esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 4 Results and Discussion
Since the proposed algorithm is based on the changes in a pitch contour, when the intervals between notes are bigger, and there are fewer soft onsets, the algorithm can estimate onsets more accurately. The accuracy of the proposed algorithm may be improved by considering more spectrogram channels, i.e., including other related frequency components from the spectrogram and not only the fundamental frequencies. In this way, a more comprehensive formula weighted together with the measurements for each channel could improve the overall measure. Therefore, a new series of numbers will be generated to find the onsets, offsets, and transitions from the trajectory changes in the new contour. In this approach, the adverse effect of the incorrect \(F0\) estimation may be reduced, especially in a real-time environment. Moreover, the accuracy of the proposed algorithm can be improved by incorporating a function tracking significant changes in the magnitudes of each spectral channel that are also associated with the onset. Another possible approach instead of using the starting pitch explained in Section 3.2 is to scale down all F0s to one specific octave and then use a log frequency axis. This approach may help in regularizing the slopes and making them comparable. In addition, the algorithm is based on two parameters, window size (as explained in Section 3.5) and the proportion of the standard deviation to calculate the thresholds, as discussed in Section 3.7. By evaluating the algorithm on other larger datasets such \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average** & **STD** & **Min** & **Max** \\ \hline
**Erkomaishvili** & 57.44 & 40.77 & 16.67 & 98.21 \\
**SVNote1** & 56.25 & 44.68 & 11.57 & 100.93 \\
**Overall** & 57.4 & 40.91 & 16.49 & 98.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average, standard deviation, the minimum, and the maximum typical duration of transitions in both the datasets and overall. as VocalSet [41], these parameters could be fixed to be a constant value that is generally applicable to all singers or could be determined by a formula and therefore be adaptive to the nature of the style of input singing. Furthermore, the algorithm's efficiency and accuracy could be evaluated on notes performed by musical instruments to see if it is also applicable in that domain. Lastly, making the algorithm more computationally efficient requires a smaller buffer size to work faster in real-time environments.
###### Abstract  
To solve the early rutting failure of asphalt pavement, the application of rock asphalt from Sichuan, China, based on anti-rutting performance, was studied. Preparations of North Sichuan rock asphalt (NS RA) and NS RA-modified asphalt mixture were elaborated in detail. Using Zhonghai AH-70 asphalt, Esso AH-70 asphalt, North American rock asphalt (NA RA) and NS RA, the performances of NS RA modified asphalt were researched based on index tests, Brookfield rotary viscosity test and bending beam rheometer test. A performance verification of NS RA-modified asphalt was carried out using rutting calculation, the rutting, indirect tensile fatigue, freeze-thaw split and small beam bending tests based on five kinds of selected gradations. The results indicated that in comparison with NA RA, the NS RA has a good modification effect as well. The NS RA can obviously improve the anti-rutting ability of the asphalt binder, and it can enhance its anti-aging performance as well. For the NS RA-modified asphalt mixture, it is feasible to determine the optimum NS RA content, based on its anti-rutting performance, and its optimum NS RA content is about 8%. The dynamic stability values of NS RA-modified asphalt mixtures are at least 3-fold higher than those of the base asphalt mixtures, and they are all far greater than the summer hot area requirement (no less than 2800 times/mm). NS RA-modified asphalt mixtures used in the middle course of asphalt pavement can obviously improve the anti-rutting performance of the pavement, and to enhance the anti-rutting ability of pavements, it should be used in the middle course of the pavement. The fatigue life values of NS RA-modified asphalt mixtures are at least 14.5-fold higher than those of the base asphalt mixtures. The freeze-thaw splitting strength ratio values of NS RA-modified asphalt mixtures are improved by at least 9.5% over the base asphalt mixtures, and their freeze-thaw splitting strength ratio values are all greater than the requirement (no less than 75%). In comparison with the base asphalt, the low temperature performances of NS RA-modified asphalt and its mixtures slightly decline, but they can meet the requirements for the zones with a minimum temperature of no less than -21.5 \({}^{\circ}\)C too. Therefore, except for the extremely low temperature area, it is an effective method for solving the rutting problem of pavement for using NS RA-modified asphalt.  
Keywords:NS RA; asphalt mixture; anti-rutting performance; low temperature performance; fatigue performance; pavement +
Footnote †: journal: Applied Soft. 2019, 9, 870
## 3 Results
### Multivariate Analysis  
For the multivariate analysis, a binary logistic regression model based on the forward LR method was also used, as shown in Table 3. The overall prediction accuracy for the diagnosis of HRVs was initially 62.5%, and all predictors introduced in the model were identified as significant, except for the SGPT concentrations. The SS was demonstrated to be the most important independent predictor for HRVs, followed by platelet count, child class, albumin levels, and LS values; although other predictors were also initially found to be significant, they were not included in the final model.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameter** & **AUC** & **AUC 95\% CI** & \(p\)**-Value** & \begin{tabular}{c} **Cutoff** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Sensitivity** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} \\ \hline PV caliber & 0.746 & 0.586 \(\div\) 0.907 & 0.005 & 13.45 & 77.80\% & 73.30\% \\ SBD & 0.731 & 0.583 \(\div\) 0.878 & 0.008 & 137 & 72.20\% & 73.30\% \\ LS & 0.747 & 0.600 \(\div\) 0.895 & 0.004 & 15.16 & 55.60\% & 86.70\% \\ Albumin & 0.178 & 0.041 \(\div\) 0.315 & 0.000 & 4.555 & 5.60\% & 96.70\% \\ BT & 0.719 & 0.565 \(\div\) 0.872 & 0.012 & 1.82 & 72.20\% & 66.70\% \\ INR & 0.699 & 0.531 \(\div\) 0.867 & 0.022 & 1.375 & 55.60\% & 86.70\% \\ Platelets & 0.088 & 0.003 \(\div\) 0.172 & 0 & 265.5 & - & 96.70\% \\ SS & 0.987 & 0.964 \(\div\) 1.000 & 0.000 & 17.95 & 94.40\% & 96.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of ROC curves for the investigated parameters.  
Figure 3: ROC curve analysis for SS values.  
The Hosmer-Lemeshow fit test indicated that the built model is viable, with its results being statistically insignificant (\(p=1.00\)); the identified predictors increased the overall prediction accuracy for the diagnosis of HRVs up to 95.8%. Subsequently, the Wald test and odds ratio Exp(B) were calculated for the SS. As shown in Table 4, SS was significantly directly correlated with the risk of bleeding (OR 4.985, 95% CI: 1.57-15.73, \(p=0.006\)).
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 2 Methodology
### Storage Units  
In our case study, the only energy storage system considered is an electrochemical battery. For simplicity, a single BESS unit is modeled; therefore, none of the related parameters or variables are indexed over the unit set \(u\). A fast piecewise model based on experimentally derived look-up tables (LUTs) is implemented. As demonstrated in [34], this approach significantly reduces computational time compared to other similar modeling methods while still ensuring a reliable and accurate representation of BESS operation.
## 5 RT Real-Time Simulation and Experimental Validation
### RT Real-Time Simulation Results
Moreover, the convergence is obtained for the estimated signals of rotor resistance, rotor speed, and rotor flux, which reveals that the estimation process is well managed with minimum error; meanwhile, the flux orientation is achieved accurately. Table 3 summarized the results obtained using two criteria: the tracking error in the steady state and the convergence time. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Tracking Error** & **Convergence Time** \\ \hline Speed estimation & 0\% & 200 ms \\ \hline Rotor resistance estimation & 0\% & 20 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Convergence time and tracking error of the proposed method. Figure 2: Block diagram of real time implementation. Figure 3: _Cont._  
Figure 3: Real time results of sensorless control of five-phase IM drive. (**a**) Speed, (**b**) torque, (**c**) Stator currents (complete view), (**d**) Rotor flux (Reference and estimated signals), (**e**) Zoomed view of stator currents (actual and estimated), (**f**) Rotor flux and rotor resistance variation, (**g**) Rotor flux dynamics under rotor resistance variation. In order to further investigate the effect of rotor resistance variation on the estimated values of rotor flux with and without using the proposed SMO observer, the Matlab/Simulink tool is used for this purpose, and the results are shown in Figure 4. In Figure 4a, a variation in the rotor resistance value is made to \(1.5*R_{m}\), then the SMO observer is used to track and estimate the resistance. From this figure, it can be realized that the SMO is effectively succeeded in estimating the resistance change with high precision. In Figure 4b,c, the estimated values of the rotor flux with and without using the proposed SMO are shown respectively. From these figures, it can be concluded that the proposed SMO plays a vital role in estimating the rotor flux under the system uncertainties. Figure 4: Dynamics of estimated rotor flux with (**b**) and without (**c**) sliding mode observer (SMO) under varying rotor resistance (**a**).
## 5 Tailoring the Sensitivity of LSPR-Based Sensors Using Simulation Models  
### Important Parameters Influencing the LSPR-Based Sensor Performance  
According to the literature, so far the work carried out about surface plasmon resonance sensing has been mainly based on SPPs [16; 49; 193; 194]. Yet, since surface plasmon effects can be much more prominent in nanoparticles than in continuous thin film layers [154; 158; 160; 195; 196; 197], the nanoparticles can be exploited as intrinsic refractive index sensors [13], by monitoring shifts and curvature changes on the LSPR band (Figure 17a,b).  
Optical sensors based on the LSPR phenomenon are recognized as advantageous candidates in the design of lab-on-chip platforms, providing a valid approach to achieve quantitative analysis while offering the possibility to be miniaturized [69; 70; 166; 198; 199; 200].  
For refractive index changes to produce a measurable shift in the resonance band, they must occur in a region of appreciable field intensity, namely, in a sensing volume contained within the plasmon's electric field decay length (\(L_{d}\)). Since the plasmon field decays exponentially with the distance from the nanoparticle, the effective sensing volume is typically limited to a length ranging from 10 to 30 nm, depending on the nanoparticle shape and size [52]. To quantify the sensitivity of a LSPR sensor to a refractive index change (\(RIS\), refractive index sensitivity, or just S, sensitivity) caused by an infinite surrounding environment (such as a gas), a simple phenomenological equation may be used--\(\Delta P=RIS\times\Delta n_{s}\times\left(1-e^{-d/L_{d}}\right)\)--where \(\Delta P\) is the variation of the measured parameter (which can be the LSPR resonance wavelength shift, \(\Delta\lambda\)), \(\Delta n_{s}\) is the refractive index difference between the two environments, \(d\) is the thickness of the layer, and \(L_{d}\) the plasmon decay length.  
From experimental and theoretical results in the literature, several parameters have been proposed as having an important influence in the sensitivity of plasmonic thin films. The proposed parameters, namely, the existence of sharp vertices in nanoparticles of exotic shapes (such as stars, pyramids, bipyramids, rings, crescents, and branches), the position of the LSPR resonance, or the full width at half height of the LSPR band, failed to give a complete explanation [173]. Meanwhile, it has been made clear that the aspect ratio (AR), independently of the shape (Figure 18a), is the most probable candidate parameter to tailor the sensitivity of plasmonic nanoparticles (Figure 18b) [202].  
Figure 17: Refractive index sensing using LSPR in gold nanoparticles with a water drop on top. A clear (**a**) colour change and (**b**) LSPR band shift can be seen between the areas covered by air (\(n_{s}\approx 1.00\)) and water (\(n_{s}\approx 1.33\).
# Impact of LGE-MRI in Arrhythmia Ablation
## 4 Use of the Processed LGE-MRI for Ablation Procedures
### Determination of the Optimal Access Approach  
In VT ablation, the location of the myocardial fibrosis is important for determining and designing ablation access: An epicardial approach for patients with epicardial or transmural scars and an endocardial approach for those without epicardial or transmural scars. In addition, LGE-MRI accurately defines intramural scarring, which is a major determinant of VT ablation failure [59]. For this specific case, ablation techniques such as septal alcholization [60], bipolar ablation or ablation using needle ablation catheters can be chosen to enhance outcomes [61].
## 2 Related Work
### CA Middleware Systems  
Many middleware systems are proposed in many IoT installations, typically for sensors abstraction but also for covering the modeling and reasoning functionality of the context life cycle. Next, a non-exhaustive list of middleware solutions for various IoT applications is presented. [PERSON] et al. [7] present a comprehensive survey of middleware, including pioneer solutions, such as Gaia [63], Carisma [64], CoBrA [65] and Hydra [66]. Among the later approaches, C4 IOT [67] focuses on fusing context data from various streams in order to produce more comprehensive and meaningful information. [PERSON] et al. [68] present a CA middleware that is specialized in mobile applications by recognizing user behaviour accurately. [PERSON] et al. [69] propose a hybrid architecture that exploits data correlation for enriched context awareness. Their proposed system, called HACCD, integrates rules, ontologies and supervised learning toward a hybrid processing layer. [PERSON] and [PERSON] [70] describe an ambient intelligence middleware that supports multi-modal context data. Their novel awareness cognition framework aims to address the scalability issues of processing multiple context sources. [PERSON] et al. [71] present a middleware for mobile environments that focuses on scalability. Kali-smart [72], an autonomic CA platform for mobile pervasive environments, exploits semantic services to provide semantic multimodal event detection, centralized semantic decision making and optimization of the response time of the situation.  
Middleware systems applied in cultural spaces are very scarce. [PERSON] et al. [73] present a platform hosting an integrated middleware system named gateway server, which performs the acquisition of information from mobile devices for cultural institutions. The authors extend their work by adding a context manager, which stores data from the environment and the user in [25]. [PERSON] et al. [74] propose IRME, a framework for smart museums that transfers middleware functionality to the Cloud. SCRABS [22] adapts a generic middleware called Perla, integrating it into their CA assistant for cultural environments. The multi-protocol middleware for location-aware indoor smart museums implemented in [75] integrates transparent accessibility to heterogeneous IoT protocols, a functionality typically executed by middleware. Finally, the middleware integrated into the framework described in [76] manages installed sensors in a recommender system for cultural paths.  
The middleware solutions presented above and summarized in Table 1 do not fully integrate every stage of the context lifecycle. They typically address heterogeneity issues and provide abstraction of the sensor layer, while providing limited context modeling of location and user profiles, and also limited reasoning. In contrast, the proposed middleware integrates a complete context model and a hybrid reasoning technique, addressing all stages of the context life cycle and supporting a fully operational CA computing procedure in CH environments.
## 4 Trends and Future Work
Figure 8 illustrates the key advancements in ML and DL for earthquake magnitude estimation. It highlights the transition from traditional empirical methods to feature-based models, the adoption of DL architectures such as CNNs and Transformers, and emerging hybrid approaches that integrate physics-based constraints and probabilistic models. These trends outline the direction of future research in optimizing real-time EEW applications.
## 3 Results and Discussion
### Engine Performance Parameters
#### 3.2.3 Exhaust Temperature  
The influence of various stages of engine warm up on the exhaust temperature is discussed in this section. There are thermocouples where the exhaust manifolds of Cylinder 1-3 and Cylinder 4-6, respectively, merge. The exhaust temperature was calculated as an average of the temperatures measured by these. Figure 14a,b show the exhaust temperatures at each load in each stage of the custom cycle, at 1500 and 2000 rpm. During Stage 1 (engine cold start), the exhaust temperatures showed an increment of approximately 10% to 15% and 6% to 10%, from Segment 1 to Segment 2, with a higher percentage difference being observed at lower loads (50% and 25% loads) at both engine speeds, 1500 and 2000 rpm. Due to the ECU restrictions applied at 100% load in Segments 1 and 2, at 1500 rpm, the exhaust temperature in Segments 1 and 2 showed a difference of approximately 20%.  
During Stage 2 (engine intermediate start--I), in Segment 3, the exhaust temperatures further increased by approximately 4% to 9% and 2% to 5%, at 1500 and 2000 rpm. The increment in the exhaust temperature in Stage 2 was an after effect of the higher temperatures produced in Segment 2 due to the advanced injection strategy followed in Stage 1 (Segment 1 and 2). The engine switched to a retarded injection strategy in Stage 2, due to which the cylinder temperatures were lowered and the effects of which were observed later in Stage 3 (Segments 4 and onwards). During Stages 3 and 4 (intermediate start--II and hot start), in Segments 4 and onwards, the exhaust temperatures remained approximately consistent within \(\pm\)1%.  
The exhaust temperatures at all loads at 1500 rpm were observed to be higher than 2000 rpm, as 1500 rpm was the engine speed for rated torque. However, the exception was at 100% load in Segment 1, due to the engine restrictions applied at 1500 rpm. Moreover, it has been observed that at 75% load at 2000 rpm, the exhaust temperature decreased in Segments 3 and onwards. The most likely reason for this could be due to an unusual combustion phenomenon taking place at this load, such as retarded injection complemented with a longer combustion duration. This could have caused the combustion to finish late in the expansion stroke, thereby causing more fuel to burn in the diffusion combustion phase and releasing less heat, consequently leading to lower exhaust temperatures.  
Figure 14: Average exhaust temperature at each load in each segment at (**a**) 1500 and (**b**) 2000 rpm. The blue circle represents 100% load, the red diamond 50% load, the green pentagon 75% load and the pink hexagon 25% load.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 5 Barcode Data Extraction
### Transform a Square Grid Format Image to an Edge Image
#### 5.3.1 Pair Odd-Degree Vertices in the Edge Image  
A proper path is chosen to eliminate two odd-degree vertices in the edge image, which begins at one vertex and ends at another vertex. In the meantime, we should modify the edge image as little as possible, which means that the sum of the lengths of all paths in the edge image is minimum. Based on this principle, the knowledge of graph theory is used to pair these odd-degree vertices. Assuming that the number of these vertices is 2N, we calculate distances between these vertices and draw a weighted undirected graph. It is known that N edges can cover all vertices in the graph according to the edge-covering number of an undirected graph. In order to minimize the sum of the lengths of N edges, the proposed pairing method is used and costs less time than the exhaustive method, which takes the split point operation to turn a weighted undirected graph into a weighted bipartite graph and the KM (Kuhn-Munkres) algorithm [26] to find an approximate solution.  
The flow of the KM algorithm is as follows.  
For a bipartite graph \(G=(V,E)\), V can divide two disjoint subsets \((X,Y)\), and \(E\) is a set of edges.  
Figure 11: (**a**) A barcode image; (**b**) a standard barcode edge image; (**c**) odd-degree vertices in the edge image; (**d**) pair odd-degree vertices in the edge image.  
1. Starting from any feasible vertex labeling \(L\), determine the equality subgraph \(G_{L}\) of \(L\) and select the matching \(M\) in \(G_{L}\). If \(M\) is a perfect matching, the algorithm stops. Otherwise, go to step 2.
2. Hungarian algorithm begins. When \(N_{G_{L}}(S)=T\), with \(S\in X\) and \(T\in Y\), the Hungarian algorithm stops. Calculate \(\alpha L\). Determine a new feasible vertex labeling \(L^{\prime}\). Replace \(L\) with \(L^{\prime}\), replace \(G_{L}\) with \(G\), and then move to step 1.  
In the KM algorithm, the Hungarian algorithm [27] is used. In addition, the Hungarian algorithm can solve the perfect matching of a bipartite graph. Its algorithm flow is as follows.  
1. Select any matching \(M\) of \(G\). If any vertex of \(X\) is the saturated vertex of \(M\), the algorithm stops. Otherwise, take a point \(x\) of \(X\) and \(x\
otin M\). Let \(S=\{x\}\) and \(T=\emptyset\).  
2. If \(N(S)=T\), the algorithm stops. There is no perfect matching in \(G\). If \(N(S)\
eq T\), take \(y\
otin N(S)\setminus T\).  
3. If \(y\) is the saturated vertex of \(M\), there exist \(z\
otin X\setminus S\) and \(yz\in M\). Replace \(S\) with \(S\cup\{z\}\), and replace \(T\) with \(T\cup\{y\}\). Turn to step 2. If \(y\) is not the saturated vertex of \(M\), there is an augmenting path \(P\) of \(M\) with \(x\) as the starting point and \(y\) as the ending point in \(G\). Replace \(M\) with \(M^{\prime}=M\bigtriangleup E(P)\) and turn to step 1.  
The KM algorithm pairs the odd-degree vertices in the edge image, as shown in Figure 11d.