esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
To continue with, the CCF samples were studied, as presented in Figure 6:1 CCF consists of 40% red mud. Red mud, a byproduct of the Bayer process used in aluminum extraction from bauxite ore, is primarily composed of iron oxides, aluminum oxides, silica, and other trace elements [46]. In principle, pure red mud has low electrical conductivity due to its high content of insulating materials like alumina, silica, etc. [47; 48; 49]. The addition of electrically insulating PCM does not contribute to the EMI shielding effectiveness of the samples, as seen in Figure 12c. As one can notice in Figure 12, even the addition of MWCNTs cannot significantly enhance the EMI shielding effectiveness of the samples, due to the low electrical conductivity of red mud. As a result, in order to achieve effective \(SE\), carbon-based materials are needed in order to achieve as high as possible electrical conductivity values.
# Research Progress on Carrier-Free Phase-Retrieval Receivers
## 3 Modified CF-PR Receiver Scheme
### Improve the Performance of the CF-PR with a Reduced Number of Iterations and Better Steady Accuracy
#### 3.1.2 Enhanced CF-PR Algorithms
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] Carrier-Assisted Phase Retrieval with Multiple Projections. In Proceedings of the 2022 Asia Communications and Photonics Conference (ACP), Shenzhen, China, 5-8 November 2022; pp. 465-468. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Carrier-less phase retrieval receiver leveraging digital up-sampling. _Opt. Lett._**2020**, _45_, 6070-6073. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Digital subcarrier multiplexing-enabled carrier-free phase-retrieval receiver. _Adv. Photonics Nexus_**2023**, \(2\), 046004. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Simplified Phase Retrieval Receiver Employing Transmission Fiber for Alternative Projection. In Proceedings of the 2022 European Conference on Optical Communication (ECOC), Basel, Switzerland, 18-22 September 2022; pp. 1-4. * [PERSON] et al. (1999) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multistage dispersion compensator using ring resonators. _Opt. Lett._**1999**, _24_, 1555-1557. [CrossRef]
* [PERSON] and [PERSON] (2008) [PERSON]; [PERSON] Fast evaluation of nonlinear functions using FPGAs. _Adv. Radio Sci._**2008**, \(6\), 233-237. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Data-Aided Iterative Algorithms for Linearizing IM/DD Optical Transmission Systems. _J. Light. Technol._**2021**, _39_, 2864-2872. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Window-split structured frequency domain kalman equalization scheme for large PMD and ultra-fast RSOP in an optical coherent PDM-QPSK system. _Opt. Express_**2018**, _26_, 7211-7226. [CrossRef] [PubMed]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Ultrafast and low overhead training symbol based channel estimation in coherent M-QAM single-carrier transmission systems. _Opt. Express_**2012**, _20_, B171-B180. [CrossRef]

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# Investigating Bayesian Parameter Identification Using Non-Standard Laboratory Specimens
## 2 Modeling of Fracture Propagation
### Q6 ED Model with the Embedded Strong Discontinuity  
During the simulation of material failure, each finite element in the Q6 ED model consists of two parts: the bulk material and a crack. Initially, as long as the stress within a finite element remains below the defined material strength, the entire element behaves as bulk material, which is first modeled using a linear elastic law, followed by an elastoplastic isotropic hardening model. However, when the stress exceeds the defined material strength of a given element, a crack followed by material softening is introduced.  
The crack embedding follows the statically and kinematically optimal nonsymmetric formulation (SKON) formulation of the embedded strong discontinuity method [48], which has demonstrated effectiveness in capturing complex fracture behaviors in materials [10]. In this approach, the crack inside each finite element is represented by a local jump in the displacement field over the defined crack line. This approach ensures that the results remain independent of the finite element size. Figure 1 illustrates the Q6 ED finite element, which is capable of simulating the constant separation of material during cracking in both Mode I and Mode II. The separation of the material is controlled by softening parameters \(\alpha_{i}\) (\(\alpha_{1}\) and \(\alpha_{2}\)).  
As the crack is formed and keeps opening, the phenomenon of energy dissipation occurs. This phenomenon is defined by the exponential softening law and its internal variables \(\xi_{n}\) and \(\xi_{m}\). The values of the variables indicate the level of element degradation for both separation modes.
## 4 Experimental setup
### Datasets  
The speech data used in the evaluation consist of a database recorded for the purpose of developing TTS synthesis. Three English speakers were chosen from the CMU-ARCTIC ([[http://www.festvox.org/cmu_arctic/](http://www.festvox.org/cmu_arctic/)]([http://www.festvox.org/cmu_arctic/](http://www.festvox.org/cmu_arctic/))) database [57], denoted BDL (American English, male), JMK (Canadian English, male), and SLT (American English, female); each one can produce one hour of speech data segmented into 1132 sentences, restricting their length from 5 to 15 words per sentence (a total of 10,045 words with 39,153 phones). Moreover, CMU-ARCTIC are phonetically balanced utterances with 100% phonemes, 79.6% diphones, and 13.7% triphones.  
The speech waveform of this database was recorded at a 32 kHz sampling rate at a 16-bit resolution; one channel was the waveform, the other laryngograph (from which a reliable pitch estimate can be derived). 20 sentences from each speaker were chosen randomly to be analyzed and synthesized with the baseline and proposed vocoders. These 60 utterances were subsequently down-sampled by a factor of 2 in order to reduce its sampling rate from 32 kHz to 16 kHz, as this is a more typical use in the early baseline vocoder [13].  
With the purpose of assessing true performance of the refined contF0, a reference pitch contour (ground truth) is required. The ground truth is estimated from the electro-glottal graph (EGG), as it is directly derived from glottal vibration and is largely unaffected by the nonharmonic components of speech [58]. In our evaluation, the ground truth is extracted from EGG signals using Praat [59]. Additionally, it is of the greatest importance to select some state-of-the-art algorithms for the purpose of comparison. Although there is a lack of such algorithms dealing with the continuous F0 approach in the literature, YANGasf [60] is the only F0 estimator method that can be compared along with adContF0 and the baseline. The choice of YANGasf is confirmed by the fact that it was recently shown in [61] to outperform other well-known F0 estimation approaches like YIN, RAPT, or DIO. Moreover, TANDEM-STRAIGHT [62] vocoder that has mostly become the state-of-the-art model in SPSS was used in this experiment as the highest quality vocoder. For all methods across all speakers, the floor and  
Figure 6: Importance of the SLM in MVF estimation. Top is the speech waveform from a female speaker, bottom is the spectrogram and MVF contours.  
ceiling frequencies of the F0 estimation range were set to 50 and 250 Hz for a male speaker, while for a female speaker, the default range was set to 150 and 350 Hz. The frame shift was set to 5 ms, while all other parameters remain at their default values.
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 3 Dark-Center Fog Insulator Detection Algorithm
### Defogging Algorithm
#### 3.1.2 Double Dark Channel Fitting Transmittance Estimation  
The transmittance estimation uses guided filtering instead of soft matting to optimize transmittance and suppress contour effects, reducing the high computational complexity, though the efficiency of estimating transmittance is still low and does not solve the distortion problem in principle [29; 30].  
Improper template selection may distort detailed information such as boundaries. The larger the template, the larger the dark channel converges to 0. To enhance the detailed information in the recovered image and avoid increasing the computational complexity of the transmittance estimation, this paper adopts a double dark channel fitting approach to optimize the transmittance using the image's detail information, avoiding high computational complexity such as soft matting and bootstrap filtering. We selected a field of \(\Omega_{35}(x)\) of 35 \(\times\) 35. A small field of \(\Omega_{25}(x)\) is selected for the minimum filtering of the original image as well, and the best experimental result showed its size of 25 \(\times\) 25. The algorithm flow is outlined as follows.  
Step 1: Use the 25 \(\times\) 25 of \(\Omega_{25}(x)\) as the field to obtain one dark channel \(I_{25}^{dark}(x)\).  
\[J_{25}^{dark}(x)=\min_{y\in\Omega_{25}(x)}\biggl{\{}\min_{c\in\{r,g,b\}}(J^{c }(x))\biggr{\}} \tag{5}\]  
Then, use this \(I_{25}^{dark}(x)\) to estimate \(t_{25}(x)\).  
\[t_{25}(x)=1-\omega_{25}\times J_{25}^{dark}(x) \tag{6}\]  
Step 2: Using the 35 \(\times\) 35 of \(\Omega_{35}(x)\) as the field, obtain the other dark channel \(I_{35}^{dark}(x)\).  
\[J_{35}^{dark}(x)=\min_{y\in\Omega_{35}(x)}\biggl{\{}\min_{c\in\{r,g,b\}}(J^{c }(x))\biggr{\}} \tag{7}\]Then, use this \(\int_{35}^{dark}(x)\) to estimate \(t_{35}(x)\).  
\[t_{35}(x)=1-\omega_{35}\times\int_{35}^{dark}(x) \tag{8}\]  
Step 3: Obtain the final transmittance estimate.  
\[t(x)=\alpha t_{25}(x)+(1-\alpha)t_{35}(x) \tag{9}\]  
where \(\alpha\) is the adjustment factor, \(0\leq\alpha\leq 1\). In this paper, \(\alpha\) is selected as 0.1.  
The above method optimizes the transmittance with the image's information, enriches the detail information of the transmittance map, better suppresses the contour distortion of the image, significantly enhances the detail information, and reduces computational complexity.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 5 Understanding the Impact of Instrument Operating Modes  
### SRAL Acquisition Mode Comparisons for Sea Surface Height  
The Sentinel-3 tandem phase has been a unique opportunity to investigate the impact of different operating modes of the altimeter. More precisely, measurements of SRAL-A in pseudo-low-resolution mode (PLRM) could be compared to measurements of SRAL-B in low-resolution mode (LRM). In addition, it has been possible to compare measurements of SRAL-A operating in closed-loop tracking mode with SRAL-B operating in open-loop tracking mode. These analyses are necessary for assessing the consistency between the different operating modes and, in particular, for improving our understanding of the altimeter SAR-mode signal processing, which is of high importance for the upcoming missions that tend toward more use of SAR-mode altimeter in the future (as for the Sentinel-6 mission).  
Two of the main results that come out from these analyses are that (1) the SAR-mode altimeter estimates are sensitive to the position of the waveform within the altimeter tracking window as evidenced in assessing SAR-mode data acquired in open-loop mode, and (2) the pulse-to-pulse correlation effects impact PLRM estimates to such an extent that the use of adapted LRM look-up tables is needed to ensure the data continuity of this mode (also implemented in the upcoming Sentinel-6/JASON-CS mission) with previous JASON time series.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 4 Model System
### Service Cache Model  
We assume that the edge computing nodes in this chapter require the local caching of corresponding services to provide services to mobile terminal users. However, due to the needs for data security and privacy protection, load balancing constraints, and the limitations of storage space on edge computing nodes, a single edge computing node cannot store all types of service caches. \(\mathcal{S}\) is a set of service caches, which includes \(S\) different types of service cache resources. The data size of each cache resource is represented by \(D_{s}\), where \(s=\{1,2,\ldots,S\}\). The deployment status of service caches on edge computing nodes can be represented by the variable \(\mathbf{C}=\{C_{s}\}_{s=1}^{S}\), where \(C_{s}\) is a Boolean variable. When the \(s\)-th type of service cache is deployed on the edge computing node, \(C_{s}=1\); otherwise, \(C_{s}=0\). Tasks generated by mobile terminal devices depend on a specific type of service cache, and the edge computing node can only correctly execute the corresponding computing tasks if it has pre-deployed the service cache resources required by the tasks. If the associated edge computing node of the mobile terminal device lacks the service cache resources needed for task execution, the task needs to be migrated to other edge computing nodes that have the required cache resources to execute. If the edge computing nodes also lack the necessary resources, the service cache must be downloaded from the central cloud.
# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
## 3 Fuzzy DEMATEL for Object-Oriented Metrics
### Fuzzy Direct-Relation Matrix  
Each expert generates a direct-relation matrix with a pairwise comparison \(\mathrm{M_{e}}\), where \(e\) represents the expert, \(M\) is a (\(n\)\(\times\)\(n\)) non-negative matrix, and \(M\)(\(e\), \(i\), \(j\)) represents the direct impact of the Metric \(i\) on the Metric \(j\) for the expert \(e\). When \(i=j\), the diagonal elements \(M\)(\(e\), \(i\), \(j\)) = 0. In Table 3, we present an example of the linguistic scores of one expert evaluation for the Blob anti-pattern.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** & **C13** & **C14** & **C15** & **C16** \\ \hline
**C1:NC** & 0 & VH & H & VH & H & H & NO & VH & VH & VH & NO & H & H & VH & VH & H \\ \hline
**C2:PS** & NO & 0 & NO & NO & NO & NO & NO & NO & NO & NO & NO & NO & NO & VH & VH & VL \\ \hline
**C3:NOA** & VL & VL & 0 & VH & NO & NO & H & VH & VH & VH & VH & VL & H & H & NO & NO & NO \\ \hline
**C4:NOM** & VH & H & L & 0 & L & L & VH & VH & VH & VH & VH & VH & H & L & H & H \\ \hline
**C5:NOOD** & L & L & VH & VH & 0 & VH & H & L & L & VL & NO & VL & L & NO & VL & L \\ \hline
**C6:NODD** & L & L & VH & VH & VH & 0 & H & VL & VL & VL & NO & L & L & NO & VL & L \\ \hline
**C7:NMSC** & NO & NO & H & NO & NO & NO & NO & 0 & VH & VH & VH & NO & H & NO & H \\ \hline
**C8:NOC** & NO & NO & H & H & NO & NO & H & 0 & VH & VH & NO & H & VH & NO & VH & VH \\ \hline
**C9:NCC** & L & VL & NO & L & VL & VL & VH & VH & 0 & VH & NO & VH & H & NO & H & VH \\ \hline
**C10:ATFD** & VL & VL & H & VL & VL & VH & VH & VH & 0 & VL & H & H & NO & VH & VH \\ \hline
**C11:NOP** & NO & NO & H & L & NO & NO & L & L & L & VL & 0 & H & H & NO & NO & NO \\ \hline
**C12:NIC** & L & H & H & H & VL & VL & VH & VH & VH & VH & VL & 0 & H & VL & H & VH \\ \hline
**C13:CM** & L & L & VL & L & NO & NO & L & VH & VH & VH & NO & H & 0 & NO & H & VH \\ \hline
**C14:NOPM** & VL & VH & NO & NO & NO & NO & NO & NO & NO & NO & NO & NO & NO & 0 & H & NO \\ \hline
**C15:PUC** & H & VH & NO & L & NO & NO & NO & NO & NO & NO & NO & NO & NO & VL & 0 & VL \\ \hline
**C16:NMR** & NO & NO & NO & L & NO & NO & VH & VH & VH & VH & NO & L & H & L & & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linguistic evaluation of criteria interdependence.  
Figure 1: Fuzzy DEMATEL method.  
Based on Table 2, the fuzzy linguistic matrix is then converted into a fuzzy scaled direct-relation matrix \(M\). Table 4 presents the aggregated fuzzy direct-relation matrix collected from the different experts' judgments.
## 5 Experimental Results
### Results Using the Dataset with Pathologies
#### 5.1.2 Results for Gradient Boosting with Classical Feature Selection (GB-RFECV)  
Moreover, in Table 2, using RFECV, an average accuracy rate of 0.7324 was achieved, reaching a maximum of 0.7797. A total of 17 variables were selected, including stress; some of the variables related to alcoholic drinks, education, time since quitting smoking, and physical exercise. Some of the most selected variables, such as sex, age, or apnea, are among the variables obtained. Figure 3 shows the evolution of the accuracy rate for different algorithms and datasets, taking the average of the cross-validation runs with RFECV in relation to the number of variables chosen. The average accuracy rate increases until it reaches 17 variables and then starts to decrease, probably due to the selection of variables that introduce noise.
## 3 Results and Discussion
### Analysis of the Electrolyte Absorption Rate  
The level of diaphragm absorption is usually determined by the porosity, which can hold a certain amount of electrolyte; the more pores, the more the diaphragm can hold the electrolyte, and the absorption rate of the diaphragm increases [31]. As shown in Table 3, the lowest electrolyte absorption rates were 116% for the Celgard 2400 diaphragm, 412% for the PU diaphragm, and 454% and 493% for 18% PU:PAN = 8:2 and 18% PU:PAN = 7:3 diaphragms, respectively. In the PU/PAN composite diaphragm, the electrolyte absorption rate increased as the proportional content of PAN increased. Compared to the Sb\({}_{2}\)O\({}_{3}\)-modified polyvinylidene fluoride-co-chlorotrifluoroethylene (PVDF-CTFE) fiber membrane prepared by [PERSON] et al. [32] with an absorption rate of only 356%. This was due to the presence of the nitrile group (CN) in PAN, which interacted with Li\({}^{+}\) for better liquid retention. The commercial Celgard 2400 diaphragm had a relatively poor wettability by electrolytes, but due to its unique chemical structure and highly interconnected porous structure between fibers, the porous PU-based film was readily wettable by electrolytes. The strong polar organic group (-CN) in PAN had good interfacial compatibility with polar electrolytes, which improved the absorption capacity of electrolytes, and also directly affected the ionic conductivity of the PU/PAN lithium-ion battery diaphragm.
## Materials and Methods
### Boundary Conditions  
In laser dermatologic surgery, cryogen spray cooling (CSC) is applied to avoid unwanted damage from epidermal burning due to the melanin absorption of light energy. In this study, the refrigerant R134a was sprayed to the epidermis before laser pulses were administered to protect the epidermis. The spray duration was \(t_{c}=100\) ms, and the spray distance was 30 mm [30]. After CSC was conducted (\(t>t_{c}\)), the natural convection of air was considered the boundary condition. The standard convection condition was used to describe the cooling effect of CSC on the skin surface (\(z=0\)):  
\[k_{e}\frac{\partial T}{\partial z}\bigg{|}_{z=0}=\left\{\begin{array}{c}h_{ c}\cdot(T-T_{c}),\,t\leq t_{c}\\ h^{\star}(r^{\star},\tau)\cdot(T-T_{a}),\,t>t_{c}\end{array}\right., \tag{5}\]  
where \(k_{e}\) is the thermal conductivity of the epidermis, \(T_{a}=25\)\({}^{\circ}\)C is the ambient temperature, and \(h_{a}=10\) W\(\cdot\)m\({}^{-2}\)-K\({}^{-1}\) is the convective heat transfer coefficient of air. \(T_{c}\) of \(-48.5\)\({}^{\circ}\)C [30] is the temperature of the refrigerant liquid film. The dimensionless surface heat transfer coefficient \(h^{\star}(r^{\star},\tau)\) based on a large number of experimental data was expressed as follows [30]:  
\[\begin{split} h^{\star}(r^{\star},\tau)=\left\{\begin{array}{ll} h^{\star}_{o}(\tau)&0\leq r^{\star}\leq 0.4\\ \frac{5(1-r^{\star})}{3t^{\star}_{o}(\tau)}&0.4<r^{\star}\leq 1.0\end{array} \right.\tau\leq 1.0\\ h^{\star}(r^{\star},\tau)=\left\{\begin{array}{ll}h^{\star}_{o}(\tau)&0\leq r ^{\star}\leq 0.2(\tau+1)\\ h^{\star}_{o}(\tau)+\frac{(5r^{\star}-\tau-1)[0.09(\tau-1)-h^{\star}_{o}(\tau)] }{(4-\tau)}&0.2(\tau+1)<r^{\star}\leq 1.0\end{array}\right.1.0<\tau<4.0\end{array}, \tag{6}\]  
where nondimensional time \(\tau=t/t_{max}\), and nondimensional space \(r^{\star}=r/r_{spray}\); the expression of the dimensionless convective heat transfer coefficient at the center of refrigerant \(h_{0}{}^{\star}(\tau)\) is presented as follows [30]:  
\[h^{\star}_{o}(\tau)=\left\{\begin{array}{ll}\tau&\tau\leq 1.0\\ 1.0-0.35(\tau-1)&1.0<\tau\leq 3.0\\ 0.3-0.02(\tau-3)&3.0<\tau\leq 8.0\\ 0.2-0.0125(\tau-8)&\tau>8.0\end{array}\right.. \tag{7}\]  
Adiabatic conditions were adopted for the side and bottom boundaries. The initial skin temperature was assumed to be \(35\)\({}^{\circ}\)C, and the inlet velocity of the blood was set as \(1\times 10^{-3}\) m/s [31].