esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Smart Conservation of Cultural Heritage
### Evaluation and Results
#### 5.3.1 Representation  
The proposed modeling method (see Section 5.1) implemented in the proposed middleware efficiently represents the context of a cultural space, capturing sensed and profiled data and modeling them appropriately. The representation of context exploits the NoSQL characteristic of non-strict data structures, which preserves semantics and allows interoperability.  
MongoDB, the NoSQL DB of choice, proved to be very efficient at sustaining a large variety of data collections. Although the application built on top of the middleware, addressing preventive conservation, had limited interaction with user devices, it required a diverse range of sources, including user and staff devices, sensors, environmental devices and smart lamp infrastructure. Overall, the observation tuples proved to be an efficient tool to capture events, which are the building blocks of context-awareness.
# Augmented Reality in Precision Farming: Concepts and Applications
## 2 Augmented Reality in Agriculture
### AR Types and Coupled Technologies
Similarly, machine learning and computer vision algorithms (together with databases to cross-reference data) enable marker-basedAR applications to perform their functionalities, as in [31]. Both AR types present advantages and challenges that might skew/narrow their application to specific scenarios within precision farming. For marker-less applications, position is often not the only factor involved in the correct overlay of data. For example, the multirobot system developed by [PERSON] et al., requires information on the orientation of the AR headset used by the driver to correctly overlay objects in the real world [11, 15]. The accuracy of the internal headset orientation sensor was, however, not sufficient for this application; therefore, an extra sensor is introduced into the framework.
# Dynamic Changes in the Export Competitiveness of the South Korean Oil Refining Industry
## 4 Results  
### Status of Exports by the South Korean Oil Refining Industry  
#### 4.1.1 Export Share of Petroleum Products  
Figure 1 shows the trend of export share by petroleum product. Considering the export value of each product as a proportion of the total export value of petroleum products, diesel, jet oil, automobile gasoline, and lubricants, in that order, have the highest values. The export share of diesel rose from 35.2% in 2000 to 40.4% in 2012 and then fell to 37.7% in 2019, but diesel still has the highest proportion among South Korea's petroleum product exports. The export shares of automobile gasoline and lubricants have increased continuously. The export share of automobile gasoline increased from 5.2% in 2000 to 10.9% in 2010 and 15.6% in 2019. The export share of lubricants also increased, from 1.9% in 2000 to 9.7% in 2010 and 10.1% in 2019. The total share of the four major export items--diesel, jet oil, automobile gasoline, and lubricants--among the total exports increased from 55.4% in 2000 to 85.0% in 2019.  
The share of naphtha among the exports of petroleum products since 2003, and that of bunker C oil since 2004, has been steadily decreasing. The share of naphtha among petroleum product exports peaked at 18.1% in 2002 and has continued to decline, dropping to 6.7% as of 2019. The share of bunker C oil exports peaked at 19.2% in 2003 and has continued to decline, dropping to 2.7% as of 2019. Bunker A oil, bunker B oil, aviation gasoline, and other heavy oils account for less than 0.1% of the exports of petroleum products. Kerosene also fell to 0.78% in 2019.
## 3 Results
A set of 305 examples was given to an expert ophthalmologist, along with the developed algorithm, for evaluation. They were tasked with deciding whether to change the patient's treatment plan after diagnosing the early stage of the disease during a prior examination. [PERSON]'s Kappa coefficient is calculated using the following formula:  
\[k=\frac{p_{o}-p_{e}}{1-p_{e}}, \tag{1}\]  
\[p_{o}=\frac{N-(c+d)}{N}, \tag{2}\]  
\[p_{e}=\frac{(a+b)(a+c)+(c+d)(b+d)}{N^{2}}. \tag{3}\]  
where \(a\) is the number of times both test participants answered affirmatively; \(b\) is the number of times test participant 1 answered affirmatively and participant 2 answered negatively; \(c\) is the number of times test participant 1 answered negatively and participant 2 answered affirmatively; \(d\) is the number of times both test participants answered negatively; and \(N\) is the total number of test examples. During the testing of the algorithm and the expert, the following results were obtained:  
\[p_{o}=\frac{305-(20+20)}{305}=0.86885, \tag{4}\]  
\[p_{e}=\frac{(181+20)(181+20)+(20+84)(20+84)}{305^{2}}=0.55057, \tag{5}\]  
\[k=\frac{0.86885-0.55057}{1-0.55057}=0.708191 \tag{6}\]  
An analysis of Equation (5) demonstrates that the degree of concordance between the solutions provided by clinicians and the hybrid classifier reached 87% of the total test cases, which is considered a very high indicator. Additionally, the number of instances in which the hybrid classifier adjusted the AMD stage was comparable to its precautionary conclusions based on expert opinions, accounting for 13% of the total test set. According to clinicians, it would be advisable for the algorithm to show increased sensitivity to the stages of AMD that pose the greatest threat to the patient's visual function. This would enable the diagnostic working with the CDSS to assess potential risks or dismiss them in cases of apparent false alarms. We will focus on these aspects in the future to enhance the proposed method for developing a hybrid classifier. [PERSON]'s Kappa coefficient analysis shows a high agreement between expert assessments and the algorithm's responses, demonstrating its effectiveness as a clinical decision-support tool. The results indicate that the issues of class imbalance and complexity in identifying the intermediate stage of AMD have been effectively resolved. Additionally, the conservative classification for classes N and V has been minimized. This framework can be seamlessly integrated into the CDSS to help determine when to adjust a patient's treatment plan. The hybrid classifier offers an accurate and interpretable assessment of the intermediate stage and early and late AMD outcomes.
## 2 Materials and Methods
### Batch Adsorption Experiments  
To estimate the Repanil Blue (RB) dye adsorption capacity of the biochars (CCB500, CCB600, and CCB700), batch experiments were carried out. To obtain a concentration of 1000 mg/L, a stock solution of RB dye was prepared by dissolving 1.0 g of the dye in one litre (1 L) of distilled water. Calibration and absorption experiments were carried out using a standard solution diluted to the required concentrations. Tests were first performed to determine the optimal experimental parameters, i.e., the contact time (1-180 min), initial dye concentration (10-125 mg/L), solution pH (2-12) controlled with 0.1 M HCl and 0.1 M NaOH and dose (0.01-0.8 g).  
adsorption tests were carried out in 100 mL glass flasks containing 50 mL of RB dye solution with an initial concentration of 60 mg/L. The flasks were stirred at 400 rpm using a magnetic stirrer and then filtered. Experiments were conducted in triplicate at room temperature. A Perkin Elmer Lambda 25 UV VIS Spectrometer was used at \(\lambda\) = 590 nm.  
The amount of dye adsorbed at a given time, t, (qt = mg/g) by the produced biochars was calculated using Equation (2):  
\[\mathrm{qt}=\frac{\mathrm{C_{0}-\mathrm{C_{t}}}}{\mathrm{M}}\times\mathrm{V} \tag{2}\]  
where \(\mathrm{C_{0}}\) and \(\mathrm{C_{t}}\) (mg/L) are the initial and t time aqueous RB concentrations, respectively; V is the volume of the aqueous solution; and M is the mass of the used biochar (g).
# Genus, Species, and Subspecies Classification of _Salmonella_ Isolates by Proteomics
## 3 Results and Discussion
In contrast, there was a pronounced difference when compared to reference strains from a different subspecies or species: an average of 75 \(\pm\) 1.7% and 70 \(\pm\) 1.2% similarity when compared to different _Salmonella_ subspecies and different species, respectively. The genus-level difference was even more significant: 48 \(\pm\) 1.1% to _E. coli_ and _Shigella_ and 5.1 \(\pm\) 9.2% to other common foodborne bacteria. While more isolates would be required to establish a statistical cut-off, this method is able to provide a rapid assessment of _Salmonella_ genus, species, and subspecies and can provide an estimate of similarity to a set of serovars for previously unsequenced isolates. Two other foodborne bacteria, _E. coli_ and _Shigella_, were also evaluated. They are closely related to _Salmonella_ and all three pathogens belong to the family _Enterobacteriaceae_. While food safety methods require the ability to distinguish between all three genera, it can be a challenge to differentiate _Shigella_ from _E. coli_. These two genera were historically considered as the same species because they share many common biochemical characteristics and are indistinguishable phylogenetically based on DNA homology by 16S ribosomal gene sequencing [37]. Indeed, the average ANI of the eight _E. coli_/_Shigella_ reference strains included in the database used in this work (Figure 1B) is 98 \(\pm\) 0.9%, comparable to the number obtained within _E. coli_ (98 \(\pm\) 1.2%) and _Shigella_ (99 \(\pm\) 0.8%). Similar to _Salmonella_ subtyping, _E. coli_ and _Shigella_ cannot be reliably differentiated by conventional MALDI TOF MS analyses due to this high sequence homology [38]. Two isolates each of _E. coli_ and _Shigella_ were analyzed and the results were shown in the bottom 4 rows of Figure 3. The data clearly showed that _E. coli_ and _Shigella_ were closely related but quite distinct from _Salmonella_, i.e., the _E. coli_ and _Shigella_ isolates showed > 82% similarity to each other but <53% to the reference _Salmonella_ strains. Specifically, the tested _E. coli_ isolates showed an average of 98 \(\pm\) 1.0% similarity to reference proteomes of the correct serotype, 90 \(\pm\) 1.0% to _E. coli_ proteomes of a different serotype, 86 \(\pm\) 1.8% to _Shigella_, 48 \(\pm\) 1.0% to _Salmonella_, and 5.1 \(\pm\) 9.2% to other foodborne bacteria. Similarly, the _Shigella_ isolates showed an average of 98 \(\pm\) 0.6% to reference proteomes of the correct species, 90 \(\pm\) 1.1% to a different species  
Figure 5: Degree of similarity of the fifteen _Salmonella_ isolates to reference proteomes with various taxonomic distances. of _Shigella_, \(91\pm 1.1\%\) to _E. coli_, \(51\pm 1.1\%\) to _Salmonella_, and \(5.6\pm 9.9\%\) to other foodborne bacteria. Again, the percent similarity correlated to their taxonomic relatedness and the best matches were to the correct bacteria to the species level, demonstrating our approach is capable of typing bacteria that are otherwise difficult to differentiate.
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
## 4 Experiment and Analysis
### Analysis of DD Atmospheric Correction at Reference Stations
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**B1C+B2a**} & \multicolumn{6}{c}{**B1I+B3I**} \\ \hline
**PRN** & **ION** & **TRP** & **P-B1C** & **P-B2a** & **L-B1C** & **L-B2a** & **ION** & **TRP** & **P-B1I** & **P-B3I** & **L-B1I** & **L-B3I** \\ \hline B24 & 0.012 & 0.016 & 0.526 & 0.541 & 0.006 & 0.003 & 0.019 & 0.015 & 0.703 & 0.643 & 0.004 & 0.002 \\ B25 & 0.011 & 0.034 & 0.551 & 0.394 & 0.006 & 0.003 & 0.016 & 0.037 & 0.711 & 0.492 & 0.005 & 0.003 \\ B26 & 0.024 & 0.039 & 0.831 & 0.708 & 0.006 & 0.003 & 0.019 & 0.051 & 0.994 & 0.833 & 0.006 & 0.003 \\ B33 & 0.013 & 0.074 & 0.606 & 0.389 & 0.004 & 0.002 & 0.015 & 0.073 & 0.738 & 0.559 & 0.004 & 0.002 \\ B34 & 0.017 & 0.024 & 0.796 & 0.692 & 0.004 & 0.002 & 0.019 & 0.04 & 1.118 & 0.844 & 0.005 & 0.003 \\ B35 & 0.012 & 0.023 & 0.669 & 0.894 & 0.006 & 0.003 & 0.018 & 0.029 & 0.762 & 0.927 & 0.006 & 0.003 \\ B39 & 0.01 & 0.01 & 0.752 & 0.568 & 0.003 & 0.001 & 0.012 & 0.015 & 0.86 & 0.521 & 0.003 & 0.002 \\ B41 & 0.029 & 0.016 & 0.787 & 0.355 & 0.008 & 0.004 & 0.033 & 0.024 & 0.677 & 0.617 & 0.007 & 0.004 \\ B42 & 0.017 & 0.078 & 0.958 & 0.671 & 0.007 & 0.003 & 0.021 & 0.068 & 1.355 & 0.761 & 0.006 & 0.004 \\ B44 & 0.011 & 0.009 & 0.566 & 0.649 & 0.003 & 0.002 & 0.017 & 0.014 & 0.79 & 0.797 & 0.003 & 0.002 \\ AVG & 0.016 & 0.032 & 0.704 & 0.586 & 0.0053 & 0.0026 & 0.019 & 0.037 & 0.871 & 0.699 & 0.0049 & 0.0028 \\ \hline \end{tabular} Note: B40 is the reference satellite of this period; AVG represents the mean value of the corresponding index RMS of each satellite; P: pseudo-range; L: phase.  
\end{table} 
Table 3: The RMS of double-difference solutions from different combinations of BDS-3 in WH04-HG02 (m). Figure 6: Comparison of average RMS of the DD solutions from different combinations of BDS-3 in WH04-HG02.
## 3 Safety Evaluation of the DSM
### Radiation Shielding Analysis  
Various structures and concrete walls of the DSM not only maintain its structural integrity, but also function to reduce the exposure of workers to radiation during loading and unloading of SNF, according to the ALARA principle. Radiation shielding analysis determined that the concrete thickness meets the allowable surface dose rate standard of the DSM outer wall, and then calculated the radiation dose rate at the air inlet/outlet. In particular, the inlet/outlet radiation dose rate analysis derived the shape of the air inlet/outlet flow path in the form of steps that can reduce the exposure of workers in dry storage facilities. MCNP 6.0 [14], based on the Monte Carlo method, was used for the radiation shielding analysis, and the assumptions used in this analysis were as follows:  
* A homogenization model was used for the effective nuclear fuel area and upper/lower structures of the SNF;
* The axial burn-up profile was not considered;
* A sufficient air layer for the calculation of scattered radiation due to collision between radiation and air was considered;
* The flux-to-dose conversion factor ICRP-74 was used [15];
* The SDSS outer wall's allowable surface dose rate was limited to 0.5 mSv/h.  
The MCNP model used in the radiation shielding analysis considered the canister (applies to MPC-24), carbon steel cylinder, airflow region, and concrete structure. Figure 4 shows the MCNP model used to calculate the wall thickness of DSM. Figures 5 and 6 show models for calculating the surface radiation dose rate at the air inlet/outlet flow path, respectively. Figure 8 shows the results of the radiation shielding analysis. Since the effect of neutron rays is insignificant, only gamma rays were considered. As a result, the minimum thickness of concrete that satisfied the allowable surface dose rate of 0.5 mSv/h was 60 cm, and the permissible surface radiation dose rate of the inlet and outlet areas was also 0.5 mSv/h. The results of the radiation shielding analysis are summarized in Figure 7 and Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & \(K_{\mathit{eff}}\) \\ \hline \(K_{\mathit{cal}}\) & 0.92325 \\ Bias & 0.00749 \\ Uncertainty & 0.01079 \\ Maximum \(K_{\mathit{eff}}\) & 0.94153 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum \(K_{\mathit{eff}}\) of the DSM.  
Figure 3: Criticality analysis model of the DSM.  
Figure 4: Radiation shielding model of the DSM.  
Figure 5: Inlet radiation shielding model of the DSM.  
Figure 6: Outlet radiation shielding model of the DSM.
## 3 Results and Discussion
### Solar Cell Characterization  
In Figure 2a, the device architecture is shown with the different layers in the device, and Figure 2b shows a cross-sectional image of a CQD solar cell device using a Zeiss 1550 scanning electron microscope (SEM) with at an accelerating voltage of 5 kV. From the SEM cross section, the TiO\({}_{2}\) layer was estimated to be \(\sim\)60 nm (six layers), the thickness of the quantum dot layer is \(\sim\)108 nm, and the thickness of the P3 HT layer is 80 nm, and the gold layer is 80 nm.  
Figure 1: (**a**) Shows the absorbance spectrum of a 0.5 mg/mL Ag\({}_{2}\)S dispersion and a normalized PL emission spectrum of a 2 mg/mL Ag\({}_{2}\)S dispersion. The PL spectrum was excited with a wavelength of 550 nm; (**b**) Shows QDs at different concentrations: 2 mg/mL (left) and 30 mg/mL (right); (**c**) Shows a TEM image of the synthesized nanocrystals, while (**d**) is an HR-TEM image of the same sample, showing the distance between two planes in one of the crystals.  
Figure 3a shows the absorption spectra of samples with glass/FTO/TiO\({}_{2}\) and a CQD film with a layer of spiro-OMeTAD, a CQD film with a layer of P3 HT and films with P3 HT and spiro-OMeTAD. Comparing the absorption of the CQD film (Figure 3a) and the CQD solution (Figure 1a) the light absorption spectra are rather similar, with light absorption extending to around 900 nm. For the CQD film with P3 HT, there is a slight enhanced absorption in the 450 to 650 nm region due to the P3 HT layer. Many of the features in the IR region of the spectra of the films with only P3 HT and only spiro-OMeTAD are difficult to interpret. This is due to interference effects of the very thin films as well as reflection from the TiO\({}_{2}\).  
Figure 3: (**a**) Shows the absorbance of HTMs with and without QDs. The films are of incomplete devices on which only the gold layer is missing; (**b**) Shows JV curves of the best devices made; (**c**) Shows an image of a device; (**d**) Is the IPCE of a solar cell device with P3 HT as the HTM. The peaks between 800 and 900 nm are residual peaks from the Xe lamp.  
Figure 2: (**a**) Shows a schematic representation of a finished device and (**b**) is the SEM cross section of a real device with the different layers labeled.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper  
### Design of the Particle Damper  
A pipeline with an outer diameter of 0.016 m was selected, and a particle damper was designed for the pipeline system based on the structural characteristics of the pipeline. A damper design should meet the following requirements: (1) it can accommodate enough particles; (2) it has good structural sealing; (3) it is lightweight; and (4) it is easy to install and disassemble. Two materials and three cavity structures were used to fabricate pipeline particle dampers, and their structural diagrams and shape parameters are shown in Tables 1 and 2, respectively.
## 3 Literature Review
### Cooperation and Participation  
Public cooperation and participation are crucial aspects of the census process. Historically, there has been public reluctance regarding census data collection, often stemming from concerns about potential increases in taxation or other adverse consequences. However, in recent years, this reluctance has decreased, although government still need to exert efforts to improve coverage.  
To encourage participation, governments invest in media coverage and advertisements to raise public awareness about the census. For example, in the United States, in-post mail is sent to addresses of households to encourage responses. In 2010, about 83 percent of households responded to the in-post mail on time. The remaining non-responsive households required on-field workers to conduct in-person data collection. In developing countries like Bangladesh, where literacy rates may be lower, schoolteachers were engaged to spread census awareness and later perform in-person enumeration duties. This approach can be costly for the government, especially in developing states, as additional resources are needed to ensure widespread census knowledge and coverage [22].  
In conclusion, census data collection faces challenges in counting hard-to-reach populations, commonly known as \"missing persons\". These individuals reside in difficult-to-enumerate situations, such as unregistered buildings, non-compliant house extensions, shared residences, and mobile or hidden locations. Certain minorities and undocumented immigrants intentionally avoid detection, complicating their inclusion in the census. Human involvement in data management introduces errors and leakages, prompting the exploration of a formal solution minimizing human interaction for more accurate and secure results.  
Addressing these challenges necessitates a multidimensional approach involving technological advancements, community engagement, and adaptive enumeration methods. Utilizing modern technologies, like blockchain-based solutions, could enhance accuracy, efficiency, and inclusivity in census data collection while preserving data privacy and security. Collaborating with local communities and incorporating their knowledge ensures a more comprehensive enumeration, especially for historically underrepresented groups. These steps contribute to building a more reliable and equitable census system that accurately reflects the diverse population of a region or country.
## 1 Introduction
RL has shown promise in applications of condition monitoring, fault diagnosis, and remaining useful life estimation for power electronic converters [19] and has been used to maximize the energy generated by HKT systems in various applications [20; 21; 22; 23; 24; 25]. However, few works have been published that utilize RL to extend the lifetime of power electronic converters. References [26; 27] present a neural-network-based approach to predict the driving behaviors and device junction temperature for a given point-to-point trip in electric vehicle applications. The predicted temperature profile is then used alongside a rainflow counting algorithm that identifies the most harmful thermal cycles the devices may face over the drive cycle and creates a reference junction temperature at or near the peak value of the thermal cycle to reduce junction temperature fluctuation. This reference temperature is then used to control the cooling system employed in the vehicle, which requires a complex and highly capable cooling system that is not suitable for most HKT applications. This work develops a model-free approach that reduces the thermal stresses on a device through a lightweight stochastic gradient descent (SGD) state-action-reward-state-action (SARSA) algorithm with a Gaussian radial basis function (RBF)-based approximation method, similar to the method presented in [25]. The proposed RL method in this work is built within a common k-omega-squared (or optimal torque controller [11; 12]), thereby removing the need for a complex actively controlled thermal management system. Also, this work utilizes a similar economic-based reward function to that developed in [15] to train the RL agent. The work presented in [25] was primarily focused on maximizing energy generation with the consideration of environmental impacts on HKT performance through a linear feedback torque control law. Although the proposed RL method is similar, the state space in this work is significantly more complex (considering not only the flow velocity but also the water temperature, energy cost, and current feedback gain \(k\)), leading to additional challenges in designing the RL method and thoroughly training the RL agent. The remainder of the paper is structured as follows. Section 2 covers the modeling methods employed to represent the turbine, permanent magnet synchronous generator (PMSG), and power electronic converter. Section 3 presents an overview of the general RL framework and the specific details of the proposed SGD SARSA method. Section 4 explains how the RL framework has been adapted for this specific application (including how the monetary-based reward function has been defined), the method used to train the RL agent, and presents a case study comparing the proposed RL-based control method to a baseline controller in two different environmental settings. Section 5 presents a discussion of the performance of the proposed method, followed by the implications of the assumptions made throughout this paper and a statement regarding future work. Finally, the manuscript is concluded in Section 6.