esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The high porosity and high specific surface area of the broken rock mass in abandoned mine goal make it an excellent thermal storage space. The void structure is an important factor that affects the permeability characteristics of broken rock mass, which determines the efficiency of extracting geothermal water from abandoned mine shafts. To accurately describe the void structure of broken rock mass, the effect of particle erosion on the fracture of rock blocks is considered in this study, based on which an impact-induced strength corrosion calculation model was proposed. Then, this calculation model was embedded into the three-dimensional numerical simulation of broken rock mass for secondary development. A discrete element numerical calculation model was established for broken rock masses with different size grading distributions under water immersion and lateral compression conditions. On this basis, considering the strength erosion effect of impacts, this study investigated the deformation and fracture characteristics of broken rock masses with different size grading distributions and analyzed the evolution laws of porosity in the broken rock masses. The main findings are as follows: The impact effect has a significant influence on the growth of microcracks and the breakage rate of broken rock mass. When the particle size of the broken rock mass differs significantly (size grading as G3), impact-induced strength erosion exerts the greatest impact on the growth of microcracks and the breakage rate. When the particle size of the broken rock mass is uniform (size grading as G1), impact-induced strength erosion minimally impacts the secondary fracturing of the broken rock mass. When the strain of the broken rock sample is less than 0.175, the distribution of microcracks is scattered; when the strain reaches 0.275, microcrack propagation accelerates and exhibits a clustered distribution; and when the strain reaches 0.375, microcracks exhibit a reticular distribution and their connectivity is enhanced. With the increase in deformation, the broken rock mass porosity decreases, and the porosity curve fluctuates along the z-axis with a decreasing trend and gradually becomes more uniform. This study provides a theoretical foundation for assessing the efficiency of extracting and storing mine water with heat in abandoned mine geothermal mining projects.  
digital rock reconstruction; pore structure evolution; macro-meso scale characterization; broken rock mass; grain size gradation; macro-deformation mechanism
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 1 Introduction
In this formula, correction term as a function of far-field gain has been successfully applied to characterize the radiation properties in the Fresnel region. An approach to the generalization of the Friis formula based on the fundamental Gaussian beam has been proposed by [PERSON] [31]. While the formulas are related to the rapid calculation based on the simple far-field gain, the integral coupling takes advantage of the fast Fourier transform (FFT) based on two vector far-field patterns [32; 33; 34; 35; 36; 37]. This feature allows diverse scenarios of the wireless link to be easily computed from far-field patterns obtained from measurements or full-wave simulations. The effectiveness of the formula has been demonstrated through various cases of the power transmission in a free-space and more complex circumstances containing dielectrics between two antennas [36]. Recently, the formula has been applied to the wireless link for implanted biomedical devices [10]. Even though there are several recent advancements for the mid-range biomedical implanted devices, relatively little attention has been paid for accurately and efficiently analyzing the mid-range wireless link. Moreover, comprehensive evaluations based on diverse techniques using the radiative near-field characteristics have been rarely reported. In this paper, the radiation characteristics around the implanted antenna are studied, and several techniques based on the far-field radiation characteristics are revisited and its applications to the wireless link for the implanted antennas are presented. The mid-range radiation characteristics are studied through the near-field distribution where the antenna is immersed with different depths of human tissue for pacemaker application. In the following section, the phenomenon of the reduction in the far-field gain, known as \"gain reduction factor\", is used to describe the radiation characteristics in the radiative near-field region. This work provides an overview of important features for the several techniques used in the mid-range wireless link: (i) asymptotic Friis formula, (ii) Chu formula, and (iii) integral coupling formula. The detailed procedure to obtain the wireless link is discussed, and the formulas are applied to the various cases of the biomedical wireless link. In order to include all near-field wireless links, this work revisits the two-port network for the investigation of inductive coupling where the wireless link is made in a reactive near-field region.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 3 Results and Discussion
### Performance Comparison and Discussion
#### 3.3.1 Comparison to State-of-the-Art Methods
The false alarm rate of CNN-based methods is relatively substantial because they are trained via datasets to extract more stable features. However, ROLO needs to pretrain the detector, and then, the LSTM solely  
Figure 12: A description of the convergence. The training loss and learning rate of our model tend to be stable after 25 epochs. (**a**) The training error curve and the validation variation tendency; (**b**) The learning rate curve of the training phase. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameter Setting** \\ \hline TVF & T = 16, S_Z = 8 \\ DP & T = 8 \\ WSLCM & K = 9, \(\lambda\) = 0.8 \\ MCLoG & K = 4 \\ Proposed Method & T = 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Partially set parameters for SOTA methods. learns the temporal features of the spatial results. It is inevitable that the model needs more trajectory data to train. In our small dataset, it performs badly. The IRSTD-GAN is a single-frame method, which works well for static targets, but the results of the moving targets are fair. One can see that the proposed method reaches the trade-off between detection accuracy and false alarm. This is attributed to the fact that the Conv-LSTM structure learns the spatio-temporal constraint of the moving target and the deep feature extractor based on 3D-CNN and fuses multi-scale local features between the target and the background. The proposed method yields better performances than the baseline algorithms in terms of both the detection rate and false alarm rate. The CNN-based methods are generally better than the traditional methods. This benefits from the flexible feature learning. However, the ROLO and IRSTD-GAN require more datasets, limiting their performance. The multiple-frame-based methods suffer less from the influence of pseudo-targets and achieve lower false alarm than the single-frame method. The traditional methods depend heavily on fixed parameter kernels. They sacrificed false alarm rates to ensure detection rates. Our method is more robust to moving targets and complex environments.
# The Impact of an Office Fire Combined with the Stack Effect in a Multi-Story Building
## 3 Results and Discussion
### Air Velocity Variation Inside the Building  
Given the selected ventilation condition, there was already a noticeable thermal draft (S1) inside the building, with maximum velocities recorded at points P1, P2, P3, P4, and P5 being 0.8, 0.5, 0.2, 1.5, and 0.4 m/s, respectively. The onset of the fire (S2) caused an intensification of the thermal draft effect, leading to significant increases at points P1, P2, P3, P4, and P5, reaching maximum values of 2.5, 8, 2.8, 3.2, and 4 m/s, as shown in Figure 10.  
Figure 10: Velocity distribution in slice Y = 5.75 m at time T = 239.6 s.  
Figure 9: Temperature distribution in slice Y = 5.75 m at time T = 238.0 s.  
The time T = 239.6 s was chosen because it best captures the movement of gasses generated and displaced by the fire. Additionally, from monitoring planes that section the building longitudinally (Y = 5.75 m) and transversely (X = 7.5 m), a considerable increase in velocity values was observed in the stairwell compared to the rest of the rooms (Figure 11). The building's geometry caused recorded velocities to exceed 7 m/s in some exterior areas, generating turbulent currents.  
In Figure 12, the temporal evolution of velocities recorded at points P2, P3, and P4 in the stairwell are graphically represented. The velocity values in Scenario S2 (solid line) were considerably higher compared to those in Scenario S1 (dashed line). A significant difference was highlighted between the values recorded at point P2 (ground floor), located closer to the fire source, compared to points P3 (fifth floor) and P4 (ninth floor). This substantial increase was attributed to the proximity of the monitoring point to the fire source, as well as the draft induced by the open position of the door and window on the ground floor.  
Additionally, a strong oscillation in the velocity values was observed, compared to the temperature variation over time, at each of these control points (Figure 9). This oscillation is due to the highly turbulent air movement in these zones. A strong oscillation in the velocity values was also observed, compared to the temperature variation over time at each of these control points (Figure 9).  
Figure 11: Velocity distribution in slice Y = 5.75 m and X = 7.5 m at time T = 239.6 s.  
Regarding occupant safety, although the velocity values increased up to 16 times in some cases (P2), they do not pose a threat to human life, with the maximum absolute value being only 29.9 km/h (8.3 m/s). The study [50] showed that a speed between 20 and 30 m/s poses a risk to human life.
## 2 Materials and Methods
### TMI 1B11 V8 (Gpm05)
#### 2.1.2 Importance of the Stability Assessment  
Since the TRMM constellation have provided almost two decades of calibrated microwave radiances, these observations have great potential for use in climate studies. However, this requires a very strict inter-satellite radiometric calibration between sensors to assure consistent environmental parameter retrievals. Thus, the GPM inter-satellite radiometric calibration working group (XCAL) was formed and charged with the responsibility of developing robust techniques to preform and maintain the radiometric calibration of constellation radiometer instruments, relative to one another. Because TRMM's orbit was non-sun-synchronous, it had frequent near-simultaneous collocations with the other radiometers in polar orbits, therefore it was selected as the radiometric transfer standard for the constellation. Thus, by having a standard calibration for all these instruments, the global brightness temperature dataset was unified [14].  
Moreover, for TMI to serve as the radiometric transfer standard, it was necessary to carefully assess the calibration accuracy and stability of this instrument. Over the TRMM mission, there have  
Figure 7: TMI’s Main Reflector physical temperature (yaw 0\({}^{\circ}\) and post-boost altitude = 403 km).  
been several versions of the TMI brightness temperature products 1B11, which have been evolutionary developments that included a number of ad-hoc corrections. Therefore, the new version, 1B11 V8 produced from this final \"legacy\" processing of the TMI brightness temperatures needs to be carefully inspected before being released to the science community for further research.
# Comparison of Temperature Dependent Carrier Transport in FinFET and Gate-All-Around Nanowire FET
## 3 Results  
The drain current (\(I_{DS}\)) at different temperature conditions are shown for NMOS GAA NW-FET (Figure 3a) and FinFET (Figure 3b). A slightly higher \(I_{DS}\) is observed for GAA NW-FET (\(\sim\)6%, at 25 \({}^{\circ}\)C) compared to FinFET. For both GAA NW-FET and FinFET, the \(I_{DS}\) decreased as the temperature increased at the same overdrive voltage (\(V_{OV}\) = \(V_{GS}-V_{TH}\)), which shows the phonon scattering limited mobility behavior [19].  
It can also be seen that V\({}_{\text{TH}}\) down-shifted from 0.56 to 0.52 V for GAA NW-FET and 0.30 to 0.27 V for FinFET as the measurement temperature increases. In Figure 3c, compared to the transconductance (\(g_{m}\)) at different temperatures, FinFET and GAA NW-FET show the disparate degree of reduction at the same \(V_{OV}\). For example, as the measurement of the temperature increases, the \(g_{m}\) of FinFET and GAA NW-FET decreases 0.4% (from 127.1 to 126.6 \(\upmu\)S/\(\upmu\)m) and 12.8% (from 188.2 to 164.1 \(\upmu\)S/\(\upmu\)m) at \(V_{OV}\) = 0.5 V, respectively.  
Figure 3: Measured \(I_{DS}\)-\(V_{GS}\) curves (linear region, \(V_{DS}\) = 50 mV) under various temperature conditions from 25 to 125 \({}^{\circ}\)C for (**a**) GAA NW-FET. (**b**) FinFET with \(I_{\text{G}}\) = 30 nm. Insets: \(I_{DS}\)-\(V_{OV}\) (\(V_{OV}\) = \(V_{GS}-V_{TH}\)) curves. (**c**) \(g_{m}\)-\(V_{GS}\) curves for GAA NW-FET and FinFET for 25 and 125 \({}^{\circ}\)C.  
Figure 2: Fabrication process flow in FinFET and GAA NW-FET.  
The temperature dependences of \(V_{TH}\) for GAA NW-FET and FinFET are plotted using the maximum transconductance method [20] in Figure 4a.  
The \(V_{TH}\) as a function of temperature can be explained by the following equation [13]:  
\[\frac{dV_{TH}}{dT} = \frac{d\varnothing_{F}}{dT}\Bigg{[}k\sqrt{\frac{q\varepsilon_{si}N _{eff}}{\varnothing_{F}C_{ox}}^{2}}+2+\frac{C_{it}}{C_{ox}}\Bigg{]} \tag{1}\]  
where \(\varnothing_{F}\) [eV] is the Fermi potential, \(q\) [C] the electron charge, \(\varepsilon_{si}\) [F/cm] the silicon permittivity, \(N_{eff}\) [cm\({}^{-3}\)] the effective doping level, \(C_{ox}\) [F/cm\({}^{2}\)] the oxide capacitance, and \(C_{it}\) [F/cm\({}^{2}\)] the interface trap density, respectively, in Equation (1). The fitting parameter \(k\) is 1 for partially depleted devices and closed to 0 for fully depleted channels. The \(V_{TH}\) is predicted to decrease at a high temperature due to the reduction of \(\varnothing_{F}\)[21]. The \(\varnothing_{F}\) decreased by excited carriers from the valence band to conduction band when the temperature rises. In this study, a similar temperature dependent \(V_{TH}\) is observed between GAA NW-FET (\(dV_{TH}/dT\) = \(-\)0.44 mV/\({}^{o}\)C) and FinFET (\(dV_{TH}/dT\) = \(-\)0.43 mV/\({}^{o}\)C). In the case of bulk planar NMOS, \(dV_{TH}/dT\) was approximately \(-\)0.7 mV/\({}^{o}\)C [22]. For the GAA NW-FET and FinFET, the lowering of \(dV_{TH}/dT\) is attributed to the fully depleted channel with the thinning of the channel.
## 4 Comparisons and Results
### Simulation Comparison of GRU, GWO-GRU, and GWO-LSTM Models  
This study presents a new approach for optimizing the hyperparameters of the GRU model and the LSTM model using a grey wolf optimizer. A dataset comprising 900 preprocessed DO datasets was utilized to train the GRU, GWO-GRU, and GWO-LSTM models separately. The prediction results are visualized in Figure 13, with the original data curve shown in black, the prediction results and absolute errors of the GRU model in blue, the prediction results and absolute errors of the GWO-GRU model in purple, and the prediction results and absolute errors of the GWO-LSTM model in green.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **Training Duration** \\ \hline LSTM & 0.49517 & 4.3764\% & 65.23 s \\ GRU & 0.40713 & 3.4169\% & 36.35 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation indicators for LSTM and GRU.  
Figure 12: (**a**) LSTM and GRU prediction results, and (**b**) LSTM and GRU absolute errors.  
Figure 13: (**a**) GRU and GWO-GRU and GWO-LSTM prediction results, and (**b**) GRU and GWO-GRU and GWO-LSTM prediction absolute errors.  
By employing GWO to finetune the GRU and LSTM model parameters, including the number of hidden layer units, the initial learning rate, and the number of iterations, the best parameter set [101, 0.005426, 392] was identified after 20 iterations. Specifically, the optimal number of hidden layer units was 101, the most effective initial learning rate was 0.005426, and the ideal number of iterations was 392. The best parameter set [98, 0.008426, 389] for LSTM was identified after 20 iterations. Implementing these parameters in the GRU model and the LSTM model led to a substantial enhancement in model performance. The evaluation indicators presented in Table 5 compared the performance of the GRU model with that of the GWO-GRU model and the GWO-LSTM model. The GWO-GRU model demonstrated superior results, with a 22.2% decrease in RMSE and a 22.3% decrease in MAPE compared to the standalone GRU model. The RMSE and MAPE of the GWO-LSTM model in Table 5 decreased by 16.5% and 12.6%, respectively, compared with the LSTM model in Table 4. These findings underscore the efficacy of leveraging the GWO algorithm for parameter optimization in enhancing the predictive accuracy of the GRU model and the LSTM model. In the course of this experiment, it was found that when using the GWO to adaptively adjust the hyperparameters of the GRU model, the modeling time of the GWO-GRU model increased significantly. The reason for this shortcoming is that when using the grey wolf optimization algorithm to iterate, in order to improve the prediction performance of the model and obtain more accurate prediction results, the number of iterations will affect the modeling speed of the model.
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 2 Materials and Methods
### Methods  
#### 2.2.1 Data Representation  
Optical remote-sensing images contain spatial and temporal information about the surface of Earth [37], and are composed of pixels marked with row and column numbers. NDVI is a widely used vegetation index to monitor and project conditions of vegetation cover [38] that can be calculated from optical remote-sensing images as follows [39]  
\[\mathrm{NDVI}=\frac{\rho_{\mathrm{Nir}}-\rho_{\mathrm{Red}}}{\rho_{\mathrm{Nir} }+\rho_{\mathrm{Red}}}, \tag{1}\]  
where \(\rho_{\mathrm{Nir}}\) and \(\rho_{\mathrm{Red}}\) represent the near-infrared and red bands of optical remote-sensing images, respectively.  
The value of NDVI is between \(-1\) and \(1\); a negative value indicates that the ground is covered with water, snow, etc.; \(0\) indicates that the ground has rocks or bare ground etc.; and a positive value indicates ground with vegetation. The higher the value is, the greater the vegetation cover. The NDVI of a vegetated area tends toward positive values, whereas  
\begin{table}
\begin{tabular}{c c c c} \hline
**Datasets** & **Acquired Date** & **Platform** & **Spatial Resolution** \\ \hline
4 March 1973 & Landsat 1 & 60 m \(\times\) 60 m \\
22 January 1974 & Landsat 1 & 60 m \(\times\) 60 m \\
27 December 1978 & Landsat 3 & 60 m \(\times\) 60 m \\  
arch 2004 & Landsat 5 & 30 m \(\times\) 30 m \\
7 February 2005 & Landsat 5 & 30 m \(\times\) 30 m \\
14 March 2006 & Landsat 5 & 30 m \(\times\) 30 m \\
2 April 2007 & Landsat 5 & 30 m \(\times\) 30 m \\
16 December 2008 & Landsat 5 & 30 m \(\times\) 30 m \\
2 February 2009 & Landsat 5 & 30 m \(\times\) 30 m \\
21 February 2010 & Landsat 5 & 30 m \(\times\) 30 m \\
8 February 2011 & Landsat 5 & 30 m \(\times\) 30 m \\
14 December 2013 & Landsat 8 & 30 m \(\times\) 30 m \\
4 March 2014 & Landsat 8 & 30 m \(\times\) 30 m \\
20 December 2015 & Landsat 8 & 30 m \(\times\) 30 m \\
6 February 2016 & Landsat 8 & 30 m \(\times\) 30 m \\
9 December 2017 & Landsat 8 & 30 m \(\times\) 30 m \\
16 November 2018 & Landsat 8 & 30 m \(\times\) 30 m \\ \hline \end{tabular}
\end{table}
Table 1: Optical remote-sensing images of study area from 1973 to 2020.  
water and urban areas are represented by near-zero or negative values [3]. Thus, optical remote-sensing images can be represented as a multidimensional array with NDVI.
## 3 Results
### Rheology of the Selected Creams and Gels
The addition of 1% nNAG or 1% NAG to G3 increased both the minimum and maximum values compared to the baseline values, whereas in G1, it increased the minimum viscosity and only slightly changed the maximum viscosity (Table 2). The determination of the yield value (Yv) is a method that predicts flow behavior. Under our experimental conditions, the inclusion of 1% NAG or 1% nNAG in the eudermic cream (C1) decreased the Yv by approximately 75% with respect to the baseline. On the other hand, 1% nNAG's inclusion in the lipophilic cream (C3) greatly reduced Yv compared to 1% NAG. Regarding the hydrophilic gel (G3), the inclusion of both 1% NAG and 1% nNAG increased Yv, with 1% NAG having a greater effect compared to nNAG. The inclusion of 1% nNAG in the lipophilic gel (G1) decreased Yv by about 35% compared to G1 alone and G1 plus 1% NAG (Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emulsion** & **Minimum Viscosity** & **Spindle/Speed Combination** & **Maximum Viscosity** & **Spindle/Speed Combination (rpm)** \\ \hline C1 & 5136 & \#5/100 & 266,666.7 & \#4/0.6 \\ \hline C1 + 1\% NAG & 5056 & \#4/100 & 302,720.0 & \#6/0.3 \\ \hline C1 + 1\% nNAG & 4416 & \#5/100 & 244,266.7 & \#6/0.3 \\ \hline C3 & 5984 & \#5/100 & 182,400 & \#6/0.3 \\ \hline C3 + 1\% NAG & 7173 & \#5/100 & 544,000 & \#4/0.3 \\ \hline C3 + 1\% nNAG & 3184 & \#5/100 & 336,000 & \#6/0.3 \\ \hline G3 & 8176 & \#4/100 & 848,000 & \#5/0.3 \\ \hline G3 + 1\% NAG & 21,960 & \#4/60 & 1,650,667 & \#5/0.3 \\ \hline G3 + 1\% nNAG & 13,248 & \#4/100 & 1,068,800 & \#5/0.5 \\ \hline G1 & 2040 & \#2/100 & 3,061,333 & \#4/0.3 \\ \hline G1 + 1\% NAG & 19,808 & \#3/100 & 3,040,000 & \#2/0.3 \\ \hline G1 + 1\% nNAG & 17,600 & \#3/100 & 2,800,000 & \#2/0.3 \\ \hline \hline \end{tabular} The minimum viscosity was obtained using the largest spindle at the highest speed. The maximum viscosity was obtained using the smallest spindle at the slowest speed. #number/RPM represents the spindle/speed combination resulting in the minimum or maximum.  
\end{table} 
Table 2: Viscosity and spindle/speed combinations at the slowest and highest speeds. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Va** & **Vb** & **Yv** \\ \hline C1 & 13,776 & 5136 & 86.4 \\ \hline C1 + 1\% NAG & 7320 & 4864 & 24.56 \\ \hline C1 + 1\% nNAG & 6933 & 4416 & 25.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determination of the yield value.