esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### The Applicability and The Robustness of The Existing Community Detection Methods  
The topological positions and the properties of nodes directly affect the interactions and spreading phenomena in the network [30]. A node participating in more than one community is a common phenomenon in complex networks. In the air transport industry, the shared nodes reflect a few central gateway airports. They connect different regions with a high density of routes, propagating passengers, cargo, and even diseases to a large portion of the network. [PERSON] et al. claimed that the airports connecting different communities are typically hubs within their low-order communities [3]. Rather than being classified into one community, those airports are, if not demanded equally, proportionately needed by both sides. In this sense, it is the shared nodes that also indicate the existence of community overlaps. Accordingly, methods and algorithms have been proposed to detect overlapping community structures using modularity, spectral, and matrix factorization [17].  
Although scholars successfully identified the overlapping vertices, their algorithms are usually node-driven rather than clique-driven [7; 16; 17].  
In addition, previous studies have usually constructed the aviation network as unweighted and undirected. Their network frameworks decrease the complexity of the air transport system by reducing the model to pair-wise interactions, without considering the particularities of the structure. Although it helps to stay focused on the structural properties of connections, and identify the most relevant mechanisms, additional information is naturally neglected under those circumstances, including flight schedule, aircraft type, and operator [31; 32]. While the traffic en route affects the network aspect spatially, and impacts the passenger rerouting choices, it is as important as the topological characters [20]. Since flights are not equivalent, the dynamics of weights along the routes should be considered proportionally, by either flight frequency or passenger number [2]. [PERSON] et al. investigated the fully connected subgraphs with the clustering coefficient and the belonging degrees [21; 22]. However, no evidence shows the robustness of those methods in weighted networks. [PERSON] et al. accounted for unweighted and weighted networks, and they extracted the maximal cliques to find overlapping vertices or bridge vertices between communities [23]. However, they simply merged two maximal cliques into a larger subgraph for weighted graphs, which is incapable of quantitatively characterizing the effects of weights during the calculation.  
To overcome the above-mentioned challenges, this paper attempts to fill the gap and tackle the highly interconnected parts in a weighted network, by introducing a clique-based community detection method to the air transport industry.
## 2 Materials and Methods
### Data Processing and Analysis  
First, nirsLAB v. 2013.1 software (Version 14, Revision 2, NIRx Medizintechnik GmbH, Berlin, Germany) was used to convert the collected original data into MATLAB format data. Then, the differential function DIFF in MATLAB was used to observe whether there was a horizontal signal in the signal. If there were more than 25% invalid horizontal signals in the signal, we excluded the signal of this channel. Then, Butterworth filtering was used to reduce the interference of high-frequency noise (0.3 Hz respiration and 1 Hz heart rate) and low-frequency noise (metabolic tremor of less than 0.01 Hz) in the signal and improve the signal-to-noise ratio. Using the principal component analysis method proposed by [PERSON] to remove movement artifacts, the calculated monitoring time of the oxygenated hemoglobin concentration was 3 min [43]. Finally, the oxygenated hemoglobin concentration data of all subjects were calculated, and the change in the prefrontal lobe oxygenated hemoglobin concentration (HbO\({}_{2}\)) was calculated according to the improved Beer-Brown law. After the abnormal fluctuations and pseudo-error were eliminated by Polar software (Polar Precision Performance SW5.20, Polar Electro, Kempele, Finland), 5-min signal samples were selected for the HRV time domain and frequency domain analysis.  
The statistical analysis was carried out using SPSS22.0 (SPSS Inc., Chicago, IL, USA) and Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA), and the results were expressed by mean \(\pm\) standard deviation (M \(\pm\) SD). The Shapiro-Wilk test was used to examine the normal distribution of the outcomes. Two-factor repeated measures analysis of variance (two-way ANOVA, SPSS22.0) was used to analyze the effects of experimental variables (intervention program \(\times\) measurement time) on the HRV HbO\({}_{2}\) in the moderate fatigue group and the severe fatigue group, respectively. The intra-group variable was the intervention program (denoted as programs I-V); the measurement time was recorded at two time points (pre- and post-intervention). Post-hoc analysis was used if a significance in the interaction was observed and corrected using Bonferroni correction. The significance level was set at \(p<0.05\), and the highest significance level was set at \(p<0.01\).
# Study on Vibration Characteristics of Multi-Beam Structures with Stick and Slip at Joints
## 2 Theoretical Modeling
### Nonlinear Connecting  
In the case of friction, the connecting force between adjacent beams resulting from relative displacement is represented as \(f_{u}\). It becomes essential to account for the energy variations in the system caused by the motion state of the joint. In the vibration process,  
Therefore, we can conclude the following:  
\[\left\{\begin{array}{l}\theta_{S}^{P}=0\\ \theta_{S}^{N}=\pi\\ \theta_{P}^{S}=\pi+\cos^{-1}\Bigl{(}1-\frac{2\mu_{f}F_{N}}{\lambda k_{u}}\Bigr{)} \\ \theta_{N}^{S}=\cos^{-1}\Bigl{(}1-\frac{2\mu_{f}F_{N}}{\lambda k_{u}}\Bigr{)} \end{array}\right. \tag{22}\]  
The segmented representation of the bonding force is as follows:  
\[f_{u}=\begin{cases}k_{u}(\cos(\theta)-1)+\mu_{f}F_{N}&\theta_{P}^{S}\leq\theta \leq\theta_{N}^{S}\\ -\mu_{f}N&\theta_{N}^{S}\leq\theta\leq\theta_{S}^{N}\\ k_{u}(\cos(\theta)-1)-\mu_{f}F_{N}&\theta_{P}^{S}\leq\theta\leq\theta_{P}^{S} \\ \mu_{f}N&\theta_{P}^{S}\leq\theta\leq 2\pi\end{cases} \tag{23}\]  
The translational connecting force periodic function can be expanded by a first-order Fourier series, as follows:  
\[f_{u}=\frac{f_{0}}{2}+f_{c}\cos\theta+f_{s}\sin\theta \tag{24}\]  
where the standard integral form of the Fourier coefficient is the following:  
\[\left\{\begin{array}{l}f_{0}=0\\ f_{c}=\frac{1}{\pi}\int_{0}^{2\pi}f_{u}\cos(\theta)d\theta=\frac{\chi k_{u}}{ \pi}\bigl{[}\theta_{N}^{S}-\cos(\theta_{N}^{S})\sin(\theta_{N}^{S})\bigr{]}\\ f_{s}=\frac{1}{\pi}\int_{0}^{2\pi}f_{u}\sin(\theta)d\theta=\frac{\chi k_{u}}{ \pi}\bigl{[}\cos^{2}(\theta_{N}^{S})-1\bigr{]}\end{array}\right. \tag{25}\]  
Following Equations (6), (24), and (25), the equivalent stiffness \(K_{eq}\) and damping \(C_{eq}\) can be represented as follows [55]:  
\[\left\{\begin{array}{l}K_{eq}=\frac{f_{c}}{\chi}=\frac{k_{u}}{\pi}\bigl{[} \theta_{N}^{S}-\cos(\theta_{N}^{S})\sin(\theta_{N}^{S})\bigr{]}\\ C_{eq}=\frac{f_{s}}{-\omega\chi}=\frac{k_{u}}{\omega\pi}\bigl{[}1-\cos^{2}( \theta_{N}^{S})\bigr{]}\end{array}\right. \tag{26}\]
## 3 Results
### GO Annotation and KEGG Enrichment of Differentially Expressed Genes in G. sinensis  
Among the differentially expressed genes (DEGs), the number of up-regulated DEGs continued to rise until GS3, then declined until the GS5 period, consistent with the fluctuation pattern of gledtisioside content across different periods of pods. Additionally, during the period from GS1 to GS3, the pods transitioned from tender to mature. The gledtisioside gradually accumulated in the later stages, suggesting that there was an absence of expression or a lack of differential change in some unigenes during the GS4 phase, potentially due to a combination of developmental regulation, epigenetic factors, and other related mechanisms (Figure S3). To further annotate the DEGs involved in gledtisioside biosynthesis in _G. sinensis_, a gene ontology (GO) enrichment analysis was performed. Among these, 418 unigenes were annotated with \"antioxidant activity\" (n = 418, 0.07%) in the molecular function category. Some DEGs encoded enzymes related to triterpen saponin biosynthesis (such as oxidoreductase and transferase), binding capabilities, and transport functions. Interestingly, UGTs (GO:0008194), which play an essential role in the gledtisioside synthesis pathway, and ENIN (enzyme inhibitor activity, GO:0004857), which acted as a critical factor inhibiting the derivative branch of the gledtisioside synthesis pathway, were significantly enriched. Moreover, hydrolase activity was significantly enriched in the glycosyl bonds pathway (AGBH, GO:0016798), which might be the main factor accelerating the degradation of gledtisioside (Table S2). A total of 78,334 genes were annotated in 53 KEGG pathways (q-value \(\leq\) 0.05). All six different groups were enriched in metabolic pathways. In total, 162 unigenes showed significant enrichment in the sesquiterpenoid and triterpen biosynthesis (STTB) pathway, while 703 unigenes were notably enriched in the terpenoid backbone biosynthesis (TBB) pathway. Similarly, ten branch pathways, including carotenoid biosynthesis (CTB), zeatin biosynthesis (ZTB), ubiquinone, and other terpenoid quinone biosynthesis (UTB), were  
Figure 2: Annotation and classification of unigenes in _G. sinensis_. (**A**) Length distribution of all unigenes in the pods. (**B**) Annotation of unigenes of _G. sinensis_. (**C**) Expressed genes in the top ten KEGG pathways. (**D**) Cluster of Orthologous Groups (COG) function categories of unigenes in _G. sinensis_. COG function classes were demonstrated on the _x_-axis, and the full names of the functions were annotated below. The number of unigenes in each class was displayed on the _y_-axis. significantly enriched in terpenoid synthesis. Seven secondary metabolic pathways were substantially enriched, including anthocyanin biosynthesis, flavonoidonoid biosynthesis, etc. Diverse KEGG enrichment suggested that _G. sinensis_ pods might contain various chemical components, including gleditsioside and flavonoids (Table 2).
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 7 Task 5: Story Presentation and Querying  
### Intelligent Widget for Narrative Discovery  
In the past decades, numerous images have been uploaded to online platforms containing hidden, interesting information that should be discovered. However, the uploaded raw data do not deliver any insights. Accordingly, these raw data should be curated and transformed into knowledge. Data analytics helps understand the data; however, it is insufficient, and a solution to accomplish such knowledge could be storytelling with image data.  
Personalized storytelling demands human-computer interaction, since direct interaction with the end-users is indispensable. Story creation has complicated challenges; however, representing the generated story to end-users is another issue that needs careful attention. Creating graphical representations of data is known as data visualization. Visualization techniques translate information into a visual context, such as a map or graph, so the human brain can comprehend and extract hidden insights. For example, large datasets can be visualized to identify patterns, trends, and outliers. Therefore, an interactive visualization module, a dashboard, is the most proper environment for engaging users and being able to recommend various data based on the user's needs. Regardless, storytelling with image data is more than simply visualizing data. Instead, this process transforms raw image data into insightful narratives by highlighting valuable information and proving key points. Together, storytelling and visualization make a powerful combination. An intelligent widget allows for presenting a contextual and visual component alongside content in a platform, supplying the end-users with a visual fragment of the most relevant and representative data. It also enables user interaction and uses domain knowledge and expertise to achieve a subjective demand. Visualization objectives, benefits, and challenges are discussed in the following.  
**Visualization Objectives.** Data visualization serves a straightforward objective. It is to comprehend the data and use them for the end-user benefit. However, since data are complex, they attain more value when visualized. Therefore, visualization is crucial for communicating data findings, identifying patterns, and interacting with data.  
**Visualization Benefits.** In addition to providing a better understanding of the data, data visualization facilitates sharing information with some audiences, helps accurately analyze data, and uncovers relationships between events and entities.  
**Visualization Challenges.** Visualization delivers estimation, not accuracy. It may not be objective, may have improper design issues, and the core messages may be skipped.
# Toward Semi-Supervised Graphical Object Detection in Document Images
## 2 Related Work
### Deep Learning Methods  
With the success of Convolution Neural Networks (CNN) in the field of object detection and computer vision, the CNNs are also applied to predict and localize the documents; [PERSON]. [20] proposes a method that first identifies table-like areas using a rule-based approach and applies a CNN on the output to detect potential tables. This method also considers non-visual features such as characters for better localization. To eliminate the dependency of rules and other metadata in training, [PERSON]. [21] introduced DeepDeSRT, a two-stage approach for detection and structure detection, and later proved that this method generalizes to new structures. A Deep Convolution Neural Network (DCNN), which adds fully connected conditional random fields to convolution layers, performs multi-scale reasoning on visual cues for localizing objects. [PERSON] et al. [22] made a few modifications to Cascade Mask R-CNN to identify mathematical formulas in document images.  
The state-of-the-art object detection networks that depend on the regional proposals are introduced to the field of graphical object detection by [7]. They applied a Region Proposal Network (RPN), which shares full-image convolution features, enabling cost-free region proposals. There are many [23; 24; 25] region, pixel and connected component-based models proposed to identify textual and non-textual components in a document. The Faster-RCNN and Mask-RCNN detectors are used by [26] to localize and segment objects.  
Based on a dynamic programming approach, [27] identifies region proposals in page object detection and [28] outlines and summarizes multiple deep learning approaches for graphical page object detection. The authors from [29] conduct performance analysis on neural networks which recognize tables in document images. The deep learning-based approaches are highly effective with some detectors trained on a particular dataset capable of detecting and localizing graphical structures on unseen new datasets [30]. The major disadvantage of the deep learning-based methods is the hunger for annotated data. These approaches require a lot of characterized data that is hard to obtain, and the annotation labeling process is manual, error-prone, and time-consuming.
## 5 Conclusions  
In this work a single sensor element based on an etched OM4 GI-MMF with integrated BG was proposed and investigated for simultaneous determination of applied temperature and SRI. Experimental results verify that different PMGs respond differently to applied SRI but equally to applied temperature. Therefore, by applying PMG multiplexing and a single sensor element consisting of an etched GI-MMF with BG, the simultaneous measurement of SRI and temperature is feasible. Experiments show that the sensitivity to SRI increases with increasing order of the PMG. Sensitivities of 3.04 nm/RIU and of 0.22 nm/RIU to applied SRI in the range from 1.33 and 1.47 for the highest and lowest PMG, respectively, were obtained. The temperature sensitivity of all PMGs was almost equal. The obtained standard deviation of the SRI and temperature measurement were 0.064 nm (for _P1_) and 0.014 nm (for _P11_). Further work is being carried out presently on a new interrogation system which would allow the excitation of individual modes of the etched GI-MMF. Thus, the interrogation system will then compensate for the envelope fluctuations of the measured reflection spectrum which limits the detection of the wavelength shift of the Bragg reflection peaks due to applied SRI. Additionally, the enhancement of the sensitivity to SRI will be investigated by using thin polymer overlays [11] as well as whether the new sensor element can be applied for simultaneous measurement of humidity [12] and temperature or strain and temperature by using different sensor coatings. In future experiments, the temperature range of the proposed sensor element will also be investigated.  
Conceptualization: K.B.; software: K.B.; validation: K.B., L.S.M.A. and Y.Z.; investigation: K.B., Y.Z., L.S.M.A.; writing--original draft preparation: K.B. and L.S.M.A.; writing--review and editing: B.W.R.; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
[PERSON] acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453).  
The authors would like to acknowledge the work from [PERSON], [PERSON] and [PERSON] from the Laser Zentrum Hannover for etching the GI-MMF.  
The authors declare no conflict of interest.
## 7 New Challenge: Multimodality
### Fail-Safe Mechanisms  
A system is said to be fail-safe if--in case of any fault--it can cease its functionality and transition to a well-defined safe state [87]. So, a key safety issue is what happens when nominal mode fails. For example:  
* If satellite connectivity drops and V2V is unavailable (e.g., in a rural area), the vehicle must rely on onboard sensors alone, which might not detect distant hazards.
* Over-reliance on any single mode (e.g., satellite navigation) without robust backups increases the risk of catastrophic failure.  
Again, these kind of issues are similar to those encountered on an aircraft, suggesting that AVs are getting closer to aircraft from the safety point of view.
## 3 Solution Method
\begin{array}{cccc}\delta^{\prime\mathsf{T}} &\frac{\partial\hat{\delta}^{\prime\mathsf{T}}_{w}}{\partial X}&\frac{ \partial^{2}\hat{\delta}^{\prime\mathsf{T}}_{w}}{\partial X^{2}}&\frac{ \partial^{2}\hat{\delta}^{\prime\mathsf{T}}_{w}}{\partial X^{3}}\end{array}  \Big{]}^{\mathsf{T}}, \tag{28}\]  
Equation (27) can be rewritten in a compact form in state space:  
\[\frac{\partial\mathbf{\eta}(X,i\omega)}{\partial X}=\mathbf{F}(i\omega)\mathbf{ \eta}(X,i\omega), \tag{29}\]  
where  
\[ \begin{array}{c}\mathbf{F}(i\omega)=\left[\begin{array}{cccc}\mathbf{0}& \mathbf{I}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{I}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{I}\\ \mathbf{f}_{1}&\mathbf{f}_{2}&\mathbf{f}_{3}&\mathbf{f}_{4}\end{array}\right],\\ \mathbf{f}_{2}=-i\omega\Big{(}\widetilde{\mathbf{K}}^{(4)}\Big{)}^{-1}\tilde{ \mathbf{K}}^{(1)},\\ \mathbf{f}_{3}=-\Big{(}\widetilde{\mathbf{K}}^{(4)}\Big{)}^{-1}\Big{(}\widetilde {\mathbf{K}}^{(2)}+(i\omega)\widetilde{\mathbf{K}}_{1}^{(2)}+(i\omega)^{2} \widetilde{\mathbf{K}}_{2}^{(2)}\Big{)},\\ \mathbf{f}_{4}=-(i\omega)^{2}\Big{(}\widetilde{\mathbf{K}}^{(4)}\Big{)}^{-1} \widetilde{\mathbf{K}}_{1}^{(3)}.\end{array}  \tag{30}\]Introducing the vector \(\mathbf{\eta}(X,i\omega)\) and performing the Fourier transform to the boundary conditions in Equation (17), we get:  
\[\mathbf{M}_{b}(i\omega)\mathbf{\eta}(-0.5,i\omega)+\mathbf{N}_{b}(i\omega) \mathbf{\eta}(0.5,i\omega)=\mathbf{0}, \tag{31}\]  
where \(\mathbf{M}_{b}\) and \(\mathbf{N}_{b}\) are selection matrice of boundary condition at the left and right edges of nanoplate, respectively, as shown below. \[\mathbf{M}_{b}(i\omega)=\left[ \begin{array}{cccc}\mathbf{I}&\mathbf{0}& \mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{I}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}\end{array} \right],\mathbf{N}_{b} (i\omega)=\left[ \begin{array}{cccc}\mathbf{0}&\mathbf{0}&\mathbf{0}& \mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \mathbf{I}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{I}&\mathbf{0}\end{array} \right]. \tag{32}\]  
According to the transfer function method, the solution of Equation (29) can be expressed as follows:  
\[\mathbf{\eta}(X,i\omega)=\mathrm{e}^{\mathbf{F}(i\omega)X}\mathbf{\eta}(0,i \omega). \tag{33}\]  
Substituting Equation (33) into Equation (31) results in:  
\[\left[\mathbf{M}_{b}(i\omega)\mathrm{e}^{-0.5\mathbf{F}(i\omega)}+\mathbf{N}_ {b}(i\omega)\mathrm{e}^{0.5\mathbf{F}(i\omega)}\right]\mathbf{\eta}(0,i \omega)=\mathbf{0}. \tag{34}\]  
For the dynamic analysis of the nanoplate, the existence of a nontrivial solution of the corresponding Equation (34) requires that the determinant of the coefficient matrix must vanish  
\[\mathrm{det}\left[\mathbf{M}_{b}(i\omega)\mathrm{e}^{-0.5\mathbf{F}(i\omega) }+\mathbf{N}_{b}(i\omega)\mathrm{e}^{0.5\mathbf{F}(i\omega)}\right]=0. \tag{35}\]  
The dimensionless natural frequency \(\omega\) of the axially-moving nanoplate resting on a viscoelastic foundation can be obtained by solving the above transcendental characteristic equation.
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
## 2 Materials and Methods
### MB Adsorption Experiments  
An MB stock solution, with a concentration of 1000 ppm, was prepared by dissolving an appropriate quantity of MB in 1 L of deionised water. MB solutions with 1, 2.5, 5, 10, and 15 ppm (initial concentration, \(C_{0}\)) were obtained by stock solution dilution with deionised water. To prevent photodegradation of the dye, all containers of the solutions were wrapped with aluminium foil and stored in the dark.  
The cubic monoliths were sliced (Struers Secotom-10, Cleveland, OH, USA) in smaller parallelepiped (0.5 \(\times\) 1.5 \(\times\) 2.0 mm\({}^{3}\)) samples that were used in the MB adsorption tests. Their weight, which depends on the porosity of the samples, was between 0.752 g and 1.638 g. The MB adsorption was tested by immersing each monolith in glass beakers with 100 mL of MB solutions, at room temperature (22 \(\pm\) 2 \({}^{\circ}\)C), under constant magneticstirring for a predetermined period (25 h). All adsorption experiments were performed at a pH value of around 6.5.  
The MB concentration was determined through the absorption intensity at 664 nm (the main absorption band of MB) by UV spectroscopy (Shimadzu UV-3100, Kyoto, Japan). For that, aliquots of 1 mL were collected from the solution, at predefined time intervals (0-25 h). Two replicas were used, and the average value was presented. The conversion of the absorbance values into MB concentration was carried out using a calibration curve previously obtained from solutions of known concentration also obtained by stock solution dilution. The pH value during the experiments was measured (Thermo Fisher Scientific, Orion Star(tm) A211, Waltham, MA, USA). The tested variables were the contact time and MB initial concentration.