esa-sceva/satcom-chunk-collection huggingface dataset:
# Readability Formulas for Elementary School Texts in Mexican Spanish
## 2 Related Work
### Readability Models Produced by Machine Learning Algorithms  
Recent research on readability formulas has evolved to consider syntactic, semantic, and discourse-level complexities, driven by advancements in natural language processing (NLP) tools such as dependency and constituency parsers, anaphora resolution systems, and resources like WordNet [6]. NLP tools, combined with machine learning (ML) algorithms, have been applied to predict readability across some domains (e.g., web pages [25] or online health resources [26]).  
In the context of the Spanish language, [PERSON] et al. [27] utilized classical machine learning algorithms--such as LinearSVC, Multilayer Perceptron, Random Forest, and Naive Bayes--to create a classification model aimed at determining the appropriate reading age for children's Spanish texts. They compared these algorithms using traditional NLP representations like Word2 Vec and TF-IDF to identify which produced the most accurate predictions. Similarly, [PERSON] et al. [28] used both feature-based machine learning and deep learning models to predict the educational grade level of Spanish science texts in obligatory secondary education.  
Despite the advancements in predictive accuracy achieved through ML approaches for readability assessment, the issue of interpretability remains a significant concern. Few studies have focused on developing ML models that generate more interpretable results. For instance, [PERSON] et al. [29] evaluated the readability of Spanish e-government websites using a decision tree generated by the C4.5 algorithm, which provides a more transparent  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**[PERSON] and \(\mu\)**} & **[PERSON]** & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**Inflesz**} & \multirow{2}{*}{**Readability Levels**} \\  & **(6 th Grade)** & & & \\ \hline
91–100 (4 th grade) & \textgreater{}70 & 86–100 & 80–100 & Very easy \\
81–90 (5 th) & 61–70 & 76–85 & 65–80 & Easy \\
71–80 (6 th) & 51–60 & 66–75 & & Relatively easy \\
61–70 (7 th to 8 th) & 41–50 & 51–65 & 55–65 & Standard \\
51–60 (9 th to 10 th) & 34–40 & 36–50 & & Relatively difficult \\
31–50 (11 th to 12 th) & 21–33 & 16–35 & 40–55 & Difficult \\
0–30 (College) & \(\leq\)20 & 0–15 & 0–40 & Very difficult \\ \hline \hline \end{tabular}
\end{table}
Table 1: Readability levels based on the scales defined by traditional readability formulas developed for the Spanish language.  
and interpretable model. However, there remains a gap in the literature regarding the integration of interpretability with high predictive accuracy in readability assessment models. This gap highlights the need for further research into developing models that can balance these two critical aspects, enabling more effective and understandable tools for assessing text readability across different languages and domains.
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 4 Evaluations
### Quantitative Evaluation
#### 4.1.3 Real Datasets
Using Equation (34), \(\Psi\) denotes the set of visible edges in the restored image, and \(r_{i}\) is the ratio determining the improvement of visibility level. Therefore, higher values of \(e\) and \(r\) are desired in an image processing task. Tables 4-6 show the average evaluation metric results on the IVC, O-HAZE, and I-HAZE datasets. Similarly, the best results are shown in bold. The values of \(e\) and \(r\) are directly proportional to the number of newly restored edges in the dehazed image. [PERSON] et al. [45], however, stated that any set of edges which have a local contrast above 5% are assumed to be the visible edges. Accordingly, in methods susceptible to the background noise, like FFD and HPM, it appears that noises may be misinterpreted and then adversely affect the accuracy of \(e\) and \(r\). Hence, even though ICAP has lower \(e\) and \(r\) results than FFD and HPM, it may still be the best performing method when considering the background noise resistant characteristics. This can be observed in the qualitative evaluation in the next section. Using the O-HAZE dataset, our proposed ICAP is the best method in terms of TMQI and FADE, and the second best under SSIM and FSIMc metrics. The dehazing performance is even more impressive on the I-HAZE dataset, where the proposed ICAP exhibits the best dehazing power under SSIM, TMQI, and FSIMc metrics. The clear superiority of ICAP over CAP is attributed to all the improvements presented in Section 3. The adaptive linear weight for handling color distortion makes ICAP outperform CAP in dark areas, resulting in smaller FADE, except for in the I-HAZE dataset, where FADE = 0.8053 may be too low, causing loss of image details. This, coupled with the adaptive constraints on the transmission map and the background noise reduction, leads to the higher TMQI and FSIMc. Adding to that, the adaptive tone remapping post-processing also contributes toward the high scores of TMQI and FSIMc. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(e\) & \(r\) & **FADE** \\ \hline DCP & 0.39 & 1.57 & 0.56 \\ FFD & **1.30** & **2.15** & 0.53 \\ CAP & 0.78 & 1.17 & 0.83 \\ HPM & 1.27 & 2.07 & 0.73 \\ ICAP & 1.11 & 2.03 & **0.50** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average rate of new visible edges (\(e\)), quality of contrast restoration (\(r\)), and FADE results on the Image and Vision Computing (IVC) dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SSIM** & **TMQI** & **FSIMc** & **FADE** \\ \hline DCP & **0.7709** & 0.8403 & **0.8423** & 0.3719 \\ FFD & 0.7263 & 0.8416 & 0.7733 & 0.4013 \\ CAP & 0.6647 & 0.8118 & 0.7738 & 0.6531 \\ HPM & 0.4702 & 0.6509 & 0.6869 & 1.1445 \\ ICAP & 0.7322 & **0.8935** & 0.8219 & **0.3647** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average SSIM, TMQI, FSIMc, and FADE results on the O-HAZE dataset.
## 4 Analysis and Interpretation of Results
### Regression Analysis of Price Trends
#### 4.5.1 Results of the Daily Regression Analysis
In the Czech Republic, relatively low R\({}^{2}\) values have been observed for stand-alone sources (Solar, Wind), but in the case of Wind + Solar combined generation, R\({}^{2}\) values have been higher, especially in recent years (e.g., 0.344 in 2025). These results suggest that although solar and wind generation alone do not show significant trends, their combined effect is statistically stronger. In the case of Estonia, overall generation trends have been very weak, with most R\({}^{2}\) values below 0.1. The exception is in recent years, particularly 2025, where combined Wind + Solar generation has reached R\({}^{2}\) = 0.407, indicating incipient structural changes in generation dynamics. Overall, the combination of wind and solar generation (Wind + Solar) shows more systematic trends than the individual sources alone. The results of the regression analysis support the assumption that with increasing integration of RES, there is more organisation and predictability of production profiles, although the extent of this effect varies significantly between the countries analysed. Significant trends are found particularly in countries with high levels of renewable investment and infrastructure flexibility.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 2 Datasets
### Satellite Image: Unifrom-1  
UNIFORM-1 is the first satellite in a satellite constellation plan for quick detection and monitoring of wildfires from space using thermal infrared sensors [3]. The satellite has successfully and continuously operated since its launch on 24 May 2014 as a piggyback satellite for Advanced Land Observing Satellite-2 (ALOS-2), operated by Japan Aerospace Exploration Agency (JAXA) [13]. UNIFORM-1 has observed ground surface temperatures with an un-cooled bolometer imager (BOL) that covers the thermal infrared wavelength region (8-14 \(\upmu\)m) [3; 14]. The BOL observes from an altitude of 628 km and has a ground sampling distance (GSD) of 157 m under the nadir condition. The guide imager for BOL, also installed on UNIFORM 1, is a monochromatic camera that integrates the visible wavelength region (VIS) with a GSD of 86.1 m. Both instruments are 2-D array imaging sensors. Their specifications are summarized in Table 1. Note that the pixel scales (and thus the GSDs and image widths) have been empirically modified from the specification values in [3] through the two-year operation of UNIFORM-1.  
UNIFORM-1 has a STT and the planned geometrical accuracy of the BOL images from UNIFORM-1 was 500 m per pixel after registration with VIS and STT [3]. The planned accuracy of thermal detection should have provided practical fire alarms to the fire department. However, after its launch, the planned accuracy was not delivered and the need for post-processing corrections was recognized. The degraded accuracy of UNIFORM-1 was traced to an interface trouble in its onboard system, which caused difficulties to use the STT and reduced the attitude information. As a result, attitude control of UNIFORM-1 was not as accurate as planned. Furthermore, the registration errors in the map projections of VIS images are as large as 50-100 km (Figure 1). These errors prevent the delivery of useful thermal information to firefighters and other users.  
Both BOL and VIS images of UNIFORM-1 have been published on a web data publishing service [15], in which images are projected onto latitude-longitude map coordinates by a procedure of satellite attitude determination and map projection described in Section 3.1.
## 2 Modelling and Methods  
### One-DimensionalModel Development  
For the SCR model, an internal proprietary simulation tool designed by BASF, CatSim, was utilized for advanced modelling and computation, encompassing numerical models for the catalyst and pipes, cones, injectors, junctions, and exhaust gas recirculation (EGR) valves, as stated in the previous work [19]. Homogeneous reactions in the gaseous phase were taken into account, whereas non-catalytic surface reactions along the exhaust pipe were disregarded. The SCR reactions comply with the Eley-Rideal reaction mechanism, namely, gaseous nitrogen oxides react with strongly adsorbed NH\({}_{3}\), and the adsorption and desorption processes of NH\({}_{3}\) are contemplated. The SCR reactions comply with the Eley-Rideal reaction mechanism; namely, the gas-phase NOx reacts with strongly adsorbed NH\({}_{3}\), and the process of NH\({}_{3}\) adsorption and desorption is contemplated. The global kinetics form is used to create an overall kinetic model for each component and the kinetics form is expressed as an Arrhenius equation. The reaction framework for the Cu-SCR model includes the following reactions:  
\[\text{NH}_{3}(\text{g})\leftrightarrow \text{NH}_{3}(\text{ads}) \tag{2}\] \[\text{NO}+\text{O}_{2}\to\text{NO}_{2}\] (3) \[\text{4 NH}_{3}(\text{ads})+\text{4 NO}+\text{O}_{2}\to 4\text{N}_{2}+ \text{6H}_{2}\text{O}\] (4) \[\text{2 NH}_{3}(\text{ads})+\text{NO}+\text{NO}_{2}\to 2\text{N}_{2}+ \text{3H}_{2}\text{O}\] (5) \[\text{2 NH}_{3}(\text{ads})+\text{2 NO}_{2}\to\text{N}_{2}+ \text{3H}_{2}\text{O}+\text{N}_{2}\text{O}\] (6) \[\text{4 NH}_{3}(\text{ads})+\text{3 NO}_{2}\to 3\text{\%} \text{N}_{2}+\text{6H}_{2}\text{O}\] (7) \[\text{4 NH}_{3}(\text{ads})+\text{3O}_{2}\to 2\text{N}_{2}+ \text{6H}_{2}\text{O}\] (8) \[\text{4 NH}_{3}(\text{ads})+\text{5O}_{2}\to 4\text{NO}+\text{6H}_{2}\text{O}\] (9) \[\text{2 NH}_{3}(\text{ads})+\text{3 NO}_{2}\to 3\text{NO}+ \text{N}_{2}+\text{3H}_{2}\text{O} \tag{10}\]  
Assuming that the conditions within each individual channel in the monolith catalyst model are precisely identical, there exists no radial fluid flow velocity nor component concentration gradient in the normal section perpendicular to the flow direction. The SCR model is constructed by means of 1D single-channel approximation in the monolith catalyst, derived from the processes of each component's reactions, the mass balance equation (Equation (11)), the energy balance equation (Equation (12)), and the momentum balance equation (Equation (13)), along with kinetic reactions on the washcoat surface [20]. The SCR reaction kinetics for both the Cu-zeolite SCR employed for calculation were developed and calibrated using testing data from a bench reactor [19].
# Hand Gesture Recognition on Edge Devices:
## 6 History and Categorization of Existing Approaches  
This section introduces diverse processes and algorithmic approaches for HGR. Figure 16 depicts an overview of these processes and algorithms. First of all, certain HGR algorithms are based on ML, while others are not. Depending on whether machine learning is applied, the entire setup, development, and assessment process is different. ML-based algorithms can use different processes and implement distinct neural network types. The processes are listed as child nodes to the right of processes. Available neural network types used for HGR are based on specific principles: spiking, attention, gate, and convolution. Child nodes to those network principles show variances of each of them. Each variance serves a specific purpose. More detailed information about the neural network types and their variances can be found in Table 3. A summary of advantages and drawbacks of different sensor types is given in Table 4.  
HGR began with vision-based systems such as [50]. This system uses vision-based depth calculation and feature extraction without ML. It was already published in 1998. Ref. [51] is also one of the earliest works about HGR, being published in 2005. This vision-based system still comes with many constraints. The system presented in [51] requires the user of the system to wear a glove and to keep their hand still at the beginning of the gesture. In 2015, Ref. [52] presented one of the earliest hand gesture recognition systems in radar. Ten hand motions and a thumb motion are recognized using radar data in the system that is being displayed. To evaluate the system the detected coordinate was compared  
Figure 16: HGR classification algorithms and processes. to one measured by a depth sensor. At about the same time the first ultrasound-based systems like [53; 54] emerged, which classify several hand gestures using the audio system of commodity devices like a laptop or a smartphone respectively. First attempts to use WiFi for HGR have also been made, as shown in [34]. Despite of reseachers' efforts since the 2000s, even today HGR systems still require improvements in terms of robustness to interference, environmental changes, different users and similar.
## 2 Preliminaries and Problem Statement  
In this section, the mathematical models of the scheduling and path planning problem in the aerial recovery process are presented. For simplicity, the following assumptions are made:  
1. Trajectory assumption. The trajectory of the mother aircraft is predefined and it is projected to a two dimensional plane for consideration. The aerial recovery process is considered to be a long-range mission and the execution time of the process is concerned with the mother aircraft trajectory.
2. Homogeneous assumption. UAVs in the swarm are homogeneous and fly at the same constant velocity, i.e., they can be recovered by the mother aircraft in an arbitrary sequence.
3. Recovery assumption. The mother aircraft employs the airborne actuators to recover the UAVs when they rendezvous at specified recovery positions with the same orientation in the sequence.
4. Communication assumption. The mother aircraft is always connected to the UAVs and the communication is perfect without delay and any other unfavorable factors.
5. Based on the assumptions, the schematic of the aerial recovery process is illustrated in Figure 1.  
Figure 1: Schematic of the aerial recovery process. (**a**) Single-UAV recovery by using an airborne actuator on the mother aircraft. (**b**) Multi-UAV recovery (in an arbitrary sequence).
## 5 Experimental Validation and Parameters Identification
### Simulation of Active Dynamics Based on the Identified Parameters  
Since the tests were performed under three different added masses, three sets of key parameters were identified by the test results. The results are compared in Table 2. The key parameters identified by the different added-mass tests showed good consistency, and the maximum relative standard deviation was only 2.94%.  
Moreover, the physical rationality of the key parameter results shown in the Table 11 should be noted. The results identified in the fluid-filled state were much higher than those in the non-fluid state, such as the mover mass (\(m_{3}>>m_{3,\text{not}}\)) and viscous damping of the mover (\(c_{3}>>c_{3,\text{not}}\))  
A series of numerical simulations were carried out based on the mathematical models in the mid- and high-frequency bands using the parameters in the third column in the Table 11 to verify the correctness of the key parameters. The simulation results were compared with the experimental dynamics results, as shown in Figures 8 and 9. For the curves related to force \(f_{1}\) and acceleration \(\ddot{y}_{3}\), the simulations agreed well with the experiments in the mid- and high-frequency bands.  
\begin{table}
\begin{tabular}{l|c c c|c c c} \hline
**4.1. Added Mass, \(m_{\text{ad}}\) (g)** & \(m_{\text{ad,1}}\) & \(m_{\text{ad,2}}\) & \(m_{\text{ad,3}}\) & \multicolumn{4}{c}{**Consistency of Obtained Results**} \\  & **8.90** & **38.97** & **93.03** & **Mean** & **Standard Deviation** & **Relative Std** \\ \hline \({}^{*}\)4.8. Linear stiffness of the decoupler membrane, \(k_{3}\) (N\(\cdot\)mm\({}^{-1}\)) & 61.95 & 61.91 & 62.62 & 62.16 & _0.40_ & _0.64\%_ \\ (independent of added mass) & & & & & & \\ \hline \({}^{*}\)5.1. Equivalent piston area of the decoupler membrane, \(A_{3}\) (mm\({}^{2}\)) & 1320.03 & 1319.21 & 1334.48 & 1324.57 & _8.61_ & _0.65\%_ \\ \hline \({}^{*}\)5.2. Dynamic bulk stiffness of the decoupler membrane, \(K_{3}\) (GN\(\cdot\)m\({}^{-5}\)) & 35.55 & 35.57 & 35.17 & 35.43 & _0.23_ & _0.64\%_ \\ \hline \({}^{*}\)5.3. Dynamic bulk stiffness of the main rubber spring, \(K_{1}\) (GN\(\cdot\)m\({}^{-5}\)) & 121.53 & 121.27 & 126.25 & 123.02 & _2.80_ & _2.28\%_ \\ \hline \({}^{*}\)5.4. Total mover mass including attached fluid in the fluid-filled state, \(m_{3}\) (g) & 113.93 & 113.62 & 119.66 & 115.74 & _3.40_ & _2.94\%_ \\ \hline \({}^{*}\)5.5. Total viscous damping of the mover system in the fluid-filled state, including damping of the fluid and damping of the decoupler membrane, \(c_{3}\) (N\(\cdot\)s\(\cdot\)m\({}^{-1}\)) & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Consistency check of identified parameters for an AHM-IT-DM-OCA.
###### Abstract
* The challenge of dynamic changes in satellite images, such as illumination changes, shadows, and transient objects, is effectively addressed by radiance modeling and learning transient embeddings. * Stochastic structural similarity loss is used to exploit structural information in satellite images to improve the quality of scene reconstruction.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 3 Human Mobility Research in Evolution Mobile Network
### Human Mobility Research from 1G to 4G

Table 1: Characteristics of the human mobility metrics in Mobile Networks. and the physical world in terms of human mobility patterns. In addition, spatial-temporal data of 3G links cyberspace and the physical world and then contribute to investigate their links with urban functional areas. In other words, from 3G information we are able to investigate how people behave in cyberspace and the physical world, and we further our understanding of people's behaviors by linking them, for example, with social context. The speed of the 3G network quickly became insufficient as technology and smartphones evolved. Despite enabling any mobile device to use a wi-fi hotspot and connect, via dongle, to internet access through a computer, the 4G drastically improved data transfer speeds. Around the year 2010, 4G was split into two categories 4G and 4G LTE (referred to as just LTE). 4G had even better quality for voice calls as the data transmitted in the pure packet form due to orthogonal frequency division multiplexing or multiple access (OFDM/OFDMA) technique. In this generation, operators collect a ton of spatio-temporal data which enable location analytics to be applied in a wide range of applications, such as in human mobility by identifying how subscribers are moving across a given area (e.g., most visited location), call detail records data from mobile operators, GPS or wi-fi-based data from location-based social networks, third-party mobile apps data such as Twitter, Facebook or Uber, and so on. Besides that, it opens the door to monetize human mobile data by selling location intelligence insights to third parties [87].
## 3 Methodology
### Baseline Data Processing Methods  
The methodology we used to analyse information entropy in packet traces and calculate service level baselines can be summarised in two phases, as illustrated in Figure 4. The first phase analyses a set of raw packet datasets (listed in Figure 5), calculating payload entropy per packet, grouping packets into flows, and calculating the final payload entropy per flow. The second phase takes the resulting flow datasets, grouping all flows by service type (i.e., based on TCP or UDP destination port), and calculates the service level baseline entropy features for all datasets.  
Sample size is included in the analysis, since some services are more widely represented in the packet distributions than others (for example, in a typical enterprise network packet trace, we would expect to see a high percentage of web traffic and much less SSH traffic [5]).
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 4 Control Algorithms
### Integrated Position/Force Control Method Taking the Demonstration Position as the Desired Position  
As a compliant control algorithm, the impedance control algorithm shows great performance in automatic control, such as in involved automatic hole finding [32; 33] and automatic compliance hole assembly [34]. However, the application in combination with teleoperation requires further investigation. In this control strategy, to eliminate hand jitters and achieve accurate assembly by manual teleoperation, we designed an integrated force/position impedance control method in which the operator position was taken as the desired position. Human manipulation was incorporated into the autonomous conductance control loop to combine the human operational initiative and the compliance of impedance control. Figure 4 shows the structure of this controller.  
Figure 4: The strategy of integrated position/force controller.  
Figure 3: The strategy of manual/automatic mixed control.  
The position correction controller at the end of the remote robot is a typical impedance controller:  
\[m_{d}\Delta\ddot{x}+b_{d}\Delta\dot{x}+k_{d}\Delta x=F_{d} \tag{6}\]  
where \(m_{d}\), \(b_{d}\), and \(k_{d}\) still represent the virtual inertia, damping, and stiffness of the system, respectively. Unlike the automatic compliant assembly method discussed in Section 4.2, the desired position in \(\Delta x\) is no longer a theoretically expected value, but rather the operator's command position \(x_{operator}\), which is directly controlled by the operator's demonstration position; that is:  
\[\Delta x=[x-x_{operator}] \tag{7}\]  
\[\Delta\dot{x}=\dot{x}\]  
\[x_{d}=x_{operator}=p_{robot\_0}+(p_{tracker\_i}-p_{tracker\_0}) \tag{8}\]  
The output command position of the integrated position/force controller is a combination of the operator movement position and the compliant correction position  
\[x_{send}=x_{operator}+\Delta x \tag{9}\]  
where \(x_{send}\) is the adjusted command position sent to the remote robot.