esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Approach
### Prediction Model Training
#### 3.2.1 Basic OS-ELM  
Since ELM learns a big amount of data at once, the output weights \(\beta\) of the hidden layer are also tuned for once and cannot be updated. Therefore, a new problem of weights updating appears when data change with time. Consequently, ELM releases the OS-ELM as one of its variants that depend on an online learning attitude with chunks in varied or fixed size, as it also could learn data that come consecutively in one by one instance [23].  
OS-ELM experimentally has given the training rules as explained in the following steps:  
For a given small dataset \(N_{0}=\{(X_{0},T_{0})\}\big{|}X_{0}=\{x_{1},\dots,x_{n}\},T_{0}=\{t_{1},\dots, t_{n}\}\), initialization of the network parameters:  
* Generate randomly all the input weights and hidden layer biases \(\{(A,B!)\}\big{|}A=\{a_{1},\dots,a_{s}\},BI=\{b_{1},\dots,b_{l}\}\). The parameters of hidden nodes (weights and biases) must be normalized between {-1,1} [24].
* Calculate the initial hidden layer output \(H_{k}\) (\(k=0\)) as the same basic ELM theories as presented in Equation (1), where \(G\) can be generated independently from the training data according to any continuous bounded piecewise activation function.
* Calculate the first output weight matrix \(\beta_{k}\) (\(k=0\)) as shown in Equation (2).
* Set \(k=0\).
* Recursive updating of \(\beta\) for new coming mini batches.
* Calculate the new hidden layer \(H_{k+1}\); Equation (1).
* Update the output weight matrix \(\beta_{k  
DFF formula that is proposed as illustrated in Equation (10), so Equations (4) and (5) will be changed, respectively, into (9) and (10).  
\[P_{0}=\left\{\begin{array}{c}\left(\frac{1}{\zeta}+H_{0}^{T}H\right)^{-1},N\leq l \\ \left(\frac{1}{\zeta}+H_{0}^{T}H\right)^{-1}\!H^{T},N>l\end{array}\right., \tag{8}\]  
\[\beta_{k+1}=\left\{\begin{array}{l}\beta_{k}+M_{k+1}\delta_{k+1},\,\delta_{k +1}>\delta_{k}\\ \beta_{k},\,\delta_{k+1}\leq\delta_{k}\end{array}\right., \tag{9}\]  
\[\delta_{k+1}=T_{k+1}-(H_{k+1}-\gamma H_{k+2})^{T}\beta_{k}, \tag{10}\]  
\[P_{k+1}=\frac{1}{\lambda}(P_{k}-k_{k+1}(H_{k+1}-\gamma H_{k+2})^{T}P_{k}), \tag{11}\]  
\[K_{k+1}=\frac{P_{k}H_{k}}{\lambda+1+(H_{k+1}-\gamma H_{k+2})^{T}P_{k}H_{k}}, \tag{12}\]  
\[\lambda_{k+1}=\lambda_{\min}+(1-\lambda_{\min})e^{\mu\left\|\delta_{k+1} \right\|}, \tag{13}\]  
The dynamic forgetting factor changes according to the proposed formula in Equation (13) where \(\lambda_{\min}\in\left[0,1\right]\) and \(\lambda_{\min}\leq\lambda\leq 1\).
# Experimental and Empirical Study for Prediction of Blast Loads
## 3 Results  
The experimental measurements included five repeated trials for each charge weight. The charge weights were determined in the range of 100 to 400 g. Table 4 presents the values of selected explosion parameters (peak overpressure and positive phase duration) for each charge weight and scaled distance. Since the peak overpressure values from the repeated trials for each charge varied only slightly (only in tenths of kPa), the averaged values are presented. In the case of the positive phase duration values, the variations were quite negligible. Figure 4 shows the blast curves for each distance for a 100 g charge. From the graph it is possible to read the values of peak overpressure, positive phase duration and possibly other explosion parameters. The value \"0\" on the horizontal axis indicates the moment of initiation of the charge. The values calculated according to the individual predictive models for a 100 g charge are provided in Table 5. For some models, it was not possible to calculate positive phase duration values, either because the scaled distance values were outside the range of applicability of the formulas, or the method used did not allow this calculation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset  Parameters**} & \multirow{2}{*}{**Charge**} & \multicolumn{4}{c}{**Distance (m)**} \\ \cline{3-6}  & & **2** & **3** & **4** & **5** \\ \hline Peak incident & 100 & 44.1 & 23.4 & 14.3 & 10.5 \\ overpressure & 200 & 66.6 & 31.1 & 20.1 & 13.6 \\ \(P_{ii}\) (kPa) & 300 & 81.3 & 41.6 & 25.9 & 17.9 \\  & 400 & 93.4 & 46.2 & 28.0 & 20.4 \\ \hline Positive phase & 100 & 1.2 & 1.5 & 1.7 & 1.8 \\ duration & 200 & 1.2 & 1.6 & 1.7 & 1.9 \\ \(t_{0}\) (ms) & 300 & 1.4 & 1.5 & 1.9 & 2.0 \\  & 400 & 1.4 & 1.6 & 1.7 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured peak overpressures and positive phase durations. Figure 4: Pressure time course for a 100 g charge. Table 6 summarises the relative deviations of the calculated and measured values for a charge of 100 g. The relative deviations are calculated for the individual scaled distances for both blast parameters. Positive values of the relative deviations indicate an overestimation of the blast parameters compared to the experimentally determined values, and conversely, negative values of the relative deviations indicate an underestimation. For a more comprehensive evaluation of the applicability of the computational relationships, all relative deviations were averaged within one model, separately for each observed explosion parameter. The values calculated according to the individual predictive models for a 200 g charge are presented in Table 7. Table 8 lists the relative deviations of the calculated and measured values for a 200 g charge.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 3 Related Work-Coordination Analysis
### Coordinated VNE
#### 3.2.1 Two Stages Coordination  
An early proposal to illustrate the two stages coordination was by [PERSON] et al. in [PERSON] et al. (2018). The objective of their study was to achieve a lower embedding cost by introducing new constraints, such as the non-negative distance \(D^{V}\).  
After receiving each incoming VNR and before the node mapping stage is commenced, the substrate nodes are clustered into groups of candidate nodes, obeying the location and capacity constraints for every virtual node. Afterward, a set of meta-nodes, one per virtual node, are placed on top of the SN. By connecting each one to their respected neighboring clustered nodes with edges of infinite resources, a temporary augmented substrate graph is constructed.  
Then, an algorithm of a Mixed Integer Programming (MIP) formulation is introduced and applied over the aforementioned augmented graph with its main goal being the minimization of the embedding cost. To circumvent the obstacle of the NP completeness of the MIP, its linear programming relaxation is solved by rounding the obtained solution either deterministically (DR) or randomly (RR). After a path between two meta-nodes is chosen for the realization of a successful mapping, the computational demands of the actual virtual nodes represented by those meta-nodes are embedded onto each of the respected neighboring physical nodes of that path. Finally, the VLM function is performed by implementing either the single path or the multi-path technique proposed in [PERSON] et al. (2017) and documented above.  
Coordination between both stages may prove to be robust, since the MIP formulation used for the virtual node mapping ultimately takes into account the embedding of virtual links existing onto the path between the source and destination meta-nodes. Therefore, the goal of low embedding cost is likely to be achieved.
## 2 Structure of PV Panel and Causes of Power Loss
### Power-Loss Factors of PV Panels
#### 2.3.2 Power Loss Caused by Solar Altitude Change and Tracking System  
The altitude of the sun changes from sunrise to sunset and over different seasons. PV panels are most efficient when they are positioned perpendicular to the position of the sun; thus, it is efficient to use a solar tracking system. The methods for positioning panels at the optimal angle include tracking the sun through a software program, utilizing an optical sensor, or employing both methods. In a previous study, the use of a tracking system for one month led to an increase in the output power of a 100 W panel of approximately 18% compared to a case where no tracking system was used [21]. Nevertheless, the implementation of the corresponding systems required additional structures and power systems and incurred maintenance costs and additional power consumption.
## 4 Predictive Model for Bearing Capacity of Columns with Stirrup Corrosion
### Axial Bearing Capacity of Damaged Concrete Sections
The size of the damage \(\lambda\)\({}_{\rm c}\) can be calculated based on the average width and total length of the cracks,  
The decay factor \(K_{\rm c}\) is defined as the ratio of the ultimate compressive load of a corroded column \(N_{\rm uc}\) to the ultimate compressive load of an intact uncorroded column \(N_{\rm u0}\):  
\[K\)\({}_{\rm c}=\frac{N_{\rm uc}}{N_{\rm u0}} \tag{3}\]  
where \(K_{\rm c}\) is a dimensionless parameter. It was observed that stirrup corrosion did not lead to concrete expansion and cracking in the core restraint area; therefore, the effect of corrosion on the core concrete bearing capacity can be ignored and ultimate bearing capacity degradation is considered to be mainly caused by damage in the cracked area of the concrete cover. Therefore, \(K_{\rm c}\) can describe the degradation of the ultimate bearing capacity in the cracked area of the concrete cover. The decay factor \(K_{\rm c}\) is related to the damage level \(\lambda_{\rm c}\) in the cracked area of the concrete cover and the curve of \(K_{\rm c}\)-\(\lambda_{\rm c}\) is given in Figure 13, from which it can be seen that \(K_{\rm c}\) decreased with increased \(\lambda_{\rm c}\) and decreased faster in the early stage than in the later stage. Fitting the test data in Figure 13 gave the relationship between the decay factor and the damage level in the cracked area of the concrete cover. \[K_{\rm c}=285335\lambda_{\rm c}{}^{2}-672.05\lambda_{\rm c}+0.9895 \tag{4}\]  
The fitted decay factor in Equation (4) had \(R^{2}\) = 0.9846, indicating a good fit. It can be used to describe the degree of degradation of the ultimate bearing capacity in the concrete cover cracking area. Therefore, the axial load borne by the concrete section in a stirrup-corroded axial compression column can be expressed as:  
\[N_{\rm c}=A_{\rm c1}f_{\rm c}+K\)\({}_{\rm c}A_{\rm c2}f_{\rm c} \tag{5}\]where \(A_{\rm cl}\) is the concrete cross-sectional area in the core restraint area; \(A_{\rm c2}\) is the net concrete cross-sectional area in the concrete cover cracking area; \(f_{\rm c}\) is the concrete axial compressive strength; and \(K_{\rm c}\) is the bearing capacity decay factor in the concrete cover cracking area.
# A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition
## 4 Experiment Setup
### Parameter Setup  
The experimental data processing method is as follows: speech greater than 3 s is randomly segmented into a segment of 3 s, samples less than 3 s are zero filled, the frame length is 25 ms, the frame shift is 10 ms, the number of Mel filter banks is 64, and the offset of 0.01 is added to make the extracted features of the filled part not 0, so as to obtain the Log-Mel spectrum with the input of [64,298] dimensions. The experiments use the stochastic gradient descent (SGD) optimizer with a learning rate of 0.001, learning rate decay (decay) of 10-6, momentum of 0.9, batch size of 32, the maximum number of training rounds (num_epochs) of 150, and the model with the highest UAA is saved during training. Each set of experiments was subjected to a five-fold cross-validation with a training, validation, and test set sample ratio of 7:1:2, and the UAA and WAA with confusion matrix were reported as the results for comparison and analysis.
studied the effect of pulsatile perfusion for the decellularization of a rat heart, demonstrating a more profound and efficient decellularization in the pulsatile group [17]. Cells are needed for the recellularization of the decellularized ECM in order to engineer a bioinformaticial heart. Such a complex organ as the heart needs an important number of cells specialized to differentiate into ECs, CMs, smooth muscle cells (SMCs), fibroblasts and conducting cells [18]. The use of stem cells to create a bioartificial heart has gained a lot of support in recent years. Embryonic stem cells [19], human mesenchymal stem cells (hMSCs) [20] and induced pluripotent stem cells (iPSCs) [21] were studied and differentiated into cardiomyocytes in several studies with promising results. Mesenchymal stem cells (MSCs) are multipotent stem cells found in bone marrow, which are capable of differentiating into cardiac cells. They have been used in the treatment of heart disease [22], showing a lot of advantages, such as a strong capacity for proliferation, ease of availability and low immunogenicity [23; 24; 25]. The purpose of this study was to obtain a bioartificial rat heart from a decellularized heart used as a matrix for grafting hMSCs. The decellularization protocol in the presence of an electric field decreases the exposure time of the tissue to the detergent and preserves the ECM components. Recellularization of the 3D ECM at a later timepoint was performed using hMSCs differentiated into cardiomyocytes with 5-azacytidine (5-aza).
## 2 Materials and Methods  
The demand for a packet-switching-based fronthaul network has led to an enhanced version of CPRI (eCPRI [29]), which is designed for packet networks, namely Ethernet and IP. Even though Ethernet was not originally designed for delay-sensitive networks or real-time applications, intensive research efforts have been devoted [30] which are mainly based on the IEEE 802.1 CM published in 2018 [31]. Apart from the above efforts towards Time Sensitive Networking for Fronthaul, eCPRI recommends that vendors optionally implement either MACsec or IPSec as security options [28]. In this context, we propose and study the use of a QKD solution to make quantum safe the key exchange which is needed for the implementation of the above security standards. We extend the study of physical layer implementation of the proposed quantum layer by considering the strict latency-related requirements of the fronthaul segments.
IOSs record single consecutive images, which are assembled and realigned through a best-fit alignment algorithm using the processing software. Anatomical landmarks such as teeth, if present, improve this method of image processing called \"stitching\". In cases of dentate patients, IOSs reported an acceptable accuracy that was similar to polyether impressions and superior to alginate impressions [19]. Concerning FDPs, some studies have reported that digital impressions with ISBs of a single implant or units of three implants were as accurate as elastomer impressions [20]. In cases of dendulous patients, a complete arch scan remains a challenge for clinicians due to the lack of stable tissue landmarks, the distance between ISBs, and to the difficulty in distinguishing between multiple identical ISBs [21; 22]. All these factors may limit the clinical application of IOSs. The accuracy of the scan could also be influenced by the experience of the operator using the IOSs; however, no consensus on the learning curve of IOSs has yet been established. In 2014, [PERSON] et al. determined that the accuracy of scanning using Itero IOSs (Align Technology Inc.) was influenced by operator performance, which was not necessarily associated with experience [23]. Similarly, a 2015 in vitro study demonstrated that the use of Lava C.O.S. IOSs (3M ESPE) by skilled operators resulted in more accurate scans compared to inexperienced operators [24]. Conversely, one study proved that, when using two different IOSs, 3D Progress (MHT) and ZFX Intraccan (Zimmer Dental), the accuracy was not affected by operator experience [25]. To overcome these limitations, some clinicians have proposed techniques to increase the accuracy of a scan of a complete dendulous arch through the use of various materials and instruments. The purpose of this narrative review was to analyze these techniquesand to compare the results obtained to understand if it is currently possible to increase the accuracy of scans of complete edentulous arches.
## 2 Numerical Method
### Boundary Conditions and Solution Procedure  
In the present work, the MHD pump-based cooling system is designed for a 22 kW traction inverter. In the simulation, the volume heat source approach is used to evaluate the heat generation from the inverter. Based on this approach, the heat generation was calculated to be 3% of 22 kW, which is equivalent to 0.66 kW and heat flux of 569,976 W/m\({}^{3}\). The temperature of the surrounding environment is 25 \({}^{\circ}\)C and the temperature of coolant at the inlet of the cooling channel is 25 \({}^{\circ}\)C. The mode of heat transfer with the environment is defined as a natural convection. In Figure 1, the inverter is acting as a heat load with heat generation of 569,976 W/m\({}^{3}\) employed in the simulation. In the simulation, the coolant inlet temperature is set to 25 \({}^{\circ}\)C, considering it gets cooled using a radiator in actual automotive applications. The liquid flow regime is defined as laminar owing to a lower Reynolds number, within 2000 value, for internal flow. The velocity of coolant for conventional pump circulation is varied as 2 mm/s, 3 mm/s, 4 mm/s, 5 mm/s and 10 mm/s, respectively. The volume fraction of nanoparticles in ferrofluid is considered as 2%, 4%, 6%, 8%, and 10%, respectively. The magnetic intensity of both magnets combinedly varies as 0.1 T, 0.2 T, 0.3 T and 0.4 T, respectively, whereas, the voltage of the electrode is changed as 0.1 V, 0.2 V, 0.3 V, 0.4 V, and 0.5 V, respectively. All boundary conditions are mathematically expressed using Equations (7)-(12).  
\[q^{\prime\prime}=569,976\ W/m^{3}, \tag{7}\]  
\[T_{i}=25\ ^{\circ}\text{C},\text{ and }T_{a}=25\ ^{\circ}\text{C}, \tag{8}\]  
\[2\ mm/s<U_{c}<10\ mm/s, \tag{9}\]  
\[2\%<\varphi<10\%, \tag{10}\]  
\[0.1\ T<B<0.4\ T, \tag{11}\]  
\[0.1\ V<V<0.5\ V, \tag{12}\]  
The Multiphysics characteristics of the MHD pump-based cooling system for a traction inverter are solved in the COMSOL Multiphysics 6.0 commercial software. The coupled numerical model is developed to solve the governing equations of the electrical, magnetic, thermal, and fluid flow fields with appropriate boundary conditions.
###### Abstract  
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer's/User's accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use.  
flood mapping; temporary flooded vegetation (TFV); Sentinel-1; time series data; Synthetic Aperture Radar (SAR) +
Footnote †: journal:  
10 July 2019; Accepted: 12 September 2019; Published: 18 September 2019
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 2 System Description  
### Mppsk Modulation  
MPPSK is a high spectral efficiency technique and a member of the sine-like modulation family [18]. MPPSK modulation is a modulation method extended from EBPSK to multi-ary modulation, which uses M-ary symbol to control the phase hopping position of sinusoidal carrier in each symbol period. Since MPPSK makes full use of the position change of the phase jump in the symbol, the information transmission rate is increased by \(\log_{2}M\) times under the same bandwidth condition, thereby further improving the spectrum utilization rate. The expression of MPPSK modulation in a symbol period \([0,NT]\) is given by  
\[f_{k}(t)=\left\{\begin{array}{ll}Asin2\pi f_{c}t&0\leq t<NT,k=0\\ Asin2\pi f_{c}t&0\leq t<(k-1)KT\\ Bsin(2\pi f_{c}t+\theta)&(k-1)KT<t<(k-r_{g})KT,1\leq k\leq M-1\\ Asin2\pi f_{c}t&(k-r_{g})KT\leq t<NT\end{array}\right. \tag{1}\]  
where \(A\) is the carrier amplitude of the symbol \"0\" and the non-zero symbol non-phase modulation interval, \(B\) is the carrier amplitude of the non-zero symbol phase modulation interval, \(k=0,1,2,...\), \(M-1\) is the actual transmitted symbol with \(M\geq 2\) values, \(T=\left(\frac{2\pi}{w_{i}}\right)\) is the carrier period, \(N\) is the number of carrier cycles in the symbol period, \(T_{s}=NT\) is the symbol period, \((1-r_{g})K\) is the number of carrier cycles that the phase jump continues, \(0<\theta\leq\pi\) is the angle of the phase jump, and \(r_{g}(0\leq r_{g}<1)\) is the Guard Interval factor. In order to meet the requirements of different application indicators, different modulation signal bandwidths, transmission rates, and system demodulation performance can be obtained by adjusting the values of modulation parameters \(A,B,M,N,K,\theta\) and \(r_{g}\). The MPPSK signal in the specific case \(A=B=1,r_{g}=0,N=10,K=2,\theta=\pi\) and \(M=4\) are depicted in Figure 1.