esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 3 Time-Localization Verification and Smoothing Localization Method
### Localization Verification
\tag{12}\]  
When GNSS inducing spoofing occurs, since the small drift (usually less than the positioning accuracy of GNSS) applied by spoofing terminal is not easy to be found at each moment, it is necessary to observe the cumulative error within a certain time window. Suppose the size of the time window is W, then the total spoofing deviation \(\Psi_{i}\) in the time window at time \(i\) is  
\[\Psi_{i}=\sum_{t=i}^{i+\text{W}}\|\mathbf{\chi}_{t}^{G}-\hat{\mathbf{\chi}}_{t}^{V}\| \tag{13}\]  
If \(\Psi_{i}\leq E_{s}\), the deviation between GNSS and visual SLAM system is within the threshold range, the localization verification is judged to be normal, and the localization domain verification flag is set to \(C_{i}^{\text{L}}=0\). If \(\Psi_{i}>E_{s}\), the deviation between GNSS and visual SLAM system exceeds the threshold range, the localization verification is judged to be abnormal, and the localization domain verification flag is set to \(C_{i}^{\text{L}}=1\). Therefore, regardless of whether forward spoofing or progressive spoofing occurs, GNSS frames need to be discarded. Then, the localization domain verification flag needs to meet  
\[C_{i}^{\text{L}}=\left\{ \begin{array}{ll}0&\psi_{i}\leq E_{d}\text{ and }\Psi_{i}\leq E_{s}\\ 1&\psi_{i}>E_{d}\text{ or }\Psi_{i}>E_{s}\end{array} \right. \tag{14}\]  
The localization verification is shown in Figure 3. Only the GNSS frames that are verified with the visual SLAM frame will enter the smoothing localization process. If location verification threshold is too large, spoofed GNSS frames that meet the threshold may be input into the smoothing localization process, resulting in localization errors in the back-end of the system, and if location verification threshold is too small, normal GNSS data frames will be discarded. Therefore, the selection of the localization verification threshold is also important. So, we conduct localization verification threshold analysis in Section 3.3. After time and localization verification, the verified GNSS data frames will be input into the smoothing localization process. Then, the system enters the smoothing localization method. Figure 3: Localization Verification.
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
###### Abstract  
The United Nations Organization established 17 sustainable development goals in 2015, including No Poverty, Good Health and Well-Being, Quality Education, Reduced Inequalities, and Responsible Consumption and Production. Latin America stands out as a region marked by significant levels of inequality, encompassing disparities in income and inadequate social protection. The rural municipality of Mecayapan in the Selvas region of Veracruz state, Mexico, is a community where these issues are prominently present. Another specific challenge that exacerbates the situation is the absence of connectivity, which hinders the utilization of technological advancements in three fundamental areas: health, education, and the economy. The project began with a thorough evaluation of the geographical, social, economic, cultural, educational, and health factors within the region. Following this, the network design and implementation phase was executed, considering the available resources, prioritizing recycled materials, and utilizing simulations when required. The subsequent step involved deploying the network in a prioritized manner, utilizing strategic management approaches. To assess the impact of this implementation, a qualitative descriptive study was conducted. This likely entailed the collection of data through interviews, observations, or surveys to gain comprehensive insights into how the WiFi network influenced the community. Results indicate that the implementation of a long-distance WiFi network with wide, open-access coverage in Mecayapan will yield favorable outcomes in terms of social inclusion, poverty reduction, and the overall enhancement of residents' quality of life. By leveraging technological advancements and promoting connectivity, the WiFi network can contribute to long-term sustainability by fostering economic development, enabling access to educational resources, facilitating communication and networking opportunities, and promoting sustainable practices in various sectors.  
sustainability; social inclusion; cost strategy; WiFi; homemade antennas +
Footnote †: journal: _Sustainability_  
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Footnote 0: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Multi-Access Edge Computing (MAEC)
#### 2.1.2 Smart City  
Smart cities are in high demand nowadays. [PERSON] et al. proposed using MAEC to transfer data in smart cities [22]. Mobile devices have become one of the primary data sources. An edge server near the mobile terminal can rapidly receive and store data. [PERSON] et al. [23] presented a scenario that involves MAEC for detecting abnormal or suspicious events such as terrorist threats and natural disasters. The MAEC architecture enables cooperation among the Base Transceiver Stations. Experimental results demonstrate that this system can notify users in high-risk areas promptly.
# An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network
###### Abstract
In [7], the Geographical Position Mobility Oriented Routing protocol (GPMOR) is described where the main aim is to reduce the number of hops based on the Gauss-Markov (GM) mobility model, which further improved the performance of routing by efficient packets. In summary, we make two contributions:  
* For FANETs, we propose the specific functions F\({}_{1}\) and F\({}_{2}\) with the optimization process by taking different parameters such as end-to-end delay (EED), packet delivery ratio (PDR), fuel emission, and throughput. The technique is used to resolve the constraints of the optimization problem with the firefly algorithm, which is used to estimate the exact match of the dynamic network topology. The primary problem is therefore converted into a distributed solvable problem, allowing senders to compute the attractiveness of flying nodes to execute congestion control. * To reach the best solution, we present a distributed traffic congestion control algorithm that incorporates the delay constraints. We propose Rs, Rd, and Rp variables for all flying nodes to verify the incoming flow of the flying nodes and outgoing flow of the flying nodes probability to exploit network utilization and decrease transmission delay in a circulated manner. Finally, we examine the optimization method's performance and demonstrate its convergence using a simulator. In this paper, we use the GPMOR protocol and the firefly algorithm to develop a load-balancing technique for position estimation in FANETs. Section 2 discusses related research in this field. The suggested network model, shown in Section 3, is completed in two phases: (i) optimizing the flying nodes using the firefly method in Section 3.1; (ii) using the traffic congestion control algorithm to predict the position of all unknown nodes in Section 3.2. Section 4 contains the findings and analyses. Lastly, Section 5 concludes the research paper.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 2 System Structure
### LSTM Neural Network Model  
The hidden layer of the LSTM neural network is composed of LSTM cells. A unique gating mechanism is introduced to control the accumulation speed of information. The standard unidirectional LSTM network transmits information in the positive order of time. In addition, to enhance the performance of the network, the BiLSTM network is proposed [23]. Compared with the standard unidirectional LSTM network, it adds a network layer that transmits information in the reverse order of time and connects the two hidden layers to the same output layer. In phoneme classification [24] and speech recognition [11], the performance of the bidirectional network is better than that of the unidirectional network. In order to explore the DL-based CSK-SS UWA communication system, this paper analyzes the application effects of the unidirectional LSTM and BiLSTM network models in the system, respectively.  
As a fundamental component of the one-way LSTM and BiLSTM network hidden layer, the LSTM cell is introduced below. The structure of the LSTM cell [25] is shown in Figure 4.  
For a given input sequence \(X_{1:T}=(x_{1},x_{2},\ldots,x_{t},\ldots,x_{T})\), in each time step, \(t\), \(x_{t}\in\mathbb{R}^{d}\) is used as the input vector feed to the LSTM cell. The LSTM cell outputs a cell state vector  
Figure 4: LSTM cell structure.  
Figure 3: Correlation results of original despreading and despreading with multipath in DSSS.  
\(c_{t}\in\mathbb{R}^{m}\) for the transmission of cyclic information, and a hidden state \(h_{t}\in\mathbb{R}^{m}\) is an output as the output vector of the LSTM cell, which can be expressed as  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\widetilde{c}_{t}, \tag{8}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}), \tag{9}\]  
where \(f_{t}\in(0,1)^{m}\), \(i_{t}\in(0,1)^{m}\), and \(o_{t}\in(0,1)^{m}\) are forget gate, input gate, and output gate, respectively. They are used to control the path of information transmission, \(c_{t-1}\) is the cell state at the previous moment, and \(\widetilde{c}_{t}\in\mathbb{R}^{m}\) is the activation state vector of the cell. They can be expressed as  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)}, \tag{10}\]  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}), \tag{11}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}), \tag{12}\]  
\[\widetilde{c}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}). \tag{13}\]  
where \(\sigma(\bullet)\) is the Logistic function, \(W_{\xi}\in\mathbb{R}^{d\times m}\), \(U_{\xi}\in\mathbb{R}^{m\times m}\), and \(U_{\xi}\in\mathbb{R}^{m\times m}\) are the weight matrix and bias vector parameters that the network needs to learn during the training process, \(\xi\in\{f,i,o,c\}\), \(h_{t-1}\) is the hidden state of the LSTM cell at the last moment.
## 3 Results and Discussion
### Single Substitutional Silicon Impurity in H-, O-, and N-Terminated C (001) Surfaces
Meanwhile, the formation energies of single Si in diamond (001) surfaces below the fifth C layer under H, O, or N termination were close to the formation energy of single Si in a 216-atom bulk diamond (4.73 eV), calculated using Equation (1). The above situations were also true for single V in H, O, or N-terminated diamond (001) surfaces. Figure 3: Formation energies of a single substitutional silicon impurity in various positions (in _L2_ to _L7_) near the diamond (001) surface under H, O, or N termination and in bulk diamond as a reference. The potential silicon impurity sites under different surface states are depicted in Figure 2.  
In conclusion, in terms of formation energy, it is easier for single Si to be embedded into _L2_ to _L4_ C layers when under H or N termination as compared with O termination. However, their formation energies are similar when Si is placed below the fifth C layer (the fifth C layer included).
###### Abstract
Solving this problem involves using mathematical models, simulations, and optimization techniques to find the best controller locations for the network's needs. This study introduces two new models for solving a variant of the aforementioned SDN problem, starting from an initial complex model. The first derived model is obtained by changing the original problem objective into constraints. The second one is stated by breaking down each part of the complex problem using extra variables, leading to a bi-linear nonconvex problem. Both models involve quadratic terms related to switches and controllers, and they are NP-Hard problems that are difficult to solve optimally. In order to solve them faster, this study proposes new models and a novel meta-heuristic algorithm. The research uses both real and randomly generated networks to test the models. It also uses larger random networks to better assess the performance of the algorithm. The models are tested under different conditions, particularly by setting limits on the number of controllers in each network. All tests are conducted using the Gurobi solver [4] to find the optimal solutions. This work constitutes a new approach to the management of latency in SDNs, particularly when measuring network efficiency. The results show that the model's effectiveness depends on the size of the networks being studied. The second model performs better than the first one, with faster solution times and smaller gaps between the best solutions obtained and the lower bounds given by the Gurobi solver. These findings help us understand how the models contribute to improving SDN performance. This study is divided into several sections to help readers understand the research thoroughly. Section 2 examines the previous studies and approaches relevant to the topic. Section 3 provides a feasible solution and the mathematical formulations used to address the problem. Section 4 explores the algorithmic approach used to solve these models effectively, discussing the methodologies and strategies applied. In Section 5, detailed and substantial computational experiments are presented to validate the models and algorithms through the specific scenarios tested, and the numerical results are reported. By focusing on analyzing and discussing the outcomes of these experiments, we highlight the key findings and insights gained from the study. Finally, Section 6 summarizes the conclusions drawn from the research findings, outlining their implications for the field. This section also suggests potential areas for future research and development based on the study outcomes and identified limitations.
# Comparing Link Budget Requirements for Future Space-Based Interferometers
## 3 Simulations of Phase Tracking Performance
### Hardware-in-Loop Simulation  
Figure 3 displays the architecture of a time domain hardware-in-loop simulation constructed to explore the phase tracking performance. The initial carrier frequency, \(f_{0}\), was combined with numerically generated laser frequency noise (LFN). LFN was either free running (approximated as \(1/f\) in frequency) or stabilized (approximated as white in frequency with a \(1/f\) roll off past some corner frequency). The generated LFN signal could also be passed through an arm response simulator. GRACE-FO-like gravity signal was added into the signal before being fed into a numerically controlled oscillator (NCO). For generality, the gravity signal was kept identical in amplitude for all experiments, regardless of satellite separation. The GRACE-like signal was interpolated from GRACE-FO mission data up to the simulation clock rate of 62.5 MHz. After GRACE-like signal insertion, the simulated optical signal was split in two before amplitude scaling and being combined with additive noise to simulate a weak tracking field and a truth tracking field. While noise insertion in the truth signal was not strictly necessary, it simulated such high carrier-to-noise density ratio that it was functionally negligible. For all simulations performed, the simulated detection quantum efficiency, \(\eta_{qc}\) is 0.7923.  
The two phasemeters were identical in design except for the gain tuning of each phasemeter's controller. The truth phasemeter controller, \(G_{2}(f)\), was tuned to track the high carrier-to-noise density ratio reference signal with large gain while the weak phasemeter controller, \(G_{1}(f)\), was varied to explore how well the phasemeter tracked the weak field signal. Both phasemeter controllers featured proportional single integration and double integration paths, each with independent gain control. The cross-over frequencies of the first and second order integrators in the controller were at factors of 10 and 100 below the unity gain frequency, respectively.
## 2 Materials and Methods  
### The Description of the Orbital Angular Momentum  
The LG beam, one of optical vortex (OV) beams carrying the OAM, is widely used in OV-based communication system because it is relatively easy to produce in experimental environments. The original transmitted LG beam in the electrical field can be expressed in cylindrical coordinates as [32; 33]  
\[\begin{array}{ll}LG_{lp}&=\sqrt{\frac{2p!}{\pi(p+\|0\|)!}}\frac{1}{\omega(z )}\left[\frac{r\sqrt{2}}{\omega(z)}\right]^{\|0}\exp\left[\frac{-r^{2}}{ \omega^{2}(z)}\right]L_{p}^{\|0\|}\left(\frac{2r^{2}}{\omega^{2}(z)}\right)\\ &\times\exp\left[\frac{-\hat{k}r^{2}z}{2\left(z^{2}+z_{R}^{2}\right)}\right] \exp\left[i(2p+\|l\|+1)\tan^{-1}\left(\frac{z}{z_{R}}\right)\right]\exp(il\phi ),\end{array} \tag{1}\]  
where \(r\) is the distance from the propagation axis, \(\phi\) is the azimuthal angle, \(z\) is the distance along the propagation axis, \(k=2\pi/\lambda\) is the wave number, \(\lambda\) is the wavelength, \(l\) denotes the topological charge which is an integer representing OAM mode, \(p\) is radial index (in our simulation we consider \(p=0\)), the term \(L_{p}^{l}(.)\) is the Laguerre polynomial, \(\omega(z)=\omega_{0}\sqrt{1+\left(z/z_{R}\right)^{2}}\) is the Gaussian beam radius at distance \(z\) where \(\omega_{0}\) is the beam waist, \(z_{R}=\pi\omega_{0}^{2}/\lambda\) is the Rayleigh range, and the phase profile expressed as \(\exp(il\phi)\) is what allows these beams to exhibit OAM.
## 3 Results
### Changes in RVGLS during Pacing  
Significant overall improvements in RVGLS were observed in response to higher-paced HR (Figure 1). Only eight participants (18%) exhibited worsening in RVGLS that were \(\geq\)10%. Twenty-one participants (47%) exhibited improvements and 17 (38%) maintained stable values at higher HRs (Figure S1). The findings shown in Figure 1 document changes of LVGLS in participants who experienced HR-related changes (i.e., improvement or worsening) in RVGLS as well as in those in whom this parameter remained unchanged. The percentage of patients who exhibited deterioration of RVGLS in response to changes in HR was similar in patients with ICD and with CRT at 19% and 24%, respectively. None of the participants carrying PM exhibited a deterioration of RVGLS. Interestingly, PM patients were more likely to experience HR-related improvements in RVGLS when compared to the responses of participants in Groups 1 and 2 (71% versus 59% and 51%, respectively). Figure 3 reports three different cases of RVGLS HR-related changes.  
Figure 2: Two examples of changes in LVGLS. The example in (**A**) documents improved LVGLS with increasing HR. The example in (**B**) documents a deterioration in LVGLS in response to increasing HR.  
As shown in Table 3, participants who exhibited worsening of RVGLS were those who presented initially with depressed RV function as determined by baseline RVGLS and TAPSE. These participants also presented with higher PASP and diminished levels of RV arterial coupling. Maximum relative changes of RVGLS did not correlate significantly with LVGLS, E/e', LV end-diastolic and end-systolic volumes, LV Tei index, stroke volume, cardiac output, or TAPSE.
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 4 Discussion
### Experimental Evidence
Specifically, the peak-to-peak absolute amplitude for vertical averages at 6.88 V while for the horizontal propagation this is 5.81 V. The FFT curves of Figure 14c confirm this trend, showing higher magnitude for the vertical excitation case compared to the horizontal surface excitation. The data imply that the sensor size effect influences the surface propagation leading to a received wave magnitude decrease, even though physically surface waves are stronger than longitudinal. The main content of the FFT curve (Figure 14c) is positioned around 150 kHz. Considering this frequency, the Rayleigh wavelength is calculated at around 16 mm, a value similar to the R15 sensor size (19 mm) and three times greater than the Pico size (5 mm). It is shown that considering the same propagation distance and for sensor physical size similar to the wave length (case of R15), the aperture effect diminishes the Rayleigh wave magnitude to levels lower than the corresponding longitudinal of the vertical excitation. However, for a smaller sensor, the Rayleigh wave magnitude remains higher than the corresponding longitudinal impinging vertically. This is confirmed in recent literature, in which the sensitivity of specific AE sensors was measured against both vertically incident and bar waves propagating parallel to the sensor surface. For various sensors, the sensitivity to parallel waves  
Figure 12: Experimental setup showing the receiver at the top concrete surface and pencil lead breakage applied at the horizontal and vertical direction. was lower than the sensitivity to vertical waves as the frequency increased [16]. It is characteristic that concerning sensor \"R6\" of Mistras, the sensitivity curves start to deviate at approximately 300 kHz, corresponding to a diameter over wavelength ratio of approximately 0.5 (element size according to [16] is 12.7 mm and the wavelength on reference aluminum bar for this frequency is approximately 22 mm). This agrees with the presented master curve of Figure 9, where the relative amplitude has high values only for \(D/\lambda<0.5\). Furthermore, in another study related to surface wave measurements, it is stated that the optimal operation frequency of the sensor is when the aperture size is no longer than half or even a quarter of the wavelength [27]. Figure 13: Individual waveforms received by Pico (5 mm) sensor in the case of (**a**) horizontal excitation; (**b**) vertical (**c**) the average FFT curves of previous waveforms.
## 3 Results
### Technical Analysis
#### 3.2.5 Control Devices  
Power quality control devices are introduced to eliminate and/or diminish the disturbances that negatively affect the quality of electrical energy. These devices are classified depending on their operation, physical structure, applications, and when they were first pioneered. The devices were comprehensively synthesized in the review articles [10; 68; 69]. In this review, we divided the control devices by their applications. Subsequently, the complete list of these devices that we found in the analyzed set of papers is summarized in Figure 7: APF (active power filter--47 papers), STATCOM, UPFC (Unified Power Flow Controller), UPQC (Unified Power Quality Conditioner), FACTS (Flexible Alternating Current Transmission System), SVC (Static Var Compensator), DVR (dynamic voltage restorer). In addition to this list, as shown in Figure 7, there is the \"Controller\" category, which includes the applications where the researchers combine diverse components of the power system, i.e., converters, generators, storage systems, transformers, power line conditioners, and capacitor banks, with the fuzzy-based controller to improve power quality issues. Of these devices, the most popular are the converters, with 25 appearances, followed by storage systems in 10 papers. Table 8 is a collection of data that show information on 10 important articles. These articles used various control devices to improve the quality of power and employed FCSs. The table provides details about the characteristics of each study presented in the selected papers, which are from each category of the chart in Figure 7. Moreover, the table also gives information about the efficacy testing and efficiency demonstration of the proposed control methodology.