esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Results and Discussion
### Subjective Evaluation  
As a subjective evaluation, the idea was to select the closeness between the re-synthesized and original speech signal that fits our goal. To evaluate which proposed system was closer to natural speech, we conducted a web-based MUSHRA (MUlti-Stimulus test with Hidden Reference and Anchor) listening test [73].  
The advantage of MUSHRA is that it enables evaluation of multiple samples in a single trial without breaking the task into many pairwise comparisons. Our aim was to measure the perceived correlation of the ratio of the voiced and unvoiced components; therefore, we compared natural sentences with the synthesized sentences from the baseline, proposed and a hidden anchor system (the latter being a vocoder with simple pulse-noise excitation). From the 60 sentences used in the objective evaluation, 14 sentences were selected. Altogether, 84 utterances were included in the test (6 types \(\times\) 14 sentences). Before the test, listeners were asked to listen to an example from the male speaker to adjust the volume. In the test, the listeners had to rate the naturalness of each stimulus relative to the reference (which was the natural sentence), from 0 (highly unnatural) to 100 (highly natural). The utterances were presented in a randomized order (different for each participant). The listening test samples can be found online ([[http://smartlab.tmit.bme.hu/adContF0_2019](http://smartlab.tmit.bme.hu/adContF0_2019)]([http://smartlab.tmit.bme.hu/adContF0_2019](http://smartlab.tmit.bme.hu/adContF0_2019))). Twenty-one participants (12 males, 9 females) with a mean age of 29 years, mostly with an engineering background, were asked to conduct the online listening test. On average, the test took 10 min to complete. The MUSHRA scores for all the systems are shown in Figure 10, showing both speaker by speaker and overall results.  
According to the results, the proposed vocoders clearly outperformed the baseline system (Mann-Whitney-Wilcoxon ranksum test, \(p<0.05\)). In particular, one can see that in the case of the female speaker (SLT), all proposed vocoders were significantly better than the STRAIGHT and baseline vocoders (Figure 10c). For the male speaker (JMK), we found that proposed #3 reached the highest naturalness scores in the listening test (Figure 10b). Meanwhile, for the BDL male speaker in Figure 10a, proposed #3 and #2 were ranked as the second and third best choices, respectively. When taking these overall results, the difference between STRAIGHT and the proposed system is not statistically significant (Mann-Whitney-Wilcoxon ranksum test, \(p<0.05\)), meaning that our methods reached the  
Figure 9: Mean PDD values by sentence type.  
quality of the state-of-the-art vocoder. This positive result was confirmed by metric measures in the statistical aspects of the objective's experimental test.
# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
## 3 Results
### Waveform Processing  
The acoustic signals from experiments were recorded by the AE system. Figure 5a shows a typical waveform of the acoustic signal of carbon steel in NaHCO\({}_{3}\) + NaCl solution. It shows that some information of the waveform, the beginning and end, was not useful. So, additional processing of the signals was necessary for the preparation of further analysis. Herein, three steps, including pre-trigger removal, tail-cutting, and shape preserving interpolation (SPI), were applied to process the waveform in Matlab. In pre-trigger removal, a value tells the software how long to record (in usec) before the trigger point (the point at which the threshold is exceeded), and is aimed at removing digital noise from the acquisition. Tail-cutting was used to remove the \"zero-padding\", which may have been applied at the end of some waveforms during the acquisition process (Figure 5b). SPI was an effective tool to ensure that each waveform had the same number of points to be stored, which is based on the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) technique and the Weierstrass approximation theorem. The waveform after processing is shown in Figure 6. In this study, wavelet denoising was performed by the Matlab wavelet tool box (Figure 7). The treated waveform by denoising was stored for the processing and extraction of the parameters.  
Figure 5: The waveform processing of a typical acoustic signal obtained from Q235 carbon steel in NaHCO\({}_{3}\) + NaCl solution: (**a**) original, (**b**) pre-trigger removal and tail cutting.  
Figure 6: The processed waveform of a typical acoustic signal obtained from Q235 carbon steel in NaHCO\({}_{3}\) + NaCl solution (after SPI and wavelet denoising).  
Figure 7: Denoising method for a typical signal of the acoustic emission of carbon steel in NaHCO\({}_{3}\) + NaCl solution. (**a**) Signal before denoising, (**b**) signal after denoising.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Contributors to Changes in the Resonant Behavior
#### 3.2.1 Contributors to Perturbations in the Cavity  
The improvement of the resolution of MCRS in measuring changes in the permittivity made it possible to probe a range of processes that were previously out-of-range. Examples of this expanded functionality are the detection of acoustic waves and small changes in gas composition [20]. These contributions were separated from the electron dynamics and each other by deploying filters in the time and frequency domain. However, this required, due to the complexity, an extensive understanding of the investigated processes.  
Some of the other properties that could be probed (non-selectively) are the temperature of the gas, thermal expansion of materials, humidity, position and shape of an item, gas flow fluctuations, oxidation processes, vapour in walls, bulk density of particles in the air, structure of materials, and water density [47; 48]. Indirectly, MCRS could resolve information concerning photon energies, photon fluxes, ionization cross sections, magnetic fields, diffusion properties [1; 13; 14; 15; 49], radioactive decay, etc.  
The electron density and permittivity are macroscopic material properties and with the improved detection limit presented in this article an electron density of only \(\sim\)0.1 mm\({}^{-3}\) can be resolved. With the typical dimensions of the cavity, electron densities as such may no longer be described by macroscopic properties. Moreover, the collection of electrons and ions may no longer be a plasma.  
Furthermore, with the improved resolution, the background ionization level may no longer be neglected. For example, changes in the temperature of the electrons could lead to anomalies like negative shifts as discussed in reference [49].  
All the properties discussed above are related to the permittivity. However, with the recently developed very high resolution in the resonant behavior, it might be that the assumption of no change in the permeability is no longer valid.
## 6 Security and Efficiency Analysis
### Security Analysis  
Detailed security analysis of the proposed protocol is presented in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Threat** & **Description** & **Analysis** \\ \hline \multirow{6}{*}{Data Integrity} & During communication, it ensures that the data sent from the source are not changed until they reach the destination. & In the proposed protocol, ECC is used for communication between SN and CN, while smart contract is used for communication between CN and BS. Communications between SN-CN cannot be changed without knowing the private keys of the nodes. Since there is a blockchain between CN-BS, data cannot be changed. Data integrity is successfully provided in the protocol. Also, data tampering, which is the act of deliberately altering (destroying, manipulating or editing) data by the attacker, cannot occur in the proposed protocol. & In the proposed protocol, a new node can join the network with permission. The security level is better as there is permission participation. At the same time, a node may leave the network for any reason. This node is removed from the system. That is, the system model can be shaped according to the addition of a new node to the network or the separation of nodes. A large-size blockchain has negative effects on performance. Both the need for storage capacity and processing time are increasing. In this case, it affects scalability negatively. One of the reasons why the private blockchain model is preferred in the system and the blockchain is used only in CN-BS communications is to keep the scalability of the network at a high level. In the blockchain, as data are produced, the number of blocks increases and the chain is constantly growing. The capacity of the nodes holding these blocks should not be limited. Under normal conditions, this chain cannot be kept in SNs or CNs because there is not enough storage. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security analysis.
# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions
#### 2.4.4 Neural Network Based Methods
An ANN-based FL evaluation of FSW AA2024-T351 aluminum alloy was created by [PERSON] et al. [82]. In this context, the multi-objective optimization method was used to identify standard welding parameters. A sensitivity analysis was then carried out to ascertain the influence of rotational and traverse speeds on fracture toughness and the rate of fatigue crack propagation. Time-dependent examination of fatigue in RD-welded connections was conducted by [PERSON] et al. [83] utilizing ANN-based techniques embedding an atmospheric corrosion model, and a hypothetical fatigue vehicle load model. However, by considering the uncertainty of parameters in limit-state functions, Monte Carlo (MC) simulations were used to compute the time-variant fatigue reliability. [PERSON] et al. [84] developed an ANN-themed prediction of SCF at the intersection of Concrete-Filled Steel Tube (CFST) Y-joints. The system involved using the BPNN model and FEA. In this context, the multi-dimensional scaling relationships between the affecting parameters and the SCF distributions were ascertained. [PERSON] et al. [85] detected damages in offshore jacket structure using ANN and FEA. They created an ANN to identify damage using the modal parameters. This ANN was then verified using a test set before being used to forecast the structural damage in an offshore jacket structure. [PERSON] et al. [86] performed a fatigue damage projection in OSDs with Long Short-Term Memory (LSTM). It was discovered that data-driven LSTM may greatly maintain the precision of fatigue damage predictions over time. In contrast, [PERSON] et al. [87] conducted a thermal elastic-plastic analysis on butt-welded joints by varying the member thickness, lateral constraint, and bending constraints.
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation
### Experiment II: Outdoor Navigation
#### 4.2.1 Study Participants and Experimental Procedure  
This experiment complies with Panamericana University ethics guidelines concerning human test subjects.  
Two blind subjects participated in this experiment. Both were male adults aged 31 and 35. The younger subject (subject A) was congenitally blind, while the older (subject B) developed retinitis pigmentosa in early infancy. Due to the severe loss of vision [46], subject B is considered legally blind. None of them reported any known impairment in tactile sensory or cognitive functions. Both are considered expert white cane users as they reported more than 10 years of experience.  
Two urban environments in the city of Aguascalientes, Mexico were chosen for the experiment. For safety reasons, they were carefully selected for exhibiting low vehicle traffic. Static and dynamic obstacles caused by objects and other people were present in both locations. Starting and finishing points representing 380-420 m distances were fixed prior to the test.  
During the test the remote computer was placed inside a slow-moving vehicle, which kept a comprehensive distance from the subject to ensure RF transmission at all times. A human assistant walked 10 m ahead of the subject with the sole purpose of previewing/intervening in any unexpected risky situation.  
All system components of Figure 2a were tested in this experiment.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 5 Detailed Description of the Proposed Protocol
### DQN-Based Routing Decision Mechanism
#### 5.2.3 Network Training and Routing Decision  
At time step \(t\), the agent gathers state information from candidate relays and constructs the state vector \(\mathbf{s}_{t}\) according to Equation (16). \(\mathbf{s}_{t}\) is fed into the DQN, which outputs Q-values corresponding to each EN. The agent then selects action \(a_{t}\) using an annealing \(\varepsilon\)-_greedy_ strategy, where \(\varepsilon\) decays as follows [31]:  
\[\varepsilon=\varepsilon_{end}+(\varepsilon_{start}-\varepsilon_{end})\cdot \exp\left(-\tau\cdot N_{eps}^{now}\right), \tag{25}\]  
where \(\varepsilon_{start}\) and \(\varepsilon_{end}\) represent the initial and terminal values of \(\varepsilon\), respectively. \(N_{eps}^{now}\) denotes the number of current training iterations, while \(\tau\) is the attenuation rate. With probability \(1-\varepsilon\), the agent exploits by selecting the EN associated with the maximum Q-value. During exploration, a Weighted Probability Selection method that prioritizes \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\tilde{r}_{t}^{1}\) & \(\tilde{r}_{t}^{2}\) & \(\tilde{r}_{t}^{3}\) \\ \hline \(\tilde{r}_{t}^{1}\) & 1 & 2 & 4 \\ \(\tilde{r}_{t}^{2}\) & 1/2 & 1 & 3 \\ \(\tilde{r}_{t}^{3}\) & 1/4 & 1/3 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pairwise comparison matrix for three criteria in \(\hat{r}_{t}\). candidates with lower RFRV is employed to enhance efficiency. The selection probability \(pe_{i}\) for \(EN_{i}\) during exploration is given by:  
\[p_{\varepsilon_{i}}=\left\{ \begin{aligned} & 1-\frac{RFRV_{i}}{\sum_{E_{N_{i}}\in \mathbb{N}_{i}^{cf}}RFRV_{i}},& EN_{i}\in\mathbb{N}_{i}^{cf}\\ & 0,& otherwise\end{aligned} \right. \tag{26}\]  
Upon determining \(a_{t}\), DEN forwards the data packet to the corresponding EN. At the next time step, the state transitions to \(\mathbf{s}_{t+1}\), and the environment provides the agent with reward \(r_{t}\) computed via Equation (17). The agent then constructs the transition tuple \((\mathbf{s}_{t},a_{t},r_{t},\mathbf{s}_{t+1})\) and stores it in replay memory \(\mathbf{M}\). When \(\mathbf{M}\) accumulates a sufficient number of samples, the agent randomly samples a minibatch from \(\mathbf{M}\) every \(C_{\text{exp}}\) timesteps to train the DQN by minimizing the loss \(\overline{L}(\omega)\) defined in Equation (2). Additionally, the target network is periodically synchronized with the evaluation network every \(C_{t}\) timesteps. The overall framework of the DQN-based routing decision mechanism is illustrated in Algorithm 3. Notably, the learning process is conducted in the virtual environment configured on computers to avoid high-performance demands on EN entities.
## 7 Conclusions  
In this study, prestressed beams were produced either using a newly proposed anchorage or existing anchorages. Experiments based on these beams were conducted to evaluate the bending behavior and performance of the anchorages. Seven concrete beams were manufactured. One was the control beam, while the others were prestressed beams. The following conclusions are drawn:  
1. The beams that were prestressed with an external tendon showed significant increases in both their yield load and ultimate load compared with the control beam. In particular, the yield and ultimate loads of the beams with the newly proposed anchorage increased by 110% to 148%, and 132% to 140%, respectively, relative to the control beam. The increases at each load (yield and ultimate) are approximately twice as high as those of the conventional anchorages, indicating that the proposed anchorage has better reinforcing effects.
2. As increasing the prestress forces applied to the steel rebar mounted in the lifting hole, the yield and ultimate loads of the prestressed beams increased by 7% to 14%, and 2% to 3%, respectively. The incorporation of the prestressed steel rebar significantly affected the yield load of the beams, but had almost no effects on the ultimate load.
3. Compared with the control beam, the prestressed beams exhibited very low ductility. Furthermore, in the same way as for the ductility, the prestressed beams had a lower inelastic energy than the control beam. However, the beams using the newly proposed anchorage showed a higher inelastic energy than those with the conventional anchorage.
4. In the beams with the conventional anchorage, problems such as cracks around the anchorage, stud bolts pull-out, and local failure of the anchorage arose. However, the newly proposed anchorage exhibited no such problems, and instead, behaved stably at the ultimate load, with only the epoxy being expelled from the concrete. This was caused by the proposed anchorage having a structurally superior shape, which enables the efficient transmission of the prestress force.  
This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2013R1A1A2059122).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] participated in manuscript writing and revision. All authors collaborated on the interpretation of the results and on the preparation of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Variation in inelastic energy ratio. \(P_{\text{cr}}\), \(P_{y}\), \(P_{\text{u}}\): cracking, yield and ultimate loads; \(\delta_{\text{cr}}\), \(\delta_{\text{y}}\), \(\delta_{\text{u}}\): deflections corresponding to the cracking, yield and ultimate.
###### Abstract  
Greece is among the European Union members topping the list of deaths related to coronary artery disease. Myocardial Perfusion Imaging (MPI) with Single-Photon Emission Computed Tomography (SPECT) is a non-invasive test used to detect abnormalities in CAD screening. The study proposes an explainable deep learning (DL) method for characterising MPI SPECT Polar Map images in patients with suspected CAD. Patient data were recorded at the Department of Nuclear Medicine of the University Hospital of Patras from 16 February 2018 to 28 February 2022. The final study population included 486 patients. An attention-based feature-fusion network (AFF-VGG19) was proposed to perform the diagnosis, and the Grad-CAM++ algorithm was employed to reveal potentially significant regions. AFF-VGG19's agreement with the medical experts was found to be 89.92%. When training and assessing using the ICA findings as a reference, AFF-VGG19 achieved good diagnostic strength (accuracy of 0.789) similar to that of the human expert (0.784) and with more balanced sensitivity and specificity rates (0.873 and 0.722, respectively) compared to the human expert (0.958 and 0.648, respectively). The visual inspection of the Grad-CAM++ regions showed that the model produced 77 meaningful explanations over the 100 selected samples, resulting in a slight accuracy decrease (0.77). In conclusion, this research introduced a novel and interpretable DL approach for characterising MPI SPECT Polar Map images in patients with suspected CAD. The high agreement with medical experts, robust diagnostic performance, and meaningful interpretability of the model support the notion that attention-based networks hold significant promise in CAD screening and may revolutionise medical decision-making in the near future.  
Keywords:explainable artificial intelligence; coronary artery disease; Myocardial Perfusion Imaging; Polar Maps; deep learning +
Footnote †: journal: Medical Imaging
# [PERSON] Coding-Based Medical Image Data Hiding Method against Salt and Pepper Noise
### Literature Review  
The methods proposed for hiding data in medical images vary. From histogram-based methods [1] and [2], hide data on peaks and zeros in the histogram of images separated into non-overlapping blocks. In [3], a data hiding is proposed according to the improvement of medical images by histogram stretching and histogram equalization methods. In addition, there are studies which propose data hiding with the difference expansion of adjacent pixels [4; 5; 6; 7]. In the medical image, some methods separate the region of interest (ROI) and region of non-interest (RONI). These methods hide data into encrypted RONI and the least significant bit (LSB) [8; 9; 10], wavelet coefficient [11; 12], discrete cosine transform (DCT) coefficients [13] and dividing blocks [14].  
Studies using error control coding methods for data hiding in medical images are limited. In [14], both noise reduction block and a simple error control coding named repetition codes are used. Repetition codes repeat the secret message several times. The author of [14] repeated the secret message eight times and create a new thread. The new thread was embedded in MRI images. These stego MRI images were exposed to salt and pepper noise. For the extraction of the secret message, in the first stage they removed noise with the noise reduction block. In the second stage, they extracted the thread, which included the secret message eight times. In the study [15], after the pixels in the cover object were duplicated by over-sampling, a secret message was added to the bits of the appropriate pixels. BCH coding was used as an error control method in wavelet-based hiding of [11] and [16] studies.  
Reed Solomon (RS) coding, which is a common error control coding method, has been used limitedly in data hiding methods. Moreover, [17] evaluated the use of error coding methods and RS codes in data hiding. Additionally, [18] hides the message by selecting the polynomial coefficients in RS codes, with the matrix coding method which is used in various [19] data hiding applications. In study [20], which hides the codeword, it selects the place where the message will be hidden according to the base of the polynomial coefficient. The authors of [21] use the RS code to fix errors in the video file from which the hidden message was extracted. The authors of [22] hide the RS encoded  
Figure 1: General structure of data hiding in medical images.  
message into the image, which is divided into blocks. In [23], the cover file is made suitable for RS by sampling. The data part of the message coded with RS is added to the image and the control part is added to the region where the upper sampling is performed. The methods proposed in [22; 23] have a complicated data hiding process due to the preferred data hiding algorithms and they take a lot of the processing load.