esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Demonstration
### Base Cases  
In order to show how the frequency of the system behaves in the presence of contingencies as a base case, we simulate the generation loss and load shedding performed by PU in the absence of the governor and attacks. The initial generation from the generators in the WSCC model at bus 10 and bus 11 is 163 MW and 83 MW, respectively.  
Suppose during a hot summer day the power demand is at its peak and the system must satisfy the demand by providing a relevant generation. If there is a sudden increase in load, more power will flow through lines, which may result in line overloads and ultimately, line tripping. This is because power flow is computed according to Kirchhoff's law, which does not have any capacity constraint. Once line starts tripping, lines connecting the generators to the grid will trip and, thus, generation loss occurs. To model this scenario, we reduce the generation on bus 10 by 20% at the 50s, 50% at 55s and bus 11 by 45% at 60s. After some time, we restore the generation of bus 10 by 45% at 200s and bus  
Figure 5: P(t): Maximum load that can be dropped. This is the result of sensitivity analysis.  
11 by 40% at 210s. Figure 6 shows how frequency deviates from its normal state (60 Hz), moves to a contingent state and, finally, goes beyond the under-frequency threshold of 57.60 Hz.  
In the above case, PU does not perform load shedding to bring frequency to the normal state. Let us examine what occurs when utility performs the load shedding in the above simulation. PU knows about the generation loss; it sends a load shedding command to DRAS 1 and 2 to reduce the load by 40% in each area. DRAS 1 and 2 receive the load shedding command and compute the number of meters to send the load shedding command using Tables 1 and 2. DRAS 1 is responsible for controlling the load at bus 5, which is 125 MW and 40% of 50 MW load means. Similarly, DRAS 2 is responsible for bus 8, and it must shed 40 MW of the load. Suppose DRAS 1 sheds 30 MW of load from residential meters and 20 MW from commercial meters. DRAS 1 sends load shedding commands to 70.88% of residential meters and 26.49% of commercial meters. Similarly, DRAS 2 sheds 30 MW of load using residential meters and 10 MW from commercial meters. Therefore, DRAS 2 sends commands to 88.48% of the residential meters and 16.53% of the commercial meters.  
Figure 7 presents the frequency curve over time for the load shedding scenario during generation loss. The load shedding signals from DRAS 1 reached 55, and this is why we see an increase in frequency. Due to the loss of generation on bus 11, the frequency drops. At 65 s, the load shedding commands from DRAS 2 leads to an increase in frequency and prevents the frequency from dropping further. After a certain duration, when generation is restored, the frequency goes back to normal.  
Figure 6: Base case—generation loss.  
Figure 7: Base case—generation loss and load shedding.
## 2 Optical Frequency Comb Self-Referencing
### CEO vs. CEP Stabilization
(**a**) \(f_{\mathrm{CEO}}\) = \(f_{\mathrm{rep}}\)/4, \(\Delta\phi_{\mathrm{CEO}}\) = \(\pi\)/2: an identical pulse is retrieved after four pulses; (**b**) \(f_{\mathrm{CEO}}\) = 0, \(\Delta\phi_{\mathrm{CEO}}\) = 0: all pulses are identical (shown here for the particular case where CEP = 0, however, the absolute value of CEP can be arbitrary). this case. Self-referencing a frequency comb with \(f_{\text{CEO}}\) = 0 cannot be achieved using the standard negative feedback loop approach used to lock \(f_{\text{CEO}}\), but an alternative method based on a feedforward stabilization [52] makes it possible. A passive stabilization of \(f_{\text{CEO}}\) to zero was also realized in an Er:fiber modelocked laser via difference frequency generation between two distant parts of a SC spectrum generated in an \(\text{HNLF}\), namely a 2 \(\upmu\)m soliton and a dispersive wave at 860 nm, to produce a broadband CEO-free output spectrum centered at the oscillator wavelength of 1.55 \(\upmu\)m [53].
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 4 Results
### Influence of RA Binder Aging  
#### 4.2.1 Inspection of Aging  
Figure 5 presents the rheological properties of two types (SK70, SBS) of RA-Art binder under different aging times. It can be seen from the results that the complex modulus of RA-Art binder increases with aging time from the unaged (0 h) to the 8 day (196 h)-aged samples. In addition, from the regression results, the value of the complex modulus (|G*|) of SK70 asphalt has a good correlation (R\({}^{2}\) = 0.89) with aging time. However, the complex modulus values of SBS binder fluctuate with aging time, which may be related to the degradation of SBS modifier. Therefore, GPC and FTIR tests were conducted on the SBS-modified asphalt based on the molecular and chemical characteristics.  
In this study, the chromatogram was evaluated between \(\sim\)15.0 min and \(\sim\)31.7 min and was divided into 13 slices, as shown in Figure 6. The first five slices of the chromatogram were used to determine the percentage of LMS, reflecting the aging of RA binder. Figure 7 shows the LMS% of artificial RA binder with different aging times. The LMS% increases with the aging time ranging from 10.3% to 14.7%, except for the results of 4 h-aged binder. The data yields a linear trend (R\({}^{2}\) = 0.89), which is attributed to the oxidation during the asphalt mixture aging process.  
The FTIR spectrum of SBS-modified binder is shown in Figure 8. The value of A1695/A1455, called the aging index (AI), is shown in Figure 9. The FTIR evaluation of artificial RA binder found similar results to that of the GPC. The AI gradually increases with the aging time with a good correlation (R\({}^{2}\) = 0.86). The AI ratios of unaged (0 h), 4 h-aged and 5 day (124 h)-aged binder were 0.86%, 1.40% and 2.88%, respectively, showing an increasing trend with the aging times. It also indicates that the SBS-modified binder in the mixtures was aged to different degrees during the aging process.  
Figure 5: Complex modulus at 58 \({}^{\circ}\)C for two types of recovered binder (SK70, SBS) from the dynamic shear rheometer test.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 4 Experiment and Result Analysis  
### Experimental Intersection Information  
An intersection in Xi'an of China is selected as an experimental case. Wi-Fi data acquisition devices are deployed at the intersection. This system is designed to verify the traffic information detection method of a single intersection based on Wi-Fi data acquisition devices.  
Figure 8 shows the locations of Wi-Fi data collection devices. Table 3 shows the information of each Wi-Fi data collection device group.  
Figure 8: Location of the Wi-Fi data acquisition device.  
This experimental intersection is a typical cross-shaped one. The traffic volume in the north-south direction is relatively large, serving as the main arterial road, while the traffic volume in the east-west direction is relatively small, serving as the secondary arterial road. The specific channelization design of the experimental intersection is shown in Figure 9. Considering the traffic demand of the main roads, both the south and north entrances are designed with four lanes, namely, one left-turn-only lane, two straight lanes, and one lane for both straight and right turns. The traffic flow at the east and west entrances is relatively small. Both entrances are designed with two lanes. The first one is a shared lane for straight-going vehicles turning left and right, while the second one is a shared lane for straight-going vehicles turning right only.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 4 Structural Transformations of Edge Couplers in Vertical Direction
### Edge Couplers Based on Cantilever Structure
to find that for edge coupler configurations with vertical transformations, the fabrication process is commonly more complicated and infeasible than those configurations with horizontal transformations. Monolithically fabrication processes are more compatible with planar photonic integrated circuits. In addition, excess manufacture of either the Si substrate or the cladding of the standard SOI wafer will bring about high cost and inferior integrability.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 2 Related Work  
Driven by the demand for multi-satellite collaboration, satellite networks have become the core platform for achieving broad area observation and efficient computing. In recent years, research on complex task decomposition, allocation, and resource mapping has mainly focused on static task scheduling and single-satellite resource optimization. However, existing methods generally lack dynamic multi-tasking parallel processing capabilities, neglect the joint optimization of priority and dependency relationships, and have rigid resource mapping mechanisms, leading to prominent issues of system response delay and resource fragmentation, making it difficult to adapt to high real-time and heterogeneous satellite collaboration scenarios.
## 2 Materials and Methods
### Fepa-Net Network Model  
In this study, the network structure refers to the encoding-decoding structure of classical U-Net. The encoding component on the left side serves as the backbone feature extraction network. It is constructed by incorporating convolutional and max-pooling layers, enabling the acquisition of low-level spatial features. In contrast, the decoding component on the right side functions as the enhanced feature extraction network. This network is composed of up-sampling and convolutional layers, which are employed to restore the high-level abstract features of the feature map that were lost during the up-sampling process. Additionally, a stacking approach is utilized to integrate the information from diverse features, facilitating a more comprehensive feature representation, and the final obtained feature map is of the same height and width as the image taken as input.  
The composition of the Fepa-Net network is presented in Figure 5, which also adopts a symmetric encoding-decoding structure. The left half of the network is an ordinary convolutional network structure, which consists of five groups of convolution and four groups of maximum pooling; the first two groups of convolution have two 3 \(\times\) 3 convolution operations, and the last three groups have three 3 \(\times\) 3 convolutions, and each convolution is followed by a RELU activation function. The number of channels is 64, 128, 256, 512, and 1024. The pooling layer reduces the scale of the input feature map, allowing the model to have fewer parameters. The right portion of the network conducts up-sampling on the outputted deep abstract features and down-sampling on the outputted shallow sub-local features, with the aim of fusing these two types of features. Instead of using ordinary convolution, a feature extraction module is applied to acquire multi-scale feature information. Moreover, a position attention module is incorporated. This module is employed to fuse the feature maps at different levels for the purpose of implementing jump connections. By doing so, it enables the acquisition of contextual information relying on the feature maps of various levels; thus, it improves the network's capability to represent features. To avoid gradient explosion and gradient disappearance, a BN (Batch Normalization) layer and a RELU activation function are added after the convolutional layer of the feature extraction module, and finally, dropout regularization is added to prevent overfitting after performing \(1\times 1\) convolutional compression.
## 4 Empirical Study and Result
### Analysis of Merging Behavior in Different Traffic States
#### 4.2.2 Merging Gap and Speed  
Figure 7a,b show the relationship between the speed when merging starts (\(v_{M_{\mathrm{s}}}\)) and the accepted gap (\(Gap_{M}\)). The colors on the scatter points represent the merging location of the vehicle in Figure 7a and the density in Figure 7b. When colors are not shown, we may wrongly conclude that the start speeds of vehicles are insensitive to gap size because there is no apparent trend between the start speed and gap distribution. The colors in Figure 7a show that the merging vehicles select a smaller gap with a lower speed under a light traffic flow rate. And when facing a particular gap, vehicles tend to merge at a slower pace as they approach the taper. And the density color indicates that vehicles take a shorter gap with relatively low speed when surrounded by other vehicles and have more gaps to choose from when density is low. However, in congested traffic states, vehicles are forced to accept gaps under 50 m and start merging at a speed of less than 30 km/h, regardless of how far away from the taper area.  
The scatter plots in Figure 7a,b highlight the relationship between the speed at which vehicles start merging and the gap they accept, showing how both factors are influenced by the merging location and traffic density. In light traffic conditions, vehicles tend to merge at higher speeds and accept larger gaps, particularly when they are further from the taper. However, as traffic density increases, vehicles are forced to accept smaller gaps, often below 50 m, and must merge at lower speeds, typically below 30 km/h, regardless of how far they are from the taper. This pattern indicates that under congested conditions, drivers have fewer opportunities to select optimal gaps, aligning with existing research that suggests increased density constrains drivers' decision-making, pushing them to accept less favorable gaps and reduce speed.  
Figure 6: Merging location distribution of Scenario 1. In light traffic conditions, vehicles tend to merge earlier, around 300 m from the taper, whereas in heavy traffic (Site C1), most merging occurs closer to the taper, within 50 m, due to reduced gap availability.
## 2 The Modified Empirical Wavelet Transform Method
### The LWM-EWT Method
#### 2.2.1 Picking Out the LWM  
Here the example how to pick out the first LWM is given to explain the detection theory of LWM. First, the detected local maxima in the magnitude of the Fourier spectrum is sorted descending and the set can be described as:  
\[\{M_{k}\}_{k=1}^{N},\ M_{1}\geq M_{2}\geq\ldots\geq M_{N}, \tag{7}\]  
where \(M_{k}\) is one of the local maxima.  
The corresponding angular frequencies of these maxima are given as:  
\[\Omega=\{\Omega_{k}\}_{k=1,2,\ldots,N}, \tag{8}\]  
where \(\Omega_{k}\) is the angular frequency of \(M_{k}\).  
If \(M_{1}\) is the first LWM, denoted by LWM-1, then zero the amplitude that the frequency is in the window of \(W\), except for the center frequency of \(\Omega_{1}\). The equation is as follows:  
\[|X(\omega)|=0,\ \Omega_{1}-\frac{W}{2}\leq\omega\leq\Omega_{1}+\frac{W}{2}, \ \omega\
eq\Omega_{1}, \tag{9}\]  
where \(|X(\omega)|\) is the spectrum amplitude; \(\omega\) is the angular frequency.  
When there are four LWM that need to be detected, the detection process of the first LWM is illustrated in Figure 3.  
Figure 2: Flowcharts of (**a**) the EWT method and (**b**) the proposed LWM-EWT method.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Packet Erasure Codes in ODYSSE
#### 3.7.1 Preliminaries  
The terminology is the same as the one from RFC 5170 [36]. Packets are supposed of equal size and are considered as vectors of GF(2), i.e., a Galois field of two elements (a Galois field is a field that contains a finite number of elements). We depart from some traditional presentations, as in our context the symbols are actually these packets. Hence obviously vectors of symbols can be represented as matrices. Consider a source node having \(k\) original packets, which can be considered as row vectors of a matrix, denoted \(S\). Along with the \(k\) source packets, \(m\) additional repair packets are generated yielding \(n=k+m\) packets (systematic codes). The matrix representing all packets (source and repair packets) is denoted \(P\).  
In general, LDPC codes (systematic or not systematic) are linear mappings: \(P\) is obtained as \(P=G^{T}S\), where \(G\) is a generator matrix. By definition of LDPC, the code has one parity check matrix \(H\), and the generator matrix must satisfy \(HG^{T}=0\) and hence \(HP=0\).  
For decoding, the method is to recover the source packets from the received coded packets: because of the linearity, we have just to solve the linear equations induced by \(HP=0\), where the unknown are the source packets while the constants are the received ones. This could be solved by Gaussian Elimination, but for LDPC codes, more efficient, algorithms exist [45].
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 4 Advanced Theories and Methods for TD-NIRS
### Hybrid RTE and DE Models  
Hybrid RTE and DE models have been developed to overcome the limitation of the DE which is not applicable to early time regimes and regions close to the boundary, source, and with low-scattering, and to save the computation loads of calculating the RTE for the whole space and time. In the fields of DOT and fluorescence diffuse optical tomography (FT) for small animals and small organs like human fingers and teeth, the early-time regime plays an important role, and simple application of the DE may be inappropriate. Several hybrid models have been proposed for CW [116], FD [117; 118], and TD [119; 120; 121]. In the FD version of the coupled RTE-DE model [117], the whole computation domain is divided into two subdomains of near-field regions using the RTE and far-field regions using the DE, and the continuity of the intensity and its derivatives at the interface between the two subdomains are given for connecting the solutions of the RTE and DE. In the FD version of the overlapped or buffered RTE-DE model [118], the subdomains overlap in an artificially defined buffer zone where smooth transition from the RTE to the DE is achieved. One example of the overlapped RTE-DE model for TD was proposed by [PERSON] et al. [118]. In their model, the RTE and DE regions are separated spatially and temporally by using a crossover length for spatial separation and a crossover time for temporal separation, succeeding in describing light propagation accurately and reducing computational load by a quarter when compared with full computation of the RTE. Further development of hybrid RTE-DE models for TD is expected in the future.
# Tomographic Inversion Methods for Retrieving the
## 3 The External Reconstruction Tomographic Algorithm (ERTA)
These functions generate the _null space_ of the transform, denoted as \(\mathrm{S}_{\mathrm{N}}\). For similarity, the functions \(f_{n,l}(r,\theta)\) that produce a nonnull Radon transform will generate the _nonnull space_, referred to as \(\mathrm{S}_{\mathrm{R}}\). It is worth noting that in our problem the observable quantities are samples of the Radon transform, so that the null space cannot be reconstructed from the measurements; in other words, the null space may have a role in the reconstruction of the function \(f\) only if some _a priori_ knowledge is available (for example, if we knew that the solution is smooth). The third important fact is that the elements \(f_{n,l}(r,\theta)\) and \(g_{n,l}(\rho,\phi)\) are orthogonal in the space domain and in the Radon domain, respectively, if particular weight functions are used to define the inner product in such spaces. More specifically, the Hilbert spaces which \(f\) and \(g\) belong to are composed by functions defined on \(x\in E=\left\{{x\in\mathbb{R}^{2}:\left\lVert{x}\right\rVert\geq 1}\right\}\). The functions \(f_{n,l}(r,\theta)\) and \(g_{n,l}(\rho,\phi)\) are orthogonal in their respective spaces according to the following inner product  
\[\left\langle{f_{1},f_{2}}\right\rangle=\iint_{E}f_{1}(x)f_{2}^{*}(x)w(x)\ dx \tag{13}\]  
with weight function \(w(x)=\left(\pi\right)^{-1}\left\lVert{x}\right\rVert\ \sqrt{\left({1-\left\lVert{x}\right\rVert^{-2}}\right)}\) in the space domain and \(w(x)=\left(\pi\right)^{-1}\left\lVert{x}\right\rVert^{-2}\) in the Radon domain. The reconstruction algorithm, based only on the nonnull space \(\mathrm{S}_{\mathrm{R}}\), starts with a decomposition of the acquired Radon transform \(g\) (the IWV measurements) by using the set of functions \(g_{n,l}(\rho,\phi)\), i.e.,  
\[g(\rho,\phi)=\sum_{n=-\infty}^{+\infty}\sum_{l=0}^{+\infty}d_{n,l}\ g_{n,l}( \rho,\phi) \tag{14}\]  
where \(d_{n,l}\) are the coefficients of the expansion. The coefficients \(d_{n,l}\) are computed according to Equation (13) which, expressed in polar coordinates, becomes  
\[d_{n,l}=\iint_{E}g(\rho,\phi)\ g_{n,l}^{*}(\rho,\phi)\rho^{-1}\ d\rho\ d\phi \tag{15}\]According to the mapping property in Equation (11) [26], we have  
Figure 2:0  
where \(f_{R}(r,\theta)\) denotes the function (belonging to \(\mathrm{S_{R}}\)) that can be achieved from the Radon transform. It is worth noting that for each \(n\)-th angular Fourier expansion coefficient, all the terms of the expansion along \(r\) with index \(l<\lfloor n/2\rfloor\), are transformed by the Radon operator into a null function: thus, the higher the frequency component index of the WV field to be reconstructed, the higher the number of radial basis functions that will not be reconstructed by the procedure (because they belong to the null space). The only coefficients of the angular Fourier series that are completely reconstructed by the procedure are those relative to the continuous and fundamental frequencies.
# COVID-19 Outbreak Prediction with Machine Learning
## 2 Materials and Methods
### Machine Learning (ML)
#### 2.2.2 Adaptive Neuro Fuzzy Inference System (ANFIS)  
An adaptive neuro fuzzy inference system is a type of ANN based on the Takagi-Sugeno fuzzy system [87]. This approach was developed in the early 1990s. Because this system integrates the concepts of neural networks and fuzzy logic, it can take advantage of both capabilities in a unified framework. This technique is one of the most frequently used and robust hybrid ML techniques. It is consistent with a set of fuzzy if-then rules that can be learned to approximate nonlinear functions [88; 89]. Hence, ANFIS was proposed as a universal estimator. An important element of fuzzy systems is the fuzzy partition of the input space [90; 91]. For input \(k\), the fuzzy rules in the input space make a \(k\)-faced fuzzy cube. Achieving a flexible partition for nonlinear inversion is non-trivial. The idea of this model is to build a neural network whose outputs are a degree of the input that belongs to each class [92; 93; 94]. The membership functions (MFs) of this model can be nonlinear, multidimensional and, thus, different to conventional fuzzy systems [95; 96; 97]. In ANFIS, neural networks are used to increase  
Figure 5: Architecture of the multi-layered perceptron (MLP).  
the efficiency of fuzzy systems. The method used to design neural networks is to employ fuzzy systems or fuzzy-based structures. This model is a kind of division and conquest method. Instead of using one neural network for all the input and output data, several networks are created in this model:  
* A fuzzy separator to cluster input-output data within multiple classes.
* A neural network for each class.
* Training neural networks with output-input data in the corresponding classes.  
Figure 6 presents a simple architecture for ANFIS.  
In the present study, ANFIS is developed to tackle two scenarios described in Table 2. Each input included by two MFs with the Tri shape, Trap shape, and Gauss shape MFs. The output MF type was selected to be linear with a hybrid optimizer type.