esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving CNN Fish Detection and Classification with Tracking
## 2 Material
### Tracking Algorithm  
The tracking algorithm goal was to keep each object tracked when the detector failed to detect it. Our tracking module took the latest detected bounding box of a fish and predicted the next possible position of the bounding box. The module was able to predict  
Figure 3: Faster R-CNN object detection on a fish image.  
Figure 2: Tracking same fish on three consecutive frames.  
bounding box positions for a set number of frames. This time window was called _timeout frames_. If the detector was able to detect the fish individual during timeout frames, then CNN detection took over the tracking module (Figure 1). If the detector was not able to detect the individual before the end of the _timeout frames_, the individual was no longer considered on screen. This heuristic was very important because it prevented the creation of false negatives bounding boxes in the event of a tracking module failure. When the tracking module was triggered, it maintained the last species attributed to the individual until the detection was resumed again (see pseudo-code in Appendix A).  
Detection confidence threshold: A value that we use in order to decide whether we are confident to accept the detected bounding box or throw it away. If we obtain a detection rate that is higher than or equals this threshold, we accept the detection and we take its bounding box and class. If we obtain a detection rate that is lower than this threshold, we assume the detection is not acceptable.  
Timeout frames: The value of the timeout frames is chosen based on how many frames the detection model is approximately unable to detect the objects consecutively. The lower the timeout frames, the better, as it may result in more false positives that can decrease the performance of the tracking algorithm. A good value choice may lead to a significant decrease in false negatives at the cost of a slight increase in false positives.  
We used the OpenCV legacy tracker [30] algorithm for our tracking module as it provided a more accurate tracking compared to other statistical methods such as Mean Shift [31]. This algorithm took a bounding box region and tried to predict the next possible position for that bounding box based on its color distribution. It also remembered the color distribution for every frame so the object was still trackable.  
Given that the tracking system is activated only upon the initial detection's availability, it is possible that in some cases, there may be fish present in the initial frames that go undetected by the detector. Consequently, we may experience a loss of information right at the beginning of the video. To address this issue, we implemented a tracking process on the same videos but in reverse order, moving from the end of the video to the beginning. This approach enables the tracking module to follow and recover fish data that might have been missed during the normal forward tracking process (from the start to the end of the video).
## 3 Materials and Fabrication Techniques
### Conducting Polymers
#### 3.1.2 Polyaniline  
Polyaniline (PANI) (Figure 3d), a conducting polymer obtained through the oxidative polymerization of aniline, has been extensively studied in the framework of OECTs. As for polypyrrole, the electronic conductivity of PANI can be modulated through the modification of its oxidation state: the low conductivity of the reduced state (leucoemeraldine state, Figure 2d with n = 1 and m = 0) sharply increases upon oxidation (emeraldine state, n = m = 0.5), up to a certain point (pernigraniline state, n = 0, m = 1)) after which the conductivity decreases. As in the case of PPy, the conducting  
Figure 3: Commonly used conducting polymers: (**a**) Polypyrrole; (**b**) Polythiophene; (**c**) Poly(3,4-ethylenedioxythiophene); and (**d**) Polyaniline.  
properties of PANI can also be modulated through pH changes, reinforcing its attractiveness as a candidate for OECT-based biosensors. As with PPy, PANI can be readily synthesized through chemical or electrochemical oxidation of its monomer, aniline.  
As an early example, similar to the one published with PPy, [PERSON] et al. functionalized an array of Au-microelectrodes with thin films of PANI, and were able to turn their transistor-like device \"ON\" or \"OFF\" through modifications of the pH of the solution, or through the addition of redox reagents (Fe(CN)\({}_{6}\)\({}^{3-/4-}\) or Ru(NH\({}_{3}\))\({}_{6}\)\({}^{3+/2+}\)) in the solution [25]. The authors noted that PANI films deteriorated rapidly and irreversibly lost their conducting properties when held at potentials higher than 0.7 V vs. SCE, or when operated in neutral or alkaline solutions (pH > 6). This lack of stability upon moderate oxidation of mild pH is detrimental when one seeks to design an efficient OECT-based biosensor; for this reason, more stable materials were developed.
# Innovative Approaches to Semi-Transparent Perovskite Solar Cells
## 5 Device Structure for ST-PSCs
Light scattering caused by undesirable optical scattering from the microstructure of ST-PSC can reduce visual transparency and overall device performance. Therefore, light scattering and human luminosity factors should be considered to improve ST-PSCs. It has been reported that nanostructures can improve transparency by reducing surface roughness and can also stabilize the photo-active cubic phase of perovskite [123; 124; 125; 126]. To this end, [PERSON] et al. used anodized aluminum oxide (AAO) as a scaffold layer for vertically aligned 1D-nanostructure-based ST-PSCs. They controlled the transparency of the perovskite layer via AAO pore size and height. The 1D-nanostructure ST-PSC showed a PCE of 9.6% and a 33.4% AVT, and the self-packing perovskite via the AAO layer improved stability under continuous illumination and reduced hysteresis (Figure 6a) [127]. As another approach to enhance the visual transparency of ST-PSC, [PERSON] et al. considered the human luminosity factor, representing the average spectral discernment for the human visual perception of light. They designed the ST-PSC to absorb photons in blue and red wavelengths, which are less sensitive to human eyes. Photon absorption increases in red regions via plasmon interactions between the TE and Ag nanotube. Consequently, they demonstrated an ST-PSC with a 9.73% PCE and 17.8% AVT via a combination of a perovskite layer that strongly absorbs blue wavelengths and a transparent electrode that improves the absorption of red wavelengths (Figure 6b,c) [128].
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 3 Proposed Method  
### General Concept  
As shown in Figure 2, we assumed that the initial values of the state estimation and covariance matrix are known. The initial values of the model probability and the transition matrix are given according to empirical knowledge, and the mixed probability, mixed state estimation, and mixed covariance matrix are calculated to complete the interactive process, and then model matching is conducted. The proposed algorithm uses an extended Kalman filter in the LOS model and proposes an improved modified probabilistic data association filter in the NLOS model. The two filters work in parallel to achieve the model-matching process. In the NLOS model, the improved modified probabilistic data association filter first divides the measured values into \(N\) different groups. Each group gets a corresponding position estimation via a least-squares estimation. The \(N\) different position estimates are obtained for NLOS recognition through a validation gate, where the position estimates falling in the validation gate around the predicted position estimates are the correct position estimates. Furthermore, the position estimates that do not fall in the validation gate are discarded. The correlation probability corresponding to the correct position estimation is calculated, the correct position estimation with the corresponding correlation probability is weighted, and the updated position estimation is produced. Through the NLOS recognition, the position estimates with a large error are discarded to reduce the NLOS errors. When the NLOS interference is relatively serious, there will be no correct position estimation, which will have no significant impact on the positioning results. If there is no correct position estimation after successive attempts, a positioning failure will occur. Therefore, the NLOS tracer method is proposed to record the situation of incorrect position estimation. When no correct position estimation occurs the first time, the predicted state is regarded as the updated state. If no correct position estimation occurs twice or more times, the EKF will be used to update the position. The model probability is updated according to the result of model matching, and the updated model probability is used to weight the updated state estimation of the corresponding model to obtain the final state estimation.
## 3 Materials and Methods
### Volumetric Change Detection  
Ground volume changes were assessed by analysing the available MP data using the 2.5D volume computation plug-in in CloudCompare open-source software v2.12 beta (CC) [39; 40]. The plug-in allows the creation of a grid composed of square bins generated over the selected study area, with a dimension of 125 \(\times\) 125 m chosen with respect to the distance between the MP (100 m). With this approach, all the areas without MP were regarded as empty cells and were therefore not useful for computing variations in volume. The average value of the vertical displacements (L3) relative to all MPs falling in a bin was assigned to the bin itself. This procedure was repeated twice, once taking into consideration the minimum vertical MP displacement based on the natural gas withdrawal (the maximum negative displacement value with respect to the average surface elevation) and the second taking the highest MP vertical displacement during the injection period (the maximum positive displacement value). The two obtained grids, each with different elevation values (max z\({}_{1}\) and min z\({}_{2}\)) but the same coordinates (x, y), were used to calculate the variations  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Observation Geometry** & **Layer Name** & **Dataset** & **Scene** & **Time Interval** \\ \hline Ascending & Calibrated—level 2B & (A15-001) & 302 & 2 February 2015–27 December 2021 \\ \hline Ascending & Calibrated level 2B & (A16-103) & 245 & 5 March 2015–22 December 2021 \\ \hline Descending & Calibrated—level 2B & (D16-081) & 297 & 8 February 2015–21 December 2021 \\ \hline Vertical component & Ortho—level 3 & (vertical) & 304 & 5 January 2016–16 December 2021 \\ \hline Horizontal component & Ortho—level 3 & (east/west) & 304 & 5 January 2016–16 December 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main characteristics of the Sentinel-1 datasets used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ancillary Data** & **Purpose** & **Source** \\ \hline Injection/withdrawal data (daily) & Useful for displaying the curve of gas in storage within the reservoir & [18] \\ \hline Volume of gas in storage & Comparison with InSAR data & [18] \\ \hline Atmospheric temperature & Comparison with InSAR and UGS data & [23; 24] \\ \hline Precipitation & Comparison with InSAR and UGS data & [23; 24] \\ \hline Digital Elevation Model (Spain) & 3D visualization & [37] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ancillary data and descriptions of their purposes.  
in volumes between the two periods. The calculated volume (V) of the selected area is the sum of the volumes of each elementary parallelepiped (dV):  
\[\mathbf{V}=\sum_{i=1}^{n}\mathbf{dV}_{i}=\sum_{i=1}^{n}\mathbf{gs}^{2}\cdot\Delta z_{i}\]  
where \(i\) is the number of non-empty MP elementary parallelepiped, \(\mathbf{gs}\) is the grid step, and \(\Delta z\) is the difference between the minimum and maximum heights.
# Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
## 3 Parametric Study  
The performance of the proposed design is analyzed through a number of parametric studies. The parametric study is performed for three design parameters, such as \"D\",  
Figure 3: Surface current distribution at (**a**) 3.5 GHz; (**b**) 5.4 GHz.  
Figure 2: Design evolution of the proposed single antenna element.  
\"H\", and \"Z\". First, the effect of parameter \"D\" on the antenna's performance is analyzed. The value of \"D\" is changed from 1 to 3 mm, with a 0.5 mm interval. As seen from the figure, the parameter \"D\" has a significant effect on both resonances. As the length of the strip is increased, the resonant frequencies shift towards the lower bands, as shown in Figure 4a. The optimum response is achieved for \"D\" = 2 mm, where the antenna resonates at 3.5 GHz and 5.4 GHz (see Figure 4a). The same kind of effect is observed for parameter \"H\" when its value is changed from 6.5 to 8.5 mm, as shown in Figure 4b. The optimum response is achieved at \"H\" = 7.5 mm. In the last, the value of \"Z\" is changed from 3 to 4 mm, and its effect on the antenna's performance is observed and depicted in Figure 4c. The parameter \"Z\" has more impact on the first resonance compared to the second resonance (see Figure 4c). The second resonance slightly shifts towards the lower frequencies.
## 4 Presentation and Discussion of Findings
### Validation Using Holdout Samples
In Table 9, the predicted classification of the CPM has 70.4% of the distribution for the training. The testing samples have 75% of the CPM, and the holdout validation proved 81.8% of the CPM make the most decision. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Predictor**} & \multicolumn{6}{c}{**Predicted**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Hidden Layer 1**} & \multicolumn{2}{c}{**Hidden Layer 2**} & \multicolumn{2}{c}{**Output Layer**} \\ \cline{3-8}  & **H(1:1)** & **H(1:2)** & **H(2:1)** & **H(2:2)** & **QS** & **CPM** \\ \hline \multirow{8}{*}{**Input Layer**} & (Bias) & 0.046 & 0.322 & & & \\  & CFLOW & \(-\)0.138 & 0.181 & & & \\  & HDATA & \(-\)0.012 & \(-\)0.474 & & & \\  & SIMP & 0.452 & 0.360 & & & \\  & TCACT & \(-\)0.234 & \(-\)0.174 & & & \\  & MONLC & \(-\)0.038 & 0.306 & & & \\  & MONMC & \(-\)0.005 & 0.150 & & & \\  & MONEC & 0.422 & \(-\)0.110 & & & \\  & MONOV & \(-\)0.210 & 0.299 & & & \\  & VARMAN & \(-\)0.261 & 0.086 & & & \\  & COSTR & \(-\)0.135 & 0.274 & & & \\  & INCMILE & 0.017 & 0.398 & & & \\  & COMPUNI & 0.499 & \(-\)0.359 & & & \\  & IDCOV & 0.398 & 0.313 & & & \\  & FORCOMP & 0.148 & \(-\)0.201 & & & \\  & COSTFOR & 0.100 & \(-\)0.286 & & & \\  & PampL & \(-\)0.118 & 0.032 & & & \\  & INTERIMV & 0.117 & 0.364 & & & \\  & WORKBUD & 0.139 & 0.132 & & & \\ \hline \multirow{2}{*}{Hidden Layer 1} & (Bias) & & & 0.018 & 0.033 & \\  & H(1:1) & & & \(-\)0.461 & 0.083 & \\  & H(1:2) & & & \(-\)0.505 & 0.424 & & \\ \hline \multirow{2}{*}{Hidden Layer 2} & (Bias) & & & & 0.575 & 0.222 \\  & H(2:1) & & & & 0.318 & \(-\)0.474 \\  & H(2:2) & & & & 0.324 & 0.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Synaptic weights for normalised two hidden layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Observed**} & \multicolumn{3}{c}{**Predicted**} \\ \cline{3-5}  & & **QS** & **CPM** & **Percent Correct** \\ \hline \multirow{3}{*}{**Training**} & QS & 11 & 26 & 29.7\% \\  & CPM & 8 & 19 & 70.4\% \\  & Overall Percent & 29.7\% & 70.3\% & 46.9\% \\ \hline \multirow{3}{*}{Testing} & QS & 6 & 14 & 30.0\% \\  & CPM & 5 & 15 & 75.0\% \\  & Overall Percent & 27.5\% & 72.5\% & 52.5\% \\ \hline \multirow{3}{*}{Holdout} & QC & 6 & 14 & 30.0\% \\  & CPM & 2 & 9 & 81.8\% \\ \cline{1-1}  & Overall Percent & 25.8\% & 74.2\% & 48.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Validated classification of predicted observed outcome from the multilayer perceptron.
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 3 Proposed Spectral Compensation for the MPEG-H 3 DA BR
### Combination of PGC and BSC  
The downside of BSC is the discrepancy between the rendered signal and the target signal when there exists more than one acoustic object in a single band. In those cases (\(m>1\)), the BSC gain \(\hat{\beta}^{j}(b)\) is likely to be incorrect for every object. Especially, when two objects exist in different critical bands belonging to the same QMF band \(b\), experiments show that artifacts can be audible. Therefore, although the BSC is computationally much simpler than the PGC, it is applied only to QMF bands with a comparable or narrower bandwidth than the critical bandwidth in our implementation.  
It can be noted that, when \(p=2\), the BSC gain in Equation (12) is equivalent to the active downmixing gain in [11]. The purpose of developing the BSC in this paper, however, is to compromise between the increase in computational complexity and the deterioration of the sound quality of MPEG-H 3 DA BR within a unified framework comprising two different compensation strategies.
# Effect of Process Parameters on Distortions Based on the Quantitative Model in the SLM Process
## 2 Materials and Methods  
### Materials  
Ti-6 Al-4V has a high strength-to-weight ratio and good corrosion resistance [24]. In this study, SLM-printed components and substrates use Ti-6 Al-4V. The thermal physical parameters of Ti-6 Al-4V are listed in Table 1, and the mechanical properties are listed in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Property (Unit)** & **Value** \\ \hline Latent heat \(\Delta H\) (J\(\cdot\)kg\({}^{-1}\)) & \(2.86\times 10^{5}\) \\ Density \(\rho\) (kg\(\cdot\)m\({}^{-3}\)) & \(\left\{\begin{array}{cc}4420-0.15\ (T-296)&T<1923\ \text{K}\\ 3920-0.68\ (T-1923)&T\geq 1923\ \text{K}\\ \end{array}\right.\) \\ Specific heat \(c_{p}\) (J\(\cdot\)kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\)) & \(\left\{\begin{array}{cc}483+0.22T\ \ \ \ T\leq 1268\ \text{K}\\ 413+0.18T\ \ \ 1268\ \text{K}<T\leq 1923\ \text{K}\\ 831\ \ \ \ T>1923\ \text{K}\\ \end{array}\right.\) \\ Thermal conductivity \(k_{\lambda}\) (W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\)) & \(\left\{\begin{array}{cc}1.260+0.016T\ \ \ \ T\leq 1268\ \text{K}\\ 3.513+0.013T\ \ \ 1268\ \text{K}<T\leq 1923\ \text{K}\\ -12.752+0.024T\ \ \ T>1923\ \text{K}\\ \end{array}\right.\) \\ Viscosity \(\mu\) (N\(\cdot\)m\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) & \(\exp(-1.6+5346/T)\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal properties of Ti-6 Al-4V (data is obtained from Reference [25]).
## 3 Mathematical Model and Numerical Calculation  
### Governing Equations  
The fluid flow was modelled by using two-dimensional unsteady Reynolds-averaged Navier-Stokes (RANS) equations together with the Spalart-Allmaras (S-A) turbulence model [34]. The incompressible fluid dynamics equations are:  
\[\frac{\partial U_{i}}{\partial x_{i}}\ =\ 0 \tag{1}\]  
\[\rho\frac{\partial U_{i}}{\partial t}+\rho U_{j}\frac{\partial U_{i}}{ \partial x_{j}}\ =\ -\frac{\partial P}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}(2\mu S_{ji}-\rho\overline{u_{j}^{\prime}u_{i}^{\prime}}) \tag{2}\]  
where \(\rho\) and \(P\) are the fluid density and flow field pressure, respectively, and the space and time coordinates are \(x_{i}\) and \(t\), respectively. \(U_{i}\) is the mean flow velocity vector, and \(S_{ij}\) is the mean strain-rate tensor.  
\[S_{ij}\ =\ \frac{1}{2}(\frac{\partial U_{i}}{\partial U_{j}}+\frac{\partial U _{j}}{\partial U_{i}}) \tag{3}\]  
Compared with the Navier-Stokes equation, there is a Reynolds stress tensor \(\tau_{ij}\ =\ -\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\) in the RANS equation, which is modelled through the Boussinesq eddy viscosity approximation as \(2\mu_{t}S_{ij}\), where \(\mu_{t}\) is the turbulence eddy viscosity.  
This paper adopts the S-A turbulent model with a good stability and small amount of computation, which is designed for wall-bound flow in aviation applications, has been shown to give acceptable results for a wide variety of situations, and is known for its stability. The S-A model is different from other one-equation models and algebraic models in the sense that it is a local model, which means the equation in one location does not depend on the solution at other points.
## 3 Methods  
### Random Forest Classification Method  
The random forest algorithm, an ensemble learning method, constructs multiple decision trees through bootstrap sampling of training data and aggregates their predictions for final classification. By leveraging random feature selection during tree construction, the algorithm demonstrates superior accuracy and robustness in remote sensing classification compared to other machine learning methods [41].  
For land use classification, this study acquired four high-resolution (1 m) satellite images (2014, 2016, 2019, and 2022) to collect >200 classification samples. These samples were randomly partitioned into training (60%) and validation (40%) datasets for supervised classification of Landsat images. To improve phenological discrimination, sixteen Landsat images (four seasonal images per year) were preprocessed through radiometric calibration, atmospheric correction, and mosaicking, resulting in 15 m resolution imagery.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Infrared Radiation (IR)  
The IR is a type of electromagnetic radiation invisible to human eyes and can only be felt as heat. Its name is derived from the electromagnetic spectrum frequencies, since infrared waves occur above microwaves and below the red visible light frequencies [123]. It is widely used for numerous applications, such as remote controls. Additionally, the infrared data association (IrDA) protocol is used for wireless devices and motion detectors. IR is also used for localization systems, such as a moving robot equipped with an IR LED camera, and localized from the reflecting artificial landmarks [124].  
An IR-based indoor positioning system was used in [125] for obtaining traffic flow data of pedestrians. The system was evaluated in the passageway of a laboratory and consisted of an IR beacon, a receiver, and a processing unit. The IR beacons transmitted their ID signal using pulse position modulation (PPM) and were attached to shopping bags. The receivers were deployed on the ceiling and measured the angle of incidence of the beacons via a pinhole camera, which was used for estimating the beacon position and identifying the ID signals. The arrival position on the side of the photodiode array changed according to the angle of incidence. A total of 12 photodiodes were used, and the IR beacons were deployed every 0.5 m along the passageway, resulting in a maximum error of 0.7 m.  
Passive infrared (PIR) sensors can also be used for detection and localization. The PIR-based localization system's accuracy depends on the sensors' density and their distribution, as well as the intersection of their FoVs. For instance, a grid-based accessibility map was developed in [126] and was subdivided into a finite position grid, in which each grid point provided its location in the environment and the probability of a person being present at that location. The system was evaluated indoors using the people's daily visits. For finding the optimal path from the initial node to the destination node, the A-Star algorithm was used. The PIR data were collected every 0.05 s from each sensor, and the people's locations were estimated. Two different routes were evaluated with mean distance errors of 0.227 m and 0.188 m, respectively.