esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model of the Proposed MEMS Gyroscope  
The lumped-parameter model of the MEMS gyroscope design is shown in Figure 2. The drive mass \(m_{d}\) is nested inside the sense frame \(m_{s}\) through the mechanical spring \(k_{x}\) and is free to oscillate along the drive direction, while the sense frame is anchored to the substrate by means of the mechanical spring \(k_{y}\) and is constrained to oscillate only in the sense direction. The air viscous damping between the comb-drive-based actuators attached to the drive mass \(m_{d}\), tuning combs and sensing parallel plates attached to sense frame \(m_{s}\) are represented by \(c_{x}\) and \(c_{y}\), respectively. When an angular velocity \(\Omega_{z}\) is applied along the \(z\)-axis, the equations of motion for the MEMS gyroscope design can be written as:  
\[m_{d}\ddot{x}+c_{x}\dot{x}+k_{x}x=F_{d} \tag{1}\]  
\[(m_{d}+m_{s})\ddot{y}+c_{y}\dot{y}+k_{y}y=-2m_{d}\Omega_{z}\dot{x} \tag{2}\]  
where \(F_{d}=F_{0}\sin(\omega t)\) is the electrostatic actuation force applied to the mass \(m_{d}\) and \(-2m_{d}\Omega_{z}\dot{x}\) is the Coriolis force acting on the one degree-of-freedom oscillator in the sense direction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Device size & \(1557\times 1816\) & \(\mu\)m \(\times\)\(\mu\)m \\ Structural layer thickness (\(t\)) & 25 & \(\mu\)m \\ Mass value of the drive mass (\(m_{d}\)) & \(3.0625\times 10^{-8}\) & kg \\ Mass value of the sense frame (\(m_{s}\)) & \(2.4338\times 10^{-8}\) & kg \\ Sensing plates length (\(l_{s}\)) & 65 & \(\mu\)m \\ Sensing plates width (\(w_{s}\)) & 4 & \(\mu\)m \\ Number of sensing plates pairs (\(N_{s}\)) & 100 & - \\ Moving and fixed sensing plates & 55 & \(\mu\)m \\ overlap length (\(x_{0s}\)) & 3 & \(\mu\)m \\ Smaller sense gap size (\(d_{1}\)) & 3 & \(\mu\)m \\ Larger sense gap size (\(d_{2}\)) & 9 & \(\mu\)m \\ Drive comb pairs (\(N_{d}\)) & 74 & - \\ Drive combs width (\(w_{d}\)) & 4 & \(\mu\)m \\ Drive combs length (\(l_{d}\)) & 50 & \(\mu\)m \\ Moving and fixed drive combs & 40 & \(\mu\)m \\ fingers overlap length (\(x_{0d}\)) & 3 & \(\mu\)m \\ Gap between drive combs (\(y_{0d}\)) & 84 & - \\ Drive tuning comb pairs (\(N_{t}\)) & 4 & \(\mu\)m \\ Width of tuning combs (\(w_{t}\)) & 50 & \(\mu\)m \\ Length of tuning combs (\(l_{t}\)) & 40 & \(\mu\)m \\ Overlap length between the moving and fixed tuning combs fingers (\(x_{0t}\)) & 40 & \(\mu\)m \\ Gap between tuning combs (\(y_{0t}\)) & 3 & \(\mu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters and their values for the proposed dual mass resonant MEMS gyroscope.  
Figure 2: Lumped-parameter model for the dual mass MEMS gyroscope design.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 8 Advanced BGP attack Detection Candidates
### Federated Learning  
There are ongoing research efforts to enhance the performance of Federated Learning (FL) models and mitigate communication costs in distributed network environments [244; 245]. FL has been used for privacy-preserving route leak detection research, and a technique known as Federated Learning Route Leak Detection (FL-RLD) has been described in the literature [129]. The research showed that ASs with more peers were valuable for route leak detection and that a method using multiple ASs produced a better performance than those using only a single AS observable. For example, the FL-RLD method had better accuracy, precision, recall, and F-score metrics. Whilst [129] studied direct route leak events, it did not consider other BGP incident categories, such as indirect cyber events or outages. This research suggests the importance and value of more peers for improved anomaly detection and may have the potential for higher-dimensional capabilities. It remains unclear whether the model amalgamation processes underlying FL achieve the criteria specified in Section 7; there was no evidence identified in the surveyed literature that FL can capture and quantify group-level interactions and dynamics information for the anomaly detection of collective ASs. There is also complexity associated with tuning FL's multiple parameters that might hinder its adaptability and swift deployment in dynamic network environments, though this requires further research.
## Materials and Methods
### Column Experiments  
#### 2.4.1 Experimental Setup  
A 99.1% pure-quartz sand (Kristall quartz-sand, [PERSON], Hirschau, Germany) was used as a packing material for the columns. A sieve analysis on the sand stock reported an effective grain size (D\({}_{10}\)) of 0.45 mm, a uniformity coefficient of 2.0, and a maximum grain size of 2.0 mm [23]. The size of the hydrochar particles was smaller than 0.425 mm. In order to remove undesirable impurities, the sand stock was immersed in 5% HCl overnight and rinsed with DI water until the pH of the washing water became stabilized. Finally, the residual water was removed by draining and successive drying at 105 \({}^{\circ}\)C.  
A certain amount of hydrochar suspension was added to the washed sand grains to achieve a 1.5% (dry \(w\)/\(w\)) hydrochar concentration, and the mixture was then thoroughly mixed. The hydrochar-sand mixture was then loaded into two types (small or large) of columns: Omnifit borosilicate glass columns (250 mm length \(\times\) 25 mm inner diameter; Diba industries, Cambridge, UK) for supporting short (10 cm) column beds, and acrylic poly vinyl chloride (PVC) pipes with an inner diameter of 5.6 cm for supporting long (50 cm) column beds. During the packing process, the columns were manually tapped and agitated in order to prevent channelling in the column bed. Also, the packing material loaded in the column was compacted carefully using either a glass rod or a steel rod to avoid possible air entrapment. Each type of column was assembled using appropriate connectors and fitting materials. Throughout the experiments, the packing materials were tightly fixed in the columns by stoppers at both ends, without under-drain media and exposure to the atmosphere. Then, the columns were washed with DI water overnight using a Masterflex pump (model 77201-60, Vernon Hills, IL, USA). During the washing process, the DI water was fed into columns at an upward flow rate of 1 mL(0.2 cm)/min for small columns, or 33.3 mL (1.35 cm)/min for large columns. Chloride tracer tests, performed on both the small and large columns (data not shown), reported a void ratio of \(\sim\)40%.  
The columns were vertically positioned using tripod stands or a steel frame rack. Prior to the main _E. coli_ flushing experiment, chloride tracer tests were performed to estimate the pore volume (PV) and to verify a stable aqueous flow in the column bed. The columns were flushed with the tracer solution (0.02 M NaCl) and successively with the DI water. The tracer concentration in the effluent was measured using ion chromatography (ICS-1000, Dionex, Sunnyvale, CA, USA).
## 7 Benchmark Problems and Case Study
### L-Bracket  
The L-bracket was chosen as a more-challenging benchmark problem [53; 113; 114], characterized by its initial geometry containing a stress-concentration point in the internal corner. Unconstrained compliance-minimization problems tend to ignore the stress concentration point, leading to an angular shape. On the other hand, stress-minimization or stress-constrained compliance-minimization problems tend to create rounded shapes that avoid the stress concentration. For these reasons, the L-bracket problem will be used in this research to validate and demonstrate the functioning of the code implemented when solving stress-dependent topology optimization problems. Furthermore, the strain and stress state generated by this L-bracket geometry is used in Section 8 to validate the correct implementation of the element formulations and maximum stress differentiation process.  
The dimensions and boundary conditions considered are represented in Figure 3. Unless stated otherwise, this problem was modeled using a regular mesh with an element size of 3.0 mm and the element type CPS4. The load \(F\) was equal to 1500.0 N, with this value being distributed over the nodes included in the 12.0 \(\times\) 12.0 corner represented in Figure 3. The elements within the 12.0 \(\times\) 12.0 corner are not editable during the optimization process. The problem was solved using an implicit analysis and considering a material with a 70.000 MPa Young's modulus, a 0.33 Poisson's ratio, and a material density of \(2.7\times 10^{-9}\) ton/mm\({}^{3}\).
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 2 Materials and Methods
### Detection of Feature Points with SIFT
#### 2.2.4 Keypoint Descriptor  
After assigning the scale, position, and direction information to each feature point, a feature descriptor needs to be defined for object matching. In this research, a 16 \(\times\) 16 template-sized window is taken as the center of feature points and divided into 16 \(\times\) 4 subblocks. The gradient size and histogram of the gradient direction in 8 directions including \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), \(225^{\circ}\), \(270^{\circ}\), and \(315^{\circ}\) are calculated on each subblock [35].  
Therefore, a 4\(\times\)4 subblock can obtain a descriptor with 8 directions, and then 16 subblocks can obtain 128 direction descriptors, and the 1 \(\times\) 128 dimensions vector is named as a feature descriptor vector. Figure 1 describes the key points through SIFT.
## 8 Conclusions  
In the distributed architecture, due to resource pooling, the vertical mapping relationship between applications and the infrastructure layer is extremely complex, making it difficult to effectively drill down from the abnormal application to locate the abnormal object in the infrastructure layer, thus reducing the efficiency of the emergency response. This paper studies a model method of vertical infrastructure-level localization and provides a systematic solution. In terms of positioning the longitudinal foundation layer, current research focuses on methods such as knowledge graph and indicator correlation analysis. Knowledge graph technology has high complexity and is only effective in local scenarios such as networks, leaving a gap between practical use. Indicator correlation analysis lacks configuration correlation information between operation and maintenance objects, limiting accuracy. This paper proposes a foundation layer fault positioning method based on dynamic operation and maintenance maps and alarm commonality analysis. During the fault period, various alarm entities are associated with the dynamic operation and maintenance map, and the commonality of alarms is obtained based on graph search addressing, covering deployment relationship commonality points, connection commonality points (physical, logical), access flow commonality points, etc., thus positively locating the possible root cause of the fault. This method is more concise and efficient than the knowledge graph method and is more accurate than the indicator correlation analysis method due to the addition of configuration correlation information. It has been implemented in a key business scenario of payment type in a bank's actual production environment, with an alarm positioning accuracy rate of over 82% and an analysis time of less than 2 min.  
Conceptualization, S.W.; Methodology, S.W.; Software, S.W.; Resources, J.G.; Writing--original draft, S.W.; Writing--review & editing, J.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets presented in this article are not readily available because the data are from a 3 rd party business.  
The authors declare no conflicts of interest.
## 3 Satellite Signal Measurements in Various Weather Conditions
### Changing Weather Conditions during the Measurements

Table 5: Weather conditions recorded during the measurements. letter class there is a finer scale from 1 to 9. This means that, for example, an M2 flare is half of an M4 flare, and so on. As written in [61] \"C-class and smaller flares are too weak to noticeably affect Earth. M-class flares can cause brief radio blackouts at the poles and minor radiation storms that might endanger astronauts\". It may also affect the satellite navigation. The number of solar flares increases approximately every 11 years, which means that the upcoming solar maximum is likely to appear in 2024-2025. Thanks to the continuous observation of the ionosphere and the sending of ionospheric corrections in navigation messages, during increased solar activity, the errors do not reach the maximum values to which this layer of the Earth's atmosphere is able to contribute. Unfortunately, it was not noted whether the three M-class solar flares quoted had an Earth-directed component and whether the measurement place was facing the sun side, where the eruption took place. However, as a general effect on positioning accuracy may be noted, it was assumed they were directed to Earth. The influence of mentioned circumstances may be taken into account in the future development of research. During measurements, significant M-class flares were noted for measurements nos. 11, 12, and 18. It can be seen, especially in measurement no. 18 (maximal error along Y axis), but for no. 12 as well (high positioning error around 16 m for both axis), that the positioning errors are larger, while the solar flares are stronger. On the other hand, the weakest solar flares identified are those for measurements nos. 4 and 5--B1.6. This also confirms the theory that the stronger the solar activity, the worse positioning (as such, a flare significantly affects the ionization of the ionosphere), and vice versa, as measurement no. 5 has the smallest positioning error along the Y axis. Tropospheric refraction does not have that much of an effect. It is usually omitted due to small errors and the difficulty of reducing the delays associated with it.
## 3 Results
### Modulus of Rupture (MOR)  
Another important property of composite materials is the resistance to bending strength or modulus of rupture (MOR). For poplar PW, the bending resistance decreases by 45.27% in the case of torrefied samples in the T1t2 regime and by 24.52% in the case of the T2t2 regime, compared to the control sample, as can be observed from Figure 6. This means that the increase in temperature negatively influences the resistance of this composite material, all due to the irreversible changes occurring in the structure of the wood during treatment at high temperatures. For MDF, the bending resistance decreases by 13.06% for the torrefied samples with the T1t2 regime and by 32.51% for the torrefied samples with the T2t2 regime, compared to control sample. The regression curves that highlighted the decrease in bending resistance for poplar PW and MDF were the polynomial ones of the second degree, with Pearson coefficient R2 = 1.  
The gluing quality is one of the most effective factors related to modulus of rupture of poplar PW because the improper gluing of veneer sheets in plywood manufacturing can cause the delamination of layers, and such samples exhibit a low modulus of rupture value. The maximum modulus of rupture for the control sample of poplar PW was 70.06 N/mm2. This value falls within the limits obtained by other researchers (95.65 N/mm2 for poplar plywood and 117.37 N/mm2 in the case of MDF) [39; 46]. The maximum modulus of rupture for torrefied poplar PW at 170 \({}^{\circ}\)C was 64.39 N/mm2, and 40.47 N/mm2 for torrefied poplar PW at 190 \({}^{\circ}\)C. The maximum modulus of rupture for the control MDF sample was 46.46 N/mm2, while the maximum modulus of rupture for torrefied MDF at 170 \({}^{\circ}\)C was 45.86 N/mm2, and for torrefied MDF at 190 \({}^{\circ}\)C, it was 31.27 N/mm2.
# FMNSCF: Fine-Grained Multi-Domain Network Interconnection Security Control Framework
## 2 Framework  
In order to facilitate the management and application, a security policy language is designed, which fits users' usage habits. However, this security policy language has low performance and complex logic, which cannot be directly applied to high-throughput security interconnection gateway. Therefore, a map method is proposed, which interprets security policies as security rules.  
Consider a situation where user \(u_{1}\) of domain \(d_{1}\) and user \(u_{2}\) of domain \(d_{2}\) can communicate with each other using \(app_{1}\). The communication time is limited from 9:00 a.m. to 10:00 a.m. every day, and there is no restriction on feature contents. The details of \(app_{1}\) communication among domains \(d_{1}\) and \(d_{2}\) are stored in a relationship and do not appear directly in the policy script. We can express it as follows:  
\[\begin{bmatrix}default&:&drop\\ whitelist&:&\{d_{1}-d_{2},app_{1}\}\end{bmatrix}.\]  
By means of map components, we can translate this policy into rules that can be loaded by the gateway. See the following:  
\[\begin{bmatrix}10.0.0.1-10.0.0.1,20.0.0.2-20.0.0.2,6-6,\\ 0-65,535,23-23,287,485,200-287,488,800,\_:accept\end{bmatrix},\]  
where 10.0.0.1 is the address of user \(u_{1}\), 20.0.0.2 is the address of user \(u_{2}\), 6 is the type of TCP, 0-65,535 is the source port range, 23-23 is the destination port of range, 287,485,200-287,488,800 means 9:00 a.m.-10:00 a.m. every day, the symbol _ means that there is no restriction on feature contents, and \(accept\) is the action of rule. The detail of time code can be shown in Formulas (38) and (39).  
Once the gateway loads this rule, it allows only packets that conform to the rule to pass through, with the rest discarded by default.  
Generally speaking, security rules are a kind of language which can be loaded by a security interconnection gateway. The security interconnection gateway has a large capacity and high efficiency of execution to meet the requirements of fine-grained security control of the integrated network of air-ground in multiple domains and achieve the purpose of in-depth protection. In this paper, we propose a multi-category of joint protection security rules.  
Security rules have life cycle management, which consists of the generation state of security rule, distribution state, and execution state. The elements mentioned above form the framework of fine-grained multi-domain network interconnection security control, FMNISCF for short. The specific relationships will be shown in Figure 1.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 5 Feasibility Analysis
The results of several experiments using pictures from MATLAB R2020b image library are reported in Figure 3, along with the values of peak signal-to-noise ratios (PSNRs, dB) recorded in Table 1.  
By observing the second and fourth columns of Figure 3, it is crystal clear that the assumed critical information in the original images is concealed, and other information contained in the original signals is reconstructed successfully. Simultaneously, from the third and fifth columns of Figure 3, we can obtain vivid reconstructions of the entire image without any concealment. The PSNR values that are calculated based on pixels of the original images and the reconstructed images from red, green, and blue channels are listed in Table 1. From these figures, we surmise that, although the values of PSNR varies due to the difference between \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Compression Rate 0.7**} \\ \hline
**Image** & **Channel r** & **Channel g** & **Channel b** & **Average** \\ \hline trailer & 30.53562 & 31.50471 & 32.48743 & 31.50925 \\ hallway & 35.46249 & 35.70703 & 37.18625 & 36.11859 \\ kids & 33.41342 & 33.73252 & 33.98569 & 33.71054 \\ llama & 32.72819 & 32.25344 & 32.39616 & 32.30926 \\ car & 31.82822 & 31.75158 & 31.79017 & 31.78999 \\ football & 31.67541 & 31.59554 & 31.27668 & 31.51588 \\ \hline \hline \multicolumn{4}{c}{**Compression Rate 0.8**} \\ \hline
**Image** & **Channel r** & **Channel g** & **Channel b** & **Average** \\ \hline trailer & 30.10448 & 30.32083 & 31.74121 & 30.72217 \\ hallway & 34.85190 & 35.30434 & 36.45469 & 35.53697 \\ kids & 32.72328 & 33.06132 & 33.37811 & 33.05727 \\ llama & 31.45512 & 31.51228 & 31.61599 & 31.52780 \\ car & 31.44679 & 31.26573 & 31.26015 & 31.32422 \\ football & 30.78486 & 30.87273 & 30.58413 & 30.74724 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PSNR values under compression rates 0.7 and 0.8 (Unit: dB). images and compression rates, all the PSNR values exceed 30 dB, which could be regarded as a benchmark for acceptable image reconstruction qualities. From Figure 4, we see that flexibility is achieved in terms of concealing portions and the number of concealed sections, and the related PSNR values in Table 2 all exceed 30 dB, which represents an acceptable quality of image reconstruction. Figure 3: The first column shows the original images named trailer, hallway, kids, llama, car, and football, respectively. The second column shows the reconstructed images obtained by restricted-authorized receivers under compression rate 0.7. The third column shows the reconstructed images obtained by full-authorized receivers under compression rate 0.7. The fourth column shows the reconstructed images obtained by restricted-authorized receivers under compression rate 0.8. The fifth column shows the reconstructed images obtained by full-authorized receivers under compression rate 0.8.
## 2 The Influence of Different Factors on Oral Microbiome
### Diseases
#### 2.2.2 Current Viral Infections  
Acute viral infections, such as the common cold or flu, can transiently disrupt the oral microbiome, leading to shifts in microbial composition that may impact oral health [72; 73]. These infections cause inflammation and immune responses that can alter the balance of microbial communities in the oral cavity. During a viral infection, the immune system's response to the virus often includes the production of cytokines and other inflammatory mediators, which can affect the oral environment. This inflammatory response can lead to changes in the microbial composition, such as an increase in opportunistic pathogens and a decrease in beneficial commensal bacteria. For example, the flu virus has been shown to increase the levels of pathogenic bacteria like _Streptococcus pneumoniae_ and _Haemophilus influenzae_ in the oral cavity, potentially leading to secondary bacterial infections, which may exacerbate inflammation and tissue damage in the oral cavity [74; 75; 76]. Furthermore, the common cold can result in alterations to the oral microbiome due to changes in salivary flow and composition, which are crucial for maintaining microbial homeostasis. Reduced salivary flow and the presence of inflammatory exudates can create a favorable environment for the growth of pathogenic microorganisms [77]. Although the effects of acute viral infections are typically short-lived, repeated or prolonged infections may predispose the oral mucosa to persistent inflammation, a recognized risk factor for OPMD and OSCC. Additionally, antiviral medications and symptomatic treatments for these infections can also influence the oral microbiome by altering the chemical environment in the mouth [78]. Changes in salivary pH and composition due to medication use may exacerbate microbial imbalances, indirectly contributing to conditions that favor the progression of OPMD to OSCC [79]. Chronic viral infections, such as those caused by herpes simplex virus (HSV) or HPV, can have more prolonged effects on the oral microbiome. These infections can lead to persistent inflammation and immune modulation, further disrupting the balance of microbial communities and promoting the growth of oncogenic microbial species, contributing to development of HNSCC [44; 51; 80].