esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Analysis Methods
### PL Detection and Tracking Algorithms  
Here, we applied two methods for the detection and tracking of PLs based on different atmospheric parameters--sea level pressure (SLP) and relative vorticity at 850 hPa (\(\zeta_{850}\)). The first procedure, developed by [PERSON] and [PERSON] [42] and [PERSON] and [PERSON] [11], searches for the minima in the band-pass filtered hourly SLP fields and concatenates the minima in consecutive time steps to tracks. From the criteria proposed by the authors, we excluded a north-to-south average direction of the track and land mask limitation. The second procedure is mostly identical to one developed by [PERSON] et al. [40] which is based on the detection of local maxima in the filtered \(\zeta_{850}\) fields. We modified some parameters to obtain a good match with the satellite observations. The modification affected:  
* a low stability criterion, defined as the difference between the sea surface temperature (SST) and the temperature at 500 hPa (T\({}_{500}\)) \(\geq\) 39 K;
* \(\zeta_{850}\)\(\geq\) 10 \(\times\) 10\({}^{-5}\) s\({}^{-1}\); such threshold exhibits good agreement between a vorticity-tracking-based and a satellite-based PL data sets [40].  
A Gaussian band-pass filter is applied in both methods with wavelengths 100 and 800 km to focus on the mesoscale nature of PLs and remove synoptic- and micro-scale variabilities. We converted a grid spacing from degree to km because of the longitudes convergence towards the pole, and linearly interpolated it to 25 km \(\times\) 25 km resolution for all reanalysis datasets.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 2 Materials and Methods
### Visible Boundary Delineation Method and Workflow
#### 2.3.2 Visible Boundary Delineation Workflow
(ii) Extracted and filtered object boundaries were smoothed and simplified to be used for the interpretation of possession boundaries aiming to support a cadastral mapping (i.e., land plot restructuring in this case, as the situation requires a new cadastral survey or land consolidation). The smoothing of extracted/filtered object boundaries was done by using the Snakes algorithm [49]. The Douglas-Peucker algorithm was applied to the smoothed object boundaries in order to further simplify the object boundaries [22; 49]. These objects both smoothed and simplified were labeled as simplified extracted/filtered objects. 4. _Accuracy assessment:_ The accuracy assessment was object-based since the results were in vector format. The buffer overlay method was used for accuracy assessment. The method is described in detail in [PERSON] et al. [50]. The accuracy assessment was based on computing the percentages of extracted (or reference) boundary lengths which overlapped within a buffer polygon area generated around the reference (or extracted) boundaries (Figure 3) [50]. To determine the completeness, correctness, and quality of extracted boundaries, calculated boundary lengths of true positives (TP), false positives (FP), and false negatives (FN) were used. The completeness refers to the percentage of reference boundaries which lie within the buffer around the extracted boundaries (matched reference). The correctness refers to the percentage of extracted boundaries, which lie within the buffer around the reference boundaries (matched extraction). The accuracy assessment was performed on buffer widths of 25 cm, 50 cm, 100 cm, and 200 cm. The selection of buffer widths is in line with other studies and was based on the most common tolerances regarding boundary positions in land administration, especially for rural areas [26; 27]. The percentage indicating the overall quality was generated from the previous two by dividing the length of the matched extractions with the sum of the length of extracted data and the length of unmatched reference [50]. The accuracy assessment was applied to automatic extracted objects, simplified extracted objects, filtered objects, and simplified filtered objects (Figure 2).
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
A path-planning method based on a multi-intelligence deep reinforcement learning framework is proposed to maximize the coverage of the target area. 2. To address the path-planning problem under the condition of partially incomplete information, we utilized an S-LSTM to store and utilize previous states. Furthermore, to address the non-convex problem in optimizing the objective function, we employed multiagent collaboration and experience sharing within the MATD3 algorithm. 3. The simulation results demonstrate that the proposed MATD3-Stacked_LSTM algorithm has better convergence and significantly improves the coverage of the target area compared to the other two algorithms.
###### Abstract  
Measurements of grazing angle GNSS-R ocean reflections combined with meteorological troposphere data are used for retrieval of ocean wave heights and surface roughness parameters. The observational results are compared to multiphase screen simulations for the same atmosphere conditions. The retrieved data from observations and simulations give equal results within the error bounds of the methods. The obtained ocean mean wave-heights are almost proportional to the square of the wind speed when applying a first-order approximation model to the high-wave-number part of the measured GNSS-R power spectra. The spectral variances from the measurements link directly to the ocean surface roughness, which is also verified by the performed multiple phase-screen wave propagation simulations. Thus, grazing angle GNSS-R techniques are an efficient method for determining the ocean state and the conditions in the boundary layer of the troposphere.  
sea surface roughness; GNSS-R ocean observations; sea surface waves; bistatic radar; scatterometry; GNSS-R ocean reflection simulations; multiphase screen simulations; GPS signals; retrievals; performance 2023153794[[https://doi.org/10.3390](https://doi.org/10.3390)]([https://doi.org/10.3390](https://doi.org/10.3390))
## 3 Activation Functions
### Rectified Activation Functions  
#### 3.1.1 ReLU  
ReLU [50], illustrated in Figure 1, is defined as:  
\[y_{i}=f(x_{i})=\left\{\begin{array}{l}0,\ |\ x_{i}<0\\ x_{i},\ |\ x_{i}\geq 0.\end{array}\right. \tag{1}\]  
The gradient of ReLU is  
\[\frac{dy_{i}}{dx_{i}}=f^{\prime}(x_{i})=\left\{\begin{array}{l}0,\ |\ x_{i}<0\\ 1,\ |\ x_{i}\geq 0.\end{array}\right. \tag{2}\]  
Figure 1: ReLU.
## 7 Conclusions  
In this paper, a rapid method for pre-identifying tangential acceleration based on ground radar observation is proposed for the problem of identifying the maneuvering parameters of non-cooperative space targets. A dynamic model of a satellite under continuous low thrust is given that can derive the relationship between the change in satellite orbital elements and the direction and magnitude of acceleration during continuous thrust control. We can pre-determine continuous acceleration using a single-arc segment orbit determination method based on the least-squares approach. Initial values of orbital elements and thrust parameters can be used for precision orbit determination to improve its accuracy and efficiency. However, in our study, we did not consider changes in the mass of the electric propulsion system. This is because we assumed that changes in propellant mass could be ignored and neglected its effect on acceleration. In future studies, we will adjust our research assumptions to improve the practical value of the experiment.  
Conceptualization, W.G., X.T. and M.H.; methodology, W.G., X.T., and M.H.; validation, W.G. and W.X.; writing--original draft, W.G.; writing--review and editing, X.T. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
Figure 24: Relationship between average relative error and time interval in acceleration identification at different range measurement accuracies.
## 2 Forward Model
### First Approach: Multi-Objective Optimization Problem  
The inverse problem is formulated in terms of a multi-objective optimization problem, based on the GPR surrogate model for the evaluation of the objective functions. Specifically, two objective functions have been considered and minimized according to the Pareto optimization [20]:  
\[v_{1}=N\{1\;if\;T_{i}:|T_{i}-T_{i}|>\alpha\},\mathrm{i}=1,\ldots,200 \tag{17}\]  
with  
\[\alpha=\frac{\delta\;T_{t}}{100}, \tag{18}\]  
and \(\delta=1\%\), while  
\[v_{2}=\underset{i}{max}T_{i}-\underset{i}{min}T_{i} \tag{19}\]  
The objective function \(v_{1}\) is the number of points along the reference line (Figure 1), the temperature of which falls out of the range \(\pm 1\%\) with respect to the target temperature \(T_{t}\) of 1100 \({}^{\circ}\)C, to be minimized. The objective function \(v_{2}\) is the discrepancy between maximum and minimum values of the actual temperature profile, to be minimized. So, with the first objective function, the algorithm tries to find a temperature profile that is as close to 1100 \({}^{\circ}\)C as possible, and with the second, the algorithm tries to find a homogeneous response.  
This multi-objective optimization is performed by utilizing the well-known Non-Dominated Sorting Genetic Algorithm NSGA-II [21] available from the Mathworks file exchange website [35]. It is a genetic optimizer that exploits the concept of solution ranking by means of the non-dominated sorting algorithm: the population is subdivided into several Pareto fronts and a fitness value is assigned to each front depending on the vicinity to the Utopia point (the closer, the better). Within each Pareto front, a fitness value is assigned to each solution, depending on the crowding distance i.e., the distance of the current solution from the closest solutions (the wider, the better). Iteration after iteration, by applying crossover, mutation, and tournament selection operators, just one Pareto front results, and the solutions are usually equally distributed along the front.
## 3 Results and Discussion
### Rutting Property (G*/sin \(\delta\))
Modifications with TLA show improvement, with 10% and 20% TLA increasing % recovery (0.39% to 0.67% original, 2.38% to 2.86% RTFO) and decreasing Jnr (4.22 k/Pa to 1.64 k/Pa original, 3.43 k/Pa to 1.45 k/Pa RTFO), suggesting enhanced elasticity and reduced deformation. However, recovery percentages remain lower compared to advanced modifications. Adding 5% CRM significantly boosts % recovery (2.05% to 13.01% RTFO) and reduces Jnr (2.81 k/Pa to 0.95 k/Pa RTFO), indicating improved performance. When combined with LC, these metrics further improve, underscoring the additive's positive impact. For example, with 10% TLA and 5% CRM plus LC, % recovery reaches 3.23% in the original and 13.69% in the RTFO, with Jnr values of 1.99 k/Pa for the original and 0.80 k/Pa for the RTFO. The modification with 20% TLA and 10% CRM shows exceptional performance, achieving 10.67% recovery original and 41.75% RTFO, with Jnr at 0.95 k/Pa original and 0.30 k/Pa RTFO. Inclusion of LC further enhances performance, with 8.80% recovery original and 43.49% RTFO, and Jnr at 0.85 k/Pa original and 0.42 k/Pa RTFO. This signifies LC is an effective additive for warm mix, enhancing binder performance by increasing % recovery and reducing Jnr, thus improving elasticity and minimizing deformation, even under aging conditions. The synergistic effects of LC with TLA and CRM produce high-performance binders, with the 20% TLA + 10% CRM + 1.5% LC modification demonstrating superior performance, highlighting LC's crucial role in creating durable high performance warm mix asphalt binders.
## 4 Results of the Study
### Numerical Simulation Analysis of the Amount of Consolidated Surface Settlement  
#### 4.4.1 Different Tunnel Depths in Reinforced Soils  
In order to verify the effect of different tunnel depths on the amount of surface settlement after soil grouting reinforcement, numerical simulations were carried out using several working conditions, as shown in Table 7. The results after simulation are shown in Figure 11.  
By analyzing Figure 11, the maximum value of surface uplift shows a gradual decrease with the gradual increase in burial depth. When the burial depth of the underground tunnel increases, the settlement of the ground surface is controlled within the range of \(-1\) mm to \(2.5\) mm. This indicates that after the soil body is reinforced by grouting the tunnel, burial depth has some effects on the settlement of the soil body above. However, these effects are not obvious. When the burial depth reaches \(35\) m, the settlement of the surface is further limited to between \(-0.5\) mm and \(1.5\) mm, which has the best effect. When the distance from the center of the tunnel reaches \(15\) m, the change in surface settlement tends to be constant, because shield tunnel excavation causes a negligible change in surface settlement when the distance from the centerline of the tunnel reaches three times the diameter of the tunnel. Through comparative analysis, the grouting reinforcement of the soil body can appropriately reduce the influence of the tunnel burial depth on the change in surface settlement.
## 1 Introduction  
Odontometrics has become a valuable tool for age and gender estimation in forensic investigation. Whilst biochemical analysis such as DNA profiling, fingerprints, and anthropometric data are among the standard methods employed for human identification in forensic science, odomometric measurements also can provide comparatively reliable results [1]. Gender dimorphism has been reported using various anthropometric parameters including foot and hand dimensions [2]. Moreover, studies suggest that it can also be explored through the analysis of teeth based on the pattern of dental development and eruption, the expression of amelogenin protein, dental morphology, and tooth dimensions [3].  
Quantitative analyses, such as discriminant function analysis, have been reported to have a crucial role in forensic applications for gender differentiation. Moreover, any method which depends on the least number of predictors possible to improve the percentage of correct classification would be a significant addition to the forensic anthropology [4]. Gender estimation using the permanent teeth dimensions has been the subject of previous studies, and a variety of measuring methods and equations have been developed in different populations. Recent advancements have enabled the digitization of patient dental casts by capturing the surface data of these casts using holography and photo-optical or laser-optical scanning [5]. The advantage of digital models is their ability to analyze tooth characteristics in a three-dimensional aspect ensuring clinically acceptable and reproducible results. Odontometric assessment using canine dimensions has been proven to be valuable in gender identification owing to their ability to withstand vulnerable conditions. Mesiodistal and buccolingual widths and the canine index of permanent teeth were the most commonly used parameters in predicting an individual's gender based on dental measurements [6]. Permanent canines are given importance during orthodontic treatment as they play a vital role in achieving an ideal occlusion and aesthetics owing to their anatomical and functional value. Moreover, tooth size discrepancies or variations assessed using odontometrics might aid in customized orthodontic management in individuals. Odontometric values vary between different population groups and also within the groups [7], and hence the use of external databases does not seem advisable even though they come from genetically or geographically related people [8]. Odontometric standards for estimation of gender in the Saudi population are scarce, and no population-specific standard is available in the literature. Hence, the present study aimed to analyze the gender dimorphism in the mesiodistal width and intercanine distance of the permanent maxillary canines using a three-dimensional (3D) digital technique and to test the accuracy in gender estimation using discriminant analysis in a sample of the Saudi population from Al Ahsa in the Eastern region of Saudi Arabia.
## 2 Fault Detection Method
### Convolutional Layer  
A convolution operation refers to the use of multiple convolution kernels to multiply the coefficients of the corresponding location in the local area of the input signal to achieve feature extraction. The main advantages of a convolution operation are weight sharing and sparse connection, which can reduce the number of hyperparameters to be trained and the amount of calculation. A convolutional layer operation is defined as:  
\[\mathbf{x}_{i}^{(l)}=\varphi\bigg{(}\big{(}\mathbf{k}^{(l)}\big{)}^{\mathsf{T}}\mathbf{x}_ {i}^{(l-1)}+b_{c}^{(l)}\bigg{)} \tag{1}\]  
where \(\varphi\) denotes convolution operation, \(\mathbf{k}^{(l)}\) is the kernel weight of the \(l\)th convolution layer, \(b_{c}^{(l)}\) is the bias of the \(l\)th convolution layer, \(\mathbf{x}_{i}^{(l)}\) is the output of the \(l\)th, \(i\) denotes the sample number, and \(\mathbf{x}_{i}^{(l-1)}\) is the input of the \(l\)th.  
Usually, a batch normalization layer (BN) needs to be connected after the convolution layer. The batch normalization layer is designed to alleviate the 'gradient dispersion' problem in deep networks and accelerate model convergence. The operation of a batch normalization layer can be expressed as:  
\[\mathbf{\hat{x}}_{i}^{(l)}=\frac{\mathbf{x}_{i}^{(l)}-\mathbf{E}\big{[}\mathbf{x}_{i}^{(l )}\big{]}}{\sqrt{\mathbf{Var}\big{[}\mathbf{x}_{i}^{(l)}\big{]}}} \tag{2}\]  
\[\mathbf{x}_{i}^{(l)}=\kappa\mathbf{\hat{x}}_{i}^{(l)}+\tau \tag{3}\]  
where \(\mathbf{E}\) is the expectation, \(\mathbf{Var}\) is the variance, \(\kappa\) is the scale factor, and \(\tau\) is the translation factor.  
After the batch normalization layer, an activation function needs to be connected to improve the nonlinear expression ability of the neural network. A rectified linear unit is adopted in this paper:  
\[\mathbf{r}_{i}^{(l)}=\max(0,\mathbf{x}_{i}^{(l)}) \tag{4}\]  
Figure 6: Overlap sampling.  
In addition, layer skip layer connection is designed to overcome the problems of gradient dispersion and gradient explosion of a neural network and accelerate convergence [34].
## 3 Results
### Evaluation the Robustness of the Proposed Model  
Next, to verify the robustness of the designed MCNN-BLSTM model (\(l=25\) s) for noisy rhythm, we make some classifications in the Normal, Atrial Fibrillation, Other rhythms, and Noise of the CinC2017 database. The results are shown in Table 5. We found that the MCNN-BLSTM network (\(l=25\) s) has an average F1 value of 78.88% for the four categories of Normal, AF, Other, and Noise rhythms. Compared with the results of the three classifications, the average F1 values of N, AF, and Other rhythms are reduced by 0.09%, 2.74%, and 1.29%, respectively. This shows that MCNN-BLSTM has good robustness to noise interference.
## 3 Results and Discussion
### Analysis of Numerical Simulation Results
#### 3.2.3 Cyclic Loading and Unloading Stress-Strain Analysis
Moreover, the trend of Cumulative Strain Difference shows that the plastic strain difference between simulated and measured decreases with increasing cycle levels.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Image Pre-Processing  
The multispectral bands for the Landsat imageries were calibrated to the correct imperfections encountered during image acquisition because of systemic errors, topographic errors, atmospheric conditions, and the earth-sun distance [28]. The radiometric calibration coefficients derived from the metadata files were used to convert the Landsat imageries to top-of-atmosphere (TOA). Then, an atmospheric correction was performed on Landsat imageries using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) in ENVI 5.5 software. The geometric corrections were conducted on the Landsat imageries to avoid misalignment because of different viewing angles, which affect change detection accuracies [29]. The geometric correction based on the image-to-image registration was performed because the images were acquired from two different sensors. The Landsat imageries for 1988, 1993, 1998, 2003, 2008 and 2013 were registered to the 2019 imagery, producing a maximum error of 5% per tie point. All the corrected Landsat imageries were rectified to the Universal Transverse Mercator (UTM), World Geodetic System (WGS) 1984--Zone 35 South coordinate system.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 5 General Discussion
### Potential Impact  
In terms of practical applications, the listener-centred paradigm opens up the possibility for future speech technology adapting to individual needs. For example, readily available feedback from listeners in the form of comfortable volume settings might be used to assess the effectiveness of audio adjustments in a process of the continual online adjustment of speech parameters by speech output devices. This paradigm might be applied in the development of algorithms that are specifically designed to enhance speech intelligibility for listeners with hearing loss. For example, we would expect that individual variations in frequency selectivity exhibited by hearing-impaired listeners will result in distinct spectral modification preferences.  
Additionally, our study provides significant insights that can be utilised in near-end listening enhancement (NELE) algorithms, such as the algorithm proposed by [PERSON] and [PERSON] (2010), which is grounded in audio engineering knowledge. Specifically, for that algorithm, our findings can contribute to the identification of the optimal spectral power distribution (Figure 1 in their paper) for enhancing speech. NELE algorithms in general can benefit from the decision making, regarding the selection of different speech feature values for speech enhancement.  
Finally, this experimental paradigm offers the potential to go beyond intelligibility and explore additional applications. One such application is the prevention of fatigue and annoyance in environments where audio announcements are frequently used. By testing different speech modifications, it may be possible to optimise the speech of audio announcements to be more pleasant and less effortful to understand, enhancing the overall listener experience.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 4 Test Setup  
The analysis according to the above methodology was performed on measurements acquired by a multi-sensor platform in continuous acquisition during a pedestrian motion. In order to acquire representative data of seamless navigation, the pedestrian user carrying the platform has followed a path that, starting from an open environment, develops towards an indoor space, and then returns outdoors. The data acquisition was performed in January 2020 in the geomatics laboratory of the Politecnico di Torino (Italy, Latitude45.063332\({}^{\circ}\), Longitude 7.660458\({}^{\circ}\) considering the WGS84 reference system with the UTM32N projection), an experimental laboratory that overlooks an outdoor terrace.  
The trajectory travelled, in addition to presenting several changes of direction, also presents a variation in level in conjunction with the passage from the terrace to the laboratory (Figure 2).