esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Moreover, this study adds scientific evidence to support the traditional use of these extracts to relieve respiratory infection symptoms. In addition, a decline in immune function in older people has been attributed, at least in part, to an increase in oxidative stress [37]. Therefore, increases in dietary antioxidants, such as anthocyanins of elderberry and other phenolic compounds present in elderberry and reishi extracts, may contribute to improving immune function in the elderly. Indeed, both elderberry and reishi extracts have been confirmed to exert antioxidant properties during antiradical activity assays in vitro [38; 39]. Additionally, one study showed a significant increase in the total antioxidant capacity and total thiol concentration in the serum of a group of healthy subjects who consumed an elderberry infusion for 30 days with similar content of anthocyanins to our extract [40]. Although we did not analyze any antioxidant capacity biomarkers in our volunteers, we cannot discard a possible relationship between the reduction in the duration and severity of respiratory infections and the possible reduction in oxidative stress, as observed by other authors. In a crossover study performed in older adults, regular consumption of gold kiwifruit increased plasma antioxidants and reduced the duration and severity of some symptoms associated with respiratory infections [41]. However, further studies should be performed to test this hypothesis. Interestingly, the older adults who took the combination of extracts reported fewer sleep problems than those who took the placebo. This effect could be attributed to reishi, which has been traditionally used for insomnia treatment [16]. Indeed, a randomized controlled trial described that the polysaccharide extract of _Ganoderma lucidum_ improved the insomnia severity scores in patients with neurasthenia [42]. Moreover, _Ganoderma lucidum_ extract has been shown to increase total sleep time and nonrapid eye movement sleep time in animal studies [43; 44], with a probable mechanism linked to the modulation of cytokines such as tumor necrosis factor-\(\alpha\)[44]. One strength of the present study was the design as a double-blind randomized controlled trial controlled by placebo. This methodology allowed us to avoid bias related to confounding factors (through a control group), selection bias (through randomization), and interpretation bias (through double blinding). However, a limitation of the study was the small sample size, limited by the fact that this is a pilot study. Additionally, the diagnosis of flu was based on a confluence of symptoms rather than viral detection. Moreover, further studies in other population groups should be performed.
## 3 Results and Discussion
### Influence of Hinge Joint Damage on Lateral Load Distribution Factor  
According to the characteristics of the bridge, all damage cases of hinge joints are listed in Table 3. Here, the damage degree refers to the average damage degree of a hinge joint within the span of the bridge. According to the theory of load lateral distribution, the hinge joint damage will affect the load lateral distribution factor (LLDF), and then result in the change in the design value of live load for each slab segment. The reliability of each slab segment varies with the changes in the LLDF. Therefore, the influence of hinge joint damage on LLDF should be considered first.  
Figure 3: Geometry and cross section for the case study bridge (all dimensions are in m): (**a**) a side view of the bridge, (**b**) the cross–section of the bridge and (**c**) the detailed cross–section of a slab segment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Number** & **Damaged Hinge Joints** & **Damage Degrees** \\ \hline
1 & hinge joint 1 & \\
2 & hinge joint 2 & \\
3 & hinge joints 1 and 2 & \\
4 & hinge joints 1 and 3 & \\
5 & hinge joints 1 and 4 & \\
6 & hinge joints 2 and 3 & \\
7 & hinge joints 1, 2 and 3 & \\
8 & hinge joints 1, 2 and 4 & \\
9 & hinge joints 1, 2, 3 and 4 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Damage cases of hinge joints.  
Using the modified hinge plate method (MHPM) proposed by our previous research [28], the influence lines of load lateral distribution under different damage cases of hinge joints are analyzed. According to the most unfavorable loading position of live load, the LLDF of different cases are calculated as shown in Table 4. In order to visually analyze the influence of different damage cases on the LLDF of each slab segment, the range analysis of the LLDF is conducted and the results are shown in Figure 4.  
As can be seen from Table 4 and Figure 4, the hinge joint damage has an effect on each slab segment. The LLDF increases as the damage degree increases. Meanwhile, the range values of LLDF increase with the number of damaged hinge joints. The effect of damaged hinge joint 2 or 3 on LLDF is much greater than that of damaged hinge joint 1 or 4.
## 2 State of the Art
A dual-channel diplexer using linked open-loop microstrip resonators is proposed and investigated in the article [27] using an ADS simulator. Simulated results for this device indicate insertion loss values of 1.8 and 1.0 dB at 1 st and 2 nd loads, respectively. With the use of microstrip open-loop resonators and source-load coupling lines, 35 dB-isolated channel bands have been efficiently created. In article [28], the researchers have a definition for diplexers, which are dual-filter circuits with three ports, that allow for the simultaneous use of an antenna by two separate frequency channels. However, this technology can be used with several transmitters operating at different frequencies as long as each band is used for sending and receiving signals. SIR combined with UIR provides all the benefits of fractal filters with much simpler modeling and manufacturing requirements. Kappa substrate, which has a very low loss tangent value, can be employed in diplexer, triplexer, and quadruplexer components. This research paper describes an investigation of a highly narrowband microstrip diplexer that makes use of meander line resonators, tiny square patches, UIR, and SIR. An investigation into this diplexer was the purpose of the study that was carried out. In addition to having low profile topologies, and compact dimensions, efficient band isolation of 31 dB between frequency channels has been obtained.
## 3 Smart and Secure HIE Systems: SSHES
### Integrating AI and Blockchain into HIE Systems: Practical Challenges and Solutions
#### 3.6.8 Human Factors: Challenges and Solutions  
Similarly to implementation cost, human factors play a vital role facing the implementation of AI-blockchain systems in health sector. Some of the challenges along with the solutions are shown as follows:  
* Resistance to embrace new technologies: Healthcare professionals might be hesitant to adopt new technologies because they are comfortable with current systems or fear job displacement. It is a challenge to persuade stakeholders of the benefits and ensure a seamless transition without disrupting ongoing operations. This hesitation can delay the implementation process and reduce the overall efficiency of integrating AI-blockchain techniques into HIE systems. To overcome this challenge, health entities must engage their staff early in the process to gather feedback and address concerns. In addition, they may conduct workshops, seminars, and continuous training programs to educate staff about the benefits and functionalities of blockchain and AI.
* Technical skill deficiency: Unfortunately, the health entity may lack the skills required to effectively utilize and manage blockchain and AI technologies. One solution is to offer sufficient training and resources to build technical proficiency. Inadequate expertise can result in improper use of the system, potential security risks, and diminished effectiveness. To deal with this challenge, the health entity should develop comprehensive training modules covering the technical aspects of blockchain and AI. Additionally, they can provide hands-on training sessions and ongoing support to ensure proficiency.
* User-centered design: Complex interfaces and workflows may hinder user adoption and efficiency. Designing user-friendly interfaces and intuitive workflows that cater to the needs of healthcare professionals is a challenge. Furthermore, poor user experience can result in errors, inefficiency, and dissatisfaction among users. Hence, it is necessary to design the system with a focus on user experience, involving healthcare professionals in the design process to ensure that the interface is intuitive and meets their needs.
## 2 Materials and Methods
### DPPH Assay  
Antioxidant capacity of BCSO in different stages of oxidation and its extracts were determined using the stable 2,2-diphenyl-1-picryhydrazyl (DPPH) radicals. To determine the scavenging efficiency of DPPH, the traditional method was applied which includes the measurement of absorbance at 517 nm after 30 min of incubation. Then, 2 mL volume of various dilutions of the oil in ethyl acetate or their methanolic extracts (prepared from different amount of oils) were mixed with 0.2 mL of 0.001 M methanolic solution of DPPH. The absorbance at 517 nm was measured against a blank of pure solvent. The free radical scavenging activity of all solutions was then calculated as percentage of inhibition on the basis of the following equation:  
\[\%\;inhibition=(A-B)/A\times 100 \tag{2}\]  
where \(A=\) absorbance of control and \(B=\) absorbance of the sample.  
Antioxidant activities of tested samples were expressed as IC50, whose value represented the concentration of the compounds, that caused 50% inhibition. All experiments were carried out in triplicate. Trolox and \(\alpha\)-tocopherol were used as a control samples.
## 1 Introduction
### Summarizing the Structure  
This paper is organized into five chapters, as shown in Figure 1. Section 1 provides an introduction detailing the paper's background, purpose, and structure.  
Section 2 reviews prior research and methodologies necessary for developing this study's proposed ship route clustering algorithm. It also discusses the selection of the sea area and the collection of necessary data for applying the methodologies.  
Section 3 describes the development process of the ship route clustering algorithm. Using ship navigation data, the process includes data cleansing, measuring the similarity between ship routes, and dimensionality reduction, which form the three stages of data preprocessing. The performance of the preprocessed dataset is compared, and the method for parameter setting for DBSCAN cluster analysis is proposed. Additionally, the criteria for the sub-clustering of ship routes are presented.  
Section 4 applies the proposed ship route clustering algorithm to the investigated sea area.  
Finally, Section 5 presents the results and suggests directions for future research.  
Figure 1: Flowchart of the study.
## 4 Integration of Biosensors
### Server Setup  
Server setup in multi-hop IoT systems is a complex yet integral component meticulously designed to handle the intricacies of data flow from edge devices to the core. This comprehensive technical configuration encompasses several key aspects, where gateways are configured with MQTT or CoAP, facilitating efficient data transmission within the network. These protocols ensure seamless communication between edge devices and the designated servers [103; 104].  
High-speed wired or wireless connections are leveraged to optimize data transfer between gateways and servers. This ensures swift and reliable data ingestion, which is critical for real-time analytics and decision making [105].  
Upon receiving data, the server environment is tailored for optimal storage and organization. Databases like MongoDB are employed, providing an efficient structure to store diverse datasets generated by distributed sensor nodes [106].  
The server setup includes implementation of advanced analytics engines, utilizing powerful algorithms and ML models [107]. These engines unravel intricate patterns, trends, and anomalies within the incoming data, enabling meaningful insights.  
Robust security measures are integrated to fortify the integrity and confidentiality of processed data. This includes implementation of TLS/SSL for secure data transmission, access-control mechanisms, and intrusion detection systems [108]. Compliance with industry standards such as ISO/IEC 27001 and GDPR is ensured through continuous security audits using tools like Nessus.  
The server setup is designed to be scalable, accommodating the dynamic nature of incoming data in multi-hop IoT networks. This scalability ensures that the system can seamlessly handle a growing volume of data and devices [109]. Acting as the nerve center of the multi-hop IoT system, the server setup orchestrates a seamless flow of data from the edge to the core. The configuration is tailored to support efficient storage, advanced analytics, and fortified security protocols.  
The server setup is a pivotal element that establishes the foundation for informed decision making in multi-hop IoT systems. The technical configuration ensures a holistic approach to data management, from reception and storage to analysis and security, contributing to the overall effectiveness and reliability of the network.
## 2 Network Model
### The Mobility Model  
Nodes in DUANs are always moving continuously with water currents and sea waves etc. The mobility pattern of drifted node in DUANs is a complex but not totally random process. The pattern is in fact a 6 DOF (Degree of Freedom) motion including pitch, roll, yaw, pitch, roll, and heave. In addition, the mobility pattern also contains 2 parts, the low frequency part and the high frequency part. The low frequency part is the main portion of the movement caused by the second order wave force. The high frequency part is a reciprocating motion created by the first order wave force. The drifted movement is an overlay of these 2 motion parts as can be seen in Figure 2.  
Since the high frequency part is a reciprocating motion which is not the main portion for DUANs, in this paper, we only focus on the low frequency movement part which indicates the primary trajectory of the drifted node.  
Figure 1: The network architecture.  
Figure 2: The mobility model of drifted node.
###### Abstract  
Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1 DTempCNNs), one-dimensional spectral CNNs (1 DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1 DTempCNN and LSTM models, and spectral features using the 1 DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1 DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1 DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS.  
satellite image time series (SITS); land cover classification; remote sensing (RS); deep learning (DL); geospatial artificial intelligence (GeoAI); beehives; Quebec; Canada +
Footnote †: journal: Remote Sensing  
1 Laboratoire de Geosimulation Environnementale (LEDGE), Departement de Geographie, Universite de Montreal, 1375 Avenue Therese-Lavoie-Roux, Montreal, QC H2V 0B3, Canada; [EMAIL_ADDRESS]  
2 Nectar Technologies Inc., 6250 Rue Hutchison #302, Montreal, QC H2V 4C5, Canada; [EMAIL_ADDRESS]
## 2 Brief Review of Previous Occultation Mission and Orbit Design Studies
In the LEO-LEO occultation constellation design, the orbital parameters of both the Rx and Tx are unknown, which provides more free dimensionalities to optimize the occultation observation system but makes the constellation more complicated and difficult to design. On the other hand, LEO-LEO occultation is commonly considered to require many satellites to provide global coverage of the atmosphere, which leads to unaffordable costs, so there is currently no operational space-borne LEO-LEO constellation. Therefore, thenumber of studies on LEO-LEO occultation constellation design and evaluation, especially constellations with a large number of satellites, is less than that on GNSS RO. Specific simple LEO-LEO constellations with one pair or two pairs of Rx and Tx satellites for demonstration missions have been designed, in which the polar orbits are commonly used and the daily occultation event distribution along the latitude and longitude are illustrated by the use of maps and histograms [19]. Walker and 2D lattice flower constellations composed of a certain number of satellites for a LEO-LEO occultation mission have been proposed and evaluated [52].
# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 3 Methods
### Intensity Differential Image  
The intensity-differential image is created from the sigma-naught image by using the NL-means and median filter operations. Then, the image is used as the input layer of the ANN approach. The intensity-differential image is created by the following procedure:  
1. applying the NL-means filter to the sigma-naught image,
2. applying the median filter to the NL-means-filtered image,
3. subtracting the median-filtered image from the NL-means-filtered image.  
In this process, it is important to determine the optimal parameters of NL-means and median filters. The NL-means filter is designed to minimize noise effects in the ANN processing. That is, the reason why the NL-means filter is used is to reduce noise without smoothing object edges. Thus, the optimal parameters used for the NL-means filter should be determined by considering the noise reduction as well as the edge preservation. The median filter is used to remove ship objects. Thus, the kernel size of the median filter should be larger than the size of ship objects, because the median value should not come from ship-object pixels but sea-object pixels. If the filter is successful, we will not be able to see any ships in the median-filtered image. The median filter does not remove objects larger than ships, including fish farms and offshore bars, but it smooths the objects. If the larger objects disappear in the image, the ship false-detection might increase. Finally, the intensity-differential image can be created by subtracting the median-filtered image from the NL-means-filtered image. The background is almost zero in the intensity-differential image, and the brightness values of fish farms and offshore bars are low in the image. The intensity-differential image enhances ship targets, and hence it must increase the ship-detection probability.
## 2 Hybrid-Energy Micro Gird Modeling  
### Thermal System Modeling  
CHP modeling: A micro gas turbine CHP system has two kinds of operation models, namely the power load-based model and the heat load-based model. The former gives top priority for power load demand, while the latter firsts meet heat load demand. The CHP system is stable and economic, but the micro gas turbine (MT) has a strong ability to adapt to external load changes in the power load-based operation model. The mathematical model of the CHP system with a micro gas turbine can be expressed as follows [17]:  
\[\begin{cases}R_{MT}(u)=Q_{e}(u)(1-\eta_{e}(u)-\eta_{l})/\eta_{e}(u)\\ R_{he}(u)=R_{MT}(u)L_{he}\\ W_{MT}=\sum\left(Q_{e}(u)\Delta u\left/\left(\eta_{e}(u)\,M\right)\right.\right) \end{cases} \tag{1}\]  
where \(R_{MT}(u)\) is the gas turbine exhaust heat at time \(u\); \(\eta_{e}(u)\) is the power generation efficiency of the gas turbine at time \(u\); \(\eta_{l}\) is the heat loss coefficient of the gas turbine engine; \(Q_{e}(u)\) is the output of the electric power of the gas turbine at time u; \(R_{he}(u)\) is the heating capacity provided by the waste heat of blue gas from the gas turbine at time \(u\); \(L_{he}\) is the heat exchanger coefficient; \(W_{MT}\) is the consumption of natural gas for the gas turbine; \(\Delta u\) is the running time of the gas turbine; and \(M\) is the low calorific value of natural gas, taking a value of 9.7 kWh/m\({}^{3}\).  
Gas boiler modeling: The natural gas consumption of the gas boiler depends on the size of the thermal load with \(Q_{heat\text{-}load}\) and the thermal efficiency of the boiler with \(\eta_{boiler}\). The thermal efficiency is the key economic indicator in the design and operation of the boiler, which shows the improvement of boiler equipment technology and operation management level. In general, the thermal efficiency is taken as 0.88 [18]. Natural gas consumption \(W_{boiler}\) can be calculated as [18]:  
\[W_{boiler}=\frac{Q_{heat\text{-}load}}{\eta_{boiler}\cdot M} \tag{2}\]  
where \(M\) is the calorific value of natural gas.