esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Sensing Coverage Model  
A given target area \(R\) is a two-dimensional plane of \(L_{x}\times L_{y}\) m\({}^{2}\). A set of sensor nodes in \(R\) is thus defined as \(C=\{c_{1},\,c_{2},\,c_{3},\,...,\,c_{N}\}\), where \(c_{i}=\{x_{i},y_{i},\,r_{s}\}\), \(i\,{\in}\,[1,\,N]\) and \(N\) is the total number of deployed sensor nodes. The coordinate and sensing radius of a sensor node are \(\{x_{i},y_{i}\}\) and \(r_{s}\), respectively. A set of POIs distributed over the region \(R\) is defined as \(P=\{p_{1},\,p_{2},\,...,\,p_{M}\}\), where \(p_{j}\) is a POI located at \(\{x_{j},y_{j}\},\,j\,{\in}\,[1,\,M]\), and \(M\) is the number of POIs. In addition, we introduce a binary variable \(\mathfrak{R}_{i,j}\) to represent whether \(c_{i}\) is able to cover \(p_{j}\). \(\mathfrak{R}_{i,j}\) is defined as:  
\[\mathfrak{R}_{i,j}=\begin{cases}1&\text{if }\ (x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2} <r_{s}^{2}\\ 0&\text{otherwise.}\end{cases} \tag{1}\]  
If the distance between \(p_{j}\) and \(c_{i}\) is less than \(r_{s}\), then \(p_{j}\) is covered by \(c_{i}\) (i.e., \(\mathfrak{R}_{i,j}=1\)). Thus, the event signal generated at the POI can be detected. However, \(p_{j}\) may be covered by \(v\) sensor nodes at the same time. For a particular \(p_{j}\), if it is covered by two or more sensor nodes (i.e., \(c_{1},\,c_{2},\,...,\) and \(c_{v},\,v\)  
Figure 1: For the coverage-preserving application for a WSN, the events at the position marked by stars will be detected and reported to the sink node through the surrounding nodes.  
\(\leq N\)), the union of \(\mathfrak{R}_{i,j}\), \(\mathfrak{R}_{2,j}\),..., and \(\mathfrak{R}_{v,j}\) for \(p_{j}\) can be calculated using the basic operation of the Boolean algebra:  
\[\mathfrak{R}_{1,j}\vee\mathfrak{R}_{2,j}...\vee\mathfrak{R}_{v,j}=1-\overline{ \mathfrak{R}}_{1,j}\wedge\overline{\mathfrak{R}}_{2,j}...\wedge\overline{ \mathfrak{R}}_{v,j}, \tag{2}\]  
where \(\overline{\mathfrak{R}}_{i,j}\) is the Boolean inverse of \(\mathfrak{R}_{i,j}\), i.e., \(\overline{\mathfrak{R}}_{i,j}=1-\mathfrak{R}_{i,j}\), \"\(\vee\) \" denotes the Boolean union, and \"\(\wedge\) \" denotes the Boolean intersect.
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
## Appendix A Cases of \(\mathbf{C(\hat{\gamma})}\) Converging to \(\mathbf{C_{1}}\)  
The results presented in [22; 24] demonstrate that the 2 SP can achieve equivalent asymptotic properties to those of the DPE when \(\mathbf{W}=\mathbf{I}^{-1}(\mathbf{\omega})\) or when the energy of the received signals is completely known [25]. Here, two additional equivalent cases are considered. From Equation (25), the covariance matrix of \(\gamma\) with a large sample is given by  
\[\lim_{K\rightarrow+\infty}\mathbf{C(\hat{\gamma})}=\bigg{[}\frac{\partial^{2 }\ell}{\partial\gamma^{2}}\bigg{]}^{-1}\frac{\partial\omega}{\partial\gamma^ {\top}}\mathbf{W}\mathbf{I}^{-1}(\mathbf{\omega})\mathbf{W}\frac{\partial\mathbf{ \omega}^{\top}}{\partial\gamma}\bigg{[}\frac{\partial^{2}\ell}{\partial \gamma^{2}}\bigg{]}^{-1}. \tag{11}\]  
Using Equation (22), we obtain  
\[\frac{\partial\ell}{\partial\gamma} =-2\frac{\partial\mathbf{\omega}}{\partial\gamma^{\top}}\mathbf{W}( \hat{\mathbf{\omega}}-W(\gamma))^{\top},\] \[\frac{\partial^{2}\ell}{\partial\gamma^{2}} =2\frac{\partial\mathbf{\omega}}{\partial\gamma^{\top}}\mathbf{W} \frac{\partial\mathbf{\omega}^{\top}}{\partial\gamma}. \tag{12}\]  
Substituting Equation (12) into Equation (11) yields  
\[\lim_{K\rightarrow+\infty}\mathbf{C(\hat{\gamma})}=\bigg{[}\frac{\partial\bm {\omega}}{\partial\gamma^{\top}}\mathbf{W}\frac{\partial\mathbf{\omega}^{\top}}{ \partial\gamma}\bigg{]}^{-1}\frac{\partial\mathbf{\omega}}{\partial\gamma^{\top} }\mathbf{W}\mathbf{I}^{-1}(\mathbf{\omega})\mathbf{W}\frac{\partial\mathbf{\omega}^{ \top}}{\partial\gamma}\bigg{[}\frac{\partial\mathbf{\omega}}{\partial\gamma^{\top }}\mathbf{W}\frac{\partial\mathbf{\omega}^{\top}}{\partial\gamma}\bigg{]}^{-1}.\]  
When all visible satellites have the same value of \(C/N_{0}\), i.e., \(\mathbf{W}\) is an identity matrix, we have  
\[\lim_{K\rightarrow+\infty}\mathbf{C(\hat{\gamma})}=\bigg{(}\frac{\partial\bm {\omega}^{\top}}{\partial\gamma}\bigg{)}^{\dagger}\mathbf{I}^{-1}(\mathbf{\omega}) \bigg{(}\frac{\partial\mathbf{\omega}}{\partial\gamma^{\top}}\bigg{)}^{\dagger}, \tag{13}\]  
where \((\cdot)^{\dagger}\) denotes the Moore-Penrose pseudo-inverse. Since the number of satellites is greater than that of the unknown parameters and \(\partial\mathbf{\omega}/\partial\gamma^{\top}\) is a full row rank, we have \(\mathbf{C(\hat{\gamma})}=\mathbf{C}_{1}\).  
Another special case is that \(\gamma\) and \(\mathbf{\omega}\) have equal dimensions, and \(\partial\mathbf{\omega}/\partial\gamma^{\top}\) is invertible. In this case, \(\mathbf{C(\hat{\gamma})}\) can be expressed as  
\[\lim_{K\rightarrow\infty}\mathbf{C(\hat{\gamma})}=\bigg{[}\frac{\partial\bm {\omega}}{\partial\gamma^{\top}}\mathbf{I}(\mathbf{\omega})\frac{\partial\mathbf{ \omega}^{\top}}{\partial\gamma}\bigg{]}^{-1}.\]  
In both cases above, \(\mathbf{C(\hat{\gamma})}\) is equal to \(\mathbf{C}_{1}\) as \(K\) tends to infinity.
# Sex-Based Differences in Bronchial Asthma: What Are the Mechanisms behind Them?
## 5 Factors Contributing to Gender-Based Differences in Asthma
### Genetically Conditioned Factors  
5.6.1. Sex-Specific Genes, Epigenetic Changes, and miRNA  
The influences of the genetic framework, epigenetic changes, and miRNA on gender differences in asthma have been thoroughly described in the excellent review by [PERSON] and [PERSON] [179]. Recently, researchers have identified male- and female-specific genes, as well as male-and-female specific genes, that are mainly expressed in the airway epithelium, among which several are involved in the pathogenesis of asthma. [PERSON] et al. found that the male-specific genes _FBXL7_, _ITPR3_, and _ALOX15_ are increased and _RAD51B_ decreased in asthma, while a female-specific gene _HLA_DQA1_ is decreased. Additional differences were observed for more male-specific hypoxia-inducible factor 1 signaling pathways and for more female-specific IL-17 and chemokine signaling pathways [223]. In a genome analysis of Latin American and African American populations, the _17q12-21_ asthma locus was identified as a significant contributor to asthma susceptibility in women [224]. In addition, several asthma-risk sex-specific alleles have been identified for women (_2q23.3_, _2q34_, _6q27_, _17213.3_) and men (_5q31.1_, _10q26.1_) [225], as well as sex-specific single nucleotide polymorphisms (SNPs) in \(\beta\)2-adrenergic receptors [226] and TSLP [227; 228]. Determination of gene alleles or SNPs in asthma patients may indicate which polymorphisms are more or less sensitive for a given therapy, as was demonstrated in the differential response to \(\beta\)2-agonists [229; 230].  
Gene expression is regulated by small non-coding microRNAs (miRNA). Recent studies have demonstrated that there are sex-specific miRNAs associated with lung function, regulation of immune responses, and asthma [231; 232; 233]. Circulating miRNA correlate well with clinical parameters and the response to therapy [234; 235], suggesting their potential for precise diagnostics and personalized medicine in the future.
## 3 Efficient Learning Algorithm
### Data Preprocessing  
The FFT algorithm operates on data on a grid. Thus, our first step is to attach the set of raw observations \(\mathcal{D}_{d}\stackrel{{ def}}{{=}}\{(\kappa_{l},\,r_{l}),\,l=1,\,2,\,\ldots d\}\) to a grid.  
We already have one axis of the grid, namely, points \(\rho_{m}\), \(m=1\), \(2\),..., \(M\). Define \(\mathcal{B}_{m}\), \(m=1\), \(2\),..., \(M\) in the following way. For \(j=1,\,2,\,\ldots,\,d\) check:  
\[x_{mj}\in\mathcal{B}_{m}\,\,\,\text{iff}\,\,\exists(\kappa_{l},\,r_{l})\in \mathcal{D}_{d}:r_{l}\in[\rho_{m}-\Delta_{r}/2,\,\rho_{m}+\Delta_{r}/2),x_{mj} =\kappa_{l}. \tag{10}\]  
Notice that the contents of each \(\mathcal{B}_{m}\)'s depends on \(\Delta_{r}\), but it is not displayed in the notation. Denote by \(\hat{n}_{m}\) the cardinality of \(\mathcal{B}_{m}\). Clearly, we must have \(\sum_{m=1}^{M}\hat{n}_{m}=d\). Informally, one can consider \(x_{mj}\), \(j=1\), \(2\),..., \(\hat{n}_{m}\) in bin \(\mathcal{B}_{m}\) as slightly distorted realizations of r.v.'s in (6).  
The second split of the grid goes along the \(x\)-axis. Denote by \(\chi_{1}<\chi_{2}<\,\dots\chi_{N}\), \(N>1\) equidistant points such that for \(\Delta_{x}=\chi_{2}-\chi_{1}\) the intervals \([\chi_{n}-\Delta_{x},\,\chi_{n}+\Delta_{x})\), \(n=1,\,2,\,\dots,\,N\) cover the support \([S_{1},\,S_{2}]\) and \(S_{1}=\chi_{1}-\Delta_{x}/2\), \(S_{N}=\chi_{N}+\Delta_{x}/2\).  
Now, we are ready to define the number of observations \(q_{mn}\) that are attached to each grid point \((\chi_{n},\,\rho_{m})\). Namely, \(q_{mn}\) is the number of observations \(x_{mn}\) that are contained in bin \(\mathcal{B}_{m}\) and simultaneously take values in \([\chi_{n}-\Delta_{x},\,\chi_{n}+\Delta_{x})\), \(n=1,\,2,\,\dots,\,N\), \(m=1,\,2,\,\dots,\,M\). Let us define \(M\times N\) matrix \(P\) with elements:  
\[p_{mn}=q_{mn}\Big{/}\sum_{l=1}^{N}q_{ml},\quad n=1,\,2,\,\dots,\,N,\,\,m=1,\,2,\,\dots,\,M. \tag{11}\]  
Clearly, \(p_{m}\), sum up to 1. Notice, however, that--strictly speaking--\(p_{m}\).'s are not histograms of r.v.'s \(X(\rho_{m})\)'s, since they are based on the observations contained in bins \(\mathcal{B}_{m}\). Nevertheless, we shall later interpret them as such because - as \(\Delta_{r}\to 0-p_{m}\).'s converge to \(f(x;\,\rho_{m})\), assuming that also \(\Delta_{x}\to 0\) in an appropriate way (see [13] for precise statements).
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 2 Learning-Based Approaches
### Conclusions  
In this section, we introduced learning-based approaches and classified the appraches into three categories: keypoints-based approaches, holistic approaches, and RGB-D-based approaches. Keypoints-based approaches are two-step approaches that extract 2D-3D point pairs and then use PnP to calculate the 6D pose of the object. Holistic approaches use an endto-end structure to measure the 6D pose, which is faster and more robust than keypoints-based approached. RGB-D-based learning approaches combine color information and depth information to achieve a more accurate and robust 6D pose result.  
The comprehensive performance of learning-based approaches is better, as can be seen in the Amazon Robotic Challenge. The first Amazon Robotic Challenge was held in 2015 in the USA [47]. The competition presents a challenging problem that integrates many fields, including pose estimation. Many teams use learning-based approach and obtain robust results. In [48], the researchers presented a method for multi-class segmentation from RGB-D data. Objects were segmented by computing the possibility of each pixel belonging to each object by using a network. However, learning-based approaches still have some weaknesses. They require plenty of storage for storign training data and enough time to train the model. In conclusion, there are three challenges facing learning-based approaches. Firstly, the approaches require plenty of training data to train the model. Secondly, they require plenty of time to train the model before online use. For some complex objects that need much training data, this may take serval hours or even several days to train the model, thus restricting the possible applications of such approaches. Finally, these approaches cannot perform well when measuring poses that do not exist in the dataset.
###### Abstract  
1-Butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (BMI.NTf\({}_{2}\)) is a hydrophobic ionic liquid with potential antibacterial properties for dental materials. This study aimed to (1) incorporate different mass fractions of BMI.NTf\({}_{2}\) into a dental adhesive and (2) assess its impact on physical and chemical properties. Adhesive resins were prepared with 1 (G\({}_{1\%}\)), 2.5 (G\({}_{2.5\%}\)), and 5 wt.% (G\({}_{5\%}\)) BMI.NTf\({}_{2}\), with a control group (G\({}_{\text{CTRL}}\)) lacking the ionic liquid. Evaluations included polymerization kinetics, degree of conversion (DC), softening in solvent (AKHN%), and ultimate tensile strength (UTS). Groups with BMI.NTf\({}_{2}\) showed accelerated polymerization kinetics, with G\({}_{5\%}\) achieving a higher DC and \(\Delta\)KHN% compared to G\({}_{\text{CTRL}}\) (_p_ < 0.05). Lower concentrations (1% and 2.5%) did not affect \(\Delta\)KHN%, and no significant differences were found in UTS across groups (_p_ > 0.05). Notably, 2.5 wt.% of BMI.NTf\({}_{2}\) increased the DC without affecting other properties, indicating optimal polymerization rates and handling characteristics. These results support the development of BMI.NTf\({}_{2}\)-based antibacterial adhesives that may assist in preventing secondary caries in restorative dentistry.  
dental caries; dental materials; dentin-bonding agents; ionic liquids; polymers; in vitro techniques Article
# Empowering Large Language Models to Leverage Domain-Specific Knowledge in E-Learning
## Appendix A Appendix A
#### a.1.2 Generated Content from LLM + DSK  
Please explain disruptive innovation and give an example to illustrate it.  
Disruptive innovation is a concept coined by Harvard Business School professor [PERSON] to refer to the emergence of new technologies or business models and how they can gradually replace existing products or services in the market, thereby disrupting traditional industries.  
Disruptive innovation often fails to meet the needs of traditional markets in the early stages, but as technology continues to advance and costs decrease, new products or business models begin to meet the needs of more and more consumers, and thus defeat the previously dominant product or service. This process is often carried out in a low-cost, simplified manner, and disruptive innovation establishes new rules of competition in the marketplace.  
A classic example of disruptive innovation is the impact on the traditional music industry. When music in MP3 format began to mature and the Internet became popular, Napster, an online music sharing service, emerged in 1999, allowing users to download music for free over the Internet. This has had a huge impact on the traditional record industry, as they cannot generate revenue in the traditional way.  
Another example of disruptive innovation is Netflix's challenge to the film and television industry. At first, Netflix was an online DVD rental platform, but with the popularity of the Internet and the advancement of video streaming technology, Netflix began to offer video streaming services and make its own titles. This disruptive innovation has changed the way people watch videos, eliminating the need for physical DVDs and the need to rely on traditional movie theaters and television stations.  
These disruptive innovations have had a huge impact on existing industries, and many traditional enterprises are at risk of being eliminated. However, at the same time, because of these innovations, new markets and business opportunities have emerged, bringing new competitiveness and opportunities to enterprises. Disruptive innovation has changed the way we live and work, and it has also challenged traditional business models, providing inspiration and reference for future innovations.
## 2 Quantum Dots in Sensing Applications
### Graphene Quantum Dots  
Graphene quantum dots are zero-dimensional (0D) nanomaterials that are prepared by converting 2D graphene. The particles of these materials are non-quasi-spherical, which is the feature that makes them different from CQDs [44]. These QDs can be synthesized from different sources, such as coal [45], natural graphite [46], rice husk [47], etc. Recently, GQDs have also emerged as fluorescent probes because of their advantages, such as a large surface area, biocompatibility, lower toxicity and ease of surface modification [48]. These QDs are prepared via two types of methods, namely the \"top-down\" approach and the \"bottom-up\" approach. The \"top-down\" approach involves breaking down large-scale macroscopic carbon-based materials such as natural graphite, carbon fibers, graphene oxide, and metal-organic frameworks and so on. The \"bottom-up\" method involves the growing of GQDs from different sources, such as plant extracts [49; 50], rice husks [47], wood charcoal [51], rice grains [52], coffee grounds [53], etc. [PERSON] et al. explored the synthesis of GQDs through a \"bottom-up\" route using starch as the precursor [54]. Though these QDs have been mostly investigated for the fluorescence sensing of PAHs and NACs,there are few reports on their use in sensing PAHs. Furthermore, these materials' sensitivity and selectivity have been shown to depend on size and shape, surface modification and heteroatom doping [55]. [PERSON] et al. explored the fluorescence detection of trinitrophenol (TNP) based on nitrogen-doped GQDs [56]. The doping with nitrogen atoms enhanced the interaction between the QDs and the phenol. Fluorescence quenching was observed owing to the transfer of energy from N-GQDs, which act as a donor to TNP, which is electron-deficient due to nitro-groups (Figure 2). [PERSON] et al. also did a similar study using creatinine-capped nitrogen-doped GQDs (Figure 3) [57]. Recently, [PERSON] et al. synthesized ferric-ion-modified GQDs for the recognition of pyrene in an aqueous medium. The ferric ions have been reported to turn off the fluorescence intensity of the QDs, while pyrene turned on the intensity [58].  
Figure 3: (**a**) FL behavior of N-GQDs in the presence of various TNP concentrations (from top to bottom (0–50 M)). (**b**) FL emission curve of N-GQDs with increased concentration of TNP (inset: Stern–Volmer plot for the quenching process) [59].  
Figure 2: (**A**) Fluorescence emission spectra of BSA/creatinine/N-GQDs–chitosan solid film in the presence of PA at 0 (red line I0), 2, 5, 10, 20, 80, 100, 150 and 200 ng/mL (from top to bottom). (**B**) Stern–Volmer curve. I and I0 were the fluorescence intensities of BSA/creatinine/N-GQDs–chitosan fluorescent solid film at 360 nm in the presence and absence of PA, respectively [57].
###### Abstract  
A significant amount of livestock manure is generated in Bangladesh, creating challenges for sustainable manure management. Bioenergy and organic fertilizer production from manure are expected to provide opportunities for renewable resources, including environmental benefits. Therefore, this research aimed to spatially assess the potential of manure for biogas and compost using GIS (geographic information system) symbology and hot spot analyses, based on theoretical estimations. This study identified hot spots for biogas and compost production from various types of livestock manure at the district and sub-district levels, whereas previous studies have only explored these at a national level. The estimated total biogas and compost potential was approximately 15,035.50 million m\({}^{3}\) and 67.36 million tons, respectively, from livestock manure in 2024, distinguishing it as a feasible alternative to fossil fuels for electricity generation and synthetic fertilizers for crop production. Overall, the regional pattern maps of the socio-economic potential, hot spot identification, and environmental benefits assessments of manure will provide a more localized approach to planning sustainable manure management strategies for biogas and compost production in Bangladesh.  
biogas production; compost production; GHG emissions reduction potential; manure nutrient leaching; hot spots analysis; synthetic fertilizer use; spatial analysis Article
## 3 Preliminaries  
### Assumptions  
Several assumptions are made following some aspects of DT-IoV in general. Nodes can move and communicate only by exchanging messages wirelessly with other nodes within radio range. The wireless link can be broken, for instance, if the node in contact goes beyond radio range, which results in an incomplete message transmission being dropped.  
It is assumed that every node has a unique identifier that allows other nodes to distinguish them in the network. Besides, messages carry the list of the nodes' identifications they have passed by. Starting with the source, each node adds its signed identification upon receiving a message that is not destined to itself, as in RCAR [17]. Moreover, it is assumed that nodes, when receiving a message, can know its source.  
The proposed detection schemes only focus on simple black-hole attacks. This means that a black-hole node maintains its behavior during the whole simulation and does not collude with other black-hole attackers.
# Time-Varying Reliability Evaluation of Concrete Based on Carbonation Depth
## 8 Example  
In order to appraise the structural safety and carry out an optimum maintenance plan, several concrete beams from a building in Yokohama, Japan, were tested. Table 5 shows detailed information for these tested components.  
On the basis of the analysis in this paper, the effect of \(f_{\text{c}}\) on carbonation depth was negligible. Therefore, the concrete failure time of the beams was determined by the service age and concrete cover. The failure mode of concrete beam is defined as the predicted carbonation depth reaching the corresponding concrete cover. The analysis of structural safety is presented below.  
Calculation I:  
Using Equation (2), the mean values of carbonation depth of six concrete beams were predicted as:  
\(\mu_{\text{I}}=17.99\)\(\mu_{\text{II}}=16.89\)\(\mu_{\text{III}}=15.86\)\(\mu_{\text{IV}}=16.89\)\(\mu_{\text{V}}=17.99\)\(\mu_{\text{VI}}=17.99\)  
According to the failure mode, when the carbonation depth was determined only by its mean value, all concrete beams were still in service.  
Calculation II:  
On the basis of the analysis in this paper, exponential distribution was applied to describe the distribution of the carbonation depth. Using Equations (1), (3), and (6), the corresponding failure probabilities were calculated as:  
\(p^{\prime}_{f\text{I}}=0.059\)\(p^{\prime}_{f\text{II}}=0.071\)\(p^{\prime}_{f\text{III}}=0.034\)\(p^{\prime}_{f\text{IV}}=0.079\)\(p^{\prime}_{f\text{VI}}=0.033\)  
Figure 16: Time-varying reliability indices with different concrete covers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Beam Number** & **Service Age (Year)** & \(f_{\text{c}}\)**(N/mm\({}^{2}\))** & **Concrete Cover (mm)** \\ \hline I & 46 & 18.8 & 25 mm \\ II & 45 & 21.0 & 20 mm \\ III & 44 & 23.1 & 30 mm \\ IV & 45 & 27.5 & 25 mm \\ V & 46 & 25.3 & 20 mm \\ VI & 46 & 31.8 & 35 mm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detail information for the tested components.  
According to the concrete guaranteed rate of 95% obtained in the building code calibration, the limit failure probability was considered 0.05. Hence, beam III and beam VI were in service, while the other beams had been destroyed.  
Calculation III:  
Using Equations (3), (7)-(12), the corresponding reliability indices were calculated as:  
\(\beta_{\text{MM-I}}=0.726\)\(\beta_{\text{3M-II}}=0.539\)\(\beta_{\text{3M-III}}=1.057\)  
\(\beta_{\text{3M-IV}}=0.784\)\(\beta_{\text{3M-V}}=0.485\)\(\beta_{\text{3M-VI}}=1.117\)  
According to the corresponding limit reliability indices, the results of the safety evaluation coincided with those obtained in Calculation II.