esa-sceva/satcom-chunk-collection huggingface dataset:
# Consumer Acceptance of Cultured Meat: An Updated Review (2018-2020)
## 6 Barriers to Acceptance
### Disgust  
Our previous review identified several studies which mentioned disgust, and this was generally connected to the perceived unnaturalness [12; 39; 53]. Building on this theme, many recent studies mentioned disgust as a barrier to cultured meat adoption [12; 21][31; 34]. Disgust sensitivity and experienced disgust have been shown to predict cultured meat rejection [23; 31; 32], though interestingly cross-country studies suggest that this may be more influential in Western cultures [21].  
Research has indicated that consumers find cultured meat less disgusting than GMOs [24] and eating insects [23; 34], but more disgusting than synthetic food additives and similar food technology plant products [24; 34]. Interestingly, [PERSON] and [PERSON] [23] found that their sample judged cultured meat to be less disgusting than eating insects, although they judged eating insects to be more natural and more healthy, indicating that a disgust reaction can be independent of evaluations which seem to be related. Similarly, [PERSON] et al. [24] found that although participants found cultured meat more disgusting than a synthetic food additive, they also judged its benefits to society to be greater. Again, this highlights the emotional nature of the disgust response, and shows how it can occur independently of rational evaluations.  
The heuristic associative emotional nature of the disgust responses is neatly illustrated in an experimental study by [PERSON] et al. [16]. The researchers found that German participants were more willing to try cultured meat when they were asked about it in their non-native English rather than their native German, and that this effect was mediated by evoked disgust. Similarly, [PERSON] and [PERSON] [14] find that the positivity of associations was the key factor accounting for differences in the attitudes related to different cultured meat names.  
Previous research has linked this disgust to the perceived unnaturalness of food in Western cultures [62], though more recent work contends that it instead stems from norm violation [17]. This is an important distinction, because if we are addressing norm-violating moral disgust rather than food-related disgust, such objections are more likely to be surmountable in the future as cultured meat becomes more familiar. Indeed, this gives credence to the view that consumers objecting to the \"unnaturalness\" of cultured meat are often objecting in fact to its unfamiliarity.
## 4 Results
### Tests with STFT
#### 4.4.2 Tests with Eight Classification Classes and LFTO  
In this case, for the sake of brevity, only the result with a decimation factor of 20 is reported. The eight class setup with the spectrograms gave a classification accuracy of 75.8%. The confusion matrix for this case is reported in Figure 19.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Decimation Factor** & **Validation Accuracy** & **Test Accuracy** \\ \hline \multirow{2}{*}{Four localized bearing faults + LFTO} & 20 & 75\% & 79.2\% \\  & 30 & 61.7\% & 62.5\% \\ \multirow{2}{*}{Four localized bearing faults without LFTO} & 20 & 90\% & 87.5\% \\  & 30 & 91.6\% & 87.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation and test accuracy for the two classifications with four classes each (spectrogram images).  
Figure 18: Confusion matrices (test set) for the classifications with four classes (spectrograms with decimation factor of 30): (**a**) four localized bearing faults and (**b**) four localized bearing faults + LFTO.  
In this case, it should be noted that the test accuracy was lower than that found in the previous section (see Figure 20); in fact, the test accuracy here was 66.7% instead of 91.7% as for the classification based on scalograms.  
Figure 19: Confusion matrix (test set) for the eight class setup with spectrogram images.  
Figure 20: Confusion matrix (test set) for the eight classes setup (with scalogram images).  
The validation accuracy achieved was about 90% and the convergence was reached in about 18 epochs. The confusion matrix for this setup is shown in Figure 20. Note that in Figures 19 and 20 the names of the rows and columns correspond to the denotations (acronyms) of the different classes.
## 5 Simulation and Industrial Application  
In order to validate the effectiveness and superiority of the proposed method, a piece of pure sound signal of a motor was recorded. Then, Gauss white noise with different signal to noise ratio were added into the pure signal. The mean square error (MSE), peak value error (PVE) and the computation time (CT) were regarded as evaluation criteria of the noise elimination solutions. The denoising performance of standard soft threshold denoising method (SST), a threshold function-based  
Figure 4: Process of the proposed denoising method.  
noise elimination solution proposed in [10] (TFB), wavelet threshold denoising optimized by genetic algorithm (WTD-GA), wavelet threshold denoising optimized by fruit fly optimization algorithm (WTD-FOA) and the proposed WTD-IFOA were compared subsequently. Finally, an industrial application for the shearer of coal mining working face is exhibited. All calculations in this section were conducted on a workstation configured as shown in Table 1.
## 4 PSO Techniques for Adaptive Equalization  
PSO variants have been extensively utilized to enhance adaptive equalization in communication systems. These variants aim to improve the optimization capabilities of PSO algorithms and enable better performance of adaptive filters [118]. Dynamic multi-swarm  
Figure 7: Flow chart of the cooperative PSO algorithm.  
PSO introduces multiple swarms dynamically adapting to different regions of the search space [132]. By assigning specific areas of exploration to each swarm, this variant efficiently explores and exploits the equalizer's tap weight space, making it suitable for handling complex frequency response variations and mitigating distortions [133]. Another variant, FIPS, enhances information exchange among particles by allowing them to communicate with all others in the swarm. This global information sharing promotes better exploration of the search space, leading to improved convergence and accuracy in adaptive equalization tasks [133].  
HPSO combines PSO with other optimization algorithms such as the GA or simulated annealing (SA) [134]. This integration enables leveraging the strengths of different algorithms [135], allowing efficient navigation of the tap weight space and achieving improved convergence and adaptation in the presence of frequency response variations [136]. Cooperative PSO introduces cooperation mechanisms among particles, facilitating information exchange and adaptation based on shared knowledge. In adaptive equalization, cooperative PSO enhances exploration and adaptation capabilities, particularly when dealing with varying channel conditions [137]. Self-organizing hierarchical PSO introduces a hierarchical structure among particles, promoting effective exploration and exploitation within localized regions of the search space. This variant adapts to different subsets of the tap weight space, enhancing adaptability to varying channel conditions and improving equalization performance. Lastly, CLPSO incorporates a learning mechanism where particles adapt their behavior based on their own experiences and the best experiences of the swarm. By combining personal and swarm knowledge, CLPSO achieves faster convergence, improved exploitation of promising solutions, and better adaptation to frequency response variations in adaptive equalization tasks [138]. These PSO variants, with their unique characteristics and capabilities, have been successfully applied in adaptive equalization to optimize tap weights and enhance convergence rates, accuracy, and adaptability, ultimately improving the overall signal quality in communication systems [139].
###### Abstract  
Mitral regurgitation (MR) is the systolic retrograde flow from the left ventricle (LV) to the left atrium. Despite the recognized importance of hemodynamic force (HDF) in cardiology, its exploration in MR has been limited. Therefore, we aimed to explore non-invasively assessed HDF as a novel biomarker for evaluating MR utilizing 4D-flow MRI. The study cohort comprised 15 healthy controls (19-61 years, 53% men) and 26 MR patients with preserved ejection fraction (EF) (33-75 years, trivial-severe, 54% men). The HDF analysis involved the semi-automatic calculation of systolic-diastolic root mean square (RMS), average, and transverse/longitudinal ratio across three directions (S-L: septal-lateral, I-A: inferior-anterior, and B-A: basal-apical) using Segment, v2.2 R6410 (Lund, Sweden, Medvoso). A noticeable trend shift emerged in HDF as the MR severity increased (\(p\)-value < 0.05). The MR severity demonstrated a noteworthy correlation with systolic RMS B-A, average B-A, diastolic average B-A, systolic average S-L, B-A, and systolic-diastolic ratio (rho = 0.621, 0.457, 0.317, 0.318, 0.555, -0.543, -0.35, respectively; \(p\)-value < 0.05). HDF significantly correlated with LV function (end-diastolic volume, end-systolic volume, EF, and mass; \(p\)-value < 0.05). Systolic RMS B-A and diastolic RMS S-L emerged as significant predictors of MR (Beta, 95% CI [3,253, 1.204-5.301], [5.413, 0.227-10.6], \(p\)-value < 0.05). This study emphasizes HDF as a potential hemodynamic biomarker for evaluating MR.  
h 2024 14 8577. [[https://doi.org/10.3390/app14198577](https://doi.org/10.3390/app14198577)]([https://doi.org/10.3390/app14198577](https://doi.org/10.3390/app14198577))
## 2 The Principle of Extracting Transmission Characteristics
According to Equation (3), \(V_{L}(f)\) can be derived as  
\[V_{L}(f)=V_{IN}(f)H_{L}(f)\approx V_{IN}(f),f<f_{c}. \tag{9}\]  
Therefore, when \(V_{L}(f)\) is calculated using Equation (4), \(V_{IN}(f)\) below \(f_{c}\) is also derived. Based on that, the time domain waveform \(v_{in}(t)\) can be reconstructed using the parameters extracted from the partial spectrum of \(V_{IN}(f)\)[26]. Afterwards, \(V_{IN}(f)\) in the whole frequency domain can be calculated according to  
\[V_{IN}(f)=F\{v_{in}(t)\}, \tag{10}\]  
where \(F\) represents the Fourier transforms. According to the above derivation, the process of preforming the transmission characteristic extraction from the near-field emission of interconnects in a PCB is as follows. Firstly, a microstrip line electric probe connected to an SA is placed right above the tested trace to measure the output near-field emission spectrum \(V_{P}(f)\) of the wideband signal flowing in the printed trace. Secondly, the electromagnetic field simulation, VNA testing, or equivalent circuit modeling is conducted to obtain the transfer function \(H_{T}(f)\). Thirdly, the time domain parameter extraction method is used to obtain the whole band input signal spectrum \(V_{IN}(f)\). Finally, data for \(V_{P}(f)\), \(H_{T}(f)\), and \(V_{IN}(f)\) are utilized to calculate the transmission function \(H_{L}(f)\) according to Equation (6). Note that the methods of the transfer function \(H_{T}(f)\) modeling and the whole band spectrum of input signal \(V_{IN}(f)\) estimation are shown in the following sections.
## 2 Mathematical Model and Controller Design  
### Mathematical Model  
In order to carry out the parametric analysis, the simplified analytical model of the suspended structure with the ARID system was developed, which is shown in Figure 1. The model consists of two degrees of freedom used as generalized coordinators, which are the structural swing angle \(\theta\) and the mass inertia relative rotation angle \(\phi\) with respect to the ground base. The length of the suspended structure is \(l\). The suspended particle mass is \(m\), and the structural damping coefficient is \(c\). The ARID system rotary inertia mass is \(m_{a}\), the rotary inertia is \(J_{a}\), the rotational stiffness coefficient is \(k_{a}\) and the damping coefficient is \(\mathrm{c_{a}}\). The acceleration at the suspended structure lifting point is \(a_{x0}(t)\)and the control torque is \(M_{a}(t)\). The motion equations of the model were deduced based on Lagrangian principle as:  
\[(m+m_{a})l^{2}\ddot{\theta}+c\dot{\theta}+(m+m_{a})gl\sin\theta \tag{1}\] \[=-(m+m_{a})l\cos\theta a_{x0}(t)+c_{l}\Big{(}\dot{\varnothing}- \dot{\theta}\Big{)}+k_{l}(\varnothing-\theta)-M_{a}(t)\]  
\[J_{a}\ddot{\varnothing}+c_{t}\Big{(}\dot{\varnothing}-\dot{\theta}\Big{)}+k_{ l}(\varnothing-\theta)=M_{a}(t) \tag{2}\]  
where \(J_{a}=m_{a}r^{2}\), \(c=2\Big{[}m+m_{a}l^{2}\Big{]}\omega\xi\), \(\omega=\sqrt{g/l}\), \(c_{a}=2m_{a}r^{2}\omega_{a}\xi_{a}\) and \(\omega_{a}=\sqrt{k_{a}/J_{a}}\) are the needed parameters. \(r\) is the radius of the mass inertia, \(\xi\) is the structural damping ratio and \(\xi_{a}\) is the ARID damping ratio. Generally, its damping force and torsion resistance are very small compared with control torques, which can be neglected (\(c_{a}\) and \(k_{a}\) are 0).
## 5 Conclusions  
This paper proposes a simple yet effective data augmentation method named GRM that aims to discover the potential sub-clinical lesions ignored by the DNNs. The visual interpretation results are used as guidance to help locate the ROIs. Random masking of those ROIs enforces the DNNs to better utilize the context information. Moreover, it also increases the DNNs' robustness to the input since the model is required to predict the category from the incomplete input. Conventional data augmentation method (e.g., cutout) requires to specify the size of the mask, which increases the difficulty during practice when the size of the target varies. On the contrary, the proposed GRM adaptively changes the size and location of the mask according to the characteristics of the target.  
Ablation experiments on multiple network architectures are carried out to validate the effectiveness of GRM. The GRM can substantially increase the networks' recognition accuracy on different tasks compared to the vanilla network. The network applied with GRM exhibits evident lower loss on the validation dataset, implying that the GRM helps to increase the networks' generalization capacity. Visualization experiments further demonstrate that the GRM contributes to exploit the sub-clinical lesions and helps reduce the false predictions during practice. In the training phase, the GRM leverages the CAM of the inference result as guidance to randomly mask the input, which is later used to train the network. From a more general point of view, the GRM can be applied iteratively, i.e., the inference and training of the sample can be repeated multiple times till the stability of CAM is achieved. The iterative method may contribute to the learning process of the network because of the enhanced regularization effectiveness. The exploration of the iterative version of GRM is left as a future work.  
Methodology, J.H.; writing--original draft, X.Y.; writing--review and editing, S.W. and J.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62106162, China Postdoctoral Science Foundation under Grant 2021M692269, and Sichuan University Postdoctoral Science Foundation under Grant 2022 SCU12080.  
The authors declare no conflict of interest.
# Validation of an RF Image System for Real-Time Tracking Neurosurgical Tools
## 3 Experimental Validation and Results
### Image Acquisition  
Before starting the experiment, a measurement only with the antennas, with nothing inside the system (empty measurement) was made. This empty measurement was used as the reference and calibration measurement throughout the whole experiment, and the rest of images presented in this work included the subtraction of this empty measurement. Then, two initial measurements were made with the microwave image system for the proposed setup: one only having the cranium (no tumor, no tool), and another one having the cranium and the tumor (no tool) in the right positions. During the navigation sequence, 8 measurements were made with the microwave image system at 8 different moments, so that the 8 corresponding images could be assessed. Parallel to each of them, the coordinates of the robotic arm at each moment were saved, which were later transformed to tool's final-end coordinates for reference. Within these 8 positions, hereinafter referred to as p\(i\), where \(1\leq i\leq 8\), p1 and p2 had the tool's final end out of the antenna system space, p3 and p4 had it inside the antenna system space but out of the cranium, p5 had it approximately in the cranium boundary (entering the hole), p6 and p7 had it inside the cranium and gradually approaching the tumor, and p8 had it inside the cranium and well-nigh touching the tumor boundary. Finally, the 10 resulting measurements (2 initial ones + 8 during the tool's navigation) were processed with both DAS and DMAS algorithms. The flowchart for the imaging algorithm applied to each measurement is depicted in Figure 11.  
The resulting images for both DAS and DMAS algorithms for the 2 initial measurements are shown in Figure 12, whereas the corresponding images for a selection of some of the 8 navigation measurements can be seen in Figure 13 (positions 1 and 2 are not shown since the tool was out of the antenna system area). The images in Figure 13 show the evolution of the tool's final-end position during the navigation experiment, although some other reflections are detected due to the long-shaped tool's body. Considering a long enough tool (which is the most usual case in these operations), these reflections are approximately constant for two consecutive images or positions (provided that a low or moderate differential movement was made), the new information only being related to the position change. Therefore, aiming at a better detection of the tool's final-end position for navigation purposes, the images were further processed by subtracting the previous image from the current one, so that only the tool's displacement information was left, and the new position can be easily tracked. The resulting images for both algorithms are shown in Figure 14, again excluding positions 1 and 2 for the same reason.  
Figure 10: Experimental setup with the tool entering the cranium within the antenna system.  
Figure 11: Flowchart for the imaging algorithm applied after each measurement.  
Figure 12: Initial images with DAS and DMAS algorithms.
# Review of Photogrammetric and Lidar Applications of UAV
## 1 Introduction
However, using a UAV with an onboard GNSS RTK revealed less accuracy in the vertical component of the resulting model or systematic elevation error. This inaccuracy can be improved by adding only a few GCPs or combining multiple flights. This article's main contribution is summarising and expanding our knowledge on applications using UAVs. We focused on analyzing the results achieved by different UAVs and different sensors and in different areas of their application. This contribution is based on a comprehensive review of the scientific literature. The primary source for the literature overview was the Web of Science database, followed by Scopus and Google Scholar databases using their search engines. The main keyword in the search was \"UAV\", along with alternatives such as \"UAS\", \"RPAS\", and \"drone\". Additional keywords in he search were products of UAV missions such as point cloud, DEM, DTM, etc., and the field of application of UAV such as surveying and mapping, industry, archeology, etc. Separate keywords associated with the main keyword UAV formed the basis for the analysis of co-occurrence, which we present in a processed network of co-occurring high-frequency words. This search yielded a sample of 213 records. This part of the study is mainly focused on the co-occurrence method to analyze articles from various indexed journals obtained for 2013-2022. The most frequently occurring words express and identify the relationship between other conceptual sub-subjects with which they are associated. The open-source VOSviewer ([[https://www.vosviewer.com/](https://www.vosviewer.com/)]([https://www.vosviewer.com/](https://www.vosviewer.com/)), accessed on 1 January 2023) was used to analyze and visualize the obtained results. As a result, our literature review can save time for readers of this topic to quickly find and understand the content and orientation of the literature on the subject of UAVs in combination with several sensors, mainly in the field of spatial data collection.
## 3 MEC Implementation in Vehicular Networks
### User-Side Layer  
Each smart car is equipped with a Vehicle Terminal (VT) which has its own storage and computing capacity and handles all the connection processes for the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) links in order to transfer data. Every VT is responsible for sensing and collecting data from the internal and external environment--such as GPS data or video streams from onboard cameras--and sending them to the MEC layer, if deemed necessary. Usually, computing tasks related to local applications are handled within the VT.  
Several technologies enabling V2X communications can be used, as highlighted by [61]. The IEEE 802.11p technology is the basis for the Dedicated Short-range Communication (DSRC) in the United States [62] and ETSI ITS-G5 in Europe [63], which operates in the 5.9 GHz spectrum for ITS. It is a mature technology and the de facto standard for V2X communications and favored due to its ease of deployment and the native support for V2V communications in an ad hoc manner. Cellular-V2X (C-V2X) and its evolution NewRadio (NR)-V2X have been proposed by 3 GPP as an extension of the 4G and 5G networks, respectively, to support vehicular communications by exploiting the existing high-capability cellular infrastructure [64; 65]. A thorough comparison of the V2X communications performance between these two technologies has been carried out in [66]. Visible Light Communication (VLC) has also been proposed as a V2X communication technology. It employs the rapid modulation of light sources, such as LED bulbs, to encode and transmit data. It is mostly used for direct communications in the vicinity through head-to-tail links, especially for safety applications when radio-frequency-based technologies are not available or may suffer congestion [67].