esa-sceva/satcom-chunk-collection huggingface dataset:
# Cascaded Dual-Inpainting Network for Scene Text
## 2 Related Work
### Scene Text Inpainting  
Recent research has shifted focus from general image inpainting to specialized tasks, particularly incomplete scene text inpainting, which has emerged as a critical area of investigation. [PERSON] et al. [28] proposed a specialized incomplete text inpainting network (INIT) employing a fully convolutional encoder-decoder architecture. Under dual constraints of reconstruction loss and semantic loss, the INIT framework demonstrated enhanced performance in both text reconstruction fidelity and recognition accuracy. [PERSON] et al. [29] developed a two-stage incomplete text inpainting algorithm (TSINIT). The TSINIT framework adopted a dual-branch architecture comprising a text extraction module and a text reconstruction module, both of which employed encoder-decoder architectures to minimize structural reconstruction errors. [PERSON] et al. [30] proposed a global structure-guided diffusion model (GSDM) comprising two modules: a structure prediction module (SPM) and a reconstruction module (RM). The SPM, constructed in a U-Net architecture, generated text structures as segmentation masks. The RM took the predicted masks and corrupted images as inputs, employing a diffusion-based reconstruction module to reconstruct missing text regions. [PERSON] et al. [31] proposed a dual-discriminator generative adversarial network (D2 GAN) for restoring missing components in ancient Yi script characters. The D2 GAN framework enhanced the deep convolutional generative adversarial network (DCGAN) by incorporating a Yi-character screening discriminator and establishing a refined Yi script generator, which effectively performed text inpainting on degraded ancient books. At present, scene text inpainting is primarily based on encoder-decoder architectures and has a single-model structure. To enhance the performance of scene text inpainting, it is critical to develop more diverse multi-level models.
## 3 Results
### Mitigating Surface Roughness  
To assess PVA performance in mitigating the impact of damaged, uneven or unconventional print surfaces, PLLA samples with PVA sacrificial layers were printed on a distressed Bakelite block. A photograph of this damaged surface can be seen in Figure 9a. Schematic and idealised representations of the sequential deposition of PVA and PLLA onto the block are shown in Figure 9b, with the former 'filling in' surface imperfections to facilitate improved PLLA printing.  
Micro-CT cross sections of the resultant PLLA and PVA layers are shown in Figure 9c, with average thicknesses of 120 \(\pm\) 9 \(\upmu\)m and 230 \(\pm\) 110 \(\upmu\)m measured, respectively. Distinct increases in PVA layer thickness, corresponding to deep grooves on the damaged surface are observed. In stark contrast, the PLLA layer appears uniform and shows minimal impression from the distressed Bakelite block. Minor PLLA thickness changes of around 15 \(\upmu\)m are noted at sites of larger scratches (-1 mm); however, these are significantly reduced in comparison to the 100 \(\upmu\)m to 300 \(\upmu\)m protrusions present in the PVA layer.
## 3 Results and Discussion
### Bacterial Community Analysis in CTS and Fermented Dough
crustorum_, _Pediococcus acidilactici_, _W. cibaria_, _F. sanfranciscensis_, _Levilacto-bacillus brevis_, _Latinactobacillus curatus_, _Limosilactobacillus pontis_, _Gluconobacter axydans_, and _Gluconobacter fateurii_. _P. pentosaceus_ and _C. crustorum_ are usually found in fermented wheat flour products and demonstrated to be homofermatative LAB in sourdough, contributing significantly to the development of steamed bread flavor [25]. For example, [PERSON] et al. [26] clarified that _P. pentosaceus_ was an efficient starter that improved bread quality and flavor by forming specific organic acids and volatile compounds. _P. pentosaceus_ was present in the samples from LY, LK, HZ, and WH and predominated in LK and WH samples. _C. crustorum_ was the most abundant species in WN samples; its relative abundance was decreased by adding alkali in fermented doughs. [PERSON] et al. [27] reported that _C. crustorum_ could convert the citrate into L-lactic acid, acetoin, and diacetyl in sourdough and endow wheat bread with a buttery odor. In addition, _W. cibaria_ and _F. sanfranciscensis_, which are also obligate heterofermatative LAB [25], dominated in samples from BZ and YC, respectively. The relative abundance of _W. cibaria_ increased from 62.8% in CTS to 98.3% in FD from BZ, indicating that _W. cibaria_ was highly competitive in this dough ecosystem. _F. sanfranciscensis_ was a typical LAB in European type I sourdough and the predominant species in CTS, AFD, and FD from YC, with relative abundances of >96.5%. _L. pontis_ showed poor competitiveness in dough fermentation, with relative abundance in LK samples decreasing from 25.9% in CTS to 1.2% in FD. Figure 4: (**A**) Venn diagram displaying the distribution of shared orthologous bacterial species among CTS, FD, and AFD groups. (**B**) Co-occurrence network analysis of the bacterial communities among 24 samples at the species level. Overall, LAB predominated in the bacterial community of the CTS group because of their high adaptability to the environment of dough fermentation [28]. With the addition of alkali, the relative abundances of _P. pentosacues, P. acidilactici, F. sanfranciscensis_, and _L. brevis_ increased, while that of _C. crustorum, W. cibaria_, and _L. curvatus_ declined in the AFD group compared with the FD group (Figure 3E). Adding alkali changed the relative abundance of dominant LAB in fermented dough, which could be explained by the fact that exogenous alkali altered their competitiveness during dough fermentation.
## 3 Results and Discussion  
Physicochemical Composition of the Wines Produced by Sequential L, Conventional S and Spontaneous N Fermentation  
The physicochemical properties of nine Plavac Mali wines produced using _Lachancea thermotolerans_ sequential-inoculated with _Saccharomyces cerevisiae_, and spontaneous fermentation are shown in Table 2. Wines fermented by the L yeast strain sequentially inoculated with S had a lower alcohol content of 14.93 \(\pm\) 0.06% \(v/v\) in comparison to those obtained by N of 15.23 \(\pm\) 0.12% \(v/v\). L wines had a significantly higher total acidity of 5.9 \(\pm\) 0.00 expressed as g/L of tartaric acid. The pH of L wines was significantly lower only when compared to N wines. A similar decrease in alcohol content and pH was reported in Merlot wines produced by different inoculations by _Lachancea thermotolerans_ and _Saccharomyces cerevisiae_[11]. However, the reducing sugar amount of 7.13 \(\pm\) 0.81 g/L indicated the production of semi-dry Plavac Mali wines by using the L strain. Aroma Profile of the Plavac Mali Wines Produced by Sequential L, Conventional S and Spontaneous N Fermentation  
One hundred thirteen aroma compounds were identified and quantified using GC-MS analysis in wines of Plavac Mali produced by three types of alcoholic fermentation (Table 3). Aroma compounds are grouped into nine chemical classes of terpen, C13 norisoprenoids, sesquiterpenoids, higher alcohols, lactones, esters, fatty acids, volatile phenols, and othercompounds, as shown in Figure 1. The ANOVA results showed significant differences (\(p<0.05\)) for the 108 aroma compounds between wines obtained by different fermentations.
# Impacts of Flight Operations on the Risk of Runway Excursions
## 5 Calculation and Analysis of Curve Similarity
### Typical Correlation Analysis  
Typical correlation analysis is a multivariate statistical analysis method that reflects the overall correlation between two sets of variables by utilizing the interrelationships among comprehensive variables. It is commonly employed in the fields of economics and biology. In this study, the curve similarity calculation indicators are designated as one set, while the other set comprises touchdown distances. The aim is to compute and analyze the correlation between these sets, investigating the nature of the differences with the standard curve and identifying the highest correlation with touchdown distance.  
When conducting bivariate correlation analysis between the curve similarity indicators and touchdown distances, it was observed that trend, horizontal scaling, vertical scaling, and shift exhibited a significant but weak correlation with the touchdown distance, as shown in Table 8. Table 9 shows the results of the calculation of the typical correlation for Style 3, where it can be seen that the significance is obviously less than the set significance level of 0.05, then the typical correlation can be considered significant. To comprehensively investigate the collective impact of these indicators on touchdown distances, canonical correlation analysis was employed, as depicted in Table 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indictors** & **Pearson Correlation** & **Significance (Two-Tailed)** \\ \hline Trend & 0.201 ** & 0.003 \\ MSE & 0.026 & 0.711 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlation coefficient with touchdown distance.  
Figure 5: Comparison of projected and actual curves for control column operation.  
Following comprehensive computation and analysis, a notable correlation was observed between touchdown distances and curve similarity indicators. Within Style 3, the indicators demonstrated a moderate correlation with touchdown distances, as depicted in Table 10. Notably, the trend exhibited a substantial impact on the correlation, followed by curve similarity and shift. This highlights the operational trend of the pilot as a significant factor influencing touchdown distances.  
After conducting a correlation analysis between all the indicators of curve similarity and touchdown distance, it was observed that the indicators of Style 3 exhibited the highest coefficient of correlation with the touchdown distance. It is noteworthy that the influence of wind speed on this correlation was found to be notable. This suggests that the operations associated with Style 3 have an effective impact on the touchdown distance, particularly in mitigating the risk of runway excursion under the influence of wind. Additionally, the typical correlation coefficient of Style 1 was found to be moderately correlated, indicating that the operations associated with Style 1 effectively affect the touchdown distance and contribute to the control of runway excursion risks.
## 1 Introduction
If the out-station beam of a GEO satellite is closed in the aforementioned situation, the RDSS services cannot be provided to users in the beam coverage area of that satellite. To solve the aforementioned problem, RDSS data can be used for GEO satellite orbit determination, which can be adopted as a backup method during GEO satellite orbit manoeuvre and recovery, improving the reliability of the GEO satellite orbit determination mode. Dr. [PERSON] adopted the RDSS data to make the orbit determination for the BDS-3 GEO satellite, and the orbit accuracy for GEO is better than 10 m [19]. In the BDS GEO satellite orbit determination method by RDSS data, the RDSS equipment time delay calibration is the first problem to be solved. Dr. [PERSON] adopted the combined orbit determination method using the RNSS data and RDSS data to solve the RDSS equipment time delay. But before the time delay calibration, the time delay value of one RDSS station needs to be accurately calibrated off-line. Then, based on the RNSS data, the equipment time delay value of the other RDSS stations can be solved simultaneously when the orbit and dynamical parameters are solved in the combined orbit determination. Since the RNSS equipment also has a time delay, it is necessary to adopt SLR data or the user equivalent range error (UERE) method to regularly calibrate the RNSS equipment time delay [20], so it needs two equipment delay calibrations to achieve the accurate RDSS equipment time delay calibration. This study proposes a new precise orbit determination (POD) method for GEO satellites based on BeiDou short-message communication ranging data. The method uses high-precision SLR data as the benchmark for the calibration of RDSS equipment time delay. We need not calibrate the time delay value of a certain RDSS station before the time delay calibration. Based on the precisely calibrated RDSS equipment time delay, RDSS data are used only in the orbit determination process. Thus, the high dependency of the existing GEO satellite orbit determination method on RNSS data can be reduced, the continuity and reliability of the precise orbit products of BeiDou GEO satellites during the orbit manoeuvre and recovery will be improved, and the integrated application of short-message communication and navigation services of BDS will be realised. In this paper, we first describe the new POD method for the GEO satellites based on RDSS data and the principle of solving time delay parameters of RDSS equipment based on the SLR data. Then, we present the experiments by the measured RNSS, RDSS, and SLR data of the BDS. Finally, we evaluate the accuracy of the estimated RDSS time delay value, the orbit accuracy of GEO satellites based on the observation residuals, overlap orbit differences, and orbit accuracy in radial direction based on the SLR data.
## 3 Results  
The amounts of heat extraction required to condense moisture for the three humidity reduction percentages (25%, 50% and 75%) and for the percentages of relative air humidity (50%, 60%, and 70%) are shown in the following figure:  
The Figure 8 shows the behaviour of heat to be extracted in order to condense the air humidity and the behaviour of each at the different amounts of relative humidity. For 25%, 50% and 75% of humidity reduction, emphasis is placed on the minimum and maximum values as can be seen in the following table.  
Table 1 is very important for the purposes of this paper and for AWG dimensioning as it defines the range of heat that must be extracted from the air entering the system. With the above results, the maximum amount of heat to be extracted is 46.2541 KJ/kg and the minimum of 13.449 kJ/kg. To carry out a study that covers the maximum and minimum values, the graph lines whose value is the highest for each of the lines of 25%, 50% and 75% reduction were used, since by studying these lines the values below them will be covered. As mentioned above, once the amount of heat that must be extracted (Q) from the dehumidification process is known, any type or medium of possible energy can be used to carry out the heat transfer of that amount of heat. The possible energy sources that can be used directly in the processes can include geothermal energy or solar energy, or indirect means that produce electrical energy by means of wind energy or photovoltaic solar energy that provide the AWG with electrical energy for its operation. It is noteworthy that the Matehuala region has all these energy resources available for use.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Humidity Reduction**} \\ \cline{2-4}
**Humidity Content** & **25\%** & **50\%** & **75\%** \\ \cline{2-4}  & \multicolumn{3}{c}{**Heat Q (kJ/kg)**} \\  & \multicolumn{3}{c}{**Max @ 22.88 °C/Min @ 13.14 °C**} \\ \hline
50\% & 19/16.8 & 31/24.5 & 44/34.8 \\
60\% & 21/17 & 31/24.3 & 44.3/34.5 \\
70\% & 17.5/13.7 & 30.15/22 & 46.8/34 \\ \hline \end{tabular}
\end{table}
Table 1: Maximum values, minimum heat required to extract with respect to relative humidity.  
Figure 8: Heat to be removed for the three percent humidity reduction (25%, 50% and 75%) with air humidity content of 50% (**c**), 60% (**b**), and 70% (**a**).
## 4 Numerical Results and Discussion
### Free Vibration of a Two-DOF System
#### 4.1.1 Accuracy Comparison  
Let time domain be [0, 19], which is taken as an element with \(N=33\) non-uniform points in DQTFEM, and 32 uniform time steps are used for both RKM and NMA. The first stable interval for \(N=33\) is [0, 43.97], hence the stability is satisfied since \(19\omega_{2}=42.49<43.97\), and the stability \(2\sqrt{2}\) of RKM is also satisfied by \(h=19/32=0.59375\) since \(\omega_{2}h=1.3277<2\sqrt{2}\). Additionally, \(N=33\) and \(h=0.59375\) implies that the three methods have the same number of interpolation points in the domain [0, 19]. The exact solutions used for comparisons are obtained by the mode superposition method.  
It follows from Figures 2 and 3 that DQTFEM is much more accurate than RKM and NMA, RelErr (DQTFEM) is about \(10^{-6}\), while RelErr (RKM) and RelErr (NMA) are \(10^{-2}\) and \(10^{-1}\), respectively; and it is noticeable from Figure 3 that the errors of RKM and NMA accumulate over time.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 3 Proposed Scheme
### Image Encryption  
Suppose that the size of the original color halftone image \(\mathbf{O}\) is \(N_{1}\times N_{2}\times 3\). As each pixel in a halftone image is represented by one bit, the total number of bits \(N\) is \(N_{1}\times N_{2}\times 3\). Let the pixel value at position \((u,v,t)\) be \(a_{u,v,t}\), where \(\{1\leq u\leq N_{1}\}\), \(\{1\leq v\leq N_{2}\}\), and \(\{1\leq t\leq 3\}\). To encrypt \(\mathbf{O}\), we calculate exclusive-or operations between \(a_{u,v,t}\) and the pseudo-random bits \(k_{u,v,t}\) as  
\[e_{u,v,t}=a_{u,v,t}\oplus k_{u,v,t} \tag{4}\]  
where \(e_{u,v,t}\) is the pixel value of cover image \(\mathbf{I}\), \(k_{u,v,t}\) is generated by a stream cipher scheme with encryption key \(K_{e}\). For example, one may use any secure stream ciphers such as RC4 [31].
# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 2 Related Work  
### Semantic Summaries  
Several works in recent years have focused on summarizing large Knowledge Graphs for indexing, estimating the size of the query results, making queries more specific, source selection, graph visualization, understanding, and schema discovery. For an overview of the entire field, the interested author is referred to our comprehensive summary [1].  
Non-quotient semantic summaries is a specific category of semantic summaries that focuses on extracting the most important part of the graph and presenting it as a summary. Notable works in this category are the RDFDigest+ [5], which uses various centrality measures to identify the most important schema nodes and then link them, KCE [6], which extracts only the most interesting schema nodes, and [7], which, again, extracts the most interesting parts over the schema of the Knowledge Graph. Note that in order to handle the large amount of triples in a Knowledge Graph, most of the works in this category focus only on summarizing the schema graph, which is relatively small, and also usually incomplete.  
Lately, works have started to appear that try to exploit query logs (which are usually small) instead of Knowledge Graphs (which are usually large) for this task [4; 8; 9]. These works identify the most interesting nodes as the ones mostly queried, and queries are also exploited, in order to find the most frequent paths, so as to link the most interesting nodes.  
ULYSSES exploits the very same ideas, using query logs to extract the most frequent questions; however, it moves beyond summaries, to construct fully fledged question/answer sets in natural language. More specifically, it identifies the most frequent questions as the most important ones; then, it proceeds to issue those queries to the corresponding SPARQL endpoints, in order to retrieve and textually summarize the presented answers.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 1 Introduction
Neural networks take inspiration from the structure of the human brain and can have both simple and extremely complex architectures, depending on the intended application. The resources that neural networks require both in terms of available data for training and computation power can be extensive, but the results obtained using neural networks present state-of-the-art results in many fields, ranging from image [14; 15] and signal [16; 17] processing all the way to large language models [18] and data synthesis [19]. In line with everything previously stated, neural networks can often be found in the communication optimization literature alongside other conventional optimization techniques.
## 2 Structural Models Used for Generating the Calibration Data
These earthquakes are classified into categories of low, medium, and high amplitudes (refer to Table 2 and Figure 4). For each earthquake scenario, model responses were generated, resulting in a total of 10,773 earthquake responses. These earthquake responses will be combined with the corresponding 3591 ambient responses, bringing the total number of cases in the created dataset to 32,319. Again, it is important to mention that all signals in the dataset are sampled at 100 Hz and have a duration of 60 s. \begin{table}
\begin{tabular}{c c} \hline \hline
**Geometry** \\ \hline Plan & 10.00 \(\times\) 7.00 (m\({}^{2}\)) \\ Stories & 1 to 7 \\ Story height & 3.50 (m) \\ Slab thickness & 0.25 (m) \\ Columns & 0.50 \(\times\) 0.50 (m\({}^{2}\)) \\ Beams & 0.40 \(\times\) 0.70 (m\({}^{2}\)) \\ \hline \multicolumn{3}{c}{**Loads**} \\ \hline Dead & 806.75 (kN) \\ Live & 806.75 (kN) \\ Safety factor & 1 1 \\ \hline \multicolumn{3}{c}{**Dynamic Characteristics**} \\ \hline Mass (per story) & 110.78 (tons) \\ Damping ratio \(\zeta\) & 5\% \\ Eigenfrequency & 1 to 10 Hz with step of 0.5 \\ \hline \multicolumn{3}{c}{**Material**} \\ \hline Reinforced concrete & \\ Bilinear material & Figure 3 \\ Yield point (u\({}_{y}\)) & 0.0105 (m) 2 \\ Post yield stiffness & \\ (K\({}_{\text{eff}}\)) & 50\% of geometric one (Kg) 3 \\ \multicolumn{3}{c}{K matrix for N = 3 (stories)} \\ Shear building model & k1 + k2 & \(-\)k2 \\ \(-\)k2 & k2 + k3 & \(-\)k3 \\  & 0 & \(-\)k3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Models’ generation parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Name** & **Absolute Peak Acceleration (m/sec2)** \\ \hline
1 & ML431\_100\_60 sec & 0.1579 \\
2 & ML5.5 Larissa2021 & 0.1750 \\
3 & ML6.0 Create2021 & 0.1973 \\
4 & ML512\_100\_60 sec & 1.2467 \\
5 & ML6.3 Crete2021 & 3.4509 \\
6 & ChiChi & 3.5414 \\
7 & Kobe\_100\_60 sec & 3.3815 \\
8 & ERZ0002\_100\_60 sec & 5.0536 \\
9 & Northridge\_100\_60 sec & 5.5750 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of seismic records used for developing the training and validation sets. The split to training and validation datasets was made on the model's level. Meaning that from the 1197 models, \(\sim\)75% of them are used for training (training set), while the other \(\sim\)25% (299 models/\(\sim\)8100 samples) are used as the validation sample (validation set). Therefore, the network learns to predict the response on unforeseen models for the 9 \"known\" EQs. Figure 4: _Cont._