esa-sceva/satcom-chunk-collection huggingface dataset:
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Trilateral Region's Construction  
As mentioned in Figure 2, the communication region of node \(S\) is circular in shape, as the goal is to construct a trilateral region (highlighted as grey colored) so that only a limited number of reliable nodes may participate in the communication process. It is presumed that vehicles have an on-board navigation system. Each vehicle is considered to have GPS receivers and preloaded street maps. GPS receivers identify the location and direction, which is helpful for nodes in calculating vehicle density. The trilateral zone, \(\triangle AEF\), must be constructed to limit the communication area (as illustrated in Figure 3). The last known coordinate information of the target node is utilized by the source node to compute the slope and the distance between them using Equations (14) and (15), respectively.  
\[m_{straight}=\frac{\Delta y}{\Delta x}=\frac{y_{D}-y_{S}}{x_{D}-x_{S}} \tag{14}\]  
\[Euclidean_{dist}=\sqrt{(\Delta y)^{2}+(\Delta x)^{2}} \tag{15}\]  
Then, we can determine the coordinate information of the perpendicular points \(C\) and \(D\) by solving Equation (15) with Equation (16).  
\[m_{perpendicular}=\left(\frac{1}{-m_{straight}}\right) \tag{16}\]  
Figure 2: Clipped communication zone.  
After calculating the perpendicular slope, we must use the Euclidean distance formula to obtain the distance between \(\overline{BC}\) and \(\overline{BD}\). We may also obtain the coordinates of \(E\) by finding the slope of straight-line \(\overline{AC}\) because that straight-line connects all three points; therefore, lines \(\overline{AC}\) and \(\overline{CE}\) have identical slopes. Similarly, we can calculate point \(A\), \(D\), and \(F\) as they are located on a straight-line by using the slope of line \(\overline{AD}\).
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 3 Main Reflectance Indices
### Photochemical Reflectance Index and Its Modifications
Finally, it should be noted that the measurements of PRI can be successfully performed on the basis of different variants of the mobile platforms, including satellites (e.g., MODIS and GOME) [54]; in the last case, the PRI can be used for the estimation of GPP on ecosystem spatial scales.
## 3 Proposed Method
### Data Sparsity Mitigation  
Computer vision scholars acknowledged that facial age estimation in masked and unmasked faces is a complex problem in this research area. In particular, masked face age estimation has evolved as an obligatory research topic because of the pandemic. It is known that solving a complex subject requires a deep structure of problem-defining components along with sufficient data to train. If not, the trained model will endure the overfitting issue. The main solution to the network overfitting problem is to ensure adequate data for model training. As a matter of fact, there is a solo competent dataset, IMDB-WIKI, which builds upon an unconstrained setting while having ample facial images. We found that the IMDB-WIKI dataset is well-suited for training our age estimation deep network. Although this dataset is rich enough in the context of samples, the inter-class disparity is a concern. After screening this dataset, we observed that data in the age range 15-65 were rationally incomparable with other age classes, which may have misled the empirical results. As a solution to the data sparsity problem that exists in the aforementioned dataset, the following steps can be useful:  
* Set a threshold for choosing the image samples from each class considering the data ratio all over the age classes.
* Remove the erroneously annotated samples of each age class using human visual perception, which is shown in Figure 3.
* Enhance the image for the classes with very limited samples by (1) appending images from the very pertinent Adience dataset [64] and (2) oversampling through data augmentation operations such as flipping, rotating in the range of \(-30^{\circ}\)-\(30^{\circ}\) maintaining the steps of \(5^{\circ}\), and scaling to ensure the threshold value for the class balancing.  
Figure 2: Schematic diagram of the proposed masked face real age estimation method.  
Additionally, some online data augmentation was also performed by rescaling the images into 256 \(\times\) 256 pixels, and a crop of 224 \(\times\) 224 pixels from the center was input to the network during training as a measure to alleviate the overfitting issues as well as the robustness of the model. The subsequent data distribution of the underlying age classes prior to and after initiating parity in the IMDB-WIKI dataset is presented in Figure 4.
## 3 Multi-Energy Coordination and Optimization Dispatching Strategy of Urban Power Grid
### The Solution of the Model Based on ADMM
In essence, power grid dispatching is a large-scale dispatching problem, with various and complex constraints. From the perspective of energy Internet, the coupling of electric with other sources further increases the difficulty of optimal scheduling. For the traditional grid dispatching calculation method, the general procedure is determining the objective function and the constraints and then solving the problem directly by existing software (such as CPLE, etc.) [35]. Although this solution method is simple, when the number of units increases and the coupling relationships between electric and other energy sources becomes complex, the solution speed will be greatly reduced. The advantage of the ADMM algorithm is that the power network and heating network can be solved separately and the interaction between the heating network and the power network can be carried out through the thermoelectric coupling elements. In this way, the complexity of each iteration can be greatly reduced, and the computing time can be reduced.
###### Abstract  
Thanks to the development of deep learning, various sound source separation networks have been proposed and made significant progress. However, the study on the underlying separation mechanisms is still in its infancy. In this study, deep networks are explained from the perspective of auditory perception mechanisms. For separating two arbitrary sound sources from monaural recordings, three different networks with different parameters are trained and achieve excellent performances. The networks' output can obtain an average scale-invariant signal-to-distortion ratio improvement (SI-SDRi) higher than 10 dB, comparable with the human performance to separate natural sources. More importantly, the most intuitive principle--proximity--is explored through simultaneous and sequential organization experiments. Results show that regardless of network structures and parameters, the proximity principle is learned spontaneously by all networks. If components are proximate in frequency or time, they are not easily separated by networks. Moreover, the frequency resolution at low frequencies is better than at high frequencies. These behavior characteristics of all three networks are highly consistent with those of the human auditory system, which implies that the learned proximity principle is not accidental, but the optimal strategy selected by networks and humans when facing the same task. The emergence of the auditory-like separation mechanisms provides the possibility to develop a universal system that can be adapted to all sources and scenes.  
sound source separation; separation mechanisms; proximity principle +
Footnote †: journal: Applied sciences
## 2 M&S Software Design for Multi-Radio Integrated Navigation System
### Message Broker-Based Software Design Method
#### 2.2.2 Function Module  
As shown in Figure 4, the function module consisted of an I/F, an encoder, a decoder, and a procedure. The binary data in the received messages were converted into internal variables of the procedure by the decoder. The internal variables were converted into the binary data by the encoder. The procedure was the program code of the function module of the M&S software. Due to the encoder and decoder, each function module did not need to share variables of the procedure with the other function modules. If the operators and/or developers updated the encoder and decoder, developed programs in other platforms could be reused or easily ported in the function module [18; 20; 24; 25].
## 1 Introduction
With the development of technology, an elastic optical network has been widely used because it can break the limitation of the rigid capacity for optical channel and provide efficient on-demand spectrum allocation with fine granularity spectrum slots. Most of the equipment that has been deployed is based on fixed-grid, on and in order to improve the utilization of spectrum resources, some nodes will be selected to support flex-grid technology in the future. However, the decision as to which nodes will be selected for upgrade, and the number of nodes selected for upgrade, depends on a number of factors such as network bottleneck, network topology, traffic profile, economic cost, network operator's policy and so on. Consequently, in such optical networks with potential upgrade nodes, how to avoid service interruption on the path is a significant problem to study. In this study, on the basis of deployed fixed-grid optical networks with potential upgrade nodes, in order to avoid service connection interruption and traffic loss caused by node upgrade and keep stable and reliable service supply at the same time, we identify how to provide migration-aware dynamic connection provisioning, when the nodes in fixed/flex-grid optical networks gradually upgrade from fixed-grid to flex-grid. We evaluate the performance through a simulation and analyze the simulation results compared with the baseline algorithm in terms of the bandwidth-blocking ratio and connection-interruption ratio under uniform and non-uniform traffic models. The major contributions of this work are two-fold: (1) we describe the probabilistic migration label model (_PML_) to establish the relationship between link distance and node upgrade (2) and we devise a _brown-field migration-aware routing and spectrum assignment (BMA-RSA)_ algorithm to provide migration-aware dynamic service provisioning upon service arrival. The rest of paper is organized as follows. Section II briefly introduces the problem of service provisioning during the process of node upgrade in fixed/flex-grid optical networks. In section III, we give the probabilistic migration label model to describe the relationship between distance and node upgrade. Then, we elaborate the proposed _brown-field migration aware routing and spectrum assignment (BMA-RSA)_ algorithm. Section IV includes the simulation results and detailed analysis of the proposed algorithm and other algorithm. Finally, section V concludes the work.
## 3 Proposed Method
### Facial Emotion Feature Extraction Based on Geodesic Distances and Sample Entropy
#### 3.3.2 Extraction of Cognitive Emotional Features Based on Sample Entropy  
In order to highlight the pattern of facial emotion features and provide more friendly input for the classifier, the facial emotion features were further optimized by extracting the sample entropy.  
Sample entropy is a quantitative feature that reflects the regularity of the time series fluctuations. Due to its better consistency (i.e., if the sample entropy value of an emotion description point is higher in the current time series than in another time series, it will maintain the same difference relationship for the parameters \(m\) and \(r\)), each emotion description point will reflect unique and stable sample entropy feature values for different cognitive emotions [62]. In addition, sample entropy does not depend on the quality of the data, and only a small amount of data is needed to accurately calculate the complexity and regularity of samples. Therefore, we used sample entropy to characterize the complexity of local dynamic changes in faces when expressing cognitive emotions. When a sequence of \(N\) data is set as \(X=\{x(1),x(2),\ldots,x(N)\}\), the calculation process for the sample entropy of the sequence is as follows.  
First, construct a sequence of sequences \(\{\boldsymbol{X}_{m}(1),\boldsymbol{X}_{m}(2),\ldots,\boldsymbol{X}_{m}(N-m+1)\}\) with dimension \(m\) based on  
\[B^{m}(r)=[\sum_{i=1}^{N-m}B_{i}^{m}(r)]/(N-m) \tag{12}\]  
Fourth, add a dimension to \(m+1\), count the number of times the distance between \(\mathbf{X}_{m+1}(i)\) and \(\mathbf{X}_{m+1}(j)\) is less than or equal to \(r\), and record it as \(A_{i}\). Define the parameters \(A_{i}^{m}(r)\) and \(A^{m}(r)\) as  
\[A_{i}^{m}(r)=\frac{1}{N-m-1}A_{i} \tag{13}\]  
\[A^{m}(r)=\frac{1}{N-m}\sum_{i=1}^{N-m}A_{i}^{m}(r) \tag{14}\]  
Finally, the sample entropy of the sequence \(X\) can be expressed as follows:  
\[SampEn(X;m,r)=\ln B^{m}(r)-\ln A^{m}(r) \tag{15}\]  
where \(B^{m}(r)\) is the probability that two subsequences of the sequence match \(m\) points under the similarity tolerance \(r\), and \(A^{m}(r)\) is the probability of two subsequences matching \(m+1\) points.  
Set \(m\) to 2 and \(r\) to 0.2 times the standard deviation of the original sequence. Calculate the sample entropy of each row of data in the spatial and temporal feature matrix \(\mathbf{Feature}_{st}\) to complete the deep extraction of cognitive emotional features. The feature vector \(\mathbf{Feature}\) is obtained as follows:  
\[\begin{split}\mathbf{Feature}&=SampEn(\mathbf{Feature}_{ \text{st}})\\ &=SampEn\Big{\{}\big{|}d_{g}(V_{1,1},V_{i,1})\quad d_{g}(V_{1,2},V_{i,2})\quad\ldots\quad d_{g}(V_{1,49},V_{n,49})\big{|}^{T}\Big{\}},1\leq i \leq n\\ &=\Big{\{}\;\;\;SampEn(1),\quad SampEn(2),\ldots,SampEn(49)\; \Big{\}}\end{split} \tag{16}\]  
It is worth mentioning that the data size of each sample is reduced from 2 \(\times\) 49 \(\times\)\(n\) to 49 through extracting sample entropy features.
## 4 Analysis Example and Methods
### Numerical Solution and Experimental Methods
#### 4.2.4 Boundary Conditions  
The boundary condition of the left end of the pipe is the velocity-inlet, which is the flow pulsation. The boundary condition of the right end of the pipe is the pressure-outlet, which is described by the linear characteristics of a throttle valve. The flow characteristics of the throttle valve can be expressed [45] as:  
\[q=k_{q}A\Delta P^{m} \tag{48}\]  
where \(q\) is the flow rate, \(k_{q}\) is the throttle coefficient, \(A\) is the throttle hole area and \(m\) is a parameter depending on the structure of the throttle valve. When there is linearity between  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Flow** & & & & & & & & & & \\
**Velocity** & \(\omega_{1}\) & \(V_{A1}\) & \(\omega_{2}\) & \(V_{A1}\) & \(\omega_{3}\) & \(V_{A1}\) & \(\omega_{4}\) & \(V_{A1}\) \\ **(m/s)** & & & & & & & & & & \\ \hline
2 & 1430.584 & 0.00851 & 3040.148 & 0.00154 & 4470.732 & 0.00108 & 5901.316 & 0.00070 \\
8 & 1430.584 & 0.03418 & 3040.148 & 0.00620 & 4470.732 & 0.00433 & 5901.316 & 0.00281 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation parameters of flow velocity.  
Figure 6: Flow pulsation curves of axial piston pump (The flow velocity is 8 m/s). (**a**) Pulsation curve in time domain; (**b**) Pulsation curve in frequency domain.  
the flow and pressure characteristics of the valve, \(m=1\), and the output pressure of the throttle valve is 0. Hence, a linear relationship between the flow and pressure characteristics of the pipe is as follows:  
\[P=k_{q}(V-\dot{\omega}_{z}) \tag{49}\]  
The throttle coefficients corresponding to the four system work conditions are listed in Table 5.  
Both ends of the pipe are fixed and the stiffness factor of the support [19] is set to a maximum value (\(\mathrm{e}^{12}\)). The boundary matrices at both ends of the pipe are obtained according to Equations (A1) and (A2).  
\[\mathbf{D}_{0}=\left[\begin{array}{cccccccccccc}1&0&-1&0&0&0&0&0&0&0&0&0&0&0 \\ 0&-A_{f}&k_{2}\Delta t+\frac{m_{0}}{\Delta t}&1&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&k_{x}\Delta t+\frac{m_{0}}{\Delta t}&1&0&0&0&0&0&0&0\\ 0&0&0&1&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&k_{y}\Delta t+\frac{m_{0}}{\Delta t}&1&0&0\\ 0&0&0&0&0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&0&0&1\end{array}\right] \tag{50}\]  
\[\mathbf{D}_{L}=\left[\begin{array}{cccccccccccc}-k_{q}&1&k_{q}&0&0&0&0&0&0&0& 0&0&0&0&0\\ 0&-A_{f}&-(k_{2}\Delta t+\frac{m_{L}}{\Delta t})&1&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&-(k_{x}\Delta t+\frac{m_{L}}{\Delta t})&1&0&0&0&0&0&0&0\\ 0&0&0&1&0&0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&-(k_{y}\Delta t+\frac{m_{L}}{\Delta t})&1&0&0\\ 0&0&0&0&0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&0&0&0&0&0&0&0&1\end{array}\right] \tag{51}\]
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Indoor Search and Rescue (SAR) Operations for Swarms of Drones
#### 2.1.4 Communication Routing  
Once the maze has been solved (in our scenario and domain, _solving the maze_ is equivalent to locating the rescue target) a swarm of ad hoc agents can further accommodate the rescue process by setting up a relay network for the base station to monitor the trapped target. In the literature, communication services have been identified as an integral functionality of swarms [31] and indeed, this can be of significant benefit while a relief team is deployed to reach the victim at the rescue site.  
This requires a routing algorithm that can engineer a reverse route between the already dispersed search agents, under relevant communication constraints. There is a large number of such constraints and challenges for inter-UAV communication; we refer to [43] for an overview of the literature.
## 3 Results
### Results of Individual Studies
#### 3.3.3 Outcome: Surgical Training  
Four studies were included (three RCTs, one prospective observational) [5; 21; 22; 36]. The authors of these studies reported that VR showed promising results, significantly improving trainee and resident skills. These studies showed that VR-trained residents performed surgery faster and with fewer errors compared to those who were traditionally trained. Moreover, VR-trained residents have proven more skillful in performing surgical tasks rather than other control groups, even with minimal guidance, resulting in overall better performance. All the mentioned studies showed high construct validity, as the levels of skill of trainees were successfully measured and improved. Moreover, one of the studies [5] showed evidence of criterion-based validity, because it compared a group of trainees using a simulator with one using conventional methods. The overall quality of evidence in the NRCT was assessed as \"high\", according to MINORS. According to the Cochrane Risk of Bias Tool, one RCT had an overall score of zero, indicating high  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author and** & **Country** & **Study Design,** & **Sample** & **Purpose** & **Hardware** & **Procedure** & **Conclusion** \\ \hline [PERSON], 2020 & & & & & & & Statistically higher \\
[35] & Sweden & POS, II & 20 & Intraoperative & AR & Spine surgery & accuracy compared to the free-hand technique & accuracy compared to the free-hand technique & accuracy compared to \\  & & & & & & & & the free-hand technique \\ Tsukada, 2019 & & & & & & & & \\
[24] & Japan & PS, II & 10 & Intraoperative & AR & arthroplasty & & reliable accuracy total \\  & & & & & & & & knee arthroplasty. \\ Zheng, 2018 & & & & & & & & Statistically higher \\
[38] & China & & & & & & & accuracy was reported \\ \hline \hline \end{tabular}
**AR**: augmented reality; **CSM**: Cervical spondylic myelopathy; **MR**: mixed reality; **NA**: not applicable; **PCCS**: Prospective case-control study; **PCohS**: Prospective cohort study; **PComS**: Prospective comparative study; **POS**: Prospective observational study; **PS**: Pilot study; **RCohS**: Retrospective cohort study; **RComS**: Retrospective Comparative Study; **RCT**: Randomized Controlled Trial; **VR**: Virtual reality.  
\end{table}
Table 3: _Cont._quality [22] and the other two had an overall score of two, indicating moderate quality [5; 21]. All the results are reported in Table 3.
## 1 Introduction  
In the past several decades, spectral domain optical coherence tomography (SD-OCT) has played an important role in many significant fields due to its non-contact, non-invasive nature, and it does not threaten human health. It is currently widely used in high-precision detection of pathological tissue [1; 2], the detection of composite materials [3], and medicine imaging [4] etc., since it was proposed by [PERSON] et al. [5]. In these significant applications, the quality and the depth range of SD-OCT system is very important. In SD-OCT system, a grating is used to spread the interfering two beams by wavelength and capture the frequency domain information of the interference signal through the charge-coupled devices (CCD) camera. A challenging problem which arises in this field is traditional SD-OCT system yields only the real part of the interferometric signal due to spectrometer limitations, which will produce complex conjugate artifacts (CCA) [6]. To achieve better imaging quality and imaging depth, there are many alternative methods available for solving the problem. These methods include phase-shifting method [7; 8], achromatic sinusoidal phase modulation [9], off-axis illumination [10], electro-optic phase modulation [11], attenuation-based methods [12; 13], using a scanner to modulate spatial interferograms [14; 15], full-bandwidth k-domain linearization method [16], and so on. As far as we know, the CCA of SD-OCT system cannot be completely removed by using the traditional five-frame phase-shifting method because of the effects of polychromatic errors whilst achromatic sinusoidal phase modulation method requires too much data. To solve these problems, a new phase shifting strategy named five-frame variable phase-shifting (FVP) method, was proposed. The FVP is dependent on the wavelengths whilst traditional five-frame invariant phase-shifting (FIP) method is correlated with the center wavelength. In comparison with traditional FIP method, this novel phase shifting method has the advantage of higher resistance to polychromatic errors and better error tolerance.