esa-sceva/satcom-chunk-collection huggingface dataset:
## OCR4 all--An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings
###### Abstract  
Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4 all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4 all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19 th Century novels showed that OCR4 all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5%. The architecture of OCR4 all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageRank, thus aiming at continual higher automation for historical printings.  
Keywords:optical character recognition; document analysis; historical printings +
Footnote †: journal: Applied Computer Science
## 5 Experiment and Results Analysis  
### Preprocessing of the Experimental Datasets  
In the field of network-security situation analysis, current studies often rely on outdated datasets or data collected in specific virtual environments, which may not be widely  
Figure 3: Attention mechanism structure.  
representative. The data for this study are derived from real data published by the National Internet Emergency Response Center, covering the 171-week weekly network-security situation report between 2018 and 2021. These weekly reports evaluate the network security situation from five key dimensions: the number of domestic hosts infected by the virus, the total number of domestic websites illegally tampered with, the number of domestic websites implanted with backdoors, the number of counterfeit pages of domestic websites, and the number of newly discovered information security vulnerabilities. To translate these data into intuitive network-security situation indicators, this paper quantifies them by assigning varying weights to different security threat levels. Specific weight assignments are shown in Table 2. Subsequently, Formula (11) is used to calculate the weekly network-security situation value in order to more accurately reflect the overall network security situation.  
\[SA=\sum_{i=1}^{5}\frac{NT_{i}}{NT_{imax}}\cdot w_{i} \tag{11}\]  
where \(NT_{i}\) represents the number of a specific network security threat in a particular week (where \"\(i\)\" represents the type of security threat), \(NT_{imax}\) represents the maximum number of this type of security threat in the 171 selected periods of data, and \(w_{i}\) represents its corresponding weight. The computationally processed data are shown in Figure 4.
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
## 3 Results and Discussion
### Microalgae and Bacteria Synergistic Process Behavior  
Figure 6 shows microscopic photos of the algae and bacteria interaction at 40\(\times\) and 10\(\times\) magnification at the end of the 14-day system monitoring. PBR1, which exclusively used microalgae inoculation, was able to keep the initial _Scenedesmus_ sp. culture alive. This may mean that the paper pulp wastewater is not toxic for this microalgae. PBR2 appears to have grown by the photoautotrophic route of microalgae, primarily filamentous cyanobacteria such as _Planktonrix_ sp., even though the system began with only bacteria present in the aerobic sludge.  
Figure 6: Microalgae–bacteria consortia in photobioreactors after 14 days of operation.  
As previously mentioned, a good process of bioaglutination was observed in the biomass of PBR3, PBR4, PBR5, and PBR6, which worked with inoculation of microalgae and bacteria [51], with the presence of morphological structures of cyanobacteria, such as _Planktothrix_ sp., and filamentous microalgae, such as _Stigeoclonium_ sp., having been observed. Finally, in PBR4 and PBR5, the biomass was found to be more stable, with more aggregated structures, as illustrated in the photographs with 10\(\times\) magnification. There are still research gaps and challenges for better understanding the relation between algae and bacteria, such as identification of the best-performing algal strains and bacteria species, optimization of control parameters, overcoming seasonal variations of the effluent quality, and biomass harvesting, among others [68].  
This development feature would mean the start of maturation in the photogranules, noted after scanning electron microscopy (SEM) study of floccular structures and filamentous bacteria in the biomass (Figure 7). The dominance of green microalgae in the nucleus of the granular structure was detected, as also found in the study of Arcila and Buitron [31]. EPS excreted during in the symbiotic phase also aid in the production of granules [27]; thus, the pH control could also have influenced the good formation of photogranules, since high pH could have reduced microalgae and bacteria's aggregation performance. Finally, fibers found in the effluent of wastewater from the paper pulp sector may have aided the photogranules structure and stability.
## 3 Inherent Challenges in S2I Translation Processes
### Computational Creativity and Divergence/Convergence Methods  
Another inherent characteristic of cross-modal generation processes is computational creativity, which is fundamental to providing original outputs. To 'imagine' the surrounding scene corresponding to the sound, the system may need to blend known images, gathering visual'memories' from the imagery acquired during the training phase in order to create an original image. To reach this goal, the system must analyze the input sound, involving the encoded patterns learned from the training sounds, to generate a representation of it that effectively drives the 'creation' of the output image. The outlined process follows ideas frequently exposed in philosophy, psychology, and cognitive science about the interrelations between imagination, perception, memory, knowledge, and creativity [41]. [PERSON] and [PERSON] [42] emphasize the importance of extending the established AI techniques to improve the divergence/convergence abilities [43, 44] of algorithms in order to achieve 'creativity'. Among other AI approaches, genetic algorithms are probably the first to employ divergence/convergence methods to obtain original results, but are mostly limited to exploring narrower knowledge spaces [42]. More recent AI techniques, such as GANs, are capable of exploring wider spaces. The adaptation of this architecture with a conditioned generator [12] is a common approach in current studies on cross-modal tasks. However, despite their effectiveness in producing realistic results, GANs are known to be unstable due to adversarial training [45, 46]. A solution presented by [PERSON] et al. [15], named Deep Convolutional GANs (DCGAN), helps overcome this behavior. Their approach comprises a set of architectural constraints that have demonstrated stabilization of GAN output in most settings, although other forms of instability, like filter collapse, persist [15]. These issues lead to a fundamental problem in training generative models, namely, the necessity to increase the diversity of synthesized content [47, 48]. Addressing this becomes especially crucial when pursuing creativity, as in our case. Greater diversity in training data can indeed enhance the model's ability to generate diverse output [49]. On the other hand, excessive diversity may hamper model generalization. We are acutely aware of the challenge posed by data diversity, both in training and in data generation. In our work, we utilize diverse audiovisual content to train the translator and aim to obtain a model with enough generalizability to 'imagine' informative images from unknown sounds. Also, the model must be sufficiently 'creative' to generate diverse and original images. This task is undoubtedly challenging, and despite the low percentage of informative images obtained (around 14%), we demonstrate that it is possible to take steps towards the S2I translation objective.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Data Sources
In Figure 5, it is possible to observe the number of replacements, by type of component, during 2015, due to the occurrence of a failure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Datetime** & **machineID** & **Volt** & **Rotate** & **Pressure** & **Vibration** \\ \hline
0 & 2015-01-01 06:00:00 & 1 & 176.217853 & 418.504078 & 113.077935 & 45.087686 \\
1 & 2015-01-01 07:00:00 & 1 & 162.879223 & 402.747490 & 95.460525 & 402.747490 \\
2 & 2015-01-01 08:00:00 & 1 & 170.989902 & 527.349825 & 75.237905 & 34.178847 \\
3 & 2015-01-01 09:00:00 & 1 & 162.462833 & 346.149335 & 109.248561 & 41.122144 \\
4 & 2015-01-01 10:00:00 & 1 & 157.610021 & 435.376873 & 111.886648 & 25.990511 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Typical example of real-time telemetry recording. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Volt** & **Rotate** & **Pressure** & **Vibration** \\ \hline count & 876100 & 876100 & 876100 & 876100 \\ mean & 170.777736 & 446.605119 & 100.858668 & 40.385007 \\ std & 15.509114 & 52.673886 & 11.048679 & 5.370361 \\ min & 97.333604 & 138.432075 & 51.237106 & 14.870754 \\
25\%  
Figure 2: Evolution of Telemetry data over the first fifteen days of January 2015, for the machine 1. Figure 4: Representation of the number of components replaced, by type. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Datetime** & **machineID** & **comp** \\ \hline
0 & 2014-06-01 06:00:00 & 1 & comp2 \\
1 & 2014-07-16 06:00:00 & 1 & comp4 \\
2 & 2014-07-31 06:00:00 & 1 & comp3 \\
3 & 2014-12-13 06:00:00 & 1 & comp1 \\
4 & 2015-01-05 06:00:00 & 1 & comp 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Typical example of maintenance records. Figure 3: Representation of the number of errors by type. Finally, this data set contains information about the model and number of years of service for each of the 100 machines--Table 6. Figure 6 shows a histogram showing the distribution of the number of machines and service time, by model.
## 1 Introduction
Furthermore, the interpretable model SHAP, which was introduced in recent years to address the issue of machine learning's lack of interpretability, can be used to explain the specific relationships between driving factors and WUE by leveraging the interpretability of SHAP in conjunction with XGBoost. The southwestern region of China, particularly the Yunnan-Guizhou Plateau, is a typical karst landscape area with a relatively fragile ecosystem. In a plateau environment such as the Yunnan-Guizhou Plateau, due to its unique geological structure and complex hydrological and climatic conditions, the soil is thin and poorly permeable, and water resources are unevenly distributed, often leading to challenges such as water scarcity and ecological degradation. it is necessary to clarify the evolution characteristics of WUE in this region and the factors that drive these changes. Therefore, this study focuses on the Yunnan-Guizhou Plateau, calculating the WUE for the years 2003-2020 using the GPP and ET ratio methods. The spatiotemporal evolution of WUE in this region is explored using Theil-Sen slope estimation and Mann-Kendall significance analysis. Finally, an XGBoost machine learning model and the SHAP interpretable model are used to analyze the relationships between the driving factors and WUE. The findings of this study are significant for ecological civilization construction and sustainable development in the region.
# Enhanced Path Planning by Repositioning the Starting Point +
## 5 Conclusions
### Primary Contributions and Research Outcomes  
This study presents a novel decomposition methodology for drone path planning that fundamentally challenges conventional approaches by examining individual path components rather than treating flight distance as an indivisible whole. The central innovation involves identifying and optimizing the connection segments between launch pads and terminal stations, components that prove to be prime candidates for enhancement when launch pad repositioning remains feasible.  
This research develops a comprehensive graph-based framework that models multi-drone operations using enhanced multiple Traveling Salesman Problem formulations. Two new performance metrics, starting point distance and Starting Point Factor, were introduced to quantify how launch pad positioning affects overall mission efficiency. Seven distinct approaches for optimal launch pad positioning were proposed and systematically evaluated, spanning from basic geometric centroid calculations to sophisticated optimization algorithms.  
Extensive experimentation (\(\approx\)1,320,000 tests) across 130,000 unique operational scenarios validated the proposed methodology. These scenarios encompassed variable drone configurations ranging from two to ten units and station arrangements from five to one hundred. The geometric median approach consistently demonstrated superior performance, achieving distance improvements between 4% and 22% across different operational configurations. Average improvements reached 22% for starting point distance calculations and 13% for total flight distance measurements. The approach also delivered computational efficiency gains of several orders of magnitude compared to exhaustive search methods while maintaining solution quality.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## Appendix A Delay Correlation
### Doppler Correlation  
Doppler correlation describes how variations in received signals are correlated across different Doppler frequencies, reflecting the time-varying nature of wireless channels due to relative motion between transmitter and receiver or moving scatterers in the environment. The Doppler shift, caused by relative motion, is crucial in understanding this correlation. The Doppler spectrum, which characterizes the power distribution across different Doppler shifts, provides fundamental insights into the time-varying channel.  
The Doppler correlation function, denoted as \(R_{v}(\Delta v)\), quantifies the correlation between channel responses at different Doppler frequencies:  
\[R_{v}(\Delta v)=\mathbb{E}[H(v)H^{*}(v+\Delta v)],\]  
where \(H(v)\) represents the channel's response at Doppler frequency \(v\), \(H^{*}(v+\Delta v)\) is its complex conjugate at \(v+\Delta v\), and \(\mathbb{E}[\cdot]\) denotes the expectation operator.  
Understanding Doppler correlation is critical for characterizing the time-variant behavior of wireless channels, optimizing receiver algorithms, and analyzing system performance, particularly in scenarios involving mobility and high-speed movements. Key practical implications include Doppler spread, indicating the extent of frequency dispersion due to motion, and coherence time, which signifies how long the channel remains relatively stable. Techniques such as equalization and diversity are directly influenced by Doppler correlation, emphasizing the importance of accurate modeling in designing robust communication systems.  
When the relative velocity \(v\) between transmitter and receiver is considered [24, 50], the resulting Doppler shift falls within the range \([-\
u_{M},\
u_{M}]\), where:  
\[\
u_{M}=\frac{v}{c}f_{c},\]  
with \(\Delta_{D}\) representing the distance between correlated antennas. For MIMO systems, \(\Delta_{D}\) denotes the distance between antennas \(m\) and \(n\). The \(m-n\) entry of the Doppler correlation matrix \(R_{\text{doppler}}\) is expressed as shown in Equation (A7) [2].  
\[R_{h}(\Delta t)=\int_{-\
u_{M}}^{\
u_{M}}\frac{P(-\arccos(\
u/\
u_{M}))+P( \arccos(\
u/\
u_{M}))}{\sqrt{\
u_{M}^{2}-\
u^{2}}}e^{j2\pi\
u\Delta t}dv,\] (A7)where \(P(\theta)\) represents a specific function. This formulation highlights \(R_{h}(\Delta t)\) as the Fourier transform of \(S_{h}(\
u)\), where  
\[S_{h}(\
u)=\begin{cases}\frac{P(-\arccos(\
u/\
u_{M}))+P(\arccos(\
u/\
u_{M}))}{ \sqrt{\
u_{M}^{2}-\
u^{2}}},&\
u\in[-\
u_{M},\
u_{M}]\\ 0,&\text{otherwise}\end{cases}\]  
This relationship underscores the spectral nature of the Doppler correlation and its impact on channel behavior in varying wireless environments.
# Wind Vulnerability of Flexible Outdoor Single-Post Billboards
## 4 Structural Behavior
### Structural Design  
After calculating the internal forces for all adopted reference wind speeds, the resistant efficiencies of column, base plate and anchor rods were calculated according to the ANSI/ASISC 360-22 [20] steel design code. The main purpose of that was to prove if billboard structural dimensions are adequate or not. Here, structural efficiency is defined as the ratio between the ultimate internal load, \(F_{u}\), and the factored resistance, \(F_{R}\). In Figure 12, the structural efficiencies in the above-mentioned structural elements of the billboard can be seen. The maximum allowable efficiency when internal loads are equal to resistances is shown with a gray dashed line. Below this line all the evaluated elements are considered adequate. From Figure 12 it can be deduced that anchor rods were overdesigned due to uncertainties about the ultimate tensile force having enough capacity to offer support with winds of up to 220 km/h. On the other hand, we have the case of the column base and base plate that have capacity to support wind speeds of up to 185 km/h and 190 km/h, respectively. Therefore, in the reliability analyses, only the base column and base plate were considered. It is important to emphasize that structural elements of billboard were designed for an initial wind reference of 180 km/h.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wind Reference**} & \multicolumn{2}{c}{**Shear Force (\%)**} & \multicolumn{2}{c}{**Bending Moment**} & \\ \cline{2-5}  & **Head-On** & **North** & **Head-On** & **North** & **Eastbound** & **Torsion (\%)** \\ \hline
160 & 0.10 & 11.17 & 1.71 & 9.78 & 2.47 \\
170 & 0.31 & 11.25 & 3.61 & 10.09 & 3.77 \\
180 & 0.50 & 13.80 & 2.17 & 7.59 & 1.72 \\
190 & 0.69 & 11.33 & 4.20 & 10.17 & 3.07 \\
200 & 0.95 & 11.58 &
## 3 Results
### Materials Characterization
#### 3.4.2 DRS UV-Vis  
The band gaps of the photocatalysts were obtained from their absorption spectra, illustrated in Figure 5, using the Kubelka-Munk method and fitted with the Tauc equation. The samples all had indirect band gaps; thus, \(r=\frac{1}{2}\) was used.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Crystallite Size (nm)** & **Phase \%** & **BG (eV)** \\ \hline P25 & 15 (A); 26 (R) & 78 (A); 22 (R) & 3.22 \\ P25-500 & 18 (A); 25 (R) & 90 (A); 10 (R) & 3.12 \\ Ag/P25-L & 19 (A); 28 (R) & 92 (A); 8 (R) & 3.60 \\ \hline \end{tabular}
\end{table}
Table 2: Textural and optical properties of the photocatalysts employed.  
Figure 4: Diffraction patterns of P25 and Ag/P25 deposited over glass slides (JCPDS card #085-1326 for reference), compared with the commercial P25.  
The absorption spectra obtained from the immobilized and impregnated P25 (with a 0.07 M solution of silver nitrate) show very interesting results. The latter's bandgap is much broader compared to that of P25 (3.6 eV, 1 eV = 1.60218 \(\times\) 10\({}^{-19}\) J), which is usually associated with a very high loading of silver NPs. This hypothesis is also confirmed by the XRD spectra, which shows reflections of Ag\({}^{0}\), and by the broad adsorption band in the visible region due to localized surface plasmonic resonance (LSPR). The LSPR band is usually located around 460 nm, whereas here it is broader, since it is likely that Ag nanocluster are formed extensively on titania NPs [38; 39]. The effect of co-catalyst addition is not always predictable on a band gap. The appearance of LSPR is almost always reported after Ag addition, while a red-shift of a blue-shift have been reported [40; 41]. In particular, a blue-shift (also observed in our case) has been correlated with an increase in silver content due to the quantum confinement effect [41]. In addition, the thermally treated P25 retained its characteristic band gap (Figure 5). The optical properties of the catalysts are reported in Table 2.
## 2 Materials and Methods
### Dataset Preparation and Data Description
The samples were then transported to the laboratory and subjected to particle size analysis. Land use/land cover [71] and lithology were surveyed from different sources (maps, drones, and satellite); a buffer of 0.5 km for land use and 1 km for lithology was used. An additional project output involved the establishment of a Wi-Fi-connected remote surveillance network of ten sites, with a central control room feeding a specific sector of the GIS. Each site was equipped with one or more cameras and with an anemometer powered by solar panels. In three of these sites, the Italian National Institute of Geophysics and Volcanology (INGV) placed a monitoring system composed of accelerometric/velocimetric devices aimed at detecting the microseismicity induced by sea waves in three selected PBs. The data ordered within the GIS were uploaded within the WebGIS, dividing them into two macro areas: raster and vector file format. Raster images are those with variable resolution, depending on the object they show and the degree of detail they intend to represent, including orthophotos, satellite images, and bathymetry. Vectors are instead points, lines, and polygons, imported by GIS, which contain an attribute table with all the element details within the shapefile--sediment samples, in situ photo collections, remote surveillance systems (points), geometric and morphological parameters (points, lines, and polygons) and lithology, land use, _Posidonia oceanica_ (polygons). The coordinate system chosen was WGS84 33N (EPSG: 32633). The data uploaded to WebGIS amounted to about 200 GB.