esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Two-Phase Flow of Shotcrete in a Bent Pipe Based on a CFD-DEM Coupling Model
## 2 Governing Equations
### The Continuous Phase Model  
The gas flow is treated as a continuous phase, the equation for conservation of mass, or continuity equation, can be written as follows  
\[\
abla\cdot\left(\begin{array}{c}\overset{\rightarrow}{\underset{\
u}{ \rightarrow}}\\ \overset{\rightarrow}{\underset{\
u}{\rightarrow}}\end{array}\right)=S_{m} \tag{1}\]  
\[\rho\left(\frac{\partial}{\partial t}\left(\overset{\rightarrow}{\underset{ \
u}{\rightarrow}}\right)+\
abla\cdot\left(\overset{\rightarrow}{\underset{ \
u}{\rightarrow}}\overset{\rightarrow}{\underset{\
u}{\rightarrow}} \right)\right)=-\
abla p+\
abla\cdot\left(\tau\right)+\rho\overset{ \rightarrow}{\underset{\mathbf{g}}{\rightarrow}}+\overset{\rightarrow}{ \underset{\mathbf{F}}{\mathbf{F}}} \tag{2}\]  
where \(S_{m}\) is the mass added to the continuous phase from the dispersed second phase, \(\rho\) is fluid density, \(\overset{\rightarrow}{\underset{\mathbf{v}}{\rightarrow}}\) is fluid velocity, \(p\) is the static pressure, \(\tau\) is the stress tensor (described below), and \(\rho\overset{\rightarrow}{\underset{\mathbf{g}}{\rightarrow}}\) and \(\overset{\rightarrow}{\underset{\mathbf{F}}{\mathbf{F}}}\) are the gravitational body force and external body forces, respectively. \(\overset{\rightarrow}{\underset{\mathbf{F}}{\mathbf{F}}}\) also contains other model-dependent source terms such as porous-media and user defined sources.  
In this paper, the transportation model selected was the standard \(k-\varepsilon\) model. The turbulence kinetic energy, \(k\) and its rate of dissipation, \(\varepsilon\) are obtained from the following transport equations [33; 34; 35]:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{i })=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}} \right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho\varepsilon-Y_{ M}+S_{k} \tag{3}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} \left(\rho\varepsilon u_{i}\right)=\frac{\partial}{\partial x_{j}}\left[\left( \mu+\frac{\mu_{t}}{\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{ \partial x_{j}}\right]+C_{1\varepsilon}\frac{\varepsilon}{k}(G_{k}+C_{3 \varepsilon}G_{b})-C_{2\varepsilon}\rho\frac{\varepsilon^{2}}{k}+S_{\varepsilon} \tag{4}\]  
In these equations, \(G_{k}\) represents the generation of turbulence kinetic energy due to the mean velocity gradients, \(G_{b}\) is the generation of turbulence kinetic energy due to buoyancy, \(Y_{M}\) represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, \(C_{1\varepsilon}\), \(C_{2\varepsilon}\), and \(C_{3\varepsilon}\) are the turbulent Prandtl numbers for \(k\) and \(\varepsilon\). \(S_{k}\) and \(S_{\varepsilon}\) are user-defined source terms.
## 4 Fatigue Life Analysis of Suspension Control Arm  
### Cumulative Fatigue Damage Theory  
It is well known that materials subjected to alternating load may lead to fatigue failure even at a quite low stress level. The fatigue can be divided into high cycle fatigue and low cycle fatigue. The number of high cycle fatigue cycles is usually greater than \(10^{5}\)-\(10^{7}\) times, and the alternating load is generally less than the yield limit of the material. Low cycle fatigue refers to the number of cycles is usually less than \(10^{4}\)-\(10^{5}\) times, and the alternating load is close to or higher than the yield limit of the material [33].  
The fatigue cumulative damage theory includes linear, nonlinear, and bilinear fatigue cumulative damage theory. Miner cumulative damage theory is a representative linear  
\begin{table}
\begin{tabular}{c c} \hline
**Order** & **Natural Frequency/Hz** \\ \hline
1 & 324.1 \\
2 & 357.1 \\
3 & 602.5 \\
4 & 1003.9 \\
5 & 1254.2 \\
6 & 1439.4 \\ \hline \end{tabular}
\end{table}
Table 1: Natural frequency of the first six modes of control arm.  
Figure 5: Loading and constraints of suspension control arm.  
damage theory. It assumes that the damage of component under every stress is independent. Without considering the loading sequence, the fatigue damage can be linearly superimposed, which can be expressed as:  
\[D=\sum_{i=1}^{m}\frac{n_{i}}{N_{i}} \tag{17}\]  
where \(D\) is the total fatigue damage, which equals 1, meaning fatigue failure; \(m\) is the number of stress levels; \(n_{i}\) is the number of cycles accumulated at the \(i\)th stress level; and \(N_{i}\) is the fatigue life at the \(i\)th stress level.
## 4 Modelling and Simulation
### Topology Optimization  
A general optimization problem is identified firstly as a problem of finding the optimal topology and then as a problem of finding the optimal shape or finding the optimal cross sections [38]. Topology optimization is a mathematical process that aims to find the optimal distribution of the material of a construction, while satisfying its support conditions and loads. It is implemented by combining finite elements for analysis and mathematical programming techniques for solving [38; 39]. ANSA's SOL200 was used to conduct topology optimization simulations in the present work [37]. The first important parameter defined is the design area, corresponding to the area of the part where the problem of topology optimization will be solved, while areas not included in the solution are called non-design areas [38]. In our case, the design area is the swingarm excluding the rear brackets connected to the chassis. The second parameter is the definition of the objective function and constraints. The objective function for this application corresponds to the minimization of the weighted deformation energy (min compliance). Using this objective function, the residual mass percentage and the static loading scenario are taken as constraints. The first restriction refers to the percentage of mass removed from the original mass of the swingarm.
## 3 Results and Discussion
### Cellulose Isolation
In this case, the final fiber residues yield is slightly higher (23.9%) than the one obtained in this work, but it must be taken into account that, as already mentioned, auto-hydrolysis process has not been optimized yet. Moreover, steam explosion pretreatment presents the main disadvantage of using critical operating conditions as high pressure and temperature involved [55] with the related high costs and safety problems. In a more recent work [56], [PERSON] et al., studied a pre-treatment methodology of wheat straw with 1-ethyl-3-methylimidazolium acetate (i.e., an ionic liquid) and its subsequent fractionation to cellulose, hemicellulose and lignin. The fractionation of completely dissolved biomass led to cellulose-rich and hemicellulose-rich fractions. The main differences from the auto-hydrolysis process are related to the use of methylimidazolium acetate and to the pre-treatment operating conditions (i.e., 120 \({}^{\circ}\)C and 6 h of stirring), while using alkaline step to recover high pure cellulose was similar. This process had a high final fiber cellulose yield equal to 41.8%. On the other hand, this process has some major disadvantages, such as the long pretreatment operational time (6 h) and the use of methylimidazolium acetate as reagent, which increases the environmental impact and the cost of the process. Concerning this, a life cycle assessment (LCA) comparison between these two processes should be conducted as a future study. For what has been explained above, auto-hydrolysis has many technological advantages compared to other pretreatment processes. These include low by-product generation, limited problems derived from equipment corrosion, and the reduction of capital and operational costs, making it one of the main interesting choices for industrial applications. Figure 2: Cellulose residues obtained from acid hydrolysis (**A**) and auto-hydrolysis (**B**) of wheat straw.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Vegetation Dataset  
The Normalized Difference Vegetation Index (NDVI) is a commonly used remote sensing index to measure the greenness and photosynthetic activity of vegetation. It is calculated from the red and near-infrared (NIR) spectral bands of remote sensing data. The formula for NDVI is (NIR-Red)/(NIR+Red), where NIR is the reflectance in the near-infrared band and Red is the reflectance in the red band. The datasets we use contain a gridded daily NDVI sourcing from the Surface Reflectance Climate Data Record (CDR). We use the dataset spans from 1982-2021 derived from polar orbit satellites of NOAA (Advanced Very High Resolution Radiometer (AVHRR) and the Visible Infrared Imaging Radiometer Suite (VIIRS)). The resolution is generated daily on a 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) global grid. In this work, we apply the maximum NDVI (NDVI\({}_{m}\)) of the growing season as a proxy of vegetation growth. NDVI\({}_{m}\) is researched to be significantly correlated with NPP (Net Primary Product) in a large range of dryland ecosystems [26]. Moreover, in the Desertification chapter of the 2019 IPCC report, NDVI\({}_{m}\) acts as a proxy of vegetation growth according to the UNCCD definition of land degradation (IPCC AR6). The dataset can be searched and downloaded from [[https://www.ncei.noaa.gov/products/climate-data-records](https://www.ncei.noaa.gov/products/climate-data-records)]([https://www.ncei.noaa.gov/products/climate-data-records](https://www.ncei.noaa.gov/products/climate-data-records)) (accessed on 20 May 2023).  
Figure 2: Sensitive analysis of different factors. (**a**) is for CO\({}_{2}\) and (**b**) is for land use rate. Solid line is the stable state and dotted line is the unstable state. There exists one bifurcation when the system approaches a certain value under different climate conditions.
## 5 Performance Evaluation
### TCP Throughput  
Table 3 gives the throughput analysis when there are different numbers of TCP flows.  
With the increased number of TCP flows, the total throughput sometimes tended to decline due to the increased channel competition. For example, while the number of TCP flows with RECN became four, the total throughput declined compared with three. When the number of TCP flows without ECN became five, the total throughput declined compared with four.  
However, comparing with the no ECN situation, it is obvious from Table 3 that the total TCP throughput has an improvement by using RECN. For example, when the number of TCP flow is five, the total throughput increases by 95%.  
Figure 6 gives the comparison of the instantaneous value of \(R_{FTE}\) when there are five TCP connections.  
Figure 5: Comparison with other metrics.  
It can be shown in Figure 6 that the average value of \(R_{FTE}\) by using RECN is higher than that of the traditional TCP most of time. Combined with the statistics in Table 3, it can be concluded that the improvement of TCP throughput results from the improvement of data frame transmission efficiency.
# A Bounded Scheduling Method for Adaptive Gradient Methods
## 3 Basdam: Bounded Scheduling Method for Adam  
### Preliminaries  
Firstly, we use an empirical study to illustrate the existence of the generalization gap in Adam. We use SGD and Adam to do image classification for CIFAR-10 data set on ResNet-34 architecture and present training accuracy and test accuracy in Figure 1. As can be seen from Figure 1, the training and test accuracy of Adam both increased faster than that of SGD in the early stage. However, when the learning rate is reduced by 10 after 100 epochs, the training and test accuracy of Adam are lower than that of SGD. Although the final training accuracy of Adam reaches the level of SGD, its test accuracy is still 1% to 2% lower than that of SGD, which means that its generalization gap is larger than SGD.  
There may be various factors that may lead to the weakly empirical generalization capability of Adam. Based on previous researches [13; 19; 31; 32; 33], we summarize these factors and work to eliminate them. The main factors can be listed as follows.  
* The non-uniform scaling of the gradients will lead to the poor generalization performance of adaptive gradients methods. SGD is uniformly scaled and low training error will generalize well [19; 31].
* The exponential moving average used in Adam can't make the learning rate monotonously decline, which will cause it to fail to converge to an optimal solution and arise the poor generalization performance [32; 33].
* The learning rate learned by Adam may circumstantially be too small for effective convergence, which will make it fail to find a right path and converge to a suboptimal point [13].
* Adam may aggressively increase the learning rate, which is detrimental to the overall performance of the algorithm [13; 18].  
Taking all these factors into account, some improvements needs to be considered for Adam. Upper and lower bounds should be specified to avoid the side effect caused by extreme large and small learning rate. At the later stage of training, learning rate should be monotonous decreased to ensure the convergence and be uniformly scaled to improve generalization performance.
## 1 Introduction
Our work addresses these deficiencies through a comprehensive digital twin framework incorporating three fundamental innovations: a U-Net architecture for real-time thermal field reconstruction, a multi-objective DQN agent coordinating PUE reduction with thermal uniformity and hotspot mitigation, and a Unity-based visualization platform enabling interactive management. In this paper, we established a U-Net algorithm firstly to predict the thermal environment for an IDC prototype in real time. The predictive model works as a surrogate model to fast reconstruct a temperature field in the IDC system. Moreover, to achieve the intelligent decision-making for thermal management, the DQN-based technique is further developed considering PUE reduction, system temperature uniformity and local hotspot inhibition, respectively. Finally, the entire real-time prediction and smart decision-making neural networks are integrated in our IDC digital twin platform based on Unity, allowing the real-time visualization, analysis and intelligent thermal management for IDC prototype. Our work advances IDC operational intelligence beyond energy optimization toward holistic operational excellence, encompassing predictive maintenance, automated thermal diagnostics, and energy efficiency enhancement. By establishing a closed-loop control paradigm integrating physical infrastructure with virtual management systems, this research contributes both methodological frameworks and empirical validation for next-generation sustainable data centers. Our paper's structure is arranged as follows: Section 2 introduces the setup details of the experimental IDC prototype and the real-time thermal environment reconstruction method. Section 3 provides the DQN-based intelligent thermal management technique development, which is one of the main focus and developments in this proposed work. Finally, Section 4 exhibits a case study using DQN-based intelligent thermal management based on the digital twin platform. Section 5 concludes with research contributions and future directions.
## 3 Link Cost Metric With Reliability Assurance  
In EO missions, in order to ensure the stability of data transmission, it puts forward high requirements for topology and ISLs reliability, due to satellites' relative movements, unstable and periodic changes in topology, and also the data transmitting errors of satellites navigation information. This section focuses on the topology reliability and link cost metric with consideration of the network properties of prediction and periodicity.
## 2 Materials and Methods
### Simulation Experiment of Probe Spring Operation  
The tensile testing apparatus for the wire is depicted in Figure 3a. Uniaxial tensile tests were performed under displacement-controlled mode using constant crosshead speeds of 15, 30, 45, and 60 mm/min, corresponding to nominal engineering strain rates (\(\dot{e}\)) of 5.0 \(\times\) 10\(-\)3, 1.0 \(\times\) 10\(-\)2, 1.5 \(\times\) 10\(-\)2, and 2.0 \(\times\) 10\(-\)2 s\({}^{-1}\), respectively. The engineering strain was calculated where L0 = 50 mm. The configuration for the current-assisted tensile test is shown in Figure 3b. Current-assisted tensile tests were performed on as-received 631 stainless steel wire under ambient conditions to evaluate the effects of simultaneous mechanical loading and electrical current. Tests were conducted at an engineering strain rate of 5.0 \(\times\) 10\(-\)3 s\({}^{-1}\), with current levels of 0.00, 0.10, 0.20, 0.25, and 0.30 A. Higher current levels were excluded as they induced immediate fracture upon tensile loading, precluding meaningful mechanical characterization. The objective was to simulate the mechanical-electrical-thermal coupling encountered during spring operation in functional applications.  
Figure 2: Schematic of the setup to measure I–V characteristics and the electrical fatigue method (EFM).  
To replicate application-relevant electrothermal fatigue conditions, tensile tests were conducted on specimens previously subjected to electrical fatigue cycling (referred to as electrical fatigue tensile failure tests). All tests were performed under displacement control at an engineering strain rate of 5.0 \(\times\) 10\(-\)3 s\({}^{-1}\) to evaluate tensile properties, assess changes in I-V behavior, and characterize microstructural degradation. Each fatigued specimen was labeled using a \"current-cycle\" naming convention (e.g., 0.37-C100 denotes 0.37 A applied for 100 cycles).  
Electrical fatigue tensile failure tests were conducted on as-drawn wires subjected to defined current amplitudes and cycle counts. Test conditions included the following: 0.35 A for 100, 200, and 300 cycles (0.35-C100, C200, C300); 0.37 A for 20, 40, 100, 200, and 300 cycles (0.37-C20 to C300); and 0.39 A and 0.41 A for 20 and 40 cycles (0.39-C20/C40; 0.41-C20/C40). A current amplitude of 0.37 A was used as the threshold to distinguish between low- and high-current regions. Below this value, fracture behavior and degradation were gradual and non-catastrophic. In contrast, currents at or above 0.37 A consistently induced rapid oxide thickening, interfacial cracking, and brittle fracture within a limited number of cycles. Accordingly, the results are discussed in two sections based on this transition in failure mode.
## 6 Simulation Results and Discussions
### Study Small-scale Fading for the Proposed Model  
Lastly, we extend our LoS channel model (12) and study the effect of small-scale fading on the performance of spectral efficiency. To account for small-scale fading, we assume the Rician fading channel model for all channels involved [8]. Thus, based on (12), the BS to the \((m,n)\)-th element of the \(i\)-th RIS channel with small-scale fading can be expressed as  
\[G_{\text{BS}}^{i_{m},i_{n}}=\underbrace{\sqrt{\frac{\beta}{1+\beta}}E_{\text{ BS}}^{i_{m},i_{n}}}_{\text{LoS component}}+\underbrace{\sqrt{\frac{1}{1+\beta}}E_{\text{BS}}^{i_{m},i_{n}}}_{ \text{NLoS component}}, \tag{45}\]  
where  
\[E_{\text{LoS}}^{i_{m},i_{n}}=\frac{\lambda_{c}}{4\pi d_{\text{BS}}^{i_{m},i_{ n}}}e^{-2\pi f_{\text{eff}}\frac{\pi\pi\pi\pi}{\text{BS}}}, \tag{46}\]  
with \(\mathrm{t}_{\text{BS}}^{i_{m},i_{n}}=d_{\text{BS}}^{i_{m},i_{n}}/c_{0}\), and \(F_{\text{Rayleigh}}\) are Rayleigh fading components. The BS-vehicle and RIS-vehicle channels are also generated by following a similar procedure. Obviously, the proposed Rician fading model is reduced to the LoS model (12) when \(\beta\rightarrow\infty\) or Rayleigh fading channel when \(\beta=0\).  
In Figure 13, we plot the spectral efficiency for different Rician fading factors \(\beta=\{10^{9},5,3,1\}\) for one vehicle pass. Note that \(10^{9}\) approximates the LoS model. It can be seen that the spectral efficiency decreases with the diminishing Rician fading factor \(\beta\). This is because the reduction of \(\beta\) leads to the mismatch of the direct and cascaded links. However, the proposed system with optimal phase shift set \(\phi_{i_{m},i_{n}}^{\text{opt}}(t)\) in (33) is still effective when small-scale fading exists.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 4 Proposed Hardware Architecture for the Digital QSM Detector
### Top View of the QSM Detector Architecture
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Size (Bits)** & **Goes into** & **Description** \\ \hline \multirow{2}{*}{yt\_real} & \multirow{2}{*}{_WL_\(\times\)_NR_} & **det1** & Real part of the received data, \(\mathbf{y}\), \\  & & & in antennas for det1 \\ \multirow{2}{*}{yt\_imag} & \multirow{2}{*}{_WL_\(\times\)_NR_} & **det1** & Imaginary part of the received data, \(\mathbf{y}\), \\  & & & in antennas for det1 \\ \multirow{2}{*}{aux\_real} & \multirow{2}{*}{_WL_\(\times\)_NR_\(\times\)_NT_} & **det1** & Real part of the operation \(\mathbf{h}^{(i)}\mathbf{s}\) \\  & & & for det1 (Algorithm 1, Line 3) \\ \multirow{2}{*}{aux\_imag} & \multirow{2}{*}{_WL_\(\times\)_NR_\(\times\)_NT_} & **det1** & Imaginary part of the operation \(\mathbf{h}^{(i)}\mathbf{s}\) \\  & & & for det1 (Algorithm 1, Line 3) \\ \multirow{2}{*}{yt1\_real} & \multirow{2}{*}{_WL_} & **det2** & Real part of the received data, \(\mathbf{y}\), \\  & & & in antennas for det2 \\ \multirow{2}{*}{yt1\_imag} & \multirow{2}{*}{_WL_} & **det2** & Imaginary part of the received data, \(\mathbf{y}\), \\  & & & in antennas for det2 \\ \multirow{2}{*}{aux1\_real} & \multirow{2}{*}{_WL_\(\times\)_HXQIS_} & **det2** & Real part of the operation \(\mathbf{h}^{(i)}\mathbf{s}\) \\  & & & for det2 (Algorithm 2, Line 3) \\ \multirow{2}{*}{aux1\_imag} & \multirow{2}{*}{_WL_\(\times\)_HXQIS_} & **det2** & Imaginary part of the operation \(\mathbf{h}^{(i)}\mathbf{s}\) \\  & & & for det2 (Algorithm 2, Line 3) \\ \multirow{2}{*}{yl\_real} & \multirow{2}{*}{_WL_} & **det2** & Real part of the received data, \(\mathbf{y}\), \\  & & & in antennas for det2\_P2 \\ \multirow{2}{*}{yl\_imag} & \multirow{2}{*}{_WL_} & **det2** & Imaginary part of the received data, \(\mathbf{y}\), \\  & & & in antennas for det2\_P2 \\ \multirow{2}{*}{auxl\_real} & \multirow{2}{*}{_WL_} & **det2** & Real part of the operation \(\mathbf{h}^{(i)}\mathbf{s}\) \\  & & & for det2\_P2 (Algorithm 2, Line 14) \\ \multirow{2}{*}{auxl\_imag} & \multirow{2}{*}{_WL_} & **det2** & Imaginary part of the operation \(\mathbf{h}^{(i)}\mathbf{s}\) \\  & & & for det2\_P2 (Algorithm 2, Line 14) \\ \hline
**Output** & **Size (Bits)** & **Goes to** & **Description** \\ \hline \multirow{2}{*}{detected\_index1} & \multirow{2}{*}{_log2(NT_\(\times\)_HXQC_)} & Detector output & First detected index \\ \multirow{2}{*}{detected\_index2} & \multirow{2}{*}{_log2(NT_\(\times\)_HXQC_)} & Detector output & Second detected index \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inputs and outputs of the detector.