esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Examples
### High Mach Number Astrophysical Jets  
This numerical experiment simulates a two-dimensional high Mach number astrophysical jet without radiation cooling [52]. The simulated Mach number is 80, and the computational domain spans from [0, 2] in x and [\(-0.5\), 0.5] in y directions. The environmental gas parameters are \((\rho,u,v,p)=(5,0,0,0.4127)\). At the left boundary, when \(-0.05\leq y\leq 0.05\), then \((\rho,u,v,p)=(5,30,0,0.4127)\), or else, \((\rho,u,v,p)=(5,0,0,0.4127)\). The remaining boundaries are outlet boundary conditions. A uniform grid is used with a  
Figure 17: KH instability problem: Density contours with 40 equally spaced isolines ranging from 0.866 to 2.125 were obtained using the LF, ROE, and URLBFS schemes. The color gradient represents the corresponding density values, with blue indicating the minimum density value. The sim-ulation was conducted at t = 0.5 with a resolution of 400 \(\times\) 400. Panels (**a**–**c**) display results using the WENO5 reconstruction method, while panels (**d**–**f**) show corresponding results with the WENO7 method. Each scheme’s ability to capture the development of vortices, maintain solution smoothness, and suppress numerical artifacts is compared.  
grid resolution of 448\(\times\)224. The total simulation time is \(t=0.07\), with a fixed time step size of \(DT=1\times 10^{-5}\).  
Figures 18 and 19 present the density and pressure distributions at the final simulation time, computed using the ROE and URLBFS schemes. As seen in the figures, the ROE scheme, due to its high numerical dissipation, struggles to accurately capture fine structures within the flow field. This leads to oscillations in the results and the appearance of non-physical solutions, which is especially noticeable with the WENO5 reconstruction scheme. While the WENO7 reconstruction method improves stability and accuracy, it still fails to capture the shock wave with sufficient precision. In contrast, the URLBFS scheme demonstrates superior performance, offering both accurate reproduction of the astrophysical jet phenomenon and enhanced resolution of the flow field, making it an ideal choice for simulating complex fluid dynamics with high fidelity.  
Figure 19: High Mach number astrophysical jet problem: Pressure contours from ROE and URLBFS schemes with a grid resolution of 448 \(\times\) 224. The results are shown at t = 0.2. Panels (**a,b**) display results using the WENO5 and WENO7 reconstruction methods combined with the ROE scheme, while panels (**c,d**) show results using WENO5 and WENO7 with the URLBFS scheme.  
Figure 18: High Mach number astrophysical jet problem: Density contours from ROE and URLBFS schemes with a grid resolution of 448 \(\times\) 224. The results are shown at t = 0.2, capturing the flow dynamics in a highly compressible, high\(-\)velocity jet. Panels (**a,b**) display results using the WENO5 and WENO7 reconstruction methods combined with the ROE scheme, while panels (**c,d**) show results using WENO5 and WENO7 with the URLBFS scheme.
## 3 Results and Discussion
### Design Review with Back-Analysis  
Instrumental observations revealing smaller horizontal displacements indicated that the geotechnical parameters used in the design phase were conservatively chosen. As a result, numerical analyses predicted higher displacements than those measured in the field. To improve the accuracy of the model and ensure consistency with field data, the geotechnical parameters were reassessed. Initially, due to limitations in representing the non-linear behavior of rock masses, the Hoek-Brown (HB) constitutive model was adopted. The HB model is more preferred and validated for application to rock masses [29], so we continued this study using the HB model. A parametric analysis was conducted to determine the most influential parameters. Since the effective parameter has a direct relationship with geomechanical classification, classification values were re-evaluated. Based on the results obtained, geometric parameter analyses were carried out to optimize the design for improved efficiency and cost effectiveness.
###### Abstract
The proposed approach differs from existing model-based optimization methods for HEMSs in the following ways: (1) a model-free fuzzy Q-learning method is used to schedule energy for a smart home appliance, PV power generation, SB, and a V2H unit based on time-of-use (ToU) and real-time pricing (RTP) tariffs; and (2) prediction of appliance consumption, PV generation, prices, EV availability, and SOC using appropriate DL algorithm aids the proposed fuzzy Q-learning algorithm. Combined with the DL models, the suggested RL-HCPV algorithm lowers customer expenditure while maintaining the desired appliance operation. The simulations depict a single home with PV solar panels, SB, smart appliances, and a V2H device under ToU and RTP tariffs. The simulation results demonstrate that the proposed DL and RL models can minimize the consumer's power costs while maintaining user comfort. Furthermore, the RL-HCPV algorithm's performance with and without the V2H unit is analysed, and it is confirmed that integrating the V2H unit results in considerable energy savings under the different penalty constraints of the reward function. The remainder of this paper is organized as follows: Section 2 defines the system modelling of the PV generation, appliance energy consumption, V2H units, EV availability, and prices, Section 3 presents the proposed RL-HCPV algorithm formulation using various DL algorithms and fuzzy Q-learning methods, The simulation results for the proposed RL-HCPV model are provided in Section 4. The conclusions are provided in Section 5.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes  
The radio resource management scheme of base stations affects the performance of applying dual connectivity in HetNets. In this section, we propose three novel schemes in resource scheduling, cell selection, and enhanced intercell interference coordination, which are named Max--min Threshold Scheduler (MTS), HetNet Congestion Indicator (HCI), and Q-learning Dynamic Almost Blank Subframe (QD-ABS), respectively. The details for these schemes are outlined in the following.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 6 Simulation of 5G RAN Deployment and Operation Approaches
### Types of 5G RAN Deployment Approaches
#### 6.1.2 Approach 2--Maximal Number of Small BSs in Sleep Mode  
This deployment approach is characterized by the possibility of having a maximal number of small BSs in sleep mode during RAN operation (Table 3). In this approach, all small BSs needed for accommodating expected DVs in the upcoming period of 10 years are initially installed in the RAN by MNO. This approach exploits the small BSs sleep mode strategy, which enables the preservation of energy through putting small BSs in a sleep  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Approach** & **Approach** & & **Approach** & **Approach** \\
**1—Variable** & **2—Maximal** & **Approach 3—All** & **4—Variable** & **5—Variable** \\
**Number of Small** & **Number of Small** & **BSs Constantly in** & **Number of Small** & **Number of All** \\
**BSs in Active** & **BSs in Sleep** & **Active Mode** & **BSs in Tx Power** & **BSs in Tx Power** \\
**Mode** & **Mode** & & **Scaling Mode** & **Scaling Mode** \\ \hline \hline \multicolumn{5}{c}{Number of macro} \\
**BSs in Tx power** & N/A & N/A & N/A & N/A & Based on DV \\
**scaling mode** & & & & & changes \\ \hline \multicolumn{5}{c}{Number of small} \\
**BSs in Tx power** & N/A & N/A & N/A & \
\begin{tabular}{c} Based on DV \\ changes \\ \end{tabular} &
\begin{tabular}{c} Based on DV \\ changes \\ \end{tabular} \\ \hline \multicolumn{5}{c}{Number of small} \\
**BSs in active mode** & \begin{tabular}{c} Based on DV \\ changes \\ \end{tabular} & \
\begin{tabular}{c} Based on DV \\ changes \\ \end{tabular} &
\begin{tabular}{c} Based on DV \\ changes \\ \end{tabular} \\ \hline \multicolumn{5}{c}{Number of small} \\
**BSs in sleep mode** & \begin{tabular}{c} N/A \\ \end{tabular} & \begin{tabular}{c} Maximal \\ for satisfying full \\ DV demand \\ \end{tabular} & \begin{tabular}{c} Maximal \\ for satisfying full \\ requirements \\ \end{tabular} & \
\begin{tabular}{c} Changes according \\ to DV \\ requirements \\ \end{tabular} &
\begin{tabular}{c} Changes according \\ to DV \\ requirements \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analyzed network deployment and operation approach.  
operation mode during the periods lacking the data needed to be transferred by those BSs. When the demand for capacity exceeds the available capacity of the currently active macro and small BSs, the required number of small BS(s) that are in sleep mode are activated. Moreover, this approach does not assume the implementation of any RRM method for improving EE of macro BS(s).
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
## 3 Semantic Segmentation Algorithm Based on Track Fasteners--Mask FRCN
### Channel-Wise Distillation Based in Mask-RCNN  
The distillation process employing the Channel-Wise Distillation (CWD) algorithm, which emphasizes the neck layer of Mask R-CNN integrated with the Feature Pyramid Network (FPN), consists of multiple stages. Initially, a ResNet101 model is pre-trained to serve as the teacher model, whereas a student model with a ResNet18 architecture is established. Subsequently, the neck layer, which utilizes FPN in Mask R-CNN, is designated as the target layer for distillation. FPN combines high-level and low-level features from various layers of the network, enhancing detection and segmentation capabilities.  
Channel-Wise Distillation (CWD) is a channel-based distillation approach that aims to minimize the distance between the teacher and student models' channel feature maps. The CWD loss function is expressed as follows:  
\[L_{\text{CWD}}=\frac{1}{N}\sum_{i=1}^{N}\|F_{i}^{i}-F_{\text{s}}^{i}\|_{2}^{2}. \tag{4}\]  
where \(L_{\text{CWD}}\) represents the Channel-Wise Distillation loss, \(N\) denotes the number of channel pairs, and \(F_{\text{s}}^{i}\) and \(F_{\text{s}}^{i}\) correspond to the i-th channel feature map of the teacher and student models.  
Throughout the training process, channel feature maps are extracted from the FPN layers of both the teacher and student models. For each corresponding pair of teacher and student channel feature maps, the L2 distance between them is calculated. The CWD loss function is subsequently defined as the average of the L2 distances across all channel pairs.  
The student model is trained using backpropagation, striving to minimize the CWD loss while concurrently optimizing the primary task loss (e.g., object detection and instance segmentation loss). This procedure iterates until convergence is reached or a predefined stopping criterion is fulfilled.  
By adopting the CWD knowledge distillation algorithm to replace ResNet101 with ResNet18 in Mask R-CNN, a degree of performance improvement can be achieved. The ResNet18 architecture is more lightweight than ResNet101, thereby reducing the model's computational complexity and memory footprint, and accelerating inference speed. Although the reduction in model size may potentially impact overall accuracy, the CWD algorithm ensures that the student model retains essential knowledge from the teacher model, mitigating performance loss.
## 4 Simulation Results  
In this section, we set two transmitters and two receivers as an example to investigate the effectiveness of the algorithm proposed above. Without loss of generality, the locations of the LEDs are randomly placed. The size of the room is 5 m \(\times\) 5 m \(\times\) 3 m. For simplicity, we assume the variance of the noise corresponding with the system is \(\sigma^{2}=1\), and the channel gain can be absorbed by normalization. The locations of the PDs are randomly chosen from an array satisfied with a uniform distribution in the floor to guarantee the generality. The number of RIS elements is a variable in our simulations, and meanwhile, it is much larger than the number of transmitters to guarantee that each transmitter is able to choose one RIS element to construct the NLoS channel. All the parameters are summarized in Table 1.
## 1 Introduction
[16] constructed a physical similarity model to investigate the fracture characteristics of hard main roofs in large mining height fully mechanized top-coal caving faces, revealing three fracture positions and two structural patterns as the working face advances. Through a mechanical model, they analyzed the superimposed effects of large and small periodic mine pressure induced by different fracture modes, and proposed hydraulic fracturing technology to pre-split the roof, thereby weakening the dynamic load caused by composite structure instability. This study provides a basis for the stability control of roadways in such mining conditions. Certainly, the stability of gob-side entry roadways can also be considered for inversion prediction through real-time monitoring of hydraulic support loads and surrounding rock stress in the roadways [17]. [PERSON], [PERSON] et al. [18] analyzed the evolution law of the roof mine pressure in the fully mechanized top-coal caving face based on the zonal bearing mechanical model of the caving roof, proposed the calculation method for the evolution of the roof mine pressure in the large-mining-thickness fully mechanized top-coal cavity face and the criterion for support crushing, and predicted the support crushing disasters in the fully mechanized top-coal cavity face. For geological conditions in different countries, such as the Guangning Coal Field in Vietnam [19] and the Karaganda Coal Field in Kazakhstan [20], this method can also be used to conduct a preliminary assessment of the application feasibility when implementing gob-side entry retaining. Reference [21] indicates that the total thickness extraction of thick coal seams in a single lift must be adapted to seam conditions (e.g., thickness, rock mass behavior, and stress environment). It was found that a compressive strength of 25 MPa in coal serves as a critical threshold for the successful application of longwall top-coal cavity (LTCC), providing valuable insights for similar mining engineering practices. However, the fracture characteristics of the roof in three-soft coal seams differ from those under conventional geological conditions. To address the feasibility of implementing gob-side entry retaining projects in shallow-buried three-soft coal seam fully mechanized mining faces (SB-TSCS FMMFs), this study establishes an elastic foundation beam model for SB-TSCS FMMFs through a combination of theoretical analysis and particle flow numerical simulation. It reveals the quantitative relationships between the main roof fracture position and coal seam/roof parameters, investigates the movement characteristics of roof strata under different parameter conditions, and proposes parameter thresholds for gob-side entry retaining suitable for three-soft coal seams. The goal is to enable feasibility assessment of gob-side entry retaining in SB-TSCS FMMFs based on roof fracture laws. Additionally, by incorporating the characteristics of shallow-buried coal seams in China and supplementing comparative analyses under similar international geological conditions, the universality of the research is enhanced.
# Review and New Aspects in Combining Multipoint Moulding and Additive Manufacturing
## 2 Vacuum Assisted Multipoint Moulding
### Definition and Historical Development
[23] follow the classical idea of multipoint moulding by mounting rounded pinheads on studdings. Moreover, they combine this classical approach with a silicone made interpolation layer and mount it inside a vacuum chamber. This allows the manufacturing of concave parts but there is a risk of transferring dimpling from the interpolation layer to the component. As it can be seen in Figure 4a,b, the thinner interpolation layers cause a clearly markable dimpling at the top side. The thicker interpolation layers in Figure 4c,d eliminate the dimpling, but also worsen the shape reproduction. Overall, the thickness of the interpolation layer is one major effect in the shape reproduction, but there also several other influences. Therefore, predicting the behaviour of the interpolation layer and the corresponding control of the system remains an issue to be dealt with [23]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Year Published** & **Main Contribution** \\ \hline Bayerische & & \\ Forschungssifitung [15], & & forming of plastic sheets with wide apart pins and a thick interpolation layer without vacuum for car \\ [PERSON] [18], [PERSON] et al. & 2011–2014 & prototype windows \\ [19; 20] & & forming of thermoplastic resin sheets without \\ [PERSON] et al. [21] & 2012 & interpolation layer \\ \hline [PERSON] [18] & 2014 & dimpling evaluation method for transparent multipoint \\  & & moulded plastic sheets \\ \hline [PERSON] et al. [22] & 2014 & variable pin distance for CFRP parts without \\  & & vacuum support \\ \hline [PERSON] et al. [23] & 2016 & densely packed pin field with silicon made interpolation \\  & & layer and two vacuum circuits for CFRP parts, research \\  & & of the influence of the thickness of the interpolation \\  & & layer on dimpling \\ \hline [PERSON] et al. [24; 25] & 2018 & system for combination of small cuboids with magnets \\  & & to larger objects as a faster alternative to additive \\  & & manufacturing, the object creation is very similar to \\  & & multipoint moulding \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)  
Figure 3: Principle of vacuum-assisted multipoint moulding (VAMM), shown in a smaller test setup. The setup from top to bottom: (**a**) interpolation layer, (**b**) pin array, (**c**) threaded rods, (**d**) vacuum container, (**e**) base plate and (**f**) actuator couplings [23]. Vacuum-assisted multipoint moulding according to [PERSON] et al. [23] constitutes the basis for the further analysis as being the actual state of technology.
## 5 Results and Discussions
### Daytime Processing Heat and Total Power Consumption
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Operation** & \begin{tabular}{c} **Power Consumption** \\ **[MWh]** \\ \end{tabular} & \begin{tabular}{c} **COP** \\ **[-]** \\ \end{tabular} & \
\begin{tabular}{c} **EER** \\ **[-]** \\ \end{tabular} &
\begin{tabular}{c} **SEER** \\ **[-]** \\ \end{tabular} \\ \hline Case WTES-C1-7 & Cooling & 71.5 & 2.9 & 10.0 & 11.1 \\ Case WTES-H1-45 & Heating & 43.7 & 2.4 & 8.3 & 8.8 \\ \hline Case ATES-C1-7 & Cooling & 86.8 & 2.4 & 8.2 & 8.7 \\ Case ATES-H1-45 & Heating & 89.1 & 1.2 & 4.1 & 3.9 \\ \hline Case ATES-C2-7 & Cooling & 72.8 & 2.9 & 9.8 & 10.8 \\ Case ATES-H2-30 & Heating & 42.3 & 2.5 & 8.6 & 9.2 \\ \hline Case ATES-C2-14 & Cooling & 57.3 & 3.7 & 12.4 & 15.3 \\ Case ATES-H2-25 & Heating & 37.7 & 2.8 & 9.6 & 10.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Seasonal power consumption and energy efficiency. The COP for cooling operation was 2.4-3.7, with the ATES-C1-7 was the lowest at COP 2.4 and ATES-C2-14 was the highest at COP 3.7. The COP for heating operation is 1.2-2.8, with the ATES-H1-45 was the lowest at COP1.2, and in other cases, it was about the same at COP2.4-2.8. As mentioned in Chapter 5.1, the ATES system has difficulty maintaining heat for long periods. Therefore, it was found that the operation using the pre-cooling/pre-heating coil performs an efficient operation. In terms of energy efficiency, the heating operation ATES-H1-45 had a low energy efficiency of 4.1 for EER and 3.9 for SEER. In other cases, the energy efficiency was 8.2-12.4 for EER and 8.7-15.3 for SEER. The COP of the ATES system operation in this study is about two lower than that of the ATES system coupled with a chiller for cooling operation in [PERSON] et al. [21]. The pre-cooling/pre-heating operations considered in this study have a similar SEER compared to the work of [PERSON] et al. [22], [PERSON] et al. [24]. However, it is about 10 lower than the summer SEER in [PERSON] et al. [22]. In the [PERSON] et al. [22] study, the aquifer  
Figure 15: Power consumption and energy efficiency. (**a**) Summer operation. (**b**) Winter operation. temperature is 10 \({}^{\circ}\)C lower than the aquifer temperature in this study, which is considered to be specialized for cooling operation. \[COP=\frac{Output\ power}{Input\ power} \tag{4}\]  
\[EER=3.413\times COP \tag{5}\]  
\[SEER=\frac{Total\ seasonal\ cooling\ output}{Total\ electrical\ energy\ input} \tag{6}\]  
where \(COP\) is the coefficient of performance of heat pump [-], \(output\ power\) is the power drawn from the heat pump as cooling or heat [W], \(input\ power\) is the power supplied to the compressor [W], \(EER\) is an energy performance rating for cooling devices [-], \(SEER\) is the seasonal cooling performance [-], \(total\ seasonal\ cooling\ output\) [Wh], \(total\ electrical\ energy\ input\) [Wh].
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 5 Experimental Setup of Photodarkening
### Absorption Spectrum Measuring Device  
The absorption spectrum measuring device is for measuring the transmission spectrum before and after the sample fiber PD experiment, and subtracting the two spectra to obtain the PD absorption spectrum. Because it is not necessary to measure when the pump is working, the noise caused by the pump does not need to be considered, so the whole device is relatively simple [15; 17]. The absorption spectrum could also be measured by changing the device in the previous section [22].  
Figure 8: Experimental setup for measuring both PD rate and TB.
## 3 Active Control Design
### Numerical Simulations
Figure 8: Control vector **u** applied to the displacements and poles chart with \(\gamma=-0.1\) and \(\mu=0.7\). (**a**) Control vector. (**b**) Poles chart (\(+\) uncontrolled poles; \(\circ\) controlled poles). Figure 10: Control vector **u** applied to the displacements with \(\gamma=-0.1\) and \(\mu=0.7\). (**a**) Linear contribution. (**b**) Nonlinear contribution. Figure 9: Control vector **u** applied to the displacements with \(\gamma=-0.1\) and \(\mu=0.4\). (**a**) Linear contribution. (**b**) Nonlinear contribution. 3.2 Influence of the Control Gain and Sensitivity of the Controller to the Signal-To-Noise Ratio (SNR)  
The influence of the control gain \(\gamma\) on the response \(X_{1}\) is presented in Figure 11a for \(\mu=0.4\) and in Figure 11b for \(\mu=0.7\). These results show that the behavior of the system is not the same in the two configurations when \(\gamma\) increases from -0.1 to 1.5. For \(\mu=0.7\), the increase of \(\gamma\) induces a more faster damping. But for \(\mu=0.4\), the increase of \(\gamma\) seems to change the dynamics of the controlled system probably due to couplings between the different modes. The sensitivity of the controller to different SNR, from 30 dB to 15 dB, between the response \(X_{1}\) and the signal used by the controller is presented in Figure 12a for \(\mu=0.4\) and in Figure 12b for \(\mu=0.7\). The control remains effective in the two cases up to a minimum value equal to -15 dB but with a severe loss of efficiency. For a lower value of SNR, the control can become fully inefficient and the controlled system can differ. Moreover, the decease of performances is more pronounced for \(\mu=0.4\).