esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital 3D Hologram Generation Using Spatial and Elevation Information
## 3 Results and Discussion  
### Printed Hologram Map  
The results obtained are presented in Figure 5.  
Earlier hologram maps focused on 3D visualization [16; 24; 26]. This experiment aimed to overcome a limitation identified in previous hologram maps by developing a new type that integrates both 2D and 3D features, enhancing measurement functionality and overall usability. A preview of our developed hologram map is available in Supplementary Video S1.  
Figure 5: View of the hologram map from various angles.  
The design allows viewers to access additional geographic information from a top-down perspective, simulating the experience of a traditional paper map. This technique is effective when the viewing angles are close to perpendicular to the map surface. At such angles, a three-dimensional object can be represented as a two-dimensional plane projection [33]. By incorporating two-dimensional scale data, the map facilitates a deviation of up to 10\({}^{\circ}\) from the perpendicular axis. Notably, it offers a 60\({}^{\circ}\) viewing angle on one side relative to the vertical axis, along with a 360\({}^{\circ}\) horizontal and a 120\({}^{\circ}\) vertical field of view. When viewed from above, this enables the visibility of additional geographic information, allowing for the measurement of distances between any two points using standard measuring instruments, akin to a 2D map. The scale is determined based on the dimensions of the selected area, the size of the printed map, and other relevant parameters. According to the scale of our map, 1 cm represents 220 m in the real world.
## 3 Duckweed as an Alternative Source of Nutrients and Bioactive Compounds
### Vitamins  
The available research suggests that duckweed, like many other plants, is capable of synthesising, and even absorbing and concentrating a variety of essential vitamins, such as vitamin C and vitamin E (especially topcuberol, which is crucial for human health), from its surrounding environment [77].  
According to multiple studies, duckweed has been found to provide a new natural plant source of vitamin B12 (cobalamin). The vitamin B12 content of duckweed can vary between 0.5 and 10 \(\upmu\)g/100 g dry matter [26; 29; 88].  
The B12 content of duckweed has a particular importance since, traditionally, vitamin B12 has been primarily obtained from animal-based sources, such as meat, eggs, and dairy products, and not from any plant-based materials, thus causing serious B12 deficiency for strict vegetarian individuals, which can be prevented by supplementation with duckweed. The increasing demand for plant-based and vegan diets has sparked interest in exploring alternative sources of this vital nutrient. [29; 57; 89; 90; 91]. Vitamin B12 is essential for the normal functioning of the brain and nervous system and is also involved in red blood cell production; fatty acid, DNA, and RNA synthesis; and in the production of cellular energy [92]. In cases of B12 deficiency, red blood cell counts can be critically reduced and anaemia can occur. This is the reason why B12 is especially known for its role in the treatment and prevention of pernicious anaemia [93]. To ensure that people get enough vitamin B12, the European Union has set a minimum daily intake of B12. The European Food Safety Authority (EFSA) recommends an adequate daily intake (AI) of vitamin B12 of 4 ug/day for the European population and 4.2 to 8.6 ug/day in some less developed countries [94].  
In early studies, it was assumed that duckweed, like other plants, could synthesise vitamin B12 through its own metabolic processes. However, recent studies have challenged this notion. Duckweed, being a simple flowering plant, does not possess the necessary biosynthetic pathways to produce vitamin B12, which is a complex molecule typically synthesised by certain bacteria and archaea [21; 24; 26; 42].
###### Abstract
In another example, [36] executed the 2 SFCA method using the GIS platform to identify and analyze spatial disparities in access to healthcare in Alwar district of Rajasthan. The results of study will help decision makers improve the road network and allocate future healthcare centers in the less-served areas in this district. Jeddah city has been witnessing a continuous increase in its population and urban growth. Globally, it has become one of the fastest growing urban areas due to its high population growth rate, which is about 3.5% annually [45]. According to the report of \"Global Metro Monitor 2011\", Jeddah is among the 200 fastest growing metropolitan areas, surpassed only by Shanghai [46]. The rapid population growth in Jeddah will naturally lead to an increase in the demand for various services and creating disparities in the access to such services [47]. One of the most important strategic goals of Saudi Vision 2030 is to facilitate access to healthcare services at the right time and place [48; 49]. Consequently, providing healthcare with equal access for all individuals is considered an important issue for the Saudi Ministry of Health and relevant decision makers. Hence, this study aims to identify and analyze spatial disparities in access to healthcare centers run by the Ministry of Health (MOH) in Jeddah city, Saudi Arabia. However, based on previous studies, this is the first study that applies the 2 SFCA method to measure GIS-based spatial accessibility of the MOH healthcare centers in Jeddah depending on the travel time (i.e., drive-time threshold). This will help identify and analyze the spatial access disparities to healthcare centers in Jeddah. The results of this study could be helpful to the local health planners in Jeddah through giving the less-served districts a priority when allocating future healthcare centers. That will hopefully improve equity of healthcare service access, thus improving the quality of life for the Jeddah population.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### Problem Motivation
#### 1.2.1 Lack of End-to-End Traceability  
Current systems rarely capture the whole journey of a coffee batch from production and harvesting to consumption [7]. For instance, coffee beans collected from multiple farms are usually pooled and repackaged without adequate tracking. When the beans are blended or repackaged, the ability to trace them back to a specific region, farm, or even harvest season is lost. This lack of granular tracking limits the ability of stakeholders to verify ethical sourcing claims, support fair trade certifications, and ensure compliance with quality assurance protocols.
## 4 Implementation Method for Improving Accuracy
### Filter of \(E_{1}\) and \(E_{2}\)  
\(\Delta t\) is an invariant constant; \(\tau_{1}\) and \(\tau_{2}\) vary with GC, so \(E_{1}\) and \(E_{2}\) are also variations. Counter \(k\) is increased once every measurement period \(T_{C}\), as shown in Figure 5. To make \(k\)th measurements \(E_{1}\) and \(E_{2}\) close to the actual value, the average value of \(M_{1}\) and \(M_{2}\) phases are taken according to Equation (6). \(E_{1}(k)\) and \(E_{2}(k)\) can be rewritten as  
\[\left\{\begin{array}{l}E_{1}(k)=(\frac{v_{11,1}(k)-v_{11,2}(k)}{v_{11,2}(k)-v _{11,3}(k)}+\frac{v_{12,2}(k)-v_{12,2}(k)}{v_{12,2}(k)-v_{12,3}(k)})/2\\ E_{2}(k)=(\frac{v_{21,1}(k)-v_{21,2}(k)}{v_{21,2}(k)-v_{21,3}(k)}+\frac{v_{22,1 }(k)-v_{22,2}(k)}{v_{22,3}(k)-v_{22,3}(k)})/2\\ \end{array}\right. \tag{14}\]  
The estimated value of the \(k\)th \(E_{1}\) and \(E_{2}\) is set as \(\hat{E}_{1}(k)\) and \(\hat{E}_{2}(k)\), respectively, which can be obtained by the following first-order filter, where A is a filter coefficient that satisfies \(0<\mathrm{A}<1\).  
\[\left\{\begin{array}{l}\hat{E}_{1}(k)=\mathrm{A}\hat{E}_{1}(k-1)+(1-\mathrm{ A})E_{1}(k)\\ \hat{E}_{2}(k)=\mathrm{A}\hat{E}_{2}(k-1)+(1-\mathrm{A})E_{2}(k)\\ \end{array}\right. \tag{15}\]
## 2 Concepts of Clustering Approaches
### Formation of a Cluster  
Cluster formation considers two stages, a cluster head election process, and nodes identification to the elected cluster heads. A cluster head organizes all the transmissions within the cluster. Thus, a cluster head deals with delivering the collected packets for the cluster, i.e., inter-cluster traffic [1].
# Resonant Asymmetrical Half-Bridge Flyback Converter
## 3 Design Considerations
### Design of Ideal \(D\) and \(T_{on}\)  
First, the ideal duty cycle is obtained as (69) and (70) without considering the leakage inductance \(L_{r}\). Then, the ideal \(V_{Cr(24V)}\) and \(V_{Cr(48V)}\) can be obtained as (71) and (72). The ideal values of \(T_{on}\) are shown in (73) and (74).  
\[D=\frac{T_{on}}{T_{on}+T_{off}}=\frac{V_{O}}{V_{in}\cdot\frac{N_{2}}{N_{1}}}, \tag{69}\]  
\[D_{(24V)}=\frac{2.5\;\mathrm{V}}{24\;\mathrm{V}\cdot\frac{N_{2}}{N_{1}}}=20.8 \%,\;D_{(48V)}=\frac{2.5\;\mathrm{V}}{48\;\mathrm{V}\cdot\frac{N_{2}}{N_{1}}}= 10.4\%, \tag{70}\]  
\[v_{Cr}=D\cdot V_{in}, \tag{71}\]  
\[V_{Cr(24V)}=D_{(24V)}\cdot V_{in}=5\;\mathrm{V},\;V_{Cr(48V)}=D_{(48V)}\cdot V _{in}=5\;\mathrm{V}, \tag{72}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(V_{in}\) & 24 V\(\cdot\)48 V \\ \hline \(V_{O}\) & 2.5 V \\ \hline \(I_{O}\) & Rated 8 A \\ \hline \(f_{sw}\) & 120 kHz\(\cdot\)180 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the prototype circuit.  
Figure 5: Circuit structure of the prototype circuit.  
Figure 6: Design flow chart.  
\[T_{on}=\frac{V_{O}}{f_{sw}\cdot V_{in}\cdot\left(\frac{N_{2}}{N_{1}}\right)} \tag{73}\]  
\[T_{on(24V)}=\frac{V_{O}}{120\;\mathrm{kHz}\cdot 24\;\mathrm{V}\cdot\left(\frac{N_{2 }}{N_{1}}\right)}=1.736\;\mathrm{\upmu s},\;T_{on(48V)}=\frac{V_{O}}{180\; \mathrm{kHz}\cdot 48V\cdot\left(\frac{N_{2}}{N_{1}}\right)}=0.579\;\mathrm{\upmu s}, \tag{74}\]
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.9 Sent Time of RTP Data Packet in System Time Clock Units  
Data packet's RTP timestamp is the instance when the packet was sampled. The NTP equivalent of this RTP timestamp can be calculated by correlating it with SR packet's media timestamp and NTP timestamp. Let us consider Figure 8 to understand this.  
Receiver observes the mapping between the media clock and reference clock as assigned by the sender. The mapping is conveyed in the periodic RTCP-SR packets. The nominal clock rate of media clock is known to both the sender and receiver by payload format. The receiver can easily calculate the reference clock capture time, once it has received an RTCP-SR packet. As shown in Figure 8. Reference clock capture time \(T_{s}\) for RTP packet can be calculated as:  
\[T_{s}=TS_{sr}+\left[\frac{M_{ts}-M_{sr}}{R_{mts}}\right] \tag{6}\]  
\(M_{ts}\) is the media timestamp of the data packet.  
\(M_{sr}\) is the media timestamp in last received RTCP SR packet.  
Figure 8: A detailed overview of the proposed system.  
\(TS_{sr}\) is the corresponding reference clock (NTP) timestamp.  
\(R_{mts}\) is nominal media timestamp clock rate (media clock ticks per second).
# Dynamic Characteristics Analysis of a Rod Fastening Rotor System Considering Contact Roughness
## 5 Conclusions  
For the rod fastening rotor system adopted in gas turbines, the influences of the joint's interface on the dynamic characteristics are investigated in this paper. Based on the fractal theory, the contact mechanism of the joint's interface is characterized by the analytical method and numerical simulation. According to the different deformation conditions of different asperities, the normal contact stiffness of the joint's interface includes three parts, namely elastic, elastic-plastic and plastic. After that, the influences of the joint parameters, such as fractal dimension, characteristic length scale and grade of asperities, on the contact stiffness of the joint's interface and the dynamic characteristics of the rod fastening rotor system are discussed in detail. Some conclusions are summarized as follows:  
1. For the rod fastening rotor system, its dynamic characteristics are different from that of a traditional integral rotor system due to the contact effects of the joint's interface.
2. The contact stiffness of the joint's interface is closely related to the grade of asperities and the fractal parameters. The contact stiffness increases with an increase in the fractal dimension and a decrease in the characteristic length scale, respectively.
3. For the rod fastening rotor, the roughness of the joint's interface can directly affect the dynamic characteristics and natural frequency of the system. Because the fractal parameters of the joint's interface are mainly determined by the contact roughness, the natural frequencies of the system are changed accordingly.
4. During the dynamic analysis of the rod fastening rotor system, both the structural stiffness of the flexible shaft and the contact stiffness of the joint's interface should be taken into consideration.  
Methodology, W.Z. and Y.Y.; writing--original draft preparation, W.Z., K.L., Y.G. and Y.Y.; review and editing, K.L., Y.G. and X.R.; supervision, X.R. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of HeiLongliang Province, Project No. LH2020A017.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Effects of the fractal dimension \(D\) and characteristic length scale \(G\) on the natural frequency of the rod fastening rotor system.
## 3 SDR Technology
### SDR Architecture
#### 3.1.1 SDR Transmission Mode  
During the transmission process, the baseband in-phase and quadrature (I/Q) samples are synthesized by the host computer and fed to the onboard FPGA of the SDR at a specified sampling rate. The FPGA interpolates the data to a higher sampling rate using a digital up-conversion (DUC) process. Thereafter, it converts the signal to the analog domain with a digital-to-analog converter (DAC). The signal will be mixed with a specified carrier frequency signal that is generated by a local oscillator (LO). After this stage, the signal is amplified and ready to be transmitted [19].
## 4 Results
### Sensitivity Analysis
#### 4.5.4 Effects of Joint Abatement Benefit Coefficients and Free-Riding Benefit Coefficients  
The evolution of agents' strategies with different joint abatement and free-rider benefit coefficients is shown in Figure 12. PBSC synergy is proportional to the joint abatement benefit coefficient and inversely proportional to the free-rider benefit coefficient; the more significant the synergy of the PBSC, the more the agent chooses the \"abatement\" strategy. Since the Planning and Designing Party (X) is at the top of the PBSC, it is complex to obtain benefits from downstream abatement agents by free-riding, and it is most sensitive to the response to synergistic capacity, there exists \(\beta_{Xi}^{*}\in[0.3,\ 0.5]\), when \(\beta_{Xi}\geq\beta_{Xi}^{*}\) and x converges to 1, otherwise it converges to 0.  
Since the free-rider benefit coefficient is set to be less than or equal to the joint abatement benefit coefficient, the strategy probabilities of downstream PBSC agents are more inclined to converge toward 1. The larger the difference between the two coefficients, the faster the convergence rate. However, Y and Z converge toward 0 when both synergizing over hitchhiking benefits are low and only begin to converge toward 1 later as the effects accrue. For Y, the inflection points of the two lines are (39, 0.32) (59, 0.31). For Z, the inflection points of the two lines are (55, 0.36) and (86, 0.36). Y is more sensitive to these two indicators. Since the Dismantling and Recycling Party (U) is at the end of the PBSC, it can obtain free-rider benefits from multiple upstream agents. When the joint abatement benefit coefficients and free-riding benefit coefficients are the same, the number of iterations for u to converge to 1 significantly increases to 232. Although the direction of convergence remains the same, the rate of evolution slows down significantly.  
Figure 11: Evolution pathways of the four parties under different absorption and transformation efficiencies. (**a**) Evolution pathway for X; (**b**) Evolution pathway for Y; (**c**) Evolution pathway for Z; (**d**) Evolution pathway for U.  
The free-riding phenomenon among agents in PBSC creates a mismatch between abatement responsibilities and benefits. The upstream abatement agent's cost is not shared, but the benefit is. When long-term synergy with other agents is not apparent, or other agents refuse to share abatement responsibilities, the upstream PBSC agent will gradually choose the \"no-abatement\" strategy. By then, the free-rider effect in PBSC will be profound, reducing the number of agents choosing \"abatement\" strategies. The gaming system will gradually evolve to the (0, 0, 0, 0) state, which is not conducive to the sustained implementation of abatement strategies.
## 3 Defect Recognition Using CNNs at Different PD Measurement Duration  
CNNs have demonstrated significant success in image recognition tasks, making them suitable for analyzing and classifying PRPD patterns [15; 17; 18; 19; 20]. The goal of this study was to enhance defect type recognition in epoxy resins using CNNs, particularly focusing on PRPD pattern images associated with three different void positions within the epoxy material; hence, the improvement of the CNN method was not and is supposed not to be included.  
The CNN architecture is particularly well-suited for image recognition owing to its inherent characteristics. CNNs are comprised of input, hidden, and output layers, with the hidden layers incorporating four convolutional layers, two max-pooling layers, and three rectified linear unit activation functions. The convolutional layers are responsible for learning local features within the input images using filters. The pooling layers then downsample the learned features, reduce the spatial dimensions, and extract the most relevant information. The activation functions introduce nonlinearity into the network, enabling it to capture complex patterns and make accurate predictions. The Adam optimizer was used to update the network-learnable parameters, and a maximum of 20 epochs were used for training. In addition, a cross-entropy method was used to adjust the weights during model training.  
PRPD pattern images were captured from epoxy resin samples containing void defects positioned at three different locations: top, center and bottom. These void positions were deliberately selected to represent different scenarios encountered in real-world applications. The PD data associated with each void position were recorded, and the corresponding PRPD pattern images were generated for defect type recognition.  
The images of the PRPD patterns of all three types of defects were stored and individually labeled as Void_Top, Void_Center, and Void_Bottom. All images considered for training the CNN had a size of 88 \(\times\) 66 px. This size was chosen because of its effectiveness and best performance when training and testing a CNN [17].
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Data Dissemination Protocol
#### 3.4.2 Message Format for a Data Dissemination Process  
Figure 16 shows the packet structure used in the data dissemination process proposed in this protocol. As described above, in the data dissemination process, the receiving node changes the source ID and source cluster ID fields. The receiving node inputs its own ID into the source ID field when retransmitting and inputs the ID of the cluster to which the node belongs in the current cluster ID field.  
Figure 16: PCDP Dissemination Message.  
Figure 14: Message Storage List.  
Figure 15: Forwarded Message Table.  
If the member finds a cluster that is different from the neighbor of the neighbor, it sends a cluster detect message as shown in Figure 17. If it is not the head of the sender, it drops it and the head rebroadcasts the saved message as described above.
## 4 Discussion
This means that the seaweed underwent significant colour changes, which could indicate changes in the biochemical composition of the biomasses submitted to different treatments, namely in the concentration of pigments [87]. Indeed, different culture media and distinct light qualities can significantly influence photosynthesis and pigment production in seaweeds [88]. The changes in \(\Delta\)E suggest a darkening of the biomass, which is due to the increase in the pigment content [89]. The changes in a* values suggest that the red seaweed grown under NB and blue LED light shifts towards the green spectrum, which may indicate a decrease in phycobilin content. As stated, the high nitrate content in the NB medium may cause nutrient imbalances, potentially affecting the production of phycobilins, and blue LED light might also cause stress, reducing the efficiency of light-harvesting pigment [76; 79]. As to the b* values, although not significant, their slight decrease indicates a shift towards the blue spectrum, probably due to an increase in the blue pigment phycocyanin registered in all samples compared to the wild. The chromaticity (C*) values decreased except for the blue LED light medium, for which the hue (\({}^{\prime}\)h) decreased. Phycobilisomes have an intensity-dependent photoprotection mechanism, so the production of phycobilins is light-dependent. Exposure to intense light increases the blue structural colour and simultaneously favours the capture of light at green and red wavelengths by the phycobilisomes [90]. On the other hand, less intense light tends to reduce the production of photoprotective pigments. The relatively stable hue angle values indicate that, despite the changes in lightness and chromaticity, the overall hue of the seaweed remains consistent. This suggests that the primary colour characteristics of the seaweed are maintained. Hence, white LED light appears to provide a broad spectrum that can support balanced growth and pigment production, while blue LED light shows the greatest colour shifts, indicating major variation in the chemical composition of the seaweeds, probably due to stress-induced conditions. These findings are consistent with the pigment content of the samples. There was a significant increase in the production of phycoerythrin and phycoocyanin, while chlorophyll \(a\) was higher in NB. These changes were induced by the availability of nutrients and by light intensity and wavelength, as stated by several authors, namely [PERSON] et al. [91], [PERSON] et al. [92], and [PERSON] [87]. Different wavelengths activate specific photosynthetic pathways, with red light primarily activating Photosystem I (PSI) while blue light activates both Photosystem I (PSI) and Photosystem II (PSII) [76]. The increase in phycoerythrin under red light and of phycocyanin under blue light optimized light absorption for these pathways.