esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Synthesis
The reaction mixture was refluxed for 21 h. Then, the methanol was evaporated under reduced pressure, the residue was poured with water (30 mL), and the mixture was acidified with diluted hydrochloric acid (1:1) to pH 4. The crystalline solid was filtered off, washed with plenty of water, and recrystallized from propan-2-ol. Light brown powder, yield 0.8 g (70%), m. p. 163-164 \({}^{\circ}\)C.  
\({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\): 2.00 (s, 6H, 2 CH\({}_{3}\)), 2.78-3.03 (m, 2H, CH\({}_{2}\)CO), 3.63, 3.77 (2s, 9H, 3 OCH\({}_{3}\)), 3.88-4.27 (m, 3H, NCH\({}_{2}\), CH), 6.97 (s, 2H, H\({}_{\rm Ar}\)), 14.39 (br s, 1H, NH) ppm. \({}^{13}\)C NMR (101 MHz, DMSO-\(d_{6}\)) \(\delta\): 27.82, 35.24, 50.37, 55.93, 60.12, 98.09, 134.42, 134.93, 152.69, 163.85, 170.88, 178.07 ppm. IR (KBr): \(\gamma_{\rm max}\): 3138 (NH), 1689 (2C=O), 1274, 1230 (3 OCH\({}_{3}\)) cm\({}^{-1}\). Anal. Calcd. for C\({}_{15}\)H\({}_{17}\)N\({}_{3}\)O\({}_{5}\), %: C 51.27; H 4.88; N 11.96. Found: C 51.17; H 4.72; N 11.82. 4-(4-Amino-5-thioxo-4,5-dihydro-1\(H\)-1,2,4-triazoleol-3-yl)-1-(3,4,5-trimethoxyphenyl)pyrrolidin-2-one (26)  
A mixture of hydrazide 4 (1.5 g, 4.8 mmol), potassium hydroxide (2.72 g, 48 mmol), carbon disulfide (3.69 g, 48 mmol), and methanol (50 mL) was refluxed for 18 h. Then, the solvent was removed under reduced pressure, and the residue was poured with diethyl ether (25 mL). The crystalline solid was filtered off, washed with ethyl ether, and dried. The obtained product was dissolved in the mixture of solvents (propan-2-ol (40 mL), 1,4-dioxane (10 mL), and water (10 mL)), and hydrazine monohydrate (0.73 g, 14.4 mmol) was added, and the mixture was refluxed for 24 h. After completion of the reaction, volatile solvents were removed on a rotary evaporator. The crude product was poured with water (15 mL), and the mixture was acidified with diluted hydrochloric acid (1:1) to pH 6. The crystalline solid was filtered off, washed with plenty of water, and recrystallized from methanol. Light brown powder, yield 1.15 g (65%), m. p. 101-102 \({}^{\circ}\)C.  
\({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\): 2.00 (s, 6H, 2 CH\({}_{3}\)), 2.82-3.03 (m, 2H, CH\({}_{2}\)CO), 3.63, 3.76 (2s, 9H, 3 OCH\({}_{3}\)), 3.82-4.26 (m, 3H, NCH\({}_{2}\), CH), 5.58 (s, 2H, NH\({}_{2}\)), 6.97 (s, 2H, H\({}_{\rm Ar}\)), 13.63 (s, 1H, NH) ppm. \({}^{13}\)C NMR (101 MHz, DMSO-\(d_{6}\)) \(\delta\): 27.13, 35.40, 50.92, 55.92, 60.14, 97.93, 134.31, 135.17, 152.63, 152.68, 167.28, 171.62, ppm. IR (KBr): \(\gamma_{\rm max}\): 3261, 3164 (NH2, NH), 1692, 680 (2C=O), 1274, 1236 (3 OCH\({}_{3}\)) cm\({}^{-1}\). Anal. Calcd. for C\({}_{15}\)H\({}_{19}\)N\({}_{5}\)O\({}_{4}\)S, %: C 49.31; H 5.24; N 19.17. Found: C 49.20; H 5.15; N 19.05.
## 1 Introduction
However, the TMD is proved to be an effective device for the vibration control of long-span structures as the tacit mechanism is inertia. TMD is a classical device with a mass, a spring and a damper attached to attenuate undesirable vibrations of engineering structures [25]. However, it is known that the performance of the TMD is sensitive to the frequency variation of the main structure [4; 26; 27]. A solving scheme is realized by the MTMD, which is equipped with a series of TMDs with distributed frequencies. Hence, there is a frequency band formed in the MTMD to cope with any target frequency lying in this band, which means the robustness of the TMD has been significantly improved. A lot of pioneering work has been done on the performance of the MTMD [26; 28; 29]. Also, a few studies have been conducted on the buffeting control of the double-tower cable-stayed bridge with MTMD. For example, [PERSON] et al. conducted a parametric study on the MTMD for buffeting control of Yangpu Bridge, which is a cable-stayed bridge with a main span of 602 m [27]; [PERSON] et al. presented a simulation study on the optimal control of a long-span cable-stayed bridge with MTMD via the first-order optimization method [4]; However, few studies are reported on the buffeting control of the novel long-span triple-tower suspension bridge. In this study, a parametric sensitivity analysis is conducted on the buffeting control of a long-span triple-tower suspension bridge. The Taizhou Yangtze River Bridge, \"Taizhou Bridge\" for short, is taken as the research object. The buffeting performance of the long-span triple-tower suspension bridge is firstly analyzed with the unconditionally simulated wind field. Then, a step-updating framework is presented to guide the parametric analysis and determine the preferred values of the mechanical parameters of the MTMD. Four parameters, which are the number of TMDs, the mass ratio, the damping ratio and the frequency bandwidth ratio, are specifically included in the parametric analysis. Meanwhile, the robustness of the MTMD is also evaluated by changing another parameter of MTMD which is called the frequency ratio. The analytical results are expected to provide references for the buffeting control and wind-resistant design of long-span triple-tower suspension bridges.
## 2 Materials and Methods
### Microstructure Observation  
SEM and TEM observations were performed on the pure diatom and kaolin and their mixture to explore the microstructure characteristics of DS. The SEM observations involved a Quanta 250 (FEI, Eindhoven, The Netherlands) with an accelerating voltage of 20 kV. The samples were prepared via vacuum freeze-drying to avoid disturbing the microstructure during drying [22]. Samples were observed at the magnifications of 100\(\times\), 800\(\times\), 2000\(\times\), and 5000\(\times\), with particular attention paid to the interparticle linkages at the latter two magnifications. The TEM observations involved a field-emission TEM microscope (JEM 2100F; JEOL, Tokyo, Japan) operated at 200 kV. The samples were prepared by dropping a suspension of diatoms and kaolin onto a carbon-coated copper grid; the suspension was obtained by dispersing dry diatom and kaolin particles in deionized water.  
Additionally, MIP tests were performed to reveal the pore size distribution of the DKM. DKM slurry was pre-consolidated at 60 kPa, vacuum freeze-dried, and cut into 5 mm \(\times\) 10 mm \(\times\) 5 mm pieces to prepare a sample for an MIP test. The MIP tests involved a Quantachrome Pore Master 33, which could detect pores with a diameter in the range of 0.006-300 \(\upmu\)m, and they were based on the equation proposed by [PERSON] [23]:  
\[P=-\frac{4\
u_{\text{Hg}}\cos\theta_{\text{Hg}}}{d} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Diatom** & **Kaolin** \\ \hline Specific gravity \(G_{\text{S}}\) & 2.25 & 2.65 \\ Activity \(A_{\text{d}}\)1  & 0.87 & 0.26 \\ Average particle diameter \(d_{\text{S0}}\) (\(\upmu\)m) & 6.80 & 0.59 \\ Specific surface area (m\({}^{2}\)/g)2  & 179.5 & 28.3 \\ Liquid limit \(w_{\text{L}}\) (\(\%\)) & 175 & 42 \\ Plastic limit \(w_{\text{P}}\) (\(\%\)) & 153 & 25 \\ Plastic index \(I_{\text{P}}\) & 22 & 17 \\ USCS classification & MH & CL \\ pH & 8.11 & 7.26 \\ Cation exchange capacity (meq/100 g) & 32.3 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of pure diatom and kaolin.  
Figure 1: Mineral and chemical compositions of diatoms and kaolin as determined by (**a**) X-ray diffraction and (**b**) X-ray energy-dispersive spectroscopy.  
which describes the correlation between the applied pressure \(P\) and the equivalent pore diameter \(d\). Here, \(\theta_{\rm Hg}\) is the mercury-soil contact angle, and \(\sigma_{\rm Hg}\) is the mercury-soil surface tension; the values used in this study were 141.3\({}^{\circ}\) and 0.48 N/m, respectively. Using the pore diameter \(d_{i}\) calculated from this equation, the cumulative pore volume (\(CV\)) and the log-incremental pore volume (\(IV\)) are calculated as:  
\[CV=\sum_{i}\Delta V_{i} \tag{2}\]  
\[IV=\frac{-\Delta V_{i}}{\Delta\log d_{i}} \tag{3}\]  
The MIP results are given as cumulative and log-incremental pore volume distribution curves, i.e., \(CV\)-\(d\) and \(IV\)-\(d\) curves.
## 6 Analysis and Discussion  
As the prior works partially lack capturing the spatial and temporal variations explicitly, we frankly provide a spatio-temporal representation of 3D skeleton joint values along the front, side, and top views. We adopt data augmentation (rotation and scaling) to increase the experimental data due to the data deficiency in UTD-MHAD and MSR-Action3D datasets. By considering the design and time complexities of the deep learning model, we fine-tune the well-known pretrained models such as MobileNetV2, DenseNet121, and ResNet18 for the human action recognition in the proposed method. We also investigate the classification results with three different fusion techniques to improve the performance.  
The spatio-temporal image obtained by assembling joints mapping and line mapping between the same joints in two consecutive frames can maintain the spatial information and temporal changes with different colors in the jet color map. The variations of spatial information as well as temporal information of each action are easily distinguishable for both UTD-MHAD and MSR-Action3D datasets. The effect of the network architecture doesn't affect more on the performance of the proposed method since the lightweight MobileNetV2 works well comparable with heavy-weight DenseNet121 and ResNet18.  
The fusion techniques also accelerate a bit on the performance of the proposed method. Most of the cases, element-wise maximization ensures the highest classification results compared to average and multiplication strategies.  
The classification accuracies of individual classes as shown in Figure 10(a),b indicate that the action performed by any parts of the human body can be classified effortlessly using deep learning with the proposed spatio-temporal image formation method.  
However, the spatio-temporal representation of the 3D skeleton joints is fully confined to the regularities of the frames in an action. The irregular frames generate indiscipline spatial and temporal information that makes it more complicated to predict the correct classes of action.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline STIF with MobileNetV2 & 96.00\% \\ STIF with DenseNet121 & 98.75\% \\ STIF with ResNet18 & 97.08\% \\ \hline BOP with ELM [51] & 91.90\% \\ MIMTL with SVM [52] & 93.63\% \\ RSS with RNN [53] & 94.48\% \\ SOFGF with CNN [62] & 97.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparisons of human action recognition using MSR-Action3D dataset.
## 5 Results
### Energy Consumption  
For the example design configuration with 512-bit signatures, 200 MHz and 384 PE the Vivado power estimator calculated a power usage of 24.4 W for the FPGA. The energy consumption was in the same range (\(\pm 2\) W) for other configurations. Due to technical limitations it was not possible to measure the power consumption of the entire accelerator card. A reasonable estimate for the total system power usage can be calculated as depicted in Equation (2). In the equation a power consumption of 4 W for the DDR and 9 W for the board infrastructure are assumed, additionally, an uncertainty factor of 2 W is included as well as a power supply loss factor of 0.7.  
\[Power_{Total} = \left(Power_{FPGA}+Power_{Uncertainty}\right)\ /\ PSU_{Loss}+Power_{ DDR}+Power_{Infrastr.}\] \[Power_{Total} = \left(24.4\ W+2\ W\right)\ /\ 0.7+4\ W+9\ W \tag{2}\] \[Power_{Total} = 50.7\ W\]  
Using the performance listed in Table 4 and the estimated power consumption of 50.7 W an energy efficiency of 1.49 GH/s/W is calculated.
# Tunnel Failure Evolution and System Reliability Analysis Based on the \(\beta\)-unzipping Method
## 1 Introduction  
Because of the variability in the physical parameters and the incomplete knowledge of underground structures, researchers have realized that there are uncertainties in parameters such as rock mass, support, and ground stress [1; 2; 3; 4; 5]. Deterministic methods cannot accurately represent the uncertainties in these parameters and might even produce erroneous results [6; 7; 8; 9]. Therefore, the reliability method, which can address uncertain parameters, has attracted considerable interest from scholars [10; 11; 12]. Failure criteria and the evolution process are key areas of underground structural research. Structure failure is a process that evolves from local damage to overall destruction [13; 14]. Different initial damage causes different failure processes [15; 16; 17; 18; 19; 20]. The system reliability theory can analyze the reliability evolution of different failure modes [8; 21; 22; 23]. Because of the different stress distributions at different locations of the structure system, different structural parts have different levels of reliability. While the safety status of certain parts might be reliable, others can be dangerous. The \(\beta\)-unzipping method regulates parts whose reliability index is below a certain threshold and are considered critical parts [24]. The failure of critical parts will result in stress redistribution and create new critical parts. A failure tree consisting of multiple failure paths is thus formed [25; 26].  
We must determine a performance function that can reflect the state of the structure in order to obtain the reliability index. In many complex stress environments, the performance function of a structure may be quite complicated or even impossible to obtain. To acquire a reliability index with a complex performance function or implicit state, some authors proposed the response surface method [27], which was initially mainly applied to the reliability analysis of slopes [28; 29; 30; 31]. As research progressed, the response surface method has gradually been applied to reliability analysis in tunnel engineering [32]. In order tostudy the failure evolution of the tunnel in this paper, the surrounding rock of the tunnel is divided into nine parts. The response surface function of the different parts is obtained using the response surface method. The tunnel shear capacity is considered. The system reliability of the tunnel under different lateral pressure coefficients is analyzed using the \(\beta\)-unzipping method, and the critical part of the failure history is identified; the evolution of the failure mode is ultimately acquired. The structural risk level is analyzed according to the failure probability and the impact on the tunnel system in different failure modes during the failure process. Therefore, this paper considers the different failure evolutions of the structure and evaluates the risk level of the major failure parts for each failure history.
## 3 Results
### Tropics/Subtropics Analysis
C2 and MERRA-2 also agree well for low SH values, but MERRA-2 generally displays much more moisture than C2 as SH values increase. We also explore the relationship between the standard deviation of the SH differences between C2 and reanalysis pairs to the mean outgoing longwave radiation (OLR) from ERA5 throughout the tropics and subtropics in Figure 7. OLR has been used in many past studies as a proxy for deep convection [36; 37; 38; 39] with lower mean OLR values (in W/m2) indicating more frequently occurring deep convection (and vice versa), and we speculate that deep convection may play a role in some of the larger humidity differences observed. At 3 km (Figure 7a,b), standard deviation patterns are comparable for both C2/ERA5 and C2/MERRA-2, although variation is generally larger across all regions for C2/MERRA-2, similar to [PERSON] et al. [32]. Except for the eastern Pacific Inter Tropical Convergence Zone (ITCZ) and the South Pacific Convergence Zone (SPCZ), large variability \"hotspots\" do not match up extremely well with lower OLR regions at 3 km. At this altitude, most  
Figure 6: **(a,c)** Sampling distribution of C2 and ERA5 specific humidity (g/kg) at 3 km and 7 km, and (**b,d**) sampling distribution of C2 and MERRA-2 specific humidity (g/kg) at 3 km and 7 km within 10\({}^{\circ}\)N–10\({}^{\circ}\)S from September–December 2019. The number of collocated profile pairs is shown in the title and the correlation coefficient is also shown on each panel. hotspots are located along the edges of low OLR regions, such as over the Gulf Stream and Kuroshio currents and over Argentina. However, deep convection occurs in these regions as well [40] and may play a role in the observed variation. At 7 km (Figure 7c,d), OLR matches up extremely well with the standard deviation of SH differences for both C2/ERA5 and C2/MERRA-2. For example, in regions with a higher mean OLR (such as over the stratocumulus cloud regions near the western coasts), standard deviations are at a minimum whereas regions with a low mean OLR (including the ITCZ, Pacific Warm Pool, and the central Indian Ocean) are at a maximum. While it is unclear why agreement is better at 7 km, the relationship between the tropical vertical water column and convection may play a role. Water vapor has much less spatial uniformity than temperature in the tropics and is more locally influenced by evaporation, precipitation, and advection [41], especially in the free troposphere well-above the boundary layer. In the tropics, the boundary layer height can often be near or even above 3 km [42; 43] which could limit variability at this height. While OLR and SH difference standard deviation patterns lend support to the idea that larger moisture differences between C2 and the reanalyses can occur due to deep convection, a strong relationship was not observed between transient OLR and profile pair SH differences (not shown). Thus, the influence that deep convection may have on moisture will receive more in-depth study in future research.
## 3 Methodology
### Trajectory Determination  
In order to resolve the UAV's states (position and orientation) from the observation of the multiple on-board sensors, a state based estimator is used. The aim of this estimator is to make use of all information and optimally combine the results. The core algorithm of the state-based estimator used in this work is the Square Root Unscented variant of the Sigma Point Kalman filter (SPKF) outlined in [33]. This variant of the SPKF was chosen over the traditional Extended Kalman Filter (EKF) as it partially addresses the issues of approximation present in the EKF [34]. Furthermore, the SPKF has been shown to converge faster, thus allowing a greater section of each flight to be used for mapping [23]. This implementation of a SPKF is a straightforward extension of the sigma-point approach to the recursive estimation of a non-linear discrete time system. Error within the system is estimated by propagating sigma points selected from within an _a priori_ measurement noise distribution. These sigma points are applied to the current augmented state through the kinematic model to determine a corresponding set of updated sigma-points. A complete overview of the SPKF algorithm can be found in [33] and for brevity is not included in this paper. The application of the SPKF to the determination of vehicle states does however requires state-based process and observation models to be defined.
## 5 Experiments and Results
### Experiment II: Comparison with Different Models
On the contrary, when the MTL is used, the degree of confusion decreases and the accuracy of other emotions improves, which is the same as the confusion of anger and happiness in EMO-DB. Hence, MTL can alleviate the problem of unbalanced sample size and data sparsity through data augmentation, thus solving the emotional confusion issue in SER. For the second comparison, the recognition accuracy of almost all emotions in the three corpora on MSFL outperforms MTL_DNN. Although compared with MTL_DNN, the performance of sadness recognition in EMO-DB on MSFL is slightly lower, the degree of confusion in recognizing anger and happiness in EMO-DB significantly decreases. The same is true for SAVEE, where the performance of neutral recognition is slightly lower while the degree of confusion in recognizing neutral and sadness significantly decreases. In CASIA, the degree of emotional confusion was not evident on either MSFL or MTL_DNN. While the recognition of neutral and happiness on MSFL is slightly lower than MTL_DNN, there is a significant recognition effect on anger recognition. Overall, MSFL is very sensitive to recognizing anger and achieves the best results on all three corpora. Moreover, it mitigates emotional confusion for EMO-DB and SAVEE, which is benefited from the transformation and selection of acoustic features by the LSTM and attention mechanisms.
## Materials and Methods
### Retrieval Algorithm
#### 2.3.1 PROSAIL Model  
From the perspective of model inversion, an appropriate model should be as realistic as possible to explain the variation of reflectance and, at the same time, as simple as possible to effectively solve the ill-posed inverse problem [33]. PROSAIL model was selected in this study, because it can achieve a proper comprise between reality and simplicity.  
PROSAIL model is a combination of the SAILH canopy reflectance model [21] and the PROSPECT leaf optical properties model [20]. LAI and Cm are input parameters of the SAILH and PROSPECT model, respectively, and therefore can both be estimated.  
SAILH simulates the canopy bi-directional reflectance in the range of 400 nm to 2500 nm as a function of input variables related to the structure of the canopy, the leaf optical properties (reflectance and transmittance), the background soil reflectance, fraction of diffuse incoming solar radiation and the sun-view geometry [21]. Canopy structure parameters include leaf area index (unitless), averaged leaf inclination angle (\({}^{\circ}\)), hot spot size (unitless). The leaf optical properties used for SAILH was simulated through the PROSPECT model [20]. The entire inputs include leaf chlorophyll content (mg-cm\({}^{-2}\)), leaf structure parameter (unitless), leaf equivalent water thickness (g-cm\({}^{-2}\)), leaf dry matter content (g-cm\({}^{-2}\)). The background reflectance spectrum can be simulated using the reflectance spectrum of fluvio-aquic soil multiplied by a brightness coefficient, which was used to depict changes induced by moisture and roughness in soil brightness [34; 35]. Concerning the fraction of diffuse incoming solar radiation, a constant value across all wavelengths is often employed in many similar studies, as this parameter showed very limited influence on the simulated reflectance [18; 34; 36; 37; 38]. Finally, the sun-view geometry is described by the incidence zenith angle, view zenith angle, and the relative azimuth angle.  
The parameterization of PROSAIL model is the prerequisite for our physically-based grassland AGB retrieval method. To improve the generality of the physically-based grassland AGB retrieval method, no field measurement data was used during the determination of the distribution of the canopy, leaf, and soil parameters. Rather, they were determined according to the literature regarding grassland parameters retrieval from PROSAIL model [23; 34; 37]. See Table 2 for the selected parameter intervals of main model parameters.  
Figure 2: Temporal variation of the incidence zenith angles for the MCD43A4 imageries over our study area in the study year (2017).
# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 4 Metrics with Environmental Implications
### Runtime  
Runtime, a key measure of program execution duration, is linked to energy consumption and carbon footprint estimation, despite not directly indicating energy efficiency. Its ease of measurement makes it a practical tool for performance evaluation across different platforms, despite requiring additional data like hardware power consumption and power mix composition.