esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we propose a weakly supervised video instance segmentation network MLGW-VIS that complements multi-scale deep semantic information to achieve accurate object localization and segmentation without mask labeling. Firstly, aiming at the problem of rough spatial information of high-level features and weak correlation with low-level features of FPN, MSL-FPN is proposed. SIR is used to extract rich spatial location information and multi-scale receptive field of low-level features, and it is added to high-level features to make up for the missing part of spatial location information. The correlation between the output features of FPN and the low-level feature set is enhanced. Secondly, GWM is added after the mask branch, and the mask features are divided into four groups and the weighted residual operation is performed on the features of each group so that each group of feature subspaces is separately mapped to supplement the depth semantic information and enhance the information interaction between channels in the subspace. Further, the mask loss part is modified by the projection loss and the color similarity loss so that the network can supervise the prediction mask only using the bounding box annotation and the semantic information of the image itself. Finally, an air cruiser platform is built to deploy the trained MLGW-VIS model for segmentation and localization of target instances. The experimental results show that MLGW-VIS is on par with mainstream fully supervised CNN-based networks in terms of segmentation accuracy and speed and has positive effects on improving the perception and localization accuracy in air cruiser application.
## 3 Large-Scale Triaxial Test on the Shear Failure Characteristics of a Soil-Rock Mixture
### Analyzing the Impact of the Breakage Index \(B_{c}\) on the Deformation Characteristics  
The dilatancy of a soil-rock mixture is described by the dilatancy rate, which is defined as, \(\zeta=d\varepsilon_{v}^{p}/d\varepsilon_{s}^{p}\), representing the ratio of the plastic volumetric strain increment to the plastic partial strain increment. The sample's volume exhibits a relative dilatation deformation trend (dilatancy rate \(d\varepsilon_{v}^{p}/d\varepsilon_{s}^{p}<0\)) during the shear process, known as dilatancy, as per the definition provided above. [PERSON] et al. [31] analyzed the triaxial CD test results of coarse-grained soil to compare the plastic strain increase ratio with the total strain increase ratio. They discovered that the dilatation ratio was minimally affected by elastic strain. Hence, the dilatation ratio of \(d\varepsilon_{v}^{p}/d\varepsilon_{s}^{p}\) can be represented as \(d\varepsilon_{v}/d\varepsilon_{s}\), and the general form of the dilatation equation was derived. \(d\varepsilon_{v}/d\varepsilon_{s}\) is utilized to characterize the dilatancy of soil-rock mixtures in this paper.  
The dilatancy of soil material is influenced by its initial physical and mechanical state, which refers to the change in soil volume due to shear [32]. The main focus of this paper is to study the impact of various dry densities on particle breakage characteristics. The dry density and initial void ratio exhibit a negative correlation. By transforming dry density into the initial void ratio, relevant data on dry density can be indirectly acquired. The failure dilatancy rate \(\zeta_{f}\) and particle breakage index \(B_{C}\) were plotted on a two-dimensional plane under various initial void ratios. As shown in Figure 14, under the same initial void ratio, the failure dilatancy rate gradually increases with increasing breakage index \(B_{C}\). This study indicates that the size of the soil-rock mixture decreases as the number of particle breakages increases. During shearing, the staggered tumbling amplitude of the particles decreases, resulting in a decrease in shear volume dilatation and a transition from shear dilatation to shear shrinkage.  
After fitting the scattered points, it was found that there is a strong power function correlation between the failure dilatancy rate and the particle breakage index [33], expressed as:  
\[\zeta_{f}=mB_{c}^{n} \tag{13}\]  
where \(m\) and \(n\) are parameters that are adjusted to fit the data.  
Figure 13: The relationship between fractal dimension \(D\) and fragmentation index \(B_{C}\).
# Frequency Selective Surfaces: A Review
## 5 Future Challenges and Potential Applications of FSSs
\begin{tabular}{l l l} \hline \hline
**Applications** & **Applications** \\ \hline \(\bullet\) & Dynamically reconfigurable FSSs & \(\bullet\) & Wearable sensing FSS \\ \hline \(\bullet\) & Tunable and software-defined FSSs & \(\bullet\) & Multi-functional FSSs \\ \hline \(\bullet\) & Mechanically tunable FSS & \(\bullet\) & Smart absorbers FSS \\ \hline \(\bullet\) & High performance tunable FSS Absorber & \(\bullet\) & Frequency selective rasorbers \\ \hline \(\bullet\) & Doubly curved FSS surface & \(\bullet\) & FSS for high-power radar applications \\ \hline \(\bullet\) & Integrated design of antenna-FSS system & \(\bullet\) & Graphene based FSS \\ \hline \(\bullet\) & 3D printed all-dielectric FSSs with wide BW & \(\bullet\) & FSS for wireless power transfer applications \\ \hline \(\bullet\) & Metamaterial inspired FSS for & \(\bullet\) & FSS for earth observation remote sensing and satellites \\ \hline \(\bullet\) & FSS for selective shielding of frequencies in critical places such as airport and military communication. & \(\bullet\) & FSS for isolating harmful \& unwanted radiation in hospitals, schools, domestic environments at microwave low frequency bands (L, S) \\ \hline \(\bullet\) & Chipless passive structural strength monitoring & \(\bullet\) & Switchable FSS based on plasma shells for aerodynamics \& terrestrial EM \\ \(\bullet\) & FSS based wireless sensor & & systems protection \\ \hline \hline \end{tabular}
# Design and Fabrication of a MEMS Bandpass Filter with Different Center Frequency of 8.5-12 GHz
## 2 Design and Simulation of RF MEMS Filter
### Microwave Performance  
This section presents the design process utilized for the size-dependent RF properties of the MEMS switch, including insertion loss, isolation, and actuation voltage. The simulation model of the RF MEMS switch is illustrated in Figure 6.  
First, a CPW line with the dimensions of W/S/W = 100 \(\upmu\)m/340 \(\upmu\)m/100 \(\upmu\)m was designed for operation in the 8.5-20 GHz range. Second, an impedance value of close to 50 \(\Omega\) was calculated using the LineCalc tool from ADS software (Advanced Design system 2014, Keysight Technologies, Santa Rosa, CA, USA). Finally, to improve the performance of the broadband RF MEMS switch, the switch dimensions for the \"off\" and \"on\" states were designed and optimized using High-Frequency Simulation Software (HFSS) version 13.0 for full-wave analysis. These dimensions included the length and width of the top electrode. In this study, the substrate material of the designed broadband MEMS switch was quartz glass, the dielectric layer was Si\({}_{3}\)N\({}_{4}\), and the other structures (including CPW, top electrode, actuation electrode, etc.) were all aluminum materials with high stability and less loss. It can be seen in Table 1 that the conductivity of aluminum is superior. Although the conductivity of metals such as gold and copper is slightly higher than that of aluminum, their prices are too expensive, so aluminum was selected as the main metal material in this study.  
As a unit structure in the filter, the filtering characteristics of a single RF MEMS switch and a single resonator are particularly critical. Therefore, before designing the multi-frequency adjustable bandpass filter, the performance of the filter cell structure should  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metal** & **Platinum** & **Aluminum** & **Gold** & **Tungsten** & **Copper** \\ \hline
**Resistivity (\(\Omega\)-m)** & 2.22 & 2.83 & 2.40 & 5.48 & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resistivity of some common metals.  
Figure 6: Simulation model of the RF MEMS filter.  
be designed and studied. The design model of the single-band bandpass filter based on the MEMS switch is shown in Figure 3. By optimizing the width of the filter, the center frequency was changed from 8.5 GHz to 12 GHz.  
In this study, the single-frequency bandpass filter was modeled in microwave simulation software Ansys HFSS15.0 (Ansys HFSS15.0, Ansys, Canonsburg, PA, USA), and its geometrical parameters were simulated and optimized. The results are shown in Figure 7, and the filtering performance is shown in Table 2.  
Figure 7: (**a**) S21 simulation results of the bandpass MEMS filter with different center frequency; (**b**) S11 simulation results of the bandpass MEMS filter for different widths of the top electrode.
# Time-Series Classification Based on Fusion Features of Sequence and Visualization
###### Abstract  
For the task of time-series data classification (TSC), some methods directly classify raw time-series (TS) data. However, certain sequence features are not evident in the time domain and the human brain can extract visual features based on visualization to classify data. Therefore, some researchers have converted TS data to image data and used image processing methods for TSC. While human perception consists of a combination of human senses from different aspects, existing methods only use sequence features or visualization features. Therefore, this paper proposes a framework for TSC based on fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features extracted from Area Graphs converted from TS. Deep learning methods have been proven to be useful tools for automatically learning features from data; therefore, we use long short-term memory with an attention mechanism (LSTM-A) to learn sequence features and a convolutional neural network with an attention mechanism (CNN-A) for visualization features, in order to imitate the human brain. In addition, we use the simplest visualization method of Area Graph for visualization features extraction, avoiding loss of information and additional computational cost. This article aims to prove that using deep neural networks to learn features from different aspects and fusing them can replace complex, artificially constructed features, as well as remove the bias due to manually designed features, in order to avoid the limitations of domain knowledge. Experiments on several open data sets show that the framework achieves promising results, compared with other methods.  
time series data; classification; fusion feature; visualization; area graph; attention +
Footnote †: journal: Applied Sciences
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Algorithm Complexity
#### 9.3.3 Position-Mixing Algorithm Complexity  
The position-mixing algorithm computation complexity consists of the position mixer selection and position exchange process between the vehicles. Let \(D_{n}\) be the distance calculation cost between vehicles and \(V_{n}\) be the number of vehicles taking part in this process. The computation cost of the position mixer selection process is \(O(D_{n}*V_{n})\). After the selection of the position mixer, the vehicle starts to exchange position coordinates with each other. Let \(PS_{n}\) be the position coordinates exchange between vehicles \(V_{n}\), then the time complexity for the position exchange process is \(O(PS_{n}*V_{n})\). The overall time complexity of the position-mixing algorithm is given below.  
\[\begin{split} Time\ complexity(Position\ mixing)=O(D_{n}*V_{n})+O(PS_{n}*V_{n})\\ =O(D_{n}+PS_{n})V_{n}\\ =O(D+PS)V(n)\\ =O(n)\end{split} \tag{64}\]
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 2 Mission Analysis
### Separation Mechanism Design Requirements  
The separation mechanism's primary purpose is to obtain approval from launch vehicle service providers by ensuring the launch vehicles' and other satellites' safety. The separation mechanism must meet all requirements imposed by launch vehicle providers and other satellite producers.  
First, in the event of the MF satellite's catastrophic failure, the separation mechanism must protect the launch vehicle and other satellites from the mechanical, electrical, or electromagnetic interference caused by the MF satellite. Second, the MF satellite must be released from the separation mechanism with as little spin as possible and with the least chance of colliding with a launch vehicle or other satellites. Finally, the separation mechanism must be adaptable to a wide variety of launch vehicles and satellites with minimal modification while maintaining a low weight [36].  
Figure 1: The MF satellite model. (a) Launch state; (b) On-orbit state.  
Figure 2: Exploded view of the MF satellite.  
Table 1 summarizes the design indexes for the separation mechanism that must be satisfied based on the mission requirements and satellite attributes. Each of the separation mechanism's dimensions, mass, separation velocity, separation angular velocity, and protrusion storage capacity must be taken into account when making the design of the separation mechanism.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 4 Linking Genetics and Imaging
### Future Directions in Genetic and Imaging Integration  
Advances in molecular imaging and next-generation sequencing (NGS) provide an opportunity to combine genetic and imaging biomarkers for risk stratification. Practically, this can be achieved by integrating genetic screening results with imaging-derived parameters into diagnostic algorithms or predictive models. For instance, patients identified with NOTCH1 or SMAD6 mutations can be prioritized for advanced imaging techniques such as 4D Flow MRI to quantify wall shear stress (WSS) and flow abnormalities. Combining these findings into machine learning-based risk stratification tools could allow earlier identification of high-risk patients, enabling timely interventions such as surgical planning or close monitoring. Additionally, predictive models incorporating genetic and imaging data can inform personalized management strategies, bridging the gap between research insights and clinical practice. Genetic screening for mutations such as NOTCH1, GATA, andSMAD6, combined with imaging-derived parameters like WSS, flow helicity, and energy loss, can help identify high-risk BAV phenotypes early. Emerging techniques, including elastography and molecular imaging, can further integrate with genetic findings to provide a personalized approach to BAV management [54; 55; 56].  
This integrative approach not only improves diagnostic accuracy but also aids in monitoring disease progression and optimizing surgical planning. Future studies are needed to validate these combined methodologies and explore their clinical utility in larger BAV cohorts. Specific research directions could include longitudinal studies that track patients with known genetic mutations such as NOTCH1 and SMAD6, correlating these findings with imaging biomarkers like WSS, flow eccentricity, and aortic dimensions over time. Additionally, multi-center trials could focus on the development of integrative diagnostic algorithms combining genetic sequencing and imaging data to improve early detection and risk stratification. The use of artificial intelligence and machine learning models should be explored to identify patterns and predict outcomes based on genetic and imaging parameters. Clinically, integrating genetic screening with imaging workflows could streamline personalized treatment pathways, enabling earlier interventions for high-risk patients and optimizing surgical planning. Emphasizing the combined utility of genetic data and imaging-derived parameters will pave the way for more comprehensive, individualized approaches to managing BAV and its associated aortopathy.
# Impact of Cryogenic Condition and Drill Diameter on Drilling Performance of CFRP
## 3 Results and Discussion
### Evaluation of Thrust Force and Delamination Factor
Figure 5: The effect of drill diameter, feed rate, dry and cryogenic conditions on the delamination factor. and 6 mm drill. Thrust force and delamination were measured for every 36 holes and Figure 6 was created from the measurements. As shown in the figure, the thrust force and delamination factor increased with increasing hole numbers. In addition, larger thrust forces and delamination factors occurred under cryogenic conditions when compared to dry conditions.
###### Abstract
Most of the resource allocation algorithms in the aforementioned papers use the link length as the link weight, and carry out the routing allocation with the Dijkstra algorithm, while other schemes dynamically allocate resources for the security requirements of different service requests. Nevertheless, the quantum key generation rate is low, and the wavelength channels for data transmission are limited. We need to consider resource surplus status in QKDON. In addition, multiple service requests may arrive at the same time under practical application scenarios. These services compete for wavelength resources as they occupy the same link, resulting in service blocking. This paper reconstructs the routing scheme and proposes a resource-adaptive algorithm with wavelength conflicts (RAWC). Firstly, the number of real-time network resources available is taken as an important factor in the scheme to achieve network load balancing. Then, we introduce the wavelength conflict mechanism to determine the communication path, so that the communication success rate is improved by minimizing wavelength competition. The rest of this paper is organized as follows. Section 2 introduces the point-to-point QKD mechanism. Section 3 shows the QKD relay method used. Section 4 describes the SDN-based QKP technique. Section 5 presents the problems and application scenarios addressed by the scheme. Section 6 discusses the proposed RAWC scheme and algorithm. Section 7 shows and analyzes the simulation results. Section 8 discusses several issues that can be explored in future work. Finally, Section 9 concludes this paper.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
###### Abstract  
Background: [PERSON]'s disease (AD), a neurodegenerative disorder characterized by cognitive decline, has been linked to systemic inflammation. Periodontal disease (PD), a chronic inflammatory condition, may contribute to neurodegeneration via microbial dysbiosis and inflammatory pathways. This systematic review explores the potential association between PD and AD, emphasizing microbial and systemic mechanisms. Materials and Methods: Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science for studies published between 2015 and 2024. The boolean keywords \"Alzheimer\" AND \"parodont\" were used. The inclusion criteria focused on human studies evaluating salivary and blood biomarkers, as well as periodontal therapies. Data extraction adhered to the PICO framework, assessing study design, outcomes, and quality using the ROBINS-I tool (original 2016 version), as provided by the Cochrane Bias Methods Group. Results: Out of the 1244 articles screened, 19 studies met the inclusion criteria. Evidence indicates that periodontal pathogens, such as _Porphyomonas gingivalis_, promote neuroinflammation, amyloid-\(\beta\) aggregation, and brain atrophy. Elevated inflammatory markers and oral dysbiosis correlated with increased AD risk. Periodontal treatment demonstrated benefits in reducing systemic inflammation and stabilizing cognitive decline. Conclusion: The findings suggest a strong link between PD and AD through systemic inflammation and microbial invasion. Maintaining oral health may serve as a preventive strategy against cognitive decline, underscoring the need for integrated medical-dental care and further longitudinal research.  
Alzheimer's disease; periodontal disease; neuroinflammation; microbial dysbiosis; systemic inflammation; cognitive decline; oral microbiome licenses/by/4.0/.
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 3 The LoRaUAV System
### Architecture  
The architecture of LoRaUAV consists of a two-layer system: the first layer is composed of GNs that transmit data using LoRaWAN, while the second layer is composed of a swarm of relay drones communicating over an WiFi ad hoc network. LoRaUAV is composed of three entities: GNs, UAVs and a BS. GNs are equipped with devices that aggregate data coming from various sensors and transmit it using a LoRaWAN module. The UAVs must be equipped with all the necessary sensors, controls and software for navigation and stabilization, e.g., speed meters and accelerometers. We assume that drones already come with all the necessary components and with a software API to interact with them. The UAVs considered in this work are multi-rotors able to hover over a location or target. The UAVs carry a module that integrates a LoRaWAN Gateway (GW) chip and a WiFi chip supporting ad hoc communications. End-to-end packet delivery over WiFi is accomplished thanks to a traditional TCP/IP stack, with the assistance of a proactive MANET routing protocol. The module also containsa relay application, responsible for adapting LoRaWAN packets received by the GW, and sending them over the ad hoc network to the destination. The most important part of the system is the VSF distributed mobility algorithm, responsible for planning the movements of the drone according to virtual spring forces on the basis of parameters collected from the GPS, routing tables and messages exchanged with GNs and other UAVs. Therefore, the algorithm needs to interface with almost all UAV systems. The BS contains the same WiFi equipment as a UAV, runs the same MANET routing protocol and integrates a LoRaWAN Network Server (NS) to manage the network. Other LoRaWAN entities (Application Servers (ASs) and Join Server (JS)) can be placed anywhere provided that a form of network connectivity is available (e.g., satellite or terrestrial link).