esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Phonation Types by Clustering of Multiple Metrics
## 3 Results
### Description of the Classification Voice Maps
#### 3.2.3 Acoustic Versus EGG Metrics  
Instead of showing just one metric across the voice range, the voice map of clusters gives a solution for simultaneously picking up information from several metrics of both acoustic and EGG signals. For example, Figure 7 shows the map layers of all metrics for comparison with \(k=4\) clusters in another male, M06.  
The Crest factor contributes the most to the cluster corresponding to the speech range on the left of this voice map, colored by the pale yellow in the phonation type layer (and the redder region on the left of the Crest factor layer). At the same time, in EGG metrics, the \(Q_{\text{ci}}\) and \(Q_{\Delta}\) seemingly imply the tendency from the lower left to the upper right. The CPPs and CSE as stability indicators for voice signal and EGG signal each independently expose the lower breathy and soft regions. Though the CPPs and CSE show similar impacts on distinguishing the loud and soft regions, a slightly different portrait in the transitional region affects the boundaries of the loud and the transition voices.  
Are all these metrics needed? To address this question, the classification was performed with EGG only (three metrics), acoustic only (three metrics), and EGG + acoustic (six metrics), as can be seen for one participant in Figure 8. We see a better portrait of parallel layering in (Figure 8b) with acoustic metrics, but a better approximation of the modal speech range (the grey on the left around 50 semitones and 80 dB) in (Figure 8c)  
Figure 7: Voice map layers of the phonation types and the underlying metrics, from a male speaker, M06, when \(k=4\). Clockwise from top left: four phonation type clusters, the Crest factor, the spectrum balance (SB), the cepstral peak prominence smoothed (CPPs), the EGG cycle-rate sample entropy (CSE), the normalized peak dEGG (\(Q_{\Delta}\)), and the EGG quotient of contact by integration (\(Q_{\text{ci}}\)). The \(x\)-axis is \(f_{\text{o}}\) in semitones (on the midi scale, 48 = 110 Hz), and the \(y\)-axis is SPL in dB(C) at 0.3 m.  
with EGG metrics. With combined acoustic and EGG features, we obtain a more structured voice map.  
This confirms that acoustic metrics and EGG metrics manifest different aspects of vocal function. Combining the metrics of the airborne signal and the EGG signal resolves the phonation types in greater detail. However, if limited by conditions, only one set of metrics can also be well interpreted, as in this case, the acoustic metrics are better at revealing the structural feature of the voice map. As a complement, the EGG metrics are more suited for finding the transition zone at the onset of vocal fold collision.
## 3 Numerical Solution: The Temperature Particles Method (TPM)
### Viscous Splitting Algorithm  
The Temperature Particles Method relies on the discretization of the vorticity field by a linear combination of Lamb discrete vortices, and the heat by temperature particles. All particles move with the local flow velocity [7; 9; 41]. The motion of these particles is governed by the vorticity transport equation, Equation (1), and the energy equation, Equation (5).  
The viscous term in Equation (1) can be split according to the viscous splitting algorithm proposed by [PERSON] [7]. Thus, the advection and the diffusion of the vorticity can be separately solved. The velocity vector of the filtered field \(\overline{u}\) associated to Equation (2) is computed over the Lamb discrete vortex i due to contributions of the incident flow, source panels and vortex cloud (the Biot-Savart Law) when the circular cylinder is used. When the ground plane is used, the source panels are replaced by mirror image Lamb discrete vortices. The discrete vortices are treated as fluid material particle and the vorticity advection solution is obtained by integrating the vortex particle trajectory in the following manner [23]:  
\[r_{i}(t+\Delta t)=r_{i}(t)+[\alpha\overline{u}_{i}(t)-\beta\overline{u}_{i}(t -\Delta t)]\Delta t, \tag{14}\]  
where \(r_{i}\) is the particle position vector, \(\overline{u}_{i}\) is the velocity vector of the filtered field, \(\alpha=1\) and \(\beta=0\) when using the first-order Euler scheme, and \(\alpha=1.5\) and \(\beta=0.5\) when using the second-order Addams-Bashforth scheme.  
On the other hand, the velocity vector computed over temperature particle i is due to contributions of the incident flow, source panels and vortex cloud for cylinder configuration. When the ground plane is used, the source panels are similarly replaced by mirror-image Lamb discrete vortices. Equation (14) is also used to calculate heat advection through a similar interpretation of the viscous splitting algorithm applied to Equation (5).  
The evaluation of the velocity component \(\overline{u}_{i}\) for a cloud of \(Z\) discrete vortices using direct calculation based on Equation (10) requires \(Z^{2}\) operations. Since this computational cost is very expensive, the present TPM uses parallel processing (OpenMP) in Fortran to calculate the induced velocities (vortex-vortex interaction). The induced velocity over temperature particles due to discrete vortices (vortex-temperature interaction) also uses parallel computing.  
If particles migrate to the interior of a solid surface, then they are reflected from their paths.
_Applied_  
_sciences_  
Modeling Analysis of Thermal Lesion Characteristics of Unipolar/Bipolar Ablation Using Circumferential Multipolar Catheter  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,* and [PERSON] 1,2,3,*  
* Department of Electronic Engineering and Academy for Engineering & Technology, Fudan University, Shanghai 200438, China; [EMAIL_ADDRESS] (K.G.); [EMAIL_ADDRESS] (Y.W.)
* Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200433, China
* Shanghai Engineering Research Center of Assistive Devices and Department of Electronic Engineering, Fudan University, Shanghai 200093, China
* Correspondence: [EMAIL_ADDRESS] (S.Y.); [PERSON]DRESS] (X.W.); Tel.: +86-31242518 (X.W.)  
Received: 26 October 2020; Accepted: 15 December 2020; Published: 18 December 2020  
**Abstract:** The circumferential multipolar catheter (CMC) facilitates pulmonary vein isolation (PVI) for the treatment of atrial fibrillation by catheter ablation. However, the ablation characteristics of CMC are not well understood. This study uses the finite element method to conduct a comprehensive analysis of the ablation characteristics of multielectrode unipolar/bipolar (MEU/MEB) modes of the CMC. A three-dimensional computational model of the CMC, including blood, myocardium, connective tissue, lung, and muscle, was constructed. The method was validated by comparing the results of an in vitro experiment with the simulation. Both ablation modes could create contiguous effective lesions, but the MEU mode created a deeper and broader lesion volume than the MEB mode. The MEB mode had an overall higher average temperature field and allowed faster formation of the effective contiguous lesion. The lesion shape tended to be symmetric and spread downward and superficially in the MEU mode and MEB mode, respectively. Results from the simulation for validation agreed with the in vitro experiment. Different ablation trends of the MEU and MEB modes provide different solutions for specific ablation requirements in clinical applications. The MEU mode suits transmural lesion in thick tissue around pulmonary veins (PVs). The MEB mode profits fast and durable creation of circumferential PVI. This study provides a detailed performance analysis of CMC, thereby contributing to the theoretical knowledge base of application of PVI with this emerging technology.  
**Keywords:** finite element method; pulmonary vein isolation; radiofrequency catheter ablation; unipolar ablation; bipolar ablation; circumferential multipolar catheter
## 3 Results and Discussion  
### Microrheological Parameters During Fermentation Process  
The exact gel-point time of each sample was measured using time-cure superposition (TCS) method; gel point values are reported in Table 1. As can be seen from the obtained results, there is no correlation between carob seed flour (CSF) amount and gel point time; so, we can suppose that the flour amount does not affect the gelation time. This trend can be observed for all three types of milk. As can be seen in the graph (Figure 1), MVI and EI values during the fermentation process increased until they reach a plateau; both the MVI and EI values are higher as the amount of carob seed flour was higher; in particular, after gel point time, these values increase for all the samples, indicating an increase in the macroscopic viscosity index and elastic properties of the samples. These results probably are due to the fact that, after reaching the gel point, gel-like structure are formed. Solid-Liquid Balance (SLB) is a parameter that allows for determining if the sample has solid-like or liquid-like behavior; it is directly proportional to the viscoelasticity of the sample. For all the studied samples, before fermentation, SLB values between 0.5 and 1, indicating that the liquid behavior was predominant, while, after the fermentation process, these values decreased to between 0 and 0.5, indicating a solid-like behavior [34] of the samples and thus confirming the formation of yogurt-like products. In Table 2, SLB values before and after fermentation are reported. All samples show a SLB value lower than 0.5 after fermentation, confirming that the protocol used in this study is suitable for obtaining the yogurt-like structures. Moreover, these finding confirmed again that the presence of different carob seed flour concentrations did not affect the fermentation process. \begin{table}
\begin{tabular}{c c c c} \hline
**CSF (\%)** & **Coconut Milk** & **Almond Milk** & **Soy Milk** \\ \hline
**0** & 3 h 45 min 1 s \(\pm\) 1 s & 1 h 12 min 26 s \(\pm\) 4 s & 2 h 2 min 49 s \(\pm\) 0.5 s \\ \hline
**0.25** & 2 h 55 min 1 s \(\pm\) 2 s & 1 h 39 min 20 s \(\pm\) 0.5 s & 1 h 24 min 32 s \(\pm\) 1 s \\ \hline
**0.5** & 1 h 7 min 36 s \(\pm\) 2 s & 1 h 30 min 2 s \(\pm\) 1.5 s & 1 h 46 min 44 s \(\pm\) 2 s \\ \hline
**0.75** & 1 h 2 min 50 s \(\pm\) 2 s & 1 h 39 min 51 s \(\pm\) 1.5 s & 4 h 0 min 14 s \(\pm\) 3.5 s \\ \hline
**1** & 1 h 42 min 50 s \(\pm\) 8.5 s & 2 h 22 min 51 s \(\pm\) 7 s & 3 h 31 min 56 s \(\pm\) 7 s \\ \hline \end{tabular}
\end{table} 
Table 1: Gel point time for different milk samples in function of carob seed flour (CSF). After the end of the fermentation process, the samples were stored at 4 \({}^{\circ}\)C and other microrheological parameters were measured with Rheolaser MasterTM after 24 h, 48 h and 5 days of storage. The macroscopic viscosity index is an important parameter to evaluate the viscosity of the studied sample, while elasticity index values give information about the sample recovery after a solicitation.
# Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
## 4 Simulative Verification
### Simulation Set Up
#### 4.2.2 Current Ripple  
For the evaluation of the current ripple, the current in the \((c,b,a)\) phase coordinate system is examined. Figure 14 shows both the deviation of the phase current from the respective reference frame \(\Delta i^{\prime}_{c,b,a}\) and the corresponding spectrum.  
Figure 12: State trajectory \(\underline{x}(t)\) for reference trajectory \(\underline{x}_{\mathrm{ref}}(t)\) by (62) and initial value \(\underline{x}_{0}\) by (63).  
It can be seen from Figure 14a,c,e that the ripples of the phase currents are at no time greater than 12% of the nominal current. In addition, Figure 14b,d,f shows a broad spectrum with values smaller than the 2% nominal current. The total harmonic distortion (THD) of the currents, defined by  
\[\text{THD}_{I}=\sqrt{\left(\frac{I}{I_{1}}\right)^{2}-1} \tag{66}\]  
according to [25], is used as a quality criterion. Here, \(I\) denotes the sum of root mean square values (RMS) of the current harmonics and \(I_{1}\) the RMS of its fundamental component, considering harmonic components up to the 50 th order [26]. The values listed in Table 4 are below the limit of 5 % nominal current required in [26] for all phases.
# Brown Rot Caused by _Gnomoniopsis smithogilvyi_ (syn.
## 5 Strategies to Mitigate the Damage Caused by Moulds and, in Particular, _G. smithogilvyi_
In industry, the chestnuts are generally subjected to a hot-water treatment, called \"curatura\", with the main objective of killing the larvae of pests, mainly _Curculio_ spp. and _Cydia_ spp. However, the addition of enzymes to this water, with the ability to lysate fungal cell walls, has been tested to control moulds in chestnuts during storage. For example, [PERSON] et al. [63] showed that the addition of a mixture of enzymes obtained from the fungus _Trichoderma lurazianum_ Rifai strain T22 to the treatment with hot water (50 \({}^{\circ}\)C, 45 min) significantly reduced the incidence of rot caused by _G. smithogilvyi_. Additionally, they found that in chestnuts subject to this treatment, after two months of storage at room temperature, 50% remained in good condition, as opposed to 15% in untreated fruits. [PERSON] et al. [81], when using hot water at different temperatures for the treatment of chestnut in the post-harvest period, found that the application of a temperature of 50 \({}^{\circ}\)C for a time of 30 or 45 min completely inactivated _G. smithogilvyi_, regardless of the level of colonization and stage of rot. However, this treatment did not reduce the impact of other moulds responsible for the chestnut's external contamination and mycotoxin production. However, the same authors [81] observed that the increase in temperature and time of the treatment caused significant losses in fruit quality. In Portugal, all the chestnut fruits for exportation must be subjected to a hot-water treatment of at least 48 \({}^{\circ}\)C for 45 min in a continuous system (and 50 \({}^{\circ}\)C for 45 min in the case of exports to Canada) [82]. This treatment aims to eliminate insects or live larvae of _Cydia pomonella_, _Cydia flagiglandana_, _Cydia splendana_, and _Curculio elephas_. However, this process requires a large consumption of water and energy, and its efficacy in the inactivation of _G. smithogilvyi_ is unknown. In general, these methods show the importance of applying the correct pre- and post-harvest methods, and both must be considered and carried out together. On the one hand, it is essential to reduce the microbial load and dispersion in the orchards. Therefore, the application of agricultural practices and the addition of fertilizers and fungicides must be adequately performed. However, the possible creation of resistance to fungicides must be considered and always avoided. On the other hand, concerning the post-harvest methods, it is crucial to study the effect of common disinfectants, ozone, and hot-water treatment (with and without enzymes) in controlling _G. smithogilvyi_ because the role and impact of these methodologies on this fungus are not well-known.
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit
#### 4.4.5 Application  
In power grid wireless communication, 5G is a popular method. Managing power distribution networks, which face a growing integration of distributed energy resources and increasing demands for reliability, efficiency, and flexibility, requires advanced communication technologies and services. These technologies are essential for functions such as protection, control, and remote monitoring [110]. However, no real-world application of 5G for time distribution in power grids has been reported so far.  
Reference [111] designs a high-precision wireless network time synchronization module based on PTP. This module can achieve a time synchronization accuracy of up to approximately 150 ns, with a maximum fluctuation range between the slave clock and the master clock of 400 ns.  
Considering its accuracy performance, 5G can be implemented in applications such as fault and event recorders, protection systems, and network communication timing.
## 4 Performance Analysis
### Bit Error Rate Analysis  
For various binary and non-binary modulation schemes, the expression of the average BER can be given as  
\[\bar{P}_{b}=\frac{\delta}{2\Gamma(p)}\sum_{k=1}^{n^{\prime}}q_{k}^{p}\int_{0}^{ \infty}\gamma^{p-1}e^{-q_{k}\gamma}F_{\gamma_{c}}(\gamma)d\gamma, \tag{19}\]  
where \(n^{\prime}\), \(\delta\), \(p\), and \(q_{k}\) are used to denote the parameters for different modulation schemes [7]. Specifically, for binary phase-shift keying (BPSK), we have \(p=0.5\) and \(q=1\), while for on-off keying (OOK), \(p=0.5\) and \(q=0.25\), and for \(M\)-ary pulse position modulation, \(p=0.5\) and \(q=M\log_{2}(M)/16\). By substituting (7) into (19) and using Equation (3.381/4) in [16], Equation (1.1) in [12], the average BER of the proposed system can be derived as  
\[\bar{P}_{e}= \frac{\delta\Delta_{1}}{2\Gamma(p)}\sum_{k=1}^{n}H_{32}^{2}\bigg{[} \frac{\theta_{1}}{\theta_{1}\gamma_{1}}\bigg{|}\begin{matrix}(1-p,1)(1-b_{1}, r_{1}),\big{(}1+z_{1}^{2},r_{1}\big{)}\\ (a_{1},r_{1}),\big{(}z_{1}^{2},r_{1}\big{)}\end{matrix}\bigg{]}\] \[-P_{1}. \tag{20}\]  
Replacing (10) into (19) and employing Equation (3.381/4) in [16], Equation (A.1) in [12], after some algebraic manipulations, we have  
\[P_{1}= \frac{\delta r_{2}\Delta_{1}\Delta_{2}}{2\Gamma(p)}H_{1,02,4;3,4}^{0,1,2;2,3,2}\bigg{[}\frac{q_{k}\gamma_{1}}{\theta_{1}},\frac{C\theta_{2}}{\dot{ \gamma}_{2}}\bigg{|}\begin{matrix}(1;1,1)\\ -\end{matrix}\bigg{|}\begin{matrix}\psi_{1}\\ \psi_{10}\end{matrix}\bigg{|}\begin{matrix}\psi_{3}\\ \psi_{4}\end{matrix}\bigg{]}, \tag{21}\]  
where \(\psi_{10}=\{(1-p,1),\psi_{2}\}\). Similarly to (13), the asymptotic BER can be formulated as  
\[\bar{P}_{e}\to\varsigma_{1}+\frac{\varsigma_{2}}{\dot{\gamma}_{1}}+\frac{ \varsigma_{3}}{\dot{\gamma}_{1}^{2}}+\frac{\varsigma_{4}}{\dot{\gamma}_{1}^{2} }+\frac{\varsigma_{2}}{\dot{\gamma}_{1}^{2}}+\frac{\varsigma_{5}}{\dot{\gamma} _{1}^{2}}+\frac{\varsigma_{6}}{\dot{\gamma}_{2}^{2}}+\frac{\varsigma_{7}}{ \dot{\gamma}_{2}^{2}}, \tag{22}\]  
where \(\varsigma_{i}(i=1,2,\ldots,7)\) are constants that can be obtained from Equation (1.5.10) in [14]. Moreover, the diversity order from (22) is still \(G_{d}=\min\bigg{\{}\frac{a_{1}}{r_{1}},\frac{z_{1}^{2}}{r_{1}},\frac{2a_{2}}{ r_{2}},\frac{2z_{2}^{2}}{r_{2}}\bigg{\}}\), agreeing with the results in (14) obtained using the CDF derivation.
## 2 Related Works
### Classifier for Recognizing the Status of Signal  
Classifiers are among the most useful tools for the recognition of signal status. A classifier is first trained using well-defined features, the corresponding status of which is stipulated by experts. This makes it possible to use a trained classifier to determine the status of a signal based solely on its features. The most common classifiers include decision trees [29], K-nearest neighbors (KNN) [30], and neural networks [31]. [PERSON] et al. [29] formulated the C4.5 decision tree to recognize 20 daily activities performed by humans. [PERSON] et al. [32] developed an hierarchical binary classifier to enhance the precision of classification and recognition rates. Unfortunately, the computational and memory requirements of decision trees make them inapplicable in most real-world situations. [PERSON] et al. [30] and [PERSON] and [PERSON] [33] achieved good results using KNN to deal with signals collected associated with human activities; however, the effectiveness of KNN can be compromised by a small training dataset and computational overhead limits its applicability.  
Neural networks are currently the most popular method used in the recognition of signal status [34; 35; 36], due to their excellent learning capability in the discrimination of nonlinearly separable classes. [PERSON] [36] used a feedforward neural network with triaxial accelerometer to recognize the movements of hemiplegic patients. [PERSON] et al. [34] utilized an adaptive neuro-fuzzy inference system based on signals collected from a triaxial acceleration sensor. [PERSON] et al. [25] recently merged a fuzzy system with a neural network to identify human activities based on signals collected using only one accelerometer. The results of these studies demonstrate the efficacy of using neural-network-based techniques for signal recognition.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 5 Caching Transient Contents in V-NDN
### Quantifying Cache Inconsistency Effects
We can conclude that the vanilla caching system in V-NDN is not able to guarantee cache consistency in presence of transient contents. This motivates our proposal in the next Section. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hit Ratio [\%]** & **Inconsistency (FP = 20s)** & **Inconsistency (FP = 10s)** \\ \hline \(\alpha=1\) & 16.5\% & 19.42\% & 25.96\% \\ \(\alpha=2\) & 38.2\% & 50.65\% & 62.06 \% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cache hit ratio and inconsistency metrics in presence of CEE+LRU policy, when varying the Zipf skewness \(\alpha\) and the FP parameter.