esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification
###### Abstract
The most significant contribution of this work is the development of a deep ConvLSTM autoencoder-based surrogate model. The main function of the developed model is to capture and learn the full wavefield representation--the animation of propagating waves, their reflections from boundaries and defects, waveform entrapment at the delamination, etc. Subsequently, it transforms this information into a compressed domain known as the latent space. Separate training is performed for the encoder so that inputs containing both reference frames (without delaminations) and an image showing the shape and location of the delaminations are only needed to restore the full wavefield of guided waves interacting with presented defects. The inference of the DL model replaces the solver of the system's governing equations, i.e., the forward modelling step in the inverse method, thus resulting in significant time and computational cost savings. The novelty of this research lies in the proposed architecture of a deep ConvLSTM-based autoencoder, as well as its utilisation in the inverse procedure for delamination identification. The particle swarm optimisation (PSO) approach was selected and applied [30] in the inverse procedure.
## 2 Optimization
### Adaptive Learning Rate Optimizers  
While previously presented optimization algorithms use the same learning rate to modify all parameters of the model, some new optimization algorithms developed from the 2010s seek to upgrade this original behavior of SGD by allowing the algorithm to _adaptively_ change the learning rate per parameter during the training process. A brief overview of the most commonly used optimizers considered adaptive is given below.
## 3 Experiments and Results  
A classification model based on the shapelets from the current envelopes is developed to disaggregate the appliances and categorize them into different classes. The developed classification model has 12 classes with a training dataset constituting 80% of the entire database and a testing dataset constituting 20% of the entire database. This work investigates five supervised classification algorithms. Section 3.1 articulates the study of the applied classification methods. Section 3.2 describes the classification performance analysis.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## Appendix A  
According to Figure 4, the AM-AM characteristics of the nonlinear PA are as follows: as \(\dfrac{r}{r_{\text{max}}}\) increases, \(\dfrac{\gamma_{o}}{r_{o,\text{max}}}\) changes from a rapid increase to a slow increase, and even after a certain value begins to fall, the maximum value does not exceed one. We can then substitute \(g(r)\) of the polynomial model into Equation (10). When \(\dfrac{r}{r_{\text{max}}}\) is \(0\to 1\), the \(g(r)\) curve tends to be flat--that is, \(g^{\prime}(1)<g^{\prime}(0)\). Let \(r_{1}\) be the maximum point of \(g(r)\), then  
Figure 9: Power spectral density (PSD) of the information signal after nonlinear PA. In the simulation, we select the OFDM signal with the bandwidth of 5 MHz as the information signal and the multitone sine signal as the energy signal, and we create the power spectral density images of the information signal after PA of Class B when the given SNDR of I(1) is 10 dB, 20 dB, 30 dB.  
\[g^{\prime}(r_{1})=0\]  
. At this time, \(\dfrac{r_{1}}{r_{\max}}>1\), \(\dfrac{g(r_{1})}{r_{\rm o,max}}\leq 1\). We take up \(\dfrac{r_{1}}{r_{\max}}=2\) as the maximum value point and calculate the coefficient value that satisfies the above constraints; then, we obtain \(c_{1}=-\dfrac{3}{16}\), \(c_{2}=\dfrac{1}{64}\).
## 3 Adaptive Time-Frequency Analysis
### Empirical Mode Decomposition and Optimization Models
#### 3.1.3 Multivariate Ensemble Empirical Mode Decomposition  
Definition  
Multivariate ensemble empirical mode decomposition (MEEMD) is an improved method of empirical modal decomposition. It evenly distributes the extrema of the signal by adding pairs of positive and negative Gaussian white noise and reduces the number of spurious components through multiple trials. MEEMD not only inherits adaptability and the ability to address mode-mixing issues from ensemble empirical mode decomposition (EEMD), but it also further enhances the accuracy and stability of the decomposition by introducing a median operator.  
The specific steps are as follows: (1) Adding Gaussian white noise: Pairs of positive and negative Gaussian white noise are added to the original signal, ensuring that the noise has a mean of zero and equal standard deviations. (2) Empirical modal decomposition: Multiple empirical modal decompositions are performed on the noise-added signal, with each decomposition yielding a series of intrinsic mode functions (IMFs) and a residual. (3) Median operation: A median operation is applied to the IMFs and residuals obtained from each decomposition to produce the final IMFs and residual.
## 2 Methodology
### Algorithm Improvement
#### 2.3.4 Heuristic Feasibility Operator  
Many processes of the algorithm may generate infeasible solutions, such as the construction of the initial population, local search, and moving particle processes, resulting in a small-scale SCP. Due to this problem, we employ an additional heuristic operator to generate feasible solutions. A heuristic feasibility operator is usually designed by combining the specific domain knowledge of the problem and heuristic strategies. It can evaluate and select the next operation according to the current search state to increase the possibility of finding a feasible solution. These operators allow efficient exploration of the solution space and avoid blind search so that a feasible solution satisfying the constraints of the problem can be found in a reasonable amount of time. For example, in the traveling salesman problem (TSP), a heuristic feasibility operator may be designed to select the next possible city to visit based on the distances between cities and the situation of the cities that have already been visited, so as to gradually construct a feasible route that satisfies that all cities are visited and the path is the shortest.  
We calculate the percentage based on the sum of the number of uncovered rows contained in the column over the number of uncovered rows, \(K_{j}=\left|I_{j}^{\prime}\right|/\sum\limits_{j=1}^{n}I_{j}^{\prime}\), where columns with \(K_{j}\) greater than a threshold \(\alpha\) (\(\alpha=K_{j}\times pop\), with pop being a control parameter) constitute candidate columns, and a column is randomly selected from the candidate list to be added to the current solution until we obtain a feasible solution (i.e., all rows are covered). The value of \(K_{j}\) is updated each time a column is added to the solution.
## 2 Theoretical Background
### VR and Impact on Student Satisfaction and Engagement  
Previous studies have assessed various aspects of incorporating VR into teaching and learning, including user satisfaction, engagement, and attractiveness. For instance, a study found a 76% increase in satisfaction among dentistry students [34], while another reported a 4.28 motivation score (on a Likert scale of 1-5) for science students [37]. Additionally, VR was found to enhance engagement in training for industrial systems [36] and boost motivation in practical nursing studies [69].  
Regarding engineering education processes, numerous studies report positive outcomes. For example, research has shown increased engagement in testing mechanical materials [64], greater satisfaction in developing prototypes with VR and additive manufacturing [70], and a 92% student understanding that VR enhances learning and makes the process more attractive, leading to increased engagement in classes [71]. Additionally, a survey revealed that 92% of students feel more satisfied when using VR in teaching and learning processes, particularly in the context of electrical systems [72] and the simulation of welding processes with robots [73].  
Conversely, the research conducted by [74] did not find any significant increase in motivation, satisfaction, or any other attribute among participants as a result of using the virtual environment, particularly in the cultural training program for high school students. In the earthmoving simulation, participants noted numerous flaws in the simulator and found it difficult to operate, ultimately impacting their satisfaction [75]. Similarly, difficulties in manipulating the applications were observed during the construction of bridge projects, which hindered the potential benefits of the new application [76].  
Based on the search for new disruptive technologies for application in the teaching-learning processes of production engineering, future research opportunities, and the positive impacts related to the learning and satisfaction of participants in the experiments, the following three research questions are presented:  
* RQ1--Is there more significant learning from students who use VR in teaching-learning than from students who only take traditional classes?
* RQ2--Did the group that used VR in the teaching-learning processes perceive satisfaction, engagement, and attractiveness in the activities, compared to classes that only used the traditional teaching method?
* RQ3--Does applying VR impact knowledge retention three weeks after using the knowledge test acquired by students?
## 2 Materials and Methods
### FCS Measurement  
Model experiments were performed to experimentally demonstrate the proposed correction method. The FCS measurements were performed on mixtures of two fluorescent latex beads, a fluorescent bead with a diameter of 30 nm (PSYF030 NM, MAGSPHERE, Pasadena, CA, USA), and a brighter bead with a diameter of 51 nm (PSYF050 NM, MAGSPHERE, Pasadena, CA, USA). A compact FCS system (307-15471, Wako, Japan) was used for the measurements. The lateral diameter of the confocal volume was 200.3 nm, and the axial diameter was 1.14 \(\upmu\)m. The effective measurement volume was determined to be 0.255 fL. The excitation wavelength was 473 nm. The excitation laser power was 0.5 \(\upmu\)W at the focal point. The 3 s measurement was repeated 20 times, and one mean ACF was obtained. The non-linear least squares analysis was performed on the mean ACF for one experiment.
## 1 Introduction
This requirement can be written as (see [16] for details):  
\[\upsilon_{m_{s}d_{s}}=\textit{sv}_{m_{1},q_{1}},\ \ \ m_{s}=\textit{sm}_{1}. \tag{1}\]  
Unfortunately, conditions (1) cannot be exact in the non-equidistant spectrum of cylindrical waveguides. As a result, the efficiency of frequency multiplication \(K=P_{s}/P_{1}\) (where \(P_{s}\) and \(P_{1}\) are HF and LF radiation power, correspondingly) is extremely low and decreases with the harmonic number \(s\) as \(K\)-\(10^{-2s}\)[20]. Nevertheless, as demonstrated in study [21], the condition (1) can be satisfied asymptotically for selected multiplication numbers \(s=5,9,13\ldots\) and selected operating modes, whose radial indices are related as:  
\[q_{s}=\textit{sq}_{1}-3(s-1)/4. \tag{2}\]  
This relationship was established on the basis of the asymptotic representation of the derivatives of Bessel functions ([PERSON]'s asymptotic). In particular, for \(s=5\), modes TE\({}_{m,p}\) at the fundamental CH and TE\({}_{5m,5p-3}\) at the 5 th CH will be resonant to the electron beam with high accuracy. This principle has been verified in experiments with a CW 45-GHz gyrotron driven by 25 keV/2 A beam at TE\({}_{6,3}\) mode, where \(\sim\)100 mW radiation power at the 5 th CH cyclotron harmonic (225 GHz) has been detected [21]. The obtained conversion ratio \(K\approx 1.5\cdot 10^{-5}\) was an order of magnitude higher than for the case of the triple frequency multiplication estimated in [20]. In this paper, within the frame of numerical simulations, we demonstrate the possibility of using the developed approach to implement Watt-level THz radiation sources based on high-power gyrotrons intended for plasma applications. In these devices, the mode spectrum is dense and severe mode competition takes place unlike the situation presented in [16]. We show that under such conditions, efficient generation of high harmonic content is still possible.
## 4 Discussion  
### Evaluation of Proteomics Data and Selection of DEPs  
For the generation of differentially expressed protein lists, we applied two criteria, a \(p\)-value < 0.05 and a 2-fold difference, as cutoff values after the examination of \(p\)-value distributions per comparison (Supplementary Figure S2), and concurrent consideration of relevant literature in proteomic analyses. All \(p\)-values regarding the three treated sample groups (PX, RS, and RSPX) against the Control (C) produced histograms that indicated the existence of cases favoring the alternative hypothesis of the test applied (i.e., significant differences), whereas the three comparisons between treated sample groups fell in sub-optimal patterns, something that is also reflected in the much lower numbers of DEPs as shown in Table 1 (approximately one fifth to one sixth when compared to total DEPs of treated sample groups against the Control). At the biological level, these observations could suggest that certain mechanisms modulated as initial plant responses after treatment with RS and/or pathogen infection (irrespective RS treatment) are not highly divergent, a hypothesis that will be further explored in the discussion of functional analyses of DEPs and the biochemical pathways modulated.  
Under the selected criteria, a clear separation of the four treatment groups and a uniform replicates' intra-grouping of replicates within each treatment was observed in a cluster analysis (Figure 1). DEPs with an absolute fold-change in expression above two times (Figure 2, Supplementary Table S1) showed figures of similar order of magnitude regarding treated samples versus the Control, however, with a somehow higher number of proteins reported for PX (n = 199) compared to RS (n = 163) and RSPX (n = 148). A closer inspection of DEPs in PX vs. C and RSPX vs. C traced proteins that reached expression differences up to 12-fold in PX and RSPX, or even higher, up to 15-fold, in RS (Supplementary Table S1), suggesting that they could have principal roles in the relative _C. pepo_ responses to each causative agent (either the pathogen, the elicitor, or their combination). Such indicative DEPs (rated equal to or highly above 4-fold) were highlighted and their putative roles, either as members of a biochemical pathway or singular/unique ones, are discussed in the next sections.
###### Abstract  
As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node's energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs--the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes' coding opportunities based on the links' transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR.  
wireless sensor network; network coding; routing decision model +
Footnote †: journal: Applied Signal Processing
## 4 Results
### Comparisons of Satellite and GPS IWV Monthly Means
The underestimation is thus clearly larger for both MODIS climatologies, which might be related to missing nighttime IWV retrievals and thus lower temporal and possibly stronger biased sampling than IASI and MIRS. The median IWV difference does not change significantly for the various averaging time periods, but appears to be largest around the one to two weeks averaging time. The large confidence intervals, especially for short averaging time periods of days, range from \(-\)8 kg/m\({}^{2}\) to  
Figure 5: Comparison of monthly mean of IWV from GPS to IASI, MIRS, MODIS and MODIS-FUB for all months from June 2012 to May 2013. The error bars show the standard deviation. Bias and root mean square error (RMSE) are given in kg/m\({}^{2}\). 2 kg/m\({}^{2}\), and can be related to the fact that in the German domain the major IWV variability is associated with synoptic disturbances. This also corresponds well with an e-folding time of roughly 13 h that was found from autocorrelation functions for various high temporal IWV measurements obtained during HOPE [18]. However, the confidence intervals decrease significantly for the weekly and monthly averages, naturally related to smoothing due to larger sample sizes. Second, the mean IWV difference per averaging time period is computed using satellite-based climatologies for which only GPS IWV values are used if matched to a satellite pixel with a successful IWV retrieval (Figure 6 center panel). This also allows investigation of the impact of sampling due to retrieval limitations, such as cloudy conditions for MODIS. Still, the results look very similar to the ones from the first step. The impact of possible lower sampling due to retrieval limitations does hardly affect satellite climatologies computed for any of the averaging time period. Third, the mean IWV difference per averaging time period is computed using satellite climatologies with actual satellite IWV values (Figure 6 right panel). This allows further investigation of the impact of retrieval bias on satellite climatologies and the difference with GPS climatologies. Now the results have changed significantly. The previously established positive, for MIRS, and negative biases, for IASI, MODIS and MODIS-FUB, dominate the results and not the larger temporal sampling of GPS. The confidence intervals are much larger and vary more in extent between the satellite instruments, which is related to retrieval algorithms and their different limitations. Still, for all satellite instruments, the median values only change very slightly (less than 1 kgm\({}^{-2}\)) per averaging time period.
## 5 Factors That Influence the Effectiveness of In-Vehicle Messaging
### Driver Characteristics
#### 5.1.3 Driver States  
Driver states are characteristics of a driver at a given moment, and therefore are dynamic and situational [102]. Driver states are influenced by a driver's current and prior tasks, as well as environmental conditions. Driver states that may be considered in in-vehicle signage information delivery design include, but are not limited to, driver boredom, sleepiness, and fatigue, as well as driver vigilance. Despite being separate, these state measures are often related and impact each other.  
A concern with Level 3 automated driving (according to the SAE taxonomy) is that a driver may become bored during highly automated driving with activity being limited to passively monitoring the state of the vehicle [103; 104]. This is of concern because states of boredom can result in performance decrements, such as slower response times and higher variability in responses [105; 106; 107]. Previous work has demonstrated that drivers tend to cope with boredom by using approach or avoidance strategies [108]. In an approach strategy, a driver may refocus their attention to increase the amount of stimulation they receive from the driving task; thus, they are \"approaching\" the task. [PERSON] showed that drivers tend to use avoidance strategies when engaged in boring driving tasks resulting in driver distraction [105]. During automated driving, drivers may tend towards this avoidance strategy and seek stimulation from sources other than monitoring the state of the vehicle. There is research that shows drivers who are required to monitor automated systems demonstrate more signs of drowsiness than drivers who are actively engaged in secondary tasks such, as reading a book or watching a video [14]. This presents an opportunity for in-vehicle messaging of non-safety-related information, such as specific service information to potentially reduce the likelihood of driver boredom. On the other hand, during a critical event, such as a takeover, drivers could experience a sudden spike in workload and the presentation of in-vehicle messages may pose undesirable distractions, as the driver tries to comprehend the current situation. Distraction can degrade driver situation awareness under normal and abnormal hazard conditions [109]. However, a close examination is needed on whether the detrimental effects of boredom may be offset by secondary task performance and if any benefit outweighs the cost of task switching between a secondary task (e.g., sign identification) and the driving task (e.g., takeover).
# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 2 Related Works
### Three-Dimensional Human Pose Estimation  
Many recent studies have focused on estimating 2D [11] or 3D keypoint locations on the human body [12]. Normally, these keypoint locations include major body joints such as the wrists, ankles, elbows, neck, shoulders, and knees. The architectures of 2D pose detectors have been designed to map images into the 2D pose vector more accurately. [PERSON] et al. [2] proposed a sequential architecture composed of convolutional neural networks (CNNs) with multiple sizes of receptive fields. [PERSON] et al. [3] proposed a method using both a 3D geometric prior and temporal smoothness prior to treat considerable uncertainties in 2D joint locations. [PERSON] et al. [1] proposed stacked hourglass networks based on the successive architecture, which is composed of pooling and upsampling layers. As we are living in the 3D space, understanding poses in the 3D space is the natural extension to 2D pose estimation. Three-dimensional pose estimation aims to locate the key 3D joints of the human body from an image or a video, which are either in the single-view or multi-view setting. Most approaches are based on the two-stage approaches that first predict 2D joint locations using 2D pose detectors [1; 2; 3; 4; 5; 24] and then predict 3D joint locations  
Figure 1: Example image-level self-supervision: We applied three types of image manipulation and one image projection task to train our human mesh estimation network. Each row shows examples from the Human3.6M dataset, and each column corresponds to (**a**) input RGB images, (**b**) shuffled patches used for the jigsaw puzzling task, (**c**) rotated RGB images used for rotation prediction task, (**d**) output images from part inpainting task, and (**e**) neural rendered images from estimated 3D meshes and textures, respectively.  
from the 2D joints or features by performing regression [4; 25; 26; 27] or model fitting [3; 28; 29; 30; 31; 32]. [PERSON] et al. [24] proposed a graph stacked hourglass network for 3D human pose estimation, which consists of four stacked hourglasses. Estimated 2D joints are fed into the network, and the 3D pose is predicted. [PERSON] et al. [4] proposed a simple framework composed of fully connected layers to lift a 2D pose to a 3D angular pose. [PERSON] et al. [25] proposed a framework composed of a 2D joint detector based on CNN and inferred 3D poses from the detected 2D joints. These approaches have made great progress in improving the performance of 3D human pose estimation.