esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
## 4 Solution Description
### Article Extraction Architecture  
One of the main elements of the FiDisD solution is the article extraction architecture, which includes three main components: Web Crawler, Web Scraper and Offchain Core (Figure 2). Multiple web crawlers and web scrapers are employed to identify and extract the article information from various news websites, while the Offchain Core gathers and manages that data.  
The role of the Web Crawler is to go through each page of specific websites, identify if the page represents an article and extract all the URLs. The website seed list is obtained from the Offchain Core component. Each Web Crawler has an associated account (i.e., username and password), which is used to authenticate to the Offchain Core. As the crawling is performed, the URLs that are identified as containing article information are sent to the Offchain Core.  
Actual article extraction is performed by the Web Scrapers, which extract article information from the pages that were identified by the Web Crawlers. That data is then sent to the Offchain Core together with a hash on the extracted contents; the latter, gets put on the blockchain to ensure trust.  
The considered news websites from which to extract information are managed by the Offchain Core. The bootstrap process of the components involves reading the website seed list and the corresponding extraction templates. These are stored on the Offchain Database by the Data Persistence module. Another aspect of bootstrapping is populating the database with the users that are allowed to crawl and scrap the websites.  
In order to ensure trust in the system, multiple crawlers and scrapers process the same information. That data together with hash used to check the data integrity are sent to the Offchain Core, which, in turn, stores the information in the database. As soon as a large enough number of crawlers/scrapers obtain the same information then consensus is achieved. At this point, the hash on the article content is stored in the blockchain (by means of the Blockchain Store Service) and a status field is marked accordingly in the database to signify that the article data is available to be retrieved by external Public API clients.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 5 Results and Discussion
### Number Density Estimation Results  
The total scattering cross-section (\(\sigma_{t}\)) used to compute the number density for a circular particle with a diameter of 10 \(\upmu\)m is illustrated in Figure 6. The \(\sigma_{t}\) values were computed using the analytical solution in Equation (13) and a numerical simulation for a scattered wavefield by a circular particle. Here, the numerical simulation case models the scatterer as a circular particle having the material properties listed in Table 1, while the analytical solution assumes the scatterer to be a rigid cavity. Despite the discrepancy in the physical conditions of scatterers, the curves for the two cases are generally similar. In this study, we use the \(\sigma_{t}\) values computed by the analytical solution for number density computation, owing to its simplicity and ease of computation.  
Figure 5: Coherent wave attenuation for the 1% volume fraction case. An analytical attenuation curve computed using independent scattering approximation (ISA) is displayed as a reference. A smoothed coherent wave attenuation curve obtained by applying moving averaging is also displayed.  
Figure 6: Total scattering cross-section (\(\sigma_{t}\)) for a circular particle in 2-D.  
The number density values computed by the proposed approach are presented in Figure 7a. The exact number density value (123) is also indicated in the figure. Except for the low-frequency region (below 1 MHz), the estimated number density values are close to the exact number density with some variation. The mean and standard deviation of the estimated number density values in the frequency region between 1 MHz and 10 MHz are 99.18 and 91.83, respectively. The number density estimation error over the frequency region is displayed in Figure 7b. The estimation error varies with frequency. The mean of the estimation error within 1-10 MHz was 43.4%, and the minimum error was 1.19% at 1.88 MHz. Considering that the number density values were back-calculated from complicated wavefield data from a discrete random medium, the obtained number density values and the error level seem to be reasonable.
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 3 Methodologies
### Detecting Equipment Activity  
#### 3.3.1 Aligning Data Standards  
The experimental participants (five males aged 27-34 years) conducted 45 trials for each of the six movements. Here, the measurement time for each operation was set to 10 s.  
The data configuration to measure the operational activities of the equipment is shown in Figure 3. Inside the dataset folder, there are subfolders for the equipment operational activity data measured by the five participants. Inside each subfolder, there are six datasets  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Details** \\ \hline Package & Optical LGA12 \\ \hline Size & 4.40 \(\times\) 2.40 \(\times\) 1.00 mm \\ \hline Operating voltage & 2.6 to 3.5 V \\ \hline Operating temperature & \(-\)20–70 \({}^{\circ}\)C \\ \hline Infrared emitter & 940 nm \\ \hline I2C & Up to 400 kHz (FAST mode) serial bus Address: 0 \(\times\) 52 \\ \hline Detectable distance & 10–300 mm (experimental measurements) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of VL53L0X-V2 ToF Sensor.  
Figure 2: Connectivity diagram of ToF sensor.  
for each operational activity. Each dataset was measured at a rate of 20 frames per second, where 10 s of activity was considered as a single sample. Thus, data for 200 frames constitute one sample, and each activity accumulated 13,800 frames in one.m file (i.e., Matlab script file format) by acquiring 69 samples per activity. With 69 samples for each activity, for five individuals, a total of 2070 boom, arm, and bucket piston displacement samples were used.  
Note that the minimum and maximum displacements of the pistons vary at each position; thus, the piston length data for each position was normalized to 0 and 1 by dividing by the length to conduct a uniform analysis as follows:  
\[\mathrm{Pl_{n}[k]=(h_{n}[k])/(IM_{n}-lm_{n})}\]  
Here, PL denotes the vector containing the normalized percentages of piston displacement, where n specifies the piston category (1 = boom; 2 = arm; and 3 = bucket). The index k represents the frame index within the dataset, l is the measured piston displacement, IM and lm are the maximum and minimum displacements observed for the piston, respectively.
###### Abstract  
Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impacts. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep-learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders the widespread adoption of these approaches. To tackle these issues, we introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation. Using only three manually annotated images and a selection of video clips from wheat fields, we generated a large-scale computationally annotated dataset of image-mask pairs and a large dataset of unannotated images extracted from video frames. We developed a two-branch convolutional encoder-decoder model architecture that uses both synthesized image-mask pairs and unannotated images, enabling effective adaptation to real images. The proposed model achieved a Dice score of 80.7% on an internal test dataset and a Dice score of 64.8% on an external test set composed of images from five countries and spanning 18 domains, indicating its potential to develop generalizable solutions that could encourage the wider adoption of advanced technologies in agriculture.  
Keywords:self-supervised learning; semi-supervised learning; domain adaptation; deep learning; precision agriculture +
Footnote †: journal: _Algorithms_
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Convolutional Neural Network for Classification
Comparatively, when ReLU is used in models for lung nodule detection, it often outperforms other traditional activation functions like sigmoid or tanh, which can suffer from rapid saturation and severe vanishing gradients in deep networks.
## 3 Numerical Simulation
### Verification of Numerical Simulation Results  
In order to verify the reliability of the flow field in numerical simulation, this paper establishes a flow field model of the same size as the wind tunnel test in Reference [38]. In Reference [38], the wind tunnel test was used to study the law and characteristics of the wind-sand flow around the subgrade after the retaining wall was added, and the particle image velocimetry (PIV) was used to measure the particle velocity field on the leeward side, which was solved by CFD numerical simulation software. By analyzing the net wind speed around the windbreak wall, the horizontal velocity of sand particles and the deposition rate of sand particles on the track line, the influence of the setting position of the second windbreak wall on the sand accumulation effect of the railway is clarified, in order to provide reference data for the design of the later wind and sand prevention project. Based on the clear parameter settings, the horizontal wind speed changes at different heights under empty field conditions are simulated and compared with the wind speed profile of the wind tunnel test. The wind tunnel model established in Reference [38] has a size of 1.45 m \(\times\) 22 m, and the friction wind speed \(\mu\)* is 0.6395 m/s. The measured wind speed and the simulated wind speed in this paper are shown in Figure 8. It can be seen that the wind speed contour results of the wind tunnel test and numerical simulation are in good agreement. Therefore, the flow field structure and related calculation parameters set up in  
Figure 7: Flow field simulation domain—flat terrain (**a**) windward dune; (**b**) subgrade.  
this paper are reasonable and reliable, which can provide accurate calculation requirements for different subgrade models of Wuhai-Maqin Expressway.
## 5 Mathematical Analysis of Proposed OAM/OCDMA-FSO Model  
The FRS code property when SPD detection is used is  
\[\sum_{j=1}^{L}H_{x}(j).H_{y}(j)-\sum_{j=1}^{L}(H_{x}(j).H_{y}(j)(H_{x}(j))= \begin{cases}P-1,&\quad for\ x=y\\ 0,&\quad for\ x\
eq y\end{cases} \tag{12}\]  
where \(H_{x}(j)\) and \(H_{y}(j)\) represent the _jth_ element of the \(X\) and \(Y\) code sequence.  
The power spectral density (PSD), \(P(v)\), at PIN PD is [41]:  
\[P(v)=\frac{R_{p}}{\Delta v}\sum_{i=1}^{L}du\left(\sum_{j=1}^{L}H_{x}(j).H_{y}(j )-\sum_{j=1}^{L}(H_{x}(j).H_{y}(j)(H_{x}(j))\right)\left\{u\left[\frac{\Delta v }{L}\right]\right\} \tag{13}\]  
Figure 4: Block diagram demonstrating the receiver of the OAM/OCDMA-FSO system. (**a**) Receiver structure and (**b**) example of OCDMA decoder of user 1.  
where \(\Delta v\) and \(u\left[\frac{\Delta v}{L}\right]\), respectively, represents optical bandwidth and unit step function.  
The current received at PIN PD, \(I_{U}\), is expressed by [41]:  
\[I_{U}=\mathcal{R}\int_{0}^{\infty}P(v)dv=\mathcal{R}\left(\frac{R_{p}(P-1)}{L}\right) \tag{14}\]  
where \(\mathcal{R}\) is PIN PD responsivity, and \(R_{p}\) is the received power, and, in the case of FSO link, is expressed as [42]:  
\[R_{P}=T_{P}\bigg{(}\frac{D_{RX}}{D_{TX}+\theta S}\bigg{)}^{2}10^{\frac{-\theta S }{40}} \tag{15}\]  
where \(R_{P}\) and \(T_{P}\), respectively, are transmitted and received power. \(D_{TX}\) is transmitted diameter while \(D_{RX}\) is the received diameter. \(S\) denotes propagation range, \(\theta\), indicates divergence angle of laser beam, and \(\beta\) is the atmospheric attenuation.  
The shot noise power, \(P_{Sh}\), is [43]:  
\[P_{Sh}=2\ e\ B_{electric}(I_{U})=\mathcal{R}\bigg{(}\frac{R_{p}(P-1)}{L}\bigg{)} \tag{16}\]  
where \(e\) is electron charge and \(B_{electric}\) is electrical bandwidth.  
The PINN noise power, \(P_{PN}\), is [44]:  
\[P_{PN}=\mathcal{R}\int_{0}^{\infty}(P(v))^{2}dv=\frac{\mathcal{R}^{2}B_{electric }{}^{2}R_{p}{}^{2}N_{u}P^{2}}{L^{2}\Delta v}\ (P-1) \tag{17}\]  
Thermal noise can be expressed as [45]:  
\[P_{Th}=\frac{4k_{B}TB_{e}}{R_{L}} \tag{18}\]  
where \(k_{B}\) is Boltzmann constant, and \(R_{L}\) and \(T\) are load resistance of receiver and absolute temperature of receiver noise, respectively.  
The signal-to-noise ratio, SNR, is [7]:  
\[\mathrm{SNR}=\frac{(I_{U})^{2}}{P_{Sh}+P_{PN}+P_{Th}} \tag{19}\]  
The BER can be expressed as [25]:  
\[\mathrm{BER}=0.5\mathit{erfc}\bigg{(}\sqrt{\frac{\mathrm{SNR}}{8}}\bigg{)} \tag{20}\]  
where \(\mathit{erfc}\) is a complementary error function.  
Table 5 shows the parameters used in the proposed OAM/OCDMA-FSO system that are evaluated and simulated using Optisystem software version 18 [12; 46; 47].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Power of spatial laser & 15 dBm \\ Laser wavelength & 1550 nm \\ Laser linewidth & 10 MHz \\ Spatial modes & \(LG_{0,0}\), \(LG_{0,10}\) \\ Bit rate/channel & 10 Gbps \\ Electrical bandwidth & 0.75 \(\times\) bit rate Hz \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters used in the simulation [12; 46; 47].
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 2 Related Work
### Application of Deep Learning in Turbulence Modelling  
In recent years, machine learning and deep learning methods have been used in the field of fluid mechanics, demonstrating immense potential in applications such as turbulence modeling.  
Various machine learning techniques have been applied to modify traditional RANS models to enhance the flow prediction, including modifying model parameters with Bayesian methods [23], introducing a correction factor for the turbulence production term using neural networks [24], adding a spatially distributed correction field via field inversion and Gaussian process [25], etc. However, the various types of corrections trained for traditional RANS models are usually not physically interpretable.  
Other than simply adding corrections to existing model parameters, more comprehensive efforts have been made to construct new Reynolds stress closures via physics-informed machine learning. A random forest method was used in [PERSON] [26] to train Reynolds stress discrepancy functions, but the performance of mean quantities needs to be further validated. At the same time, the gene expression programming (GEP) method has been introduced [27] to develop explicit algebraic (Reynolds) stress models (EASM) based on the stress tensor decomposition proposed by [PERSON] [28].  
Although machine learning for turbulence model development is becoming a growing trend, obstacles and problems still exist in training and implementing the models to engineering applications. Using machine learning to enhance traditional turbulence models for improved accuracy in flow predictions remains an area that requires continuous and thorough exploration.  
In the field of turbulence model mapping, according to our investigation, there is currently no existing work that utilizes deep learning to extract features from flow field data and regress these features to map them to more accurate large eddy simulation (LES) data. Nevertheless, this research topic remains significant as deep learning regression and mapping hold the potential to rapidly and accurately acquire complex and precise flow field data, significantly reducing the time and resources required for traditional fluid dynamics calculations. Additionally, it provides a novel approach for modifying traditional RANS models.
# Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling
## 1 Introduction
The new contributions provided to the currently available literature are (i) presenting a resistance function determining the UAVs' mission resistance to both changes in weather conditions and changes to the times and location of deliveries, (ii) demonstrating the condition guaranteeing convexity of the resistance function in the polar coordinate system and (iii) conducting computational experiments confirming the usefulness of the proposed approach (i.e., the model and the condition based on it) for online planning of the UAVs' fleet missions. In other words, the proposed approach supports the decision-makers in their search for a plan of the missions (with routes and flight schedules), which allows for the delivery of an expected quantity of goods to a specific group of recipients. Introducing a new measure of the UAVs' mission resistance and the analysis of the conditions guaranteeing its convexity allowed us to develop a declarative model of proactive/reactive UAV mission planning. Its computer implementation enables us to solve problems of scale encountered in practice online (distribution of goods for up to 90 customers). The remainder of this paper is organised as follows: Section 2 elaborates on related research. Section 3 presents the methodology used in the approach to UAV mission contingency planning. Section 4 describes the mathematical formulation for the declarative model for reactive planning of deliveries by the UAV fleet in terms of the constraint satisfaction problem. In Section 5, we focus on the relaxation of the convex resistance function aimed at developing restrictions, reducing the time-consuming assessment of weather conditions. Section 6 presents experiments that have been carried out and elaborates on the results obtained. Finally, Section 7 provides the final conclusions followed by a description of future research.
## 2 Related Works
### Description of the Pruning Algorithm  
Sonobuoys are battery-powered devices, making energy efficiency a critical factor directly linked to operational duration. To operate effectively within a limited power environment, this study aims to minimize the computational load and the number of parameters of the encoder mounted on the sonobuoy by applying an unstructured pruning technique that removes unnecessary weights. The proposed pruning technique consists of initial training stages, pruning and retraining stages, and iterative pruning with performance evaluation. It is designed to maximize efficiency while minimizing performance degradation through iterative pruning.  
Furthermore, the acoustic signals collected by the sonobuoy are transmitted to an MPA for underwater target detection and analysis. Since the decoder is mounted on the MPA, which receives a continuous power supply, it is not constrained by energy consumption. Therefore, this study focuses on applying pruning techniques exclusively to the encoder, which is mounted on the battery-powered sonobuoy, to maximize energy efficiency and achieve high performance.
# Cocoa Bean Shell as Promising Feedstock for the Production of Poly(3-hydroxybutyrate) (PHB)
###### Abstract  
Cocoa bean shell (CBS), a by-product of the chocolate industry, has been employed as a substrate to obtain poly(3-hydroxybutyrate) (PHB) by fermentation with _ Bacillus firmus_. With this aim, acid-thermal hydrolysis of CBS (20% \(w/v\)) was conducted at 135 \({}^{\circ}\)C for 10 min so that broths rich in fermentable sugars were obtained. These broths, both non-centrifuged and centrifuged, were employed as fermentation media. Significant polymer production was obtained from the broth with solids (non-centrifuged) with a yield of 107 mg of PHB/g dry matter. These results indicated that the presence of CBS solids played an important role in microorganism metabolism, with them being fundamental to the production of PHB. Experimental data were fitted by a model based on irreversible first-order reactions, and kinetic constants were obtained for solubilisation, hydrolysis, and sugar consumption. Although, several studies on obtaining PHB from other agri-food residues have been published, this is the first work on PHB production from CBS, with the study obtaining promising results with PHB concentrations similar or even higher than the others previously reported.  
cocoa bean shell; bioplastics; polyhydroxybutyrate; fermentable sugars; hydrothermal treatment
## 6 Discussion  
Supporting pillar placement is the principal problem in VIG manufacturing because it affects the stress concentration distribution, mechanical properties and thereby the product life. In this study, comprehensive, multifaceted mechanical analysis was performed and a multi-objective solution for supporting pillar placement was established based on the stress and deformation distribution. We obtained critical parameters for the supporting pillar radius and spacing distance. The findings through case simulation and verification help us understand how the supporting pillar determines VIG mechanical properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatments** & **Strain Distribution and Maximum Deformation** & **Stress Distribution and Maximum Stress** \\ \hline R3a63 & & \\ r = 0.3 mm & & \\ a = 63 mm & & \\ \hline R3a50 & & \\ r = 0.2 mm & & \\ a = 63 mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The supporting stress and deformation of the glass substrate under different supporting pillar radii and spacing distances. R3a63, with a 0.3 mm supporting pillar radius and 63 mm spacing distance; R3a50, with a 0.3 mm supporting pillar radius and 50 mm spacing distance; R2a63, with a 0.2 mm supporting pillar radius and 63 mm spacing distance.  
Firstly, due to the quadrilateral placement of supporting pillars, the stress and deformation concentration points of the glass pane showed a quadrilateral array distribution tendency. The way to improve mechanical properties for VIG mainly focused on optimizing the maximum stress value in the stress concentration area.  
Secondly, glass pane parameters, such as elastic modulus, thickness and Poisson ratio, inevitably affect the stress and deformation distribution. Therefore, it is necessary to design the critical supporting parameters to ensure the mechanical stability for VIG, according to the pane-specific structural and material properties. This paper provided a multi-objective solution to solve critical spacing parameters, so that we could obtain the acceptable scheme for the supporting pillar placement according to the design requirements.  
However, this study has some limitations. Firstly, the feasibility of the multi-objective critical model was verified by computer simulation. In fact, the mechanical properties of VIG also need to be subjected to sample testing and microstructure comparison. Second, a more profound understanding and interpretation of the stress characteristics of VIG is necessary. The results of this study are not sufficient to explain the stress situation under other supporting pillar placement forms such as regular triangle and regular hexagon supports.