esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results
### Simulation Results for the IEEE 14-Bus Test System
As the optimization results are scattered as grey dots in Figure 6, we can define the droop coefficients of LCC and VSC HVDCs--\(R_{LCC}\) and \(R_{VSC}\), respectively--like their corresponding lines. Using Equation (15), we can derive the final droop coefficient values as \(R_{LCC}=0.227\) and \(R_{VSC}=0.179\). The calculated droop coefficients are verified by comparison with the conventional case. The conventional case is defined as the situation where the operating points are determined by the optimization, and the droop coefficients are determined proportional to their capacity [41]. To verify the proposed determination method, we utilized 10 randomly sampled load profiles. Table 2 shows the comparison of average outputs and power losses. As described in Table 2, the deviation of power loss increased using the proposed method to reduce total system loss. From the simulation results in Tables 1 and 2, we suggest that the proposed optimization problem and the droop coefficient determination method enhance the efficiency of the test system. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UPF Mode** & **VC Mode** & **Proposed** \\ \hline Output of LCC HVDC (\(P_{LCC}\)) & 112.02 MW & 111.96 MW & 84.08 MW \\ Output of VSC HVDC (\(P_{VSC}\)) & 112.02 MW & 111.96 MW & 139.26 MW \\ Power loss & 5.04 MW & 4.92 MW & 4.34 MW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of power in three cases. Figure 5: Voltage profile of the conventional and proposed methods in the IEEE 14-bus test system.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 1 Introduction  
The adsorption process has been widely applied to recover valuable elements, such as lithium (Li), potassium (K), uranium (U), and strontium (Sr) from seawater, wastewaters, or concentrated brines produced in the desalination process [1, 2, 3]. Among many methods for mining elements from seawater, the adsorption process is a relatively economic and simple process. Due to the vast amount of seawater, the presence of elements at very low concentrations in seawater can provide useful amount of elements [2]. seawater contains cations such as sodium (Na), magnesium (Mg), calcium (Ca), K, and Sr in decreasing order of amount in seawater [2].  
Potassium can be used for the production of fertilizers, but the terrestrial K resources are limited compared to K in seawater. Thus, previous studies tried to recover K from seawater, both synthetic seawater and real seawater, at a laboratory scale [4, 5]. Different methods, such as precipitation and extraction [6, 7], membrane separation [5], and adsorption on sorbents [1, 4] have been used to recover K. With adsorption, K-selective adsorbents have been synthesized to enhance the K recovery from seawater [4, 8].  
In previous studies, zeolite has been used to recover K from seawater by sorption process, and these studies synthesized or modified zeolite for better K uptake [1; 4; 9]. With surface modification of zeolite, the K adsorption capacity was increased [9]. Similarly, the sodium ion sorption capacity of zeolite increased after treatment with ammonium acetate [10; 11]. Moreover, the synthesized adsorbent (i.e., zeolite W) showed a higher selectivity to K [4].  
Manganese nodule residue that is generated during the processing of manganese nodules has been used for adsorption of heavy metals, such as chromium and lead (Pb) [12; 13]. Moreover, one previous study used ocean manganese nodules to make a K ionic sieve for K recovery from seawater [14]. However, ocean manganese nodule has not been studied for its sorption capacity of K.  
The sorption-based K recovery studies are largely limited to using zeolite as an adsorbent, and other types of adsorbents have not been explored much. Thus, there is a need for more studies on other possible adsorbents for K recovery from seawater. Therefore, this study was set to study and compare the adsorption characteristics of different adsorbents for K recovery from seawater. The zeolite-based adsorbents, both natural zeolite (i.e., clinoptiolite) and modified natural zeolite, and manganese nodule, a potential adsorbent, were used in this study to explore and compare the K recovery potential. The natural zeolite was treated with ammonium acetate to prepare the modified natural zeolite in this study.
## 3 Discussion
Seven genes showed significant up-regulation at both 24 and 48 h after feeding. These genes are candidate synthase genes for the above terpene volatiles. However, their function needs to be confirmed by protein expression and purification, in vitro enzymatic assays using geranyl diphosphate (GPP), farnesyl diphosphate (FPP), and geranylgeranyl diphosphate (GGPP) as substrates, and GC-MS analysis. Plant secondary metabolites also play important roles in plant-insect interactions [15]. Insects often identify these metabolites by taste to determine whether the plant can provide the corresponding nutrient resources. At the same time, plants can also negatively affect insect digestion and development by producing certain secondary metabolites, thereby achieving functional insect resistance [16; 17; 18]. Flavonoid secondary metabolites are particularly important in this type of interaction [17; 18]. In this study, we found that the epigallocatechin content increased significantly after 24 h of feeding, although it had decreased by 48 h. Previous studies on epigallocatechin have shown that this compound is involved in plant resistance to diseases [19]. Considering that the interaction between plants and phloem-feeding insects has many similarities with the resistance of plants to pathogens [20; 21], we hypothesize that epigallocatechin plays an important role in the interaction between _P. acerifolia_ and _C. ciliata_. We also identified the candidate key enzymes involved in the biosynthesis of this compound by analyzing our leaf transcriptome dataset. Functional studies on the proteins encoded by these candidate genes need to be performed to determine their roles in epigallocatechin biosynthesis. A biological study should be carried out to observe the effects of epigallocatechin on feeding, development, and oviposition by _C. ciliata_. Our study examined the intrinsic mechanisms underlying the interaction between _P. acerifolia_ and _C. ciliata_ using transcriptome sequencing and metabolite analysis. The high-throughput RNA sequencing technology used in this study provides a comprehensive look at the transcriptome and does not rely on the limited genomic resources presently available for _Platanus_ species. It is worth noting that this study was performed on detached leaves and this could not fully represent the response of leaves on the tree under natural conditions. In the future, similar studies using leaves under natural conditions are needed to further confirm the key candidate genes and compounds found in the study. Additionally, further functional research on DGEs identified here should be carried out in order to clarify their specific roles in plant-insect interactions, thereby enabling the development of insect-resistant sycamore trees and providing a basis for the development of strategies to protect sycamore trees and to manage _C. ciliata_ infestations.
## 7 Conclusions and Future Work  
In this paper, we studied the problem of automated parking space allocation during the transition period when both human-operated vehicles (HVs) and autonomous (driverless) vehicles (AVs) are present simultaneously in an intelligent parking infrastructure. Since not all vehicles will be autonomous in a near foreseeable future and fully automated parking systems are still being developed, the problem of interest, thus, has important and relevant applications. Based on four key factors that affect the choice of parking spaces for drivers, the fuzzy comprehensive evaluation (FCE) method is used to first predict the driver's choice of parking space, since a HV cannot communicate with the central command station. This prediction then allows an intelligent parking system to allocate the remaining available parking spaces to the AVs without conflict arising with the HVs. Furthermore, by incorporating information of the parking spaces and road map of the parking facility, an adjacent matrix in the Floyd algorithm is then developed to determine the shortest feasible paths to arrive at the parking spaces safely. The proposed algorithms are applied to three parking lot scenarios in which both a HV and an AV are entering. It is shown that a parking space can be reasonably allocated to the AV after a prediction of the driver's choice of parking space.  
This work lays the foundation for future investigation into the automated parking problem with multiple HVs and AVs. Moreover, since the test parking lot discussed in this paper is not very large, it is likely that drivers can observe the entire layout and the situation from the entry point. More complexities of the parking infrastructure, such as multiple entrances, exits, and levels, can be considered in the future. A faster and more robust dynamic planning method needs to be developed.  
[PERSON] and [PERSON] designed the algorithm; [PERSON] verified the rationality of the algorithm; [PERSON] performed the software simulation; [PERSON] and [PERSON] analyzed the results of the simulation; [PERSON] wrote the paper with help of [PERSON] and [PERSON]; [PERSON]-[PERSON] completed the final editing. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by The Major University Nature Science Research Project of Jiangsu Province (No.16 KJA580001).  
Not applicable.  
Figure 9: Comparison between the predicted parking space preference and the opinions of the most preferred parking space from fifty drivers in three example scenarios.
## 3 Results and Discussions
### Residual Stress in As-Built State
The slope of the deformation curve becomes more negative with an increasing number of legs cut in the simulations, whereas the experimentally obtained curves have relatively steady slopes compared with the simulations. However, the final deformations predicted by the simulations  
Figure 4: Equivalent stress and deformation calculated by FE model before and after cutting supporter legs for 0.5 mm thick cantilever. The deformation is magnified by 10 times for better visualization. Figure 3: FE simulation model for cantilever downward distortion for \(t=0.5\) mm. (**a**) A schematic of the model used in this study and (**b**) finite element meshes. after cutting all the legs follow the same trend as those of the experiment. Figure 5b shows the final deformations after cutting all the supporter legs, obtained from the simulations and the experiments. It can be seen that the final deformation trends between the simulations and the experiments agree fairly well. Both the experiments and the simulations show trends of diminishing the downward deformation clearly when the thickness of the cantilever is increased from 0.5 to 1.5 mm. The simulation results confirm that the main reason for the downward distortion of the thin maraging steel cantilever is the phase transformation of the maraging steel from martensite to austenite during the LPBF, which occurs due to the IHT effect, except on the top layer. It was also found that the deformations predicted by the simulations had slight deviations from the experimental results. These deviations are probably caused by the inhomogeneous residual stress distribution in the maraging steel cantilever caused by the LPBF, which was neglected in the highly idealized FE model of this study. As pointed out by [PERSON] et al. [23], each LPBF layer of the maraging steel have experienced multiple reheats to above the austenite transformation temperature except for the top layer. The reheating generates retained austenite and causes the volumetric contraction of each layer, with occurrences of compressive stress at the top layer and a complex stress state in the layers below. This complex effect was idealized by the compressive stress at the top layer in the current FE simulations, which probably resulted in the deviations between the experimental and simulated deformation behaviors shown in Figure 5.
# Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength
## 3 Results
### Compressive Strength at 7 Days  
The addition of 0.25% wt. of cement (28.85 g) molasses increased the compressive strength by 41.35%. Similarly, as the molasses percentage increased from 0.5% wt. of cement (57.7 g) to 0.6% wt. of cement (68.7 g), the compressive strength remarkably decreased from 32 MPa to 3 MPa. However, a significant decrease in compressive strength was observed for the samples with higher molasses replacement percentages. The compressive strength results at 7 days for different percentages of molasses replacement are presented in Figure 2.  
The results show that as little as 0.25% wt. of cement (28.85 g) molasses significantly affected the compressive strength of the concrete after 7 days. However, this positive effect is lost as the percentage increases. One possible reason for the decrease in compressive strength as the amount of molasses increases is the dilution effect because molasses has replaced cement and reduced the overall strength.
## 3 Experimental Results
### Impact Behavior
#### 3.2.2 Impact Load-Time History  
The impact load was measured during each impact as derived from the accelerometer measurements. The impact load-time history curves are displayed in Figure 9a-c. It was observed that all composite specimens did not fail or even crack under the effect of multi-impacts up to the maximum capacity of the testing machine (44,000 N). The peak impact force for hybrid mixtures, which incorporated 0.5%, 1%, and 1.5% SMA fiber, slightly increased by about 2.38%, 7.19%, and 3.03%, respectively, compared to that of the control mono PVA-ECC mixture. The ability of ECC specimens to resist impact load mainly depended on the fiber type and content. The material's response under impact load depends on its interactive behavior under compressive and shear stresses [34]. Since all ECC mixtures had a slight difference in compressive strength, it appears that the SMA fibers-matrix  
Figure 8: Accumulated penetration depth in composite specimens.  
Figure 7: Percent enhancement in energy dissipation capability of different composite specimens.  
bond allowed to better resist shear stresses induced by the impact load, thus leading to higher impact resistance.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 3 System Model
### Enhanced Reinforcement with Dynamic Learning  
For a more dynamic network with frequent changes in the PU appearance in channels, an enhanced RL approach is required to adapt its learning to the dynamicity of the channel. For instance, the dynamicity of \(MC\) is less than that of the \(FC\). Therefore, from MC to FC, the dynamicity increases, and the learning rate \(\alpha\) is decreased. Equation (3) is enhanced as follows [46]:  
\[\begin{split} Q^{i}_{t+1}(s^{i}_{t},a^{i}_{t})=(1-\alpha(s^{i}_{t },a^{i}_{t}))\times Q^{i}_{t}(s^{i}_{t},a^{i}_{t})+\\ [\alpha\times\min(r^{i}_{t+1}(s^{i}_{t+1}))]\end{split} \tag{4}\]  
As the bottleneck link in a route has the least channel capacity, it helps in determining the priority of the D2D route over other routers in the network. A link with a lower channel capacity has a lower priority compared to other routes. The dynamic learning rate is \(\alpha_{\min}\leqslant\alpha(s^{i}_{t},a^{i}_{t})\leqslant\alpha_{\max}\), and it is determined by the availability of channels (or white spaces) as follows:  
\[\alpha^{i}_{t}(s^{i}_{t},a^{i}_{t})=\Phi^{c_{n},l_{in},n_{in}}_{t,OFF} \tag{5}\]  
For \(\alpha^{i}_{t}(s^{i}_{t},a^{i}_{t})\), a higher value shows a higher dependency of the Q-value on the current knowledge and a lower value shows a higher dependency of the Q-value on previous knowledge. The dynamic learning of \(\alpha^{i}_{t}(s^{i}_{t},a^{i}_{t})\) can be varied based on the immediate state-action pair and previously learned rewards. In this study, the agent is myopic and relies on the immediate state-action pair with some consideration of the previously learned Q-value (rather than the next state-action pair (or discounted reward) due to the random appearance of PUs; hence, the discount value is \(\gamma=0\). Due to the need for interaction with the operating environment, the majority of the testbeds using USRP [33; 37; 46] have adopted Q-learning to adapt to the environment. Q-learning is the preferred approach because: (a) it does not need datasets required in supervised machine learning approaches [24; 25; 26] and (b) it provides definite outcomes, particularly whether a channel is utilized or unutilized, which is preferred compared to unsupervised machine learning. The non-learning approach, called non-RL, selects routes based on their priority (e.g., based on the number of hops of the routes specifically routes \(k_{2}>k_{3}>k_{4}\)). Hence, the proposed dynamic Q-learning approach is compared with the traditional Q-learning approach and the non-learning approach.  
In the following sections, the learning model and algorithm of CenTri are presented.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Formal Definitions and Properties  
In the CONIKS system, the formal definitions and properties of cryptographic primitives are foundational to ensuring security and functionality. At the heart of these primitives is the hash function \(H\), which maps arbitrary-sized inputs to fixed-sized hash values [18]. The function \(H\):{0,1}*\(\rightarrow\){0,1}\(k\) is designed with critical properties: pre-image resistance, which makes it computationally infeasible to reverse-engineer the original input from the hash value; second pre-image resistance, which prevents others from finding a different input that maps to the same hash value as a given input; and collision resistance, which ensures that it is infeasible to find two distinct inputs that produce the same hash value. Additionally, digital signatures play a crucial role in authenticating data and ensuring integrity. They employ a key pair where the private key \(sk\) is used to sign a message \(m\), producing a signature \(\sigma\) (sigma), and the public key \(pk\) is used to verify the signature through the function _Verify(pk,m,\(\sigma\))_.  
Hash Function \(H\) is a cryptographic hash function providing properties like collision resistance and pre-image resistance.  
A hash function \(H\) is a function \(H\):{0,1}*\(\rightarrow\){0,1}\(k\) that produces a fixed-size hash value from an arbitrary-size input [18]. Important properties include the following:  
Pre-image Resistance: Given \(h=H(x)\), it is computationally infeasible to find \(x\);  
Second Pre-image Resistance: Given \(x\) and \(h=H(x)\), it is infeasible to find \(x^{\prime}\
eq x\) such that \(H(x^{\prime})=h\);  
Collision Resistance: It is infeasible to find any two distinct inputs \(x\) and \(y\) such that \(H(x)=H(y)\).  
Figure 1: Structure of a Merkle tree.  
Digital signatures use a pair of keys: a private key \(sk\) for signing and a public key \(pk\) for verification. For a message \(m\), the signature \(\sigma\) (sigma) is generated as \(\sigma=Sign(sk,m)\). Verification involves checking _Verify_(\(pk\),\(m\),\(\sigma\)) to ensure that \(\sigma\) was generated using \(sk\) and that \(m\) has not been altered.  
These cryptographic constructs are essential for maintaining security and trust in the CONIKS framework.
## 2 Materials and Methods
### Literature Review and Analysis on HRC Modeling Methods
#### 2.1.2 Unified Modeling Language (UML)  
UML is a popular modeling language in the fields of software engineering and system design. Through its standardized notation, clear visualization and specification of structures, behaviors, and interactions of systems is achieved. UML is, therefore, thought to be an ideal approach to model architecture, interfaces, and interactions in HRC systems. The use of UML in the field of human-robot collaboration seems to mostly focus on safety and risk analysis. This can be seen in [36; 37; 38; 39].  
[PERSON] et al. [40] used a combination of UML and colored Petri Nets (CPNs) to model and simulate mobile robots based on wireless robotic components (WRCs). UML diagrams were customized to describe the specific WRC architecture based on the robotic software, particular task, and operational environment. The hierarchical CPNS were developed using the customized UML diagrams as a guide. From there, an executable model of the robotic system could be developed. Further work will aim to improve the translation from UML to CPNs.  
[PERSON] et al. [41] attempted to use UML in development for the design of real-time robot controllers, based on the necessity of better adaptability from robots when working in cooperation with humans. The main focuses were dependability, fault avoidance, fault removal, and fault tolerance. UML was found to be appropriate for the development and maintainability of this controller.  
As a major software engineering modeling tool, UML can model requirements and high-level system architecture very efficiently. However, UML is not well-established for robotics, including HRC. While UML can be useful for modeling certain aspects of human-robot collaboration, limitations arise when applied to the full scope collaborative scenarios. Robots often operate autonomously and need to make decisions, but UML does not offer specialized modeling constructs for representing autonomy or decision-making processes.
## 5 Conclusions  
In this paper, we have investigated a power-efficient illumination control optimization design for the CMIMO VLC system with multi-color LED. To increase the practicality of the system, crosstalk interference and quadrangle chromaticity constraint are taken into consideration. Since the VLC system is the combination of illumination and communication, an optimal power allocation strategy is proposed to maximize power efficiency via collaboratively satisfying the illuminance, chromaticity, amplitude and SINR constraints. Simulation results have testified that the proposed optimal power allocation strategy can significantly reduce the total transmission power compared with the uniform power allocation method. Moreover, higher power efficiency can be achieved under larger CCT value due to the increase of the blue and green light component in the mixed light. Furthermore, it is also concluded that better error performance can be obtained for the CMIMO model considering crosstalk interference and proper DC bias value is required to avoid the clipping distortion problem.  
All authors contributed equally to writing the paper. Formal analysis, Y.Z.; Funding acquisition, J.Z. and J.Q.; Writing--original draft, Y.Z.; Writing--review & editing, J.Z. and J.Q.  
This work is supported in part by Grant No.161100210200 from Major Scientific and Technological of Henan Province, China.  
The authors wish to thank the anonymous reviewers for their valuable suggestions.  
The authors declare no conflict of interest.  
Figure 7: Error performance for the CMIMO model and the conventional WDM model under different interference coefficient under CCT = 5700 K.