esa-sceva/satcom-chunk-collection huggingface dataset:
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 2 Innovative Positioning and Navigation Technologies  
Three key technologies are introduced in the following being the core of the HANSEL _testbed_, referred hereafter simply as _testbed_. In the context of the Smart Cities, such paradigms are all enabled by network facilities both for data sharing (i.e., Snapshot Cloud Processing and Differential GNSS (DGNSS) Cooperative Positioning) and for auxiliary Radio-Frequency (RF) ranging (i.e., Wi-Fi(tm) Ranging).
## 4 Experimental Results
### Comparison with Other Methods  
This section will compare the classification performance with the cited literature [10, 11, 20], as shown in Table 12. Based on the same BoT-IoT dataset, both models use GANs to solve  
Figure 10: (**a**) The accuracy of the proposed classification mechanism using the ToN-IoT dataset, and (**b**) the loss of the proposed classification mechanism using the ToN-IoT dataset.  
Figure 9: The training loss of MLP-based IoT attack classification with data augmentation for GANs using the ToN-IoT dataset. (**a**) Class of “DoS”; (**b**) class of “DDoS”; (**c**) class of “Password”; and (**d**) class of “Mitm”.  
classification problems. In [10], there is an attack classification model that integrates GANs and AutoEncoder but only distinguishes data into two classes: attack and non-attack. The literature [11] is a multiclass attack classification model that integrates conditional GANs and the feed-forward neural network (FNN). The comparison results show that the proposed method is better than the literature [11] in four performance indicators: accuracy, precision, recall, and F1-score. Although the performance of the proposed method in precision, recall, and F1-score is slightly lower than that of the literature [10] by 2%, the method of the literature [10] can only perform binary classification (attack and non-attack). The proposed method can achieve excellent classification performance for all five classes, reaching more than 90% in the four indicators of accuracy, precision, recall, and F1-score.  
Additionally, the proposed classification method was trained using the ToN-IoT dataset. The results demonstrate that the proposed method outperforms the approach presented in the literature [20] that also used the same dataset. Moreover, regardless of whether the BoT-IoT dataset or the ToN-IoT dataset is used, the overall classification performance in terms of accuracy, precision, recall, and F1-score consistently exceeds 95%, while individual class performance exceeds 90%.
## 2 Materials and Methods  
### Materials and Preparation of the Quartz Crystal Microbalance Sensor  
A 5 MHz AT-cut quartz crystal with gold electrodes was used in this study. The disc diameter was 25.4 mm. The frequency and series resonance residence were measured using a frequency counter Model QSR-F-5 (SRC, Itoigawa, Japan). First, the quartz crystal was coated with a polyvinyl butylval (PVB)-based coating reagent (AY-302, Sunhayato, Tokyo, Japan) to reduce the interference caused by the electrolyte in anaerobic digestate, and then coated again with a copolymer of styrene-divinylbenzene (PS-DVB) gel (PLS-2 for AQUA, GL Science, Tokyo, Japan) by random immobilization. PS-DVB was expected to efficiently absorb LCFAs in the digestate because it showed the highest frequency-shift rate with a lower increase in resonance residence among a total of five materials, including \(\alpha\)-cyclodextrintrim polymer, polyethylene glycol 2000, OV-275, C18-bound PS-DVB gel, and normal PS-DVB gel when they were applied to QCM coating by random immobilization. The PVB-based reagent was diluted eight-fold with ethanol before use. The PS-DVB gel was first disintegrated by bead beading for 5 min, then 20 mg of the gel was dispersed in 10 mL of ethanol. The solution was stirred by a magnetic stirrer.  
After rinsing with ethanol, a 15 \(\upmu\)L PVB-ethanol solution including approximately 160 \(\upmu\)g dry weight of PVB was spread on one side of the QCM disc surfaces with a micropipette and dried for approximately 30 min. Then a 3.3 \(\upmu\)L PS-DVB solution, including 6.6 \(\upmu\)g dry weight of PS-DVB obtained from a stirred glass bottle, was spread on the gold electrode with a micropipette. After briefly air-drying, the PS-DVB film was coated twice following the same procedure. In this study, the QCM disc was washed with ethanol to remove the spent coating and was newly coated every time a measurement was carried out to ensure the reproducibility. The flow cell used for the QCM measurements consists of three Teflon pieces (Figure 1). The top piece was the cell top used to hold the reference and counter electrodes, the center piece was the cell body for the solution, and the bottom piece was used for mounting. The quartz crystal was located between the center and bottom pieces, and the seal was formed through two O-rings pressed together by screws. The working volume of the flow chamber created inside the cell was 0.15 mL. The sample was introduced from the inlet port on the top of the flow cell by a peristaltic pump and flowed radially outward from the center of the cell to the exit channel at the edge. The flow rate during the measurement was 0.12 mL/min. All measurements were performed by feeding the sample solution into the flow cell, for approximately 200 s, in at least triplicate for each sample at around 25 \({}^{\circ}\)C.
# Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection
## 3 Proposed Approach
### ELM Functionality Explanation  
This section explains the functionality of an ELM using the standard ELM architecture shown in Figure 4.  
Input Layer: This layer consists of neurons that represent the input features. Each neuron corresponds to one feature of the data, denoted as \(x_{1}\), \(x_{2}\), \(x_{3}\), \(\ldots\), \(x_{p}\) features.  
Hidden Layer: The hidden layer has a set of neurons, each with a random weight and bias connected to every input neuron. The weights are denoted by \(w_{ij}\), where \(i\) indexes the input neurons and \(j\) indexes the hidden neurons. The biases are denoted as \(b_{1}\), \(b_{2}\), \(\ldots\), \(b_{q}\) for  
Figure 4: The standard ELM architecture.  
\(q\) hidden neurons. The output of the \(j\)th hidden neuron, denoted as \(h_{j}\), is calculated using the activation function (\(g\)):  
\[h_{j}=g\left(\sum_{i=1}^{p}w_{ij}x_{i}+b_{j}\right) \tag{11}\]  
Here, \(g(\cdot)\) is typically a non-linear function like the sigmoid, tanh, or ReLU [40].  
Output Layer: The output layer neurons receive the output from the hidden layer neurons. The weights between the hidden layer and the output layer are denoted as \(\beta_{ij}\), where \(i\) indexes the hidden neurons and \(j\) indexes the output neurons. The final output by the \(j\)th output neuron (\(y_{j}\)) is a linear combination of the hidden layer outputs:  
\[y_{j}=\sum_{i=1}^{q}\beta_{ij}h_{i} \tag{12}\]  
These weights (\(\beta\)) are determined by a linear system to minimize the error between the predicted outputs and the actual target values of the training data. The complete ELM system in matrix form can be given as follows:  
\[\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{r}\end{bmatrix}_{r\times 1}=\begin{bmatrix}h_{1}&h_{2}&\cdots&h_{q}\\ h_{1}&h_{2}&\cdots&h_{q}\\ \vdots&\vdots&\ddots&\vdots\\ h_{1}&h_{2}&\cdots&h_{q}\end{bmatrix}_{r\times q}\times\begin{bmatrix}\beta_ {11}&\beta_{12}&\cdots&\beta_{1r}\\ \beta_{21}&\beta_{22}&\cdots&\beta_{2r}\\ \vdots&\vdots&\ddots&\vdots\\ \beta_{q1}&\beta_{q2}&\cdots&\beta_{qr}\end{bmatrix}_{m\times r} \tag{13}\]  
or  
\[Y=H\times\text{B} \tag{14}\]  
In an ELM, the key idea is that the weights and biases (\(w\) and \(b\)) are set randomly and are not updated during training. Instead, only the output weights (\(\beta\)) are learned. This is achieved by solving the least-squares problem, typically using the Moore-Penrose pseudo-inverse:  
\[\text{B}=H^{+}T \tag{15}\]  
where \(H^{+}\) denotes the pseudo-inverse [41] representation of the hidden layer output matrix (\(H\)), and \(T\) is the target output matrix for the training data.  
The ELM is efficient because it reduces the learning process to a single-step linear problem, bypassing the need for iterative optimization, such as the backpropagation used in traditional neural networks. This makes ELMs particularly fast for training while still allowing them to approximate complex functions given an adequately large number of hidden neurons.
## 3 Results and Discussion
### GasSensitive Properties  
Furthermore, the gas-sensitive properties of the NTO NWs were studied using a self-assembly gas-sensitive testing system (Figure 5). The as-prepared NTO powders were mixed in alcohol to form a slurry and then coated onto the Al\({}_{2}\)O\({}_{3}\) microtube to form a thick sensing film. The NTO NWs-coated microtube was then fixed with six poles, of which the four Pt electrodes were used for resistance test and the other two poles were used for connecting a heating resistance wire inserted through the microtube. Then, the resistance testing was available for the as-prepared NWs coated on ceramic cylinders with an internal heater. The changeable resistance with the gas concentration indicates the gas sensitivity of the NTO NWs. In Figure 6, it is clear that the NTO NWs have good gas sensitivity for both NO or NH\({}_{3}\) at 350 \({}^{\circ}\)C, in which the response time was about 25 s and the recovery time was about 170 s, with high stability. In view of their flexible structure and good mechanical properties, the NTO NWs can be used as the gas sensor in novel wearable nano-devices, which may be used in special high-temperature environments, such as aerospace or high-temperature reaction furnaces.  
Figure 4: In situ mechanical properties of the NTO NW measured by a SEM/SPM system. (**a**) SEM images of mechanical test; and schematic diagram of experimental model (the inset); (**b**) Relationship curve between the load and displacement of the NTO NW.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 3 Preliminary
### Edge Degree Description  
Apart from the most well-known concept of 'node degree', there exists another useful concept known as 'edge degree'. The interconnection among nodes is measured by the edge degree, which provides valuable insights into the connectivity patterns. The edge degree can be determined by considering either one endpoint (a node) or both endpoints (nodes) of an edge, thereby capturing different aspects of the graph's structure. Various descriptions of edge degrees have been suggested in the graph community [63].  
Figure 1: (**a**) Arbitrary random graph. (**b**) Partitioning clustering. (**c**) Overlapping clustering. (**d**) Hierarchical clustering.  
For instance, the simplest form of edge degree assigns a uniform value of 1 to each edge regardless of the degrees of the connected endpoints. This straightforward definition provides a fundamental understanding of edge connectivity. The more complex form of edge degree is the product of the power functions of the degrees of both endpoints. This complex measure reveals deeper insights into the influence of highly connected nodes on edge strength and network robustness. Figure 2a-e illustrate five possible definitions of an edge degree, serving as visual aids to depict several distinct definitions of edge degree. These illustrations, supported by rigorous mathematical concepts, help grasp the diversity of edge degree interpretations and their implications for network analysis.  
In this work, the edge degree denoted as \(d(e_{i})\) is taken to mean the summation of edges on both sides minus one (i.e., avoiding duplicated counting as in Figure 2d). This definition is the most appropriate for this study as the density of clusters relative to the whole graph is of interest. Therefore, summation of adjacent edges from nodes at each end of the edge is directly relevant to the determination of relative density. The edge degree \(d(e_{i})\) is determined by using Equation (1):  
\[d(e_{i})=(d_{(e_{1})}+d_{(e_{2})})-1 \tag{1}\]  
where \(d_{(e_{1})}\) and \(d_{(e_{2})}\) represent the degrees of the first and second endpoints of the edge, respectively. Note that the sum of both degrees is subtracted by 1 to avoid duplicated counting of an edge. Hence, the average degree in a graph (\(\mathcal{G}\)) is represented as \(Avg_{deg}(\mathcal{G},E)\) and is determined by Equation (2) below. The edge corresponds to the average degree \(Avg_{deg}(\mathcal{G},E)\) and is average degree edge within the set of edge (\(E\)) and is denoted as (\(Avg_{deg}(\mathcal{G},e_{i})\)).  
\[Avg_{deg}(\mathcal{G},E)=\frac{\sum_{i=1}^{E}d(e_{i})}{E} \tag{2}\]  
where \(E\) is the total number of edges in the graph.
## Materials and Methods
### Evaluation of Immunostaining  
Immunostaining was evaluated using the immunoreactive score (IRS) system [30; 31]. IRS gives a range of 0-12 as a product of multiplication between a positive cells proportion score (0-4) and a staining intensity score (0-3). Staining extent was scored as 0 (no positive cells), 1 (\(<\)10%), 2 (10%-50%), 3 (51%-80%), and 4 (\(>\)80%) according to the percentage of positively stained cells. Staining intensity was scored as 0 (no color reaction), 1 (mild), 2 (moderate), and 3 (intense). Based on final IRS scores, immunostaining was determined to be negative (IRS score 0-1), mild (IRS score 2-3), moderate (IRS score 4-8), and strongly positive (IRS score 9-12) [30; 31]. Immunostaining was evaluated by two pathologists (AM and NA) who were blind to patient demographic data, clinical data, and pathological data. The concordance rate was more than 90%. Discrepancies were resolved by joint discussion. Representative examples of staining for each antibody are shown in Figure 1. Positive control slides were included, and negative controls were run with the primary antibody replaced by buffer.  
Figure 1: Immunohistochemistryistochemistry staining for IR and c-MET in breast cancer. (**A**) Negative staining for IR; (**B**) mild staining for IR; (**C**) moderate staining for IR; (**D**) strongly positive staining for IR; (**E**) mild staining for c-MET; (**F**) moderate staining for c-MET; and (**G**) strongly positive staining for c-MET. [Magnetization at x200]. IR, insulin receptor.
## 3 Methodology
### Computational Model Setup  
It was made a model by aided computer design under dimensions illustrated in Figure 3, and due to its symmetry, a 2D model was done and another with azimuthal symmetry [10]. The temperature of the hot source (HST) was extracted from the data of experimental results; the same for the cold source temperature (CST) which were introduced as boundary conditions in the finite element model, the properties of the material, in the case copper, being a reference material, Equation (3) was used  
\[\lambda_{Cu}=416.3-0.05904T+7.087\times 10^{7}/T^{3} \tag{3}\]Equation (3) was obtained from [9] because in the standard are published thermal conductivity of some materials considered such as meter bar reference materials for the cut bar method.  
ANSYS 18 software with the Mechanical APDL (Parametric Design Language) user interface and thermal module for the simulations of this work was chosen. For the flat model, it was used a PLANE 77 element of eight nodes, and it has one degree of freedom, temperature, at each node, and applies to a 2-D, steady-state or transient thermal analysis and a SOLID 90 element of 20 nodes with a single degree of freedom, temperature, at each node for the 3D model. The mesh for 3D and 2D models are shown in Figure 4a,b, respectively [11,12].  
The mesh generated for the 3D model is depicted in Figure 4a, where azimuthal symmetry was implemented. For the 2D model, the mesh that was generated is shown in Figure 4b, where symmetry was used on the \(y\) axis. For loads, were used temperature values from the experimental results and heat flux 0 on the boundaries where the insulating material is presented. To reduce resources and computational time.
## 4 Results and Discussion
### Power-Law Function FGM Beam
#### 4.2.2 Shear Stress Distribution in P-FGM Beam  
The shear stress distribution normalized with the average shear stress is presented in Figure 8. One can observe that the maximum shear stress for each power-law index value considered corresponds to the neural axis location along with the beam thickness. For this demonstration E1 = 210 MPa and E2 = 390 MPa for steel and ceramic, respectively, were used. The maximum and minimum normalized shear stress are observed for n = 0.1 and n = 2.0 with 1.45 and 1.14 times the average shear, respectively.
# Green Approaches to Carbon Nanostructure-Based Biomaterials
## 2 Green Routes to Prepare Carbon Nanomaterials for Biomedical Applications
### Carbon Nanotubes (CNTs)
With an analogous approach, oxidized CNTs were coupled to dopamine to promote the mineralization with hydroxyapatite, and CNT electronic properties were exploited to achieve directional alignment through agarose gel electrophoresis to form scaffolds with collagen, which promoted the healing of bone defects in vivo [184]. Non-covalent CNT functionalization is also a popular route, as it does not disrupt their electronic properties. Use of ultrasounds promoted the wrapping of CNTs by polyethylene glycol-pyrene polymer to achieve stable dispersions to induce apoptosis of colon cancer cells by applying nanosecond electric pulses, which affected calcium flux within cells [185]. Simple mixing of CNTs with cationic polyamidoamine polymer and anionic dsRNA, with ultrasonication at room temperature, provided coated CNTs for gene knockdown interventions [186]. Similarly, sonication of CNTs with DNA in saline solutions, followed by purification using polymers, was effective to yield dispersible CNTs that showed very good short-term and long-term biocompatibility in vivo [187]. DNA-CNT complexes were also used in hydrogel composites with silica nanoparticles to deliver doxorubicin [188]. When dispersed with Triton within a glycol-chitosan hydrogel, CNTs promoted cell migration and recruitment, with potential applications in wound healing [189]. Conductive composites for potential applications in sensing were obtained by mixing CNTs with soy lecithin, natural rubber, and the green solvent methyl isobutyl ketone in a homogenizer at high pressure [190]. Figure 7: Stem cells (nuclei stained in blue) adhere and spread onto single-walled (SWNT, **left**) or multi-walled (MWNT, **right**) CNT-coated surfaces with multiple focal adhesions (green with vinculin-staining) and actin fibers (red). Reproduced from [172].
# Development of Folded Expanded Metal Mesh with Sound Absorption Performance
## 5 Conclusions  
In this research, we develop FEMM prototypes with three different structures and measured the sound-absorption performance to confirm the characteristics of FEMM. Afterward, we selected a specimen with high sound-absorption performance to apply to the field and then discussed the improvement in office acoustics.  
The first part of stage 1 shows four types of single-panel EMM that were tested. On the premise that the specimens were not filled with porous sound-absorption materials, the \(\alpha_{w}\) of all specimens was lower than 0.35.  
To achieve better sound-absorption performance, we designed a sound-absorption material with a folding structure in three different shapes: triangle, square, and trapezoid, respectively. The results showed that the main factor influencing the octave of sound absorption was the thickness of the cavity. In this part, we carried out 30 measurement results, in which the \(\alpha_{w}\) was 0.65-0.85. Regardless of the folding shape of the FEMM and the cavity thickness, the sound-absorption coefficient was maintained at 0.6-0.9 above 1000 Hz. Finally, the folding shapes did not significantly affect the sound-absorption performance, and the difference in the sound-absorption coefficient was only 0.2 at most at a specific frequency.  
The field verification results showed that when the FEMM was installed in the office, which covered about 47.8% of the ceiling area, the RT was effectively reduced by about 0.25-0.46 s, while the \(C_{50}\) improved significantly. The overall results show that FEMM not only has high sound-absorption performance but also provides sound-absorption capability of space and could provide a better acoustics environment.  
In the end, comparing the cost of EMM and perforated panel. when perforating a perforated plate, residual material will be generated. The EMM is created with numerous holes through the stretching process, therefore, the cost of the expanded metal mesh is lower than that of the perforated plate.  
Formal analysis, P.-C.T.; Investigation, J.-Y.L.; Methodology, J.-Y.L.; Project administration, Y.-S.T.; Software, P.-C.T.; Writing--original draft, J.-Y.L.; Writing--review & editing, Y.-S.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Ministry of Science and Technology (109-2622-E-006-032).  
Not applicable.  
Figure 20: Comparison of speech clarity (\(C_{50}\)) at each band.  
Data Availability Statement: The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 6 Uncertainties Associated with Distances Derived from Point Clouds
### Distance Measurement: Uncertainty Contribution from Spatially Correlated Probing Effects (\(P\))  
See Section 5.5. We assume the spatial correlation is isotropic and specified by variance \(\sigma_{P}^{2}\) and length scale parameter \(\lambda_{P}\). The spatial correlation parameter \(\lambda_{P}\) relates to chordal distance on the unit sphere and is usually chosen so that two points that are diametrically opposed on the unit sphere are associated with independent effects, i.e., \(\lambda_{P}\) is significantly smaller than 1. In general, a value of \(\lambda_{P}=1/2\) is appropriate. Suppose \(\mathbf{x}_{i}\) is measured in probing direction \(\mathbf{n}_{i}\) using probe offset \(\mathbf{p}_{k(i)}\) with associated statistical parameters \(\sigma_{P_{0},k(i)}\), \(\sigma_{P,k(i)}\) and \(\lambda_{P,k(i)}\), etc., and \(\mathbf{n}_{ij}\) is the unit vector pointing from \(\mathbf{x}_{i}\) to \(\mathbf{x}_{j}\). For the case of different probes, the model in Section 5.5 assumes that the probing effects are independent so that  
\[u^{2}(d_{ij})=\sigma_{P_{0},k(i)}^{2}+\sigma_{P_{0},k(j)}^{2}+\sigma_{P,k(i)}^ {2}+\sigma_{P,k(j)}^{2}.\]  
For \(\mathbf{p}_{i}=\mathbf{p}_{j}=\mathbf{p}\), etc.,  
\[u^{2}(d_{ij}) =\sigma_{P_{0}}^{2}(\sigma_{i}^{2}+\sigma_{j}^{2}-2o_{i}o_{j})\] \[+\sigma_{P}^{2}\Big{(}\sigma_{i}^{2}+\sigma_{j}^{2}-2o_{i}o_{j}e^ {-d_{ij}^{2}/\lambda_{P}^{2}}\Big{)},\]  
where  
\[o_{i}=\mathbf{n}_{i}^{\top}\mathbf{n}_{ij},\quad o_{j}=\mathbf{n}_{j}^{\top}\mathbf{n}_{ij}, \quad d_{P,ij}=\|\mathbf{n}_{j}-\mathbf{n}_{i}\|.\]  
It is usually the case that \(\mathbf{n}_{i}\) and \(\mathbf{n}_{j}\) are aligned with \(\mathbf{n}_{ij}\). In this case, \(o_{i},o_{j}=\pm 1\), and we have  
\[u^{2}(d_{ij})=4\sigma_{P_{0}}^{2}+2\sigma_{P}^{2},\quad\mathbf{n}_{i}=-\mathbf{n}_{j},\quad u^{2}(d_{ij})=0,\quad\mathbf{n}_{i}=\mathbf{n}_{j}. \tag{32}\]  
The relationships above in Equation (32) show how the model accounts for the differences between uni-directional and bi-directional probing. For example, in measuring a step gauge, the probing effects do not contribute to the uncertainties associated with the distances between left-facing faces or between right-facing faces but contribute to the uncertainties in distances between left- and right-facing faces. In the comparison of two gauge blocks sitting side by side in which all four faces (two left-, two right-facing) are measured with the same probe, then the uncertainty contributions are such that  
\[u^{2}(d_{12})=u^{2}(d_{34})=4\sigma_{P_{0}}^{2}+2\sigma_{P}^{2},\quad u^{2}(d_ {12}-d_{34})=0.\]