esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The Finite Element Analysis of Specimens  
### FEA Model  
To further study the hysteretic performance of XMD, a finite element analysis (FEA) model in the commonly used program ABAQUS was established, as shown in Figure 18. An eight-node linear brick 3D element with reduced integration was used to model the connected steel plates and stiffening ribs in this FEA model. When the scale of a component in one direction is much smaller than that in other directions and the stress along the thickness direction is ignored, the mechanical behavior of a component can be modelled by the shell element [41]. Therefore, the X-shaped metal plates were simulated by a four-node quadrilateral shell element. Additionally, the geometric nonlinearity of X-shaped metal plates was taken into account in the FEA model. All freedoms of the left surface of a connected steel plate were fully fixed to simulate the rigid joint. The translational degree of freedom along the \(y\)-axis was free to apply the displacement. For specimens with stiffening ribs, the contact surface of stiffening ribs and X-shaped metal plates adopted the surface-to-surface tie constraint to model the welding joints. For specimens with cover plates, the translational degree of freedom along the \(x\)-axis was fixed to model the cover plates. However, it can be noted from the test results that the cover plates could not fully constrain the out-of-plane deformation of steel plates. Therefore, the simulated results of specimens were larger than those of the test results for specimens with cover plates. When we applied comprehensive consideration of time cost and computational accuracy, the grid edge lengths of X-shaped metal plates and other components in this FEA model were 5 mm and 6 mm, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & \(K_{\text{e}}\)**/(kN/mm)** & \(K_{\text{t}}\)**/(kN/mm)** & \(K_{\text{e}}\)**/\(K_{\text{t}}\)** \\ \hline W30-O15\({}^{\circ}\) & 13.23 & 15.09 & 0.88 \\ W30-O30\({}^{\circ}\) & 17.29 & 15.13 & 1.14 \\ W30-O0\({}^{\circ}\)-LP & 13.05 & 15.08 & 0.87 \\ W30-O15\({}^{\circ}\)-LP & 15.16 & 15.09 & 1.00 \\ W30-O30\({}^{\circ}\)-LP & 20.13 & 15.13 & 1.33 \\ W30-O0\({}^{\circ}\)-LS & 12.86 & 15.08 & 0.85 \\ W30-O15\({}^{\circ}\)-LS & 16.57 & 16.07 & 1.03 \\ W30-O30\({}^{\circ}\)-LS & 21.93 & 19.60 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of experimental and theoretical results.
## 2 DEOS Mission Scheduling Model
### Scheduling Problem Modeling
#### 2.2.2 Scheduling Solution  
The purpose of scheduling is to find an optimal execution plan for satellites that can maximize the payoff of observing targets. From the scheduling plan, the tasks of a satellite can be arranged in a timeline, and each satellite will know exactly the execution and termination time of observation, as shown in Figure 1. It is noteworthy that all the scheduling plan needs to satisfy the model constraints in Section 2.2.1 to make it executable.  
During a scheduling period, observation opportunities are strictly limited by observation windows. We naturally number the observation windows and use metaheuristic algorithms to find optimal combinations so as to deconflict and output an executable scheduling plan. In this paper, we define a 6-tuple to record an observation chance to one target of a certain satellite, namely, [Window ID, Target ID, Satellite ID, Observation Start Time, Observation End Time, Observation Slew Angle]. The scheduling solution consists of numbers of (the length of solution is the number of targets) these tuples, and detailed execution information of the scheduling plan can be indexed from the observation windows set.
## 4 Results and Discussions
### The Dynamics of Flame Response
Meanwhile, the process of variation in the height of the large vortical structures is indicated by a green dashed line. The convective velocity of perturbations \(u_{\textit{cf}}\) can be calculated using the corresponding excitation frequency period \(\tau_{e}\) and convective wavelength (\(u_{\textit{cf}}=\lambda_{f}/\tau_{e}\)). The physical meaning of the ratio of flame length to convective wavelength (\(L_{f}\)/\(\lambda_{f}\)) is equivalent to the previously defined \(S\textit{f}_{\textit{f}}\). Hence, these two parameters were compared. The calculated convective wavelengths \(\lambda_{f}\), convective velocity \(u_{\textit{cf}}\), and \(L_{f}\)/\(\lambda_{f}\) for the four excitation frequencies are presented in Table 2. The results demonstrate an increase in convective velocity with increasing excitation frequency. The convective velocity is smaller than the jet velocity of 3.52 m/s with lower frequency excitation, and relatively higher frequency excitation results in a convective velocity close to the jet velocity. This variation may be linked to the flame length under different excitation frequencies. Specifically, under low-frequency forcing, the flame length exhibits a longer extent while the convective velocity experiences a decrease downstream of the flame. This convective velocity attenuation behavior has also been observed in premixed flames, as reported in references [46; 51]. Consequently, such a variation of \(u_{\textit{cf}}\) causes significant deviations in \(L_{f}\)/\(\lambda_{f}\) and \(S\textit{f}_{\textit{f}}\) at low-frequency conditions. Therefore, when analyzing the relationship between flame response and \(S\textit{f}_{\textit{f}}\), it is necessary to consider the changes in convective velocity. \begin{table}
\begin{tabular}{c c c c c} \hline \(f_{e}\) (Hz) & 10 & 20 & 30 & 100 \\ \(\lambda_{f}\) (mm) & 190.4 & 121.7 & 87.6 & 30.9 \\ \(\tau_{e}\) (s) & 0.100 & 0.050 & 0.033 & 0.010 \\ \(L_{f}\) (mm) & 152.4 & 161.2 & 113.6 & 87.6 \\ \(u_{\textit{cf}}\) (m/s) & 1.90 & 2.43 & 2.63 & 3.61 \\ \(L_{f}\)/\(\lambda_{f}\) & 0.80 & 1.32 & 1.29 & 2.42 \\ \(S\textit{f}_{f}\) & 0.43 & 0.92 & 0.97 & 2.43 \\ \hline \end{tabular}
\end{table} 
Table 2: The calculated parameters related to convective velocity \(u_{\textit{cf}}\).
# Evaluation of Remote Sensing-Based Evapotranspiration
###### Abstract  
Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman-Monteith-Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through \(\epsilon\)-dominance non-dominated sorted genetic algorithm II (\(\varepsilon\)-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling-Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting.  
hydrological model; multi-variable calibration; evapotranspiration; PML; FLUXCOM; GLEAM 202214546. 25 July 2022  
11 September 2022
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 2 System Model  
Consider a multiuser massive MIMO system as illustrated in Figure 1. The BS, equipped with \(M\) antennas, serves \(K\) single-antenna users in the same time-frequency resource. All nodes operate in the HD mode. To reduce the power consumption and hardware complexity, the BS is fitted with low-resolution ADCs/DACs. The low-resolutionADCs affect the channel estimation in the UL training phase while the low-resolution DACs affect the DL transmit signal quality. The BS uses the RS scheme for DL multiuser transmission. We consider a block fading channel where the channel is unchanged during the coherence interval. We assume a Rayleigh fading channel model where the channel between the BS and all \(K\) users, \(\mathbf{H}\in\mathbb{C}^{M\times K}\), is modeled as [27]  
\[\mathbf{H}=\mathbf{G}\mathbf{D}^{\frac{1}{2}}, \tag{1}\]  
where \(\mathbf{G}\in\mathbb{C}^{M\times K}\) denotes the channel matrix containing the small scale fading having independent and identically distributed (i.i.d.) \(\mathcal{CN}(0,1)\) entries. The channel matrix \(\mathbf{D}\in\mathbb{C}^{K\times K}\) is a diagonal matrix containing the large scale fading in terms of the \(k\)th diagonal entries denoted by \(\beta_{k}\). In this work, the large scale fading coefficient, \(\beta_{k}\), is modeled in dB scale as [28]  
\[\beta_{k}=148+37.6\log_{10}(d_{k})+z_{k}, \tag{2}\]  
where \(d_{k}\) is the distance from the BS to the \(k\)th user and \(z_{k}\) denotes the shadow fading term. The channel between the BS and the \(k\)th user is denoted by \(\mathbf{h}_{k}\) where \(\mathbf{h}_{k}\) is the \(k\)th column of \(\mathbf{H}\).
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### Baselines  
In order to verify the performance of the LDIF approach, we compare the eight most classic recommendation methods, including the traditional collaborative filtering approaches and the state-of-the-art deep learning combined approaches.  
1. UPCC [39]: User-based collaborative filtering, using Pearson's correlation coefficient (PCC) to calculate the similarity between users, then using the real values of the top k similar user neighbors on the service to predict the missing values, so as to provide web service recommendations.
2. IPCC [40]: An item-based collaborative filtering algorithm, which first uses PCC to calculate the similarity between web services, and then uses the real values of its top k similar service neighbors on users to predict missing values, so as to provide web service recommendations.
3. UIPCC: This approach is a combination of UPCC and IPCC methods, and the values obtained by the two methods are weighted and summed to recommend web services.
4. PMF [41]: Probabilistic matrix factorization is a approach that adds a probability distribution to the traditional matrix decomposition. It uses Bayes to derive the posterior probability of the implicit features of users and items, so as to analyze web services and make a recommendation.
5. DeepFM [42]: This approach uses a deep neural network combined with a classical factorization machine, and comprehensively considers the interaction effects of low-order features and high-order features to predict the QoS value of web services.
6. DCN: This approach is the deep and cross network, which combines a cross network with a deep neural network to achieve more efficiency and comprehensively consider the interaction information from low- and high-order features, so as to predict the QoS value of web services.
7. WDL: This approach is the wide and deep network, which combines polynomial regression and deep neural networks to describe linear and nonlinear relationships, respectively.
8. DCN-V2 [43]: This approach is the improved deep and cross network, which is a mixture of low-rank DCN (DCN-Mix) to achieve a healthier trade-off between model performance and latency.  
Among the above methods, 1-3 are the traditional memory-based collaborative filtering benchmark methods, 4 is the traditional model-based collaborative filtering benchmark method, and 5-8 are the deep learning-based benchmarking methods.
## 3 A Resilient Routing Protocol
### Protocol Design  
The protocol operates through a structured approach encompassing node clustering, weight determination using AHP, and intelligent routing path selection using DQN.  
Figure 1: Illustration of the proposed system model.
###### Abstract  
This paper analyzes the presence of undesired quantization-induced perturbations (QIP) in a dc-dc buck-boost converter using a two-loop digital current control. This work introduces design conditions regarding control laws gains and signal quantization to avoid the quantization effects due to the addition of the outer voltage loop in a digital current controlled converter. The two-loop controller is composed of a multisampled average current control (MACC) in the inner current-programmed loop and a proportional-integrator compensator at the external loop. QIP conditions have been evaluated through simulations and experiments using a digitally controlled pulse width modulation (DPWM) buck-boost converter. A 400 V 1.6 kW proof-of-concept converter has been used to illustrate the presence of QIP and verify the design conditions. The controller is programmed in a digital signal controller (DSC) TMS320F28377S with a DPWM with 8.96-bit equivalent resolution, a 12-bit ADC for current sampling, and a 12-bit ADC for voltage sampling or a 16-bit ADC for voltage error sampling.  
dc-dc power converter; multisampled average current control (MACC); digital control; limit-cycle oscillation (LCO); quantization-induced perturbations (QIP) 2020 10 7179 10.3390/app10207179
## 4 Performance Analysis of Different Methods  
In this section, we will further investigate the SNR and computational complexities of different methods. Approaches such as MTD and hybrid integration (HI), which have been used in radar signal processing, are used for comparison with the MHI.
## 4 UV Prevention Strategies Based on Aging Equivalence Relationships  
### Selection of Aging Equivalent Index  
The mechanism of UV aging and the thermal oxygen aging of asphalt are similar, caused by the volatilization of light oil in asphalt, the transformation of light oil to resins, and the transformation of resins to asphaltene [43; 44; 45]. However, the two aging methods are not exactly the same. Asphal has different sensitivities to thermal aging and ultraviolet aging condition. The ultraviolet aged asphalts compositors in penetration, softening point, and ductility tests do not accord with thermally aged asphalts. Ultraviolet aging and thermal aging influence asphalt in different ways: rheological property changes greatly after ultraviolet aging [21]. Therefore, the selection of UV aging evaluation indexes should refer to the evaluation indexes of oxygen aging of hot asphalt. It is clear that the indexes that meet the requirements are penetration, softening point, ductility, mass loss rate, G*, \(\delta\), and G*/sin \(\delta\) after UV aging.  
The purpose of this paper is to give the corresponding measures to prevent and control UV aging of asphalt based on the amount of UV radiation in different areas. Therefore, it is necessary to establish the equivalent relationship of UV aging of asphalt between different UV radiation zones, and the aging equivalence indexes need to meet the following requirements:  
* The index is sensitive to UV radiation and has a certain degree of discrimination;
* The index has a strong regularity with the UV radiation, and the UV aging equation can be established to track the index;
* The index is easy to obtain, the error is small, and it can be popularized.  
According to the research results in Section 3, among the asphalt indexes after UV aging such as penetration, softening point, ductility, mass loss rate, G*, \(\delta\), and G*/sin-\(\delta\), the distinction of softening point and mass loss of asphalt are not very high, and the test error has a great influence on the results, which does not meet the requirements of aging equivalence indexes. Although G*, \(\delta\), and G*/sin \(\delta\) are sensitive to UV radiation and have stable data, such indexes, however, have not been included in \"Technical specifications for construction of highway asphalt pavement\" (JTG F40-2004) [46]. In addition, the dynamic shear rheometer to obtain such indexes is only equipped in a few universities and scientific research institutes, while design and construction companies are rarely equipped with this, so such indexes do not have the conditions of being able to be popularized in China.  
To sum up, this paper selects penetration loss rate and ductility loss rate of asphalt after UV aging as aging equivalent indexes.
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Contemporary Use of 3D-Printed Accessories in Orthodontics
#### 4.1.5 Removable Orthodontic Appliances  
Removable orthodontic appliances can be used for the treatment of Class II malocclusions, typically in the early stages of the treatment. However, the shortage of skilled technicians and financial limitations prevent the accessibility of widely recognized removable functional appliances [50]. Several studies described the manufacturing of variable removable orthodontic appliances by 3D printing [50; 51; 52; 53; 54; 55].  
A paper by [PERSON] et al. outlined a novel digital technique for creating a removable orthodontic appliance directly from intraoral scans, eliminating the need for traditional impressions. The quality of the intraoral fit was evaluated, confirming a comfortable fit. The study demonstrated the feasibility of digitally manufacturing removable dental appliances and highlighted a shift toward more integrated CAD/CAM processes in dental manufacturing [52]. [PERSON] et al., in their study, demonstrated successful manufacturing of myofunctional appliance by implementing the 3D-printed protocol [53].  
Mandibular advancement devices (MADs) are widely used for mild to moderate obstructive sleep apnoea (OSA). OSA is a prevalent and potentially life-threatening condition, primarily treated medically with continuous positive airway pressure [54]. The clinical report of [PERSON] et al. highlighted a fully digital workflow for creating a custom nonadjustable MAD, utilizing computer-aided design/manufacturing (CAD/CAM) and additive manufacturing. This innovative approach allows for the rapid production of MADs without the need for traditional intermediate materials or lab processes. Nonetheless, further research is needed to validate these findings comprehensively and the authors highlight the fact that OSA needs medical treatment and not, primarily, orthodontic treatment [55].
## 3 Results and Discussion
### Material Properties  
XRD and FTIR were used to analyze the material composition of the exoskeleton of the dactylopodites. The results of XRD analysis show a narrow band peak at 20 = 29.5\({}^{\circ}\) with a full width at the half maximum (FWHM) of 0.27 and peak intensity of 180, indicating a crystalline structure.  
Figure 8: Microstructure of longitudinal section of dactylopodite: (**a**) Layered structure in exocutic, (**b,c**) pore canal and pore canal tubule.  
Figure 9: Longitudinal section of the tip of Chinese mitten crab dactylopodite: (**a**) Layered structure at tip of dactylopodite, (**b**) highly condensed structure of exocutic, (**c**) Bouligand structure in endocutic.  
In addition, smaller diffraction peaks were seen at 20 = 36.1\({}^{\circ}\), 36.6\({}^{\circ}\), 39.6\({}^{\circ}\), 39.7\({}^{\circ}\), 43.3\({}^{\circ}\), and 48.8\({}^{\circ}\), as shown in Figure 10. Comparative analysis [22] revealed that calcite is the predominant form of CaCO\({}_{3}\) in the exoskeleton.  
Studies have shown that chitin is a common substance in the exoskeleton of crustaceans [23; 24]. [PERSON] et al. [25] used FTIR analysis of the exoskeleton of the blue crab (_Callinectes sapidus_) and found O-H stretching at wave number 3432 cm\({}^{-1}\), CH\({}_{3}\) and CH\({}_{2}\) stretching at 2919 cm\({}^{-1}\), C=O secondary amide stretching at 1657 cm\({}^{-1}\), N-H bending and C-N stretching at 1550 cm\({}^{-1}\), C-O-C stretching at 1064 cm\({}^{-1}\), and C-H ring stretching at 890 cm\({}^{-1}\). The FTIR results for the grooved tiger prawn (_Penaus semisulatus_) showed that the same functional groups appeared at wave numbers 3438, 2918, 1656, 1562, 1072, and 899 cm\({}^{-1}\), respectively [26]. In the exoskeleton components of _Gammarus argaeus_, absorption peaks were shown at 3433, 2913, 1655, 1554, 1064, and 894 cm\({}^{-1}\)[27], showing characteristics of the same functional groups.  
Figure 11 shows the FTIR results of the Chinese mitten crab dactylopodite. The graph shows absorption peaks at 3440, 1635, 1408, 1075, 870, and 560 cm\({}^{-1}\). Compared with the results above, it can be concluded that the absorption peak of 3440 cm\({}^{-1}\) is O-H stretching, 1635 cm\({}^{-1}\) is C=O secondary amide stretching, 1408 cm\({}^{-1}\) is N-H bend and C-N stretching, 1075 cm\({}^{-1}\) is C-O stretching in the phase ring, and 870 cm\({}^{-1}\) is closer to CH ring stretching. At 2930 cm\({}^{-1}\), weak CH\({}_{3}\) stretching and CH\({}_{2}\) stretching are seen.  
The above analysis suggests that the organic components of the exoskeleton of the dactylopodites of the Chinese mitten crab are mainly chitin and protein fiber and that the inorganic component is mainly CaCO\({}_{3}\) in the form of crystallized calcite.