esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Covalent Interactions
### CNT-Based Nanocomposites
#### 2.1.1 Grafting-from through Surface-Initiated Polymerization
Applying ultrasound waves in an aqueous medium generates highly reactive radicals which may then initiate the polymerization reaction ([PERSON] et al., 2019). [PERSON] et al. (2019) described an initiator-free polymerization of the monomer N-isopropyl acrylamide (NIPAM) in the presence of the crosslinker \(N\),\(N^{\prime}\)-methylene-bis-acrylamide (MBA) under the application of an ultrasonication power of 400 W into deionized water. The transient collapse of bubbles produced hydroxyl radicals which acted as initiators. Thus, the \"grafting-from\" polymerization of NIPAM onto the carbon nanofiber (CNF) surface was initiated by both the covalent introduction of chemical initiator moieties (azobisisobutyronitrile, AIBN) onto the CNFs, and the creation of hydroxyl radicals under ultrasonication that promoted the polymerization. The characteristic temperature-sensitivity of the  
Figure 6: Atomic force microscopy (AFM) image of vertically aligned (VA)-SWCNTs/polystyrene (PS) nanocomposites (Reprinted from [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Functionalization of Vertically Aligned Carbon Nanotubes with Polystyrene via Surface Initiated Reversible Addition Fragmentation Chain Transfer Polymerization. _Appl. Surf. Sci._**2012**, _258_, 2836–2843 (16), with permission (2020) from Elsevier). Figure 7: FESEM images of VA-CNTs (**a**) before and (**b**) after the grafting of polyaniline by a grafting-from method (Reprinted from [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Direct Growth of Polyaniline Chains from N-Doped Sites of Carbon Nanotubes. _Small_**2013**, \(9\), 3829–3833 (23), with permission (2020) from WILEY). resulting polymer combined with the CNFs' conductive properties due to the presence of defects make these nanocomposites very promising as electrochemical sensors.
## 3 Work-Flow
### Implementation Notes  
SNAP is implemented as a full-stack application and runs on Linux or MacOS Apache MySQL PhP (LAMP/MAMP) stack [5; 48]. The framework's back-end database uses MySQL for user account, project, and configuration management. User accounts are created using two step-account creation and the MD5 hashed passwords are stored with user account information in the database. Some of the tested NLP tool kits also use the MySQL database back-end for meta-data and book-keeping management. With exception of the interactive visualization, the front-end web interface is implemented in PhP using Codelgniter libraries, and the visualization layer uses JavaScript libraries to implement the interactive visualization as an in-browser applet [49; 50]. The NLTK libraries implement the NLP functionality and is accessed as a web service from the web interface using Python2.7 [40]. The semantic concepts are processed using Gephi's headless API that is also accessed as a web-service using Java interface [42]. The rest of the SNAP's software is implemented in Java that includes the frequency filtering, semantic flow processing, and file I/O. In case the resulting semantic network graph is too large and cannot be visualized using the in-browser applet, the data structures can be exported into a visualization using an external partiview application [51]. For the exact list of packages, binaries, and libraries, please see the project portal [5].  
Figure 1 not only illustrates the document processing work-flow, it also closely parallels the design and implementation of the modular work-flow as the framework's software components. The document processing parameters for each software component are stored and queried from the project configuration before each processing step and are stored in the user's profile in the database. As the software components are stand-alone and do not rely on each other, the overview figure also captures the software component design schema.
# Patent Analysis of the Development of Technologies Applied to the Combustion Process
## 1 Introduction
The presence of acoustic oscillations in the combustion process improves the mixing rate between the fuel and oxidizer due to the creation of intense turbulence zones in the flame region. This provides several advantages for this technique, including a higher combustion efficiency with reduced energy consumption and little loss of fuel, increased combustion intensity, increased heat- and mass-transfer rates, low capital investment for implementation, and--more importantly--reduced NOx and CO pollutant emissions [13; 15]. One study evaluated the interactions between combustion, turbulence, and acoustic excitation in a premixed flame on a turbulent Bunsen burner. The thermoacoustic interactions were produced by an active system based on speakers, and an acoustically excited and non-excited flame front were compared. The results demonstrated that acoustic excitation changes the pattern of turbulent flow, and by changing this pattern it is possible to actively impact the combustion process, therefore eliminating or reducing combustion instabilities [16]. Acoustic excitation has also been tested on a premixed jet flame (liquid petroleum gaseous flame). In this experiment, acoustic excitation was used to control flame stabilization and perturb its flow. Two external loudspeakers were used for this. The results showed that the flame length and core decreased as the amount of excitation was increased, promoting a good combination characteristic in the flame and thereby allowing a better temperature distribution and burning efficiency, and also reducing the amount of carbon produced [17]. In another study, the interaction of OEC technique with the acoustic excitation of flames was studied, and its effects on the atmospheric emissions of CO, NOx, formaldehyde, and acetaldehyde, as well as the temperature of exhaust gases, in diffusive and confined natural gas flames were evaluated. The results showed a reduction in the emissions of CO and NOx with OEC and acoustic excitation, indicating that both techniques together can reduce pollutant emissions and increase the efficiency of thermal combustion equipment [11]. It is possible to verify the improvements provided to the combustion process in burners by these techniques, allowing for the characterization of the use of acoustic excitation and OEC methods to reduce the emission of pollutant gases and increase the energy efficiency of the system. However, the number of records evaluating these two processes in combination is still scarce in the literature. For this reason, this study aimed to perform a technological prospection that focused on patents in order to verify the technological panorama evaluating the combustion process coupled to the use of OEC under the influence of acoustic excitation.
# Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds
###### Abstract  
Floods cause considerable damages worldwide and mitigation of their adverse effects through effective protection measures is needed. Along with the commonly applied \"grey\" infrastructure, \"green\" measures that can offer additional benefits, such as ecosystem services, are increasingly being considered lately. While the recent research tendencies are focused on the effectiveness and the value of green measures in urban areas, this paper presents a comprehensive financial evaluation of green and grey flood mitigation scenarios for a smaller rural watershed. A micro-scale damage model that builds on the hydrodynamic modeling of hazard, detailed asset identification, and damage assessment is presented and applied for evaluation of benefits from various flood mitigation measures in the Tamnava watershed in Serbia. Four scenarios are considered: (1) existing flood protection system; (2) green scenario involving new detention basins; (3) grey infrastructure enhancement by rising of the existing levees and diverting flood discharges; and (4) green-grey scenario that combines scenarios (2) and (3). The benefits (loss reduction) are the greatest with the green scenario and marginally higher with the combined green-grey scenario. The results suggest that for small rural watersheds, a holistic, integrative approach that includes both types of infrastructure can provide the most effective flood risk mitigation.  
flood risk; flood damage; flood mitigation measures; green infrastructure; financial appraisals of damages; depth-damage functions; rural watersheds Article
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.5 Inner Layer Filling
2:\(indication\leftarrow-1\)
3:while\(indication=-1\)do
4:\(d_{1}\gets d.south\), \(d_{2}\gets d_{1}.east\)
5:\((x_{1},y_{1})\leftarrow ArbitraryDistant(d.x,d.y,d_{1}.x,d_{1}.y,d.r,d_{1}.r,0)\)
6:\((x_{2},y_{2})\gets not\ defined\)
7:if\(d_{2}\) is not \(defined\)then
8:\(d_{3}\gets TwoNeighbours(d,d_{1})\)
9:else
10:if\(\sqrt{(d_{2}.x-d.x)^{2}+(d_{2}.y-d.y)^{2}}\leq d_{2}.r+d.r\)then
11:\(d_{1}\gets d_{2}\), \(d_{2}\gets d_{1}.east\)
12:\((x_{1},y_{1})\leftarrow ArbitraryDistant(d.x,d.y,d_{1}.x,d_{1}.y,d.r,d_{1}.r,0)\)
13:endif
14:\(d_{3}\gets ThreeNeighbours(d,d_{1},d_{2})\)
15:if\(d_{3}\) is not \(defined\)then
16:\(d_{3}\gets TwoNeighbours(d,d_{1})\)
17:else
18:\((x_{2},y_{2})\leftarrow ArbitraryDistant(d_{1}.x,d_{1}.y,d_{2}.x,d_{2}.y,d_{1}.r,d_{2}.r,0)\)
19:endif
20:endif
21:\(D\gets D\cup\{d_{3}\}\), \(d\gets d_{3}\)
22:\((x,y)\leftarrow ArbitraryDistant(x_{1},y_{1},m,y_{ref},0,0,d.r)\)
23:if\((x,y)\) and \((x_{2},y_{2})\) are \(defined\)then
24:if\((x_{2}-x)^{2}+(y_{2}-y)^{2}>d.r^{2}\)then
25:\((x,y)\gets ArbitraryDistant(x_{2},y_{2},m,y_{ref},0,0,d.r)\)
26:endif
27:endif
28:if\((x,y)\) are \(defined\)then
29:\(indication\gets 0\)
30:else
31:\(indication\leftarrow-1\)
32:endif
33:endwhile
34:if\((x_{2},y_{2})\) are \(defined\)then
35:\(indication\leftarrow EquiDistant(d,\{(x_{1},y_{1}),(m,y_{ref}),(x_{2},y_{2})\},0)\)
36:else
37:\(indication\leftarrow EquiDistant(d,\{(x_{1},y_{1}),(m,y_{ref})\},0)\)
38:endif
39:\(d_{1}\gets D[2]\)
40:if\(\sqrt{(d_{1}.x-d.x)^{2}+(d_{1}.y-d.y)^{2}}\leq d_{1}.r+d.r\)then
41:\(x_{3}\gets m,y_{3}\gets d_{1}.y-\sqrt{d_{1}.r^{2}-(m-d_{1}.x)^{2}}\)
42:\((x,y,r,h,b,e,h_{ref})\leftarrow(d.x,d.y,d.r,d.h,d.b,d.e,d.h_{ref})\)
43:\(indication\leftarrow EquiDistant(d,\{(x_{3},y_{3}),(m,y_{ref})\},1)\)
44:if\(indication=0\)then
45:if\((x_{1}-d.x)^{2}+(y_{1}-d.y)^{2}>d.r^{2}\) or \(((x_{2},y_{2})\) are defined and \((x_{2}-d.x)^{2})+(y_{2}-d.y)^{2})>d.r^{2}\)then
46:\((d.x,d.y,d.r,d.h,d.b,d.e,d.h_{ref})\leftarrow(x,y,r,h,b,e,h_{ref})\)
47:endif
48:endif
49:\(y_{ref}\gets d.y+\sqrt{d.r^{2}-(m-d.x)^{2}}\)
```  
**Algorithm 4** InnerXDirectionFilling--Filling inner layer along the \(x\) direction
# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 4 Experimental Results and Analysis  
### Dataset  
This paper uses the RDD2022 dataset [28] from the open-source Crowdsensing-based Road Damage Detection Challenge (CRDDC'2022) [29] for model training. The RDD2022 dataset consists of 47,420 road images collected from six countries: Japan, India, the Czech Republic, Norway, the United States, and China. The dataset is prepared following the format of the PASCAL Visual Object Classes (VOC) datasets [30], with XML files providing annotations for over 55,000 instances of road damage. As illustrated in Figure 10, the dataset encompasses four damage types: longitudinal cracks (D00), transverse cracks (D10), alligator cracks (D20), and potholes (D40), with respective counts of 26,016, 11,830, 10,617, and 6544 instances. The distribution of these damage types reveals that longitudinal cracks are the most prevalent, while potholes are relatively less frequent.  
The RDD2022 dataset is segmented into two sets: the training set and the test set. The distribution of images and labels across different countries in the dataset is illustrated in Figure 11. Images in the training set are accompanied by annotation files, while images in the test set do not include annotations. Therefore, the detection results need to be converted into files in a specific format and submitted to the CRDDC'2022 official website ([[https://crddc2022.sekilab.global/](https://crddc2022.sekilab.global/)]([https://crddc2022.sekilab.global/](https://crddc2022.sekilab.global/)) (accessed on 1 March 2024)) for evaluation. Consequently, we divided the data from the RDD2022 training set into our experimental training set and validation set in a 7:3 ratio, using the RDD2022 test set as our experimental test set. We then tested the trained model on the test set and submitted the results to the official website to view the outcomes.  
Figure 10: Example images for road damage categories.
###### Abstract  
Molecularly imprinted polymers (MIPs) as well as non-imprinted polymers (NIPs) were synthesized for selective sorption of lead and terbium. The ratio of raw monomers for the terbium-MIPs' synthesis was optimized based on the results of the synthesis of lead-MIP. It was found that the molar ratio of template/monomer/monomer/cross-linker = 1:5:5:8 was the most accurate for successful synthesis of the target MIP. As a result, the yields of the MIP and NIP on terbium were 59.3% and 61.2%, respectively. The structure of the imprinted samples was determined by FTIR spectroscopy. SEM analysis of the imprinted structures showed that the Tb-MIP contained a large number of pores compared to the NIP. The size of these pores ranged from 0.779 \(\upmu\)m to 1.874 \(\upmu\)m. The results of sorption experiments showed that the adsorption efficiency of Tb-MIP was seven times higher than that of NIP: the sorption degree was 70.80% for MIP and 9.95% for NIP. The imprinting factor was calculated and was equal to 7.06. The sorption process was described by the Radushkevich and pseudo-second-order kinetic models. It was shown that sorption by NIP occurred with a fast saturation of a lower Tb concentration, and the MIP's sorption passed slower and more efficiently. The desorption degrees of Tb-MIP and NIP were 90.15% and 52.67%, respectively.  
molecular imprinting; molecular recognition; imprinting factor; synthesis protocol; sorption of metals; lead nitrate; terbium nitrate
# Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
###### Abstract  
Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R\({}^{2}\)) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.  
artificial neural network; random forest; pile bearing capacity; pile foundation; sensitivity analysis 2020; 10.3390/app10051871
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 1 Introduction  
Humankind is facing an energy crisis. It is estimated that the Earth's coal reserves will last only another hundred years, while petroleum and gas will last even less. This has led, in recent decades, to the development of green and alternative energy resources. One key requirement is that these technologies should save energy without reducing the quality of life or economic growth [1,2].  
The use of renewable energy, such as solar energy, wind power, water power, tidal power, sea temperature difference, geothermal energy, and biomass energy is considered essential for sustainable development and is an active research area [3,4]. In Taiwan, wind and solar power are potentially the most important renewable energy sources. To keep noise levels low, wind power systems are only practical in the outskirts of cities, and for economic feasibility, they need to be built on a large scale [5,6]. Another source of renewable energy with great potential is solar energy; the amount of solar energy reaching the Earth in 40 min is equal to humankind's annual energy requirement. Taiwan is located in the subtropical region close to the equator and it thus receives sufficient sunshine. Solar energy is a clean energy source, and we believe that developing solar energy is a reasonable solution to meeting Taiwan's energy needs [7, 8, 9].  
Overview of the study: Tracking the sun's trajectory in the sky and aligning the solar energy system with it can greatly improve the performance. Professor [PERSON] _et al._[10] proposed to use a solar tracker which orients itself along the direction of the sunlight, and the solar tracker positions the panelin a hemispheroidal rotation to track the movement of the sun and thus increase the total electricity generation. [PERSON] _et al._[11] reported that using tracking functional solar cells can increase the output by 50% compared to a fixed system. This necessitates the use of a two-axis tracking system. The system needs to drive two sets of motors to control the angle of the solar panels, which increases hardware costs and electrical energy consumption. Professor [PERSON] [12] of Kaohsiung University of Applied Sciences has studied one-axial and two-axial tracking systems in detail. This article proposes the application of fuzzy control theory in a solar tracking system, which is fully automatic in a changing environment, and which takes into account meteorological changes and the external environment arising from a malfunction. This approach avoids cloudy skies, cloud cover, and unstable weather to reduce unnecessary energy loss. The database associated with the elevation and horizontal angle of the solar cells is used to achieve the best quality of the effect of charging the system.
## 2 The Bridge-Borne Noise Prediction Model
### Model Validation  
To ensure the accuracy of the simulation results, it is necessary to validate the model. As the freight EMU train has not been placed into official operation, relevant experiments cannot be carried out. Therefore, the tested results under the condition of a high-speed passenger train passing through a bridge are adopted to verify the model. It should be noted that the bridge and track types at the test site are consistent with those in the model, the same operating conditions are set in the simulation, and the train speed is about 220 km/h. The test content includes the bridge bottom plate acceleration and the bridge radiated noise near the deck, as shown in Figure 2a. The acceleration sensor used in the test is INV9824 with a measurement range of 1000 g (produced by COINV, Beijing, China), and the INV9206 noise sensor (produced by COINV, Beijing, China) is adopted to measure noise from 20 dB to 146 dB. The data acquisition system adopted a 16-channel device by the IMC GmbH. Figure 2b,c show the comparisons of measured and computed bridge accelerations in the time and frequency domains, respectively.  
It can be observed that the vibration amplitudes of computed results in the time domain are consistent with the measured one, and both the main frequencies of the measured and computed acceleration RMS are around 50 Hz with close amplitudes. Figure 2d shows the comparison of the measured and computed 1/3 octave band SPLs at the test point. It can be seen that both the main frequencies of measured and computed SPLs are 50 Hz, and the overall trends of the SPLs in the frequency range of 20-200 Hz are similar; correspondingly, the SPLs are close. In summary, the computed results are in good agreement with those of the tested results, which verifies the effectiveness of the simulation model.
## 3 Methodology and Algorithm Descriptions
### Performance Metrics  
The evaluation was carried out across several dimensions to assess both computational efficiency and the quality of the output. Execution time was measured in milliseconds to determine the total duration each algorithm required to complete the mining process. Memory usage was tracked by recording the peak memory consumption during execution. The number of valid association rules generated in each experiment was also counted to gauge rule density. To evaluate the strength and relevance of the discovered rules, statistical metrics such as support, confidence, and lift were analyzed. Additionally, accuracy and precision were assessed using SPSS, with an emphasis on the correctness and generalizability of the generated rules.  
Each experiment was repeated multiple times to ensure result stability and reduce the impacts of outliers.
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
## 1 Introduction
### Vehicle Driver Models  
Several different approaches to creating vehicle driver models in multi-body software were introduced. In the investigation of optimal cabin suspension parameters in [6] or [28], a rigid connection between the driver and the cabin was adopted. Typically, a spring-damper system is employed to model seat suspension with the mass of the suspended part of the seat combined with that of the driver [29; 30].  
In developing active seat suspension systems, modelling the influence of the seat cushion is a common practice. Such models with a spring-damper element between the seat and the human body are used in [13; 31; 32; 33]. Several models include additional stiffness and damping between the lower and the upper parts of the human body [34; 35].  
A study of the effects of human feet and floor interaction on whole body vibration level was conducted in [36]. A seventeen DOF human biodynamic model and a six DOF seat model were constructed in Adams software to prove that this effect is not negligible. A dynamic model of the bus riding on uneven roads was used in this simulation study.  
Different kinds of human-seat interaction models are being developed for seat design purposes. A review of these models, which often include human back and backrest interaction, can be found in [37]. Multi-body models with contact algorithms are utilised  
Figure 2: Half model of the agricultural tractor.  
to study the influence of seat geometry and friction parameters on human body loading. Anatomically based FE models capturing the physical properties of bones, soft tissues, and skin are implemented in seat development [37].
## 5 DoA Estimation Techniques  
### Delay and Sum (DAS) Technique  
In the delay-and-sum (DAS) method for estimating DoA, the power of the signal is computed for each angle, and the direction with the highest power is selected [5]. The DAS beamformer output for various satellites is expressed as follows:  
\[P_{DAS}(\theta)=\mathbf{A}^{H}Cov_{D}(\mathbf{r})\mathbf{A}, \tag{20}\]  
where \(Cov_{D}\) is the spatial covariance matrix for \(\xi\) number of datas (i.e., samples) collected from the antennas and is given by:  
\[Cov_{D}(\mathbf{r})=\frac{1}{\xi}\sum_{i=0}^{\xi-1}\mathbf{r}_{i}\mathbf{r}_{i }^{H}, \tag{21}\]  
where \({}^{H}\) represents the Hermitian matrix.