esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction  
Looking at the literature from the last few years, it is evident that glass-based rare-earth-activated optical structures represent the technological pillar of a huge of photonic applications covering health and biology, structural engineering, environment monitoring systems, and quantum technologies. Among different glass-based systems, a strategic place is assigned to transparent glass-ceramics and nanocomposite materials, which offer specific characteristics of capital importance in photonics [1; 2; 3]. These two-phase materials are constituted by nanocrystals or nanoparticles dispersed in a glassy matrix. The respective composition and volume fractions of crystalline and amorphous phase determine the properties of the glass-ceramics. The key to making the spectroscopic properties of the glass-ceramics very attractive for photonic applications is to activate the nanocrystals by using rare-earth ions as luminescent species [4]. From a spectroscopic point of view, the more appealing feature of glass-ceramic systems is that the presence of the crystalline environment for the rare-earth ions allows high absorption and emission cross sections, reduction of the non-radiative relaxation thanks to the lower phonon cut-off energy, and tailoring of the ion-ion interaction by the control of the rare-earth ion partition [5]. Here we focus on glass-ceramic photonic systems based on rare-earth activated SiO\({}_{2}\)-SnO\({}_{2}\) monoliths produced by sol-gel route. Although the system has been investigated for several years, the research activity is still undergoing because of the need to develop reliable fabrication protocols and to control the ion-ion interaction [4; 5; 6]. Both these problems are highly detrimental for the efficiency of active devices [2; 7; 8; 9]. Among the different materials that are successfully used as nanocrystals to be embedded in the silica matrix, tin dioxide presents specific interesting characteristics. Rare-earth-activated SnO\({}_{2}\)-based bulk glass ceramics have been extensively studied for improving luminescence efficiencies of several rare-earth ions by exciton mediated energy transfer from SnO\({}_{2}\) nanocrystals to the rare-earth ion [6; 10; 11]. SnO\({}_{2}\) is a wide-band gap semiconductor (E\({}_{\text{g}}\) = 3.6 eV at 300 K) with a maximum phonon energy of 630 cm\({}^{-1}\), exhibiting a broad window of transparency from visible to infrared covering a significant emission range of rare-earth ions [12].  
Here we look for two significant outcomes: (i) a fabrication protocol of SiO\({}_{2}\)-SnO\({}_{2}\):Er\({}^{3+}\) glass-ceramic monoliths, and (ii) efficient Er\({}^{3+}\) sensitizing by SnO\({}_{2}\) nanocrystals pumping. We will present recent results concerning sol-gel fabrication of SiO\({}_{2}\)-SnO\({}_{2}\):Er\({}^{3+}\) glass-ceramic monoliths and their spectroscopic assessment for the development of luminescent systems such as solid-state lasers and active fibers.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### Satellite Grid Visibility Algorithm
#### 2.2.1 Traditional Satellite Visibility Analysis  
Owing to the rapid movement of satellites, the prerequisite for the discussion in this section is to consider the satellite to be stationary at a time \(t\). At time \(t\), the distance between satellites \(A\) and \(B\) is \(l_{AB}\), the orbital height is \(d_{A}\) and \(d_{B}\) (from the center of the earth), the radius of the earth and the thickness of the atmosphere are \(R_{earth}\) and \(h_{atm}\), respectively, and the angle \(\theta\) between the inter-satellite link and the satellite-earth link (the center of the earth) should satisfy the following Equation (1) [16]:  
\[\left\{\begin{array}{l}\theta_{A}=\arccos[\left({d_{A}}^{2}+{l_{AB}}^{2}-{d_ {B}}^{2}\right)/2d_{A}l_{AB}]\\ \theta_{B}=\arccos[\left({d_{B}}^{2}+{l_{AB}}^{2}-{d_{A}}^{2}\right)/2d_{B}l_{ AB}]\end{array}\right. \tag{1}\]  
When the link between the two satellites \(l_{AB}\) is tangential to the atmosphere, the two satellites are geometrically visible to each other, and this is a critical condition for calculating geometric visibility. Therefore, to judge whether the two satellites are visible, \(\theta\) should be satisfied (2):  
\[\left\{\begin{array}{l}\theta_{A}>\arcsin[(R_{earth}+h_{atm})/d_{A}]\\ \theta_{B}>\arcsin[(R_{earth}+h_{atm})/d_{B}]\end{array}\right. \tag{2}\]  
Figure 3: Aerospace storing model and indexing method: (**a**) aerospace storage model; (**b**) spatio-temporal secondary indexing method.  
When the distance between the two satellites is used to describe the visibility between the satellites, the following Equation (3) should be satisfied:  
\[\left\{\begin{array}{rcl}l_{AB}&<&L_{AB}=L_{A\text{max}}+L_{B\text{max}}\\ L_{A\text{max}}&=&\sqrt{{d_{A}}^{2}-(R_{earth}+h_{atm})^{2}}\\ L_{B\text{max}}&=&\sqrt{{d_{B}}^{2}-(R_{earth}+h_{atm})^{2}}\end{array}\right. \tag{3}\]  
where \(L_{AB}\) is the distance between satellites \(A\) and \(B\) under critical visible conditions; \(L_{A\text{max}}\) is the distance from the tangent point to satellite \(A\) when the communication link between the two satellites is tangent to the atmosphere; \(L_{B\text{max}}\) is the distance between the tangent point and satellite \(B\) (Figure 4).
## 1 Introduction
To effectively interpret the nuanced data collected from this varied sensor array, we have developed three distinct sidewalk and pedestrian route segmentation models for the 4K images, stereo camera RGB images, and the LiDAR's reflectivity channel, each tailored to the specific characteristics of the sensor it is designed for. Moreover, the dataset is enriched with two sources of raw audio files, which are synchronized with the visual data to provide a comprehensive multisensory perspective of the urban environment. An event-based classification model further processes these audio files, identifying and classifying urban sounds. These classified audio files, segmented at 5 s intervals and geotagged, offer an immersive auditory layer to the dataset, deepening our understanding of urban soundscapes. Figure 1 presents an overview of the platform, showcasing our multisensor datasets, segmentation techniques, sound event classifications, and the area under study. Our paper is structured as follows. In Section 2, we review existing urban datasets and highlight the need for more comprehensive pedestrian-focused data, demonstrating the limitations of current road-centric resources. Section 3 details our innovative multi-sensor platform used for data collection, emphasizing the integration and synchronization of various data types like visual, LiDAR, and audio. In Section 4, we detail the methodologies employed to ensure precise alignment, synchronization, and cleaning of the collected data. In Section 5, we discuss the role of auditory analysis in urban studies and the classification of urban sounds and their importance in understanding the urban acoustic environment. In Section 6, we present our approach to segmenting pedestrian pathways. The potential applications of our dataset are explored in Section 7. In Section 8, we reflect on the challenges encountered and outline future research directions. Section 9 provides a summary of our findings and the implications for future urban studies.
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry Analysis  
The GC/MS analysis was performed as follows: 10 mg of the tested plant extract was dissolved in 1 mL of 99.8% anhydrous pyridine, and 100 \(\upmu\)L of N,O-bis(trimethylsilyl)-trifluoracetamide (BSTFA) was then added. The mixture was heated at 60 \({}^{\circ}\)C for 30 min to allow derivatization. The resulting silylated samples were then analyzed using an Agilent 7890A gas chromatograph coupled with an Agilent 5975C mass selective detector (Agilent, Santa Clara, CA, USA). A 1 \(\upmu\)L sample injection was performed using an Agilent 7693A autosampler. Analyte separation was achieved on an HP-5 MS fused silica column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness) with a helium flow rate of 1 mL/min. The injector was operated in split mode (1:10), with the temperature set to 300 \({}^{\circ}\)C. The initial column temperature was 50 \({}^{\circ}\)C, which was increased to 325 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min and held for 10 min. The ion source temperature was set to 230 \({}^{\circ}\)C, and the quadrupole temperature was maintained at 150 \({}^{\circ}\)C. Electron ionization mass spectrometry (EIMS) was performed at an ionization energy of 70 eV. Detection was carried out in full-scan mode, with a mass range of 41 to 800 \(m/z\). After data integration, the relative contributions of each compound to the total ion current (% TIC) were calculated. Retention indices for the analytes were determined using the retention times of alkanes as standards. The mass spectra and calculated retention indices were used for compound identification. Mass spectral identification was facilitated by an automated GC-MS data processing system provided by the National Institute of Standards and Technology (NIST) [73], using data from the \"Identification of Biologically and Environmentally Significant Organic Compounds Mass Spectra and Retention Indices Library of Trimethylsilyl Derivatives\". The retention indices of the identified compounds were compared with reference values.
Thus, there is an evident need for a detailed study that both offers a broad understanding of AR applications and deeply investigates the challenges and opportunities in the field. This paper serves to fill this gap by presenting a comprehensive overview of usage, innovation, and research in AR technologies. Its cross-domain analysis breaks from the norm of domain-specific studies, providing a more extensive and insightful understanding of AR technology. The work further explores the current technological advancements and forecasts future trends, helping to direct ongoing and future research in the field. The main contributions that this work provides to the scientific community are as follows:  
* **A comprehensive understanding of AR applications and trends across various domains:** By examining the relevant literature in Sections 3-5, this work aims to offer a clear view of the current state of AR applications, benefits, and challenges in different domains. In contrast to other works that focus on a specific field, this study presents a cross-domain analysis, providing a broader perspective on AR technology. * **Insight into the technological advancements and emerging trends in augmented reality:** This paper investigates the latest developments, ongoing research, and future directions in AR technology, as discussed in Section 7.2. The analysis highlights the potential impact of these advancements on the development and improvement of AR applications and their implications for various domains. * **A structured evaluation of AR implementation challenges and limitations across different domains:** By examining the challenges and limitations of AR applications in Section 7.1, this work presents a detailed analysis of the common obstacles and potential solutions for AR technology across various fields. The organization of this work is outlined as follows. Section 2 presents the systematic literature review methodology. The AR definitions and concepts, and the classification of AR applications, technologies, and devices are discussed in Sections 3 and 4 respectively. Section 5 describes the application of AR in various domains and their respective limitations and challenges. Section 6 presents the results and findings on the trends of AR technology, while Section 7 discusses the current application challenges, technological advancements, emerging trends, and the benefits and implications of AR technology. Finally, Section 8 concludes the paper. In Abbreviations all abbreviations used along the paper appear with their meanings. Figure 1: Selection criteria process for publication comparative analysis.
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 2 Analysis of Tensile Test in Anchor Cables and Evaluation of Ejection Kinetics
### Tensile Test of Cable Steel Wire  
Anchors (1860 MPa, 21.8 mm in diameter) were selected and cored to prepare specimens with a diameter of 6 mm and a length of 0.2 m. The stress-strain curves of anchor wires were acquired through a tensile test, as shown in Figure 1.  
In this test, 20 anchor wire specimens were used and tested with a W-type universal tensile machine. The test data were averaged and plotted. The stress-strain curves of the anchor steel wires are presented in Figure 2.  
Figure 1: Tensile test diagram of anchor steel wires.  
Figure 2: Stress–strain curves of anchor steel wires.  
The following data can be obtained through the stress-strain curves.  
Strain energy density in the elastic loading stage:  
\[U_{t}=\int_{0.01216}^{0.06919}f(x_{1})\mathrm{d}x_{1}=58.65\mathrm{e}^{6} \mathrm{J}/m^{3} \tag{1}\]  
Strain energy density in the plastic loading stage:  
\[U_{s}=\int_{0.06919}^{0.09184}f(x_{2})\mathrm{d}x_{2}=33.3038\mathrm{e}^{6} \mathrm{J}/\mathrm{m}^{3} \tag{2}\]  
Strain energy density in the unloading stage:  
\[U_{x}=\int_{0.05789}^{0.09079}f(x_{3})\mathrm{d}x_{3}=27.528\mathrm{e}^{6} \mathrm{J}/m^{3} \tag{3}\]  
Dissipated energy density in the loading/unloading stages:  
\[U_{h}=U_{t}+U_{s}-U_{x}=64.4258\mathrm{e}^{6}\mathrm{J}/m^{3} \tag{4}\]  
In the preceding formulas, \(U_{t}\) is the strain energy density in the elastic loading stage, \(U_{s}\) is the strain energy density in the plastic-elastic loading stage, \(U_{x}\) denotes the strain energy density in the unloading stage, \(U_{h}\) represents the dissipated energy density in the loading/unloading stage, \(f(x_{1})\) is the stress-strain curve in the elastic stage, \(f(x_{2})\) is the stress-strain curve of the plastic stage, and \(f(x_{3})\) is the stress-strain curve in the unloading stage.  
Anchor wire breaking and ejection conform to theorem of conservation of energy, as follows:  
\[\frac{1}{2}mv_{1}^{2}=W_{x} \tag{5}\]  
Stored total strain energy in anchor wire breaking:  
\[W_{x}=U_{x}\pi r^{2}L \tag{6}\]  
Partial stored strain energy in anchor wire breaking:  
\[W_{\mathrm{d}x}=U_{x}\pi r^{2}L_{1}=W_{x}\frac{L_{1}}{L} \tag{7}\]  
In the preceding formulas, \(W_{x}\) is the stored total strain energy in anchor wires, \(W_{\mathrm{d}x}\) represents the partial stored strain energy in anchor wire breaking, \(v_{1}\) is the ejection speed, m stands for the anchor wire breaking mass, \(L\) is the total tensile elongation of anchor wires, and \(L_{1}\) is the anchor wire breaking length.
## 5 Data Collection and Analysis
### Subjective Cognitive Workload Using NASA TLX  
A two-way repeated-measures ANOVA was performed for participants' responses to the NASA TLX questionnaire. [PERSON]'s test confirmed the sphericity assumption in the subjective workload measures from NASA TLX for the effect of interruption mode (\(\chi^{2}\) (5) = 7.891, \(p>0.05\)) and the effect of task set (\(\chi^{2}\) (2) = 5.743, \(p>0.05\)), and the interaction between interruption mode and task set (\(\chi^{2}\) (27) = 35.621, \(p>0.05\)). Therefore, no corrections to degrees of freedom were needed. It also showed a significant effect of interruption modes (F (2, 235) = 4.71, \(p<0.01\)) and task sets (F (3, 235) = 5.29, \(p<0.01\)) on task completion time. No interaction effect was observed. Post hoc LSD pairwise comparisons showed that there was a difference between task sets and their subjective ratings of workload in the immediate mode, but those in the negotiated mode were not statistically differentiated. In other words, as participants delicately perceived the demands in task sets in the immediate mode, they felt that the task was less workload-inducing in the negotiated mode. The analysis results indicated that there was a significant statistical difference between interruption modes and overall subjective workload. Participants felt the highest workload in the immediate mode and the lowest in the negotiated mode. Descriptive statics of NASA TLX can be found in Table 4 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**NASA TLX Measure**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Standard Deviation**} \\ \hline \multirow{3}{*}{Cognitive/cognitive task set} & Immediate & 67 & 9.62 \\  & Scheduled & 35 & 6.94 \\  & Negotiated & 29 & 4.59 \\ \hline \multirow{3}{*}{Cognitive/skill task set} & Immediate & 55 & 9.92 \\  & Scheduled & 26 & 5.56 \\  & Negotiated & 22 & 3.41 \\ \hline \multirow{3}{*}{Skill/cognitive task set} & Immediate & 42 & 8.26 \\  & Scheduled & 26 & 5.85 \\  & Negotiated & 19 & 2.71 \\ \hline \multirow{3}{*}{Skill/skill task set} & Immediate & 34 & 7.13 \\  & Scheduled & 22 & 3.95 \\ \cline{1-1}  & Negotiated & 17 & 2.63 \\ \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of NASA TLX data under each task set.  
Figure 4: Pupil diameter variation by task set and interruption coordination mode.
# Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
## 2 Design of Experiment and Data  
The data used in this research were retrieved from the experiment reported in [21]. We applied the machine learning algorithm to predict the data and compared the findings with the actual experimental results reported by the authors in [21]. The experiment was conducted using a Maxturn++ (MTAB, Tamilnadu, India) CNC lathe machine with a swing of 410 mm over the bed, a standard turning diameter of 200 mm, a weight of 2500 kg, and a maximum turning length of 360 mm. The rapid feed rate was 30 m/min, and the 7 KW spindle motor power has a speed range of 50-6000 rpm. The X-axis was 140 mm, and the Z-axis was 380 mm in length.  
The cutting forces were measured using a Kistler dynamometer (Kistler Instrument Corporation, Novi, MI, USA), and the surface roughness tester SJ-201P by Mitutoyo (Mitutoyo, Aurora, IL, USA) was used to measure surface roughness. The range of input parameters is shown in Table 1, and the experimental data results are reported in Table 2. The experimental results in Table 2 were obtained from the conventional experimental procedure and are used in this research to train the GPR algorithm to predict the cutting force. In the next section, the methodology of the GPR, ANN, and SVM is presented to predict the cutting force of the turning process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range** \\ \hline Cutting speed (m/min) & 75, 90 \\ \hline Feed rate (mm/rev) & 0.04, 0.06, 0.08, 0.1, 0.12 \\ \hline Depth of cut (mm) & 0.5, 1, 1.5 \\ \hline Tool nose radius (mm) & 0.4, 0.8 \\ \hline Air pressure (bar) & 5 \\ \hline Fluid flow rate (mL/h) & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters range values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Cutting Speed** & **Nose Radius** & **Feed Rate** & **Depth of Cut** & **Surface** & **Average Cutting** \\  & **(m/min)** & **(mm)** & **(mm/rev)** & **(mm)** & **Roughness (\(\mu\)m)** & **Force (N)** \\ \hline
1 & 75 & 0.8 & 0.04 & 1.5 & 1.01 & 22.45 \\
2 & 75 & 0.8 & 0.04 & 1 & 1.06 & 15.52 \\
3 & 75 & 0.8 & 0.04 & 0.5 & 1.26 & 7.67 \\
4 & 75 & 0.8 & 0
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 3 Identification of CSC
### Marker-Based Isolation of CSCs
#### 3.1.3 Aldh1  
ALDH1 is a useful putative marker in identifying CSCs of different origins (Figure 2). ALDH1 is a superfamily of cytosolic enzymes that metabolize aldehydes to their corresponding carboxylic acids. Among 19 enzyme isoforms that may be localized to different cellular compartments, such as the cytoplasm, mitochondria, and nucleus, the cytoplasmic isoforms involved in the retinoic acid biosynthesis have proven to be critical in regulating stemness in normal stem cells as well as CSCs [82].  
Elevated activity of ALDH1 has also been identified in HNSCC CSCs in several studies, and associated with increased invasiveness, stemness, and self-renewal properties of CSCs [83; 84]. While the prognostic relevance of ALDH1 expression in HNSCC is still debatable [85; 86; 87], current evidence indicates the potential significance of this CSC marker in the prediction of lymph node metastasis in some types of HNSCC [88]. For example, it has been demonstrated that ALDH1\({}^{+}\) cells are present in both, primary tumors and lymph node metastases from OSCC patients. The association of ALDH1 with tumor progression is further supported by studies showing that ALDH1\({}^{+}\) cells from HNSCC patients preferentially co-localize in the tumor-invasive front with other CSC markers including CD44 and CD14, and they endogenously co-express several EMT-related markers such as Snail and MMP-9 [83; 85; 89].
## 2 Basic Formulations
\tag{22}\]  
From Equations (16) and (20) one can obtain  
\[\widetilde{\mathbf{G}}\widetilde{\mathbf{U}}=\mathbf{Q} \tag{23}\]  
\[\widetilde{\mathbf{G}}=\mathbf{G}\mathbf{H}^{-1}. \tag{24}\]  
It should be noted that the solution \(\widetilde{\mathbf{U}}\) of Equation (23) does not include displacements and velocities of the \(N\)th sampling point, referred to in Equation (19), but these can be easily obtained through Equation (20) and the DQ rule, respectively. Rearranging the terms of Equation (23), it is rewritten as  
\[\left[ \begin{array}{cc}\widetilde{\mathbf{G}}_{\mathrm{ii}}&\widetilde{\mathbf{G}} _{\mathrm{id}}\\ \widetilde{\mathbf{G}}_{\mathrm{id}}&\widetilde{\mathbf{G}}_{\mathrm{dd}}\end{array}  \right]\left[ \begin{array}{c}\widetilde{\mathbf{U}}_{\mathrm{i}}\\ \widetilde{\mathbf{U}}_{\mathrm{d}}\end{array} \right]=\left[ \begin{array}{c} \mathbf{Q}_{\mathrm{i}}\\ \mathbf{Q}_{\mathrm{d}}\end{array} \right], \tag{25}\]where subscript 'i' indicates the initial conditions, and 'd' indicates unknowns. From Equation (25) we have  
\[\widetilde{\mathbf{U}}_{\text{d}}=\widetilde{\mathbf{G}}_{\text{dd}}^{-1}(\mathbf{Q}_{ \text{d}}-\widetilde{\mathbf{G}}_{\text{di}}\widetilde{\mathbf{U}}_{\text{i}}), \tag{26}\]  
where \(\widetilde{\mathbf{U}}_{\text{i}}\) is formulated by initial displacements and velocities, thus the initial conditions are dealt with in a general and convenient way. It is worth noting that here \(\mathbf{u}_{1},\mathbf{u}_{2},\,\cdots,\,\mathbf{u}_{n}\), corresponding to all time sampling points, are solved concurrently by using the exact initial conditions; this is different from classical step-by-step DIMs, in which the results of the present time step are the initial conditions of the next time step. That is, only the initial conditions of the first time step are exact; all others are approximate. However, DQTFEM can also serve as a step-by-step method; in this situation, a time element is a time step in which there are \(N\) time sampling points. Therefore, the initial conditions except for the first time step are also approximate, but the approximate initial conditions are highly accurate since the present method is a high-order method. Remarks:  
1. Usually for conservative systems, the actual path will be the one for which the integral of Lagrangian function (\(\mathbf{T}-\mathbf{V}\)) is an extremum, and for the non-conservative system considered here, the general form of [PERSON]'s variational principle is used and the integral still exists; however, there exists no functional that must be an extremum [49]. 2. The method of imposing initial conditions in the present method is standard and similar to the one used in the FEM; see Equation (25). 3. DQTFEM is a new TFEM, since Gauss-Lobatto quadrature and DQ rules are used for the first time in the construction of a TFEM. The high accuracy and efficiency due to the employment of Gauss-Lobatto quadrature and DQ rules are validated in the following numerical comparisons; see Section 4.