esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stability Analysis and Discussion
### 2-DOF Model of VHCVSS
#### 3.2.1 Modeling
\begin{array}{l}\text{Denoting }M\ =\ \left[\begin{array}{cc}m_{x}&0\\ 0&m_{y}\end{array}\right]\text{, }\boldsymbol{C}\ =\ \left[\begin{array}{cc}c_{x}&0\\ 0&c_{y}\end{array}\right]\text{, }\boldsymbol{K}\ =\ \left[\begin{array}{cc}k_{x}&0\\ 0&k_{y}\end{array}\right]\text{, }q(t)\ =\ \left[\begin{array}{c}x(t)\\ y(t)\end{array}\right]\text{, if the axial depth of cut is sliced, considering Equations (2), (3) and (28) will be changed into  
\[\boldsymbol{M\ddot{q}}(t)+\boldsymbol{C\dot{q}}(t)+\boldsymbol{Kq}(t)\ =\ -\sum_{j =\ 1}^{N}\sum_{k\ =\ 1}^{f}\boldsymbol{D}_{j,k}(t)(\boldsymbol{q}(t)-\boldsymbol{q}(t- \widetilde{\boldsymbol{\tau}}_{j,k}(t))) \tag{30}\]  
where  
\[\begin{array}{l}\boldsymbol{D}_{j,k}(t)\ =\ \left[\begin{array}{cc} \widetilde{\boldsymbol{c}}_{xx,j,k}(t)&\widetilde{\boldsymbol{c}}_{xy,j,k}(t) \\ \widetilde{\boldsymbol{c}}_{yx,j,k}(t)&\widetilde{\boldsymbol{c}}_{yy,j,k}(t) \end{array}\right]\\ \widetilde{\boldsymbol{C}}_{(\bullet),j,k}(t)\ =\ \left[\begin{array}{cc} \int_{(t-1)h}^{kh}h_{(\bullet),j}(z,t)dz&k\ =\ 1,2,\ldots,f-1\\ \int_{(f-1)h}^{k_{f}}h_{(\bullet),j}(z,t)d\phi&k\ =\ f\end{array}\right. \text{,}(\bullet)\ =\ xx,\ xy,yx\,\text{and}\ yy\end{array} \tag{31}\]  
Denoting \(\boldsymbol{p}(t)\ =\ \boldsymbol{M\ddot{q}}+\boldsymbol{cq}/2\) and \(\boldsymbol{x}(t)\ =\ \left[\begin{array}{cc}\boldsymbol{q}(t)&\boldsymbol{p}(t)\end{array} \right]^{T}\), one can transform the 2-DOF model in Equation (28) into  
\[\dot{\boldsymbol{x}}(t)\ =\ \boldsymbol{A}\boldsymbol{x}(t)+\boldsymbol{B}(t)x(t)- \sum_{j\ =\ 1}^{N}\sum_{k\ =\ 1}^{f}\boldsymbol{D}_{j,k}(t)\boldsymbol{x}(t-\widetilde{\boldsymbol{\tau}}_{j,k}(t)) \tag{32}\]  
where  
\[\begin{array}{l}\boldsymbol{A}\ =\ \left[\begin{array}{cc}-M^{-1}\boldsymbol{C}/2& M^{-1}\\ \boldsymbol{CM}^{-1}\boldsymbol{C}/4-\boldsymbol{K}&-\boldsymbol{CM}^{-1}/2 \end{array}\right]\text{, }\boldsymbol{B}(t)\ =\ \left[\begin{array}{cccc} 0&0&0&0\\ 0&0&0&0\\ -\sum\limits_{j\ =\ 1}^{N}\sum\limits_{k\ =\ 1}^{f}\widetilde{\boldsymbol{C}}_{xx,j,k}(t)&- \sum\limits_{j\ =\ 1}^{N}\sum\limits_{k\ =\ 1}^{f}\widetilde{\boldsymbol{C}}_{xy,j,k}(t)&0&0\\ -\sum\limits_{j\ =\ 1}^{N}\sum\limits_{k\ =\ 1}^{f}\widetilde{\boldsymbol{C}}_{yx,j,k}(t)&- \sum\limits_{j\ =\ 1}^{N}\sum\limits_{k\ =\ 1}^{f}\widetilde{\boldsymbol{C}}_{yy,j,k}(t)&0&0\\ \end{array}\right]\\ \boldsymbol{D}_{j,k}(t,z)\ =\ \left[\begin{array}{cccc}0&0&0&0\\ 0&0&0&0\\ \widetilde{\boldsymbol{c}}_{xx,j,k}(t)&\widetilde{\boldsymbol{c}}_{xy,j,k}(t)&0& 0\\ \widetilde{\boldsymbol{c}}_{yx,j,k}(t)&\widetilde{\boldsymbol{c}}_{yy,j,k}(t)&0& 0\end{array}\right]\\ \boldsymbol{C}_{yx,j,k}(t)&\widetilde{\boldsymbol{c}}_{yy,j,k}(t)&0&0\end{array} \right]\end{array}  \tag{33}\]
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 4 Strategy of Communication Path Selection
### Lagrange Relaxation-Based Aggregated Cost  
The optimal algorithm, as the name implies, always finds the optimal path with cost \(z_{opt}\). Since the DCLC problem is NP-hard, it cannot find the optimal solution in polynomial time. If the algorithm always finds a feasible solution (if it exists), then the algorithm is said to be complete. Completeness does not imply optimality, but it is acceptable. In this paper, the LARAC algorithm is selected as the main routing algorithm, because it has the following properties [26]:  
* In the network topology, as long as the target path can be obtained, the path always satisfies the delay constraint.
* The algorithm can be solved in polynomial time.
* It is impossible to provide general theoretical guarantees for finding the optimal cost path, but the algorithm gives the lower bound of the optimal cost. The difference between the two is within the acceptable range, so as to ensure the high quality of the result.
* The algorithm not only applies to a single QoS constraint, but also can be extended to study multiple QoS requirements.
## 5 Conclusions  
Compared to surface seismic surveys, VSP offers advantages such as lower acquisition costs, a higher signal-to-noise ratio, and better resolution. However, it also faces challenges, including a limited imaging range and difficulty in removing artifacts. In response to the difficulty in efficiently utilizing multiples in VSP seismic data in existing research, the challenges faced by conventional RTM methods when processing multiples were analytically examined in this study. One key issue was identified, that is, the presence of significant artifacts during the imaging process. To address this issue, a CR-MRTM method was proposed that replaces seismic wavelets with seismic signals at source locations to achieve the joint imaging of primary reflections, surface multiples, and interbed multiples in complex VSP data. Compared to previous studies, the method we propose effectively utilizes both primary reflections and multiple waves from VSP seismic data, significantly improving imaging quality and the effective imaging range without a substantial increase in computational cost. This approach overcomes the limitation of traditional VSP reverse-time migration, which is restricted to imaging only the vicinity of the observation well. Furthermore, our method eliminates the need for additional seismic signal separation, enabling the joint imaging of primary reflections and multiple waves and thereby enhancing the stability and efficiency of the imaging process. The proposed method demonstrates excellent performance for both interlayer multiples and surface-related multiples, addressing theshortcoming of conventional VSP multiple-wave imaging methods, which typically only image surface-related multiples.  
Conceptualization, C.L., Y.L. and J.G.; methodology, Y.L. and J.G.; software, Y.L. and J.G.; validation, C.L. and J.G.; formal analysis, C.L. and Y.L.; investigation, C.L. and J.G.; resources, C.L.; data curation, C.L. and Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, C.L., Y.L. and J.G.; visualization, Y.L. and J.G.; supervision, C.L.; project administration, C.L. and Y.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on Key Technologies of Intelligent Geological Structure Modeling Based on Structural Analysis by National Natural Science Foundation of China: 41974147.  
Not applicable.  
The synthetic data used in this paper can be obtained from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
# A Multi-Modal Person Recognition System for Social Robots
## 3 Architecture of the Person Recognition System
### Feature Extraction
#### 3.2.2 Voice-Based Feature Vector  
The voice-based feature vector is computed based on the short-term spectral, specifically, the so-called mel-frequency cepstral coefficients (MFCCs). We opted for MFCCs for many reasons: (1) MFCCs are easy to extract compared to other speech features, such as voice source features, prosodic feature, and spectro-temporal features; (2) MFCCs require relatively less amount of speech data to be extracted; and (3) MFCCs is text and language independent. Thus, MFCCs feature vector fits the nature of the person recognition for the social HRI where a real-time response and text-independent speech signature are crucial for user acceptance of social robot. A modular representation of MFCCs feature vector extraction is shown in Figure 7.  
MFCCs feature vector is computed based on a widely accepted suggestion that the spoken words cover a frequency range up to 1000 Hz. Thus, MFCCs use linearly spaced filter at low frequency below 1000 Hz and logarithmic spaced filter at high frequency above 1000 Hz. In other words, the filter-bank is condensed at the most informative part of the speech frequency (more filters with narrow bandwidths below 1000 Hz) and lengthy-spaced filter-bank is applied at higher frequencies. As depicted in Figure 7, the first step in the extraction process is to pre-emphasize the input speech signal by applying filter as in (8).  
\[Y(n)=X(n)-a\,*X(n-1) \tag{8}\]  
where \(Y(n)\) is pre-emphasized speech signal, \(X(n)\) is the input speech signal, and a pre-emphsized factor can be any value in the interval \([0.95,\ 0.98]\). In the next step (Windowing), the pre-emphasized speech signal \(Y(n)\) is multiplied by smooth window function, here, we used Hamming windows, as in (9).  
\[W(n)=0.54-0.46*cos(\frac{2\pi n}{N-1}),\quad 0\leq n<N-1 \tag{9}\]  
The resultant time-domain signal is converted to frequency domain by applying the well-known Fast Fourier Transform (FFT). The frequency range in the resultant FFT spectrum is very wide and fluctuated. Thus, the filter-bank that is designed according to Mel scale is applied in order to get the global shape of the FFT spectrum magnitude which is known to contain the most distinctive information for speaker recognition. The MFCCs are obtained by applying logarithmic compression and discrete cosine transform, as in (10). The discrete cosine transform converts log Mel spectrum into the time domain.  
\[C_{n}=\sum_{m}^{M}[\log S(m)]cos\left[\frac{\pi n}{M}(m-\frac{1}{2})\right] \tag{10}\]  
where \(S(m)\), \(m=1,2\ldots,M\) is output of, an M-channel filter-bank, \(n\) is the index of the cepstral coefficient. In this study, we retained the 12 lowest \(C_{n}\) excluding 0 th coefficient.
## 2 Related Work
### Attention Mechanism  
Attention mechanisms are designed to mimic the ability of humans to find salient regions in a scene, and such mechanisms can be used to highlight a specific part of the feature map by weighting the feature map. Attention mechanisms are usually classified into channel attention mechanisms, spatial attention mechanisms, temporal attention mechanisms, branching attention mechanisms, channel spatial attention mechanisms, and spatio-temporal attention mechanisms [28]. Spatial attention mechanisms, channel attention mechanisms, and hybrid domain attention mechanisms are more commonly used in image analysis.  
The spatial attention mechanism starts from finding regions on the image spatial domain that are helpful for the task. For instance, according to [29], it was suggested that Spatial Transformer Networks (STN) should be able to learn how to adaptively spatially transform various data to obtain spatial transformation invariance.  
On the other hand, the channel attention mechanism calculates the weight of each channel in the network to form the attention on the channel domain. [PERSON] et al. [30] weighted the original features by dimensionality using a three-step operation of the squeeze, excitation, and attention on SeNet.  
[PERSON] et al. [31] designed the Convolutional Block Attention Module (CBAM) by combining the spatial and channel attention mechanisms. A simple and efficient forward CNN attention module is designed by combining two different dimensions, spatial and channel, to generate an attention map.  
In medical image analysis, [PERSON] et al. [32] proposed a novel Attention Gate (AG) model for medical imaging, which can automatically learn to focus on target structures of different shapes and sizes. An interactive attention mechanism was developed by [PERSON] et al. [33] to implicitly instruct the network to focus on pathological tissue in multimodal data. [PERSON] et al. [16] introduced an inter-slice contextual attention mechanism and an intra-slice spatial attention mechanism in lesion detection to improve model performance using fewer slices.
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 4 Efficiency of Creating PAM versus Its Dimensions
### Metrics  
As seen in the analyzed areas, there are points (i.e., sparse PAM matrix cells) for which calculations have not been made. Path loss values in these locations must be interpolated according to Algorithm 2. On the other hand, there are points (i.e., PAM cells), where calculations were performed repeatedly (more than once). This is due to the high density of the profiles near the TX. Let us introduce the redundancy parameter \(r\) as the number of path loss value calculations at a given point. As a consequence, it is possible to define the calculated points ratio \(CPR\) for the analyzed area  
\[CPR(\%)=\frac{CP}{TP}\cdot 100, \tag{4}\]  
where \(CP\) and \(TP\) are the number of calculated points (with \(r\geq 1\)) and total number of points at the analyzed area, respectively.  
To determine the number of all calculations performed, the redundancy values for all points in the analyzed area should be summed up  
\[CN=\sum_{s=1}^{TP}r_{s}, \tag{5}\]  
where \(s\) is the point index and \(r_{s}\) is the redundancy value for a specified point \(s\).  
The \(CP\) measure introduced earlier considers the number of points, where the calculations were made at least once (i.e., \(r\geq 1\)). By correlating this parameter with the redundancy \(r\), it is possible to define a new \(CP_{r}\) measure meaning the number of points where the calculations were made exactly \(r\) times. Therefore, the previously introduced \(CN\) metric can also be expressed as  
\[CN=\sum_{r=1}^{r_{\max}}r\cdot CP_{r}, \tag{6}\]  
where \(r_{\max}\) is the maximum value of \(r\) for the analyzed area.  
The normalized number of points where the calculations were made exactly \(r\) times can be defined as follows:  
\[NCP_{r}(\%)=\frac{CP_{r}}{CP}\cdot 100. \tag{7}\]  
Additionally, for evaluating the PAM creation efficiency, we use a computation time \(CT\) expressed in seconds. The measure considers the execution of all algorithm steps presented in Section 4.2, excluding the calculation of terrain profiles. The absolute values of this measure depend on the processing power of the used computer. Therefore, this parameter is used to compare different variants, e.g., different sizes of the analyzed area.
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 4 Dynamic Sampling Techniques to Train Artificial Neural Networks
### Method 1. Dynamic Sampling  
The basic idea of the Dynamic Sampling (DyS) method, proposed in [27], is to design a simple DyS that dynamically selects samples during the training process. In this method, a pre-deletion of any sample to prevent information loss, to dynamically select the samples (hard to classify) to train the ANN and to make the best use of the dataset does not exist. According to this main idea, the general steps in each epoch can be described as follows.  
1. Randomly fetch a sample \(q\) from the training dataset.
2. Estimate the probability \(p\) that the example should be used for the training. \[p=\left\{\begin{array}{ll}1,&\text{if }\delta\leq 0\\ exp(-\delta\cdot r_{j}/min\{r_{i}\}),&\text{otherwise,}\end{array}\right.\] (3)where \(\delta=z_{j}^{q}-max_{i\
eq c}\{z_{i}^{q}\}\). \(z_{i}^{q}\) is the \(i\)-th real ANN output of the sample \(q\) and \(j\) is the class label to which \(q\) belongs. \(r_{c}=Q_{c}/Q\) is the class ratio; \(Q_{c}\) is the number of samples belonging to class \(c\); and \(Q\) is the sample number.
3. Generate a uniform random real number \(\mu\) between zero and one.
4. If \(\mu<p\), then use the sample \(q\) to update the weights by the back-propagation rules.
5. Repeat Steps 1-4 on all samples of the training dataset in each training epoch.  
In addition, the authors of the paper [27] use an over-sampling method based on a heuristic technique to avoid bias for the class imbalance problem. Beginning with the first epoch, the process consists of the samples of all classes, except the largest classes over-sampled to make the dataset balanced. As the training process goes on, the over-sampling ratio (\(\rho\)) is attenuated in each epoch (\(ep\)) by a heuristic technique (Equation (4)). It is calculated as:  
\[\rho=(r_{max}/r_{j})/ln(ep) \tag{4}\]  
where \(ep\) (\(>2\)) and \(max\) represent the largest majority class.
## 7 Economic Analysis of D-FACTS Systems
Therefore, in this paper, the economic analysis aims to compare the total cost of two types of parallel D-FACTS connected to a wind farm based on DFIGs, as presented in Figure 21.  
According to Figure 21, it is clear that the use of D-STATCOM to maintain the wind farm in service during grid fault conditions is an expensive application compared with D-SVC due to the use  
Figure 21: Cost of D-FACTS systems from different types of grid faults. Figure 20: Reactive power injected into the PCC by using D-SVC and D-STATCOM with different types of grid faults. of the transformer and the cost of power electronics [17; 18]. Consequently, one can conclude that the D-STATCOM is more cost-effective compared to D-SVC for voltage support at the PCC and the wind farm connection in the event of the most severe grid fault conditions. Figure 22 shows the cost breakdown of a proposed 12 MW wind farm installation. According to this figure, it can be seen that the proposed solution based on a D-FACTS system represents only 4% of the overall cost of the wind turbine installation. On the other hand, this solution offers good performance at the wind farm, ensuring its connection with the electrical grid and the reliability of the wind energy conversion system.
# Bat Optimized Link State Routing Protocol for Energy-Aware Mobile Ad-Hoc Networks
## 5 Simulation and Result
### Experimental Results and Discussion  
This section provides a brief discussion of the results of the proposed BOLSR protocol compared with three other protocols. The performance metric that was used to assess the performance of the proposed protocol includes the parameters of PDR, E2E delay, EC, and ROR. The three key network scenarios of the tests were related to the parameters of a number of nodes, node speed, and simulation time. These parameters were altered to observe their effects on the PDR, E2E delay, EC, and ROR of the BOLSR, CAABO, EBOLSR, and OLSR protocols. It is worth mentioning, that the figures below depict the effects of EC, ROR, and E2E delay parameters in different scenarios as the BOLSR protocol is focused on reducing energy consumption, overhead, and delay. The results obtained from implementing the three scenarios are presented in Tables 3-5. Each scenario was implemented five times to obtain the required results. The mean was used to evaluate the performance of the network routing protocols and the Standard Deviation (SD) was used to evaluate the variation in the behavior of the routing protocols within the five runs. The results obtained during the implementation of the first scenario are shown in Figure 8. Figure 8a shows the average effect of the number of nodes in the ROR of the BOLSR, CAABO, EBOLSR, and OLSR protocols. Figure 8b shows the ROR SD of the routing protocols. The results show that the overhead ratio of the four routing protocols is increased with the increase of the number of nodes (40-100). In MANET, the nodes of the network are dynamically collecting information about each other. Furthermore, during the nodes' movement, there might be link failures between these nodes which results in recalculating the routing tables. These issues lead to an increase in the overhead ratio and the energy consumption of the network. The results further show that the BOLSR maintains the lowest overhead ratio among the rest which implies finding the optimal path in terms of overhead ratio and energy consumption.
## 1 Introduction
(2) Describing the beam control and beamforming synthesis implementation process in the system, which combines prior information and spatial search, and performing phase difference calculations with formula derivations. An FPGA implementation process is also described. (3) Verifying and analyzing the beam control and beamforming algorithms in the design through an anechoic chamber experiment, including beamforming signal amplitude testing, sensitivity testing, beam uniformity testing, and beam pointing accuracy testing, and evaluating the errors in the beamforming algorithm. The second part of the paper provides the derivation of digital beamforming weights, the third part focuses on the design of digital beam control and beam synthesis algorithms, the fourth part presents anechoic chamber experimental validation and analysis, and the fifth part offers a summary and outlook of the entire paper. The organizational structure of the entire paper is shown in Figure 2.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Reactive Routing Protocols
#### 2.2.1 Dynamic Source Routing (DSR)  
This routing protocol is a representative of reactive routing, which allows a network to be self-configured, self-organized and without infrastructure [28]. The main objective when choosing DSR is its reactive nature, and it is mainly designed for multi-hop wireless mesh networks. In DSR, the source UAV only builds routing paths towards the destination UAV when needed. This depends mainly on two processes: (i) route discovery; and (ii) route maintenance. Route discovery is the process used at the source UAV to discover a route, whereas route maintenance is needed when link failures occur. [PERSON] et al. developed first test bed for UAV ad-hoc networks with the dynamic source routing (DSR) protocol [29]. [PERSON] et al. also suggested that DSR is more suitable for FANETs than proactive routing protocols, due to the high mobility of the network [30]. However, due to the repetitive mechanism, reactive path finding before each packet delivery can also be exhaustive. Therefore, this routing protocol is not suitable for scenarios where the topology of the network is highly dynamic.
## 4 Results and Discussions
### Spatial and Temporal Changes in Water Conservation Function at Different Spatial Scales
#### 4.2.3 Average Water Conservation Capacity of Different Land Types  
The water source conservation function in the study area was assessed and statistically analyzed based on the initial classification from China's multi-period land use database, enabling the calculation of the average water source conservation depth for each year. As depicted in Figure 5, the water source conservation depth exhibited the following order: forest land > arable land > grassland > urban construction land > water area > unused land. Notably, the water source conservation depth of forest land consistently exceeded the regional average over multiple years.  
It is important to highlight that when assessing the multi-year change rates, urban construction land (13.19%) demonstrated the highest increase, primarily attributed to urbanization expansion leading to a rise in impermeable surfaces. This, in turn, disrupted the water balance, reducing precipitation infiltration and elevating flood peak flows [40]. In contrast, forest land and arable land played roles in mitigating surface runoff, alongside promoting soil infiltration, thus delaying the precipitation convergence within the watershed. Consequently, they exhibited relatively low change rates [21].