esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Simulations
### Results with Feature Selection
#### 6.3.4 Comparison of EWOA and WOA  
Table 13 explores the obtained results based on AUC, the number of selected features, and fitness value for EWOA and WOA using the best TFs (i.e., S2 and V4). For Data 1, the performance of EWOA-S2 outperforms other methods in terms of avg. AUC (i.e., 0.91683) and fitness value (i.e., 0.8302). While the performance of EWOA-V4 outperforms other methods for Data 2. Figure 15 depicts the convergence for all methods. We employed the F-Test value to determine the best approach. The obtained results show that EWOA-4 outperforms all other methods with F-test value equals 1.58.
## 4 Results and Analysis  
The effectiveness of the proposed ANHVSO, NHVSO and VSO methods for fault detection and identification was evaluated using a 6-DOF PUMA robot arm. The same robot manipulator was used to test the effectiveness of the proposed AMFBVSC, FBVSC and VSC methods for fault-tolerant control. The mathematical model of this 6-DOF PUMA robot manipulator can be found in [1] and [28]. Sinusoidal signals for all joints are defined as desired reference signals. To test the accuracy and robustness of the proposed algorithm, three different faults were seeded on the desired signal at different locations: actuator, sensor, and actuator-sensor. The results are presented in two parts: a) fault detection and identification, and b) fault-tolerant control analysis.
## 1 Introduction
The average groundwater level in the alluvial deposits is approximately 2 m in Canakkale city center. Canakkale Province is located in an area of high seismic activity, according to the Turkey Earthquake Hazard Map [18]. The Municipality of Canakkale aimed to detect areas where soil stabilization should be implemented, by examining the ground conditions in the Canakkale settlement area. For this purpose, a microzonation study was performed in the Canakkale settlement area. Microzonation is a detailed and multidisciplinary study that aims to divide the area of study into zones with respect to its geological, geotechnical, geophysical, and geometrical characteristics, and to provide reliable maps of seismic ground-shaking parameters, considering local site effects, as well as induced hazards, such as liquefaction and landslides. A detailed microzonation study provides the basis for an advanced seismic risk assessment of an urban area, and for earthquake design considerations in new engineering projects [19]. Figure 1: Location map of the study area. Within the scope of this study, both geotechnical borehole studies and geophysical studies were carried out. In the geotechnical borehole studies, soil samples were taken with an SPT sampler and Shelby tube, the SPT test was applied, and the static groundwater level was identified. Within the scope of the geophysical studies, shear wave velocity data for the Canakkale settlement area were obtained via the seismic refraction method and multi-channel surface wave analysis (MASWs) studies. Based on the results, the engineering and seismic parameters for the ground in the Canakkale settlement area were determined, the liquefaction potential was examined, and the areas where precautions must be taken were identified [20]. The main purpose of this article is to develop a regression equation between V\({}_{\text{s}}\) and SPT-N as a result of the geotechnical and geophysical studies carried out on alluvial deposits in the Canakkale settlement area. More than two-thirds of the geotechnical borehole studies, seismic refraction, and MASWs studies carried out within the scope of the microzonation studies were performed on alluvial deposits. Statistical evaluations were made between the uncorrected SPT-N blow count and the corrected SPT-N(N\({}_{60}\)) blow count and shear wave velocity (V\({}_{\text{s}}\)). Based on statistical evaluations, empirical equations were developed to estimate V\({}_{\text{s}}\) from the SPT-N blow count and the SPT-N(N\({}_{60}\)) blow count, and the predictive ability of these equations was compared with that of equations proposed in previous studies.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 2 Related Works
### Loop Closure Detection  
Loop closure detection is a crucial component of visual SLAM, tasked with identifying loop nodes and refining initial pose estimates [38]. Traditional Bag-of-Words (BoW) models like DBow2, DBow3, and FBOw [39] utilize the binary descriptor and store descriptors in a k-d tree structure based on Hamming distance. Despite their utility, these conventional BoW methods require extensive offline training data, which can limit their adaptability. To address this, iBow [40] was introduced, offering online BoW training directly from testing datasets. This approach provides robust transfer performance across various scenarios, enhancing the system's adaptability. Another approach to loop closure detection, similar to NetVLAD [41], as used in [28; 42], heavily depends on the quality and relevance of the training set. Significant differences between the training and testing datasets can lead to performance degradation. In contrast, ref. [43] utilizes deep learning to handle loop closure under varying viewpoints and lighting conditions. It achieves an average precision-recall AUC improvement of 26% over DBoW2 and 18% over AlexNet [44] across six public datasets. Instead of relying on the entire image, this approach focuses on landmarks to determine loop closure, enhancing its robustness and accuracy in diverse environments. Ref. [45] presents LCDNet, a learning-based approach for loop closure detection and point cloud registration in LiDAR SLAM. It effectively detects loop closures even in challenging reverse loop scenarios.
# A Brazilian Space Launch System for the Small Satellite Market
## 2 Methodology and Launch Vehicle Configuration
### Cost Analysis
#### 2.2.3 Ground and Flight Operations Costs  
This sub-model is divided into direct (\(C_{DOC}\)) and indirect (\(C_{IOC}\)) operations costs. Direct operation costs apply to ground preparation of a vehicle, launch, and mission operation activities, including the cost of propellants. While, indirect operation costs are comprised of administration and management costs of the launch service provider, costs related to technical support along with general launch site and range costs, insurance fees, launch provider profit, and so on [16]. Ground operation cost is given by Equation (27). Essentially, this cost model considers the main activities related to the preparation of the range facilities, payload encapsulation, vehicle transportation, and erection to the launchpad, propellant loading, and other supporting direct activities relevant to the launch mission.  
\[C_{GOC}=8M_{0}^{0.67}L^{-0.9}N_{s}^{0.7}f_{o}f_{c}f_{4}f_{8} \tag{27}\]  
where,  
\(M_{0}\)--total dry mass of the vehicle;  
\(L\)--launch rate (number of launches per year);  
\(f_{v}\)--impact of vehicle type;  
\(f_{c}\)--impact on assembling integration mode;  
\(f_{4}\)--learning cost reduction factor.  
Flight operation cost is comprised of mission planning and preparation, launch and ascent flight control up to payload separation as well as flight safety control and tracking, given by the following equation  
\[C_{FOC}=20\left(\sum Q_{N}\right)L^{-0.65}f_{4}f_{8} \tag{28}\]  
where,  
\(Q\)--stage complexity factor.  
This parameter accounts for the number and type of stages of the proposed launch vehicle. The suggested value for small solid rocket stages is 0.15 and 0.4 for an expendable liquid propellant stage. Vehicle propellant cost is estimated by  
\[C_{pp}=\sum\left(C_{kg}m_{pp}\right) \tag{29}\]  
where,  
\(C_{kg}\)--cost per kilogram of a given propellant,  
\(m_{pp}\)--total mass of a given propellant.  
Indirect operation costs are related to administration and management, launch service provider, general launch site and range costs. For simplicity, we considered these costs a fraction (10-15%) of the total cost per flight, as suggested by the TRANSCOST model [16]. Additional costs, such as insurance fees, nominal profit, and vehicle development amortization charges, or royalties, are also a fraction of the cost per flight. Combining vehicle performance and system cost analysis, launch system specific transportation cost, for a given reference orbit can be estimated from  
\[STC=f_{5}(C_{F}+C_{GOC}+C_{FOC}+C_{pp}+C_{atz}+C_{pft}+C_{add})/M_{pld} \tag{30}\]  
where,  
\(C_{atz}=C_{D}/N_{f}\)--vehicle development cost amortization;  
\(C_{pft}\)--cost for nominal profit;  
\(C_{add}\)--additional costs;  
\(M_{pld}\)--mass of payload @ given orbit;  
\(f_{5}\)--correction factor for vehicle performance estimates accuracy;  
\(N_{f}\)--number of flights.
## 3 System Modelings
### Performance Evaluation  
In order to evaluate the performances of the proposed sCO\({}_{2}\)/AHP system and the sCO\({}_{2}\)/DH system, several criterions are considered in this study, including the coefficient of power performance (_COPP_), coefficient of heat performance (_COHP_), overall system exergy efficiency (\(\eta_{ex}\)) and total product unit cost (\(c_{total}\)). The _COPP_ and _COHP_ represent the electric power generation and heat production capacity, respectively, which are defined according to Equations (40) and (41) [18; 47],  
\[\textit{COPP}=\frac{W_{net}}{Q_{core}} \tag{40}\]  
\[\textit{COHP}=\frac{Q_{h,users}}{Q_{h,absorb}} \tag{41}\]  
where \(Q_{h,users}\) is the heat amount supplied to the heat users, while \(Q_{h,absorb}\) is the heat absorption from the topping cycle for the purpose of heating. The definitions of \(Q_{h,users}\) and \(Q_{h,absorb}\) can be found in Equations (23)-(26).  
The exergy efficiency \(\eta_{ex}\) can be defined by Equations (42)-(44) [32; 47],  
\[\eta_{ex}=\frac{W_{net}+E_{h,users}}{E_{core}}. \tag{42}\]  
\(E_{h,users}\) is the exergy that heat users obtain from the DHE and AHP. For the sCO\({}_{2}\)/DH system,  
\[E_{h,users}=E_{\rm H6}-E_{\rm H4} \tag{43}\]  
while for the sCO\({}_{2}\)/AHP system,  
\[E_{h,users}=E_{\rm H6}-E_{\rm H4}-E_{\rm H1}. \tag{44}\]  
The total product unit cost \(c_{total}\) presents the system performance in terms of economics, which is determined by Equation (45) [32],  
\[c_{total}=\frac{\sum_{i=1}^{n_{k}}\dot{Z}_{k}+\sum_{i=1}^{n_{f}}c_{fi}E_{fi}}{ \sum_{i=1}^{n_{p}}E_{pi}} \tag{45}\]  
where \(E_{fi}\) and \(E_{pi}\) are the exergy flow rate of the fuel and the product, respectively. \(n_{k}\), \(n_{f,}\) and \(n_{p}\) represent the number of components, fuel components, and product components.
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
## 1 Introduction
In addition, we extend the analysis results to the encoded case. The rest of this paper is structured as follows: Section 2 introduces the model of multi-user MIMO system with MMSE equalizer. Section 3 derives the corresponding SINR distribution expression. The derivation of the PEP and the upper-bounds of the BER for the convolutionally coded massive multiuser MMSE-MIMO systems are introduced in Section 4. Numerical simulations were performed in Section 5 to verify our analytical results. Finally, Section 6 summarizes the paper. Notations: The bold lower and upper case letters denote column vectors and matrices, respectively, and \([\cdot]^{T},[\cdot]^{H}\) denotes transpose and conjugate transpose, respectively. The short-hand form \(\mathcal{CN}(\mu,\Sigma)\) denotes the probability density function (PDF) of a complex Gaussian random variable (RV) with mean \(\mu\) and variance \(\Sigma\); \(\mathbf{I}\) is the identity matrix. The operation \(\mathbb{E}_{x}[f(x)]\) represents the mean of the function \(f(x)\) with respect to the RV \(x\).
# Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
## 6 Review of Industrial Applications
### Immersive Mobile Calls  
Since in 2024 almost 60% of mobile network clients use 5G networks, and by 2029 this is set to hit 84% [61], the new IVAS (Immersive Voice and Audio Services) technology presented by the 3 rd Generation Partnership Project (3 GPP) is yet to be widespread [1; 62]. Still, the development and implementation of the IVAS codec marks a significant advancement in audio communication technology. Introduced by 3 GPP, it represents a leap forward in delivering immersive audio experiences across various communication platforms. IVAS is designed to support a wide range of audio formats, including mono, stereo, binaural, ambisonics, and multichannel configurations, making it highly versatile for different use cases.  
One of the critical features of the IVAS codec is its ability to handle spatial audio metadata. Furthermore, the codec includes functionalities such as Discontinuous Transmission (DTX), which improves efficiency by detecting active voice signals and minimizing data transmission during silent periods. This is coupled with comfort noise generation to maintain a consistent audio quality. The integration of advanced rendering algorithms allows for binaural and loudspeaker outputs, enhancing the user experience in applications ranging from telephony to extended reality (XR). IVAS also provides rendering functionality for multi-loudspeaker playback as well as binaural rendering for headphone reproduction including head-tracking, scene rotation, reverberation, and support of external customized HRTFs. Recreation of the surrounding acoustic scene and combining it with HRTFs is what makes it more interesting and captivating in the aspect of typical call proceedings. In the age of remote work, being able to locate each speaking person with the use of such a renderer could prove to be extremely valuable.  
[PERSON], [PERSON] et al. [63] showed the development of an innovative multiparty conferencing system with 3D audio for mobile devices. The audio system created relies on binaural sound and the use of HRTFs. While it allows for user-specific room customization, incorporating BRIRs, which capture a room's unique impulse responses, can significantly enhance the realism of conference calls by accurately simulating the acoustic environment. Analyzing the differences between traditional and social communication, [PERSON], [PERSON] and [PERSON] [53] created immersive virtual reality social application (IVRSA). This permits its user a virtual scene building experience as well as voice acquisition and transmitting in real time. As mentioned before, with the created RIR database, instead of creating a scene themselves, the users can pick one that suits their requirements and by combining it with their personal HRIRs obtain even better acoustic experience results.
## 2 Mathematical Formulation
### Constraints Regarding PSM  
The value of \(PSM_{rmax}\) must be taken into account when computing the \(TMS\) for each relay. This represents the highest current level of the normally inverse curve programmed in the relay before reaching the definite time region. In this case, we consider a \(PSM_{rmax}\) variable between values \(\alpha\) and \(\beta\) as proposed in [21]. Equations (8) and (9) indicate the limits of \(PSM_{rf}\) and \(PSM_{rmax}\), respectively.  
\[PSM_{rmin}\leq PSM_{rf}\leq PSM_{rmax} \tag{8}\]  
\[\alpha\leq PSM_{rmax}\leq\beta \tag{9}\]
# A Real-Time Sound Field Rendering Processor
## 2 HO-FDTD Algorithm
### General Grids  
Within an enclosure, the dynamics of an acoustic field is governed by the following two basic equations [23].  
\[\
abla P+\rho\frac{\partial u}{\partial t}=0 \tag{1}\]  
\[\frac{\partial P}{\partial t}+\rho c^{2}\
abla\bullet u=0 \tag{2}\]  
Here, \(P\) and \(u\) are the pressure and particle velocity, respectively; both are functions of time and a spatial coordinate. The physical constants \(\rho\) and \(c\) are the air density and wave speed in air, respectively, \(\
abla\) and \(\
abla\bullet\) are the three-dimensional gradient and divergence operations. The differential wave equation (Equation (3)) may be derived by inserting Equation (2) into Equation (1) and eliminating the particle velocity.  
\[\frac{\partial^{2}P}{\partial t^{2}}+c^{2}\
abla^{2}P=0 \tag{3}\]  
where \(\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y ^{2}}+\frac{\partial^{2}}{\partial z^{2}}\) is the Laplacian operator in 3D sound spaces. Then, the wave equation in a 3D sound space can be described by the time domain formulation in Equation (4)  
\[\frac{\partial^{2}P}{\partial t^{2}}=c^{2}(\frac{\partial^{2}P}{\partial x^{2 }}+\frac{\partial^{2}P}{\partial y^{2}}+\frac{\partial^{2}P}{\partial z^{2}}) \tag{4}\]By applying the center differential method in Equation (4), and letting \(\Delta x=\Delta y=\Delta z=\Delta l\), the discretion of Equation (4) yields Equation (5).  
\[\begin{array}{l}P^{n+1}(i,j,k)=\chi^{2}[P^{n}(i+1,j,k)+P^{n}(i-1,j,k)+P^{n}(i, j+1,k)+P^{n}(i,j-1,k)\\ +P^{n}(i,j,k+1)+P^{n}(i,j,k-1)]+(2-6\chi^{2})P^{n}(i,j,k)-P^{n-1}(i,j,k)\end{array} \tag{5}\]  
where \(\chi=c\Delta t/\Delta l\) represents the Courant number, and n is a discretized time step. In general, \(\chi\leq 1/\sqrt{3}\) for a three-dimensional sound space. Equation (5) indicates that three multiplications, six additions, and one subtraction are needed to calculate sound pressure of a grid. When it is implemented by hardware, at least two multipliers, six adders, and one subtractor are required. In order to reduce the multiplication operations, which need more clock cycles and hardware resources, \(\chi\) is assumed to be \(1/2\), and Equation (5) is rewritten as [18]  
\[\begin{array}{l}P^{n+1}(i,j,k)=\frac{1}{4}[P^{n}(i+1,j,k)+P^{n}(i-1,j,k)+P^{ n}(i,j+1,k)+P^{n}(i,j-1,k)\\ +P^{n}(i,j,k+1)+P^{n}(i,j,k-1)+2P^{n}(i,j,k)]-P^{n-1}(i,j,k)\end{array} \tag{6}\]  
In Equation (6), multipliers are replaced by right and left shifters in hardware to save hardware resources and improve system timing performance. In principle, Equation (6) is a seven-point stencil wave equation in which to calculate sound pressure of a grid needs the sound pressures of its six neighbors at previous time step.
## 3 Results and Discussion  
### Model's Adequacy  
The adequacy of the model was determined by the square coefficient of the fitting model (R\({}^{2}\)) and the lack of fit values. The R\({}^{2}\) value was higher than 0.9 for the responses of \(\left|\,\eta^{*}\right|\), color difference coefficient, hardness, and volatile compound distance. It was also very high for the porosity, total phenolic content, \(\beta\)-glucan content, vitamin C content, total flavonoid content, and volume. It showed lower levels of efficacy, total antioxidant capacity, and overall acceptance. For most responses, the model is recognized as fit since the lack of fit values were more than 0.05, with the only exceptions being \(\left|\,\eta^{*}\right|\) and color difference coefficient. Taking into account these factors, analyzed factors influenced the responses, and the model is considered adequate.
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 3 Analytical Solution of the Time-Harmonic Electromagnetic Field around One Current-Carrying Coil
### [PERSON]'s Equations in the Complex Domain  
[PERSON]'s equations in the complex domain can be written as follows:  
\[\
abla\times\mathbf{H}=\mathbf{J}+\mathbf{J}_{S}+j\omega\mathbf{D}, \tag{6}\]  
\[\
abla\times\mathbf{E}=-j\omega\mathbf{B}, \tag{7}\]  
\[\
abla\cdot\mathbf{B}=0, \tag{8}\]  
\[\
abla\cdot\mathbf{D}=\rho, \tag{9}\]  
where \(\mathbf{H}\), \(\mathbf{B}\), \(\mathbf{E}\), \(\mathbf{D}\), \(\mathbf{J}\), \(\mathbf{S}\), \(\rho\) and are the magnetic field strength, magnetic flux density, electric field, electric displacement field, polarization current density, free current density in space and free electric charge density, respectively.  
The following relationships exist in an isotropic linear medium:  
\[\mathbf{D}=\epsilon\mathbf{E}, \tag{10}\]  
\[\mathbf{B}=\mu\mathbf{H}, \tag{11}\]  
\[\mathbf{J}=\sigma\mathbf{E}, \tag{12}\]  
where \(\epsilon\), \(\mu\) and \(\sigma\) are the permittivity, permeability and conductivity of the medium, respectively. By comparison of Equations (6) and (7), it can be found that the forms of [PERSON]'s equations in the complex domain are simpler.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 3 Results
### Inverse Adding-Doubling (IAD) Solution: Arterial Occlusion Test  
Results obtained with a GPU-accelerated LM algorithm are shown alongside measured data in Figure 6. Here, reflectance spectra of skin before, during, and after a cuff test are presented for a single spatial data point in a curvature-corrected hyperspectral image [25]. Upon examining the calculated spectra, we observed excellent agreement with the measured data across the entire spectral range of 430-750 nm. However, minor deviations can be spotted near 700 nm due to slight wiggles in the measured spectra. To assess the goodness of fit, we calculated the reduced \(\chi^{2}\) value for each spatial data point; the computed data yielded reduced \(\chi^{2}\) ranging from 10\({}^{-5}\)-10\({}^{-4}\). In terms of computational efficiency, it took approximately 5 min to process a 220 \(\times\) 100 \(\times\) 61 image (\(x\)-axis \(\times\)\(y\)-axis \(\times\) spectral-axis).  
Figure 7 shows colormaps extracted from the hyperspectral images by the inverse AD algorithm, depicting tissue oxygenation, calculated as the ratio of \(f_{HD02}\) to the sum of \(f_{Hb}\) and \(f_{Hb02}\) multiplied by 100%, and the total hemoglobin concentration, calculated as a sum of \(f_{Hb}\) and \(f_{Hb02}\) multiplied by 100%. These colormaps capture the conditions before, during, and after a cuff test. Upon analyzing Figure 7a, we observe that tissue oxygenation is visibly reduced during the cuff test, coinciding with the obstruction of blood flow when a blood-pressure cuff is inflated to around 200 mmHg. Additionally, we can discern from Figure 7b a slight increase in the total hemoglobin concentration throughout the entire experiment. Conversely, tissue oxygenation reaches its peak approximately 3 min after deflating the blood-pressure cuff, indicating rapid tissue reoxygenation following the vascular obstruction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mu_{\text{a}}\) & **25** & **50** & **100** & **150** \\ \hline
**0.1** & 10,000 & 10,625 & 18,235 & 40,556 \\
**0.5** & 5333 & 9375 & 15,294 & 18,889 \\
**1.0** & 5333 & 8125 & 11,765 & 14,444 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Computational speedup when comparing the Monte Carlo (MC) method (Table 4) and the ADIL algorithm using the GB SPF for the number of examined fluxes \(m=16\) and N = 3000 (Table 6). \(\mu_{\text{a}}\)—absorption coefficient; \(\mu_{\text{s}}\)—scattering coefficient.  
Figure 6: Measured (dashed lines) and fitted (solid lines with error bars) reflectance spectra of skin before, during, and after performing a cuff test.  
To quantify our observations from the colormaps, we present mean values and standard deviations of tissue oxygenation and the total hemoglobin concentration before, during, and after the cuff test in Table 8. These numerical findings substantiate the conclusions drawn from the visual examination of the colormaps in Figure 5 and also corroborate data reported by [PERSON] et al. (2020).