esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Answering RQ1 and RQ2
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Quality Category**} & & & & \\ \cline{3-8} \cline{8-10}
**Ref.** & **Year** & **AR as a** & **AR** & **AR-Based** & **Application** & **Technology** & **Sample** & **Results** \\  & **Leen** & **Mertical** & **Assisted** & **Solution for** & **Non-Line Quality** & & & \\  & **Tool** & **ogy** & **Control** & & & & & \\ \hline \multirow{3}{*}{[206]} & \multirow{3}{*}{2019} & \multirow{3}{*}{x} & Design & HHD, & & & \\  & & & discrepancies & Hybrid tracking & & & \\  & & & detection & (marker-based tracking, & & & \\  & & & & markerless tracking) & & & \\ \hline \multirow{3}{*}{[199]} & \multirow{3}{*}{2017} & \multirow{3}{*}{x} & Shop floor & Monitor, HHD, & & & \\  & & & procedures & Marker-based tracking & & & \\ \hline \multirow{3}{*}{[200]} & \multirow{3}{*}{2017} & \multirow{3}{*}{x} & \multirow{3}{*}{x} & Machine process & Monitor, & & \\  & & & monitoring & & CNC feedback-based & & \\  & & & & tracking & & & \\ \hline \multirow{3}{*}{[201]} & \multirow{3}{*}{2017} & \multirow{3}{*}{x} & \multirow{3}{*}{Spot-welding} & Spatial display, & & & & \\  & & & Markerless tracking & & & & \\ \hline \multirow{3}{*}{[201]} & \multirow{3}{*}{2017} & \multirow{3}{*}{x} & \multirow{3}{*}{Spot-welding} & Spatial display, & & & & \\  & & & discrepancies & Hybrid tracking & & & \\  & & & detection & markerless tracking & & & \\ \hline \multirow{3}{*}{[71]} & \multirow{3}{*}{2016} & \multirow{3}{*}{x} & \multirow{3}{*}{x} & Sampling & HHD, & & & \\  & & & acceptance & Marker-based tracking & & & \\ \hline \multirow{3}{*}{[202]} & \multirow{3}{*}{2016} & \multirow{3}{*}{x} & \multirow{3}{*}{Sampling} & HHD, & & & \\  & & & & marker-based tracking & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{3}{*}{[205]} & \multirow{3}{*}{2015} & \multirow{3}{*}{x} & \multirow{3}{*}{Spot-welding} & HHD, & & & \\  & & & Marker-based tracking & & & \\ \hline \multirow{3}{*}{[207]} & \multirow{3}{*}{2015} & \multirow{3}{*}{x} & \multirow{3}{*}{x} & \multirow{3}{*}{Lean process} & HHD, & & & \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{3}{*}{[209]} & \multirow{3}{*}{2011} & \multirow{3}{*}{x} & \multirow{3}{*}{Spot-welding} & Spatial display, & & & \\ \cline{1-1}  & & & Marker-based tracking & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._To continue answering RQI, all the collected data reported are shown in Table 8:  
In terms of Layer 1, 63.5% (127 articles) of the selected publications provide important concepts and theory in their research (Layer 1). The largest percentage of AR articles is in Layer 2 Implementation (150 articles, 75%). This indicates that AR technology has matured to the point where it can be implemented using off-the-shelf commercial packages or self-development using less expensive software infrastructure. Significant assessment (Layer 3) of the Effectiveness and Usability studies using scientific and formal methods is
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Interpretability and Complexity of Random Forests  
It is essential to address the dual nature of Random Forests (RFs) concerning their interpretability and complexity. At its core, an RF is an ensemble of decision trees. Each individual tree within the forest is inherently interpretable. We can visualize and understand the decision-making process of each tree, and the feature importance metrics derived from RFs offer valuable insights into the significance of each variable in the prediction process.  
However, the strength of RFs lies in their ensemble approach, where multiple trees vote to make a final prediction. This ensemble nature, which aggregates the outputs of often hundreds or thousands of trees, is what gives RFs their robustness and ability to capture intricate patterns in the data. However, this is also where the complexity arises. While we can interpret individual trees, synthesizing a collective interpretation from all the trees in the ensemble can be a daunting task. The combined insights of numerous trees can sometimes lead to a more complex narrative than what we might obtain from a single tree.  
Therefore, when we discuss the interpretability of RFs, we refer to the insights gained from feature importance and individual trees. In contrast, the complexity aspect pertains to the ensemble nature of the model, where the collective decision-making process of all trees is considered.  
In our study, we leverage both the interpretability and the power of RFs, ensuring that we extract meaningful insights while also benefiting from the model's robust predictive capabilities. We hope this clarification provides a more comprehensive understanding of the characteristics and advantages of using Random Forests in our analysis.
## 4 Results and Discussion  
### Simulation Results  
After entering the data into the model and taking into account the previously adopted assumptions, the simulation of the production line operation was carried out. Figure 10 presents the data generated with the FlexSim program, which illustrate the individual characteristics of the individual production stages and thus the percentage share of the use of the selected machines in the analyzed production process.  
By analyzing the presented data, a so-called bottleneck located on the cavity cutting section was identified. For about 86% of the simulation time, the station performed operations on the product. For the rest of the time, the machine waited for the failure to be repaired at the previous stations to resume production. In addition, it was noted that the column foot welding station was idle most of the time (only about 24% of the simulation time was spent by the machine working on product finishing). The second section stage that attention was paid to was the welding cell. Its operation reduced the efficiency of the press brake, which spent most of its time in the simulation waiting for the possibility of transferring the machined product to the next stage of production.  
Another area of analysis was the number of products that could be produced during one shift with the current system functioning. Figure 11 shows an analysis of the daily output of poles over a year.  
The data analysis indicated that the production line was capable of producing about 80 pieces of product per day. Due to failures and different repair times of individual machine sections, this value was not fixed for all days. After examining the \"for ship  
Figure 9: A calendar showing the operating time of the system. Source: own study.  
ment\" field, it was determined that in the simulated time (365 days), the line produced 18,200 lighting poles.  
In addition, the influence of the sheet metal composition state section was studied. Figure 12 shows a chart reflecting the execution of orders throughout the year.  
Figure 11: Daily production in pieces of the base model. Source: own study.  
Figure 12: The state of the sheet metal in the base model. Source: own study.  
Figure 10: Utilization of individual machines on the production line in the base model percentage share. Source: own study.  
The analysis of the presented data confirmed that the sheet metal storage section could meet the demand for the manufactured product. All of the above values were considered as the initial state to which the results of the next simulation will be compared.
###### Abstract  
A new transparent zinc silicate glass-ceramic was derived from the 55 ZnO-5B\({}_{2}\)O\({}_{3}\)-40 SLS glass system via a controlled heat-treatment method. The precursor glass sample was placed through the heat-treatment process at different temperatures to study the progress in phase transformation, optical performance and emission intensity of the zinc silicate glass-ceramics. For this project, material characterization was measured through several tests using densimeter and linear shrinkage measurement, X-ray diffraction (XRD), Fourier transform infrared reflection (FTIR), ultraviolet-visible (UV-Vis) and photoluminescence (PL) spectroscopy. The density and linear shrinkage measurements show that the density of the particular glass-ceramic samples increases with the progression of heating temperature. The XRD analysis displays the result in which the zinc silicate crystal starts to grow after the sample was treated at 700 \({}^{\circ}\)C. In addition, the FTIR spectra indicated that the crystallization of the zinc silicate phase occurred with the appearance of SiO\({}_{4}\), ZnO\({}_{4}\) and Si-O-Zn bands. UV-visible exhibited the small changes when the value for the optical band gap decreased from 3.867 to 3.423 eV, influenced by the temperature applied to the sample. Furthermore, the PL spectroscopy showed an enhancement of broad green emission at 534 nm upon the increased heat-treatment temperature. Thus, it can be concluded there is the progression of crystal growth as the heat-treatment temperature increased; three emission peaks appeared at 529, 570 and 682 nm for the green, yellow and red emissions, respectively.  
glass-ceramics; heat treatment; crystal growth; optical band gap; photoluminescence
## 2 Mathematical Formulation
### Reformulation Using Epsilon Constraint  
In this subsection, we describe the characteristics of the model and the proposed reformulation. A fundamental task in multiobjective optimization is to find Pareto-optimal solutions. As a biobjective approach, we decided to implement a multiobjective method of resolution to generate an exact front of efficient solutions.  
In the mathematical model, we note that the objective functions are separable. In other words, each of them involves different decision variables. On the one hand, \(y_{ij}\) allows estimating the total arrival time to the customers. On the other hand, \(I_{ij}\) computes the total tardiness for the case in which customers having a minor priority are served earlier than customers with higher priority index (regardless if they are located in the same route or belong to different routes).  
To clarify this, the particular structure of the biobjective problem proposed herein is described below:  
\[\min F_{1}=L=\sum_{i\in V^{\prime}}c_{0i}y_{0i}+\sum_{i\in V^{ \prime}}\sum_{\begin{subarray}{c}j\in V^{\prime}\\ j\
eq i\end{subarray}}c_{ij}y_{ij} \tag{33}\] \[\min F_{2}=T=\sum_{i\in V^{\prime}}\sum_{\begin{subarray}{c}j\in V ^{\prime}\\ j\
eq i\end{subarray}}I_{ij} \tag{34}\]  
\[\text{subject to :  
The aforementioned characteristics of the biobjective model are exploited by using an improved version of the \(\varepsilon\)-constraint method, named as AUGMECON2 [28], as a solution procedure. For every single routing decision in Equations (4)-(18), the minimum tardiness (min \(T\)) problem bounded by the constraints in Equations (19)-(24) is solved as principal objective function, transforming the latency function (\(L\)) into constraint. The results of the proposed method are shown in Section 4.
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
## 5 Optimization Design of the Stabilizer Component  
In this paper, kaolin (Al2O3-2 SiO2-2H2O) and carbon-white (SiO2) were selected as auxiliary components to improve the performance of the stabilizer, and the design component test is shown in Table 6 below.  
Figure 8: The relationship between stabilizer content and the 48-h segregation softening point difference of SBS-modified bitumen.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Content, \%**} & \multicolumn{3}{c}{**RTFOT**} & \multirow{2}{*}{\
\begin{tabular}{c} **Penetration,** \\ **0.1 mm, 25 \({}^{\circ}\)C** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Penetration** \\ **Index PI** \\ \end{tabular} } \\ \cline{2-2} \cline{5-6}  & & & & & \\ \hline
0 & 1.1 & 62.6 & 11.3 & 64.6 & 0.2 \\
0.05 & 0.41 & 74.3 & 16.1 & 62.3 & 0.2 \\
0.1 & 0.15 & 78.3 & 20.5 & 58.5 & 0.1 \\
0.15 & 0.08 & 79.2 & 21.0 & 55.3 & 0.1 \\ \hline \multicolumn{6}{c}{\(>\)0.2} \\ \hline \hline \multirow{2}{*}{**Content, \%**} & \multicolumn{3}{c}{**Ductility at 5 \({}^{\circ}\)C, cm**} & \multirow{2}{*}{**Softening Point \({}^{\circ}\)C**} & \multirow{2}{*}{
\begin{tabular}{c} **Brookfield viscosity** \\ **at 135 \({}^{\circ}\)C, Pa-S** \\ \end{tabular} } \\ \cline{2-2} \cline{5-6}  & & & & & \\ \hline
0 & 27.7 & 65.5 & 1.621 & 99.5 & 86.5 \\
0.05 & 28.3 & 70.1 & 1.827 & 99.5 & 85.6 \\
0.1 & 28.4 & 75.2 & 1.911 & 99.4 & 88.4 \\
0.15 & 29.6 & 78.4 & 1.972 & 99.3 & 87.7 \\ \hline \multicolumn{6}{c}{\(>\)0.2} \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of modified bitumen with different stabilizer content.  
According to the above components, the different stabilizers prepared, the amount of stabilizer (in terms of the amount of S bitumen percentage) being 0.09% control stabilizer content, prepared SBS-modified bitumen and relevant tests according to the research need to be selected.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 3 Evaluation Framework
### Link Budget and Channel Modelling
We select the values in Equation (4) for our simulation since they are consistent with our measurements shown in [70]. \[PL_{\text{rain}}[\text{dB}/\text{km}]=\left\{ \begin{matrix}5&\text{Light rain}\\ 20&\text{Heavy rain}\end{matrix} \right. \tag{4}\]  
Attenuation due to vegetation \(PL_{\text{veg}}\) can be modelled using the exponential decay model which has the form of \(PL_{\text{veg}}=\text{A}*\text{f}^{\text{B}}*\text{d}^{\text{C}}\). Three important models describe this attenuation at mmWaves, Weissberger [75], Cost 235 [76] and FITU-R Vegetation [77]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MCS** & **Constellation** & \begin{tabular}{c} **Backoff** \\ **[dB]** \\ \end{tabular} & \
\begin{tabular}{c} **Datarate** \\ **[Mbps]** \\ \end{tabular} &
\begin{tabular}{c} **SNR** \\ **[dB]** \\ \end{tabular} \\ \hline
1 & BPSK & 0 & 385 & \(-\)1.4 \\  
\hline \hline \end{tabular}
\end{table} 
Table 4: SNR values for the spirit module. Vegetation loss measurements at 60 GHz presented in [67], confirm that the Weissberg is a better fit for larger distances, but for typical urban/suburban tree densities up to 9 m wide, the FITU-R model is a better fit. We select the FITU-R model for winter (without leaves) and summer (with leaves), as shown in Equation (5). \[PL_{\text{veg}}[\text{dB}]= \begin{cases}0.37*\text{f}^{0.18}*\text{d}^{0.59}& \text{Winter}\\ 0.39*\text{f}^{0.39}*\text{d}^{0.25}&\text{Summer}\end{cases}  \tag{5}\]
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Channel Response Models under Test
#### 4.2.1 Flat Channel  
This is the channel with the simplest response function. When this channel is applied, the received signal spectrum has the same amplitude and phase as the transmitted one for every frequency point.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency band & 41.9–88.9 kHz \\ Number of subcarriers & 97 \\ Intercarrier spacing & 488.28 Hz \\ OFDM symbol length & 2.24 ms \\ Cyclic prefix length & 192 \(\mu\)s \\ Interleaver type & Block interleaver \\ Interleaver size & 1 OFDM symbol \\ Encoder rate & 1/2 \\ Subcarrier modulation & DBPSK: Differential Binary Phase-Shift Keying \\ DQPSK: Differential Quadrature Phase-Shift Keying \\ DQPSK: Differential 8 Phase-Shift Keying \\ Preamble waveform & Single chirp from 41.9–88.9 kHz in 2.048 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: ITU-T G.9904 (PRIME) physical parameters.
## 3 Materials and Methods
### Data Sources and Collection Procedures  
We focused on capturing authentic user-generated dialogue from Spanish-speaking online communities. Although multiple public platforms were initially considered, such as Meneame, Reddit, and Usenet newsgroups, we ultimately restricted our dataset to content from Mediavida, a Spanish internet forum, due to its terms of service regarding data reuse and sharing for research purposes. This decision ensured full compliance with ethical and legal standards for data collection and publication.  
Mediavida is a long-standing, high-traffic online forum in which users engage in threaded discussions on topics ranging from technology and gaming to social issues. The platform is characterized by its multi-user, multi-threaded structure, where users, often pseudonymous, contribute comments that branch off from an original post and often reply to one another in nested hierarchies. This creates a tree-like structure of conversation, where the root node represents the original post, intermediate nodes represent comments with replies, and leaves represent terminal comments.  
To collect the data, we used a custom web scraping pipeline built with requests-html and BeautifulSoup. For each thread, we extracted the original post along with its full comment tree. Each entry included (i) a unique comment identifier, (ii) the identifier of the parent comment or root post, (iii) the raw text content, and (iv) an anonymized user ID.  
We stored the data in JSON format, preserving the hierarchical structure of each thread. To extract linear dialogue sequences from these trees, we implemented a breadth-first search (BPS) algorithm. The BFS traversal allowed us to collect all root-to-leaf paths, each corresponding to a coherent conversational trajectory. To avoid redundancy, we filtered out any dialogues that were strict subsets of longer paths, a step limited to posts with fewer than 100,000 dialogue paths to maintain computational efficiency.  
During preprocessing, we applied several refinement steps to clean and standardize the dataset:  
1. Removal of URLs, email addresses, and phone numbers.
2. Discarding of comments shorter than 10 characters.
3. Anonymization of usernames and user mentions.
4. Application of a basic profanity filter adapted from [60] to exclude overtly toxic language not relevant for initial model training.  
This procedure resulted in a high-quality, ethically sourced dataset of Spanish online dialogues suitable for annotation and machine learning tasks related to hate speech and bias detection.
## 3 Membrane Performance Measurements
### Membrane Performance Calculation
#### 3.5.1 H2O Permeance  
Firstly, when the weight change of the tray containing the absorbent is measured by \(\Delta m_{\text{CaCl}_{2}}\), the mass flux of water vapor, \(J_{\text{H2O}}\), is calculated by the following equation.  
\[J_{\text{H2O}}=\frac{\Delta m_{\text{CaCl}_{2}}}{A_{m}\Delta t} \tag{1}\]  
where \(A_{m}\) is the area of the membrane through which the substance can be permeated in the cell, and \(\Delta t\) is the measurement time. The absorbed mass of H2O was converted into moles by molar mass of water, then calculated to volume. As the vapor pressure difference is the driving force for the flux, we separated the water vapor pressure from the mass flux to calculate the permeance. The water vapor pressure inside the cell is zero, hence, the steam pressure difference was calculated from the humidity of the chamber. In addition, multiplying the unit conversion constants results in the following equation of water vapor permeance, \(P_{\text{H2O}}\).  
\[P_{\text{H2O}}=\ \frac{\Delta m_{\text{CaCl}_{2}}}{M_{\text{H2O}}p_{v}A_{m} \Delta t}\times\frac{10^{10}}{3.35} \tag{2}\]  
where \(M_{\text{H2O}}\) and \(p_{v}\) correspond to water molar mass and water vapor pressure in the thermo-hygrostat chamber, respectively. The GPU value obtained for Equation (2) is mol/m\({}^{2}\)-s-Pa.
# A Mixed Reality Interface for a Digital Twin Based Crane
## 3 Results
### Control Accuracy Measurement Protocol  
The measurement protocol divided the procedure into steps and defined each step with a fixed time allocation and operational specifications. Table 1 illustrates the overall measurement procedure consisting of 20 repeated unit tests, each with registration as an initialization step, followed by unit measurements on two selected targets. Table 2 illustrates the unit measurement procedure on a selected target. Each unit measurement included a sequence of switches on the view and moving direction, with a fixed time allocation. The X, Y, and Z in the table, as consistent with the notations in Figure 2, represent the trolley, hoist, and bridge, respectively. The step-by-step protocol is as follows.  
1. **Setup of the HoloLens and crane**: The tester first switches on the HoloLens and opens the crane MR application, then switches on the crane and activates its operation through the manual controller.
2. **Registration of the holograms**: Next, the tester stays close to the _image target_ and looks at it until a green bounding box appears and stably aligns with the _image target_, which indicates that the registration step is completed.
3. **Target selection and position record**: Then, the tester selects Target 1 through air-tap on the corresponding hologram and records the target positions of the hoist, bridge, and trolley shown on the dashboard. The same works for Target 2. Since the target positions stay the same regardless of different registration setups, this step only needs to be done once for each selected target.
4. **Moving the crane manually to the selected target**: The tester navigates the crane with a manual controller to the selected target position, until the crane hook aligns with the target hologram, and then records the crane actual position. This step is an iterative process that requires observing from different views and fine-tuning in different moving directions. The protocol defines the time allocation as well as the sequence of the views and moving directions that the manual control procedure should follow (See Table 2). For example, the tester first views from the XY-face, while moving the crane along the X direction for 10 s; Then, the view should be switched to a ZY-face while the crane is moved along the Z direction for another 10s, and so on. The total duration of such a unit measurement sums up to 80 s. Once a unit measurement is done, the tester reads the crane status positions of the three subsystems from the application dashboard and records them. This step is repeated for both Targets 1 and 2.
5. **Repeat the test**: The tester conducts again Step 2 followed by Step 4 for both Target 1 and 2 as a unit test, and repeats the unit test 20 times (see Table 1).
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 5 Implementation
### Hyperparameter Optimization
#### 5.5.1 Parameter Types  
The meta simulator, called meta-simulator.cc, performs grid search on a set of parameters by iterating all combinations of the parameters' values. In each iteration, simulator.cc is executed with the current set of parameter values, and the energy consumption results are written into a separate file. Given a certain network scenario, the parameters are partitioned into three categories--_constant_, _fixed_, and _variable_:  
* _Constant_ parameters remain the same across all simulator runs for the scenario.
* _Fixed_ parameters can take on different values, and one analysis will be done for one combination of fixed parameters. Given a set of fixed parameters, the goal is to find out how the variable parameters (explained next) affect energy consumption.
* _Variable_ parameters are the target of the evaluation because we assume that it is unclear which parameter combination is the best.  
In this work, rather than focussing on specific scenarios, we analyze a large range of different fixed parameters. The exact procedure is detailed in Section 6.1.  
The shown parameter types can be specified in a.meta file, which serves as input for meta-simulator.cc. A.meta file consists of the three sections indicated before: constant, fixed, and variable. The constant section defines key-value pairs as usual, and the fixed and variable sections define mappings of keys to a list of values.