esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a novel 2D-DOA sparse estimation method with total variation regularization is proposed. It is intended to solve the sparse estimation of a non-strictly sparse sound field using the regularization of sound sources' characteristics. The main conclusions are as follows.  
The spatially extended source model is constructed using spatial positions' grouping features of sound sources and sound signals' correlation characteristics. The two-dimensional sparse representation of sound sources is realized by constructing a two-dimensional array manifold matrix and its overcomplete representation.  
These characteristics, spatial grouping, block sparse, and correlation, are converted into regularization penalty terms by constructing the total variation regularization term. It promotes the sparsity of the block sparse solution on the spatial derivatives domain. Furthermore, combined with the \(\ell_{1}\)-norm regularization term, the 2D-DOA sparse estimation model is built to be based on a joint estimate pitch angle and azimuth angle of an extended source by the convex optimization algorithm. This process promotes sparsity both on the spatial derivatives of the solution and on the solution itself, thus rapidly seeking solutions where the nonzero coefficients are grouped together.  
The numerical and experimental results demonstrate that the method with regularization penalty term significantly improves the sparsity and the resolution and detection probability in the 2D-DOA sparse estimation. It also has certain robustness to noise. The solving process avoids the feature decomposition, decoherence, and angle pairing of the high-dimensional sample covariance matrix in the traditional methods. The method is promising for the capability of sparse representation, not necessarily sparse sound fields (e.g., in acoustic near-fields, reflective environments).  
Conceptualization, Z.L. (Zhihong Liu) and Q.L.; methodology, Z.L. (Zhihong Liu); software, Z.L. (Zunmin Liu) and C.L.; validation, Z.L. (Zhihong Liu), C.L. and Q.L.; formal analysis, Q.L.; investigation, Z.L. ([PERSON]): resources, C.L. and Q.X.; data curation, Q.X.; writing--original draft preparation, Z.L. ([PERSON]) and C.L.; writing--review and editing, Q.L. and Z.L. ([PERSON]); visualization, C.L.; supervision, Z.L. ([PERSON]); project administration, Q.L. and Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 9: The dipole source’s detection probability with SNR \(=-5\) and \(K\)\(=4\) (**a**) R 2 DTV-CES, (**b**) LASSO, (**c**) Improved ESPRIT.  
**Funding:** This research was funded by the Provincial Natural Science Foundation of Shandong under grant number ZR2023 MF018 and the National Natural Science Foundation of China under grant number 61871447.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Mathematical Model
### Time Correlation of Measurement Error Time Series  
The autocorrelation of a random stationary process describes the correlation between values of the process at different times as a function of the time lag between the two times. Since a mobile node's location is defined over time as it moves, the resulting measurements form a time series. In order to show that time correlation exists among the time series elements, we use the Ljung-Box test [23]. The Ljung-Box test examines whether a group of autocorrelations of a time series are not zero by testing the overall randomness based on multiple lags. The null hypothesis in this test is that the first \(m\) autocorrelations are jointly zero.  
The autocorrelation between times \(s\) and \(t\) is defined as:  
\[R_{s,t}=\frac{E[(X_{t}-\mu_{t})(X_{s}-\mu_{s})]}{\sigma_{t}\sigma_{s}} \tag{1}\]  
If \(R\) is well-defined, its value must be in the range [-1, 1], with 1 indicating perfect correlation and \(-1\) indicating perfect anti-correlation.  
When \(X_{t}\) is stationary, then autocorrelation depends only on the lag between \(t\) and \(s\). Therefore, the autocorrelation can be expressed as a function of the time lag:  
\[R_{\tau}=\frac{E[(X_{t}-\mu)(X_{t+\tau}-\mu)]}{\sigma^{2}} \tag{2}\]which is an even function that can be stated as \(R_{\tau}=R_{-\tau}\).  
The Ljung-Box test is applied to the location measurements corresponding to two datasets corresponding to the traces of two vehicles chosen at random from the TAPASCologne mobility trace [24] and denoted as \(\
u_{1}\) and \(\
u_{2}\) for convenience. The null hypothesis was rejected for both sets of measurements at multiple increasing time lags, with the hypothesis value \(h\) being equal to 1, indicating perfect time correlation. For the location measurements of both vehicles, \(\tau\), the correlation time is found to be \(\approx\)4 min.  
To find out the time window of autocorrelation, beyond which successive
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 3 Results  
This section first executed several tests on different real-world data sets to demonstrate the effectiveness of the proposed forecasting model. Next, test evaluations between a few existing point forecasting models are demonstrated. These simulation results explain the performance of the proposed model in addition to the error metrics. Then, comparative results for probabilistic forecasting are presented and discussed. Finally, it discusses the ablation study.  
Three common covariance functions are tested to choose an appropriate one for the proposed GPR model, namely the following: squared-exponential, exponential, and Matern 3/2. The three scenarios select 95%, 90%, 80%, and 50% confidence levels of the PI for the probabilistic GPR model training. Finally, the best-performing PI result is used as input to derive PDFs of electricity demand forecasting over time, which are then checked against observed values using the KDE method.  
In this study, the offline method is used to make training as straightforward as possible. All the forecasting processes are implemented with Matlab R2022b and tested using a personal computer with Intel(R) Core(TM) i7-8650U CPU @ 1.90 GHz 2.11 GHz, 16 GB RAM.  
To determine the optimal kernel function, this paper first does comparative studies of three common kernels. Table 3 presents the forecasting errors of the proposed model. Four forecasting error measurements, MAE, RMSE, and R\({}^{2}\) are utilized to evaluate the model. To summarize the results of Table 3:  
Figure 3: The correlation coefficient (\(S\)) of different features with the electricity demand.
## 3 Particle Filter Frame
### LRD Predicting Operator Driven by Particle Filter Frame  
The flow chart of the degradation trend prognostics for the rolling bearings based on the proposed method is illustrated in Figure 2. The main procedures of the degradation trend prognostics can be described as follows:  
**Step 1**.: Particle initialization.  
1. The \(f\)-ARIMA degradation models are established by the tested EVI time series or kurtosis time series. In this stage, the initial \(f\)-ARIMA model order and coefficients distribution are determined, i.e., \(\left\{x_{0}^{i}\right\}=\left[\phi_{0}^{1},\phi_{0}^{2},\cdots,\phi_{0}^{p}, \theta_{0}^{1},\theta_{0}^{2},\cdots,\theta_{0}^{l},d_{0}\right]\), where \(p\) and \(q\) are model orders, \(d_{0}\) is initial fractional value, and \(\phi(0)\) and \(\theta(i)\) are model coefficients.
2. By drifting all of the coefficients set randomly, that is, \(\overset{\wedge i}{x}_{k|k-1}^{i}=\overset{\wedge i}{x}_{k-1}^{i}+u_{k-1}^{i}\), where \(u_{k-1}^{i}\) is noise sequence, then the particles set \(\{\overset{\wedge i}{x}_{k}^{i}\}\) is obtained, the number of particles set \(\{\overset{\wedge i}{x}_{k}^{i}\}\) is N, and their weights are initialized as \(\omega_{0}^{i}=1/N\).  
**Step 2**.: For \(k=1\), \(2\),..., \(L\) (\(L\) is step length):  
1. For \(k=k-1\), predict the time series output \(\overset{\wedge i}{y}_{k|k-1}^{i}\) one-step by \(f\)-ARIMA model with the parameters \(\overset{\wedge i}{x}_{k|k-1}^{i}\).
2. When the new output time series point \(y_{k}\) arrives, update each particle's weight: \(\omega_{k}^{i}=\omega_{k-1}^{i}p(y_{k}\Big{|}\overset{\wedge i}{x}_{k|k-1}^{i})\).
3. Normalize the particles weight, i.e., \(\overset{\sim i}{\omega}_{k}^{i}=\omega_{k}^{i}(\sum\limits_{i=1}^{N}\omega_{ k}^{i})^{-1}\).
4. Resampling is performed to remove the small weight particles and generate promising new particles with the larger weight, thus the new particles set is \(\{\overset{\wedge i}{x}_{k}^{i}\}\).  
**Step 3**.: The state vector \(\overset{\wedge}{x}_{k}\) can be estimated as \(\overset{\wedge}{x}_{k}^{i}=\sum\limits_{i=1}^{N}\overset{\sim i}{\omega}_{k} ^{i}x_{k}^{i}\).  
**Step 4**.: Train the \(f\)-ARIMA model using the update state vector \(\overset{\wedge}{x}_{k}^{i}\) to get the prediction value of \(\overset{\wedge}{y}_{k+1}^{i}\).  
Figure 1: Schematic diagram of particle filter frame (PFF).  
**Step 5**. Algorithm stops when \(k\) is equal to step length \(L\), otherwise, go to step (2).
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 5 Results and Discussion
### Simulation Results for the SIFT  
To detect the agglutination formed in a given blood sample using the SIFT algorithm, a comparison between the reference blood image shown in Figure 13 and the input blood sample is made. When SIFT algorithm is applied to the reference image, the feature points and the orientation vectors are extracted. The feature points are represented by red circles, while the orientation vectors are represented by blue lines as shown in Figure 16. Figure 17 shows the feature points and their orientation vectors for an example of an input blood sample image as well. Similarly, when the SIFT algorithm is applied to the blood spot mixed with anti-A, it can be noticed that the SIFT algorithm is sensitive to variations in brightness. As a result, some fault feature points will appear due to the high brightness. The effect of these fault feature points will be minimized later after applying matching. The blood spot that is mixed with anti-B has 64 feature points, whereas the blood spot mixed with anti-D has 28 feature points. After extracting feature points descriptors of both images, the comparison is performed by calculating the Euclidean distance between each spot of the input image and the reference image. The decision is made based on the ratio between the shortest Euclidean distance and the second shortest, which is chosen to be 0.9 according to our experimental evaluation. Then, the resulting matching points for each blood spot of the input image were: no matching points for the anti-A spot, 34 matching points for the anti-B spot, and 12 matching points for the anti-D section. Therefore, the blood type is classified as B+.  
The confusion matrix and the evaluation measures of the SIFT are shown in Tables 7 and 8. It can be observed that the results of the SIFT were improved compared with SDS. The improvement of the SIFT evaluation over SDS evaluation was about 10 percent points for all of the evaluation measures, which reflects an improvement in agglutination detection. The specificity is also lower by about 25% than the sensitivity because the decision of the SIFT is easily affected by noise and variations in the brightness. The percentage of error in blood type detection over the 500 samples is 34.2%. However, this is not acceptable for  
Figure 16: Features points and its orientation for the reference image using SIFT algorithm.  
Figure 17: Feature points and their orientation for three blood spots (anti-A, anti-B, and anti-D) of an example of a blood sample using the SIFT algorithm.  
blood typing. Simulation results also showed that SIFT requires a relatively long processing time, where the average processing time required for each sample is 7.79 s.
## 2 Design of the 1 \(\times\) 4 Channel Wavelength Demultiplexer
### Design of the 2 \(\times\) 2 MMI Coupler  
The general interference based 2 \(\times\) 2 MMI couplers were used to build the MZ interferometers of the demultiplexer. It is necessary to perform numerical simulations to accurately estimate the core waveguide length (\"\(L_{\textit{MMI}}\)\") of a MMI coupler for a given core waveguide width [14]. In this work, we use FDTD simulation from Lumerical Mode software to estimate the core MMI waveguide length \"\(L_{\textit{MMI}}\)\". Figure 3a shows the waveguide geometries of the 2 \(\times\) 2 MMI coupler designed in this work. The core waveguide width (\(W_{\textit{MMI}}=30\)\(\upmu\)m) was chosen to make the lateral separation between the two input waveguides and between the two output waveguides large enough (\"\(L_{\textit{lat}}\)\" = 10 \(\upmu\)m) such that no inter-coupling occurs between the two side-by-side _I/P_ waveguides. The access waveguide width (\(W_{a}\)) is increased to 5 \(\upmu\)m to minimize the mode mismatch loss at MMI core waveguide input and output edges [14]. A 200 \(\upmu\)m long taper waveguide is designed to lower optical loss transition from the 5 \(\upmu\)m wide waveguide to the 2.8 \(\upmu\)m wide waveguide. Figure 3b shows the electric field distribution (in linear scale) in a 2D plane of the MMI coupler for a MMI core waveguide length of \(L_{\textit{MMI}}\approx 1860\)\(\upmu\)m at 1550 nm wavelength.  
To determine the effect of the variation in the MMI coupler length on the insertion loss and the power coupling between the two output (O/P) ports of the MMI coupler, we have simulated three MMI structures with different core waveguide lengths, while keeping the core waveguide width fixed to \(W_{\textit{MMI}}=30\)\(\upmu\)m. Table 1 shows that at 1550 nm wavelength, MMI coupler with the core waveguide length \(L_{\textit{MMI}}\approx 1860\)\(\upmu\)m exhibits minimum insertion loss per O/P waveguide (\(\sim\)0.3 dB).  
Figure 2: Variation in the (**a**) effective index (\(n_{\textit{eff}}\)) and (**b**) group index (\(n_{\textit{g}}\)) of the Si\({}_{3}\)N\({}_{4}\) waveguide from the wavelength range from 1530 nm to 1580 nm.
## 3 Design of Isl Network for IoT
### Network Model  
Herein, a satellite network is defined as a set of nodes that are able to communicate over wireless links. The nodes are characterized by their high mobility, as not only fixed satellite constellations but also autonomous orbital maneuvers, reconfiguration, and swarm behavior must be considered. This means that the link availability between a particular set of two nodes is, generally, time-variant. Thus, a satellite network is usually a meshed network (see Figure 7d), which needs to be controlled by a distributed algorithm. An important design baseline is that all nodes (satellites) in a network are technically identical and have the same resources onboard; most notably, they have the same electrical energy. Hence, it is not advantageous to distinguish between routers and user terminals.  
Compared to a classical ad-hoc sensor network, a satellite network has some significant differences. First of all, in ad-hoc or wireless sensor networks, two of the most important determinant conditions are inavailability of the position information of the wireless nodes and high and unpredictable mobility of the nodes. This causes a necessity for highly dynamic collection of routing information, inevitably decreasing the data throughput. However, especially for satellite networks, the node positions (and, thus, their availabilities) are highly predictable. Hence, a meshed nanosatellite network has the following characteristics:  
* The positions of all nodes at each moment are known; indeed, orbital mechanics allow for good prediction. Orbital maneuvers may lead to changes in the physical network topology. However, they are slow compared to the changes in signal quality and the connection matrix of terrestrial wireless meshed networks, where not only the movement of the nodes, but also wave propagation effects (such as unpredictable shadowing and reflection) play a significant role.
* Significantly great distances, up to 400 km and more; consequently, signal propagation time has a large impact on the timing scheme and MAC.
* Clearly, more resources are available onboard nanosatellites (several Watts, 32 bit \(\upmu\)C, several MB of RAM), compared to a node of an ad hoc sensor network (often primary cells, 8 bit \(\upmu\)C, few KB RAM).
* Line-of-sight (LOS) wave propagation is characterized by no shadowing and almost no reflection, scattering, or diffraction; so the channel can be well approximated with the AWGN model.
* For multiple orbit constellation, considering that highly inclined \(\mathrm{SSO}\) are the first choice for small satellites, the formation will be \"stretched\" over equatorial regions and will be more compact above the poles; thus, regular changes in edge number and weights are expected.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 2: Remain the Structure of the IEEE 802.11p
#### 3.2.4 Pseudo-Pilot Based Scheme  
Besides TDLSE scheme, a pseudo pilot based channel estimation scheme was proposed in [13]. The basic algorithm of the pseudo-pilot based scheme was proposed in [37; 38] and the pseudo pilots were assumed as true pilots. This scheme is based on the regression polynomial to estimate the channel response [39]. Further, in [39; 40], the least square fitting was investigated to calculate the regression polynomial coefficients. The algorithm of pseudo pilot scheme is based on three steps, such as (1) precalculation; (2) real-time estimation; and (3) channel tracking. The first step needs predefinition of the regression polynomial and the prescoring of the related matrix. The second step at first calculates the channel impulse response on the pilot subcarriers, and then it estimates the complete channel impulse response. Afterward, this complete response is used to derive a better channel response by using a scheme that is similar to the decision directed (DD) scheme. The third and final step is an improvement of the pseudo-pilot algorithms [37; 38]. The initial coefficients are derived from the preamble and then the pilots are deployed to track the channel variations in the adjacent symbols with performance improvement in the pseudo-pilot algorithm.
# Reproducibility and Accuracy of Two Facial Scanners: A 3D In Vivo Study
## 2 Materials and Methods
### Anthropometric and Digital Measurements  
The anthropometric distances between the Tr (trichion)-Na\({}^{\prime}\) (soft tissue nasion), Na\({}^{\prime}\)-Prn (pronasion), Prn-Pog\({}^{\prime}\) (soft tissue pogonion), and left-right Zyg (_zygomatic_) points were manually measured as a reference using a digital caliper directly on the subject's face. One hour later, measurements were repeated by the same operator to verify the repeatability of the measurements. Geomagic X Control software (v2020.1.1, 3D Systems Inc., Rock Hill, SC, USA) was used to calculate the distances between the cephalometric reference points on each of the scans. The sets of measurements were compared with each other and the anthropometric data. After two days, the digital linear measurements on the face scans were repeated to verify the repeatability of the measurements. The same software was used to perform the 3D analysis. Firstly, the scans were carefully trimmed to include the entire forehead, passing in front of the tragus, and at the submandibular level. Subsequently, the software provided the automatic best-fit alignment between the two scans of the same patient. For point analysis, the operator manually inserted the cephalometric reference points on each of the scans, as described in Table 2 and Figure 2. Two days later, the points were placed again on the face scans to verify repeatability. \begin{table}
\begin{tabular}{l l} \hline
**Cephalometric Point** & **Definition** \\ \hline \multirow{3}{*}{Left zygomatic (L-Zyg)} & The most lateral point on the contour of the left zygomatic arch. It corresponds to the outermost point of convexity of the left cheekbone area when viewed from the side. This point is typically located in line with the outer corner of the left eye. \\ \hline \multirow{3}{*}{Right zygomatic (R-Zyg)} & The most lateral point on the contour of the right zygomatic arch. It corresponds to the outermost point of convexity of the right cheekbone area when viewed from the side. This point is typically located in line with the outer corner of the right eye. \\ \cline{1-1}  & Soft tissue pogonion (Pog\({}^{\prime}\)) & The most projected point of the chin. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._The 3D analysis of the points was performed by dividing the measurement results into the following tolerance ranges: 0.5 mm to 0 mm and 0 mm to \(-\)0.5 mm, considered highly reproducible; 1 mm to 0.5 mm and \(-\)0.5 mm to \(-\)1 mm, considered moderately
## 2 Materials and Methods
### PC1D Modeling Tool  
The numerical modeling software PC1D has been used to simulate the PV properties of c-Si solar cells, and this software was invented by the group at the University of New South Wales [32]. PC1D allows us to simulate the important photovoltaic properties of solar devices fabricated with semiconducting materials by regarding them as one-dimensional (axial symmetry). The PC1D software consists of a large number of libraries and files, including the parameters of a variety of PV technologies like Ge, GIN, GaAs, a-Si, InP, c-Si, and AlGaAs [33; 34]. Several key parameters, including active area, thickness of device, band gap, and reflectance, were used to perform the simulation for the photovoltaic parameters of Si solar cells. In this work, the PC1D simulation was performed using the NR lengths and the average reflectance of the ZnO NRs AR layer over the textured c-Si wafer, and the predicted performance and charge transport properties of the solar cells were based on the vertically arranged ZnO NRs AR layer. The details of the simulation parameters are summarized in Table 1. The simulation was performed under AM 1.5 solar radiation and a constant light intensity of 0.1 W/cm\({}^{2}\) (one sun) at 300 K. For all PV simulations, the bulk recombination was set to 10 us, and P-type background doping of the solar cells was set to 1.153\(\times\)10\({}^{16}\) cm\({}^{-3}\) and N-type first front diffusion to 2.87 \(\times\) 10\({}^{20}\) cm\({}^{-3}\) peaks.  
Figure 1: Schematic diagram of growth of ZnO nanorods (NRs) on a textured Si wafer.