esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Damage Identification of Beam-like Structures Considering Shear Deformation
### Example 3: Simply Supported Overhanging Beam with Five Elements  
To illustrate the effectiveness of the proposed method, a simply supported overhanging beam was studied. Figure 6 depicts the model used for identification, which is a simply supported overhanging beam with a length of 1.8 m, composed of six nodes and five beam elements.  
The material properties for this beam were the same as those used in previous examples, with a rectangular cross-sectional shape (see Table 1). The applied external forces consisted of concentrated vertical forces at nodes 1, 5, and 6, with magnitudes of \(-50\) kN, \(-50\) kN, and \(-100\) kN, respectively. The damage scenarios of the structure are shown in Table 6.  
The nodal displacements at DoFs 2, 5, 14, and 17 were selected as the necessary data for establishing the objective function. Figure 6(a)-c illustrate the identification process of three different damage scenarios using the objective function established based on the method that accounts for shear deformation.  
Figure 5: The MRE during the identification for three different scenarios.  
Figure 6: Simply supported overhanging beam with five elements.  
Figure 7: _Cont._  
From Figure 7, it can be observed that the proposed damage detection method that accounts for shear deformation successfully identified the corrosion depths for all three damage scenarios. For damage Scenarios 1, 2, and 3, the objective function was iterated 25, 33, and 46 times, respectively, to complete the damage identification for two beam elements, three beam elements, and four beam elements.  
The MRE of corrosion depth identification results for three different damage scenarios are given in Figure 8.  
Figure 8 clearly shows the variation of MRE with iterations for all three damage scenarios, which eventually decrease to near-zero values. This indicates that the proposed method successfully identified structural damage in all scenarios, confirming its effectiveness and accuracy.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 1 Introduction
The next section briefly identifies various data consumers that are present in a smart parking system and categories them as either on-site or over-the-Internet type. Section 3 summarizes the related research works, and Section 4 elaborates on the different aspects of efficiency for a communication model. The further section evaluates the models, discusses their relevance and converges on an efficient multi-collator communication model.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 2 The Case Study  
### Parameter Value Selection Criteria  
The text highlights that the filter radius (R) and penalization (p) are the two main parameters being analyzed. These parameters should not be chosen arbitrarily but require careful consideration to achieve a satisfactory outcome.  
For penalization p, previous studies [1; 26] indicate that a minimum value of three should be used for isotropic materials with a Poisson Ratio v\(\sim\)1/3, within the defined topology optimization method. The relative density of each element follows the SIMP power rule, and it is observed that higher penalization values lead to sharper trends towards either 0 or 1 density. To analyze the parameter's impact, values ranging from three (3) to six (6), inclusive, with increments of one (1) are chosen based on previous studies [1; 26]. These values allow one to observe the transitional changes in the resultant shapes when larger penalization values are employed.  
For filter radius R, the values were taken from different scopes, and they are summarized in Table 1. These sources include  
* Values used in other mentioned studies (OS).
* Values derived from the Euclidean distance between the element under analysis and the neighboring layer of elements (ED).
* Large estimated values intended to observe the behavior of the method with extreme values (LV).  
The values of the filter radii in Table 1 were rounded to reduce computation time. This approach was chosen because the filter radius is a highly recurrent value throughout the process. Rounding the number to a value that does not overlap with the next filter radius value has no impact on the result.
# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 3 Results and Discussion
### Electronic Nose Compensation  
In this study, we employ an artificial neural network (ANN) model for electronic nose compensation, a popular algorithm in the field of machine learning that has received attention for its great potential in handling compensation of electronic nose data. Previous studies, such as the work of [PERSON] et al. [25], have demonstrated the effectiveness of artificial neural networks for gas sensor compensation. The core of this study is to explore the influence of drift factors on the feature optimization process; therefore, we have used an artificial neural network model to comparatively analyze the data before and after feature optimization, with a view to revealing its specific impact on the performance of electronic noses.
## 11 Related Work  
Over the last decade, researchers from different disciplines, such as sociology, politics and history, studied the importance of the web in the context of several application domains and discussed the necessity of web archiving to enable retrospective analysis of web content in these domains (e.g., [38; 39; 40; 41; 42]). At the same time, several projects have pursued web archiving (e.g., [43; 44]). A large number of national libraries and national archives are now actively archiving the web as part of their heritage preservation mission. The Heritrix crawler [45], jointly developed by several Scandinavian national libraries and the Internet Archive through the International Internet Preservation Consortium (IIPC) [46], is a mature and efficient tool for large-scale, archival-quality crawling.  
Standard crawling methods aim to capture as much of the web as possible. In contrast, focused crawling [47] aims to only crawl pages that are related to a specific topic. Focused crawlers (e.g., [48; 49]) learn a representation of the topic from the set of initial pages (seed URLs) and follow links only if the containing page matches that representation. Extensions of this model use ontologies to incorporate semantic knowledge into the matching process (e.g., [50]), \"tunnel\" between disjoint page clusters (e.g., [51]) or learn navigation structures necessary to find relevant pages (e.g., [52]). ARCOMEM complements existing approaches to long-term preservation by developing new ways into intelligent and adaptive decision support for content appraisal and selection for web archiving by leveraging the social web and taking events, entities and topics into account. Furthermore ARCOMEM preserves the semantic context of web objects by enriching web archives with the extracted information.  
The social web provides an additional source of data for parliament applications. Many services, such as Twitter, YouTube or Flickr [53], provide through their APIs access to structured information about users, user networks and created content and are therefore attractive to researchers. Data collection from these services is not supported by standard web crawlers. Usually it is conducted in an _ad hoc_ manner, although some structured approaches exist [54; 55; 56]. However, these platforms focus on API access and do not archive web pages linked from social web platforms. In the ARCOMEM project, the first approaches were investigated to implement a social- and semantic-driven appraisal and selection model for web and social web content.  
A limited set of tools exists for accessing web archives, like NutchWAX [57] and Wayback [58]. NutchWAX provides URL and keyword-based access based on Apache Nutch [59], an open source web search project. Wayback is an open source implementation of the Internet Archive Wayback Machine. It allows browsing the history of a page or domain over time. Overall, the possibilities to explore web archives are limited to basic functionalities. In ARCOMEM, we developed and evaluated SARA--a search and retrieval application that enables users to navigate through the archived content using semantic information.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## SONAR
### Summary and Future Direction  
Satellite derived bathymetry has gained popularity among scientists because of its synoptic coverage and capacity to collect information from inaccessible locations. Bathymetric mapping research is currently focusing on the efficacy of providing operational products, with the future focusing on real-time operational application in ports, harbors, channels, creeks, etc. In this manner, several satellite agencies are currently debating the current constraints of best-fit sensors for precision data collecting for bathymetric mapping, particularly in shallow waters of coastal regions.  
In the case of classification tasks, the hybrid approach undeniably offers the opportunity to think beyond the horizon of conventional methods/algorithms. Some of the constraints, such as chlorophyll, turbidity, and other parameters in the complex water bodies pose significant challenges that has fallen by the wayside in bathymetric mapping literature for the past several years. To address these challenges, accurate datasets, as well as advanced statistical and machine learning models are required.
## 4 Result Analysis
### Optimization of Patch Location on Damping Loss Factor
CFCF and SFSF have almost the same patch optimization location, but the damping loss factor of the former is always smaller than that of the latter. For CGSF, the optimal location of the patches is always on the free boundary, which is similar to CEFF. Therefore, it can be concluded that there will be a greater loss factor away from the constraint. The damping loss factor can be obtained by solving the eigenvalue of the dynamic equation, which is related to the stiffness matrix and mass matrix of the system. The location of the patch affects the local stiffness matrix, but the influence of local stiffness on total stiffness is not clear. Therefore, it is not easy to theoretically analyze the reasons for this result, and the best way to derive a general rule is from the simulation results.
## 1 Introduction  
Instead of incandescent bulbs with poor lighting efficiency, light-emitting diode (LED) light sources offer high luminous efficacy, long lamp-life, and are mercury-free alternatives for indoor and outdoor lighting applications [1; 2; 3]. Consequently, LEDs has been widely utilized in our daily lives such as streetlights, flashlights, backlight sources, displays, decorative lighting, automotive lighting, and so on [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16].  
Streetlights, which illuminate a road, aim to provide a safe environment during the night-time for motorcycle/bicycle drivers and pedestrians [3; 17; 18]. Traditional lighting sources for streetlight applications have been high-pressure mercury lamps because of their low-cost. However, high-pressure mercury lamps are not energy efficient. In addition, the discharge tube containing mercury vapors is harmful in terms of polluting our environment when the lamp runs out. Therefore, LEDs have begun to replace the conventional high-pressure mercury streetlight. The conventional two-stage LED driver supplying a rated lamp power of greater than 70 W for streetlight applications, shown in Figure 1, consists of an input low-pass filter (\(L_{f}\) and \(C_{f}\)) connected with a full-bridge rectifier (\(D_{1}\), \(D_{2}\), \(D_{3}\) and \(D_{4}\)), an interleaved boost power factor correction (PFC) converter (including two capacitors \(C_{in1}\) and \(C_{in2}\), two diodes \(D_{B1}\) and \(D_{B2}\), two inductors \(L_{1}\) and \(L_{2}\), two power switches \(S_{1}\) and \(S_{2}\), and a DC-linked capacitor \(C_{B}\)), and a half-bridge-type LLC resonant converter (including a Direct-Current (DC)-linked capacitor \(C_{B}\), two power switches \(S_{3}\) and \(S_{4}\), a resonant capacitor \(C_{r}\), a resonant inductor \(L_{r}\), a center-tapped transformer \(T_{1}\) with two output windings, two output diodes \(D_{5}\) and \(D_{6}\) and an output capacitor \(C_{o}\)), along with a LED [19]. Due to two-stage power conversions, the circuit efficiency is limited and more power switches and components are required in the conventional driver.  
In response to these challenges, this paper presents and implements a single-stage LED driver with coupled inductors and high power factor for streetlight applications. Descriptions and analysis of operational modes, and design equations of key components in the presented LED driver, and experimental results obtained from a prototype circuit are demonstrated.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
###### Abstract  
The need for accurate tourism demand forecasting is widely recognized. The unreliability of traditional methods makes tourism demand forecasting still challenging. Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan's tourism demand forecasting. The networks are able to seize the dependence of visitor arrival time series data. The Adam optimization algorithm with adaptive learning rate is used to optimize the basic setup of the models. The results show that the proposed models outperform previous studies undertaken during the Severe Acute Respiratory Syndrome (SARS) events of 2002-2003. This article also examines the effects of the current COVID-19 outbreak to tourist arrivals to Taiwan. The results show that the use of the LSTM network and its variants can perform satisfactorily for tourism demand forecasting.  
deep learning; long short-term memory; tourism demand; Taiwan; gated recurrent unit +
Footnote †: journal:  
1
Footnote 1: institutetext: Department of Land Management and Development, Chang Jung Christian University,  
Tainan City 71101, Taiwan; [EMAIL_ADDRESS]
# Semantic Structure from Motion for Railroad Bridges Using Deep Learning
###### Abstract  
Current maintenance practices consume significant time, cost, and manpower. Thus, a new technique for maintenance is required. Construction information technologies, including building information modeling (BIM), have recently been applied to the field to carry out systematic and productive planning, design, construction, and maintenance. Although BIM is increasingly being applied to new structures, its application to existing structures has been limited. To apply BIM to an existing structure, a three-dimensional (3D) model of the structure that accurately represents the as-is status should be constructed and each structural component should be specified manually. This study proposes a method that constructs a 3D model and specifies the structural component automatically using photographic data with a camera installed on an unmanned aerial vehicle. This procedure is referred to as semantic structure from motion because it constructs a 3D point cloud model together with semantic information. A validation test was carried out on a railroad bridge to validate the performance of the proposed system. The average precision, intersection over union, and BF scores were 80.87%, 66.66%, and 56.33%, respectively. The proposed method could improve the current scan-to-BIM procedure by generating the as-is 3D point cloud model by specifying the structural component automatically.  
semantic structure from motion; deep learning; structural component classification; structure from motion; scan to building information modeling
## 3 How to Treat and Protect Cultural Heritage Assets against Biodeteriogens?
### Physical Methods  
Monochromatic visible light [10] is a very safe, useful, and simple method to reduce or prevent the growth of photosynthetic microflora. The most used wavelengths are blue (\(\lambda\) 470-490 nm), green (\(\lambda\) 500-530 nm), and red (\(\lambda\) 680-700 nm) (Figure 4). If the intent is to block the photosynthetic process, it has to be considered that most of these microorganisms also have accessory pigments. Removal of these may require different wavelengths. In addition, they are ineffective against other kinds of microorganisms, unless photosensizers (e.g., erythrosine-ERY) are applied [1]. In some cases, those that survive may grow on organic debris from dead cells that are left behind [5, 11].  
UV irradiation, especially UV-C (\(\lambda\) 254 nm) will kill most indoor colonizing microorganisms and are especially used in natural caves and hypogea [12, 13, 14] and show caves [15]. They are easy to handle, but some preliminary tests need to be performed to establish the distance of lamps and duration of treatment. As their target is DNA, they need to be carefully used (for example, they cannot be left on during tourists' visits), and any damage to the organic component and pigments should be considered.  
Thermal treatments (microwave, heat irradiation, heat shock treatment) are considered an ecologically safe alternative to traditional conservation methods [16, 17, 18, 19]. Thanks to their low-interaction with the substrate and penetration depth, they are safe for the cultural asset, for operators, and the environment. Further, they are effective against lichens and bryophytes and partially on green algae, but they do not have any effect on most of the environmental bacteria and some black fungi [1]. Some side effects, such as the possible diffusion of water into the substrate and/or the production of the organic debris of dead cells, which are readily used as a carbon source by other microorganisms (see above), have also been reported.  
Figure 3: An example of submerged archeological items lying on the seafloor subjected to several underwater environmental factors such as sea current and the nature of the seabed. Biodeterioration is mainly due to the activity of micro- and macro-foulers depending on the position of the items (Photo, [PERSON] and [PERSON], Diving Center Punta Stilo, Calabria, Italy).
## Appendix B Detailed Description and Background Data of the Calculation Model and Hourly Simulation
### Electricity and Hydrogen Hourly Balance  
The red rectangle, in Figure 3, includes both the electricity and the hourly balance. First, the electricity consumption of the services and residential buildings (E\({}_{\text{B}}\)), the HFS compressor and chiller (E\({}_{\text{HFS}}\)), SHS compressor (E\({}_{\text{SHS}}\)) is subtracted from the solar electricity production (E\({}_{\text{S}}\)). Any surplus solar electricity (E\({}_{\text{surp}}\)) is converted via electrolysis and water (H\({}_{2}\)O\({}_{\text{S}}\)) into \"solar\" hydrogen (H\({}_{\text{S}}\)). If there is a shortage of electricity, this is compensated for by electricity from the FCEV2 Gs (E\({}_{\text{V2G}}\)) by converting hydrogen (H\({}_{\text{V2G}}\)). The amount of hydrogen consumed for V2G (H\({}_{\text{V2G}}\)) is added the next  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Specific Energy Consumption**} & \multicolumn{2}{c}{**Average Annual Distance Driven d**} \\  & \multicolumn{2}{c}{**Transportation SEC\({}_{\text{T}}\) [kg H\({}_{2}\)/100 km]**} & \multicolumn{2}{c}{**[km/year/vehicle]**} \\ \hline
**Vehicle Type** & **Near Future [36]** & **Mid Century [36]** & **Hamburg, Germany** & **Murcia, Spain** \\  & & & [119; 120] & [57] \\ \hline Passenger car & 1.0 & 0.6 & 13,728 & 12,535 \\ Van & 1.3 & 0.9 & 19,388 & 17,704 1 ** \\ Truck & 4.6 & 3.7 & 31,870 2  & 37,077 \\ Tractor-trailer & 6.9 & 5.5 & 96,211 & 151,513 \\ Bus & 8.6 & 6.9 & 55,883 & 147,398 \\  & \multicolumn{2}{c}{Hamburg, Germany} & \multicolumn{2}{c}{Murcia, Spain} \\ Annual hydrogen consumption H\({}_{\text{road}}\) & Near Future & Mid Century & Near Future & Mid Century \\ Hydrogen & & & & \\ [kg H\({}_{2}\)/year] & & & & \\ Hydrogen Energy 3  & 18,913 & 12,459 & 21,486 & 15,044 \\
[MWH\({}_{\text{HHV}}\)/year] & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: The average annual distance driven d and Near Future and Mid Century scenario-specific energy consumption of transport (SEC\({}_{\text{T}}\)) for van, truck, tractor-trailer, and bus type FCEVs.  
day to the hydrogen fueling profile for road transportation (H\({}_{\text{road}}\)) and follows the same hourly pattern. H\({}_{\text{V2G}}\) and H\({}_{\text{road}}\) combined make up the total hydrogen dispensed at the HFS (H\({}_{\text{HFS}}\)).
## 3 Results and Discussion
### Effect of Experimental Parameters
#### Effect of Adsorbent Dose  
The amount of adsorbent used in the adsorption process gives the cost of the adsorbent per unit volume of the solution to be treated.  
Increasing the adsorbent dose produces a decrease in the adsorption capacity values of both adsorbents (Figure 1b), from 95.58 to 39.73 (58.43% decrease) in the case of activated carbon (increase in dose from 10 mg to 50 mg) and from 27.23 to 12.25 (55.01% decrease) in the case of activated olive stones (increase in dose from 20 mg to 100 mg).  
This decrease indicates that, in the adsorbent mass ranges used, although the mass of methylparaben adsorbed increases with the amount of adsorbent, as the total surface area available for adsorption (and, consequently, the total number of available adsorption sites) increases, the ratio of mass of methylparaben adsorbed per gram of adsorbent decreases as the initial concentration of methylparaben used remains unchanged [55].  
For the same adsorbent dose (20 mg), the adsorption capacity of methylparaben on activated carbon (77.18 mg/g) is 4.05 times higher than its adsorption capacity on activated olive stones (19.07 mg/g) due to the greater surface area and the greater proportion of functional groups of the former with respect to the latter.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Linearized Equation** & **Plot** & **Model Parameters** \\ \hline \hline \multirow{3}{*}{Weber-Morris [52]} & \multirow{3}{*}{\(\mathrm{q_{t}=k_{\mathrm{intp}}\cdot t^{1/2}+C_{i}}\)} & \multirow{3}{*}{\(\mathrm{q_{t}\ against\ t^{1/2}}\)} & \multirow{3}{*}{\(\mathrm{k_{\mathrm{intp}}}\): intraparticle diffusion rate constant (mg/g\(\cdot\)h\({}^{1/2}\))} \\  & & & \(\mathrm{C_{i}}\): contribution of the boundary layer diffusion \\ \hline \hline Boyd [53] & \(\mathrm{B\cdot t=-0.4977-\ln\left(1-\frac{\mathrm{q_{t}}}{\mathrm{q_{e}}}\right)}\) & \(\mathrm{B\cdot t\ against\ t}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adsorption mechanism mathematical models.