esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 4 Numerical Results
### Execution Times and Discussion of Parameter Values
For small \(n\) and \(s\), a larger \(c\) can give faster good results. Finally, we partially justify our choices for the number of iterations. Generally, the rule is simple: more iterations lead to better results. However, there are many factors that influence the convergence speed and there are many random elements in the algorithm (the initial dictionary, the added atoms, the initial size and, of course, the noise). We illustrate in Figure 1 the evolution of the main variables, the current dictionary size (as given by the ITC) and RMSE (on the training data), for \(n=128,s=10\) and 10 runs of ITC-ADL with random initial sizes. The values are shown for iterations that are multiple of \(c\); the size is constant between them and the RMSE typically decreases. It is visible that, after 100 iterations, the size is close to the true one and the RMSE is at about its final (and almost optimal, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\delta_{+}\delta_{-}\) & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline size & 257.90 & 257.88 & 257.88 & 258.16 & 259.86 & 258.16 & 259.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average dictionary sizes given by ITC-ADL, for \(n=256\), \(s=6\), when \(\delta_{+}=\delta_{-}\) vary. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(n_{\text{cand}}=10\) & 15 & 20 & 25 \\ \hline \(s=6\) & 263.86 & 258.34 & 257.30 & 256.80 \\ \(s=10\) & 288.18 & 286.68 & 280.66 & 274.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average dictionary sizes given by ITC-ADL, for \(n=256\), for several values of \(n_{\text{cand}}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(c=3\) & 4 & 5 & 6 & 8 & 10 \\ \hline \(s=6\) & 257.16 & 258.04 & 258.32 & 256.92 & 257.28 & 256.96 \\ \(s=10\) & 265.36 & 273.00 & 280.36 & 285.46 & 282.64 & 284.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average dictionary sizes given by ITC-ADL, for \(n=256\), for several values of \(c\). as we have seen) value. Typically, the size becomes larger than the true one when the RMSE reaches the lowest value. Then, ITC-ADL gradually trims the dictionary almost without changing the RMSE. Thus, if our purpose is dictionary learning (and perfect recovery is not sought because there is actually no true dictionary), the number of iterations can be smaller than for dictionary recovery.
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 5 Analysis on Corrosion-Fatigue of Structural Steel for Bridges
### Evaluation on the Corrosion-Fatigue Process  
The C-F crack development in UWS bridges mainly depends on the category of environment, the fracture mechanics-related properties and the stress state under live loads. Meanwhile, there are also some other conditions that lead to an accelerated C-F process under same corrosion environment and material, e.g., the relatively huge pitting flaw size due to the uncertainty in pitting corrosion.  
As suggested by [PERSON] [15; 57], the fatigue strength will be reduced due to the corrosion effect, and the degree of reduction increases with original fatigue strength. Since the base metal is believed with the highest fatigue strength, the reduction in the fatigue strength of the base metal is correspondingly the most serious. Thus, investigations were made on the C-F performance of base metal. The stress range used in the analysis was determined after the design fatigue strength at 2 million cycles in the Chinese code JTG D64 [58]. The ADTT was selected according to the number of heavy vehicles in traffic category 2 stipulated in the Eurocode 1 [59]. In addition, the pitting current was used the same as the value from the corrosion test in Qingdao [42]. The main parameters were listed as the following: \(\Delta\sigma\) = 140 MPa, \(R\) = 0.4, ADTT = 1370, \(I_{p}\) = 1.05 \(\times\) 10\({}^{-8}\) C/s, \(C_{p}\) = 1 and \(m_{asp}\) = 1. Besides, it was assumed that each passing truck generated only one stress range. The results are shown in Figure 9.  
The result indicates that crack development at the crack initiation stage was notably different from that at the crack propagation stage. For instance, the crack growth rate was remarkably lower at the initiation stage than at the propagation stage. In addition, it could also be found that the initiation life accounted for the largest part of the whole C-F life in both types of steel, i.e., 80.3% in the HPS 70W and 61.6% in the 14 MnNbq. Similar conclusions can also be found in many relevant studies [22; 60]. At the same time, the crack initiation life in HPS 70W was much longer than in the 14 MnNbq, i.e., almost 5.3 times longer. On this end, the weathering steel HPS 70W steel exhibited much better C-F performance than the normal carbon steel 14 MnNbq, even under the same pitting current. This advantage of the HPS 70W could be attributed to the higher fatigue resistance and lower decrease in fatigue limit with the increase in stress ratio. Besides, the corrosion resistance of HPS 70W  
Figure 8: Critical pitting depth of the steel HPS 70W with different plate thicknesses: (**a**) 20 mm; (**b**) 30 mm; (**c**) 40 mm and (**d**) 50 mm.  
was far better than the normal carbon steel 14 MnNbq, which led to a further enhancement in the C-F performance of the HPS 70W.
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.1 Microwave Radiometer  
Microwave radiometers can estimate OSW based on the spectrum of the microwave radiation emitted by the ocean surface [79; 82]. OSW measurements from microwave radiometers mainly rely on the physical RT models, which estimate the microwave emission from the ocean surface and the emission and absorption by the Earth's atmosphere [83]. As discussed, the nonpolarimetric microwave radiometers (e.g., Advanced Microwave Scanning Radiometers (AMSR)) can only estimate OSW speed. However, the WindSat polarimetric microwave radiometer can provide both OSW speed and direction [79]. Polarimetric microwave radiometers contain \(\pm\)45\({}^{\circ}\) polarized and right/left circularly polarized channels along with vertical and horizontal polarizations. Polarimetric microwave radiometers retrieve OSW speed and direction by measuring the complex correlation between horizontally and vertically polarized microwave radiations [32]. In fact, the difference between horizontal and vertical polarizations is related to OSW speed, and the complete state of polarization is related to OSW direction [82]. The OSW direction from polarimetric microwave radiometer systems becomes less precise when wind speed is less than 8 m/s [84; 81]. This is because the passive OSW direction signal is small in all polarizations at low wind speeds, causing noisy and inaccurate measurement of the OSW direction [79; 85]. Generally, microwave radiometers provide better results in high wind speeds than other low-resolution RS systems, such as scatterometers. For example, it has been reported that in spaceborne L-band radiometers, received signals remained sensitive to increasing wind speeds up to 70 m/s [86; 87]. Accordingly, some of the L-band radiometers (e.g., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)) can provide a reasonable estimation of extreme OSW speeds at a coarse spatial resolution [87]. The quality of the OSW measurements from microwave radiometers is negatively affected by rain [83]. However, L-band radiometers (e.g., SMOS) are almost insensitive to the presence of rain or frozen precipitation [88]. Various OSW products have been so far generated using the data collected by different microwave radiometers. For example, [PERSON] et al. [89] implemented a novel method to estimate OSW vector in severe weather conditions (i.e., the high precipitation environment of tropical cyclones) using WindSat data. This study used HWind data [90] as the reference dataset and evaluated the proposed methodology in 17 tropical cyclones from 2003 to 2009. The results showed that the mean difference between the retrieved OSW speed and HWind data for 17 tropical cyclones was 0.2 m/s. The Root Mean Square Error (RMSE) was also 24.2\({}^{\circ}\). The authors reported that the observed RMSE value was mainly related to a time or location mismatch between the HWind analysis and the WindSat data.
# Readability Formulas for Elementary School Texts in Mexican Spanish
###### Abstract
Furthermore, most of these models are trained on corpora that do not reflect linguistic variants of a language, leading to potential mismatches between the estimated readability levels and the actual curricular complexity encountered by students. In spite of the recent efforts, there is currently no approach offering both high accuracy and interpretability to assess the readability for elementary school texts in Mexican Spanish. This highlights the pressing need for readability models that are not only transparent but also explainable contextually. This paper presents two novel readability formulas tailored for the Mexican Spanish language. The contribution of this work is twofold. First, to the best of our knowledge, these are the first readability formulas specifically designed to assess elementary school texts in the Mexican Spanish language. Despite Mexico being the largest Spanish-speaking country in the world--home to over 125 million native speakers--there has been a notable absence of readability formulas adapted to its specific linguistic and educational contexts. Second, one of the proposed formulas is the first of its kind to be generated through GP, a technique within evolutionary computation that simulates natural selection and biological evolution to solve complex problems. A key advantage of GP over conventional machine learning approaches is its capacity to produce interpretable models, expressed as explicit closed-form equations. This interpretability allows for greater transparency and deeper understanding of the linguistic features that influence text difficulty. To assess the effectiveness of the proposed formulas, an experimental study was carried out, comparing their performance against several well-established readability formulas. The experimental results indicate that the proposed formulas exhibit a markedly stronger correlation with Mexican elementary grade levels. This suggests that they offer a more accurate and interpretable means of assessing text difficulty in the context of Mexican Spanish educational materials. The remainder of this paper is organized as follows. Section 2 reviews some of the most used readability formulas, with a particular focus on those developed for the Spanish language. Section 3 details the methodology adopted for the development of the proposed readability formulas. Section 4 presents the experimental setup, outlines the evaluation criteria, and discusses the results obtained from the comparative analysis. Finally, Section 5 summarizes the main findings of this study and outlines directions for future research.
## 3 Results and Discussion
### Characterization of the Membranes  
#### 3.2.1 F-Ir  
The FT-IR spectra obtained from the original DES-lignin powder and the membrane modified with 1 wt % DES-lignin are compared with that of a pure PES membrane in Figure 2. Due to the low amount of DES-lignin powder (0.25 and 0.5 wt %) used in the modification of the membranes and overlapping of most of the DES-lignin peaks with the membrane peaks, the characteristic peaks of DES-lignin, the vibration of the unsaturated carbonyl group (C=O) at 1735 cm\({}^{-1}\), and aromatic skeleton stretching (C=C) at 1511 cm\({}^{-1}\) could not be unequivocally observed in the spectrum of the modified membranes. However, the new absorbance peaks could be seen clearly in the spectrum of the membrane prepared at the DES-lignin concentration of 1 wt %. At least two possible interactions, \(\Pi\)-\(\Pi\) interaction between benzene rings and hydrogen bonding between the hydroxyl group in DES-lignin and oxygen atoms of sulfone group in polyethersulfone, could be envisaged.  
The presence of a strong and broad band between 3200 and 3600 cm\({}^{-1}\) centering around 3500 cm\({}^{-1}\), a slight peak at 1735 cm\({}^{-1}\), and a shoulder peak at 1511 cm\({}^{-1}\), which are associated to stretching vibrations of aromatic hydroxyl groups, carbonyl group, and coupled skeletal vibrations of the aromatic rings of DES-lignin, respectively, indicated the incorporation of DES-lignin within the polyethersulfone matrix. In addition, the area of C-H aromatic stretching vibration (3100-3000 cm\({}^{-1}\)) and aliphatic stretching vibration (2922-2875 cm\({}^{-1}\)) become more intense due the presence of DES-lignin in the structure of the modified membrane. The same behavior of interaction between a PES membrane and vanillin, as one of lignin derivatives, was also observed in our previous work [42] and in the study done  
Figure 2: ATR-FTIR spectra of virgin and modified membrane with 1% DES-lignin in the region of 4000–400 cm\({}^{-1}\). Non-overlapping peaks related to DES-lignin can be detected at 1511 cm\({}^{-1}\), 1735 cm\({}^{-1}\) and broad band between 3200 and 3600 cm\({}^{-1}\).  
by [PERSON] et al., where the interactions between phenolic model compound vanillin and PES surface was comprehensively investigated [43]. They also mention that hydrogen bond formation between the hydroxyl group of this phenolic compound and ether oxygen of PES could also be established, although the hydrogen bonding with sulfur is the dominant interaction. As can be seen in Figure 2, the intensity of the ether group at 1043 cm\({}^{-1}\) in the DES-lignin modified membrane becomes more intense compared to the virgin membrane, which is in accordance with the findings of [PERSON] et al.
## 2 Materials and Methods
Secondly, we assessed if a trend was significant within the historical time period from 1961 to 2019 and within the individual scenarios from 2010 to 2100. To test if trends within the historical time period or the scenarios were significant and differ between scenarios, a generalized mixed model, with scenario and time as fixed effects, was fitted as a random intercepts model with an unstructured correlation matrix (PROC GLIMMIX, SAS 9.4, SAS Institute Inc., Cary, NC, USA). For the water budget components precipitation, evapotranspiration, transpiration, SW200, and temperature, we fitted a lognormal distribution. For the variable dry days per year, we fitted a Poisson distribution, modified according to [PERSON] and [PERSON] [64], to account for overdispersion. For climate water balance and seepage, a gaussian distribution was fitted. Inferences on differences of slopes between scenarios were made by specifying contrasts. Additionally, simple linear fixed-effects models with time as fixed effect were fitted for single plots. All statistics were fitted on a 95% confidence level. Statements about significance are made on \(p<0.05\) probability value. Test statistics can be found in Tables S2-S5 in the Supplementary Materials Section.
## 5 Results and Discussion
### Methodology and Results of Approach Comparison  
In view of the fact that the manuscript outlines two approaches developed to study the bubble flows, the next fair step is to develop a methodology for a qualitative comparative assessment of the methods applied; thus a criteria set was developed in order to compare CCV and NN methods. The set included over a dozen different criteria, i.e., model and sample complexity, robustness of the model to changes in sample labeling and number of processed samples, etc. Being mostly general criteria they provide nevertheless a comparison of the two models obtained in a very different way in terms of the speed of development, data preparation, their speed and resistance. A number of quantitative assessments is represented in Table 2. Let us provide the results of applying the assessment methodology to both methods in this subsection:  
Figure 34: The arithmetic ellipsoidal surface area of bubbles was obtained from the elliptical approximation of the third axis by the CCV method (blue) and for cluster surface area for NN (orange). One can see that the ratio is similar to the one with the 2D area estimate. It requires more tests; however, the first conclusion is a 2D area estimation instead of a 3D one since we need to calibrate results anyway and for the calibration procedure absolute values are not that crucial, while the ratio matters.  
Both models were used on comparable hardware with the same amount of free RAM, namely 32 GB--however, both models did use a relatively small amount of available memory.
* Both models have a comparable single-frame processing speed with bubbles. When assuming the NN method, based on modification of StarDist, spends from 250 ms to 650 ms per frame with a resolution of 1280 by 800, then the CCV method, based on DBscan, varies from 180 to 1090 ms depending on the number of bubbles for detection.
* Data labeling in both cases takes comparable time and is used primarily to build a dataset in order to assess the quality of the model according to the selected metric.
* An advantage of the classical model (CCV method) is minor training required, while the deep learning one developed for the modification of StarDist (NN method) requires training for several hundred epochs on hundreds of labeled samples. It is worth noting, however, that, in case of obtaining new data that differs from the previous, the CCV method might require manual hyperparameter tuning, while in the NN method training occurs automatically.
* Meanwhile the strength of the model based on the NN method is the augmentation including blurring and rotations for each image that makes this model potentially more resistant to the filming condition modifications. It is worth mentioning notwithstanding that the study of stability lies out of the current paper's focus.
* Summarizing, none of the developed models has a significant advantage over another with the studied criteria considered significant for further practical use.
# Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling
## 2 Methodology Structure  
The IK function defines the joints' configuration (Q) required to reach the target frame with the Cartesian coordinates (S) of the end-effector. This study models the IK procedure adopting a sequential set of ANNs. The flow diagram of the proposed methodology is presented in Figure 1. Three macro-steps can be depicted, an initial phase (_\"IK boundary definition and pre-processing\"_) where the robot is chosen and the dataset is produced with the FK equations, obtained automatically through an iterative procedure based on the D-H parameters or by manual setting. The corresponding modeling is evaluated with the _Mean Euclidean Error_ (MEE) for a set of predefined trajectories in order to verify the dataset quality on the machine learning model. Once the FK is validated, the dataset is produced. The following step aims to define the ANN structure (_\"Network Architecture Definition\"_) for the IK model. Two different approaches may be selected, a _global_ approach, where all the joints are computed together, or the novel _sequential_ approach, where the joints are computed sequentially from the TCP or the Base, integrating the previously modeled joint as extra input for the following one. In order to avoid a manual inference of the model structure, the genetic algorithm (GA) optimization is included with a predefined tunable cost-function. Finally, the produced IK model is validated using the same trajectories employed in the initial step FK validation.  
Table 1 summarizes the methodology in terms of input and output elements of the proposed IK procedure.  
\begin{table}
\begin{tabular}{c c l} \hline \hline  & & **IK Boundary Definition and Pre-Processing** \\ \hline \multirow{4}{*}{Input} & - & Select the robot \\  & - & Joint geometrical dimensions/position \\  & - & Use 70-15-15 technique to identify the training-testing-validation \\  & - & Estimate D-H parameters \\ Output & - & FK equations, based on D-H parameters, validated with the mean Euclidean error index \\ \hline \multirow{4}{*}{Input} & - & Dataset for the defined D-H \\  & - & Define the number of joints \\  & - & Check algorithm constraints \\  & - & Activation Functions \\  & - & Feature selection and filtering \\ \cline{2-2}  & - & Hidden layers (HL) \\  & - & Hidden neurons (HN) \\ Output & - & Quantify model robustness \\  & - & Comparison between methods, dataset training, and validation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input/Output IK procedure.  
Figure 1: Flow diagram of the IK procedure.
## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System
### Treatment Continuation Module  
Once the initial treatment phase is complete and clinical outcomes are assessed, the system provides a clear path for decision-making based on the patient's response to treatment. The system classifies the patient into one of several categories, which are discussed in Section 4.3.  
The treatment continuation module is an integral part of the expert system designed to guide clinicians through the management of patients with FSGS following the initial treatment phase. This module provides evidence-based recommendations on how to proceed with therapy after six months, after assessing whether the patient has achieved remission, is in partial remission, or has experienced a relapse. The module ensures that the treatment plan remains tailored to the patient's evolving needs. The flowchart of the described approach is presented in Figure 14. The key steps in the workflow are as follows:  
* **Assessment of remission status**: The clinician is asked whether the patient has achieved complete or partial remission. If the answer is \"Yes\", the system displays further treatment options based on remission status, leading to the recommendations shown in Table 3.
* **Therapeutic adjustments for relapse**: If relapse is detected, the system prompts the clinician to choose from alternative treatment regimens. These may include second-line therapies such as calcineurin inhibitors or more intensive immunosuppressive treatments.
* **Disease resistance to first-line treatment**: In cases where steroid resistance is observed, the system provides guidance on potential second-line therapies. Options include calcineurin inhibitors, rituximab, or other immunosuppressive agents.
* **Monitoring and follow-up**: The system integrates periodic follow-up assessments to ensure that the treatment remains effective. It adjusts recommendations based on the patient's clinical response over time, with regular monitoring of key parameters such as proteinuria, serum creatinine, and albumin.  
The treatment continuation decision process is guided by an interactive interface (see Figure 15), where clinicians are prompted with specific questions based on the patient's progress. Clinicians must provide answers regarding the patient's remission status, after which the system proposes an appropriate treatment strategy. The interface is designed to allow easy navigation between different treatment options, providing detailed information about each one.
# A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation +
## 4 Evaluation  
In this section, the performance of the proposed drone-based message ferry was evaluated by conducting an experiment utilizing LACS prototype. We measured the inter-LACS data transfer rate (throughput). Thereafter, we estimate the effective transmission speed that can be achieved by the proposed message ferry.