esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since April 2022, the _Dove Classic_ and _Dove-R_ satellites have been decommissioned, but the data collected between June 2016  
Figure 1: Illustrated PlanetScope imaging configuration for cubesats operating at descending orbits from side (**A**) and top-down view (**B**). Both figures are adapted from [12]. Attributes like view angle, azimuth angle, or satellite azimuth can be found in the JSON metadata provided for each scene. and April 2022 by _Dove Classic_ and between March 2019 and April 2022 by _Dove-R_ are still available as archived data. _SuperDove_ scenes have been available since March 2020 and are still captured daily [14]. All scenes are taken with slightly off-nadir view angles (0-5\({}^{\circ}\), depending on latitude) at altitudes ranging between 475 and 525 km. The overlap along the track is about 10%, with a time interval between consecutive images of about one second. To ensure void-less coverage, the swaths of neighboring cubesats overlap by several kilometers in cross direction (approximately 40%; time interval of approximately 90 s, e.g., [15]). The PlanetScope scene (PSS) images are available as basis scene products (Level 1B) and orthorectified scene products (Level 3B). Level 1B products provide scaled top-of-atmosphere radiance values with radiometric and geometric sensor corrections applied. According to the Planets product description, the latter imply sensor optic corrections (lens distortion) and co-registration of the captured strips. Level 1B scenes are not orthorectified but accompanied by rational polynomial coefficient (RPC) describing the imaging geometry by a rational functional model (RFM), which is particularly important for 3D reconstruction when the intrinsic (physical) rigorous sensor model is unknown. The RPC consist of four polynomials, each containing 20 coefficients, used to transform geodetic coordinates (longitude, latitude, and height above the datum) into image pixel coordinates. The reader is therefore advised to select this processing stage when obtaining data if s/he wishes to use the approach presented below. More details on the PlanetScope mission, constellation, and data products are given in [12; 14; 15; 16].
Although the aforementioned neural network learning methods have been extensively researched in engine remaining life prediction, certain limitations persist. For instance, RNN neural networks exhibit poor feature extraction ability, CNN neural networks fall short in handling time series data, and LSTM neural networks show low predictive accuracy under complex operating conditions. Considering these shortcomings, researchers [14] have introduced a TCN network. This neural network architecture is capable of effectively capturing local patterns in time series data. [PERSON] [15] leveraged a multi-head probability sparse self-attention mechanism in deep learning to enhance data relevance and combined it with causal convolutions in TCN neural networks to boost the model's learning capabilities. [PERSON] et al. [16] designed a MCA-DTCN network lifespan prediction model to extract key features from engine data. These research findings indicate that TCN prediction methods offer advantages in engine life prediction. However, considering that different sensor monitoring data may impact engine remaining life to varying degrees, it is essential to thoroughly investigate these issues during model training to prevent a decrease in prediction accuracy. Hence, when utilizing TCN neural network modeling, continuous optimization and improvement of the TCN neural network structure are necessary to enhance predictive performance. To better address the above issue, this paper constructs a turbine engine RUL prediction method based on an Adam-optimized SAM-TCN. This approach aims to accurately capture significant feature data contributions to address the issue of low RUL prediction accuracy. The main contributions of this paper are as follows. Considering that turbofan engine data used for experiments are multivariate time series, TCN neural networks are employed in this study to handle these temporal data sequences effectively, thereby avoiding the problems of gradient vanishing and slow convergence speed typically encountered in recurrent neural networks during training. Simultaneously, the SAM method is utilized to capture the impact magnitude of each feature sequence on the engine's RUL, enabling the prediction model to focus more on useful feature data and enhancing its capability to extract key features. Additionally, the Adam optimization method is introduced to globally optimize the prediction model, thereby improving its training effectiveness and accuracy. Experimental verification and analysis are conducted on the C-MAPSS dataset for turbofan engines (FD001-FD004 datasets). The RMSE values obtained are 11.50, 16.45, 11.62, and 15.47, respectively, and the Score values are 225.32, 1136.27, 259.79, and 1365.40, respectively. The results of these two evaluation metrics demonstrate that the proposed method in this paper achieves lower prediction errors and better performance compared to others in the literature, effectively validating the efficacy of the proposed approach.
## 5 Conclusions  
The large amount of ship movement data collected by terrestrial networks and satellite constellations of AIS receivers requires the aid of automatic processing techniques if the data are to be fully utilized. The TREAD methodology derives knowledge of maritime traffic in an unsupervised way, in order to detect low-likelihood behaviors and to predict vessels future positions.  
The learning process is robust with respect to different number of sensors, their coverage and refresh rate and the scale of the area of interest. The traffic route extraction process is based on incremental learning and can be applied both in real-time or batch fashion.  
In this research work, vessels are analyzed as a collective entity that constructs and shapes the traffic patterns over the area of interest. The resulting low-likelihood behavior detection can often be fully explained through the interaction between objects. For example, a sudden change in course or speed can be due to collision avoidance maneuver with respect to another vessel or an intent to delay the transit to arrive at a pre-arranged time. This level of interaction, if taken into account, can help improve the interpretation of vessel behavior and intent.
## 2 Experimental Section
### Experimental Setup  
The experimental setup for the high-power, Q-switch Nd:YAG laser is shown in Figure 1. The pump laser was a pulsed laser diode array (Institute of Laser Engineering, Beijing University of Technology, Beijing, China) emitting at 808 nm with a broadband width less than 3 nm to match the Nd:YAG absorption broadband. The LD module consisted of five similar parts. A cross-section of the LD module is depicted in Figure 2. Thirty bars with single maximum output peak power of 100 W comprised the LD bar array. The diode arrays, with maximum output peak power of 15 kW, were powered by an external power source with repetition rate of 1-15 Hz and pulse duration of 100-250 \(\upmu\)s. In addition, the LD arrays formed an integrated module with the Nd:YAG crystal rod to decrease the size. The Nd:YAG crystal rod (JIEPU TREND, Beijing, China) (\(\mathcal{O}7\) mm \(\times\) 100 mm) had doping concentration of 1.1 at% and 5\({}^{\prime\prime}\) parallelism. Both surfaces of the Nd:YAG crystal rod were anti-reflection (AR) coated to minimize reflection at 1064 nm. Two TECs (Beijing Huimao Cooling Equipment Co., Ltd., Beijing, China) were adopted to maintain the temperature of the LD module, with a controlling precision of 0.01 degrees. The TEC module had a maximum power of 66 W and was mounted under the LD module with thermally conductive silicone rubber. The rear mirror was a plane mirror (Beijing Qifenglanda Optics Technology Development Co., Ltd., Beijing, China) with a coating for high reflectivity (HR) at 1064 nm. The output coupler (OC) was a plane mirror coated for partial transmittance (\(T=75\%\)) at 1064 nm. To obtain linearly-polarized 1064 nm radiation, a Brewster polarizer (BP) (Beijing Qifenglanda Optics Technology Development Co., Ltd., Beijing, China) with \(T_{\mathrm{p}}\) of 99.52% and \(T_{\mathrm{s}}\) of 0.123% was inserted into the cavity. A KD*P crystal was used as the Q-switcher, with AR coatings on both faces at 1064 nm used to generate modulated pulses. A \(\lambda\)/4 plate was AR-coated at 1064 nm and used to change the polarization direction. The total cavity length was 280 mm.  
Figure 1: Schematic diagram of the diode-side-pumped Q-switched Nd:YAG solid-state laser. HR is the high reflective mirror; BP is the Brewster polarizer; and OC is the output coupler mirror.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 5 VLSI Implementation of Dual-Mode LDPC Decoder for 5G NR Systems
### Post-Layout Implementation Results
Compared to [23; 24], the proposed dual-mode decoder using the layered INPMSA decoding achieves a high throughput of 10.86 Gb/s, which meets the requirement of 5G NR application with a high NAE of 11.40 bits/mm\({}^{2}\) and low NEE of 28.85 pJ/bit. This reveals that the INPMSA using the proposed compensation scheme achieves a low silicon area with low energy consumption. Figure 9: Chip layout of the proposed dual-mode LDPC decoder.
# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 4 Results
### Construction of the UGC Short Video Value Assessment Index System
#### 4.1.3 Construction of the UGC Short Video Value Assessment Index System  
The information ecological chain theory, introduced in the 1980s, focuses on the interconnectedness of information flow among various information subjects in information systems [45, 46]. Analysis shows that the application scenarios based on this theory involve information flow activities among three types of information subjects: information generators, information disseminators, and information consumers. These subjects exhibit a certain degree of interdependence, forming a chain-like relationship. In the context of this study, we posit that UGC short videos also involve interactions among three key subjects during the dissemination process: creators, platforms, and users. Creators primarily undertake the creation and production of UGC short videos, while platforms serve as intermediaries between creators and users, handling the operation and management of UGC short videos. Users, in turn, receive and engage with UGC's short video content.  
Thus, this study integrates the 11 identified UGC short video value features and establishes a hierarchical relationship among indicators based on three dimensions: creators, platforms, and users. The study analyzes the similarities and differences between the indicators to optimize and adjust them, ultimately constructing the UGC short video value assessment index system. Refer to Table 2 for more information.
## 1 Introduction
To improve the optimization efficiency, various surrogate models are employed to replace the FEM analysis to approximate the implicit performance functions and the corresponding limit states in the reliability analysis and RBDO. The commonly used surrogate models include the response surface method (RSM) [18], support vector regression (SVR) [19], artificial neural networks (ANNs) [20; 21], kriging models [22; 23], and radial basis functions (RBFs) [24]. This paper applies the GRNN, a deformation form of the RBF, in RBDO because the GRNN has excellent performance in nonlinear approximation, quick learning, fault tolerance, and robustness, even in the case of inadequate samples [25]. In addition, the RBDO aims to obtain a reliable optimum to ensure that the failure probability does not exceed a target level. Therefore, various corresponding approaches have been proposed. [PERSON] et al. [7] presented a BPNN-assisted reliability-based design optimization method for the two-phase tensioning system transformation process (STP) of a cable-stayed bridge erected by the incremental launching method. [PERSON] et al. [26] used the RSM and NSGA-II algorithms to execute the multiobjective optimization of a composite orthotropic bridge to improve the fatigue performance to achieve an infinite fatigue life under a relatively low structural weight. [PERSON] et al. [27] proposed a fault-tolerant interval inversion (F-TII) framework to mine the fault-tolerant capacity from the geometric nonlinear redundancy of a cable system, in which the inversion is solved as a reliability-constrained, multiobjective interval optimization problem with the aid of a GRNN-based surrogate model. These studies indicate that multiobjective optimization methods combined with the surrogate model are feasible in structure optimization. However, there are no related reports on the multiobjective optimization of controlling the NRRAP of cable-stayed bridges in the literature thus far. This paper presents a three-step pavement optimization strategy (TPOS) against the NRRAP. First, the assessment of the NRRAP under the live load was carried out based on the updated parameters. Second, the design variables were determined according to sensitivity analysis. The pavement optimization problem was formulated with the objectives of driving comfort and minimum construction cost. In addition, the reliability constraint for the bearing reaction of the auxiliary pier was adopted in the optimization model. The NSGA-II was used to solve the multiobjective optimization problem and obtain the optimal Pareto set. Third, we randomly selected an optimization scheme from the optimal Pareto set and evaluated it with C1-C3 [28]. The TPOS was applied to an asymmetric cable-stayed bridge. The results demonstrate that the TPOS can efficiently achieve the optimal pavement strategy of the cable-stayed bridge under multisource construction uncertainties and traffic growth.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 4 Linking Genetics and Imaging
### Genetic-Driven Hemodynamic Variations  
Recent research highlights the interplay between genetic mutations and hemodynamic variations observable through advanced imaging techniques. For instance, mutations in NOTCH1 and SMAD6 have been associated with flow eccentricity and elevated WSS, particularly along the greater curvature of the ascending aorta [52; 54]. Such abnormal hemodynamics contribute to localized aortic wall remodeling and increase the risk of aneurysm formation. Imaging tools like 4D Flow MRI can map these flow abnormalities, providing crucial data for correlating genetic variants with observed structural changes [50; 53].  
For example, NOTCH1 mutations not only result in early calcification but also exhibit characteristic flow disruptions on imaging, which may serve as early indicators of disease progression. Studies have shown that NOTCH1 mutations are associated with altered blood flow patterns, including increased flow eccentricity and turbulence in the ascending aorta, which can be effectively visualized using advanced imaging techniques like 4D Flow MRI [52; 53]. Such findings highlight the role of NOTCH1 in contributing to hemodynamic changes that precede structural complications, emphasizing its potential as a combined genetic and imaging biomarker for disease progression.  
Similarly, SMAD6-associated aortopathies show distinct flow abnormalities and elevated WSS, which can be effectively monitored using imaging tools. Studies have demonstrated that SMAD6 mutations, which disrupt TGF-beta signaling, result in altered hemodynamic forces, including elevated WSS and flow eccentricity, particularly in the ascending aorta. These abnormalities contribute to localized aortic wall remodeling and increase susceptibility to aneurysm formation. Advanced imaging tools such as 4D Flow MRI and computational fluid dynamics (CFD) analyses are essential in capturing these flow disruptions, providing a comprehensive link between genetic predisposition and observed structural changes [53; 54].
# Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules
###### Abstract  
In this paper, we propose a centralized task allocation and an alignment technique based on constraint table and alignment rules. For task allocation, a scoring scheme has to be set. The existing time-discounted scoring scheme has two problems; if the score is calculated based on arrival time, the agent who arrives in a task point first may finish the task late, and if the score is calculated based on end-time of the task, agents who have the same score may appear because of temporal constraints. Therefore, a modified time-discounted reward scheme based on both arrival and end-time is proposed. Additionally, an accumulated distance cost scheme is proposed for minimum fuel consumption. The constraint table made by tasks that are already aligned is also considered in scoring. For centralized task alignment based on the constraint table and alignment rules, a technique based on sequential greedy algorithm is proposed. Resolving conflicts on alignment is described in detail using constraint table and alignment rules, which are composed of four basic principles. We demonstrate simulations about task allocation and alignment for multi-agent with coupled constraints. Simple and complicated cases are used to verify the scoring schemes and the proposed techniques. Additionally, a huge case is used to show computational efficiency. The results are feasibly good when the constraints are properly set.  
task allocation; task alignment; mission planning; task planning; multi-agent systems; multi-agent planning and scheduling +
Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 3 Experimental Results
The loss values (i.e., Equation (3)) of each model on each patient's input images (HEX and FAM) that are presented to the models were computed for 160 patients (i.e., test set). Then, to compare the two models' mean absolute loss values, a t-test was performed where the null hypothesis to be tested was that both mean values were equal. As expected, because the results of the models in Table 2 are very close, the loss values of almost all models were from populations with equal means, so the null hypotheses are not rejected, as shown in Figure 8, where the value 0 indicates that the null hypothesis is not rejected. However, when comparing the VGG19 model against InceptionResNetV2, MobileNetV2, and VGG16, the null hypotheses were rejected, indicating that the mean loss of those models was significantly different at the 5% significance level. Thus, the InceptionResNetV2, MobileNetV2, and VGG16 models are better than the VGG19 model because their mean loss values are smaller than those of the VGG19 model. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & \(ACC\) & \(\mathbf{F_{i}}\) & \(MCC\) & \(TPR\) & \(TNR\) & \(AUC\) \\ \  
Further analysis of the training execution time of the pre-trained models was performed. The results indicated that although all the pre-trained models performed with the same accuracy, the MobileNetV2 architecture outperformed dramatically in terms of the training speed and fast convergence. Table 3 lists the execution times of the training processes obtained for each model. Each model was trained using 240 patients' input images (each patient had HEX and FAM images), and the current model was validated using 160 unseen patient data during a total of 100 training epochs, which resulted in the total execution time of the models.
## 2 Indoor Positioning System in 433 MHz ISM Band  
The proposed indoor positioning system is composed of two unique subsystems: GPS down-converting repeaters and an up-converter integrated with an off-the-shelf GPS receiver. The block diagrams of the GPS down-converting repeaters and the up-converter with the off-the-shelf GPS receiver are presented in Figure 3. The red dashed line represents the 433 MHz RF link.  
Figure 2: The proposed indoor positioning system. \(i\): index of the repeaters (\(i=1,2,3\)); \(j\): index of the satellites (\(j=1,2,\ldots,n\)); \(Ri\): \(i\)th repeater; \(Sj\): \(j\)th satellite; \(d_{i}\): indoor distance from repeater \(Ri\) to the receiver (with a clock bias of \(t_{bias}^{receiver}\)); \(D_{Ri}^{Sj}\): distance from \(i\)th repeater (\(Ri\):) to the \(j\)th satellite (\(Sj\)); \(\tau_{i}\): propagation delay of the \(i\)th repeater; \(t_{bias}^{Sj}\): satellite clock bias of the \(j\)th satellite.
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 2 Related Work
### Unguided Crowd Evacuation  
During unguided crowd evacuation, pedestrians direct themselves through the evacuation path and exit using signage, either static or dynamic, without the need for an external agent (e.g., human leader, mobile app, robot, UAV) to lead them. A study in this regard [3] utilized the Ant Colony Optimization algorithm (ACO) to determine a feasible route and avoid a potential obstacle. The study proposed suggesting found paths over mobile messaging or broadcasting the paths to the people under evacuation. Another study [4] focused on evacuation in a simplified scenario covering a single indoor room with one exit. In such studies, simulation tools such as Unity [5] and EXODUS [6] are generally used. Another similar study [7] used an adaptive guiding protocol based on wireless sensor networks to approach congestion problem during evacuation. None of these studies focused on the utilization of UAV for evacuation.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 3 Proposed Scheme  
This section introduces our high-efficiency and low-complexity receiving antenna allocation scheme for the downlink MU-MIMO-OFDM transmission based on IEEE 802.11 ac [6]. The proposed scheme combines the limited CSI feedback sequence based on [20] and receiving antenna decision method based on [21].
## 4 Nutritional and Bioactive Potential of Fresh Fruit and Vegetable Waste
### Fresh Fruits
#### 4.1.6 Mango  
Mangoes are one of the most popular fruits worldwide, and their production causes high amounts of waste. Mango peels and seeds are the main byproducts produced, having a considerable concentration of bioactive compounds [104]. Mango peel is rich in vitamins C and E, and in several minerals (Ca, K, Mg, Na, Fe, Mn, Zn, and Cu) [105], and contains pectins and anthocyanins [103] and a higher polyphenol content than its pulp [105]. Among the phytochemicals in mango peel reported in the literature, we can find mangiferin, benzophenone derivates, xanthones, phenolic acids, fatty acids, flavonoids, procyanidins, penta-O-galloyl-glucoside, methyl gallate, tetra-O-galloyl-glucoside, maclurin di-O-galloyl-glucose, and isoquercitrin [106]. Mango seeds also have a very interesting bioactive composition which includes high concentrations of the major flavonoids and phenolic acids, gallic acid, catechhin, chlorogenic acid, caffeic acid, ellagic acid, rutin, quercitin, quercetin, and kaempfer [106]. Additionally, these seeds are a source of fats, being particularly rich in palmitic, stearic, oleic, and linoleic acids [102].