esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Planar Double-Layer Timber Spatial Frames by Using Parametric Tools
## 3 Results and Discussion
### Comparative Analysis
#### 3.2.1 Mass-Deformation Ratio  
The mass-deformation relationship has been studied under the following parameters: support distribution, topology of the frame and module dimensions.  
In general, the three-sided support is the worst performing. From 20 to 25 m, the deformations are too large (only the D1 frame resists). The perimeter support, of course, has the best values, followed, not far behind, by the 4-point supports.  
The change can be seen in the lines drawn on the graphs of Figure 14: in small spans (\(7\times 12.5\) m), the mass remains constant (because it does not require section optimization),  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frame** & **Number of Modules** & **Module Dimensions \({}^{*}\) (cm)** & **Galapagos Depth** & **Depth/Module** \\ \hline A1 & x = 7, y = 13 & 200 & 100 & 0.50 \\  & & 300 & 210 & 0.70 \\ \hline A2 & x = 7, y = 13 & 200 & 100 & 0.50 \\  & & 300 & 180 & 0.60 \\ \hline A3 & x = 7, y = 13 & 200 & 110 & 0.55 \\  & & 300 & 210 & 0.70 \\ \hline B1 & x = 5, y = 9 & 200 & 100 & 0.50 \\  & & 300 & 210 & 0.70 \\ \hline C1 & x = 7, y = 13 & 200 & 140 & 0.70 \\  & & - & - & - \\ \hline D1 & x = 14, y = 16 & 200 & 100 & 0.50 \\  & & 300 & 190 & 0.63 \\ \hline D2 & x = 5, y = 9 & 150 & 100 & 0.67 \\  & & 250 & 170 & 0.68 \\ \hline \hline \end{tabular} \({}^{*}\) The module dimensions are decided based on two aspects: to have a reasonable number of modules to create the four types of covers and that all topologies have the same or similar dimensions to be able to compare them.  
\end{table}
Table 2: Galapagos mass–depth results.  
and it is the deformation that changes. Therefore, the line is vertical. As the spans increase, the line starts to recline (the 20 \(\times\) 40 graph is the most illustrative of this process). The inclination becomes dramatically more severe at the jump from 12.5 \(\times\) 25 m to 20 \(\times\) 40 m. This slope demonstrates that the program has enlarged the frame sections to satisfy the L/300 deflection limit (hence the increase in mass), and thereby, the usefulness of the section optimization component is demonstrated.  
Looking at the topologies individually, the D1 frame is the one with the highest mass in all cases and, at the same time, one of the least deformed. A1 and C1 are the worst  
Figure 14: Mass–deformation ratio result graphs.  
performers, as they need a lot of mass without deformations decreasing too much. The others have similar behaviors, although the A2 and D2 frames behave slightly better.
## 3 Results and Discussion
### Sensitivity of ITEC to Al\({}^{3+}\)  
The fluorescence titration experiments were carried out by adding the various concentrations of Al\({}^{3+}\) (Figure 3a). With the increased concentrations of Al\({}^{3+}\) in the range from 2 \(\upmu\)mol/L to 10 \(\upmu\)mol/L, the linear fitting curve with fluorescence intensity of ITEC was plotted in Figure 3b. The concentration  
Figure 2: (**a**) Ultraviolet spectra of ITEC (10 \(\upmu\)M) in different metal ions (20 \(\upmu\)M). (**b**) Fluorescence responses of ITEC (10 \(\upmu\)M) to various metal ions (20 \(\upmu\)M) by excitation at 365 nm. (**c**) The bar graph of fluorescence intensity of ITEC at 431 nm.  
of Al\({}^{3+}\) (7.41 umol/L) is within this linear range according to WHO regulations on drinking water [34]. According to the basis of 3 \(\sigma\)/k, the detection limit of ITEC was calculated to be 2.19 \(\times\) 10\({}^{-9}\) mol/L (k = 8.861 \(\times\) 10\({}^{8}\), \(\sigma\) = 0.647) [38; 39], which was lower than those of many reported fluorescence probes for Al\({}^{3+}\) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Structure of Probes** & **Detection Limit (mol/L)** & **Modes probe:Al\({}^{3+}\)** & **Application** & **Ref.** \\ \hline \hline \(\begin{array}{c}\text{CO}_{\text{i}}\text{Cu}_{\text{j}}\\ \text{OH}\end{array}\) & \(1.0\times 10^{-7}\) & 1:1 & - & [40] \\ \(\begin{array}{c}\text{CO}_{\text{i}}\text{Cu}_{\text{j}}\\ \text{OH}\end{array}\) & \(4.79\times 10^{-8}\) & 1:1 & - & [41] \\ \(\begin{array}{c}\text{CO}_{\text{i}}\text{Cu}_{\text{j}}\\ \text{OH}\end{array}\) & \(1.0\times 10^{-7}\) & 1:1 & - & [42] \\ \(\begin{array}{c}\text{CO}_{\text{i}}\text{Cu}_{\text{j}}\\ \text{OH}\end{array}\) & \(7.4\times 10^{-9}\) & 1:1 & Real samples & [43] \\ \(\begin{array}{c}\text{Tyrosine-stabilized fluorescent gold}\\ \text{nanoclusters}\end{array}\) & \(3.0\times 10^{-7}\) & - & Real samples & [44] \\ Salicylaldehyde modified MOF & \(6.98\times 10^{-6}\) & - & - & [45] \\ Nitrogen-doped graphene quantum dot & \(1.3\times 10^{-6}\) & 1:1 & Cell imaging & [46] \\ \(\begin{array}{c}\text{Tyrosine-stabilized fluorescent gold}\\ \text{nanoclusters}\end{array}\) & \(7.4\times 10^{-9}\) & 1:2 & Cell imaging & This work \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of probes structure for Al\({}^{3+}\) detection by fluorescence method.  
Figure 3: (**a**) Change in fluorescence spectra of ITEC (10 uM) after adding Al\({}^{3+}\) (0 uM, 0.6 uM, 0.8 uM, 2.0 uM, 2.4 uM, 3.0 uM, 4.4 uM, 5.0 uM, 6.4 uM, 7.0 uM, 9.0 uM, 10.0 uM, and 14.0 uM, respectively) with an excitation at 365 nm. (**b**) The curve was plotted with the fluorescence intensity of ITEC in 460 nm versus Al\({}^{3+}\) concentrations (2.4 uM–10 uM).
# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 3 Analyzing the AI Hub Corpus Using LIWC
### Linguistic Inquiry and Word Count (LIWC)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**KR-En Parallel Corpus (Social Science)**} & \multicolumn{3}{c}{**KR-En Parallel Corpus (Technology)**} \\ \hline
**Categories** & **Features** & **Train** & **Valid** & **Total** & **Train** & **Valid** & **Total** \\ \hline \multirow{6}{*}{summary language variables} & **Analytic** & **97.38** & **97.38** & **97.38** & **99** & **99** & **99** \\  & Clout & 53.86 & 53.87 & 53.86 & 49.13 & 49.17 & 49.13 \\  & Authentic & 23.99 & 24.26 & 24.02 & 16.69 & 16.49 & 16.67 \\  & Tone & 47.66 & 47.91 & 47.69 & 34.02 & 33.99 & 34.02 \\ \hline \multirow{6}{*}{Linguistic dimensions} & **WC** & **9,991,408** & **1,250,115** & **11,241,523** & **13,621,209** & **1,702,722** & **15,323,931** \\  & WPS & 20.77 & 20.79 & 20.77 & 17.87 & 17.87 & 17.87 \\  & Sixltr & 30.65 & 30.61 & 30.65 & 29.02 & 29.03 & 29.02 \\  & Dic & 80.71 & 80.71 & 80.71 & 67.19 & 67.17 & 67.19 \\  & function & 46.81 & 46.81 & 46.81 & 42.15 & 42.19 & 42.15 \\  & pronoun & 5.32 & 5.31 & 5.32 & 2.04 & 2.06 & 2.04 \\  &pron & 0.96 & 0.96 & 0.96 & 0.14 & 0.15 & 0.14 \\  & i & 0.14 & 0.14 & 0.14 & 0.03 & 0.03 & 0.03 \\  & we & 0.22 & 0.22 & 0.22 & 0.02 & 0.02 & 0.02 \\  & **you** & **0.1** & **0.1** & **0.1** & **0.02** & **0.01** & **0.02** \\  & shehe & 0.16 & 0.17 & 0.16 & 0.01 & 0.02 & 0.01 \\  & they & 0.34 & 0.34 & 0.34 & 0.06 & 0.07 & 0.06 \\  & ipron & 4.35 & 4.34 & 4.35 & 1.9 & 1.91 & 1.9 \\  & article & 11.42 & 11.42 & 11.42 & 14.95 & 14.95 & 14.95 \\  & prep & 16.14 & 16.13 & 16.14 & 13.2 & 13.23 & 13.2 \\ \hline \multirow{6}{*}{grammar} & **auxerb** & **7.23** & **7.21** & **7.23** & **7.68** & **7.7** & **7.68** \\  & adverp & 2.44 & 2.42 & 2.44 & 1.35 & 1.34 & 1.35 \\  & conj & 5.41 & 5.44 & 5.41 & 3.77 & 3.76 & 3.77 \\  & negate & 0.85 & 0.86 & 0.85 & 0.3 & 0.3 & 0.3 \\  & verb & 10.3 & 10.25 & 10.29 & 9.51 & 9.53 & 9.51 \\  & adj & 4.73 & 4.73 & 4.73 & 3.32 & 3.32 & 3.32 \\  & compare & 2.6 & 2.57 & 2.6 & 2.15 & 2.15 & 2.15 \\  & interrog & 0.88 & 0.88 & 0.88 & 0.54 & 0.54 & 0.54 \\  & number & 1.89 & 1.87 & 1.89 & 6.28 & 6.27 & 6.28 \\  & quant & 1.58 & 1.58 & 1.58 & 1.63 & 1.62 & 1.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LIWC results of Korean–English Parallel (Social Science) and Korean–English Parallel (Technology) Corpus.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 5 Deformation Monitoring Visualization Platform
### Map Base Management Module  
The map base management module handles the presentation of a refined 3 DRS model, remote sensing image selection, and base map switching, along with spatial calculation and analysis. It enables operations such as switching, roaming, and double-screen comparison between multi-temporal remote sensing images and various base maps. The refined 3 DRS model demonstrates the morphological characteristics of the hazard point, including its overall distribution, contour, and local texture. Figure 12 is a screenshot of the visualization system, showing a 3D model of a culvert gate.  
Figure 11: System architecture of the 3 DRS-enhanced GNSS/intelligent vision surface deformation monitoring system. Based on the Cesium framework, Spring Boot Framework, MySQL, and Tomcat, this system implements modules such as map base management, spatial analysis tools, multi-source monitoring, intelligent vision, and site selection.  
Figure 12: A screenshot depicting the map base management module of the visualization system. The upper menu includes buttons for InSAR, Verification, Field Survey, Multi-source IoT Monitoring, Basemap, Layers, and Tools. The right-side menu manages 3D Models, Remote Sensing Imagery, and Maps.  
The module facilitates spatial and temporal comparative analysis of the 3 DRS model across different time nodes. By integrating digital twin and IoT technologies, it provides managers with precise insights into the dynamic changes, deformation risks, and safety status of the monitoring object. This supports comprehensive risk assessment and informed response throughout the entire monitoring process [38; 39].
## 2 Materials and Methods
### Image Classification  
The maximum likelihood classification algorithm was also used to classify the Landsat images. This algorithm is widely used in remote sensing because of its strong theoretical foundation and can accommodate varying data, land use and land cover (LULC), and satellite systems [51]. Therefore, this study used TerrSet's hybrid classification technique, which combines segment-based and pixel-based categorization [52]. SEGMENTATION, SEGCLASS, and SEGTRAIN are the three modules that make up the technique [52]. Image segmentation was applied using the SEGMENTATION module to group pixels according to their spectral similarity, i.e., using a watershed delineation approach, the input images were segmented based on the variance similarity of pixel values. The SEGTRAIN module was then used to convert the image segments into training sites and signature classes. Finally, the SEGCLASS module was used to categorize segments based on a classified image created using pixels.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### Interferometric Waveforms Description
GPS L1 Coarse/Acquisition (C/A) code signals were employed for GNSS-R DDM measurements.The DDM has a size of 61 \(\times\) 20, with a time delay interval of 1/8 chip, encompassing 61 sampling points. The Doppler frequency interval is 500 Hz, covering 20 sampling points. The specular point predicted by the model is designed to be at the center of the DDM waveform. The coherent integration time for the DDM waveforms is 1 millisecond, with each output waveform being the result of 1000 non-coherent accumulations, yielding an output rate of 1 Hz. For comparative analysis, Figure 5d shows the DM waveform at 0 Hz Doppler frequency extracted from the DDM waveform in Figure 5c. Due to significant amplitude variations among the three DM waveforms, the y-axis in Figure 5 displays the Signal-to-Noise Ratio (SNR) for standardized comparison. Comparative analysis in Figure 5 reveals that, for given sea conditions and antenna gain parameters, interferometric waveforms possess markedly steeper leading edges. This  
Figure 4: Characteristic spaceborne GAL dual-frequency iGNSS-R DMs. (**a**) Coherent GAL E1 DM over sea ice. (**b**) Coherent GAL E5 DM over sea ice. (**c**) Incoherent GAL E1 DM over open sea, with incidence angle of 16.1\({}^{\circ}\) and wind speed of 7.5 m/s. (**d**) Incoherent GAL E5 DM over open sea, with incidence angle of 16.1\({}^{\circ}\) and wind speed of 7.5 m/s. geometric characteristic is principally responsible for high-precision altimetry through interferometric approaches.
## 5 Conclusions  
The analysis of corneal nerve fiber tortuosity to better diagnose and monitor corneal diseases is beyond doubt. However, this tortuosity assessment has been traditionally performed through manual or semi-automated methods. In this work, a fully automated, quick and simple method to quantify corneal nerve tortuosity from IVCM images is proposed. The effectiveness of the proposed tortuosity index has been demonstrated on a sample of post-LASIK patients with DED symptoms.  
Conceptualization, methodology and original draft preparation: I.F. and A.L.-M.; software: I.F.; data collected: A.V., M.C. and M.J.M.; supervised and provided intellectual contributions: M.J.M. and M.C.; review and editing: I.F., A.V., M.C., M.J.M., A.d.l.M. and A.L.-M. All authors have read and agreed to the published version of the manuscript.  
The study was supported in part by Centro de Investigacion Biomedica en Red de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN) (CB06/01/0003 grant).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institute of Applied Ophthalmobiology (IOBA) institutional review board and by the Ethics Committee of the University of Valladolid (Protocol code PI-15-301, approved on 25 May 2017).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The data that support the findings of this study are available on request from the corresponding author, [PERSON] The data are not publicly available, as they contain information that could compromise the privacy of research participants.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ANOVA Analysis Of Variance.  
CDVA Corrected Distance Visual Acuity.  
CFS Corneal Fluorescein Staining.  
CI Confidence Interval.  
DED Dry Eye Disease.  
IOBA Institute of Applied Ophthalmobiology.  
IQR InterQuartile Range.  
IVCM In vivo confocal microscopy.  
LASIK Laser-Assisted In Situ Keratomileusis.  
NRS Numeric Rating Scale.  
OSDI Ocular Surface Disease Index.  
SD Standard Deviation.  
TBUT Tear Break-Up Time.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 4 Numerical Results  
In this section, we study the TCP performance in dynamic blockage environments depending on the system parameters, which are provided in Table 1.
# Anti-Galling Cold, Dry Forging of Pure Titanium by Plasma-Carburized AISI420J2 Dies
## 3 Experimental Results
### Upsetting of Titanium Wire into Thin Flat Plate  
A titanium wire with a diameter of 1.0 mm and length of 10 mm was employed as a workpiece for cold, dry forging. The stroke (\(\delta_{\mathrm{m}}\)) was first controlled to move down from the initial position, where the workpiece contacted the punch surface. Then, it was further moved down stepwise to the specified depth; i.e., \(\delta_{\mathrm{m}}\) = 0.1 mm for \(r\) = 10% and \(\delta_{\mathrm{m}}\) = 0.7 mm for \(r\) = 70%. Figure 5a depicts the variation in the cross section for the forged titanium wire with increasing reduction ratio (\(r\)). A circular wire was shaped into a flat plate with a thickness of 0.3 mm at \(r\) = 70% without significant bulging deformation.  
Up to \(r\) = 30%, the wire was compressed in the axial direction. When \(r\) > 30%, its plastic flow became homogeneous in the lateral direction. As shown in Figure 5b, the width of the forged wire (\(W\)o) was larger than the width of the contact interface (\(W\)c) for \(r\) < 20%. \(W\)c approaches \(W\)o with increasing \(r\). This proves that the titanium workpiece flows in the lateral direction with homogeneous plastic flow velocity along the contact interface. The surface extension rate reached 170% of the original wire surface.  
Figure 6 shows the variation in these \(W\)o and \(W\)c as well as the bulging width (Bg = (Wo-Wc)/2) with increasing \(r\). A monotonous increase in \(W\)o and \(W\)c with \(r\) implies that a circular wire widens uniformly with upsetting. In particular, a linear increase in \(W\)c with \(r\) reveals that the reduced volume of the wire by compression is compensated by its widening volume in the lateral direction. As shown in the monotonic approach of Bg to zero with increasing \(r\), the bulging deformation of the wire diminishes with \(r\). After [15], this nearly zero bulging deformation for \(r\) > 30% proves that the friction coefficient approaches less than 0.1.  
Figure 4: SEM–EDX analysis of the plasma-carburized AISI420J2 punch at 673 K for 14.4 ks. (**a**) SEM image of the plasma-carburized AISI420J2 punch surface, and (**b**) its element mapping.  
Figure 7 depicts the relationship between the applied power (\(P\)) and the stroke (\(\delta\)) up to the specified reduction rate for \(r\) = 10%, 20%, 30%, 50%, and 70%, respectively. The measured \(P\)-\(\delta\) diagrams for each \(r\) superpose on a single master curve; the power increases significantly for \(r>30\%\) due to the work hardening of pure titanium.  
Figure 5: Variation in the cross section and plane views of cold dry forged pure titanium wire with increasing reduction in thickness (\(r\)). (**a**) Variation in the titanium workpiece cross sections with increasing \(r\), and (**b**) variation in the plane view of the forged wire with increasing \(r\).  
Figure 6: Surface area extension with increasing reduction rate, \(r\).
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 4 Localization Algorithms
### Alternative Trilateration Method  
To address the issue presented above, one can reduce the number of steps in the traditional method by drawing inspiration from the proposal in [21]. The steps that will be described are performed iteratively for each pair of Anchor nodes.  
1. Given a pair of nodes and their circumferences, if the point of intersection is unique, it is saved;
2. If the two circumferences do not intersect, like in Figure 8, the radii of each circumference are increased proportionally so that they intersect at a single point (the radius is increased until to the dotted lines);
3. If instead the two circumferences intersect in two points, like in Figure 9, the one that is closest to a third Anchor node is considered.  
At the end of this procedure, iterated for each node pair, the average of the coordinates is computed, which corresponds to the estimated position of the target node.  
Figure 8: A case where the circles do not intersect. The radii are increased proportionally until they meet.  
Figure 7: Non-single point of intersection due to estimation error.
## 1 Introduction  
Running on the track is very different compared to street running. Accordingly, it is well known that the speed, running disciplines (e.g., hurdles, short and long distances), trial duration, and shoes of track runners differ from street runners, along with the specific running technique [1, 2]. Among the various factors affecting running performance, shoes play an important role because the construction characteristics can have implications for running injuries and performance [3]. The volume of literature about running shoes has strongly increased in the past decade [4, 5, 6]. At the same time, the attention of researchers has mainly focused on shoes for long-distance running rather than track shoes. This is well documented by using, on PubMed.gov, the generic key words \"running shoes\" and the specific key words \"track running shoes\" (1183 and 56 records, respectively, from 1975 to 2022 -last access on 26 July 2022).  
Both long-distance and track running shoes are available on the market with new high-tech materials (i.e., carbon fiber plate in the midsole) [7]. However, the literature has focused mainly on reporting strengths and limitations of long-distance running shoes [8; 9; 10]. Accordingly, there are no definitive data available for high-tech track running shoes [7], although their use is increasing among athletes. A recent article published by [PERSON] et al. [11] suggests how the so-called \"super spikes\" shoes might provide advantages over traditional track spikes, but these can depend on the athlete's perception of the shoes and are based on specific competitions. Moreover, the authors suggest that the testing procedures on the field are not always easy, because of the aerobic and anaerobic metabolic demand of the track disciplines [11]. Therefore, it is difficult to give an exact answer about the benefits related to the use of the super spike shoes. Furthermore, the use of shoes during running must be considered in relation to the type of advantages that are produced in terms of running economy, muscle and tendon stress, and perceptions of shoe comfort. For example, it is known that the traditional track spikes help save energy at high speed for middle distances [12] but there is no knowledge of how spikes can affect the kinetics and kinematics parameters due to the lack of evidence. From a practical point of view, it is likely that running athletes can switch to \"super spikes\", and, therefore, understanding the effects of a new pair of shoes on kinetics and kinematics parameters should be the first goal of coaches. For this reason, the aim of this work was to provide an easy method to assess the implications of the different shoes on the biomechanics of running athletes to understand the modification of the running technique induced by high-tech shoes. This was achieved by reporting and analyzing kinematics, kinetics, and plantar pressure parameters of an Olympic-level running athlete using both the normal spike shoes (NSS) and the super spike shoes (SSS).