euler model and realizable \(k\)-\(\epsilon\) turbulence model.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 2 Assumptions and Considerations  
### Radial and Tangential Error Components  
Roll position errors consist of two components: radial and tangential (see Figure 2). The radial component is perpendicular to the passline (see Figure 1), while the tangential component is tangential to the passline and thus always perpendicular to the radial component. The radial component gets its name from being colinear with the casting radius in the curved section of a CCM. In the horizontal section of the CCM, there is no casting radius, but the radial component is still defined as perpendicular to the passline. In this section of the machine, the passline is straight and has no tangent, so here, the tangential component is defined as parallel to the passline.  
When a roll position deviates towards the outside of the casting arc (in the curved section) or towards the bottom of the machine (in the horizontal section), its radial error component is defined as positive. The positive direction for a tangential component is always in the direction of casting.