esa-sceva/satcom-chunk-collection huggingface dataset:
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 3 Evaluation Results and Discussion
### Discussion  
According to the experimental results provided in this section, it can be seen that the proposed new architecture for melody extraction can extract melody from polyphonic music. It obtains the highest accuracies among the compared methods at different signal-accompaniment ratios. It achieves comparably good performance on the MIR1K dataset, but not that well on ISMIR2004. Deep observations reveal that, for the ordinary melody, either from singing voice or instruments, it can track the dynamic pitch variations, but it still needs to be improved to follow deep vibratos and pitch glides, such as in opera. In the future, we will focus our work on improving the performance of chroma-level note detection for such excerpts.
## 2 Materials and Methods
### Loss Function  
The goal of PINN is to minimize the loss function in Equation (5). The total loss function value is given by Equation (5), which is composed of the residuals for the PDEs and the mean square error of the initial and boundary conditions. In the loss function (Equation (5)), we introduce a set of weighting factors \(w=(\beta_{1},\beta_{2},\beta_{3})\) to address the issue of disparate  
Figure 1: A traditional PINN architecture with soft constraint.  
Figure 2: The HWPINN architecture.  
error magnitudes among different terms. These weighting factors ensure that the different components of the loss function have equal importance in the optimization process.  
\[L=\beta_{1}\cdot L_{pde}+\beta_{2}\cdot L_{IC}+\beta_{3}\cdot L_{BC}, \tag{5}\]  
\[L_{pde}=\frac{1}{N_{c}}\sum_{n=1}^{N_{c}}|R_{pde}(x_{n},t)-0|^{2}, \tag{6}\]  
\[L_{IC}=\frac{1}{N_{IC}}\sum_{n=1}^{N_{IC}}|u(x_{n},t)-u^{*}(x_{n},t)|^{2}, \tag{7}\]  
\[L_{BC}=\frac{1}{N_{BC}}\sum_{n=1}^{N_{BC}}|f_{bc}(u(x_{n},t)-u^{*}(x_{n},t))|^{2}, \tag{8}\]  
Equation (6) displays the loss function for the residual values of the PDEs. The residual values of the PDE \(R_{pde}\) in Equation (6) can be obtained by shifting the governing equation of the original PDE. The following is an example of the 1D AC equation expressed in Equation (1):  
\[R_{pde}(x,t)=e^{2}\
abla^{2}-W^{\prime}(u)-\frac{\partial u}{\partial t}. \tag{9}\]  
In Equation (6), \(N_{c}\) represents the points generated for training in the computational domain. Equations (7) and (8) represent the loss values for the initial conditions and boundary conditions, respectively. \(N_{BC}\) and \(N_{IC}\) represent the sampled points that meet the initial conditions and the points on the boundary conditions, respectively. In Equation (7), \(u(x,t)\) represents the result predicted by the neural network, and \(u^{*}(x,t)\) represents the exact value from the initial conditions. In Equation (8), \(f_{bc}\) represents the type of boundary condition. Periodic boundary conditions [34] are used for all cases in this study.  
For hard-constraint PINNs, the weight coefficients in Equation (5) need to be set to \(w=(1,0,0)\). And, a hard constraint function needs to be implemented in the program. The hard-constraint functions need to be set based on concrete boundary conditions and initial condition information, so they are described in Section 3.
# Portable RGB-D Camera-Based System for Assessing Gait Impairment Progression in ATTRv Amyloidosis
## 2 Materials and Methods
### Statistical Analysis  
To investigate whether the considered RGB-D camera can be used as an alternative system for detecting relevant changes in the gait of a given ATTRv amyloidosis patient over time, we compared the results obtained at T0 and T1 for both Kinect v2 and Qualisys.  
For each parameter, patient, and system, we obtained the difference between the mean values for T0 and T1 when considering all gait cycles. A negative/positive value indicates a decrease/increase in the parameter mean value between the two moments, which verifies whether the trend over time for the Kinect was the same as for Qualisys.  
Furthermore, we investigated if the difference between T0 and T1 was statistically significant by carrying out the Mann-Whitney U non-parametric test over the parameter's median values for all gait cycles at T0 and T1. The test results obtained for both systems help to verify if the Kinect can detect actual significant changes in gait over time (results for the reference Qualisys system considered as the ground truth). The statistical test was performed using IBM SPSS Statistics for Windows, Version 25.0. (Armonk, NY, USA: IBM Corp) with a confidence interval of 95%.  
Based on the interpretation of the Mann-Whitney U test results regarding the difference between T0 and T1--statistically non-significant, or significant increase/decrease--we performed the Cohen's Kappa inter-rater agreement test to determine the agreement between the RGB-D camera and reference systems. The results of this test were interpretedaccording to [PERSON] et al. as \"poor\" (<0.00), \"slight\" (0.00-0.20), \"fair\" (0.21-0.40), \"moderate\" (0.41-0.60), \"substantial\" (0.61-0.80) or \"almost perfect\" (0.81-1.00) [58].
# An Algebraic Approach to Identifiability
## 3 Observability and Identifiability
### Global Identifiability of a Particular Parameter  
The statement that a system is not identifiable is not always satisfying. In some cases, only a subset of the parameters can be identified. Thus, it makes sense to test the identifiability for each parameter \(p_{i}\). This can be accomplished by computing further eliminations as in the preceding section, namely by comparing the elimination ideals \(\hat{\mathfrak{J}}\cap\mathbb{R}[p_{i},\tilde{p}_{i}]\) and \(\mathfrak{J}\cap\mathbb{R}[p_{i},\tilde{p}_{i}]\).
## 3 Methodology
### Overview  
An overview of the proposed method is given in Figure 1. It mainly contains three steps: (1) initial feature extraction, (2) data complexity measurement, and (3) progressive model training. First, random samples from the sample dataset are chosen to pretrain the DNN, and all samples are fed into the trained model according to their categories for feature extraction. Second, the K-means algorithm is employed to divide the samples in each category into three clusters according to the product of the local density and distance; then samples in each category are divided into easy-to-classify, relatively easy-to-classify, and hard-to-classify subsets according to the smallest intra-class local density in each cluster. Third, data from the same subset of all categories are combined, and the data are fed into the DNN in order from easy to hard to progressively train the DNN in three stages. Thus, the model capacity can be gradually improved gradually by progressively adding increasingly complex data into the training process.
## 3 Methodology  
Several methods are available for the availability estimation: reliability block diagram (RBD), Markov model, and Monte Carlo simulation [17; 18]. The former two are an analytical approach whereas the latter one is a simulation approach. The analytical approach calculates the availability using mathematical equations, while the simulation technique estimates it by generating scenarios. When the system is complex, the analytical approaches like RBD and Markov model are unrealistic. They are additionally difficult to apply to the system, which has nonconstant failure and repair rates. However, the Monte Carlo simulation approach can handle inconstant failure/repair rates and multi-state systems. One of the drawbacks of the Monte Carlo simulation is the long simulation time, but it can be overcome by the advanced simulation techniques. In this study, Monte Carlo simulation is employed for the availability estimation.  
Figure 6 shows the procedure for the availability estimation using Monte Carlo Simulation. First of all, the target system is analyzed, and then the reliability block diagram is drawn for the modeling of the system. The data for reliability and maintainability is collected from the data sources. The availability of the target system is estimated using the Monte Carlo Simulation. The followings are the details of each step.
# Intra-System Reliability Assessment of 2-Dimensional Shear Wave Elastography
###### Abstract  
The availability of 2-Dimensional Shear Wave Elastography (2D-SWE) technology on modern medical ultrasound systems is becoming increasingly common. The technology is now being used to investigate a range of soft tissues and related pathological conditions. This work investigated the reliability of a single commercial 2D-SWE system using a tissue-mimicking elastography phantom to understand the major causes of intra-system variability. Sources of shear wave velocity (SWV) measurement variability relates to imaging depth, target stiffness, sampling technique and the operator. Higher SWV measurement variability was evident with increasing depth and stiffness of the phantom targets. The influence of the operator was minimal, and variations in sampling technique had little impact on the SWV.  
ultrasound; phantom study; shear wave elastography; reliability study +
Footnote †: journal: Medicalcan differentiate between normal and pathological conditions. Although several studies have investigated the reliability of ultrasound derived elastography measurements [9; 12; 13; 14; 15; 17; 18; 19], there is limited published data for 2D-SWE systems and a lack of universally agreed-upon reliability criteria for this system [20].  
Modern 2D-SWE systems now incorporate software tools to assist operators to visually assess the stability of the acquired SWV and to inform them of the ideal placement of the sampling region of interest (ROI) box [21]. Alongside a 2D colour-coded map of tissue stiffness, a novel approach provided by some manufacturers is the addition of a \"propagation contour map\" (Figure 1), which uses a series of contour lines to depict shear wave arrival times at different anatomical points in the tissue being assessed [22; 23]. Regions in the image displaying parallel contour lines indicate shear waves are propagating smoothly with minimum variability [21]. These two images provide complementary information designed to produce a visual indicator of the best acquisitions to select for quantitative analysis and to optimise the placement of the ROI sampling box in an area free of significant artefacts. Operators utilising 2D-SWE technology need to have confidence that any derived measurements are a true representation of the tissue being sampled and have a good understanding of the factors that can introduce variability. Thus allowing them to take steps to minimise these at the time of imaging.  
This study aimed to investigate the reliability of a modern 2D-SWE system using a tissue-mimicking elastography phantom to identify the major causes of intra-system variability. The evaluation includes an analysis of the sources of SWV measurement variability, as they relate to imaging depth, target stiffness, sampling technique and the operator.
# Potential of Advanced Consolidants for the Application on Sandstone
## 3 Results and Discussion
### Characterization of the Consolidant Xerogels  
#### 3.2.1 Effect of Catalyst and Added Nanoparticles on Xerogel Texture Properties  
A sol-gel process was used to transform liquid consolidants into solid xerogels, whose properties were determined by an extensive adsorption study, the results of which are summarized in Table 3.  
The addition of nanoparticles to the sols whose gelation was catalyzed by dibutyltin dilaurate did not lead to the formation of mesoporous in corresponding xerogels. This might be due to formation of a narrow corona around the nanoparticles, the corona itself being identified as a micropore. Actually, an increase of about 20% in micropore volume was observed. The surface properties of the added particles, their hydrophilicity for a surface covered with hydroxyl groups and hydrophobicity for a surface covered with methyl or octyl groups, did not affect the character of porosity. Therefore, it can be assumed that there was no formation of chemical bonds between the particle surface and the surrounding gel because, if chemical bonds were formed, there would be a difference.  
When an organic amine, a substance with a higher basicity than dibutyltin dilaurate but a lower basicity than inorganic bases (e.g., ammonia or hydroxides of alkaline metals), was used as the catalyst, important changes in porosity occurred [21]. The formation of micropores was substantially suppressed and a considerable amount of mesopores were formed, their width achieving 5 nm depending on the type and concentration of organic amine used. Due to the structure of their molecules, some primary amines with a longer alkyl chain (such as octyl or dodecylamine) might function as non-ionic surfactants that reduce the surface tension.  
The texture of SiGel-ZnO-d/oa was more complex, exhibiting both micro- and mesoporous, due to the combination of both dibutyltin dilaurate and organic amine catalysts. The commercial KSE OH consolidant exhibited similar texture properties, but SP FX was practically non-porous.  
Thus, to suppress the formation of micropores, which have a negative effect on the cracking of gels due to their high capillary pressure [2] and to induce the formation of advantageous, substantially wider mesopores, an organic amine is preferable as a catalyst [21].
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 4 Results and Discussion
### Performance Result of Classifiers  
The proposed model utilizes the selected seven ML classifiers with two different feature extraction techniques. These techniques were set empirically to achieve higher accuracy. For instance, LR achieved the best accuracy and F1 score in our dataset, where the classification accuracy and F1 score are 90.57% and 0.9280, respectively. Meanwhile, there is a slight difference between LR, SGD, and LGBM classifier performance, where SGD achieved an accuracy of 90.6%, but the F1 score was lower than LR. However, the LGBM classifier achieved an accuracy of 90.55%, and the F1 score was 0.9271. This means LR performs better than other classifiers, as shown in Table 2.  
Moreover, RF and AdaBoost have achieved almost the same accuracy, but in terms of F1 Score, RF performs better than AdaBoost. Multinomial NB has achieved low accuracy and precision with a detection rate of 81.39% and 0.7952, respectively, and we can notice that the excellent recall levels-out the low precision, giving a good F-measure score of 0.8754 as illustrated in Table 2.  
Finally, SVM has achieved the lowest accuracy and precision in our dataset, as shown in Figure 5. Nevertheless, it achieved the best recall compared to the rest of the classifiers implemented in the current research. Furthermore, some studies have looked at the automatic cyberbullying detection incidents; for example, an effect analysis based on lexicon and SVM was found to be effective in detecting cyberbullying. However, the accuracy decreased when data size increased, suggesting that SVM may not be ideal for dealing with common language ambiguities typical of cyberbullying [61]. This proves that the low accuracy achieved by SVM is due to the large dataset used in this research.  
F-measure is one of the most effective evaluation metrics. In this research, the seven classifiers' performances were computed using the F-measure metric, as shown in Figure 6. Furthermore, the performances of all ML classifiers are enhanced by producing additional data utilizing data  
Figure 5: Performance Summary of Algorithms.  
synthesizing techniques. Multinomial NB assumes that every function is independent, but this is not true in real situations [115]. Therefore, it does not outperform LR in our research as well. As stated in [116], LR performs well for the binary classification problem and works better as data size increases. LR updates several parameters iteratively and tries to eliminate the error. Simultaneously, SGD uses a single sample and uses a similar approximation to update the parameters. Therefore, SGD performs almost as LR, but the error is not as reduced as in LR [92]. Consequently, it is not surprising that LR also outperforms the other classifiers in our study.
## 3 Results and Discussion
### Case Study
#### 3.2.2 Sub-Steps Convergence Test  
The sub-steps convergence test was conducted to standardize the number of sub-steps that the reference loads will be divided and applied. This study is also based on the P(2;2) plate configuration with \(h_{s}/t_{s}=1.02\), under \(P_{z}=0.16\) MPa and biaxial compression, with a finite element size of 25 mm. The results of \(\sigma_{u}\) are shown in Table 9.  
The results in Table 9 indicate that dividing the reference load into 100 sub-steps versus 200, 300, and 400 resulted in only a \(-0.9\%\) difference in \(\sigma_{u}\), but reduced computational effort by 21%, 56%, and 62, respectively. In addition, the von Mises stress distribution for each case of Table 9 is presented in Figure 15.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Element Length** & **Number of** & **Processing Time** & \(\sigma_{u}\) \\
**(mm)** & **Elements** & **(s)** & **(MPa)** \\ \hline
50 & 1134 & 82 & 101.17 \\
30 & 3114 & 112 & 99.40 \\
25 & 4140 & 158 & 97.62 \\
20 & 6426 & 216 & 97.62 \\
10 & 24,744 & 975 & 97.62 \\ \hline \end{tabular}
\end{table}
Table 8: Mesh convergence test for plate configuration P(2;2) with \(h_{s}~{}=~{}45.82\) mm and \(t_{s}~{}=~{}45\) mm.  
Figure 15: The von Mises stress distribution of plates with number of sub-steps: **(a)** 100, **(b)** 200, **(c)** 300, and **(d)** 400.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number of** & **Maximum Number of** & **Minimum Number of** & **Processing Time** & \(\sigma_{u}\) \\
**Sub-Steps** & **Sub-Steps** & **Sub-Steps** & **Sub-Steps** & **(s)** & **(MPa)** \\ \hline
100 & 200 & 50 & 168 & 97.62 \\
200 & 400 & 100 & 212 & 98.51 \\
300 & 600 & 150 & 385 & 98.51 \\
400 & 800 & 200 & 440 & 98.51 \\ \hline \end{tabular}
\end{table}
Table 9: Number of sub-steps test for plate configuration P(2;2) with \(h_{s}~{}=~{}45.82\) mm and \(t_{s}~{}=~{}45\) mm.  
From Figure 15, the stress values and distributions showed a slight discrepancy when considering the different sub-steps. Therefore, to reduce the computational effort, all case study simulations were performed with 100 sub-steps of loading, which can achieve a maximum of 200 and a minimum of 50 sub-steps according to problem convergence.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 6 Experimentation
### Fixed Job Experiments
#### 6.2.3 Results  
Figure 5 shows the results in the hybrid datasets with 1500 and 2500 jobs from [7]. The plots show the performance of each of the methods in GIPS as a function of the number of jobs that the agent has distributed so far. Note that the performance of the heuristic methods remains the same over time as they distribute jobs by following fixed policies. In contrast, the performance of PPO increases as the agent gains experience interacting with the Dew environment. In the case of PPO, the plot shows the average performance across 16 runs as well as one standard deviation in the violet shaded area and the maximum and minimum performance in light violet.  
As Figure 5 shows, PPO eventually outperforms all the heuristics methods in both environments. In the 1500 jobs environment, PPO quickly improves and exceeds the performance of the baselines. With 2500 jobs, however, the agent requires around eight times more experience to outperform the baselines than with 1500 jobs. Part of the reason is that the baseline W-RR works particularly well with 2500 jobs. The other part is that the agent has to explore a larger space of possibilities when dealing with 2500 jobs than when distributing 1500 jobs. As a frame of reference, it took 5 days to train the agent with 1500 jobs and 10 days with 2500 jobs on an AMD Ryzen Threadripper 2990 WX processor.  
Interestingly, PPO outperforms all the baselines while being competitive at job completion. Recall that jobs can be lost in our environments if they are assigned to devices that are running out of battery. Even though we did not provide a reward to the agent for completing jobs, PPO learned to maximize GIPS while completing most of the jobs. As Table 1 shows, PPO completes more jobs than BPA, E-SEAS, and W-Random in both domains and is competitive with the Round Robin methods.  
Finally, note that we also ran experiments using another RL agent, which is called A3C [22]. PPO could be considered an improved version of A3C. In particular, PPO has an extra constraint that A3C does not: PPO explicitly penalizes when the policy \(\pi_{\theta}\) changes abruptly between two consecutive learning steps. As the results show, avoiding such large changes makes learning more stable and allows PPO to find good policies, while A3C does not. In these experiments, A3C is using the same hyperparameters as PPO. Since A3C is unable to find good policies in our experiments, we focus the discussion around PPO in the rest of the paper.
# Decentralized and Secure Blockchain Solution for Tamper-Proof Logging Events
## 3 Materials and Methods
### Types of Transactions  
This section details two different implementations aimed at providing a universal, blockchain-independent solution for certifying registration events. One approach involves using a blockchain that can append metadata to transactions, thereby including the hash of the event generated by the managed equipment. The second approach provides a solution using a blockchain that avoids attaching metadata to transactions.  
When an event from a managed network device arrives at the Syslog server, it is automatically forwarded to the message broker. Once there, the event will wait for the log module service to process it for subsequent transfer to the blockchain. When the blockchain selected does not allow metadata, the log module will perform a transaction with the blockchain that will require the payment of a certain number of tokens. The transaction ID generated in response will be used as the key, and the content of the log event as the message, thus letting us calculate a SHA2-based hash message authentication key (HMAC) [26]. These three elements (i.e., log event, transaction ID and resulting hash)  
Figure 3: Main view of the log management server interface.  
are then sent to the log management server for safekeeping. Finally, the log module will also send confirmation to the message broker indicating that the log event has been registered and is waiting for further events. Figure 4 shows the interaction process, where the managed device (e.g., a router) that has the Syslog protocol configured generates the event with a size of 256 bytes (e.g., incorrect login access).  
When the blockchain allows metadata, the log module receives a log event and calculates a hash of the event's content. This hash is then included in the metadata field before the corresponding transaction is initiated, and the result will persist in the blockchain once the block is completed. As in the previous case, a transaction ID will be received and sent to the log management server along with this event's hash and content. Figure 5 illustrates the interaction process, which differs from the previous method after the log module. Specifically, the log module will generate a SHA2 hash of the event and perform the transaction in the blockchain, adding the hash to the transaction's metadata and obtaining a transaction ID. Once the transaction ID is received, the log module will send the log event, transaction ID, and resulting hash as in the previous case.