esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The current study comprehensively assessed both the individual and interactive impact of three soil physical parameters, including clay content (\(X_{1}\)), moisture content (\(X_{2}\)), and density (\(X_{3}\)), on two soil shear strength indicators, soil cohesion and internal friction angle, for paddy soils in the plastic state. A prediction model was established to quantify the response of soil cohesion and the internal friction angle to the change in clay content, moisture content, and density.  
Our results indicate that all three environmental factors had a significant impact on both soil cohesion and the internal friction angle, and the individual effects of these factors on soil cohesion were in the order of \(X_{1}>X_{2}>X_{3}\), while the order was \(X_{2}>X_{3}>X_{1}\) for the internal friction angle. For the interactive factors, the order of contribution to soil cohesion was \(X_{1}X_{2}>X_{1}X_{3}>X_{2}X_{3}\). The impact of interactive factors on the internal friction angle was in the order \(X_{1}X_{2}>X_{2}X_{3}>X_{1}X_{3}\); however, none of these factors was significant. Statistical analysis suggested good performance of the model for predicting the soil shear indicators based on soil clay content, moisture content, and density, with \(R^{2}\) at 0.91 and 0.89 for cohesion and internal friction angle, respectively. After the model setup, our models were successfully validated with another set of soil samples, with all the PBIASs between measured and estimated values within 15% and RRMSEs less than 11%. In conclusion, this study provides a quick and promising prediction of soil shear characteristics for paddy soils in the plastic state, which is a sound basis for the design and improvement of agricultural machinery in paddy fields.  
Conceptualization, Q.J. and Y.W.; methodology, Q.J.; software, M.C. and Z.H.; validation, Q.J., M.C. and J.W.; formal analysis, M.C.; data curation, M.C.; writing--original draft preparation, M.C.; writing--review and editing, Q.J.; visualization, M.C.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2016 YED0700601, and the APC was funded by Zhejiang University.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### The Communication Simulator
#### 2.2.1 UFMC Principles  
Figure 8 shows the functional blocs of UFMC adapted to VLC. As OFDM has widely been studied and standardized, its working principles are not reiterated in this paper. The interested reader can find information about it at the following reference [31]. The input data of UFMC are also complex symbols resulting from an IQ in-phase and quadrature (IQ) symbol mapping method just like OFDM. In UFMC, the total bandwidth is first divided into B sub-bands. The filtering operation in UFMC is carried out per sub-band (cf Figure 8). The data from each sub-band undergo an IFFT operation, and the Chebyshev filter (of length \(L\)) is applied in the time domain on each sub-band. To be compatible with VLC, OFDM uses the Hermitian symmetry (HS) property to make it real-valued by making the data symbols transmitted on positive frequencies the complex conjugates of those on negative frequencies. As opposed to OFDM, it is more difficult to apply the HS to UFMC signals to make it a real-valued signal. Since the filter stage is after the IFFT, it does not make sense to add HS as in OFDM. Adding an extra IFFT stage before sending on the transmission channel does not solve the problem either. Therefore, a juxtaposition method is used instead to have a real-valued signal HS-free UFMC signal, as suggested in [32] (cf Figure 9). A zero-padding technique is applied on each received symbol of length (\(N+L-1\)) at the receiver side to reach a sample number equal to twice (\(2N\)) the subcarrier number \(N\) used at the emission. This operation is followed by the FFT and the down-sampling technique for data recovery.  
The OFDM and UFMC data are generated thanks to a MATLAB program that can also be found in our Github [28]. The constraint of the VLC technology is using a real and positive signal to modulate the LED's current. This implies that the two modulations, developed in the framework of RF transmission, require adequate changes to fulfill these constraints. The juxtaposition method has been chosen to have a real-valued signal, and a constant bias is added to have a positive-valued signal. The juxtaposition method consistsof generating a conventional complex signal (OFDM or UFMC) and juxtaposing all the real parts and all the imaginary parts of the complex numbers forming the desired symbols in the time domain to obtain a real-valued signal. The mechanism of juxtaposition is illustrated in Figure 9.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Investigation of the Reaction of Zry-4 in Steam, Nitrogen/Steam, and Air/Steam Atmospheres  
In the framework of the severe accident research after the Fukushima accidents in 2011, the reaction of the nuclear fuel cladding tubes in steam/nitrogen and steam/air gas mixtures came into focus in research. This is relevant if the emergency cooling water is pressurized by nitrogen or for spent fuel pool accidents. For thermodynamic reasons, zirconium reacts at first with O\({}_{2}\) and if oxygen is consumed, and the reaction with steam  
Figure 10: Development of the hydrogen concentration during isothermal oxidation of Zry-4 in steam at temperatures of 1000, 1100, 1200, and 1300 \({}^{\circ}\)C.  
starts. If both oxygen and steam are no longer available, zirconium reacts with nitrogen. If the oxygen and steam starvations are valid for the whole sample, it is called global. If the starvation occurs only at special positions, for instance at the tips of cracks through the oxide scale, it is called local. A model for these reactions was developed, predicting the dependence of the reaction kinetics on the total gas flow: the surprising outcome that the reaction rate is the higher the lower the gas flow is, was experimentally confirmed.
# Image-to-Image Translation Using Identical-Pair Adversarial Networks
## 2 Related Work  
A variety of methods using feedforward CNNs have been proposed for image-to-image translation tasks [20]. Feedforward CNNs are trained and updated by backpropagation. The feedforward model can be run forward very fast in test time. Image semantic segmentation methods generate dense scene labels from a single input image [21; 22; 23]. Image de-raining methods aim to remove rain or snow from the scene [6]. Image inpainting attempts to recover an image that has a missing part(s) [8; 24]. Feedforward CNNs approaches have also been applied to image colorization [25].  
Recently, GANs-based approaches have been developed for image-to-image translation as well. A Generative Adversarial Network [26] learns a generative model, which generates samples from real-world data. A generative network and a discriminative network of GANs compete with each other by playing a minimax game. Through the competition, GANs can generate realistic images from random noise input. Since then, They have been proposed continuously, such as InfoGANs [27], Energy-based GANs [28], WGANs [29], Progressive GANs [30], and SN-GANs [31]. Their approaches used for image-to-image translation tasks include ID-cGANs [32] for image de-raining, iGANs [33] for an interactive application, the IAN [34] for photo modification, and Context Encoder for image inpainting [8]. The Pix2 pix-cGANs were used to solve several image-to-image translation tasks, such as object edges image to picture, aerial pictures to maps, or semantic labels to street view. It was claimed to be the first generic image-to-image translation framework of GANs-based approaches [19]. PAN [11] is another approach that results in better quality of the generated images. PAN proposed perceptual loss in addition to the generative adversarial loss. Then cycleGANs [16] approached it one step further to both pair and unpair image-to-image translation.  
Blurriness and realistic approximation at the human level are still challenges that require more study in this field.
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
## 3 Local Search Maximum Likelihood Parameter Estimator
### TF Coarse Estimator  
A simple and effective parameter estimation method of the chirp signal is to estimate the IF. The IF represents the instantaneous frequency at different time, which combines the initial frequency \(f_{0}^{\prime}\) and chirp rate \(k^{\prime}\) in a linear and clear relationship. TF coarse estimator directly uses the IF information of \(y^{\prime}(n)\). So, its performance is highly influenced by the time-frequency localization and energy concentration about the IF. It is well known that the classical WVD provides a high-resolution TF representation of a mono-component chirp signal [38]. Here we employ WVD for coarse estimation. One important factor that should be noticed in TF coarse estimator is the SNR, since we directly use IF, only when the SNR is high enough, the result is acceptable. We will analyze the improvement of SNR between \(y^{\prime}(n)\) and \(y(n)\) later.  
For a discrete signal \(y^{\prime}(n)\) with N samples, the WVD is defined as Equation (2).  
\[WVD(t,f)=\sum_{m=-N}^{N}y^{\prime}\Big{(}n+\frac{m}{2}\Big{)}y^{\prime*}\Big{(} n-\frac{m}{2}\Big{)}e^{-\frac{(2\pi nm}{N}} \tag{2}\]  
For an ideal chirp signal, its WVD is an impulse spectral line along its IF, finite length chirp signal's WVD is dorsal fin shape as shown in Figure 5. We can see that the spectrogram of the chirp signal obtains the maximum value at the frequency point \(f_{0}^{\prime}+\ k^{\prime}t\) at each discrete time \(t_{n}\). Therefore, the IF of \(y^{\prime}(n)\) can be estimated by maximizing the spectrogram at each discrete time as Equation (3).  
\[f_{n}^{\prime}(t_{n})=\underset{f^{\prime}}{argmax}|WVD(t_{n},f)| \tag{3}\]  
Figure 4: Schematic diagram of local search estimator.  
Considering that there are 2L+1 discrete IF points, then the initial frequency \(f^{\prime}_{0}\) and chirp rate \(k^{\prime}\) can be obtained by statistically averaging as Equations (4) and (5).  
\[f^{\prime}_{0}=\frac{1}{2L+1}\sum_{n=-L}^{n=L}f^{\prime}_{n}(t_{n}) \tag{4}\]  
\[k^{\prime}=\frac{1}{(t_{L/2}-t_{-L/2})(L+1)}\left[\sum_{n=0}^{n=L}f^{\prime}_{n }(t_{n})-\sum_{n=-L}^{n=0}f^{\prime}_{n}(t_{n})\right] \tag{5}\]
###### Abstract  
Recent advancements in Information Technology (IT) have sparked the creation of numerous and diverse types of devices and services. Manual data collection measurement methods have been automated through the use of various wireless or wired sensors. Single sensor devices are included in smart devices such as smartphones. Data transmission is critical for big data collected from sensor nodes, such as Mobile Sensor Nodes (MSNs), where sensors move dynamically according to sensor mobility, or Fixed Sensor Nodes (FSNs), where sensor locations are decided by the users. False data transfer processing of big data results in topology lifespan reduction and data transfer delays. Hence, a variety of simulators and diverse load-balancing algorithms have been developed as protocol verification tools for topology lifespan maximization and effective data transfer processing. However, those previously developed simulators have limited functions, such as an event function for a specific sensor or a battery consumption rate test for sensor deployment. Moreover, since the previous load-balancing algorithms consider only the general traffic distribution and the number of connected nodes without considering the current topology condition, the sustainable load-balancing technique that takes into account the battery consumption rate of the dispersed sensor nodes is required. Therefore, this paper proposes the Sustainable Load-balancing Scheme (SLS), which maximizes the overall topology lifespan through effective and sustainable load-balancing of data transfer among the sensors. SLS is capable of maintaining an effective topology as it considers both the battery consumption rate of the sensors and the data transfer delay.  
sustainable load-balancing; cloud computing; convergence processing; green communication +
Footnote †: journal: _8, 436_, doi:10.3390/su8050436
# _Ku_-Band CMOS Power Amplifier with Three-Stack Power Stage to Enhance Output Power and Efficiency
## 2 Typical CMOS Power Amplifiers
### Stack Structure
Therefore, if the \(n\)-stack and the common-source structures are designed to have the same output power, this means that the efficiency of the \(n\)-stack structure can be improved.
## 5 Experimental Results
### Evaluation Parameters  
In this paper, we use the PySLAM framework [22], with all the features integrated into this framework. PySLAM's development source code is in Python v3.9 language and programmed on Ubuntu 20.04. For the DPVO build source code, we used the code at the link ([[https://github.com/princeton-vl/DPVO](https://github.com/princeton-vl/DPVO)]([https://github.com/princeton-vl/DPVO](https://github.com/princeton-vl/DPVO)), accessed on 6 May 2024). For the TartanVO build source code, we used the code at the link ([[https://github.com/castacks/tartanov](https://github.com/castacks/tartanov)]([https://github.com/castacks/tartanov](https://github.com/castacks/tartanov)), accessed on 6 May 2024). At the same time, there is support from several open-source libraries such as Numpy (1.18.2), OpenCV (4.5.1), PyTorch (\(\geq\)1.4.0), and Tensorflow-gpu (1.14.0). The PySLAM framework was implemented on computers with the following configuration: CPU is 12,400 f, 16 G DDr4, GPU RTX 3060 12 GB. In this paper, with the DPVO and TartanVO networks, we performed three experiments (\(L1,L2,L3\)) with each subset of the TQU-SLAM benchmark dataset for estimating the camera motion trajectory. The results are shown in the next section.  
Figure 10: Illustration of TartanVO architecture [21].
## 3 Materials and Methods
### Overview of the Applicable Methods
AHP-TLF MCGP additionally uses the Taguchi loss function to minimize product or service quality errors, while AHP helps in multi-criteria decision making. Problems in the transportation sector are often characterized by a limited number of alternative solutions, a complex system of objectives and criteria, and many unknowns. As a result, these problems are usually \"weakly structured\", which makes MADM/MCA methods particularly suitable for solving them. The previously presented analysis of relevant research and case studies revealed that the most commonly used MADM/MCA methods in the transport sector are AHP (especially for weighting the criteria), MAUT/MAVT, higher-order methods (such as ELECTRE and PROMETHE), and SAW. Different methods are often combined, or certain parameters are adjusted (e.g., introduction of vague criteria) in order to better adapt the methodology to a specific decision-making problem. In addition, other methodologies, such as CBA or GIS tools, can be included in the decision-making process or the presentation of results. The application of MODM methods in the transport sector is less common and is usually used for optimization problems, where genetic algorithms or specialized heuristic procedures are most often applied (Figure 2). Table 2 provides a comparative overview of the most common types of MCDM techniques, including their advantages, disadvantages, and application. \begin{table}
\begin{tabular}{c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
Although the applied MCDM methods can have significant differences, in most cases it is necessary to define the goal, criteria, and alternatives during the decision-making process. This definition largely depends on the characteristics of each decision-making problem and is actually an important part of the decision-making process. In general, decision-making involves choosing one of the alternatives with which a certain problem can be solved. The goal refers to the state of a certain system that is desired to be achieved by making a decision, while the criteria are attributes that describe the alternatives. Also, the criteria measure the achievement of the given goals, while the weights of the criteria reflect their importance. As a rule, the importance of the criteria depends on the preference of the manager (decision-maker) who decides on their individual importance [51]. After the comparison of MCDM methods, the AHP method has the greatest application in practice, primarily due to its relatively fast and simple implementation and the availability of tools. Due to the previously mentioned advantages, most managers choose this method when solving a certain problem. Since this method is applicable to this research, where criteria and alternatives require prioritizing, weighting, and selection of the optimum to achieve the set goal, it was selected as most appropriate for this research study.
## 3 Brief Overview of the OWC Technologies  
The four main OWC technologies, namely visible light communication (VLC), light fidelity (LiFi), optical camera communication (OCC), and free space optics (FSO), are considered to be promising in meeting the demands of 5G/6G and IoT networks for their special features. Figure 1 illustrates brief architectures of these technologies. In terms of infrastructure, these technologies have differences in the type of transmitter, receiver, and communication media. The VLC uses light-emitting diodes (LEDs) or laser diodes (LDs) as transmitters and photodetectors (PDs) as receivers. Only visible light (VL) is used as the communication medium in the VLC. LiFi is similar to the wireless fidelity (WiFi) technology. It provides high-speed wireless connectivity along with illumination and uses LEDs or defuse LDs as transmitters and PDs as receivers. It uses VL for the forward path and infrared (IR) as the communication medium for the return path. However, it can also use VL as the communication medium for the return path. The receiver devices in most user equipment, such as smartphones, are not equipped with high-power LEDs; thus, the uplink communication in the VLC and the LiFi cannot perform well [37; 38; 39]. Moreover, they also cannot perform well in return path if the uplink is a diffused light and faces serious interference affected by the downlink lights. The OCC uses LED array or light as a transmitter and a camera or image sensor as a receiver. The built-in complementary metal-oxide semiconductor cameras facilitate the ability to capture photos and videos [40]. The camera can be either global shutter or rolling shutter [41] type. OCC normally uses VL or IR as the communication medium. However, ultraviolet (UV) spectrum can also be used as the communication medium. The FSO technology usually uses LD and PD as the transmitter and the receiver, respectively. However, heterodyne optical detection receiver is also used in FSO communication. It is normally operated usingthe IR as the communication medium but can also be operated using VL and UV. Table 2 presents the performance metric comparison among the various OWC technologies. The differences among these technologies are very specific. The unique characteristic of VLC is the use of visible light as communication media. A LiFi system must support seamless mobility, bidirectional communication, and point-to-multipoint, as well as multipoint-to-point communications. Only the OCC system uses camera or image sensor as a receiver among all the OWC technologies. Due to the narrow beams of focused light from an LD transmitter, an FSO system can form a very long distance as well as a high-data-rate communication link. The detail differences among the OWC technologies can be found in our previous work in [2].
# Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction
## 4 Analysis and Discussion
A vertical displacement of approximately 0.045 mm was observed approximately 80 m before the adjacent excavation work area. However, the vertical displacement resulting from the adjacent excavation work was approximately 0.555 mm, corresponding to a 0.51 mm increase. Decreasing and increasing sections are typically observed when using track bed settlement sensors. Therefore, because changes appear simultaneously when monitoring subway track structures, a safety evaluation of the minimum and maximum values is necessary. In this study, the analysis results of the measurements obtained by the tunnel convergence meter and track bed settlement sensors installed at the same location were compared. The vertical displacement analysis results of the subway track and tunnel structure revealed a significant difference in the vertical displacement between the tunnel and track in Sections A and C. By contrast, Section B, the center of the adjacent excavation work area, had a larger tunnel vertical displacement than the track. The results revealed a difference in the vertical displacement of the tunnel and track owing to the adjacent excavation work. Thus, in subways, the behavior of tunnels and tracks may differ; therefore, track and tunnel monitoring should be performed simultaneously.
# Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
## 2 Theoretical Analysis and Proposed CNN Denoiser
### Proposed CNN Denoiser  
#### 2.2.1 Network Architecture  
CNN has been successful in processing various visual tasks and has demonstrated effective performance in handling Gaussian denoising. In this work, SDCNN for denoising the wrapped phase is based on FFDNet [21]. The deeper neural network architecture is illustrated in Figure 2. SDCNN consists of a series of \(3\times 3\) convolution layers. Each layer is composed of three types of operations: convolution (Conv), batch normalization (BN), and rectified linear units (ReLU). More specifically, \"Conv + ReLU\" is adopted for the first convolution layer, \"Conv + BN + ReLU\" for the middle layers, and \"Conv\" for the last convolution layer. The noisy \(S_{N0}\) and \(C_{N0}\) fringe patterns are, respectively, reshaped into four sub-images, which are then input into the 32-layer CNN together with the noise-level map. The final output is reconstructed by the four denoised sub-images.