esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Main Implications and Future Perspectives
#### 3.5.2 Future Perspectives  
Further Process Optimisation:  
This study points to future opportunities for enhancing the extraction process through the optimisation of variables such as solvent composition, temperature, and time. This could further improve yields, reduce energy consumption, and lower costs, making polyphenol extraction from grape marc more efficient and commercially viable.  
Exploration of Other Agricultural By-Products:  
While this study focuses on grape marc, similar processes could be applied to other agricultural by-products, such as citrus peels, olive pomace, or apple cores. Expanding the scope of such research could unlock additional sources of bioactive compounds, contributing to a broader range of value-added products.  
Technological Advancements:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Water** & **Hydroalcoholic Solution** \\ \hline
**Total phenols** & 181 \(\pm\) 2 & 280 \(\pm\) 2 \\
**(mg/L of A. gallic)** & & \\ \hline
**Antioxidant activity** & 1.02 \(\pm\) 0.07 & 1.92 \(\pm\) 0.05 \\
**(\(\mu\)mol TEAC/mL of extract)** & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total phenols (mg/L of _A. gallic_) and antioxidant activity (\(\mu\)mol TEAC/mL of extract) of the powder dissolved in water and hydroalcoholic solution (50%).  
As technology continues to evolve, more advanced techniques such as supercritical fluid extraction, ultrasound-assisted extraction, or membrane filtration could be integrated into the process to enhance selectivity, reduce solvent use, and improve extraction efficiency.  
Integration with Sustainable Supply Chains:  
These findings have a strong potential for integration into sustainable supply chains, where bioactive compounds can be recovered from agri-food waste and incorporated into various products in the food, health, and cosmetic industries. This could lead to more sustainable practices in manufacturing and distribution, reducing the environmental footprint of agri-food industries.  
Scaling Up to Industrial Levels:  
The pilot plant demonstration is an important step, but scaling this technology to an industrial level will require addressing challenges such as the consistency of raw material quality, the standardisation of extraction processes, and optimising the economics of large-scale operations. Success in these areas could pave the way for the widespread adoption of this technology.  
Regulatory and Market Considerations:  
The development of value-added products like polyphenol-rich powders will also require compliance with food safety regulations and market acceptance. Future work could include studying consumer preferences, conducting safety assessments, and navigating regulatory frameworks to ensure that the products can be safely introduced to the market.
## 4 Implementation of the Proposed UniChain System
### Generating, Verifying and Appending a New Block  
The process of generating a new block begins by selecting the university that is responsible for performing this computational task. Based on the degree that each university node owns, the selection process is performed. According to the selection method in the proposed incentive mechanism, universities with more degrees maintain more academic records or records with higher values. Thus, they are less likely to be selected. The NC assigns this task to the university with the lowest degree among other universities in the system.  
Assume that University Node A is selected for the task of generating a new block. University A sends a request to the LC. The LC forwards the request to the NC to verify that University A is an authorized university on the system and it is selected for performing the task. The NC returns a verification to the LC. Upon receiving the verification, the LC sends all encrypted Logs whose status is \"new log\" to University A. After collecting all logs, University A creates a new block that includes all logs, broadcasts to the blockchain network about the new block and calls for verification.  
Each involved university node verifies its logs in the new block. Each involved node decrypts the log with its private key, computes the hash value of the log after decryption and compares the computed hash with the value stored in its DB Manager. Each node then sends a signed proof to the University Node A. Upon receiving all the signed proofs, University A notifies the NC to update the degree of University A by adding the incentive value c to its current degree. According to the proposed incentive mechanism, the university that will be chosen to generate a new block will get an incentive c as a reward upon successfully verifying the block by other universities. The value of c depends on the size of the blockchain network and the distribution of universities degrees. Thus, c will be defined as a fraction of the average degrees in the network. University A then broadcasts to all universities to append the new block. After appending the new block, the LC automatically updates the status field for all logs which are added to the chain as \"appended\" and the last_update field. The process of generating, verifying, and appending a new block is summarized in Figure 6.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 3 Mathematical Models
### Particle Collision Model  
It is of significance to establish the model that describes the contact between particles. The contact force, \(\mathbf{F}_{c,ij}\), including the elastic force and the dissipation force, is composed of the normal stiffness force, the normal damping force, the tangential stiffness force, and the tangential damping force:  
\[\mathbf{F}_{c,ij}=\mathbf{F}_{cn,ij}+\mathbf{F}_{ct,ij}=\mathbf{F}_{cn,ij}^{s}+\mathbf{F}_{cn,ij}^{d }+\mathbf{F}_{ct,ij}^{e}+\mathbf{F}_{ct,ij}^{d} \tag{15}\]  
The Hertz-Mindlin contact model features high accuracy and high efficiency and was employed in the present study [22]. The forces in this model are expressed as:  
\[\mathbf{F}_{cn,ij}=\frac{4}{3}E_{ij}^{*}\sqrt{R^{*}}\delta_{n}^{\frac{3}{2}} \tag{16}\]  
\[\mathbf{F}_{ct,ij}=-S_{t,ij}\delta_{t,ij} \tag{17}\]  
\[\mathbf{F}_{cn,ij}^{d}=-2\sqrt{\frac{5}{6}}\beta\sqrt{S_{n,ij}m_{ij}^{*}}v_{n,ij} ^{rel} \tag{18}\]  
\[\mathbf{F}_{ct,ij}^{d}=-2\sqrt{\frac{5}{6}}\beta\sqrt{S_{t,ij}m_{ij}^{*}}v_{t,ij} ^{rel} \tag{19}\]  
Taking rolling friction into consideration by applying a torque onto the contacting surfaces yields:  
\[\mathbf{\tau}_{i}=-\mu_{r}F_{n}R_{i}\mathbf{\omega}_{i} \tag{20}\]  
where \(E^{*}\) is the equivalent Young's modulus (Pa), \(R^{*}\) is the equivalent radius (m), \(\delta_{n}\) represents the normal overlap (m\({}^{3}\)), \(m^{*}\) is the equivalent mass (kg), \(\beta\) is the coefficient of restitution, \(S_{n}\) is the normal stiffness (N/m\({}^{2}\)), \(v_{n,ij}^{rel}\) is the normal component of the relative velocity (m/s), \(S_{t}\) is the tangential stiffness (N/m\({}^{2}\)), \(\delta_{t}\) denotes the tangential overlap (m\({}^{3}\)), \(v_{t,ij}^{rel}\) is the relative tangential velocity (m/s), \(\mu_{r}\) is the coefficient of rolling friction, \(F_{n}\) is the normal force (N), \(R_{i}\) is the distance of the contact point from the center of mass (m), and \(\mathbf{\omega}_{i}\) is the angular velocity of particle \(i\) at the contact point (rad/s).
## 3 Experiments and Simulations  
### Materials  
Figure 3 illustrates the structure of a typical Nomex honeycomb sandwich structure (diameter = 30 mm). As shown in Figure 3a, Nomex honeycomb sandwich structures are composed of three layers: two face sheets and the honeycomb core. The two face sheets were glued to the sides of the honeycomb core using a modified acrylate adhesive. The face sheets in the sandwich were constructed from 6061 aluminium alloy plate (with a density of 2700 kg/m\({}^{3}\), Poisson's ratio of 0.33, and thickness of \(h_{f}\)).  
The honeycomb cells in the core have a regular hexagonal structure and are made of very thin Nomex paper, as shown in Figure 3b. The static mass density of the honeycomb structure corresponds to \(\rho_{e}=48\) kg/m\({}^{3}\), the density of Nomex paper is 1100 kg/m\({}^{3}\), and the Poisson's ratio is 0.33. The parameters describing the dimensions of the honeycomb cells are the thickness of the wall \(t=0.07\) mm, the wall angle (\(\beta=30^{\circ}\)), and the length of the sides of the cells (\(l\)). The symbol \(h_{c}\) is used to denote the thickness of the honeycomb core.  
Four basic groups of specimen structures were used in the experiments. Overall, there were honeycomb cells with two different specifications, structures with face sheets of different thickness, structures with cores of different thickness, and single- and double-layered honeycomb sandwich structures. The parameters and types of the structures used are shown in more detail in Figure 3 and Table 1.  
Figure 3: Diagrams showing the composition of a honeycomb sandwich structure: (**a**) the structure of the sandwich structure, and (**b**) a unit cell of the Nomex honeycomb.  
Where D\({}_{2}\) is a double-layer Nomex honeycomb sandwich panel formed by stacking two honeycomb sandwich panels of 20 mm thickness, with an aluminium panel of 1 mm thickness in the middle.
## 2 Effects of Wind and Earthquake Excitations on Wind Turbine Tower
### Wind Load Applied to the PCSH Wind Turbine Towers  
#### 2.1.1 Aerodynamic Load Determination  
Due to differences in wind pressure, the aerodynamic wind load applied on the top of a wind turbine tower is usually calculated under four different working conditions, including the annual average wind speed, nominal wind speed, cut-out wind speed, and extreme wind speed [25]. The aerodynamic load can be determined by the following equations [26; 27]:  
\[F_{1}=C_{p}\rho V_{a}{}^{2}\pi R^{2} \tag{1}\]  
\[F_{2}=C_{p}\rho V_{n}{}^{2}\pi R^{2} \tag{2}\]  
\[F_{3}=0.4C_{p}\rho V_{c}{}^{2}\pi R^{2} \tag{3}\]  
\[F_{4}=0.5C_{t}\rho V_{e}{}^{2}A \tag{4}\]  
\[\rho=0.00125e^{-0.0001z^{3}} \tag{5}\]  
where \(F_{1}\) is the wind load under the annual average wind speed, \(C_{p}\) is the wind energy utilization coefficient which can take the value of 4/9 for an ideal wind turbine but 0.4 is chosen for the PCSH wind turbine tower in this study, \(\rho\) is the density of air, \(V_{a}\) is the annual average wind speed, \(R\) is the impeller radius of the wind turbine, \(F_{2}\) is the wind load applied to the turbine under the nominal wind speed, \(V_{n}\) is the nominal wind speed, \(F_{3}\) is the wind load under the cut-out wind speed, \(V_{c}\) is the cut-out wind speed, \(F_{4}\) is the wind load under the strongest wind speed in 50 years, the drag coefficient \(C_{t}=1.6\), \(V_{e}\) is the extreme wind speed, \(A\) is the projection of the blades in the plane perpendicular to the direction of the wind, and \(z\) is the height.
## 3 Proposed Method
### Double Bit Quantization (DBQ)  
To generate the candidate list nearest neighbor searching plays an essential role in computer vision and retrieval information. Though several nearest neighbor searching approaches are proposed previously, however, this traditional approach requires higher computation while it scans all the data point. To solve this kind of issue many researchers proposed the nearest neighbor searching technique based on hashing approach. In recent days, most of the researchers proposed single-bit quantization process where each projected dimension place on the single bit with a threshold value. However, in single-bit operation threshold typically lies on the highest density point place and also neighboring value near threshold represent totally dissimilar value. To resolve this kind of issue, we utilized Double-bit Quantization (DBQ) [9] based hashing technique. The main idea of DBQ is to quantize each projected dimension into double bits with adaptively learned thresholds and it enables the real value to be represented by four different binary codes.
# On the Capacity of Optical Backbone Networks
## 3 Channel Capacity
### Capacity of an Optical Channel
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Symbols** & **SE** & \(C_{ch}\) & **Reach (km)** & **Reach (km)** \\
**(M)** & **(bit/s/Hz)** & **(Gbit/s)** & \(L_{s}\) = 80 km** & \(L_{s}\) = 100 km \\ \hline
4 & 4 & 256 & 15,100 & 9500 \\
16 & 8 & 512 & 3020 & 1900 \\
64 & 12 & 768 & 720 & 450 \\
128 & 14 & 896 & 360 & 225 \\
256 & 16 & 1024 & 180 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of SE related reach as a function of the number of symbols for a PM-MQAM (@\(R_{s}\) = 64 Gbaud). can be compensated for by doubling the channel power, ensuring that the reach remains unchanged. A more rigorous analysis of the impact of NLI noise has shown that achieving total reach compensation is unattainable, and in reality, there is an 8% reduction in reach when duplicating the symbol rate (see Figure 2 in [28]). These trends suggest that the optimal strategy for achieving greater optical channel capacities, especially in long-haul networks, is to prioritize increasing symbol rates rather than focusing primarily on spectral efficiencies. Of course, the increase in the symbol rates comes at the cost of requiring larger channel bandwidths, which, in turn, implies a reduction in the number of channels in DWDM transmission. Furthermore, higher symbol rates come at the cost of higher power dissipation rates in the application-specific integrated circuits (ASIC) used in the BVTs [29]. It is important to note that the increase in symbol rates is a current active area of research, with numerous experimental demonstrations yielding results ranging from 100 to 200 Gbaud [30; 31; 32].
# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 3 Implementation Based on PER-D2 MQN Algorithm
### Duel DQN Structure Model  
For the bootstrap-based DQN algorithm, the estimation of the state value is crucial for each state. Duel DQN introduces a competitive network structure to update the values of all actions at the same time when the Q value is updated. More precisely, the Q value can be split into two components: the state value and the action advantage, as shown in Equation (2):  
\[Q(s,a;\theta,\alpha,\beta)=V(s;\theta,\beta)+A(s,a;\theta,\alpha) \tag{2}\]  
Centering is required due to the uncertainty in the advantage function \(A(s,a;\theta,\alpha)\) and the combination of the state value function \(V(s;\theta,\beta)\), which may lead to unstable Q-value estimates. The centering process ensures uniqueness and stability by subtracting the mean of the dominance function. The adjusted Q-value function is represented as shown in Equation (3):  
\[Q(s,a;\theta,\alpha,\beta)=V(s;\theta,\beta)+\left(A(s,a;\theta,\alpha)-\frac {1}{|A|}\sum_{a^{\prime}}A(s,a^{\prime};\theta,\alpha)\right) \tag{3}\]  
where \(s\) represents the state, \(a\) represents the action, \(a\) and \(\beta\) are the specific parameters of the advantage function \(A(s,a;\theta,\alpha)\) and the state value function \(V(s;\theta,\beta)\), respectively, \(\theta\) is a shared network parameter, and \(A\) is the size of the action space. The experiment in the reference [11] proved that this more frequent value updating method enhances the convergence speed and learning efficiency of DQN.  
The Duel DQN structure used by the PER-D2 MQN algorithm is shown in Figure 3, Its main structure comprises an input layer, three hidden layers, and an output layer. The input layer corresponds to 28 state sets, the first two hidden layers are fully connected layers, containing 64 and 128 units, respectively, and the third hidden layer is the main feature of the Duel DQN structure, which is divided into a value function consisting of one unit and an advantage function consisting of five units of action sets.  
Figure 3: The network structure of the adopted Duel DQN.
## 3 Results and Discussion
### Communication Overhead
#### 3.2.2 Private Key  
Figure 10 shows that private key size increases along with the increase in attributes in all the approaches. However, in LIBAC and the proposed MCP-ABE-BG, the private key size is minimal as compared the MCP-ABE-BG approach. Especially notable, the proposed method's private key size is four times smaller than the MCP-ABE-BG key size for 100 attributes. This shows that the bloom filter helps to reduce the key size effectively.
## 1 Introduction  
Growing nitrate pollution has become a common environmental problem worldwide [1]. Rivers are important channels which connect the nitrogen pools of mainland and ocean. The ever-increasing nutrient substances in rivers are the key changing factors of the global water quality and biodiversity. The data from the Global Environmental Monitoring System shows that the nitrate concentrations are seven times as high as the allowable maximum pollutant concentration (45 mg/L) in most rivers [2; 3]. The excessive amount of nitrate in surface water not only results in the eutrophication of water body but also poses a direct threat to human health [4]. Therefore, it is of significance to identify the sources of nitrate, and analyze the nitrate transformation mechanism for water environmental protection.  
The nitrate sources of surface water are diversified and mainly include precipitation, mineralization of soil nitrogen, farmland fertilization, septic tank emissions, manure, domestic wastewater, and industrial sewage [5]. The human activity, hydrologic process, and migration and transformation mechanisms of nitrate vary within different land uses [6]. The major source of nitrate pollution is generally determined by the geographic position, scale, land-use type, and hydrological conditions of the watershed. Because the nitrogen isotopic compositions of nitrate and the fractionation mechanisms of nitrogenous substances are different, the value of \(\delta^{15}\)N has been used to identify nitrate sources since the 1970s [7]. The continuous progress of source tracing technology has gone through four stages, i.e., source apportionment using \(\delta^{15}\)N, source apportionment using \(\delta^{15}\)N and \(\delta^{18}\)O, source apportionment using multi-isotopes, and research of mathematical models. However, a global database of \(\delta^{15}\)N and \(\delta^{18}\)O is lacking in previous researches. The comprehensive analysis on the transformation mechanism of nitrogen is also not clear. Moreover, the degree of isotopic fractionation is difficult to quantify. Until now, there is no review that systematically summarizes present limitations and future prospects of application of nitrogen and oxygen isotopes. Thus, the present work analyzed the composition characteristics of \(\delta^{15}\)N and \(\delta^{18}\)O values of various sources and the influencing factors. The application cases of stable isotope technique under different land-use types and basin conditions were also summarized. This review would provide a theoretical basis for the control of nitrate pollution in surface water.  
Fundamental Principle of Using Nitrogen and Oxygen Stable Isotopes to Identify the Pollution Source and Fate of Nitrate of Surface Water
## 8 Conclusions  
A local pre-rerouting algorithm (LPRA) is proposed in this paper, which solves the routing problem during sun outage of inter-satellite links in LEO satellite networks, and improves the load-balancing capabilities of LEO satellite networks. Considering the characteristics of LEO satellite networks, our proposed algorithm only collects link information within a limited area and performs centralized routing calculations. LPRA collects local routing information and traffic information in links that are about to fail before sun outage occurs, and calculates separate paths for each different pair of satellite source and destination nodes so that the traffic can be redistributed before failure. After the occurrence of sun outage, these services will be routed and forwarded according to the pre-calculated path. In addition, in order to optimize the uplink path of traffic from ground stations during sun outage, an algorithm for uplink satellite reselection based on feeder link load and minimum hop count is developed, and the scalability of this algorithm in multi-layer satellite networks and large-scale satellite networks is discussed. Simulation results show  
Figure 13: (**a**) Load-balancing capabilities under low average link load; (**b**) load-balancing capabilities under high average link load.  
that the algorithm proposed in this paper is superior to existing algorithms in terms of its packet loss rate and load-balancing capabilities. When the sun outage duration is long, the delay performance advantage is not clear. When the link load is high, the delay performance decreases seriously. This result is consistent with the idea that the algorithm in this paper sacrifices part of the delay performance in exchange for throughput performance. In subsequent research, the selection of alternative paths can be further optimized to improve the delay performance of the algorithm.  
Conceptualization, J.J.; methodology, J.J.; software, J.J. and Z.Y.; validation, G.L. and L.S.; formal analysis, J.J.; investigation, J.J.; resources, G.L. and L.S.; data curation, J.J.; writing--original draft preparation, J.J.; writing--review and editing, G.L., L.S. and H.W.; visualization, J.J. and H.W.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National special support plan for high-level talents (grant number WRJH19 DH01).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by the Shanghai Yuanxin Satellite Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.