esa-sceva/satcom-chunk-collection huggingface dataset:
# AlGaN Ultraviolet Metal-Semiconductor-Metal Photodetectors with Reduced Graphene Oxide Contacts
## 2 Materials and Methods  
Three Al\({}_{\chi}\)Ga\({}_{1-\chi}\)N/GaN (300 nm/1.5 \(\upmu\)m) samples with different Al mole fractions were grown on (0001) sapphire substrates by metalorganic chemical vapor deposition. Before device fabrication, the samples were cleaned by ultrasonication in acetone, methanol, isopropyl alcohol, and deionized (DI) water. Then, the samples were cleaned with buffered oxide etchant (BOE), piranha solution (H\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O\({}_{2}\)), and, lastly, DI water. As shown in Figure 1, for device fabrication, first, the aqueous GO solution prepared by the modified Hummers method [19] is sprayed over the sample, which is laid on a hot plate with a temperature of 180 \({}^{\circ}\)C. The GO/AlGaN/GaN samples are quickly transferred to a furnace chamber for the thermal reduction of the GO films by heating at 800 \({}^{\circ}\)C for 10 min. Next, a probing metal pad of Ti/Au is deposited by an electron beam (E-beam) evaporation system, assisted by a lift-off process. An etch mask of 100 nm thick SiO\({}_{2}\) for rGO etching is deposited by E-beam evaporation and patterned as an interdigitated shape by conventional photolithographic techniques and BOE treatment. Finally, the rGO film is etched out by O\({}_{2}\) plasma treatment before the remaining SiO\({}_{2}\) is dissolved using BOE.  
High-resolution X-ray diffraction (XRD) measurement was performed to determine the lattice parameters of the AlGaN films and thus obtain the Al mole fractions. The measurements of the photodetection properties were performed using a dark-room probe station system equipped with a  
Figure 1: Schematic fabrication process for reduced graphene oxide (rGO)/AlGaN/GaN metal–semiconductor–metal (MSM) photodiodes.  
Keithley 2400 source meter, a Bentham SSM150 Xe switching monochromator, and a calibrated optical power sensor.
# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
###### Abstract
* Frequency and time localization--Apply WT effectively to reduce computational complexity. * Signal-features-based compression--Apply WT to distinguish between the minute signal features during the compression. * Quantization and lossless compression--Apply the hybrid transformation to quantize the coefficients to optimize the lossless image compression during the transmission. Wavelet compression and JPEG fall under the \"transform-based lossy compression algorithms\" umbrella category. Transformation, quantization, and encoding are the three processes that make up these approaches. A picture is converted from grayscale values in the spatial domain to coefficients in another domain in a lossless procedure known as transformation. The Fourier transform, used to recreate magnetic resonance (MR) pictures, is one well-known transform. The DCT and WT are two more transformations more often employed in compression. The transformation phase does not result in any information loss. Information is lost during the quantization process. While less significant coefficients are loosely approximated, frequently as zero, efforts are made to preserve the more significant coefficients. Converting floating points to decimals might be enough to perform quantization: values are converted to integers. The quantized coefficients are finally encoded. The quantized coefficients are compactly represented in this step, which is also lossless, to allow for adequate image storage or transmission. The paper is organized as follows. Section 2 presents a literature survey, Section 3 discusses the proposed model, and Section 4 focuses on the results and analysis. The conclusions and a consideration of future enhancements are drawn in Section 5.
## 3 System Model and Problem Formulation
### User Movement Model  
In this article, rescue workers are randomly distributed in different locations in various rescue centers. Their goal is to conduct a blanket search of the designated area. We assume that the rescuers' movement follows a Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\), and that they are constantly in action throughout the period. We use \((x_{n},y_{n},h)\) to represent the position coordinates of the UE \(n\), where \(h\) represents the height of the UE. We assume that the height of all the UEs from the ground is \(h\).
###### Abstract  
The second heart sound consists of aortic and pulmonary components. Analysis on the changes of the second heart sound waveform in respiration shows that the aortic component has little variation and the delay of the pulmonary component is modulated by respiration. This paper proposes a novel model to discriminate the aortic and pulmonary components using respiratory modulation. It is found that the aortic component could be simply extracted by averaging the second heart sounds over respiratory phase, and the pulmonary component could be extracted by subtraction. Hence, the split is measured by the timing difference of the two components. To validate the measurement, the method is applied to simulated second heart sounds with known varying splits. The simulation results show that the aortic and pulmonary components can be successfully extracted and the measured splits are close to the predefined splits. The method is further evaluated by data collected from 12 healthy subjects. Experimental results show that the respiratory split can be accurately measured. The minimum split generally occurs at the end of expiration and the split value is about 20 ms. Meanwhile, the maximum split is about 50 ms at the end of inspiration. Both the trend of split varying with respect to respiratory phase and the numerical range of split varying are comparable to the results disclosed by previous physiologists. The proposed method is compared to the two previous well known methods. The most attractive advantage of the proposed method is much less complexity. This method has potential applications in monitoring heart hemodynamic response to respiration.  
sec:second heart sound; respiratory split; aortic component; pulmonary component; respiratory modulation +
Footnote †: journal: Applied
# Rendering Strategy to Counter Mutual Masking Effect in Multiple Tactile Feedback
## 3 The Proposed Rendering Algorithm
### Results  
Figure 8a shows the average rate of correct answers for the presented objects when the proposed rendering algorithm was applied or when it was not.  
The first column in the figure refers to the case when the answer to both geometry and texture was correct. The second or third columns refer to cases when the answer to geometry or texture was correct, respectively. Considering both geometry and texture (first column), the average rate of correct answers was significantly higher when the rendering algorithm was applied (0.644 \(\pm\) 0.079 vs. 0.878 \(\pm\) 0.031, \(p<0.01\)). The result implies that two independent cues, i.e., geometry and texture, were successfully presented to the users at the intended perceived intensity by applying the proposed  
Figure 8: Result of the user test: (**a**) mean rate of correct answers in the geometry and texture discerning test. Error bars represent the standard deviation. Asterisks denote a significant difference between the groups (\(p<0.01\)); (**b**) example of the expected perceived intensity without the proposed algorithm (a case of combined bump and rough).  
rendering algorithm. The mean rate of correct answers to the presented geometries regardless of the texture (second column) was also significantly higher when the rendering algorithm was used (0.719 \(\pm\) 0.076 vs. 0.943 \(\pm\) 0.022, \(p<\) 0.01). However, the mean rate of correct answers to the presented textures regardless of the geometries (third column) was not affect by the algorithm (0.880 \(\pm\) 0.053 vs. 0.928 \(\pm\) 0.022, \(p=\) 0.207). When the proposed algorithm was not applied, some distortion in both geometry and texture information could have happened, as was discussed in Section 3.1 above. Figure 8b shows an example of an expected perceived intensity when a bump with rough texture was rendered without the proposed algorithm. As expected, the perceived intensity of the MV was severely distorted due to the masking effect, unlike the reverse case, because the slope and intercept of the masking function of the MV (approximately 1.09 and 1.39, respectively) were larger than those of the EV (approximately 0.66 and \(-\)6.22, respectively). It is noteworthy that the distorted MV has the opposite profile to that of the intended perceived intensity of the EV. This might cause confusion, particularly when discriminating geometries, as it adjusts based on changes in the stimulus intensity. Texture information may not be perceived as uniformly as intended, but the participants could guess the correct answer because the intensity of the MV was high enough to allow recognition at certain positions (e.g., near the center of horizontal axis).
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 4 Big Data Collection in LS-WSNs
### Static Architectures
#### 4.1.1 Static-Sink and Static-Sensor Architecture  
This architecture consists of a large number of sensors capable of collecting and transmitting environmental data in an autonomous way. The position of these sensor nodes is not necessarily predetermined. They can be randomly dispersed across the area of interest. This type of architecture is characterized by a high density in order to find at least one path between any two network nodes. Moreover, one of the major drawbacks of this architecture is that it shortens the lifetime of the network. The authors in References [25; 80; 81; 82] reported a problem of rapid depletion of batteries from neighboring sensor nodes to the sink. The relay of data from the source sensor to the sink is the source of the network's nonuniform energy consumption. Thus, neighboring sensor nodes at the sink, being the most requested in the data transfer to the sink, quickly exhaust their energies, and the sink, in turn, is out of order. This fact reduces the lifetime of the network. For instance, applications that use this type of architecture include the VOLCANO [47], GLACSWEB [83], and the Habitat Monitoring on GDI (Great Duck Island) projects [84].
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 3 An Overview of LoRaWAN and SD-WAN Technologies
### WAN and SD-WAN Technologies
#### 3.2.1 SD-WAN Technology Definition  
SD-WAN is a new technological deployment that works as an alternative to today's WAN communication technology. An SD-WAN network provides suitable automotive procedures for better data tracing from point to point (p2p). These automated actions can evolve SD-WAN into a productive, stable, and reliable solution for an organization [38]. The term SD-WAN comes from the words Software, Defined, Wide Area, and Network, and is part of a new era of implemented SDN that drives rooting packets between data centers and headquarters to a WAN with higher speeds. Before this implementation, companies and organizations were obliged to use specific network equipment and discrete leased circuits at any monetary cost. SD-WAN can provide balanced loaded data traffic by using multiple WANs and increased bandwidth [32]. Using the SDN benefits the private network as it separates into two layers, the \"control\" layer and the \"data\" layer. The \"control\" layer is managed over the cloud, or by using on-premises infrastructure. A software management implementation is responsible for managing how \"data\" will be transferred over the network, as they can be driven by separated paths depending on the network traffic and the pre-planned plan [39].
###### Abstract  
Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China's terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05\({}^{\rm o}\)) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m\({}^{-2}\) d\({}^{-1}\) and 5.07 g C m\({}^{-2}\) d\({}^{-1}\) for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area.  
G 2019/11, 1855; doi:10.3390/rs11161855
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 4 Case Study: Shangmao Landslide
### Model Training  
The constructed dataset was divided into training and testing sets by the ratio of 7:3. The ResNet model was constructed for classification training.  
The hyperparameters were also determined by the random search algorithm as follows: the model uses four residual blocks, and is optimized using the Adam optimizer. The loss function is the categorical cross-entropy, the batch size is 32, the initial iteration number is set as 1500 and the initial learning rate is set as 0.01. To speed up the training, a learning rate dynamic decay strategy was employed to accelerate convergence and the EarlyStopping method was used to stop the iterations before overfitting and terminate training (i.e., stop training when the accuracy is not increasing within 100 iterations).  
The training results show that the accuracy, precision and recall of the algorithm are 0.993, 0.993 and 0.925, respectively. The trained ResNet algorithm was applied to the slide deformation dataset, and the deformation state classification results are shown in Figure 17.  
Figure 16: Results of gray correlation analysis for the Shangmao landslide.  
Figure 17: ResNet warning results of the Shangmao landslide.  
The above results show that the K-means-ResNet method can identify the deformation state of a landslide accurately. According to the deformation state, we can make a landslide warning.
## 3 Calculation Mesh and Boundary Condition Setting
### Independence Analysis  
#### 3.3.1 Mesh Independence Analysis  
The mesh-independence analysis takes the conventional submerged inlet as an example. In order to minimize the impact of the number of meshes on the accuracy of the results, four sets of mesh are finally obtained by adjusting the density of mesh nodes. The number of volume meshes is 0.9 million, 1.2 million, 3 million, and 5 million, respectively. The numerical simulation results and experimental results of four sets of mesh are shown in Table 1. Compared with the experimental results, the error of the calculated results is less than 0.65%, which indicates that the accuracy of the numerical solution of the flow field is basically independent of the number of meshes after the meshes are larger than 0.9 million. In order to save calculation time, this paper uses the node density of mesh scheme 1 to calculate.  
It should be noted that the computational mesh used in this paper is unstructured polyhedral mesh. At the same density, the number of polyhedral meshes is much lower than that of conventional unstructured tetrahedral mesh. Taking the polyhedral mesh with a mesh number of about 0.9 million as an example, using the same face mesh, the same boundary layer setting, and the same volume mesh growth rate, the total volume mesh number when dividing the tetrahedral mesh is about 3.7 million, of which the boundary layer prism mesh accounts for about 0.7 million, and the tetrahedral mesh far from the wall accounts for about 3 million. The geometry of the submerged inlet and projectile studied in [34; 38] is similar to that in this paper. The number of calculation mesh is 0.9 million, so the calculation mesh used in this paper can meet the requirements of calculation accuracy.
# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 3 Materials and Methods
### The EECP-CBL Model
\[x^{\prime}{}_{i}=CNN(\mathrm{x}_{i}) \tag{2}\]  
where \(x_{i}\) is the input vector to the CNN network with the energy consumption and \(x^{\prime}_{i}\) is the output of the CNN network to be fed to the next Bi-LSTM network. The proposed framework first passes the input vector to CNN and obtains the new vector \(x^{\prime}_{i}\) based on the Equation (2). Then, to understand Bi-LSTM, we introduce the LSTM with forget gate structure proposed in [32]. The formulation is denoted by,  
\[i_{t} =\sigma(W_{f}([x_{t},\ y_{t-1}])) \tag{3}\] \[f_{t} =\sigma(W_{f}([x_{t},\ y_{t-1}]))\] (4) \[o_{t} =\sigma(W_{o}([x_{t},\ y_{t-1}]))\] (5) \[g_{t} =tanh\Big{(}W_{g}([x_{t},\ y_{t-1}])\Big{)}\] (6) \[c_{t} =f\odot c_{t-1}+i\odot g\] (7) \[y_{t} =\circ\circ tanh(c_{t}) \tag{8}\]  
where \(i,f\), \(o\), \(g\) and \(c\) are input, forget, output, and input modulation gate respectively. Note that these are in n-dimensional real vectors. In Equations (3)-(6), the notation \(\sigma\) is a sigmoid function and \(W_{i}\), \(W_{f}\), \(W_{o}\), and \(W_{g}\) are fully connected neural networks for the input, forget, output, and input modulation gates respectively. In Equations (7) and (8), the notation \(\odot\) is an element-wise product operator. LSTM model only considers one directional information on a sequence which leads to reduce the effectiveness of LSTM model. Moreover, multiple directional information on the sequence contains valuable information. Therefore, bidirectional long short-term memory denoted by Bi-LSTM was developed which combines the forward and backward directions in the sequence [33]. The key idea of Bi-LSTM model is that it looks a particular sequence both from the front-to-back as well from the back to front. In which, a LSTM layer is for forward processing while the remaining layer is for backward processing. In this way, the network could capture the change of energy power both its past as well as its future. To understand this model, let's considering an input sequence \(x\) with \(n\) elements. The order of the forward LSTM is \(\{x_{1},x_{2},\ldots,x_{n}\}\) while that of the backward LSTM is \(\{x_{n},x_{n-1},\ldots,x_{1}\}\). After the training process of the forward and backward LSTMs separately, these LSTMs are integrated by fusing their outputs in the previous step denoted as,  
Figure 2:0  
where \(y_{F}\) and \(y_{B}\) are the outputs of the forward and backward LSTMs, respectively while the notation \(\oplus\) expresses any integration operator including a simple adder, a neural network etc. In other words, Bi-LSTM combines the forward and backward directions based on Equation (9). The outputs of Bi-LSTM are fed to two fully connected layers to generate energy consumption values. The structure and configuration details of proposed approach are presented in Figure 2:1
## 3 Results of Literature Review and Economic Assessment
### Replacing Air by CO\({}_{2}\) at Liquid-Vapor Equilibrium in a Hydro-Pneumatic UPHES
#### 3.2.3 Operation as Short-Duration or Medium-Duration Storage, plus as Heat-Pump
Because of light thermal irreversibilities due to the temperature pinches at heat exchangers (i.e., 5 K or more at each side) occurring during the back and forth changes of phases of CO\({}_{2}\), the RTE of this pressurized UPHES shall be slightly lower than the RTE of conventional gravity PHES or of gravity UPHES. In this respect, it is specified that although Figure 4 shows several thermodynamic cycles close to the critical point of CO\({}_{2}\), it is likely that simulation and experimentation will encourage the authors to operate alternative cycles farther apart from this point, to ensure a better stability of the volumetric mass of the fluid as well as a better heat-exchange coefficient. RTE shall however be significantly higher than the RTE of hydro-pneumatic UPHES using air or any other gas in the sole gaseous phase. Our preliminary target for RTE in the simplest application for storage of short and medium duration, without the heat-pump function, is 73%, computed from typical efficiency of 90% each for the hydro pump set and the hydro turbine set and from 5% as an arbitrary valuation of the loss of work due to the temperature pinch at each of the two heat exchanges. When the system is requested to produce heating of mid-high temperature (50-80 \({}^{\circ}\)C) and/or cooling of mid-low temperature (5-15 \({}^{\circ}\)C), the absolute figure of recovered electricity in kWh needs to be lowered by a value approximately equal to 1/10 th of the thermal energy exported or brought-in. Validation by simulation and experimentation are planned in a later stage and are absolutely needed to ascertain a more reliable value of RTE in each storage mode. A cooperation agreement has been signed with a French public research institute to assist in simulation of heat exchanges, in assessment of RTE and in the dimensioning of heat exchangers for a small-scale aerial prototype. As a crude estimate of the pure Capex for the supply of heating and cooling, we can simply divide the Capex for electricity by 500%, which brings a Capex of heat and cooling to roughly equal 300 USD/kW Thermal + 40 USD/kWh Thermal. Related to this use as storage of short and medium duration and compared to electrochemical storages such as batteries, whose Capex is expected to reach similar values of 200 USD/kWh by 2025-2030s, this novel UPHES offers the distinctive benefits of a longer lifetime (40 years expected), of not relying on foreign-sourced metals nor on high embodied-carbon metals, of not requiring challenging disposal nor recycling, plus of displacing a large amount of heating and cooling at each contraction or expansion of the CO\({}_{2}\) mass.