esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dynamic Compression Test at the High Strain Rate
### Test Results and Analysis
#### 2.3.4 Test Results of Quasi-Static  
The quasi-static compression tests are carried out by the electronic universal testing machine at room temperature via displacement loading method. The loading rate is 0.2 mm/min, and the sampling time interval is 0.2 s, the test will not stop until the specimen is obviously compacted. The three quasi-static compression tests are conducted by the universal electronic testing machine at the strain rate of \(6.67\times 10^{-4}\) s\({}^{-1}\).The average stress-strain curve of the aluminum honeycomb structure under quasi-static compression is obtained, as shown in Figure 15.  
Figure 14: Stress–strain curve at strain rate \(4.6\times 10^{3}\) s\({}^{-1}\).  
Figure 15: Stress–strain curve of Quasi-static compression test.  
As is shown in Figure 15, the yield strength (initial collapse stress) \(\sigma_{c0}\) = 2.33 MPa, and the corresponding initial collapse strain is \(\varepsilon_{c0}\) = 1.33 \(\times\) 10\({}^{-2}\). During the plateau stage, the strain increases from 0.188 to 0.792. The stress oscillates slightly, but it remains with the constant of 1.43 MPa. When the strain increases to the initial strain of densification at \(\varepsilon_{d0}\) = 0.792, the slope of the stress-strain curve changes significantly, and the specimen is gradually compacted.
## 3 Results  
### Characterization  
The surface morphology of the conductive fabric was characterized by a scanning electron microscope (SEM) shown in Figure 2. It can be seen that the rGOs homogeneously coated the fabric threads. In order to show the microfolded rGO structures, a rectangle area of the coated fabric in Figure 2a is zoomed in on. As shown in Figure 2b, two microfolded rGO layers on a single fabric thread generated by two rGO dip-coatings can be clearly distinguished. When stretching the conductive fabric, relative sliding between the lower rGO layer and the upper rGO layer causes their overlapping area to decrease, thereby leading to the increase in the contact resistance between them.
# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 4 Results and Discussion
The best result came from the support vector machine. Finally, before proceeding to the next phase, the classification dataset was divided into a 70:30 ratio, and classification was applied. Overall, gesture recognition accomplished the system and produced satisfactory results, with a word accuracy of 88 percent for the alphabet and number correctness of 96 percent, which is shown in Table 3. Figures 13 and 14 show the correct real-time output for the number \"1\" and character \"G\"; here, we used SVM as the classification model. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset Consisted of** & **Skin Detection** & **Feature Extraction** & **Accuracy** & **Reference No.** \\ \hline \multirow{6}{*}{Static signs and alphabets} & \multirow{6}{*}{HSV} & \multirow{6}{*}{SIFT} & Light HMM—83.6\%, multi stream HMM—86.7\%, and tied-density HMM—91.3\% & [18] \\  & & & Softmax activation to use logistic regression on feature extraction which was connected before the final layer & 95\% test accuracy & [19] \\ \hline \multirow{6}{*}{[PERSON]’s dataset} & Skin detection was conducted during preprocessing using gamma variation & \multirow{2}{*}{Hog feature extraction without segmentation} & 94.5\% best performing accuracy during SVM implementation & [22] \\ \cline{1-1}  & & HSV, Canny edge detection & SURF & Word accuracy—88\%, and Alphabet and number accuracy—96\% & Proposed study \\ \hline \hline \end{tabular}
\end{table} 
Table 2: In contrast to alternative strategies outlined in other referred manuscripts. \begin{table}
\begin{tabular}{l l l} \hline \hline Word accuracy & 88\% \\ \hline Number accuracy & 96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of words and numbers.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 5 Experimental Analysis
### Analysis of Forecasting Results  
#### 5.2.1 Performance Measures  
The first step of the developed regime identification models associated with the four detector stations was to determine the model parameters. To this end, the models were implemented and calibrated using the whole study data set. The determined model parameters \(\pi\) and \(A\) are listed in Tables 2 and 3. The statistical means of the traffic measures for each identified regime are depicted in Table 4.  
Figure 5: Feature importance associated with Station 400081.  
To evaluate the developed forecasting models, two performance measures were employed, including root mean square error (RMSE) and mean absolute percentage error (MAPE). The measures are defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n_{\text{s}}}\sum\
olimits_{\gamma=1}^{n_{\text{s}}} \left(y_{\gamma}-\hat{y_{\gamma}}\right)^{2}}, \tag{8}\]  
\[\text{MAPE}=\frac{1}{n_{\text{s}}}\sum\
olimits_{\gamma=1}^{n_{\text{s}}} \left|\frac{y_{\gamma}-\hat{y_{\gamma}}}{y_{\gamma}}\right|, \tag{9}\]  
where \(y_{\gamma}\) is the true value of the \(\gamma\)th sample of the considered traffic flow measure, \(\hat{y_{\gamma}}\) is the forecasted value of the \(\gamma\)th sample of the considered traffic flow measure, and \(n_{\text{s}}\) is the number of forecasted samples.
## 6 Simulation Results and Discussion
### Performance of Inverter
#### 6.2.2 Voltage Fluctuation in the Grid Side  
Voltage fluctuation in the grid side is tested at time 0.2 s, the grid voltage varies from 311 V to 280 V, and then returns to a normal level (311 V) at time 0.24 s. Figure 13 shows the adaptabilities of the inverter under grid voltage fluctuation, showing the tracking error can decrease to zero quickly.  
In order to study the advantages of the proposed controller, a comparison with sliding control is implemented under the same conditions. The sliding mode surface is designed as \(s=\dot{e}+c\cdot e\), and the sliding term \(\eta\text{sgn}(s)\) is utilized to compensate for the influence of the system uncertainties. The parameters are \(c=4000\) and \(\eta=6\times 10^{6}\). A random disturbance is added when \(t\) = 0.2-0.4 s. The comparisons of the steady performance for the inverter are studied in Figures 14-16, showing the tracking error with SMC is much bigger than that with FNNGFTSMC and AFSMC.  
Figure 11: Performance of the DC-AC inverter with the AFSMC scheme.  
Figure 12: Performance of the inverter under parameter variations.  
Figure 14: Tracking performance of the inverter using the AFSMC.  
Figure 13: Performance when grid voltage fluctuates.  
Figure 15: Tracking performance of the FNNGFTSMC scheme.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 5 Simulations  
The proposed method is applied to the nine-bus system and the New England test system. Dynamic recovery load models [23; 24] are used in the simulation, with \(\alpha_{\mathrm{s}}=\beta_{\mathrm{s}}=0\), \(\alpha_{\mathrm{t}}=\beta_{\mathrm{t}}=2\) and \(T_{\mathrm{p}}=T_{\mathrm{q}}=60\). The voltage reference \(V_{\mathrm{r}}=1\), and the voltage control deadband is 0.1 p.u., which indicates that the voltage magnitude should be kept within [0.95 p.u., 1.05 p.u.] with control. The sampling interval \(t_{\mathrm{s}}\) is 10 s. The initial prediction horizon \(t_{\mathrm{p1}}\) is 60 seconds. The diagonal elements of \(Q\) and \(R\) are set to be 1 because the correction of each bus voltage is equally important and no expensive control actions such as load shedding are considered in the simulation cases. The trajectory prediction and adaptive horizon parameter determination is realized based on the quasi-steady state model [1], and the actual control performance is illustrated by the full-time domain simulation on a detailed system model.  
The model prediction was realized with the Matlab/Simulink-based tool. The optimization problem was solved by GAMS [25]. The calculations were completed using a windows machine with Intel Core 2.50 GHz CPU and 8 GB of RAM.
## 5 Results Comparison and Characteristics Analysis
### Error Analysis  
The reasons for the errors are as follows:  
1. The influences of curvature and edge effect on the equivalent rotor impedance are neglected in ECM. Hence, the calculation values of stator phase current are less than ones obtained by FEM as shown in Figures 12a and 13a.
2. According to Figure 5 and Equations (15) and (17), each equivalent circuit parameter will affect the currents, leading to variation of the analytical results of torque and power factor. During the calculation of rotor parameters, the rotor core impedance is assumed to be infinite. Neglecting the influence of rotor core impedance causes the calculation result of equivalent rotor impedance to be larger than the actual value. Although the saturation effect is considered in the calculation of excitation reactance, the rotor core relative permeability is determined by experience and the excitation reactance is not accurate enough. Thus, there are errors between the analytical and simulation values of the torque and power factor, as shown in Figures 12b,d and 13b,d.
3. The phenomenon that the calculation values of efficiency are bigger than the simulation values is caused by ignoring the influences of harmonic effect and rotor core in ECM.
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 4 Experimental Results
### Parameters Affecting Liquefaction Behavior of Partially Saturated Sands
#### 4.3.4 Relative Density  
Figure 11 shows the test results obtained in the nearly /fully saturated and partially saturated sand tests conducted at two different relative densities and with the same effective confining stress. Whilst an increase in the relative density caused an increase in the liquefaction resistance of both the nearly/fully saturated and partially saturated sand specimens, this effect was much more pronounced in the former case. The increasing liquefaction resistance of the sand specimens with an increasing relative density can be ascribed to their contraction/dilation tendency. Loose sands have a higher tendency to contract, whereas dense sands show less contractive or dilative behavior. Under comparable test conditions, dense sands tend to generate lower excess pore pressures during undrained cyclic loading, and they exhibit greater liquefaction resistance than loose sands.  
Figure 11: Effect of relative density on liquefaction resistance of sand: (**a**) nearly /fully saturated sand specimens; (**b**) partially saturated sand specimens.  
Figure 10: Effect of back pressure on liquefaction resistance of sand: (**a**) nearly /fully saturated sand specimens; (**b**) partially saturated sand specimens.
# Thermal Fatigue Properties of Ultrasonically Bonded Copper Joints
## 2 Materials and Methods  
### Materials and Procedure  
A copper terminal was bonded onto a copper substrate via ultrasonic bonding. As shown in Figure 1a, the short-side direction of the copper terminal was parallel to the ultrasonic vibration. The energy, frequency, load and amplitude applied to the bonding material during ultrasonic bonding were 700 J, 21 kHz, 800 N and 33.2 \(\upmu\)m, respectively. As the ultrasonic bonding was finished when the energy reached 700 J, this bonding time was not uniform, ranging from 620 to 788 ms. The copper alloy used for the copper terminal was C1020, and the copper terminal had a thickness of 1.5 mm and an area of \(5\times 6\) mm. The copper alloys used for the copper substrate were C1020 and C1940. Table 1 provides the chemical composition and physical properties of C1020 and C1940. C1020, an oxygen-free copper alloy, is commonly used in electronic parts. C1940 is a copper alloy used for producing electronic parts with better mechanical properties than C1020. C1940 has higher hardness and a finer grain size than C1020. Figure 2 shows the grain orientation maps of the C1020 and C1940 substrates. The following types of joints were fabricated using these copper alloys: C1020 joints composed of a terminal and substrate made of C1020, and C1940 joints composed of a C1020 terminal and C1940 substrate.  
Figure 1: Schematic illustration of the specimen used for the ultrasonic bonding process. (**a**) The size of the bonding tool and copper terminal. (**b**) A clad material composed of copper/invar/copper used for the bonding substrate.  
To evaluate the thermal fatigue properties of ultrasonically bonded copper joints, it is necessary to induce thermal stress at the interface based on the difference in the CTE. Hence, a clad material composed of copper/invar/copper with a thickness of 1 mm (Figure 1b) was prepared. As invar, a nickel-iron alloy, has a low CTE, inserting invar between the copper terminal and copper substrate can lower the CTE at the interface [12], causing a difference in the CTE between the substrate and terminal. The apparent linear expansion coefficient of the entire copper substrate calculated using the compound law was 4.4 ppm/K for both the C1020 and C1940 substrates. Table 2 summarizes the apparent CTE and average grain size of the copper substrates.
# Multivalent Aptamer Approach: Designs, Strategies, and Applications
## 3 Design Strategies
### Spatial Arrangements  
The spatial arrangements of aptamers or their positioning relative to binding motif alignments on the target surfaces have recently become an interesting field in the study of multivalent aptamers. The optimal arrangements of aptamers--characterized by the distance and geometry of surface ligands or binding pockets--is believed to be more important than high local aptamer concentrations [5; 61], since placing aptamers at an optimal distance and position can result in increased avidity [92].  
Spatial arrangements can be achieved by either altering the distance between two adjacent aptamers [92; 93] or positioning aptamers in a desired geometry [5; 94]. Distance arrangements can be achieved by positioning aptamers onto rigid scaffolds (i.e., dsDNA, dsRNA [95], or DNA origami [96]). By conjugating two different anti-thrombin aptamers onto multi-helix DNA tiles and DNA origami scaffolds at various distances, [PERSON] et al. demonstrated that placing two different thrombin binding aptamers at an optimal distance on a rigid DNA tile nanostructure could lead to significant improvements in binding affinity, with results suggesting an estimated 50-fold enhancement in binding strength compared to aptamer monomer approach [97]. Using rigid scaffolds for spatial arrangement is particularly effective when the arrangements of binding moieties on the target surface can be perfectly aligned with those of the aptamers. However, it should be noted that even minor structural mismatches could result in suboptimal binding, due to unfavorable steric interactions in such cases [98]. In an elegant study, [PERSON] et al. [5] developed a star-shaped multivalent aptamer-conjugated DNA architecture by using tile-based DNA assemblies. In these star-shaped DNA nanoconstructs, each dsDNA intra-molecular spacer between the adjacent aptamers contained a hairpin structure, which could be unzipped and stretched to fine tune in order to fit the distance between adjacent surface ligands. This spatial arrangement of multivalent aptamers enhanced binding to their targets while reducing off-target binding through pattern matching. Significantly, the researchers also showed that aptamers with a mismatched geometric arrangement displayed an unexpected lower affinity to their targets, suggesting that specific bindings between aptamers and their targets are dependent on not only individual aptamer-ligand interactions but also geometry matching and pattern recognition. As shown in Figure 5, a heptagon-shaped complex showed a poor detection performance despite correct spacing and a higher level of valency, thus strongly suggesting the importance of geometric positioning [5].
## 2 System Architecture
### PPG Signal Conditioning
The same two bits that control the value of the resistor also select an appropriate pre-designed value for the capacitor (the \(R_{f}\)-\(C_{f}\) value pairs has been tabulated in the inset of Figure 3a) so that the stability of the TIA is maintained across the four different combinations and the transfer function does not vary significantly. The second stage in the readout circuit is an SI that adds extra gain to the signal path, and, more importantly, creates a low-pass filter (LPF) before the signal is sampled. This is required because the TIA creates a peak in the noise transfer function, so an LPF can counteract this effect and keep an acceptable signal-to-noise ratio [19]. This peaking is caused by the pole that the feedback resistor of the TIA creates with the input capacitance, usually dominated by the parasitic capacitance of the PD. The integrator is implemented using an RC circuit formed by \(R_{int}\) and \(C_{int}\) (Figure 3a), where the switched capacitor is placed in a feedback loop with another OTA with the same design as Figure 3b. \[V_{out}=\frac{T_{int}}{R_{int}\cdot C_{int}}(R_{f}\cdot I_{ph}) \tag{1}\]  
The total gain of the signal path can be described by Equation (1) where \(R_{int}\) has a fixed value of 100 k\(\Omega\), and \(C_{int}\) can be tuned between 25 pF and 100 pF in four-steps using two control bits. The integration time \(T_{int}\) is determined by the on-time of switch S1, and is another parameter that we can use to set the gain of the readout circuit. A second control signal is required for switch S2 to  
Figure 3: **(a)** Schematic of the PPG analog front end. The inset shows the programmable options for choosing among the built-in \(R_{f}\)-\(C_{f}\) value pairs. **(b)** Transistor-level schematic of the OTAs used in the AFE. discharge the feedback capacitor once the ADC samples the output signal. The waveform depicted in Figure 4 represents a snapshot of the simulated operation of the readout circuit, where the input photocurrent and the outputs of the TIA and SI are shown.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 2 Objectives  
A cloud service provider must employ a management framework that utilizes various SLA requirements, energy complications, service demands, and cost obligations of fog jobs of different characteristics to effectively complete workload executions. The framework must formalize cost-aware schedules that employ:  
* The communication bandwidth allocated by fog environments;
* The waiting delays incurred due to backlog bottlenecks delivered by fog nodes and congestion in the resource queues of the cloud;
* The cost of services provided by the cloud;
* The energy constraints developed due to serving such fog jobs;
* The SLA penalties of fog jobs incurred due to service violations.  
The scheduling framework is intended to provide services to fog jobs with heavy computations, for which it utilizes the power and speed of a remote cloud computing data center to serve heavy fog demands that cannot be processed locally in the fog environment due to the performance limitations of fog nodes.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 2 Materials and Methods
### Data and Processing
#### 2.2.2 Soil Moisture and Precipitation Data
The Integrated Multi-Satellite Retrievals for GPM (IMERG) algorithm consolidates and calibrates datasets from the GPM satellite constellation to estimate precipitation over the Earth's surface at a 10 km spatial resolution with a half-hour temporal resolution. The GPM (IMERG) Level 3 Version 06 Final Run precipitation product used in this study includes bias correction based on a monthly gauge analysis. Because there might be a lag between pre- and post-event SMAP-based soil moisture data, accumulated precipitation data in this time window for study regions, measured by GPM, were downloaded and processed. The GPM data are released in network common data form version 4 format (NetCDF4) [76]. Previous studies have shown that GLDAS and SMAP soil moisture products have similar spatial patterns and show good agreement with in situ soil moisture measurements, with both soil moisture datasets having an accuracy that is generally lower than 0.04 cm\({}^{3}\)/cm\({}^{3}\). [77; 78; 79]. As discussed earlier, GLDAS soil moisture simulations cannot reflect seismically induced soil moisture changes. Therefore, in this paper, to differentiate earthquake-induced soil moisture changes from climate-induced ones, the recorded moisture change data extracted from SMAP and GLDAS were compared.
# Double Deep Autoencoder for Heterogeneous Distributed Clustering
## 2 Distributed Clustering Algorithms  
The distributed clustering processes can be depicted as shown in Figure 1. Each local dataset, i.e., a partition of a whole dataset, is used to perform local clustering, e.g., \(k\)-means. Then, the global representation is gathered from each local clustering and perform global clustering. Finally, the result of the global clustering is used to apply the local datasets to evaluate the result of the distributed clustering algorithm. Distributed clustering algorithms enable the clustering problem to be solved under a distributed environment.  
The property of local datasets is divided into horizontally distributed (homogeneous) and vertically distributed (heterogeneous) datasets. In the former, all databases across distributed data sites share the same set of attributes. In the latter, the attributes differ among the distributed datasets in a heterogeneous situation. For example, a heterogeneous dataset is divided by a central dataset, as shown in Figure 2.  
Most DDM algorithms use the concept of parallel computing and algorithms to consider the homogeneous dataset situation [14; 15]. On the other hand, some algorithms, such as those of [16; 17], generate local data and transmit them to a central coordinator, which then performs the clustering algorithm. In addition, some researchers have focused on fuzzy datasets, e.g., the PFCM-c\({}^{\star}\) algorithm [18], IFDFC algorithm [19], and parallel fuzzy c-means algorithm [20]. Although these methods have been proposed to deal with various problems in distributed environments, these algorithms only work in homogeneous databases that all share the same attributes.  
In order to deal with the problem of the heterogeneous datasets in the distributed environment, [PERSON] and his colleagues extended the concept of CHC to propose a distributed hierarchal clustering [8; 9]. However, it may suffer from problems of computational time and memory in a big or high-dimensional dataset, similar to conventional hierarchical clustering algorithms. Hence, [PERSON] et al. [9] proposed a dimensionality reduction of CHC to solve the problem of the computation  
Figure 1: Distributed clustering in homogeneous databases.  
Figure 2: An example of a heterogeneous dataset.  
complexity. However, this method disregards the possible interaction effects among attributes in different datasets, and forgoes some levels of information in the data. Moreover, it cannot deal with categorical data with the conventional principal component analysis. Although other distributed clustering algorithms for heterogeneous datasets are proposed, e.g., OPTICS algorithm [21] and the SDBDC algorithm [17], these methods assume clusters of similar density, and may have problems separating nearby clusters [22] and the appropriate choice of parameters, such as the radius parameter, which is still an open issue [23]. In sum, none of the existing algorithms adequately address the problems we have outlined here.