esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Results of Step 1 by Type
#### 4.1.3 Weather Forecasts  
For the first question (\"On which screen were the information delivery elements arranged most efficiently?\"), as shown in Figure 16, 14 subjects selected clip no. 3, 5 subjects selected clip no. 1, and 3 subject selected clip no. 2. For the second question, (\"On which screen were the information delivery elements arranged most properly and harmoniously?\") and the third question (\"On which screen is the sign language interpreter most clearly visible\"), 12 subjects selected clip no. 3 and 5 each selected clip no. 1 and no. 2. For the fourth question (\"Which screen do you want to continue to use?\"), 13 subjects selected clip no. 3, 5 chose clip no. 1, and 4 selected clip no. 2. For the fifth question, (\"Which screen was the most difficult to understand the content?\"), 8 subjects each chose clip no. 1 and no. 2 in which the interpreter was arranged differently. Clip no. 3, which is a split arrangement of the data screen and sign language interpreter, was selected by 6 subjects, showing that all three types of clips were preferred by a similar number of the subjects.  
As for the average value of the preference of the weather forecast type, as shown in Figure 17, clip no. 3, with the weather description part split from the large-sized sign language interpreter, showed the highest preference, and clip no. 2, with the sign language screen being placed on the opposite side of the weather caster, was at the lowest in preference. In the case of clip no. 1, which has a slightly higher score than clip no. 2, the subjects' opinions were that the lower right arrangement of the sign language interpreter (like the current broadcast sign language services) is familiar, and it is easy to see as the interpreter was in the same direction as the weather caster.  
They answered that it would be difficult to see if the weather data were too small because the entire screen is a visual reference containing information. They also answered  
Figure 16: Results of experiments in weather forecast cases.  
Figure 17: Results of preference evaluation with weather report clips.  
that the visibility of the weather data was more important than the sign language interpreter or subtitles for weather report broadcasts. In addition, they gave the opinions that the sign language screen should not cover the hands of the weather caster because a weather caster's hands indicating the weather data serves an important role in weather forecasts.  
Figure 18 shows the result of the gaze-tracking hit map in the weather forecast case. The subjects mostly focused on the interpreter and sometimes watched subtitles or the weather information. Compared to the other two cases, the dependency on the subtitle is small, since the presentation and content of the weather information is very formal and easy.  
In individual interviews, the subjects presented the following opinions on the screen components of weather report clips and the proposed screen layout as presented in Table 8.
## 3 Experiments and Results Analysis
### Comparative Analysis of Experimental Results  
Generally, the performances of single models, such as TCN, LSTM and CNN, are inferior to those of hybrid models. Moreover, the TCN-based hybrid models in STLF are comparatively inferior to other hybrid models. Thus, the experimental results of the proposed model were mainly compared with other TCN-based hybrid models with VMD decomposition that include VMD-CNN-TCN-SAM (VCTA), VMD-TCN-LSTM-SAM (VTLA), VMD-CNN-LSTM-SAM (VCLA), VMD-CNN-TCN-LSTM (VCTL) and VMD-CNN-TCN-GRU-SAM (VCTGA). It must be stressed that the CNN in these hybrid models means a three-layer 1D-CNN network. Furthermore, in order to overcome the randomness of the AI algorithms, all of the models mentioned above were trained and tested enough times until the results became stable and reliable.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 3 Localization and Mapping  
In this section, we present a graph-based algorithm for maintaining a globally and locally consistent pose track using the estimated frame-to-frame egomotion parameters from the previous section and a consumer-grade GPS receiver. We also show how the optimized  
Figure 2: Gradient magnitude-based pixel selection; selected pixels shown in red. Subfigure captions indicate the number of selected pixels for 240 \(\times\) 320 resolution images. (**a**) Dense (\(\approx\)65,000 px); (**b**) 75% (\(\approx\)57,600 px); (**c**) 50% (\(\approx\)38,400 px); (**d**) 25% (\(\approx\)19,200 px).  
pose graph can be refined to generate a map of detected tree stems. Our algorithm consists of two phases: global alignment, in which we minimize errors between the odometry-based pose graph and global positions provided by the GPS receiver, and local refinement, where we relate poses by multiple observations of tree stems and simultaneously optimize the configuration of the pose graph and tree stem positions.  
In order to increase the efficiency of computations, we use the \(\mathfrak{se}(2)\) Lie algebra of rigid transformations to represent the graph, and ultimately the map, as opposed to the \(\mathfrak{se}(3)\) parameters we optimized for in the odometry section. We do this by simply extracting the translation parameters corresponding to the \(x\) and \(z\) axes in \(\mathfrak{se}(3)\) to represent the translation along the \(x\) and \(y\) axes on a planar pose graph, and the rotation component about the \(y\) axis from the \(\mathfrak{se}(3)\) parameters to represent the heading. Using \(\mathfrak{se}(2)\) parameters produces a meaningful map that can easily be presented on a 2D display monitor.
The advantages of this optimization method are that it can improve the traditional navigation filter method which only uses the observation information of the previous time, and can adjust the original filter process with the feedback of the observation information during the later time and the degree of error attenuation, which effectively improves the navigation filter accuracy and reduces the estimation error. The main contributions of this paper are as follows:  
1. We propose a new method for calculating the observable degree of a high-orbit satellite navigation system based on GNSS, which can simultaneously give the relative observability of each state component at each moment and the overall observability of the system;
2. We design an adaptive optimization method of navigation filter based on this observable degree, which maps the observable degree of the state component to a feedback weighting factor to improve the performance of the navigation filter;
3. Based on the GNSS navigation system, we combine the proposed observability calculation method as an adjustment factor of the adaptive filter for filter optimization. Numerical simulation shows that the method effectively improves the navigation filter accuracy of the navigation systems of the high-orbit satellite based on GNSS. The rest of this article is organized as follows. In Section 2, we briefly introduce the state equation and measurement equation of the high-orbit satellite navigation system based on GNSS. In Section 3, we analyze the observability of the system and propose the observability matrix of the system. Furthermore, the observable degree calculation matrix of the system is defined. In Section 4, according to the proposed framework for the observable degree, we propose an adaptive filter method. In Section 5, we verify the effectiveness of the proposed method by simulation of the high-orbit satellite navigation system based on GNSS. Section 6 is the summary and prospect of the full text.
## 3 Methodology
### Data Preprocessing  
The necessary preprocessing techniques that were applied to ensure the quality of the data were the analysis to check the existence of outliers and the need to treat them, the matching of the range of observations in the data set, and the normalization of the input values (X). These techniques are described below:  
The process of preparing the data for subsequent detailed analysis consisted of performing data cleaning by removing missing observations from the data set. In this sense, the five indicators obtained were the inflation rate, the minimum wage, GDP, GCF, and the unemployment rate; the GDP and the GCFF had a range of data from 2000 to 2020 and the inflation rate, minimum wage, and unemployment rate from 2002 to 2020, so it was decided to match the range of years and omit the year 2020 to correspond to the time of the  
Figure 1: Phases of the prediction process.  
COVID-19 coronavirus pandemic and be considered valid outliers. Likewise, the minimum wage was deflated based on 2007. No missing values were found in the data set.  
To determine outliers in characteristics, the interquartile range (IQR) technique was used using the 1.5 IQR rule, which designates any value greater than \(Q3+(1.5\times IQR)\) and any value less than \(Q1-(1.5\times IQR)\) as an outlier (Table 2).  
The unemployment rate is an economic indicator that refers to the portion of people who are actively looking for work but cannot find work. This measure of the prevalence of unemployment can be a good indication of the economic situation of the country [24]. A low unemployment rate is generally a sign of a good economy. The data set has 216 observations, four input variables, and the unemployment rate, which is the output variable (target). There is no defined way to divide the data into training, validation, and testing. In the literature, most researchers have suggested splitting the data as 60:20:10, 70:20:10, or 80:10:10 [25]. Therefore, we have divided the data set as follows: for the GA-LSTM prediction model, 80% of the data is for training, 10% for validation, and 10% for testing purposes; for the rest of the prediction models, 90% of the data is for training and 10% for testing purposes. This division allows us to predict the last 22 months of the unemployment rate using the individual algorithms (BiLSTM and GRU) and the last 19 months of this economic indicator using the hybrid GA-LSTM algorithm due to the sliding length of the temporal sequence of data (window size of 3). Before training the proposed models, the input data were standardized using the StandardScaler class of Scikit-learn's preprocessing module, where the mean is removed and the data are scaled to unit variance, as follows:  
\[z=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(z\) indicates the normalized value, \(x\) represents the actual value, \(\mu\) is the mean of the training samples or zero, and \(\sigma\) is the standard deviation of the training samples or one.
# Accuracy Comparison among 3D-Printing Technologies to Produce Dental Models
Unlike conventional stone casts, 3D-printed dental casts can be produced from 3D reconstructed models composed by digital meshes. For this purpose, optical scans from a dental arch can be digitally processed on a CAD software, in order to turn the structure of the digital mesh into a dental model shape by creating and editing a closed mesh area. The acquisition of 3D-printing devices by dental clinics, as well as by schools of dentistry in universities for research and educational purposes, is the first step in moving dentistry towards the digital era, which may offer benefits to patients and improve their satisfaction and acceptance towards dental treatments significantly. According to the recent literature, digital technologies and workflows in dentistry can simplify and make procedures predictable. Moreover, digital workflows lead to advantages for dental patients as compared with conventional procedures, such as higher quality, better reproducibility of the prosthesis, and faster treatment times [4,6]. Different 3D-printing techniques can be used to produce models. The common devices in dentistry that use light to polymerize resin are stereolithography apparatus (SLA), digital light processing (DLP), liquid crystal display (LCD), and Polylet 3D inkjet printing technology (Polylet) [7]. There is a gap in the literature on trueness and precision of dental devices for clinical use. So far, only a few studies have effectively compared accuracy of the manufactured dental models by different printing technologies with the corresponding digital files [8, 9, 10]. In addition, these studies present different outcomes concerning the clinical relevance of the deviation between printing equipment. Considering the current relevance of this matter with the rise of 3D-printing technologies in dentistry, the purpose of this study was to investigate and compare the accuracy (trueness and precision) of different 3D-printing technologies (SLA, DLP, LCD, and Polylet).
## 2 Waste Glass as Resource for Construction Materials  
### Waste Glass  
The total global production of solid waste is estimated [14] to be more than 200 million tonnes with more than 13% projected to end up as glass wastes. This makes available a vast potential resource in abundance and availability such that the potential use of unrecycled waste glass in construction materials has attracted many recent reviews [14; 15; 16]. Generally, there has been mixed outcomes on the beneficial reports of substituting glass materials with the traditional constituents of concrete. Nonetheless, the benefit resides mostly in the fact that waste glass that might eventually end up as landfill can now be functionally utilised. The substitution for replacing any mined materials also brings in a latent value in its contribution to environmental impact and improved suitability of making concrete.  
The main concern for the inclusion of siliceous aggregate in concrete is the susceptibility of silica-based content to undergo alkaline silica reaction (ASR). These are found [7; 14; 15] when siliceous materials come into alkaline mix of the binder materials. The gel from ASR when in contact with water could expand and has been implicated as a source of cracking in some applications of waste glass substitutions. The effects of ASR and its formation become significant when substitutions exceed 50% where expansions have been reported to be about 0.7%. For much smaller volume proportion substitutions, typically, expansion due to ASR gel formation has been shown [7] to be lower than 0.2%.  
There are several types of glasses that may end up as waste glass, and these have been categorised as [14]:  
* Sodium lime glasses (packaging glass)
* Borosilicate glasses
* Leaded glasses
* Barium glasses
* Aluminosilicates.
* Potentially, any of these could end up in waste system; however, the most common in the waste collection are the sodium lime glass from food and beverage packaging. Although these come in different colouration, the major components essentially are [14; 17; 18]:  
* Na2O + K2O
* Others & Al2O3, Fe2O3, MgO.  
Since most of the waste glass come in combined colours, there has been no evidence that colour makes any difference in property outcome, but the chemical and physical properties are similar irrespective of colour, which may be varied due to different compositions of minor concentrations of transition metal oxides.  
Generally, the use of waste glass in concrete substitutions is either to replace cementitious binder or to replace aggregates.
## 1 Introduction
First, it introduces an innovative approach to enhancing the security of the classical channel by combining PQC with QKD, thereby increasing overall safety. Second, it provides an in-depth assessment of various algorithm parameters, including sizes, speeds, and security levels. This comparative analysis aims to identify the optimal combination for the proposed cryptosystem. Third, it explores the practical implementation challenges of integrating PQC into a QKD system. Finally, it provides guidelines for future research and development and shows key areas for further exploration to enhance the robustness and efficiency of quantum-secure communication systems. The structure of this paper is organized as follows: First, we review relevant studies on the QKD BB84 protocol and the CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon algorithms. Next, we provide a detailed explanation of our methodology, covering the requirements and experimental simulations. Following this, we present our experimental results, compare them with RSA performance, and discuss their significance. We also examine the potential drawbacks of the proposed method. Finally, we conclude the paper with a discussion of possible directions for future research.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 1 Introduction
Dynamic frequency selection was proposed to safeguard radar systems in the 5 GHz band, and listen-before-talk (LBT) [18] was introduced to enable efficient spectrum sharing by minimizing inter-user interference in the unlicensed spectrum. Both proposed protocols highly degrade the LTE performance in terms of latency and throughput. This issue is further aggravated by the heterogeneity, massive connectivity, and ubiquity of industrial network systems and applications [19]. Therefore, 5G NR-U needs to provide mechanisms to coexist and even converge through necessary modifications to the LTE specification to combat the loss from the regulatory requirements. In this paper, we open up 5G NR-U prospects specifically for mission-critical industrial applications. Moreover, for the first time, we point out the possible challenges that arise from the use of New Radio in the \begin{table}
\begin{tabular}{l l} \hline \hline
**Use Case Type** & **Description** \\ \hline \multirow{3}{*}{**Automation**} & - Factory: floor robotics, e.g., wireless robots introduce significantly greater flexibility \\  & to reconfigure production lines. \\  & - Logistics and warehousing: e.g., pick and pack machines \\  & - Typically focused very dense deployments with low latency requirements \\ \hline \multirow{3}{*}{**Mission-Critical**} & - To monitor and control critical infrastructure, e.g., electricity distribution grids, \\  & power plants, etc. \\
**Services** & - Public safety agencies often need to create closed user group ad-hoc networks at \\  & the scene of an emergency \\ \hline \multirow{3}{*}{**Primary Industries**} & - Remote Industrial location often not covered by public wireless infrastructure \\  & - Very diverse sector for mining to agriculture, \\ \cline{1-1}  & making increasing use of automated machinery \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Use case of 5G NR for Industry 4.0 [14]. Unlicensed/shared band in industrial applications and suggest possible research directions to solve the challenges. We envision machine learning as a toolbox, which will play a fundamental role in making networks adapt to the coexistence setting by maximizing unlicensed spectrum efficiency, easing the capacity constraint for enterprises' 5G network and beyond. The rest of this paper is organized as follows. The brief overview of Industry 4.0 and NR-U technology is described in Section 2. The prospects of 5G NR-U in industrial applications are discussed in Section 3. The main technical challenges and research directions for industrial NR-U are presented in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Results and Discussion
### Analysis of Migration Tests by LC-MS\({}^{n}\)
[5], most of the oligomers found were cyclic because they lacked free functional groups, hence, they could not be incorporated into the polymeric network of the coating and, therefore, could migrate as unreactive byproducts. The exposure-based threshold of toxicological concern (TTC) is used to evaluate compounds with known chemical structure in terms of possible risks to human health. While linear polyester oligomers are mainly classified as Cramer I substances with a daily exposure threshold of 30 \(\upmu\)g/kg body weight per day, it was observed that cyclic polyester oligomers formed from aromatic dicarboxylic acids are assessed as Cramer III substances, with a daily exposure threshold of 1.5 \(\upmu\)g/kg body weight per day [10]. In addition, the fragmentation spectra were obtained when the adduct with hydrogen was the most intense in order to generate more information about the chemical structure of the proposed compounds (Table 4). When the sodium or ammonium adducts were formed and were the most intense, no fragmentation was observed, as also reported in the study of [PERSON] et al. [67]. Some of the most repeated fragments were: _m/z_ 149 corresponding to a phthalic acid (PA/IPA/TPA) with a loss of a water molecule and is protonated; _m/z_ 235, which corresponds to a protonated molecule of phthalic/isophthalic acid (PA) with neopentyl glycol (NPG); _m/z_ 383 corresponding to a molecule formed by two NPG with a PA, protonated [64]; _m/z_ 193, which corresponds to PA with EG, protonated; _m/z_ 359, which corresponds to two molecules of PA with one of EG, protonated; and _m/z_ 415, which corresponds to two molecules of PA with one of HD, protonated. There is not toxicological data available for these polyester oligomers and, therefore, no migration limits have been established in the legislation to date. Moreover, there is limited information available in the scientific literature on migration data regarding these migrants. Consequently, special attention should be paid to the safety of these compounds. Regarding the targeted analysis for the simultaneous determination of five polyvalent carboxylic acid monomers, a chromatographic method was developed. Several columns, including C18 (100 mm \(\times\) 2.1 mm, 2.6 \(\upmu\)m), Eclipse XDB-C18 (100 mm \(\times\) 4.6 mm, 1.8 \(\upmu\)m), Narrow-Bore SB-C3 (150 mm \(\times\) 2.1 mm, 5 \(\upmu\)m), and Gemini C18 110A (150 mm \(\times\) 3 mm, 5 \(\upmu\)m), were tested for the separation of the compounds, which is challenging in the case of isomers. Only Gemini C18 110A (150 mm \(\times\) 3 mm, 5 \(\upmu\)m) allowed the separation of all the carboxylic acids. None of them was detected in the migrations tests of the samples analyzed above the limits of detection (0.5 mg/L for PA, 0.1 mg/L for TPA, 0.25 mg/L for IPA and TMA, and 1.25 mg/L for AA), with the exception of TPA which was detected in sample CM1.