esa-sceva/satcom-chunk-collection huggingface dataset:
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 3 Simulation Results
### Evaluation Algorithm  
The evaluated scenarios encompassed the water type, total distance, divergence, and vector of propagation of the emitter and plane of the final sensor.  
Initially, the photons were propagated using the algorithm described in Section 2.3, and the final parameters were stored in a data file, as mentioned previously. For each scenario, the received power was obtained after the receiver architecture was simulated. The algorithm is general enough to consider the case where both the transmitter and receiver are misaligned at a certain angle and simultaneously are offset between each other.  
Then, we computed the received power at the sensors in each of the positions of the lenses' scheme. Even though the receiver plane was tilted due to angular misalignment, the lens positions were shifted in the \(x/y\) plane, which made this analysis pertinent.  
Afterward, another simple routine calculated the maximum radius the AUV could move for the receiver to have a minimal amount of relative received power. The minimum accepted ratio of received power to transmitted power was \(1\times 10^{-3}\), which is equivalent to considering an emitter with a typical power of 0 dBm and a minimum received optical  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of photons & \(1\times 10^{6}\) \\ Wavelength (\(\lambda\)) & 520 nm \\ Refractive index of water (\(n_{1}\)) & 1.33 \\ Refractive index of lens (\(n_{2}\)) & 1.46 \\ Emitter (Tx) beam waist radius & 1 mm \\ Emitter (Tx) divergence & from 0\({}^{\circ}\) to 60\({}^{\circ}\) \\ Sensor (Rx) radius & 10 mm \\ Lens thickness & 15.6 mm \\ Lens curvature radius & 52.6 mm \\ Lens diameter & 101.6 mm \\ Lens focal distance & 60 mm \\ Number of lenses (\(N\)) & 1; from 4 to 7 \\ Distance between Tx and Rx (\(z_{f}\)) & from 0.5 m to 1 m \\ Misalignment angle between Tx and Rx & from 0\({}^{\circ}\) to 20\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Water Type** & \(\epsilon(\lambda)\) & \(\omega_{\mathbf{b}}\) \\ \hline Clear waters & 0.15 m\({}^{-1}\) & 0.25 \\ Coastal waters & 0.10 m\({}^{-1}\) & 0.55 \\ Harbor I waters & 1.10 m\({}^{-1}\) & 0.83 \\ Harbor II waters & 2.19 m\({}^{-1}\) & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attenuation coefficient and albedo considered for different water types [10].  
power of \(-30\,\)dBm (suitable to achieve a forward error correction (FEC) limited bit error rate (BER) of \(3.8\times 10^{-3}\) for a data rate of \(100\,\)Mbit/s) [27].  
Figure 9 shows the scheme with seven lenses, where the blue dots mark the various positions in which the set of lenses was displaced for the calculation of the MALO. The lenses' scheme is centered in all the blue dots' positions for calculating the received power.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 6 Further Analysis and Discussion
### Tendency Analysis of DJV Robustness against Errors
#### 6.4.1 Robustness of Balance Pressure \(p_{r}\)  
It follows from Equation (30) that the derivative of the balance pressure to the installation error \(\Delta H_{s}\) is  
\[\frac{dp_{r}}{d\Delta H_{s}}=\frac{12.8\times 10^{13}}{\eta S_{r}\sqrt{\pi}} \cdot\exp(-25,585H)l_{c}^{\prime}b_{p}\int_{0}^{\frac{\pi\mathrm{im}}{2p_{r}}} \exp\left(-t^{2}\right)dt \tag{35}\]  
which is taken as a susceptibility index to evaluate the balance pressure robustness against the installation error, with its tendency displayed in Figure 26.  
Figure 24: Shunt wedge error dependence of balance pressure.  
Figure 25: Drift of average pressure gain caused by shunt wedge width variation.  
Therefore, when the deflector approaches the shunt wedge by 0.1 mm, the balance pressure susceptibility, when compared with that for the standard deflector installation (\(\Delta H_{s}=0\)), increases about 12 times. Conversely, if the deflector is further than the design location, the susceptibility will drop slightly. This result shows that a positive error of the jet distance is acceptable, while negative errors will lead to serious inconsistency of product pressures, namely, a plummet in the balance pressure robustness.  
Consider the derivative of Equation (33) to the shunt wedge width error,  
\[\frac{dp_{r}}{d\Delta l_{m}}=-\frac{p_{m}l_{c}^{\prime}}{S_{r}}\exp\left\{- \left[\frac{\eta(l_{ms}+\Delta l_{m})}{2b_{p}}\right]^{2}\right\} \tag{36}\]  
which represents the balance pressure robustness against the width error of the shunt wedge, as illustrated in Figure 27.  
In comparison to the installation error \(\Delta H_{s}\), the shunt wedge error has a larger but more gradual effect on the balance pressure robustness. Taking both Figures 26 and 27 into consideration, it can be concluded that as long as the deflector is not too close to the shunt wedge, the shunt wedge error will dominate the balance pressure fluctuation.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 2 System Model and Problem Formulation
### Problem Formulation  
In this section, we investigate the multi-objective optimization problem with different design objectives for maximizing the communication throughput and minimizing the CRB for the target location estimation simultaneously. Considering all of the buoys have to be implemented to achieve two individual missions and each object should satisfy the transmit power constraints, as a result, we first formulate the communication sub-problem which maximizes the system throughput under the NOMA protocol given by  
\[(\mathrm{P1}): \max_{\mathbf{l}u,\mathbf{p}}F_{1}=\sum_{k=1}^{K}B\text{log}_{2}\Bigg{(} 1+\frac{p_{k}h_{\text{U}k}}{\sum_{l=k+1}^{K}p_{l}h_{\text{U}li}+\sigma^{2}} \Bigg{)}\] (10a) s.t. \[P_{\text{min}}\leq p_{k}\leq P_{\text{max}},\;\forall k\in\mathcal{K}, \tag{10b}\] \[\frac{p_{k}h_{\text{U}lk}}{\sum_{k=k+1}^{K}p_{l}h_{\text{U}li}} \geq B_{\text{S}ic},\;\forall k\in\mathcal{K},\] (10c) \[R_{k}\geq R_{\text{th}},\;\forall k\in\mathcal{K},\] (10d) \[\min(l_{x})\leq x\leq\max(l_{x}),\] (10e) \[\min(l_{y})\leq y\leq\max(l_{y}),\] (10f) \[||\mathbf{l}_{\mathbf{l}}-\mathbf{l}_{\mathbf{R}}||^{2}\geq D_{\text{min}}^{2}. \tag{10g}\]  
where \(P_{\text{min}}>0\) and \(P_{\text{max}}\) is the minimum and maximum transmit power of each buoy, constraint (10c) guarantees the effective SIC at the UAV, and constraint (10d) defines the minimum uplink data rate requirements. In order to ensure each buoy sustains a relatively stable working condition, the power of the buoy must be a positive number and have a certain upper threshold. Furthermore, the position of the UAV is limited in such a way that the UAV cannot fly beyond the location of the furthest buoy which is given in (10e) and (10f). Equation (10g) constrains that the minimum distance \(D_{\text{min}}\) between the UAV location and the target location.  
As for the radar-sensing performance, the optimization sub-problem can be formulated as  
\[(\mathrm{P2}): \min_{\mathbf{p}}F_{2}=\frac{(g_{\mathbf{a}}+g_{\mathbf{b}})^{T}\mathbf{p}}{\mathbf{ p}^{T}\mathbf{G}\mathbf{p}} \tag{11a}\] \[\text{s.t. }P_{\text{min}}\leq p_{k}\leq P_{\text{max}},\;\forall k \in\mathcal{K}, \tag{11b}\]where (P2) has the same power constraints as in problem (10).  
By comparing the sub-problem (P1) and (P2), a non-trivial trade-off design for balancing the communication and radar sensing performance arises in our considered UAV-assisted ocean monitoring system. Both the sub-problems have the same power constraints, while their optimization objectives are not identical, which refer to maximizing the total transmission rate and minimizing the CRB of target location estimation, respectively. Moreover, the variables are tackled in objectives as well as constraints, which makes the problems non-convex and may yield prohibitive complexity. Toward this end, we propose an efficient algorithm to deal with the two conflict objective problem which will be detailed in the next section.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 4 Results
### Comparison of the Water Indices Enhancement Performance
In the SWIR band (Band 11), the reflectance of floating ice is slightly different from that of general water and so the NDWI value of floating ice is smaller than that of general water, with a mean value of 0.23 (Figure 3). The SWI value of floating ice is larger than that of general water and the mean value is 0.88 (Figure 3). Therefore, the SWI can achieve better performance than the NDWI in terms of extracting floating ice.
## 2 Materials and Methods
### Data Collection Process  
The systematic literature review consisted of a comprehensive data collection process from 10 repositories, which was conducted by two reviewers. To restrict the search of the publications obtained from various repositories, a set of predetermined keywords was used as detailed in Section 2.3 and Table 2. The studied and investigated data were extracted from the literature published from 2014 to 2024, which satisfied the inclusion criteria defined in Table 1. In addition, a set of data quality assessment questions in support of the PRISMA flow diagram in Section 3.1, A proposed PRISMA flow diagram and an adapted Critical Appraisal Skills Programme (CASP) systematic review checklist was used to assess the validity of the results obtained from studies, ascertain the study results, and evaluate their impact and applicability.  
* Q1. Is the research related to both machine learning and the classification of transformer faults based on DGA data?
* Q2. Does the research stipulate the analysis methodology used in the study?
* Q3. Does the research specify the performance accuracy of the methods used for DGA interpretation?
* Q4. Does the research stipulate DGA data sources and data size?
* Q5. Does the research consider DGA applications for transformers only and not tap changers and bushings?  
The extracted data were then organized in an Excel spreadsheet in preparation for further analysis of trends and patterns that could be identified from the data to be presented in the form of graphs, charts, and tables for interpretation.
## 3 Materials and Methods
### Materials
#### 3.1.5 Data Augmentation  
Data augmentation is employed to artificially expand the dataset size and introduce variability, enhancing the model's generalization ability. For DCAT, the following augmentation techniques were applied:  
1. **Image Augmentation:** * _Random Cropping:_ Random cropping of images was performed to create variations in the viewpoint. * _Horizontal Flipping:_ Images were randomly flipped horizontally to simulate different orientations. * _Color Jittering:_ Minor adjustments in brightness, contrast, and saturation were applied to simulate varying lighting conditions.
2. **Textual Augmentation:** * _Synonym Replacement:_ Random replacement of words with their synonyms from a curated Tamil synonym dictionary. * _Back-Translation:_ Some captions were translated to a pivot language (e.g., English) and then back to Tamil to introduce linguistic variations. * _Random Deletion:_ Random deletion of words in captions to mimic incomplete or noisy data scenarios.  
Both image and textual augmentations were applied in a controlled manner to avoid introducing misleading or incorrect information. The balance between the original and augmented data was carefully maintained to ensure that the model learns from a diverse but accurate representation of the datasets.
## 2 System Model
### Estimation of the Position  
In this work, the trilateration method based on the received power is applied to determine the position of the receiver. Basically, the trilateration method determines the position as the intersection of spheres or circles given by the distance between a given transmitter and receiver. The location of the optical transmitter \(l\) is defined by its Cartesian coordinates,  
\[L_{\text{pos}}=\begin{bmatrix}x_{\text{LED}_{l}}&y_{\text{LED}_{l}}&z_{\text{ LED}_{l}}\end{bmatrix}\;. \tag{6}\]  
Similarly, the position of the receiver is defined by the Cartesian coordinates  
\[P_{\text{pos}}=\begin{bmatrix}x_{p}&y_{p}&z_{p}\end{bmatrix}\;. \tag{7}\]  
Then, considering a single transmitter, the position of the receiver corresponds to any position in the circumference or sphere defined by the following equation:  
\[\begin{pmatrix}x_{p}-x_{\text{LED}_{l}}\end{pmatrix}^{2}+\begin{pmatrix}y_{p}- y_{\text{LED}_{l}}\end{pmatrix}^{2}+\begin{pmatrix}z_{p}-z_{\text{LED}_{l}} \end{pmatrix}^{2}=d_{p,l}^{2}\;, \tag{8}\]  
where \(d_{p,l}^{2}\) is the estimated distance between the receiver and the \(l\) transmitter. The architecture of the positioning system is described in Figure 1.  
Determining the receiver's position requires at least three equations, such as (8). After that, the estimated position is given by the intersections of these circumferences or  
Figure 1: Geometry of the positioning system.  
spheres composing a non-linear equations system. In this work, the trilateration equation systems are solved using the Matlab solve function, which employs iterative algorithms to calculate the receiver's position from a set of equations. At this point, note that the proposed set-up (described in the next section) includes four optical transmitters and an angular diversity receiver equipped with eight photodiodes distributed around the azimuthal angle, along with an additional photodiode oriented perpendicular to the ceiling. Then, the set of photodiodes can be employed as a reference signal to improve the accuracy of the estimated distance. According to the number of transmitters detected, three scenarios can be defined as detailed in Table 2.
## 5 Limitations of the Study  
Multiple sources of uncertainty have been considered when assessing the influence of LULC changes on soil erosion and sediment yield from 1980 to 2020. However, a key limitation of this study is centered on the precision of the land use classification. The overall classification accuracy varies within the range from 80% to 92% over the period from 1980 to 2020. Notably, a Kappa coefficient exceeding 0.70 was achieved for all three classified images. Despite meeting the benchmark for classification performance, some level of uncertainty remains intertwined with this achievement.  
It is essential to recognize that although the RUSLE model is widely acknowledged and valuable for predicting soil erosion, it does have certain limitations. These include the need for accurate input data, potential uncertainties associated with model parameters, and its focus on sheet and rill erosion rather than gully erosion. Therefore, users should be aware of these limitations and consider them in their decision-making processes. Moreover, the determination of the K factor, despite being extensively developed based on data from the physicochemical properties of soil, was influenced in the study area due to the limited data. Instead, the erodibility factor was established by employing the soil map and utilizing pedo-transfer functions.
# A Multi-Coupling 3D Frequency-Selective Surface with High Selectivity and Wide Dual Passband
The advantages in terms of bandwidth and frequency selectivity compared with other dual-band FSS designs are emphasized, and the applicability and high performance of this design in practical dual-band communication applications are explained.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 2 Antenna Design
### Quad-Port Ring-Shaped MIMO Antenna  
[PERSON]'s theorem states that channel capacity experiences a notable increase with an increase in components [39].  
Based on this study, the dual-port antenna was further modified to a quad-element antenna given in Figure 9. All the parameters of the radiating elements were the same as the antenna in Figure 3. The four-element antenna's dimensions were 35 \(\times\) 44 \(\times\) 0.8 mm\({}^{3}\), with four half-circle slots in the antenna's ground for bandwidth and isolation enhancement. The four-port antenna was divided into two blocks of dual-port antennas with a neutralization line for a better isolation enhancement of 30 dB.  
The reflection coefficient of the quad-port antenna was \(<\)\(-\)10 dB in the 28 to 46 GHz frequency range, with an isolation enhancement of \(<\)30 dB. Figure 10 reveals the S-parameter results of the quad-element MIMO antenna.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Size in mm** & **Parameters** & **Size in mm** \\ \hline W & 35 & A & 3 \\ L & 22 & S & 7 \\ T & 6 & R & 8.6 \\ V & 4.5 & M & 9 \\ D & 0.5 & N & 1.48 \\ P & 8 & U & 6 \\ Q & 6 & K & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dual-port antenna’s parameters.  
Figure 9: Quad-port MIMO antenna design. (**a**) Top and (**b**) bottom view.  
Figure 10 demonstrates that the quad-element MIMO antenna achieves an 18 GHz vast bandwidth, varying from 28 to 46 GHz, with an enhanced isolation of 30 dB. Figure 11 plots gain and radiation efficiency to further confirm the antenna's capabilities, securing a gain from 6.8 to 8.5 dBi with 91-97% radiation efficiency.  
Figure 12 presents the diversity performance of the quad-element antenna for further examination of its accuracy. The diversity performance of the MIMO antenna is calculated using ECC and diversity gain [40]. It is necessary for MIMO antennas to have ECC values below 0.5, and this is evaluated in Equation (2).  
\[ECC=\frac{\left|\iint_{4\pi}\left[\stackrel{{\rightarrow}}{{F}} _{1}(\theta,\varphi)\bullet\stackrel{{\rightarrow}}{{F}}_{2}( \theta,\varphi)\right]d\Omega|^{2}}{\iint_{4\pi}\left|\stackrel{{ \rightarrow}}{{F}}_{1}(\theta,\varphi)\right|^{2}\!d\Omega\iint_{4\pi} \left|\stackrel{{\rightarrow}}{{F}}_{2}(\theta,\varphi)\right|^{2 }\!d\Omega} \tag{2}\]  
\(\stackrel{{\rightarrow}}{{F}}_{1}(\theta,\varphi)\) is the field radiation pattern of port 1, \(\stackrel{{\rightarrow}}{{F}}_{2}(\theta,\varphi)\) is the field radiation pattern of port 2, and \(\bullet\) is the Hermitian product.  
\[DG=10\sqrt{1-(ECC)^{2}} \tag{3}\]  
Figure 11: Peak gain and radiation efficiency plot.  
Figure 10: S-parameter results for quad-port MIMO antenna.  
The quad-element MIMO antenna's diversity performance, considering ECC and DG, is examined in Figure 12, and Figure 12 confirms that the antenna secures an excellent diversity performance with ECC \(\leq\) 0.0001 and diversity gain \(\geq\) 9.999 dB.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 6 Results
### Single Speaker Model Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measure** & **Model** & **S1** & **S16** & **S25** & **S27** & **AVG** & **IQR** \\ \hline Corr2D & GaborFea2 Speech & 60.90\% & 70.10\% & 65.13\% & 60.54\% & 64.17\% & 24.81\% \\  & GaborCNN2 Speech & 63.60\% & **70.80\%** & **66.03\%** & **64.70\%** & **66.28\%** & **14.18\%** \\  & Lip2 AudSpec & **64.40\%** & 69.10\% & 56.33\% & 54.90\% & 61.18\% & 17.25\% \\ \hline PESQ & GaborFea2 Speech & 1.488 & **1.882** & 1.617 & **1.889** & **1.719** & 0.624 \\  & GaborCNN2 Speech & 1.399 & 1.816 & **1.735** & 1.805 & 1.689 & 0.550 \\  & Lip2 AudSpec & **1.646** & 1.735 & 1.378 & 1.746 & 1.626 & **0.536** \\ \hline OOSQ & GaborFea2 Speech & \(-\)0.334 & **0.667** & 0.402 & **0.391** & 0.282 & 0.848 \\  & GaborCNN2 Speech & \(-\)0.366 & 0.659 & **0.566** & 0.338 & **0.299** & 0.836 \\  & Lip2 AudSpec & \(-\)**0.145** & 0.430 & \(-\)0.039 & 0.037 & 0.071 & **0.522** \\ \hline STOI & GaborFea2 Speech & **0.149** & **0.332** & **0.335** & 0.226 & **0.261** & 0.208 \\  & GaborCNN2 Speech & 0.147 & 0.313 & 0.315 & **0.249** & 0.256 & 0.170 \\  & Lip2 AudSpec & 0.118 & 0.327 & 0.295 & 0.204 & 0.236 & **0.159** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative evaluation of GaborFea2 Speech and GaborCNN2 Speech Networks against Lip2 AudSpec for individual speaker speech reconstruction.
## 1 Introduction
### Motivation  
This work aims to find relationships between parameters impacted by the sensor system and predictive trends based on the threshold of measurements perceived by the sensors during the day. This proposed system consists of a low-cost WSN deployed at the home of seniors who want to remain independent. In the context of the provided passage, an \"independent person\" refers to an elderly individual who wishes to live independently and maintain their autonomy without relying heavily on constant assistance or care from others. This could be an elderly person who prefers to live in their own home rather than moving to an assisted living facility or nursing home.  
This system monitors the main areas of the house programmed with a low-energy cost algorithm and sends recommendations to the person at the beginning of each day. It is a simple algorithm based on the threshold of measurements perceived by the sensors during the day. In this way, the nodes change hierarchies in the network and has proactive or reactive priorities for sending information. The routing protocol takes advantage of each node's proactive or reactive nature to transmit the packets. The change that is optimized in the proposed algorithm is that when the node's measurement levels are kept within a normal range, the protocol responds to sending packets reactively. Meanwhile, if the measurement levels are outside the threshold, the protocol responds proactively to sending packets to better control the information review.  
The main contribution of this work is the novel categorization of the impact of metrics related to the person's vital signs based on the way the person sleeps. By analyzing the sleep metrics of older adults before and after using the proposed sensor network, we will be able to determine the level of impact (high, medium, or low) that the system will have on improving rest once we know the person's metrics prior to installing the sensor network. Therefore, we discuss the usability of our proposed system from the perspective of a non-invasive technological product and its use as a medical tool to improve people's rest.  
We found that recommendations based on measures from the sensors improved sleep quality. In addition, the proposed energy-saving algorithm assists in optimizing the network's performance and directing its efforts towards areas that can have the greatest impact on improving sleep quality for individuals.  
The remainder of this paper is organized into five sections. Below is the Related Work Section, in which the comparative relationship of the state of the art is explained. The Materials and Methods section describes the presentation of the sensor network, the routing algorithms, and the correlation metrics. The Results section explains the impact and relationship of the parameters measured in people and their relationship with vital signs to improve sleep quality. The results are also discussed here. Finally, in the Conclusions section, the improvement of the proposed system and possible areas of opportunity it still presents are given.