esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In double-shield TBM tunnel construction, grouting plays a vital role in consolidating the gravel backfill and maintaining the integrity of the segmental lining. To investigate the permeation behavior of grout within the pea gravel layer, a fluid dynamics model was developed in this study. The model directly simulates the flow of grout through the porous medium by solving the Navier-Stokes equations and employs the level set method to track the evolving interface between the grout and air phases. Unlike conventional continuum approaches, this model incorporates particle-scale heterogeneity, allowing for a more realistic analysis of grout infiltration through the non-uniform pore structures formed by gravel packing. Three different grouting port positions and two boundary conditions are considered in the simulation. The results indicate that under pressure boundary conditions, the grout flow rate increases rapidly in the initial stage, and then decreases and stabilizes, with the flow rate peak increasing as the grout port moves upward. Under velocity boundary conditions, the injection pressure grows slowly in the early stage but accelerates with time. Additionally, the rate of pressure change is faster when the grout port is located lower in the backfilling layer. Through theoretical analysis, the existing analytical formula was extended by introducing a gravitational correction term. When the grouting port is near the upper part of the tunnel, the analytical solution aligns well with the numerical simulation results, but as the grout port moves downward, the discrepancy between the two increases.  
double shield TBM; grouting permeability; two-phase flow; numerical simulation 2020
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 2 V2A Charging Mode  
Due to the inherent mobility of vehicles, the charging activities of EV customers are hard to predict. EV owners can choose to park their cars in private garages when they return home, in their company charging places during weekdays, or in commercial parking stations for traveling purposes, to procure energy to refill EV batteries. When EVs are connected to the power grid, the power grid provides a channel for griddable EVs to perform 'Grid-to-vehicle' (G2V) charging. With the discharging capability enabled in the near future, EVs are also envisaged to perform various V2A activities, including V2H, V2B, V2V, and V2G. An overview of the V2A paradigm is given in Figure 1. The demonstrated architecture consists of a power grid operator, smart home, smart building, charging station, AC transmission link, communication channel, and other basic power system elements. EVs are parked in different places and can be controlled to perform various discharging activities, in detail: V2H for home usage, V2B for building energy management, V2V among different EVs in EV charging station, and V2G arranged in different places.  
All the power consumption/supply units are supervised by the grid operator, which depends on the support of bidirectional communications. The details of the V2A charging mode are introduced below.
## 3 Results and Discussion  
### Characterization of Raw Materials for Energy Utilization  
Table 2 shows the most important parameters of the tested materials in terms of their energy generation potential. According to the assumptions and results of similar studies [15; 33; 34; 35; 36; 37; 38] the materials containing seeds with oils (Sea-buckthorn, grape vine pomace and grape vine seed cake) have the highest LHV. Almond seed coat also has a statistically similar high LHV, although it does not contain seeds. According to the results of the statistical comparison, materials such as fruit shells and stones achieve similar HHV. These materials have LHV comparable to wood pellets and higher than lump wood, and in this respect meet the ISO 17225-6 [39] standard (minimal 14.5 MJ\(\cdot\)kg\({}^{-1}\)). Interestingly, the almond seed coat material is very specific according to our results. While it has high ash content, it still achieves a very good O:C ratio, and a high LHV, which is unexpected, perhaps due to high contents of lignin. In addition, the calorific value will increase even more by pelletizing these materials. [PERSON] et al. [35] report an HHV for grape seeds at 18.28 MJ\(\cdot\)kg\({}^{-1}\) and for grape pomace at 18.76 MJ\(\cdot\)kg\({}^{-1}\), values very similar to those that we obtained. Our higher HHV for grape seed cake can be explained by the oil pressing process, during which excess moisture is removed. The value of grape pomace depends very strongly on the grape variety. Also, the reported ash content of 2.8% is consistent with our results. [PERSON] et al. [40] report an HHV for berry pomace of 21.36 MJ\(\cdot\)kg\({}^{-1}\) compared to our value of 18.8 MJ\(\cdot\)kg\({}^{-1}\). However, the ash content is again very similar at 3.73%. [PERSON] and [PERSON] [41] report a similar HHV value for peach stone (20.65 MJ\(\cdot\)kg\({}^{-1}\) compared to our 19.57 MJ\(\cdot\)kg\({}^{-1}\)) and a slightly higher ash content of 1.5%. [PERSON] et al. [42] report an HHV of 14.1 MJ\(\cdot\)kg\({}^{-1}\), which is, however, for the empty fruit bunch (EFB) of the date palm, whereas we analysed only date stone, as only the separated fruit is imported for further processing. The lower stone value is due to the higher moisture content and lower calorific value of the material. The waste material used also differs in terms of elemental composition. [PERSON] and [PERSON] [43] have made an extensive comparison of several fruit and nut materials in Turkey for energy purposes. Their findings fairly replicate our results in terms of HHV, or ash content. A minor difference can be found, e.g., in pistachio shells, where they report a higher value of 19.26 MJ\(\cdot\)kg\({}^{-1}\). The authors also confirmed that the oil contained in the seeds increases the calorific value.
# A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy
## 2 Materials and Methods
### The CIRS ATOM Anthropomorphic Phantoms  
The CIRS ATOM phantom (CIRS Inc., Norfolk, VA, USA) is an anthropomorphic phantom for radiation dose measurements, which can be used to assess the absorbed organ doses as well as the effective body dose. The phantom is made of tissue-equivalent epoxy resins and is composed of materials simulating the densities of average soft tissue, average bone tissue, cartilage, spinal cord, spinal disks, lungs, brain, and sinus. For the photon energy range of 40-150 keV, the linear attenuations of these simulated materials are within 3% of the actual attenuations of real tissue.  
This study adopted the adult female phantom (Model ATOM 702-G) for the experiment, which consisted of 38 sections (Section 1-38) with an individual thickness of 25 mm. Each section had several 14 mm diameter holes that could be inserted with tissue-equivalent plugs covering the densities of 21 internal organs. A radiation dosimeter could then be placed in these plugs for dose measurements.
# Design Study of a Large-Angle Optical Scanning System for MEMS LIDAR
###### Abstract  
MEMS-based LIDAR has the advantages of fast-scanning, simple structure, small volume, and light weight, which make it a hot topic for 3D imaging. In order to overcome the limited scanning angle of the current MEMS mirrors, a large angle MEMS-based optical scanning system was designed using telephoto structure. In this design an \(f\)-\(\theta\) lens group is used for flat field scanning and a symmetrical cemented lens group is used for angle expansion. The principle of this system is discussed in detail and an optical design is implemented by ZEMAX software. The designed system realized a scanning angle as large as 57\({}^{\circ}\) and is in excellent linear relation with the tilt angle of the MEMS mirror. At the target distance of 100 m, the maximum RMS radius of the light spot is only 7 cm. This optical scanning system shows a significant application potentiality for 3D imaging LIDAR.  
LIDAR; optical scanning; MEMS; optical design Article  
Design Study of a Large-Angle Optical Scanning System for MEMS LIDARAR  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], M. Design Study of a Large-Angle Optical Scanning System for MEMS LIDAR  
high-power consumption, large volume, cumbersome optical structure, and complicated assembly, resulting in low production efficiency and high cost [13]. On the contrary, MEMS technology directly integrates the micro-scanning mirror into the silicon chip, which is only a few millimeters in size. The mirror rotates through electrostatic, electromagnetic, electrothermal, piezoelectric or other driving methods, which greatly reduces the size of the system and increases production efficiency. The study of LIDAR based on MEMS mirrors is significant for the development of the new generation of miniaturized, low-cost imaging LIDAR [14; 15; 16].  
MEMS scanning technology is a quasi-solid form with high flexibility. It combines microelectronics, mechanical and optical technologies, and uses a drive circuit to achieve the desired field of view (FOV) and control the angle accurately [17; 18]. However, due to the high resonant frequency of MEMS mirror, which is above 200 Hz, the scanning angle is usually within \(\pm\)10\({}^{\circ}\), which cannot meet the large field of view detection requirements for imaging LIDAR. An auxiliary optical system must be used to extend its field of view [19; 20; 21]. So, researching auxiliary angle extending optical system design is quite significant for the MEMS scanning LIDAR. This work designed an optical scanning system for frequency modulated continuous wave (FMCW) LIDAR, which can achieve a large field of view of 57 degrees, and uniformly scanning with a 7 cm light spot for a target at 100 m.
# Failure in Confined Brazilian Tests on Sandstone
## 2 Materials and Methods
### Numerical Methods  
Numerical simulations replicating experimental testing were performed with the Hybrid Optimization Software Suite (HOSS) [21], developed at Los Alamos National Laboratory, to evaluate and improve the prediction of damage and failure. HOSS uses the combined finite-discrete element method (FDEM) to simulate fracture and damage processes in brittle materials including the separation of mesh elements to accurately model fracture networks [22; 23; 24]. Previous studies have proven the ability of HOSS to accurately model simulations of rock mechanic problems and fracture development processes [25; 26; 27; 28].  
A three dimensional, tetrahedron mesh (Figure 4a) was modeled to replicate the geometry of the experimental Brazilian disk and two loading plates by using Cubit, a modeling software developed at Sandia National Laboratories. The steel plate material blocks were treated as finite element (FEM) domains, and the sandstone disk material block was treated as an FDEM domain. FDEM modeling is more computationally intensive than the continuum-based FEM approach, so only the material block expected to fracture used the FDEM approach. By defining the steel plates as FEM domains, the computational processing power needed for each simulation is reduced. The mesh dimension of 3 mm was chosen by using a mesh convergence study performed during similar simulation testing by [PERSON] [15]. An additional simulation was performed to replicate a uniaxial compression test, shown in Figure 4b.  
Material properties used in the simulations are given in Tables 1 and 2. The steel material properties were obtained from [PERSON] [15]. For the sandstone, [PERSON]'s modulus and density were obtained from experimental testing. Poisson's ratio, and the friction coefficients were estimated based on existing literature on similar sandstones [10; 29; 30]. Shear strength was calibrated to the model based on preliminary iterative numerical trials. Similarly, the tensile strength measured in the lab was also calibrated to the model. After iterative numerical tests, the tensile strength value used in the simulations was 4.69 MPa, which is 78% of the tensile stress at failure in the unconfined Brazilian tests. The true tensile strength is typically between about 70 to 80% of the BTS value for a wide range of rocks [7].  
To simulate the displacement-controlled loading that was performed in the laboratory experiments, a constant velocity was applied to the bottom loading plate, while fixing the top loading plate. The velocity of the bottom plate was 0.3 mm/min and was chosen using a convergence study carried out for a similar simulation by [PERSON] [15]. A pressure boundary condition was placed on the exterior side sets of the sandstone disk, simulating the confining pressures of 2.76, 8.27, 13.79, 20.68, and 27.58 MPa for different tests.
# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 3 Framework and Methodology  
Due to the special nature of the underwater environment and the absorption, simple gesture recognition algorithm may not achieve good results. Therefore, we combine underwater image enhancement and human tracking, proposing an interactive underwater accompanying robot system based on gestures. The system block diagram is shown in Figure 1. First, we need to identify the underwater target person in the enhanced underwater image as the target for the next continuous tracking. Then, the gesture recognition algorithm is used to obtain the gestures made by the tracking target. Finally, according to the target coordinates and gesture recognition results, the processing results are transmitted to the STM32 control board by instruction. The results of each step can be output individually.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 2 Background
### Deep Neural Networks for Drowsiness Prediction  
Taking advantage of deep learning techniques, we apply deep neural networks with MobileNet-V2 and ResNet-50V2 for driver drowsiness detection. A detailed description of the network architectures and an analysis of their advantages is presented as follows.
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 4 Performance Evaluation
### Hyperparameter of Competing Methods  
Recall from Section 3.4 that we constructed predictive models using a decision tree, kNN, random forest, and linear SVM. Table 6 demonstrates a detailed description of each hyperparameter of competing methods. Various combinations of the parameters were applied in the experiments, and the most optimum set was selected. First, the prediction of the non-active state of machines with a decision tree was performed using the Gini impurity. Further, the kNN model was constructed using the Minkowski distance to compute the distance between an observation data point and its nearest neighbors with \(k=5\). Here, we tested various combinations of \(k\) and selected as \(k=5\) because it produced the highest accuracy. Moreover, utilizing the advantage of the bagging method, the random forest was developed using the Gini impurity. Finally, the radial basis function, a kernel SVM, was employed to map the data into a high-dimensional space. Here, the C parameter, which controls the penalty of the data points outside the margin, was set to 10. Similar to the k parameter of kNN, we tested various combinations of C parameter and selected as C = 10 as it produced the highest accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Parameters**} \\ \hline \multirow{8}{*}{**Decision Tree**} & class\_weight & None \\  & criterion & gini \\  & max\_depth & None \\  & max\_features & None \\  & max\_leaf\_nodes & None \\  & min\_impurity\_decrease & 0.0 \\  & min\_impurity\_split & None \\  & min\_samples\_leaf & 1 \\  & min\_samples\_split & 2 \\  & min\_weight\_fraction\_leaf & 0.0 \\  & present & False \\  & random\_state & None \\  & splitter & best \\ \hline \multirow{8}{*}{**KNN**} & algorithm & auto \\  & leaf\_size & 30 \\  & metric & minkowski \\  & metric\_params & None \\  & n\_jobs & None \\  & n\_neighbors & 5 \\  & P & 2 \\  & weights & uniform \\ \hline \multirow{8}{*}{**Random Forests**} & class\_weight & None \\  & criterion & gini \\  & max\_depth & None \\  & max\_features & None \\  & max\_leaf\_nodes & None \\  & min\_impurity\_decrease & 0.0 \\  & min\_impurity\_split & None \\  & min\_samples\_leaf & 1 \\  & min\_samples\_split & 2 \\  & min\_weight\_fraction\_leaf & 0.0 \\  & present & False \\  & random\_state & None \\  & splitter & best \\ \hline \hline \end{tabular}
\end{table}
Table 6: The hyperparameters of competing methods.
## 3 Analytical Methods  
### Electron Probe Micro-Analysis of Phlogopite  
Electron probe microanalysis (EPMA) of phlogopite grains was carried out on polished-thin sections. The major compositions of phlogopite were determined by Nanjing Hongchuang Geological Exploration Technology Service Co., Ltd. (Nanjing, China) using a JEOL JXA-iSP100 electron probe microanalyzer. The background counting time was half the peak at the high and low background positions. All of the data were corrected using the ZAF correction method for the matrix effect.  
The accelerating voltage, beam current and beam diameter of the phlogopite analyses were 15 kV, 10 nA and 10 \(\upmu\)m, respectively. The counting times per analysis were 10 s on peak positions and 5 s on two background positions located on either side of the peak position. The following standards were used: albite (Na\({}_{2}\)O), diopside (SiO\({}_{2}\)), almandine (Al\({}_{2}\)O\({}_{3}\)), olivine (MgO), tugtupite (Cl), orthoclase (K\({}_{2}\)O), diopside (CaO), nickel (NiO), hematite (FeO), rhodonite (MnO), chromium (Cr\({}_{2}\)O\({}_{3}\)), rutile (TiO\({}_{2}\)), and barite (BaO).
## 5 Implementation, Evaluation Details
### Metrics for Retinal Vessel Segmentation
#### 5.1.2 Recall  
Recall, also known as Sensitivity or True Positive Rate (TPR), measures the ability of the segmentation algorithm to correctly detect all positive instances (vessels) from the ground truth. It is calculated as the ratio of true positive predictions to the total number of positive instances. Recall is given by:  
\[\text{Recall}=\frac{TP}{TP+FN}\]  
where:  
* \(TP\) is the number of true positive predictions.
* \(FN\) is the number of false negative predictions.
## 2 Fundamentals
### D-Bar Algorithm  
The D-Bar algorithm can be summarized into four steps:  
1. Compute the DN map \(\Lambda_{\sigma}\) from the voltage measurements.
2. Compute the scattering transform within \(|k|\leq R_{1}\).
3. Solve the D-Bar equation, Equation (A4).
4. Recover the impedance values from Equation (A6).
###### Abstract  
The validation of numerical tools employed in the analysis of incidental transients in a fusion reactor is a topic of main concern. KIT is taking part in this task providing both experimental data and by performing numerical analysis in support of the main codes used for the safety analyses of the Helium Cooled Pebble Bed (HCPB) blanket concept. In recent years, an experimental campaign has been performed in the KIT-HELOKA facility to investigate the behavior of a First Wall Mock-Up (FWMU) under Loss Of Flow Accident (LOFA) conditions. The aim of the experimental campaign was twofold: to check the expected DEMO thermal-hydraulics conditions during normal and off-normal conditions and to provide robust data for code validation. The present work is part of these validation efforts, and it deals with the analysis of the LOFA experimental campaign with the system code MELCOR 1.8.6 for fusion. A best-estimate methodology has been used in support of this analysis to ease the distinction between user's assumptions and code limitations. The numerical analyses are here described together with their goals, achievements, and lesson learnt.  
The present paper is part of these validation studies performed at KIT. Some representative tests of the FWMU-LOFA experimental campaign have been reproduced with the MELCOR 1.8.6 for fusion. The code is adopted for the accident analyses for DEMO in-vessel components in EUROfusion [6]. The aim is to assess the capabilities of three aspects of the code:  
* The hydrodynamic model implemented in MELCOR;
* The capability to reproduce heat transfer phenomena among adjacent heat structures;
* The fluid-wall heat transfer.  
The approach to perform this study is the same adopted for the RELAP5-3D code [4]: mean tests are depicted from the experimental tests carried out, and a best-estimate methodology is used to ease the distinction between user's effects and code limitations. The adopted best-estimate methodology is the \"Best-Estimate Model Calibration and Prediction through Experimental Data Assimilation\" methodology [7]. This rigorous methodology is based on the information theory and the Bayes' theorem, and it requires an accurate model-oriented choice of parameters and responses to provide best-estimated evaluations of the selected parameters and responses. The theoretical details of this methodology can be found in [7], while the Best-estimate for SYstem Codes software (BeSYC) used to apply it is described in [8] (References [4,8] are published in the same Applied Sciences issue. The reader is advised to look at the two papers for further information concerning the LOFA experimental campaign and the software used to apply the best-estimate methodology).
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 5 Non-Orthogonal Multiple Access (NOMA)
### eMBB Rate by Treating Interference as Noise  
In the case of analog fronthaul, an enhanced strategy to jointly decode the eMBB signals under NOMA at the BBU is to treat the URLLC interfering transmissions as noise at the eMBB decoder, instead of discarding the corresponding minislot as in puncturing. The block diagram is the same as for puncturing and shown in Figure 8. Accordingly, based on the signals received over the fronthaul links, the BBU first detects the presence of URLLC transmission so as to properly select the decoding metric. Then, based on this knowledge, joint decoding is performed by TIN.  
**Lemma 4**.: _In the given C-RAN architecture with analog fronthaul links, for a given bandwidth amplification factor \(\eta\geq 1\), the eMBB user rate under NOMA by treating URLLC interference as noise yields_  
\[R_{B}=\frac{\mu}{M}\mathbb{E}_{\mathbf{A}}\left[log\left(det\left(\mathbf{I}+ P_{B}\mathbf{R}_{A,z_{\eta}}^{-1}\mathbf{H}_{\alpha\eta}\mathbf{H}_{\alpha\eta}^{T} \right)\right)\right], \tag{39}\]  
_where \(\mathbf{R}_{A,z_{\eta}}=\mathbf{R}_{z_{\eta}}+\beta^{2}P_{U}\left(\mathbf{A} \otimes\mathbf{H}_{c}^{\eta}\mathbf{H}_{c}^{\eta}\right)\) is the overall noise plus URLLC interference at the BBU; matrix \(\mathbf{A}\) is as in (5); and matrices \(\mathbf{R}_{z_{\eta}}\) and \(\mathbf{H}_{\alpha\eta}\) are the same as in Lemma 2._  
Proof.: see Appendix C.  
Differently from the two previous cases, in the case of NOMA under TIN, the noise covariance matrix \(\mathbf{R}_{A,z_{\eta}}\) needs to account also for the interfering URLLC transmissions, whose packet arrival probability is described by matrix \(\mathbf{A}\). The achievable rate is then computed by taking the average over the random matrix \(\mathbf{A}\). This average reflects the long-blocklength transmissions of the eMBB users.