esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Estimation of LST  
With the aim of identifying the amount and nature of the UHIs' spatial distribution within the area, LST for the different surface covers was extracted from the thermal ranges of the Landsat satellite images using the equations mentioned previously (Equations (1)-(7)). Additionally, by utilizing values (\(\upmu\)) and (\(\updelta\)) of LST, the heat island density over the area was calculated for the observed period of time, every 10 years starting from 1990 to 2020. Through the results of the study, we found that the surface temperatures varied between 26 \({}^{\circ}\)C and 52 \({}^{\circ}\)C throughout the study period, given in terms of least and ultimate LST, respectively. The statistics obtained from the analysis of heat islands are shown in Table 4, where it should be noted that the intensity of heat islands (which represents the difference between heat islands for urban areas and heat islands for suburbs) ranged between 10.4 \({}^{\circ}\)C in the year 1990 to 8.7 \({}^{\circ}\)C for 2020.  
Figure 5 displays the heat islands' spatial distribution over the study area during the observation time (1990-2020). The heat islands were centered over urban areas during the study period, specifically in the western, eastern, and northern parts of the study area, where urban complexes (represented by some of the main cities and villages that surround the oasis) are situated. On the other hand, heat islands with low values were centralized around the middle of the study area, where there is a lot of vegetation cover due to palm  
Figure 4: The decadal change in vegetated area and built-up zones during the study period.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **1990** & & **2000** & & **2010** & & **2020** & \\ \cline{2-8}
**LU/LC Types** & \begin{tabular}{c} **Area** \\ **(sq.km)** \\ \end{tabular} & \% & \begin{tabular}{c} **Area** \\ **(sq.km)** \\ \end{tabular} & \% & \begin{tabular}{c} **Area** \\ **(sq.km)** \\ \end{tabular} & \% & \
\begin{tabular}{c} **Area** \\ **(sq.km)** \\ \end{tabular} & \% &
\begin{tabular}{c} **Area** \\ **(sq.km)** \\ \end{tabular} & \% \\ \hline Vegetation & 117.08 & 64.24 & 107.76 & 59.13 & 88.52 & 48.57 & 85.91 & 47.14 \\ \hline Built-up & 31.25 & 17.15 & 48.23 & 26.46 & 65.13 & 35.74 & 83.48 & 45.81 \\ \hline Bare land & 10.44 & 5.73 & 15.98 & 8.77 & 14.88 & 8.17 & 4.88 & 2.68 \\ \hline Sand dunes & 23.47 & 12.88 & 10.27 & 5.64 & 13.70 & 7.52 & 7.97 & 4.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary of area coverage of LULC classes over the 30 year study period.  
plantations in the region. Starting from 2010, some aggregation of vegetation cover in the middle of the oasis began to develop, which was often due to palm plantations. However, these vegetation spots have been subjected to urban invasion (Figure 5c,d) during the last 10 years, leading to the emergence of separate heat islands permeating the vegetation cover.
## 6 Numerical Results
### Energy Consumption  
Due to the battery-driven nature of WSNs (whole network or some parts of the network), it is essential to study the amount of energy consumption to validate a proposed approach. The key factor in energy consumption is to reduce data communication among the sensor-nodes. Figure 9 declares the amount of energy consumption for all of the sensor-nodes in each round. By analyzing the four development scenarios in the figure, there are three achievements for FRM in contrast with CBS. We could balance energy consumption among the sensor-nodes in each round as the smooth line for energy consumption in each round indicates. The second achievement is reducing the total amount of energy consumption for each round because our approach eliminates all of the unnecessary hierarchical structures and attempts to reduce the number of hops in case of multi-hop communication for the sensor-nodes. And the third achievement of our proposed approach is extending the operation time of the network.  
Finally, Figure 10 presents the amount energy consumption for the whole network by increasing the number of rounds. Clearly our approach can conserve a considerable amount of energy in the network which can be used to reduce the size of batteries in the sensor-nodes for the applications in which the size of the sensor-nodes is an important factor such as battle field monitoring for hiding the sensor-nodes from the adversary.  
Figure 9: Comparison in case of energy consumption for all the sensor-nodes in each round (**a**) WSN#1; (**b**) WSN#2; (**c**) WSN#3; and (**d**) WSN#4.
# Diparlureas as Antitumor Agents
## 2 Diaylureas in Therapy or in Clinical Studies as Anticancer Agents
### Ripretinib  
Ripretinib (DCC-2618, QINLOCK(tm), Figure 3) is an oral inhibitor of tyrosine kinase that primarily inhibits KIT proto-oncogene receptor tyrosine kinase and platelet-derived growth factor receptor A (PDGFRA) kinase signaling. Ripretinib also inhibits other kinases, such as PDGFR, TIE2, VEGFR2, and BRAF. It was designed for cancers and myeloproliferative neoplasms, especially GISTs. Ripretinib is a \"switch-control\" kinase inhibitor that forces the activation loop (or activation \"switch\") into the inactive conformation. In preclinical cancer models it has shown efficacy, and preliminary clinical data show that Ripretinib inhibits a broad range of KIT mutants in patients with drug-resistant GISTs [86]. The INVICTUS study demonstrated the efficacy and safety of ripretinib as the fourth-line treatment _versus_ placebo in patients with advanced GISTs [87]. In May 2020, ripretinib received approval from the US FDA for the treatment of patients with advanced GISTs who had received previous treatment with more than two kinase inhibitors [88]. Ripretinib is being evaluated in an ongoing phase III study (INTRIGUE) as a second-line therapy in comparison with sunitinib after progressing on imatinib [89]. Recently, ripretinib is being investigated in clinical trials for systemic mastocytosis (SM) [90], and has been also proposed for the treatment of STS [91].
## 2 Related Work  
### Face Recognition with Single Sample Per Person  
With the unremitting efforts of scholars, face recognition has made great progress. However, the task becomes much more challenging when only one sample per person is available for training the face recognition model. [PERSON] et al. [17] extracted histogram of oriented gradients (HOG) features and employ support vector machine (SVM) for classification. [PERSON] et al. [41] combined Gabor wavelets and feature space transformation (FST) based on fusion feature matrix. They projected the combined features to a low-dimensional subspace and used nearest neighbor classifier (NNc) to complete classification. [PERSON] et al. [42] proposed a locality preserving projection (LPP) feature transfer based algorithm to learn a feature transfer matrix to map source faces and target faces into a common subspace.  
In addition to the traditional methods introduced above, there are many other methods that utilize the learning ability of deep learning to extract features. To make up for the lack of data in SSPP FP, some algorithms combine deep learning and sample expanding. In [34], a generalized deep autoencoder (GDA) is firstly trained to generate intra-class variations, and is then separately fine-tuned by the single sample of each subject to learn class-specific DA (CDA). The new samples to be recognized are reconstructed by corresponding CDA so as to complete classification task. Similarly, [PERSON] et al. [33] used a traditional approach to learn an intra-class variation set and added the variation to single sample to expand the dataset. Then, they fine-tuned a well-trained network using the extended samples. Sample expanding can be done not only in the image space but also in the feature space. [PERSON] et al. [43] proposed a sample expansion algorithm in feature space called k class feature transfer (KCFT). Inspired by the fact that similar faces have similar intra-class variations, they trained a deep convolutional neural network on a common multi-sample face dataset at first and extracted features for the training set and a generic set. Then, k classes with similar features in the generic set are selected for each training sample, and the intra-variation of the selected generic data is transferred to the training sample in the feature space. Finally, the expanded features are used to train the last layer of SoftMax classifier.  
Unlike these existing methods, this paper simultaneously recovers intrinsic attributes and generates diversified samples. Compared with sample expanding methods mentioned above, our method uses face modeling to decompose intrinsic property and generates images with richer intra-variation via simulating the face image formation process rather than following the idea of intra-variation migration. Our method also takes full advantage of intrinsic properties that can more robustly represent identity information. Deep learning is used as a feature extractor in our method due to its superiority demonstrated in many existing studies.
## 5 Experimental Results
### Experimental Waveforms
The different phases and amplitudes of the modulation symbols were correctly reproduced. Following that, the dynamic behaviour of the prototype was evaluated by performing amplitude and phase changes. As Figure 20 shows, the designed VLC driver required a settling time of less than one communication-signal period to perfectly track the reference after the change. This settling time is short enough to address the symbol selected (\(\mathrm{T_{sym}=4/f_{sig}}\)) to reach the 1.5 Mbps bit rate.
# High-Power, High-Beam-Quality, Long-Pulse-Width 532 nm Laser Based on a 4f Optical System
## 2 Theoretical Analysis
The calculation formula for the quality factor Q is [18]:  
\[\text{Q}=2\pi\
u_{0}\left(\frac{\text{W}}{\frac{\text{SWC}}{\text{mL}}}\right)= \frac{2\pi\text{mL}}{\delta\lambda_{0}} \tag{5}\]  
In the formula, W is the energy stored in the cavity, \(\delta\) is the single-pass energy loss rate in the cavity; n is the refractive index of the acousto-optic crystal medium, c is the speed of light, L is the geometric length of the cavity and \(\lambda_{0}\) is the laser wavelength in a vacuum. From Equation (5), it can be seen that when the single-pass energy loss rate in the cavity and the length of the laser cavity are fixed, the quality factor Q is inversely proportional to the losses of the optical resonator. This means that when the optical resonator losses are higher, the Q value is lower, the threshold is higher, and laser oscillation is harder to achieve. Conversely, when the losses are lower, the Q value is higher, the threshold is lower, and the laser is more likely to oscillate. Therefore, the threshold of the laser can be adjusted by changing the Q value of the optical resonator. The pulse width of a Q-switched laser can be calculated using the following formula [18]:  
\[\text{t}_{\text{p}}=\frac{2\text{L}}{\text{c}\left(\ln\left(\frac{1}{\text{R }_{\text{cc}}}\right)+\text{L}_{\text{oss}}\right)}\left[\frac{\text{n}_{ \text{i}}-\text{n}_{\text{f}}}{\text{n}_{\text{i}}-\text{n}_{\text{f}}\left( 1+\ln\left(\frac{\text{n}_{\text{i}}}{\text{n}_{\text{f}}}\right)\right)}\right] \tag{6}\]  
where L is the total cavity length, R\({}_{\text{cc}}\) is the reflectivity of the output coupling mirror, c is the speed of light, L\({}_{\text{oss}}\) is the cavity loss, n\({}_{\text{i}}\) is the initial inversion particle density, n\({}_{\text{f}}\) is the final inversion particle density, and n\({}_{\text{t}}=\left[\ln\left(\frac{1}{\text{R}_{\text{cc}}}\right)+\text{L}_{ \text{oss}}\right]/2\sigma\text{L}_{\text{cry}}\) is the threshold inversion particle density. Here, \(\sigma\) is the stimulated emission cross-section at 1064 nm, and L\({}_{\text{cry}}\) is the length of the Nd:YAG crystal rod. As can be seen from the above formula, the simplest and most effective method to achieve long nanosecond pulse lasers is to extend the length of the laser resonator. The characteristics of the 4f resonator ensure that the laser q-parameter remains unchanged with the extended-cavity length, thereby ensuring the stability and efficiency of the output laser.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 3 Application
### Rheological Control Agent  
The rheology of polymer/nanoclay composites is of great relevance to the development of advanced products with application in various fields such petroleum and pharmacy [152]. Most petroleum-based polymers are not compatible with nanoclay materials due to differences in surface energies, thus surfactants are used to reduce the surface energy of clay layers [9]. Nanoclay composites are used in a number of novel applications, such as in asphalt mixtures, where they improve the rutting and fatigue resistance of asphalt mixtures, and enhance the storage stability and the aging resistance of polymer-modified asphalt mixtures [153]. [PERSON] et al. [154] applied nanoclay assisted by surfactants as a stabilizer in CO\({}_{2}\) foam; according to the results, nanoclay improved the stability and formability of CO\({}_{2}\) foam, which led to improved oil recovery from a homogeneous porous medium in a microfluidic device. The success of the implementation of surfactant/nanoclay composites as a foam stabilizer points to the petroleum industry and enhanced oil recovery as potential areas of application for these composites. [PERSON] et al. [155] improved the rheological properties and resistivity of synthetic-based drilling fluids for high temperature applications via the use of nanoclay composites. The yield point and shear stress of the fluids were significantly increased, and the electrical resistivity of the drilling fluids was decreased by approximately 30% via the addition of 1% of nanoclay composites. By applying bentonite nanoclays to asphalt mixtures, [PERSON] et al. [156] improved the initial strength, cracking resistance of asphalt mixtures and reduced their susceptibility to moisture. Nanoclay composites are the primary materials applied in asphalt construction [157]. [PERSON] at al. [158] studied nanoclay composites emulsified asphalt mortar as a damping material for a ballastless high-speed rail track bed. The properties of modified asphalt mortar were examined, including its uniaxial compressive strength, indirect tensile strength, tensile strength ratio, dynamic modulus, phase angle, and shrinkage. The results showed that unlike concrete and asphalt, asphalt mortar emulsified with nanoclay composites exhibited properties that depend on the loading rate and temperature.
## 2 The Pressure Control Configurations Design
### Pressure Control System  
Because the existing fixed bed reactor in the laboratory has a small amount of catalyst loading and limited reaction heat production, it is difficult to meet the needs of this experiment, so it is necessary to design a reactor close to practical production. A water jacket encircled the fixed-bed reactor, which is extensively applied in chemical catalytic reactions conduction [14; 15; 24; 36], especially the diethyl oxalate hydrogenation and FTS, is designed and constructed (Figure 5), with pressurized circulating water with a steam drum serving as the heat exchange medium. A stainless-steel tube for the thermocouple is put in the middle of the fixed bed reactor and the determination of the temperature at different positions of the reactor can be realized by adjusting the height of the thermocouple in the reactor. Thermocoules are also installed at the inlet and outlet of the electric heating furnace, steam drum, and reactor to monitor the temperature at the corresponding position. The circulating steam-water mixture is pressurized into the jacket by entering a drum with an inner diameter of 300 mm and a height of 500 mm. Thermal insulation cotton is wrapped around the electric heating furnace, water circulation pipeline, and steam drum to reduce heat loss. An electric heating wire with a power of 2000 W is installed on the periphery of the water jacket.  
Figure 4: The schematic diagram of the control layout design in KINGVIEW.  
Having analyzed the heat transfer characteristics of FTS, we employed an electrically heated furnace of the fixed-bed reactor as a simulation of reaction exotherm, inspecting the control equality of the PID controller with fixed and BPNN auto-tuning parameters. Our control configuration consists of a PC and basic controllers, such as a pressure sensor (PS), PLC, and pneumatic valve with electrical regulators. By the programmed PLC-data reading function, Python captures the real-time steam-drum pressure signal from the sensor via the SIEMENS PLC analog-input module. The pressure data is reported and displayed in KINGVIEW by the OPC technology between Python and KINGVIEW. Next, Python carries out PID calculations based on the setting parameters of KINGVIEW and delivers control signals to the SIEMENS PLC analog-output module successively and the pneumatic valve lastly, which is turned up when the measured pressure of the drum is above the setpoint. Thereupon, drum pressure remains stable, as well as the temperature of the drum and reactor.
## 2 ARD Framework
### Sentinel Scene Discovery  
A description of the selected scene discovery mechanism is useful in order to provide clarity on the terminology used within the framework. ESA provides the following definitions related to Sentinel-1 and Sentinel-2 data [45]:  
* Products: _\"Products are a compilation of elementary granules of fixed size, along with a single orbit. A granule is the minimum indivisible partition of a product (containing all possible spectral bands).\"_
* Tiles: _\"For Level-1C and Level-2A Sentinel-2 products, the granules, also called tiles, are approximately 100 \(\times\) 100 km\({}^{2}\) ortho-images in UTM/WGS84 projection.\"_ The following terms are introduced with respect to the proposed framework:
* Patches: rectangular or square cut-outs of defined pixel sizes from the complete image.
* Scene: a collection of images covering the entire spatial extent of the target ROI.  
The scene discovery algorithm, shown in Figure 2, starts with the selection of an ROI and the required temporal frequency. The subsequent step is the process of selecting Sentinel products for every date in the time sequence. The selection of the option to obtain Sentinel-1 and Sentinel-2 pairs that cover the target ROI depends on which of the two data sources is considered the primary and secondary satellite, the latter being configurable by the user. The Sentinel-84 API queries the Copernicus Data Hub for the metadata on all Sentinel products intersecting with the ROI, including footprints, date of acquisition and percentage of cloud cover within the given temporal duration. The footprint is then used to select the primary product (P\({}_{1}\)) with the largest overlap to the target ROI, (R\({}_{r}\)), resulting in an intersection (R\({}_{P1}\)). Subsequently, all secondary products (S\({}_{1}\)) overlapping with R\({}_{P1}\) and with close temporal match are queried; the largest secondary product R\({}_{S1}\) is selected. Secondary products are searched again until the complete R\({}_{P1}\) area is covered. The next primary product is then selected to cover the remaining part of the ROI (R\({}_{r}\)-R\({}_{P1}\)). The process is repeated iteratively until all the target ROI is covered. Finally, all subsets of the ROI are collected and then rearranged in descending order of the overlapping area with the target ROI and named R\({}_{1}\), R\({}_{2}\), etc. An example of the procedure is illustrated in the case study presented in Section 3.1.
# Factors of the Technology Acceptance Model for Construction IT
## 3 Research Model and Hypotheses
### Analysis Results on TAM Variables in Construction IT
#### 4.3.2 Analysis about Internal Variables  
We conducted a multiple regression analysis of the existing variables to determine whether the TAM proposed here was applicable to construction IT acceptance. We found that perceived usefulness and ease of use were proportional to attitude and had a significant impact. By looking at beta values, we inferred that perceived usefulness and attitude had an impact on behavioral intention, which had an impact on actual usage. Table 1 and Figure 6 provide a summary of the hypothesis verification results. As in Appendix A,Appendix B,Appendix C,Appendix D,Appendix E beta values refer to the influence on the dependent variable. A beta value of '+' means an influential value; '-' means no influence on the hypothesis. A large _t_-value means a small standard deviation (high correlation), and a small _t_-value means a large standard deviation (low correlation). The hypotheses for the external variable of usage frequency and the independent variables of perceived usefulness and perceived ease of use were all rejected, but at least one of the hypotheses for the other external variables (acceptance type, educational satisfaction, usage knowledge, usage enjoyment, and usage experience) and for the independent variables (perceived usefulness and perceived ease of use) was confirmed. We found that utilization of the TAM is useful in the analysis of construction IT acceptance because the hypotheses based on internal variables were all confirmed.  
Figure 6: Analysis result model of the hypothesis test regarding the TAM of construction IT. \(\beta\) = Standardized Coefficients; \(t\) = test statistics; \(p\) = significance probability (_p_-value).
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 5 Results
### Noisy Channel Simulation Outputs  
The simulation charts below are for a noisy channel with additive white Gaussian noise (AWGN) and Rayleigh fading characteristics.  
Figures 10-15 show the output of simulating the two different QPSKs in a Rayleigh fading channel with AWGN. Distortions are expected in a fading channel [13; 37]. The severity of the distortions is determined by the weaknesses or robustness of the system. According to Equation (4) in Section 3.2, there is an expected bit error probability which is computed based on the energy per bit to noise spectral density ratio. In a system with efficient modulation and demodulation, using an envelope or coherent detector will manage to give an output waveform similar to the input waveform [19; 20]. The decision-making module can estimate the transmitted signal. However, if the modulating wave (carrier signal) is not consistent with the expected signal at the demodulation stage, more errors will be present on the received signal. This is the case with the customary QPSK which is showing a high BER in Figures 10-15.  
Figure 11: Customary QPSK output for 64 data points in a noisy channel.  
Figure 10: GAWDO QPSK output for 64 data points in a noisy channel.  
Figure 14: GAWDO QPSK output for 1024 data points in a noisy channel.  
Figure 12: GAWDO QPSK output for 256 data points in a noisy channel.  
Figure 13: Customary QPSK output for 256 data points in a noisy channel.