esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 9 Post-Processing Techniques for Error Correction in OCR for Image-to-LaTeX Conversion  
Post-processing techniques improve the accuracy of Optical Character Recognition (OCR) systems for image-to-LaTeX conversion by addressing errors that occur during the recognition process. OCR models are not infallible, and mistakes propagate and increase throughout the conversion process [11]. The creation of robust error correction strategies is essential to ensuring high-quality conversions. One critical aspect of post-processing is error detection. Various error detection methodologies include statistical analysis, linguistic analysis, and pattern matching. A study compared the OCR output with statistical models to identify discrepancies [22]. Statistical techniques can identify potential errors based on their deviation from the expected patterns by analyzing the frequency and distribution of symbols. Pattern-matching strategies apply regular expressions to identify the mistakes in the OCR output. The designs can capture inconsistencies in the OCR results, like misrecognized symbols. Linguistic analysis leverages language models and grammar rules to identify semantic errors. It can identify mistakes that violate grammatical or mathematical rules by analyzing the OCR output in the context of the surrounding text or equations [28]. By combining these approaches, error detection algorithms can identify potential errors and flag them for further correction.  
Error correction techniques come into play upon detecting errors to rectify the OCR and enhance the accuracy of the final LaTeX output. Language models provide contextual information and semantic understanding to correct mistakes in OCR outputs [43]. The models consider equations and the surrounding words to identify and rectify errors. If the OCR output contains a misspelled and unrecognized word, the language model provides alternative words based on the context to improve the accuracy of the converted LaTeX representation [54]. Language models can give valuable suggestions for error correction by leveraging the statistical properties of language and the context in which OCR errors occur. The integration of contextual information is also used to correct mistakes [55]. Contextual analysis includes analyzing the relationships between equations, symbols, and mathematical expressions to identify and correct errors. OCR errors that disrupt the overall coherence of the mathematical expressions are detected and rectified by considering the syntactic and structural context. If an OCR error results in an equation that violates mathematical rules, the contextual analysis identifies the discrepancy and proposes corrections that maintain the integrity of the equation.
## 4 Evaluation of Experimental Results
### Optimum Force Calculation for Surface Roughness Optimisation
optimum values, the optimal parameter combination projected onto the improvement factors is written as follows:  
\[f_{2}=0.1\;\frac{mm}{rev};\;v_{v1}=0.6938\;\frac{m}{s};\;F_{opt}=102\ldots 127\;N\]  
Using the same burnishing speed with a lower feed rate resulted in lower improvement, while the trendline became much steeper, leading to significantly higher force optimum values, written as follows:  
\[f_{1}=0.05\;\frac{mm}{rev};\;v_{v1}=0.6938\;\frac{m}{s};\;F_{opt}=113\ldots 190 \;N\]  
Applying higher burnishing speed produced results similar to earlier observations, with slightly lower improvement percentages, increased power demand, and, due to the speed, lower force optimums, written as follows:  
Table 4.0  
Table 4.1  
In summary, based on the force optimization data, the most favorable burnishing force for surface quality optimization ranged between 100 N and 110 N. Applying a lower force, according to this theory, may degrade surface quality but improve energy efficiency. In Section 4.3 of the analysis, we use factorial design to assess exactly how a reduced burnishing force affects surface roughness.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 2 Methods
### Edge-Guided Module
In summary, the EGM realizes the fusion of edge features and change features, increases the weight of the edge portion of the features, and assigns weights to the feature channels through the attention mechanism, which directs the network to focus on the edge region of the building, and thus improves the change detection capability of the network.
# Solar Ultraviolet Irradiance Characterization under All Sky Conditions in Burgos, Spain
## 3 Results and Discussion
### GHUV/GHI Ratio and Sky Conditions  
As explained in Section 3.1, during the experimental campaign, sky conditions were determined in Burgos using the CIE standard sky classification, or alternatively, the \(k_{t}\) criteria. Figure 9 shows the results of the statistical analysis of the average _GHUV/GHI_ ratio, calculated using 10 min intervals, for each CIE standard sky type. The highest ratio values were obtained for sky types 5 (4.85 \(\pm\) 0.92%) and 1 (4.73 \(\pm\) 0.76%), which corresponded with the overcast sky conditions.  
Figure 10 presents a comparison of the statistical analyses of the aggregated CIE sky types and the data classifications according to \(k_{t}\). The same trend can be observed in both cases (i.e., the _GHUV/GHI_ ratio was higher for overcast skies and lower for clearer skies). The mean values obtained in both cases, as well as their dispersion values, were similar; they were slightly higher for the overcast and intermediate sky types that were classified using the \(k_{t}\) taxonomy, and the results for the clear sky type were similar, as Table 6 shows.  
Figure 10: Boxplot of the _GHUV/GHI_ average values for (**a**) each CIE sky type group and (**b**) for each sky type classification according to \(k_{t}\), based on the 10 min datasets.  
Figure 9: Boxplot of the average _GHUV/GHI_ ratios for each CIE sky type using the 10 min data.  
The main characteristics of the monthly and seasonal \(GHUV\) and \(GHI\) ratios, based on the 10 min, hourly, and daily averages, were studied. The linear trend lines of both formulations (\(GHUV=a\cdot GHI+b\), and \(GHUV=a\cdot GHI\)) were employed. According to several works, the formulation of the equation affects the quality of the results, due to errors induced by forcing the intercept to zero [34]. The temporal changes were examined, together with the effect of cloudiness; therefore, the \(k_{t}\) cloudiness classification was used to characterize the sky conditions. The results of these studies are depicted in Tables 7-9.  
Figure 11 shows the high positive correlation between both magnitudes, at 10 min, hourly, and daily intervals. Better fitting was obtained using no intercept linear regression models rather than linear regression models with an intercept, and the \(R^{2}\) statistic increased as the time intervals lengthened; however, as is evident, both regressions were very similar. No significant differences were found for the slope and standard deviation values of the fitted data for the different considered time intervals.
# A Novel Real-Time Match-Moving Method with HoloLens
## 1 Introduction  
Virtual-real synthesis techniques are used for video production in the media industry because they are able to express many images that cannot be expressed in the real world. These techniques can deliver a realistic experience as well as reduction in production cost. Therefore, they have attracted the attention of many researchers. In general, virtual-real synthesis refers to the insertion of computer graphics into a real world scene with precise position, scale, and rotation information. This technique is called match-moving. This technique works by extracting internal parameters (such as focal length) and external parameters (position and rotation) from an RGB camera used to capture a real scene [1,2]. From these parameters, we can retrieve the motion of the RGB camera, which is called camera tracking [3]. As the camera tracking process belongs to post-production and takes a long time to extract a camera trajectory from an RGB camera, consequently, it increases the production cost and time. Some factors, including motion blur caused by fast camera movement and occlusion by a person or object, can affect camera tracking results. These factors result in bad feature-matching between video frames, which results in poor or no virtual-real synthesis. Due to these errors, sometimes a scene must be shot again, which wastes time and money. Therefore, an accurate virtual-real synthesis system is required to reduce production time and cost.  
In this study, we implemented a novel system consisting of Microsoft HoloLens and an RGB camera. This system works based on spatial understanding by scanning the environment in real time, which gives us the position and rotation information used for camera tracking. Our system differsfrom conventional systems because it does not need to extract feature points, which are later used for camera tracking, from the recorded video. Additionally, we built a pre-visualization system that can verify the match-moving results while recording by displaying the real and virtual objects in the scene.  
In Section 2, we present the existing match-moving method and HoloLens related works. In Section 3, we present the proposed system. Section 4 presents experimental results that compare the proposed match-moving system with existing methods and pre-visualization performance test results. Finally, Section 5 includes conclusions about the proposed match-moving system and future research plans.
## 4 Analysis of Calculation Results
### Effects of Stiffness of the Stayed Cable  
The cables of some cable-stayed bridges play a minor role in the overall forces of the bridge. However, their axial stiffness's effects on the structure's general power cannot be ignored. Changes in cable stiffness are realized by changing the section diameter. According to the finite element model of this bridge, cable stiffnesses were taken as 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, and 2 times the original stiffness, while the other parameters remained unchanged. Then, the internal force and deformation of the main girder and cable tower  
Figure 4: Effects of main girder stiffness on structural internal force and deformation. (**a**) moment; (**b**) deflection.  
were compared. The calculation results are shown in Figure 5, and the meaning \(M\)/\(\sum\)\(M\)(%) and \(\gamma\)/\(\sum\)\(\gamma\)(%) in the figure are the same as above.  
In Figure 5, with increasing stiffness of the stayed cable, the moment and deflection in the span of the main girder showed a decreasing trend; the moment as well as deflection at the root of the tower of the bridge tower did not change significantly, while the moment at the root of the main girder tower decreased to a very small extent and slowly increased. Since the cable is flexible, its internal force did not follow the linear change law. As stiffness increased, the transmission effect of the cable on the force was enhanced, and the cable transferred more vertical load to the tower, resulting in changes of the cable force and thereby structural force changes. Due to increasing stiffness of the stayed cable, the overall stiffness of the structure was increased, thus structural deformation was reduced.  
Figure 6 shows the comparisons of internal force and deformation of the main tower of the main girder that was caused by changes in stayed cable stiffness. The effects of stayed cable stiffness changes in each span of the main girder and at the root of each tower of the main girder were comparable. The bending moment in the span of the main girder decreased with increasing stayed cable stiffness, while the bending moment at the root of the main girder tower decreased first and then increased with changing stayed cable stiffness. The effects of cable stiffness changes on each tower of the bridge slightly differed. The top deflections of 2# and 3# towers increased and then decreased depending on the influence of the stiffness, while the top deflections of 1# and 4# towers continuously decreased.  
Figure 5: Effects of cable stiffness on structural internal forces and deformation. (a) moment; (b) deflection.
### Significance and Novelty of the Study  
This study marks a significant milestone in Somaliland, being the first to investigate student dropout rates using data from the 2022 National Education Accessibility Survey conducted in collaboration with the Ministry of Education. By employing a diverse set of machine learning models such as logistic regression, probit regression, naive Bayes, random forest, decision tree, support vector machine (SVM), and K-nearest neighbors (KNN), this research delves into the complexities of why students leave school early. It reveals how factors like age, household income, and family size play a role in dropout rates. Offering innovative strategies to address these challenges, this study is a pivotal contribution to enhancing educational outcomes in Somaliland, providing a nuanced understanding for policymakers, educators, and stakeholders.  
For the government, this study offers invaluable insights for policymaking and resource allocation in the education sector, enabling targeted interventions to reduce dropout rates and enhance educational outcomes. International NGOs can leverage these findings to develop tailored programs and initiatives that address the identified risk factors, fostering collaboration with local stakeholders for impactful interventions. Researchers and academicians are presented with a pioneering research framework that not only advances the understanding of educational challenges in Somaliland but also showcases the application of diverse machine learning models in educational data analysis, offering methodological insights for future studies.  
Parents and community members benefit from increased awareness about the complexities of student dropout, empowering them to actively participate in creating a supportive educational environment for children. Applied scientists and data science practitioners find a valuable case study in this research, highlighting the significance of utilizing advanced analytical techniques to uncover actionable insights from educational data. By proposing innovative strategies to tackle dropout challenges, this study lays the groundwork for sustainable improvements in educational outcomes, ultimately contributing to a more inclusive and successful educational landscape in Somaliland.
# The Accuracy Assessment of Lithospheric Density Models
## 2 Numerical Model
### Sub-Lithospheric Mantle Geoid
#### 2.2.5 Sub-Lithospheric Mantle Disturbing Potential  
The computation of the lithosphere-stripped disturbing potential \(T^{\mathrm{CL}}\) in Equation (36) enhanced a signature of the LAB geometry that has to be removed in order to enhance a gravitational signature of the sub-lithospheric mantle density structure. This procedure is realized by stripping the lithosphere with respect to the density contrast between the reference (lithospheric mantle) density and the asthenosphere density in order to obtain the sub-lithospheric mantle disturbing potential. The sub-lithospheric mantle disturbing potential \(T^{\mathrm{SM}}\) is then computed as follows:  
\[T^{\mathrm{SM}}=T^{\mathrm{CL}}-V^{LAB}, \tag{40}\]  
where the potential \(V^{LAB}\) of LAB geometry is defined by the following:  
\[V^{LAB}\big{(}r_{g},\Omega\big{)}=3\frac{\mathrm{GM}}{\mathrm{R}\;\overline{ \rho}^{\mathrm{Earth}}}\sum_{n=0}^{\pi}\frac{1}{2n+1}\]  
\[\times\sum_{k=0}^{n+2}\binom{n+2}{k}\frac{(-1)^{k}}{\mathrm{R}^{k+1}(k+1)}\sum _{m=-n}^{n}\hat{\mathrm{L}}_{\mathrm{n,m}}^{(k+1)}\;\mathrm{Y}_{\mathrm{n,m}}( \Omega). \tag{41}\]  
The coefficients \(\hat{\mathrm{L}}_{\mathrm{n,m}}^{(k+1)}\) in Equation (41) read as follows:  
\[\sum_{\mathrm{m}=-\mathrm{n}}^{\mathrm{n}}\hat{\mathrm{L}}_{\mathrm{n,m}}^{(k)} \;\mathrm{Y}_{\mathrm{n,m}}(\Omega)=\frac{2n+1}{4\pi}\Delta\rho^{\mathrm{L/A}} \iint\limits_{\Phi}L^{k}\big{(}\Omega^{\prime}\big{)}\;\mathrm{P}_{\mathrm{n}} (t)\;d\Omega^{\prime}, \tag{42}\]  
where \(L\) is the LAB depth (see also Equation (39)), and the LAB density contrast \(\Delta\rho^{\mathrm{L/A}}=\rho^{\mathrm{A}}-\rho^{\mathrm{LM}}\) is defined as the difference between the lithospheric mantle density \(\rho^{\mathrm{L}}\) and the asthenospheric density \(\rho^{\mathrm{A}}\). We note that the computation of \(V^{LAB}\) can again be restricted up to the third-order terms of a binomial series (as in the case of computing \(V^{LM}\) in Equation (37)).
# Facile Preparation of Graphene Oxide-MIL-101(Fe) Composite for the Efficient Capture of Uranium
###### Abstract  
Graphene oxide (GO)-MIL-101(Fe) (Fe-based metal-organic frameworks (MOFs) with Fe(III) as the metal anode and 2-aminobenzene-1,4-dicarboxylic acid as a ligand) sandwich composites are designed and fabricated through a facile in situ growth method. By modulating the addition amount of GO nanosheets, composites containing MIL-101(Fe) octahedrons with a tunable dimension and density are achieved. The optimized ratio between individual components is determined through adsorption experiments. Adsorption isotherms reveal an enhanced adsorption efficiency and improved adsorption capacity of GO15-MIL-101(Fe) (GO dosage is 15 mg) in comparison with raw MIL-101(Fe) nanocrystals. Experimental evidence indicates that the removal of U(VI) by the composite is based on inner-sphere surface complexation and electrostatic interaction. The improved adsorption performance originates from the optimized synergistic effects of GO and MIL-101(Fe) octahedrons. In summary, this work offers a facile synthetic method to achieve cost-effective composites towards the U(VI) capture. It also lays the foundation for the design of novel adsorbents with the full play of component's functionality.  
graphene oxide; MIL-101(Fe); composite; adsorption; uranium +
Footnote †: journal: Applied Physics Letters B
## 3 Application of Pavement Inspection Indicators to Maintenance Decision-Making
### Maintenance Decision-Making Based on Grading of Inspection Indicators  
Based on the results of the K-means clustering analysis in Section 3.2, this study further sets grading thresholds for the crack rate, rutting depth, and IRI. The upper limit of Cluster 1 is set as the threshold for \"Continuous Monitoring\", and the upper limit of Cluster 2 is set as the threshold for \"Requires Maintenance\". Based on these thresholds, the inspection results are graded into \"Requires Maintenance\", \"Continuous Monitoring\", and \"Good\". The specific grading methods are as follows:  
* Crack Rate (%) 1. Good: Crack rate \(\leq\) 4.5 2. Continuous Monitoring: 4.5 < Crack rate \(\leq\) 12.5 3. Requires Maintenance: Crack rate > 12.5
* Rutting Depth (mm) 1. Good: Rutting depth \(\leq\) 4.6 2. Continuous Monitoring: 4.6 < Rutting depth \(\leq\) 7.0 3. Requires Maintenance: Rutting depth > 7.0
* IRI 1. Good: IRI \(\leq\) 3.11 2. Continuous Monitoring: 3.11 < IRI \(\leq\) 4.42 3. Requires Maintenance: IRI > 4.42  
Subsequently, based on the grading results, a more effective maintenance decision-making model is established. Any pavement unit with any inspection indicator graded as \"Requires Maintenance\" is defined as a \"Maintenance Section\". Pavement units with all three indicators graded as \"Good\" are defined as \"Good Condition Sections\", and the remaining sections are defined as \"Continuous Monitoring Sections\". Corresponding maintenance strategies are developed for each grading, as shown in Table 14.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Clustering Analysis Results** & **Cluster 1** & **Cluster 2** & **Cluster 3** \\ \hline Maintenance Grading Definition & Good & Continuous Monitoring & Requires Maintenance \\ \hline IRI & 1.4–3.11 & 3.13–4.42 & 4.5–7.00 \\ \hline \end{tabular}
\end{table}
Table 13: IRI clustering analysis results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Grading Conditions** & **Maintenance Grading Result** & **Maintenance Method** \\ \hline Any inspection indicator graded as & & \multirow{2}{*}{“Maintenance Section”} & \multirow{2}{*}{Conduct structural surveys and perform pavement maintenance work} \\ \cline{1-1} \"Requires Maintenance\" & & & \\ \cline{1-1} \cline{3-4} All three inspection indicators graded & & & \\ as “Good” & & & \\
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 1 Introduction
### Biochar Production Model for BSG  
Biochar, as defined by the International Biochar Initiative (IBI), is a solid material derived from the thermochemical conversion of biomass in an oxygen-limited environment [17]. Biomass refers to the biodegradable portion of agricultural, forestry, and related industries' products, waste, and residues, as well as the biodegradable portion of industrial and municipal waste [17]. The characteristics of biochar, such as porosity, organic carbon content, pH, etc., are influenced by the type of biomass used in its production, including its cellulosic, sugar, and fat fractions, as well as the presence of any contaminants.  
Biochar is distinct from charcoal and other carbon products due to its predetermined use, which is primarily environmental management. This can include soil remediation, resource efficiency improvement, pollution prevention, greenhouse gas (GHG) mitigation, and so on [17]. Biochars are highly porous, amorphous materials, typically containing negligible levels of heavy metals and low nitrogen and ash concentrations while maintaining a high surface area and adsorption capacity [18; 19]. They also exhibit a low degree of aromatization, resulting in high porosity and reactivity, which is correlated with a high surface area and electrical conductivity [18; 19].  
BSG can gain value when utilized to produce biochar. The production of biochar from BSG generates bio-oil and syngas as byproducts, which can be utilized for energy applications or further purified to obtain their primary components for use as raw materials. The resulting biochar can be applied in soil amendment, carbon sequestration, energy production, and other applications. This approach has the potential to transform the brewing industry and contribute to a circular economy, revolutionizing the environmental impact associated with beer production. Additionally, this approach offers economic benefits, serving as a potential secondary income or cost reduction for breweries.
# NDN Content Store and Caching Policies: Performance Evaluation
## 5 Simulation and Discussion
### Simulations Results
#### 5.2.3 Network Traffic  
Regarding the network behavior in terms of its traffic (i.e., number of interest/data messages), Figure 7 presents the obtained results, which, as expected, show that with a smaller number of CS, the network traffic is high and vice versa. The figure shows the result for a scenario with 32 consumers and 10 producers, which is similar to the result achieved with the scenario with 41 consumers and 1 producer. This behavior is justified by the following:  
* For a scenario with less CS in the network, the interest will take a longer route before finding the requested content. In this process, the probability of delay (as shown in Figure 8, Section 5.2.4) and the possible discarding of the packet is high;
* With more CS, as noted earlier, the CHR will be high. That is, the probability of finding the requested content closer to the consumer is also high, avoiding a longer route (or  
Figure 5: Cache hit ratio for NDN testbed with all routers equipped with CS and a content request rate of 1000 packets/s.  
Figure 6: Upstream hops to content retrieval, NDN testbed with 32 consumers and 10 producers.  
high number of hops, as explained in Section 5.2.2), and decreasing the probability of packet loss.  
Figure 7(a) shows the variation in traffic with the variation in CS in the network. This result is particularly different from the one achieved when the skewness factor is \(\alpha=0.75\), which presents constant traffic for all levels of cache (i.e., 5%-100%), for both Abilene and NDN testbed.