esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Preprocessing  
The handwritten document is first scanned and a color image in PNG format is obtained. It is converted into a grayscale image, which is binarized using [PERSON]'s global thresholding algorithm [29]. In MATLAB, the function graythresh() returns an image-dependent threshold value, calculated using [PERSON]'s global thresholding algorithm. This threshold is used by the function imbinarize() to convert the grayscale image into binary with black characters on a white background. The binarized image is then inverted so that the image can be subjected to horizontal and vertical projection to segment the text lines. The preprocessed image thus obtained is named \(l_{b}\). The image \(l_{b}\) is divided into three vertical stripes of equal width. A morphological operation is performed on each vertical stripe to eliminate the isolated pixels, i.e., 1 surrounded by 0s. Following this, a morphological bridge operation is performed on all the vertical stripes to connect or bridge the pixels that are not connected. A summary of the overall processes in performing text line extraction after the preprocessing step is explained below.  
Text lines are extracted in each vertical stripe separately using the horizontal projection (HP) method described in [28]. The extracted text lines from each vertical stripe may be correctly or incorrectly segmented lines. Incorrect segmentation is due to the presence of overlapping lines or due to some special characters such as 'Chandrakkala' in the Malayalam language and the wide separation of some letters written below a particular character. The latter case contributes to the incorrect segmentation of such characters into separate lines called short lines. All these possibilities are checked for and corrected by the proposed technique. As a first step, the extracted line in the vertical stripe is checked regarding whether it is an overlapped line. If not, then it is checked for a short line. If a line is detected as an overlapped line, it is further divided into individual lines. The individual lines obtained after segmenting the overlapped lines are checked for an incorrectly segmented short line. If detected as a short line, it is joined to the correct line. The line segments obtained in the three vertical stripes after all these procedures are joined with the corresponding line segments in the other vertical stripes. These methods are developed with the assumption that none of the lines begin as a paragraph with large indentation from the margin. Moreover, it is assumed that, except for the last line in the page, no lines terminate before the end of a line in the page. These steps are taken to ensure that all three vertical stripes contain parts of every line except the last line. A detailed description of the developed techniques to detect and process overlapping lines and short lines is given in the following subsections.  
Figure 2: Flow of the processes involved in text line extraction of Malayalam handwritten documents.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
###### Abstract  
Since bus prioritization policies can help mitigate urban traffic jams, the planning of bus lanes has drawn considerable attention. Existing methods suffer from a common limitation, which is that the limited spatial adaptability resulting from certain road condition information cannot be directly specified. Many bus GPS trajectories have been accumulated and can be contiguously gathered if needed. This paper proposes a trajectory-based bus lane planning method. First, we formulize the bus lane planning problem as a multiobjective optimization problem in which the road conditions, traffic flow, connectivity of bus lanes, and construction cost are organized as four constraints, and road utilization and bus punctuality are modeled as two objectives. Then, an evolutionary algorithm-based method is presented to solve the problem. We tested the model in the Nanshan District, Shenzhen City, China. Through a comparison with existing survey-based methods, the parameters associated with road conditions in this method are directly extracted from GPS trajectories, and this method is more effectively deployed than other methods. Since GPS trajectories can cover a wide area if needed, and because the proposed method can be effectively executed, this method can be adapted to large urban scales.  
traffic congestion; bus lane planning; GPS trajectory; multiobjective optimization 2021  
[PERSON], 1,2 College of Geographic Sciences, Nanjing Normal University, Nanjing 210023, Jiangsu, China [EMAIL_ADDRESS]  
1 Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, Jiangsu, China [EMAIL_ADDRESS]  
2 College of Geographic Sciences, Nanjing Normal University, Nanjing 210023, Jiangsu, China [EMAIL_ADDRESS]  
3
## 3 Results and Discussion  
### Strength  
Generally, the mechanical properties of concrete can be characterized by the compressive strength, tensile strength, and modulus of elasticity. In this study, these properties were obtained at 28 and 365 days (Table 3). The results show that the mechanical properties of concrete mixes made with and without SP decreased by increasing the replacement ratios of OPC and NA with FA and RCA, respectively. Furthermore, the differences between the mechanical behaviour of the reference concrete (without FA, RCA and SP) and \"RCA concrete containing FA\" significantly decreased over time. This is due to the pozzolanic reaction between the SiO\({}_{2}\) of FA and the Ca(OH)\({}_{2}\) of RCA, which accelerates the rate of concrete strength development over time, relative to OPC concrete. The mentioned rate further increases with the use of SP, due to its dispersion effect on FA particles throughout the mixes. Consequently, the FA particles can easily reach the Ca(OH)\({}_{2}\) of RCA. The reasons behind these trends are explained in previous studies [20; 29; 36].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multicolumn{2}{c}{\(f_{\text{cm, mixes}}\)/\(f_{\text{cm,M1}}\)} & \multicolumn{2}{c}{\(f_{\text{ctm, mixes}}\)/\(f_{\text{ctm,M1}}\)} & \multicolumn{2}{c}{\(\text{E}_{\text{cm, mixes}}\)/\(\text{E}_{\text{cm,M1}}\)} & \multicolumn{2}{c}{**MPR (\%)**} \\ \cline{2-9}  & **28d** & **365d** & **28d** & **365d** & **28d** & **365d** & **28d** & **365d** \\ \hline M1 1 ** & 1.00 & 1.00 & 1.00 & 1.00 & 100 & 100 \\ M1 sp & 1.32 & 1.35 & 1.25 & 1.18 & 1.17 & 1.19 & 126 & 127 \\ M2 & 0.83 & 0.85 & 0.89 & 0.90 & 0.89 &  
According to the MPR values (calculated according to Equation (3)) at early and longer ages, the conventional mixes with SP had the highest mechanical performance, followed by the mixes containing low volumes of FA and coarse RCA (Figure 3). The lowest ranking concrete mixes are those containing high volumes of FA or fine RCA. These trends were expected because it is well-known that SP increases the mechanical performance of concrete, and both FA and RCA are harmful above a given incorporation ratio. Additionally, a high volume of coarse RCA in concrete is less detrimental than that of fine RCA, but much less favorable than the incorporation of FA [20; 29; 36]. Although the mechanical behaviour of concrete is essential, ranking concrete mixes based only on their strength may not be reliable from a \"business as usual\" point of view. In fact, the cost of concrete cannot be ignored when selecting the strength class, and they must be jointly optimized according to the target application. Therefore, an economic life cycle assessment was made for all concrete mixes (Section 3.2).
## 3 Materials and Methods
### Methods  
This present study aims to characterize the chemical composition of the mosaic floor mortars. The chemical analyses were performed on finely crushed mortar samples by X-ray fluorescence spectroscopy (XRF) through a Panalytical Axios spectrometer PW4400/40 X-ray (Marvel Panalytical, Almelo, The Netherlands) equipped with Rh tube under argon/methane, at University of Aveiro (UA), using Omnian37 and Pro-Trace2021 software for major and minor elements analyses, respectively. The loss of ignition (LOI) was determined by heating the mortars samples at 1000 \({}^{\circ}\)C, using an electric furnace for 3 h. The major elements analyzed were Al2O3, CaO, Cl, Fe2O3, K2O, MgO, MnO, Na2O, P2O5, SiO2, SO3, and TiO2 with detection limit of 1%. The detection limits of the trace elements analyzed were: As = 4.06 mg/kg, Ba = 6.90 mg/kg, Br = 0.78 mg/kg, Co = 4.54 mg/kg, Cr = 1.96 mg/kg, Cs = 4.78 mg/kg, Cu = 2.84 mg/kg, Ga = 0.94 mg/kg, Nb = 0.84 mg/kg, Ni = 2.00 mg/kg, Rb = 0.64 mg/kg, Sn = 3.02 mg/kg, Sr = 0.72 mg/kg, Th = 2.52 mg/kg, V = 2.78 mg/kg, Zn = 1.28 mg/kg and Zr = 0.80 mg/kg. Precision and accuracy of analyses and procedures were monitored using UA internal standards, certified reference material and quality control blanks. Results were within the 95% confidence limits. The relative standard deviation was between 5% and 10%.  
All statistical analyses were performed using IBM SPSS(r) statistics v25. The normality of the data was verified (Shapiro-Wilk's test, \(p>0.05\)). ANOVA, Tukey's test, t-student, K-means, cluster and discriminant analysis were used to determine groups and statistically significant differences (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{Layer 1: Nucleus—Opus Signinum (Upper Zone of the Layer)} \\ \hline \hline \multicolumn{5}{c}{Layer 2: Nucleus—Opus Signinum (Lower Zone of the Layer)} \\ \hline \hline \multicolumn{5}{c}{Layer 2: Nucleus—Opus Signinum (Lower Zone of the Layer)} \\ \hline \hline \multicolumn{5}{c}{Removal depth: \(\sim\)3.0/5.0 cm; color: reddish.} \\ \hline \hline \multicolumn{5}{c}{Layer 3: Between Nucleus and Rudus} & \multicolumn{1}{c}{Layer 4: Rudus} & \multicolumn{1}{c}{Layer 5: Statumen} \\ \hline \hline \multirow{2}{*}{Description} & Removal depth: \(\sim\)5 cm; & Removal depth: \(\sim\)5/10 cm; & Removal depth: \(\sim\)25/30 cm; color: \\  & color: pinkish & color: brown & dark brown \\ \hline \hline \multicolumn{5}{c}{Sample} & MA4 & MD3 & MA1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Studied samples and their characteristics.
## 5 Discussion  
This study provides a novel contribution by focusing on the patents' claims section to uncover protected innovations in hydrogen-powered aviation. Unlike previous studies that analyzed titles or abstracts, this approach reveals the core technical features and inventive  
Figure 12: Top 15 organizations by (**a**) production level and (**b**) latency from filing to grant.  
Figure 13: Measures of (**a**) category production level and (**b**) category latency.  
scope deemed valuable enough to protect. By structuring the results into seven innovation categories, this study offers a detailed view of where meaningful technical advances are occurring. The classification reveals how industry stakeholders are overcoming key hydrogen aviation barriers. For instance, fuel storage and delivery systems show a clear trend toward integrated, modular, and cryogenic-ready designs. These advancements suggest near-term feasibility for ground infrastructure and airframe adaptation. The clustering of patents in fuel cell integration, hybrid propulsion, and turbine enhancement reveals a broad push toward flexible propulsion architectures that combine multiple energy sources for performance and resilience.  
The results of the scientometric analysis further indicate growing global interest, with a recent sharp rise in participation by new inventors and diverse organizations. Leading aerospace firms and emerging innovators are engaging simultaneously. This landscape reflects both top-down strategic initiatives and bottom-up experimentation. Latency metrics show that the examination times are shrinking. This quickening of the patent lifecycle suggests growing familiarity among patent offices and clearer articulation of novel hydrogen technologies. Overall, this study offers valuable signals to industry, government, and research stakeholders. Manufacturers can benchmark the strategies of competitors, identify opportunity gaps, and refine development roadmaps. Policymakers and funding agencies can gain insights into where public investment could complement private innovation to accelerate commercialization. Researchers can better align their efforts with emerging trends to avoid redundant paths while focusing on unresolved technical challenges.  
This work is limited in scope to the claims section of USPTO-issued patents, enabling deep insight into protected innovations but not capturing all the global developments or unfiled research. **Future work** could expand coverage to integrate the academic and industrial literature. Nonetheless, the claims-based approach of this study establishes a solid methodological foundation for the ongoing tracking of hydrogen aviation innovations.
###### Abstract  
The use of X-ray fluorescence (XRF) scanning systems has become a common practice in many application sectors. In multistratified and heterogeneous samples, the simple analysis of an XRF spectrum as a response of the entire sample is not reliable, so different spectral analysis techniques have been proposed to detect the presence of surface stratification. One commonly studied case is that of gilding, i.e., the presence of a superimposing gold-leaf layer. The observation of changes in the net peak ratios of a single element or of several elements in an XRF spectrum is a well-developed practice, but is still not used in the case of XRF scanning (macro-X-Ray fluorescence scanning, MA-XRF), a technique that can be described as the extrapolation of XRF spot analysis to a second dimension, scanning a sample surface instead. This practice can yield information on the overlaying layer thickness, if some properties of the sample are known--or estimated--beforehand, e.g., the overlapping layer's chemical composition and the matrix effect contribution from the bulk material (_thick ratio_). This work proposes the use of an algorithm to calculate the thickness distribution of a superimposing gold layer accurately and automatically through the differential attenuation method by using MA-XRF datasets in a total noninvasive manner. This approach has the clear advantage over the traditional spot sampling of allowing the generation of a surface heightmap to better visualize and interpret the data, as well as a considerably larger sample space. Monte Carlo simulations were used to verify the influence of the medium used to adhere the gold leaves to the substrate and to generate known spectra to assess the algorithm's accuracy.  
Keywords:MA-XRF; Monte Carlo simulations; thickness calculation; differential attenuation +
Footnote †: journal: Applied Physics Letters
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 5 Case Studies
### IEEE 300-Bus System
#### 5.3.2 Light and Heavy Load Conditions  
For testing the effectiveness of the proposed method in light and heavy load conditions of the IEEE 300-bus system, the conditions of 80% and 110% of base load are respectively applied. The tested controlled variables are the same as those in Section 5.3.1. The ranking results of light and heavy conditions in descending order are shown in Table 16.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Outage Line** & **Light Load Condition** & **Heavy Load Condition** \\ \hline L 181(119-120) & 147.7148 & 341.7919 \\ L 309(225-191) & 40.2870 & 70.5317 \\ L 114(59-61) & 23.7148 & 39.3177 \\ L 242(162-164) & 31.3284 & 35.5648 \\ L 257(178-180) & 7.2198 & 11.5840 \\ L 322(241-237) & 6.4276 & 11.2255 \\ L 246(167-169) & 1.4334 & 9.0839 \\ L 205(133-137) & 0.9297 & 9.9235 \\ L 109(006-9007) & 0.9201 & 3.7961 \\ L 59(16-42) & 0.8912 & 3.4846 \\ L 249(173-174) & 0.8763 & 3.2126 \\ L 210(134-184) & 0.7992 & 3.0168 \\ L 174(115-122) & 0.7311 & 2.8433 \\ L 273(194-664) & 0.3252 & 2.7788 \\ L 207(1033-169) & 0.3732 & 2.5910 \\ L 308(224-226) & 0.2521 & 2.3859 \\ L 275(196-197) & 0.1436 & 2.0399 \\ L 86(38-41) & 0.4148 & 1.9632 \\ L 332(204-281) & 0.1586 & 1.9495 \\ L 140(81-194) & 0.1513 & 1.9282 \\ L 266(190-231) & 0.1151 & 1.9248 \\ L 85(57-90) & 0.1146 & 1.8892 \\ L 143(168-67) & 0.0945 & 1.8849 \\ L 9(9005-9055) & 0.0944 & 1.8741 \\ L 142(258-66) & 0.0841 & 1.7082 \\ L 274(95-219) & 0.0834 & 1.6927 \\ L 192(136-158) & 0.0834 & 1.6454 \\ L 227(143-145) & 0.0825 & 1.6394 \\ L 1815(123-124) & 0.0525 & 1.9374 \\ L 159(99-109) & 0.0524 & 1.8280 \\ L 197(128-130) & 0.0523 & 1.7938 \\ L 184(122-125) & 0.0515 & 1.7828 \\ L 163(103-105) & 0.0418 & 1.5820 \\ L 162(102-104) & 0.0491 & 1.5415 \\ L 239(157-159) & 0.0507 & 1.5416 \\ L 324(242-245) & 0.0476 & 1.5606 \\ L 128(73-79) & 0.0476 & 1.5222 \\ L 54(13-20) & 0.0477 & 1.4659 \\ L 112(57-63) & 0.0467 & 1.5399 \\ L 251(73-176) & 0.0466 & 1.5381 \\ L 201(103-132) & 0.0464 & 1.4931 \\ L 65(22-23) & 0.0464 & 1.3600 \\ L 3142(28-234) & 0.0464 & 1.2715 \\ L 248(172-1744) & 0.0455 & 1.2575 \\ L 215(137-181) & 0.0492 & 1.1494 \\ L 256(178-179) & 0.0474 & 1.1425 \\ L 295(214-242) & 0.0460 & 1.0201 \\ L 315(229-190) & 0.0388 & 1.0044 \\ L 340(10-11) & 0.0290 & 0.9898 \\ L 2211(40-145) & 0.0249 & 0.9442 \\ L 120(69-79) & 0.0243 & 0.9717 \\ L 222(140-146) & 0.0181 & 0.9353 \\ L 144(9012-9002) & 0.0155 & 0.8952 \\ L 335(3-1) & 0.0134 & 0.8542 \\ L 487(7-131) & 0.0125 & 0.8440 \\ L 80(37-38) & 0.0100 & 0.0762 \\ L 709(505-9053) & 0.0084 & 0.0761 \\ L 210(207-9071) & 0.0062 & 0.0378 \\ L 34(9003-9036) & 0 & 0.0092 \\ L 118(63-526) & 0 & 0.0076 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Security index results of light and heavy load conditions of the IEEE 300-bus system.  
From the values of the security indices in Table 16, a similar conclusion can be drawn that the rankings are almost unchanged, and increasing the loads will lead to serious consequences. What's more, the effectiveness of the proposal in the IEEE 300-bus system is verified.
## 7 Effects of Directivity on the Predictions of the Seismic Response
Examples of the Q-Q plots developed for two different records are shown in Figure 13.  
For each record the mean and standard deviation of the maximum radial displacements, obtained by applying that record at different \(\theta\) angles, can be calculated. The Normal distribution assumption can then be used to approximate the probability distribution of the maximum radial displacements at different \(\theta\) angles. If n records are used in the analyses, n values of mean and standard deviation will be determined. It is well known that the average of n Normally distributed random variables is also Normally distributed with the overall mean, \(\overline{M}\), and overall standard deviation, \(\overline{S}\), as presented by Equations (1) and (2). As a result, the mean response of a structure subjected to n records (n \(\geq\) 7) can be determined as a Normal random variable considering the uncertainty due to the angle \(\theta\) of the records. The mean displacement of the structure subjected to a number of records can then be predicted for a certain probability of non-exceedance for design or evaluation purposes. \[\overline{M}=\frac{1}{n}\sum_{i=1}^{n}M_{i} \tag{1}\]  
\[\overline{S}=\frac{1}{n}\sqrt{\sum_{i=1}^{n}S_{i}^{2}} \tag{2}\]  
Figure 13: Examples of Normal Quantile-Quantile plots for two different records. The mean and standard deviation of the maximum radial displacements of the central bridge columns are presented in Table 5 for the regular and irregular bridges studied for the same 22 records used in the previous sections of this paper. The distributions of the probability of non-exceedance for the central columns of the bridges are shown by the solid line in Figure 14.  
The radial displacements of 123 and 166 mm were predicted for the central columns of the regular and irregular bridges, respectively, from 3D analyses when the ground motion records were applied in the principal directions of the bridge (see Table 2). From Figure 14 these radial displacements correspond to 10% and 25% probabilities of non-exceedance, for the regular and irregular bridges, respectively. As presented in Table 2, the use of the 30% rule resulted in predictions of 135 mm and 178 mm for the maximum displacements of the central columns of the regular and irregular bridges, respectively. These values correspond to approximately 84% probability of non-exceedance. This indicates that the use of 30% rule can reasonably account for the variability of seismic response due to different excitation angles. It should be noted that only the variability due to the incident angle \(\theta\) in the prediction of the mean response is the subject of this study. The other sources of variability, such as the record-to-record variability, are out of the scope of this study. Figure 14: Probability of non-exceedance for the mean displacements of the central column using different averaging methods, due to variability of angle \(\theta\), for: (**a**) regular bridge; (**b**) irregular bridge.
## 2 Failure Probability Analysis
### Joint Probability Distribution Model of Ice and Wind Loads  
In this paper, data on ice, wind speed, and wind direction from 1973 to 2020 for the cities of Chenzhou, Yongzhou, and Shaoyang in Hunan Province, China, are collected through the National Oceanic and Aeronautic Administration (NOAA) website. Based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), the joint probability distribution models of IT-WS and WS-WD are established using the Clayton Copula function (Equation (3)) and Frank Copula function (Equation (4)), respectively. Figure 1 shows the joint probability density of IT-WS and WS-WD. More details on the construction of the joint probability distribution model can be found in [PERSON] et al.'s study [29].  
\[F_{XY}=C_{1}(u_{1},v_{1},\theta_{1})=\left(u_{1}^{-\theta_{1}}+v_{1}^{-\theta_ {1}}-1\right)^{-1/\theta_{1}} \tag{3}\]  
\[P_{XY}=C_{2}(u_{2},v_{2},\theta_{2})=-\frac{1}{\theta_{2}}\text{ln}\left[1+ \frac{\left(e^{-\theta_{2}u_{2}}-1\right)\left(e^{-\theta_{2}v_{2}}-1\right)} {e^{-\theta_{2}}-1}\right] \tag{4}\]  
where \(F_{XY}\) is the [PERSON] joint probability distribution model; \(u_{1}\) and \(v_{1}\) are ice thickness and wind speed cumulative probability density functions; \(P_{XY}\) is the [PERSON] joint probability distribution model; \(u_{2}\) and \(v_{2}\) are wind speed and wind direction cumulative probability density functions; \(\theta_{1}\) and \(\theta_{2}\) are the parameters of the Copula function.  
Figure 1: The joint probability density distribution: **(a)** IT-WS; **(b)** WS–WD.
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods
### Experimental Setup
#### 2.6.2 Channel  
As represented in Figure 5, we use a container to simulate the channel. We decided to fill the container with only 20 mL of water with a pH of approximately 7. On one side of the container, we placed the tubes sending the acid or basic solution, one at a time (never simultaneously). On the other side of the container, 6 cm apart, we placed the pH probe and another pump to keep the container's liquid volume always the same. Although these pumps generate some flow, the system will still mainly work via diffusion. One advantage of using acids and bases is that they are chemicals that cancel each other, and that property is very important in closed loops because the average concentration of these chemicals would remain constant assuming the same transmission concentration of bases and acids.