esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Deep Space Probe Navigation
###### Abstract  
Deep space exploration, generally referring to the space exploration activities targeting the Moon and more distant extraterrestrial celestial bodies, stands as a critical indicator of a nation's comprehensive capabilities and technological prowess. Conducting deep space exploration is beneficial for breaking through the space technology necessary for human survival and development and expanding human activity space. Within this ambitious endeavor, effective navigation serves as the cornerstone for ensuring mission success, enabling spacecraft to traverse the vast and unpredictable cosmic frontier. As humanity's reach into the cosmos continues to expand--from lunar to interplanetary voyages and beyond--the demand for real-time, high-precision, and highly reliable navigation solutions has grown exponentially.  
In this Special Issue, we have collected original research and review papers that reflect the latest advancements in the navigation theories and methods for deep space probes.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 6 Conclusions  
This work proposed a step-by-step design method for a frequency reconfigurable metasurface antenna with the ability to adjust its operating frequency by utilizing semiconductor diodes such as PIN diodes. A simplified unified demonstration on how a metasurface antenna unit cell was designed theoretically from split ring resonators in order to attain its geometrical dimensions at any frequency of resonance was shown. The radiation mechanisms of the 16 element metasurface and RMS antenna were explained in detail via the reflection phase and its magnitude characteristics. It was observed that when diodes were added, the resonating frequency decreased as the capacitance increased. The proposed frequency RMS antenna exhibited a wideband operational bandwidth from 25 to 32 GHz and approximately at 40 GHz. In addition, multiple gain variations from 27.33 dBi to 28.8 dBi were observed across the full operating band at different diode capacitances. It was also observed that the gain and directivity increased when diodes were added due to decreased angular beamwidth. However, radiation efficiency decreased because of the loss that occurred due to the dissipation of power from the added diodes. From the proposed frequency RMS antenna, the gain, directivity, and radiation efficiency did not vary as the frequency of operation changed. The proposed 16 elements structure has proven to be a double negative metasurface, which means that it has the ability to dynamically shape electromagnetic fields. There has been considerable success with reconfigurable metasurface antennas in the microwave regime, but the use of metasurfaces in future communication has enormous potential for further research.  
**Author Contributions:** Methodology, B.K.L.; supervision, M.M. and B.B.; writing--review and editing, P.M. and R.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Botswana International University of Science and Technology grant number S00190 and S00378.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
The findings support the technology-push and market-pull theories [56], which argue that technological advancements drive efficiency gains and structural transformations within economies. The significant negative correlation between digital infrastructure and greenhouse gas emissions (GHG) in Model M1 corroborates prior studies suggesting that improved digital  
Figure 2: Relations between research questions and GLM results. Source: Own processing. infrastructure can enable energy-efficient solutions, smart grid integration, and optimized resource utilization [22, 23]. At the same time, the positive relationship between user adoption and GHG emissions highlights an important deviation from the green technology paradigm, which generally posits that digitalization leads to sustainability improvements [14]. Our results suggest that digital adoption, particularly in emerging economies, may increase energy demand faster than the energy efficiency gains derived from digital technologies. This finding is in line with Jevons Paradox [57], which argues that efficiency improvements can sometimes lead to greater overall resource consumption rather than conservation. This phenomenon underscores the need for policy interventions to ensure that digital expansion is accompanied by investments in clean energy sources to offset rising energy demands. Additionally, our findings contribute to the diffusion of innovation theory [58] by demonstrating how the different dimensions of digitalization--infrastructure, affordability, and user adoption--exert distinct influences on environmental performance. While infrastructure improvements positively impact renewable energy adoption (Model M2), affordability and user adoption show a negative correlation with the share of renewable energy. This suggests that digital expansion in emerging economies may not inherently align with sustainability goals unless guided by policies that prioritize green energy integration. Furthermore, the study builds upon the institutional theory of digital transformation [8], which emphasizes that digitalization is shaped by socioeconomic and regulatory contexts. Our results indicate that while digital infrastructure advancements can contribute to lower emissions, the overall environmental impact of digitalization is contingent upon the regulatory and energy policy frameworks within each economy. This supports the notion that digital transformation alone is not a sufficient driver of sustainability; instead, its effectiveness depends on complementary policies that promote sustainable energy transitions.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Water Filtration
#### 7.1.3 Enhancing the Antibacterial Performance with Bactericidal Agents
Further research is needed to regulate the silver release content, which may restrict the application based on various nations' drinking water regulations, as well as to evaluate the long-term performance of the AgNP-containing electrospun nanofibrous membranes [175]. Many reports have suggested that incorporating non-toxic metallic nanoparticles, such as zinc oxide (ZnO) nanoparticles, into electrospun polymers improves the bacterial resistance, stability, and bacterial removal [176,177]. ZnO powder was added to the polyacrylamide(PAN) solution, which was electrospun on a rotating drum, and a chitosan/PEO solution in acetic acid at a ratio of 70:30 was electrospun on top of the non-dried PAN/ZnO membrane. It was observed that the smooth fibrous chitosan layer was strongly linked in between the PAN nanofibers, providing high physical interaction, rigidity, and stress absorption, which led to an increase in the tensile strength of 74% and in the elastic modulus of 32% for the dried nanocomposite membrane. As a result, the PAN/chitosan membrane had smaller pores on the surface, meaning the bacteria filtration performance through size exclusion killed up to 99.99999% of _E. coli_ in 24 h. However, water flux during bacteria filtration was decreased due to the clogging of pores induced by the increased bacteria concentration. Incorporating ZnO nanoparticles disrupted the polymer chains of PAN and created nano-cavities, which improved the water permeability of PAN nanofibers by 71%. In addition to bacteria filtration, studying the antibacterial effect is important to prevent biofouling. A dramatic improvement in antibacterial performance was observed in PAN/ZnO-chitosan membranes, which killed all bacteria in 3 h compared to the PAN/chitosan membrane, reducing the bacterial concentration by 99.6% after 24 h. Similar to NH3+ ions of chitosan, Zn2+ ions may interact with the anionic bacteria membrane, harming its surface structure and breaking through it to prevent nucleic acids from replication. In addition, electron pairs in ZnO nanoparticles break under light, generating highly active free radicals capable of penetrating and destroying bacterial cells [178]. Consequently, combining the synergistic effects of chitosan nanofibers with ZnO nanoparticles into the PAN/ZnO-chitosan electrospun membrane increases the antibacterial efficacy upon bacteria filtration and fights the biofouling of the membrane, giving it a self-cleaning property.
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 3 Limitations and Advances of Road Infrastructure
### Lane Support Systems (LSS)
#### 3.2.2 Advances  
Aside from addressing design issues, mainly regarding the continuity, colour and width of markings, improvements in MV detection may be achieved using modern road marking technologies and techniques. For example, profiled or agglomerate road markings have improved visibility during wet and rainy conditions when there is a reduction in flooding or water drainage. Moreover, high-quality glass beads or all-weather markings should also increase the visibility of markings in diverse conditions ([68; 69]).  
There is also a tendency to develop smart road marking. One project that deals with this is the Safe Strip Project. Here, the micro/nano sensory system is incorporated into road markings to support V2I by providing information about the traffic ahead or environmental parameters essential for vehicle dynamics. Directly communication to vehicles is supported through onboard units or mobile phones in the case of non-equipped vehicles. This also offers benefits to road authorities via the collected data, which could greatly support predictive maintenance. It is also energy efficient as power supply comes from ambient sources [70; 71; 72; 73].
## 1 Introduction
The work in this paper is a contribution towards fully enclosed actuators with on-board local control units including feedback and communication to the main controller. This was developed with consideration for the operating environment and, at the same time, maintaining an overall simple configuration as compared to hydraulic or electro-hydraulic systems which are controlled remotely from the topside and have limited dynamic control capabilities. The present work describes the topology adopted for developing a distributed control system configured using a combination of fieldbuses to automate the inspection of underwater structures. With this hardware, it is possible to power and control the actuators underwater using a simple umbilical cable consisting of only a power line and a twisted pair for communication signals. This has the advantage of easing cable management and greatly reduces the drag for underwater operations at greater depths. For underwater units, the CAN bus interface was chosen as it is more robust and fault tolerant as compared to other fieldbus networks, as suggested in [11] and [12]. For the topside units, a combination of RS 485- and CAN-based communication was used considering the wide range of actuator and sensor availability for land-based systems. Section 2 describes the main requirements for the system developed and an overview of the robotic structure and the overall system structure and components. Section 3 details the control system hardware which is based on a fieldbus to implement distributed control and also details the design of the subsea actuators to keep the underwater units and the wiring to a minimum. Section 4 provides an overview of the testing of the system in shallow waters at an underwater testing facility to prove the core functionality of the system. Conclusions of the work carried out here are summarized in Section 5.
## 2 Problem Formulation
### Target Probability Map Model  
After rasterizing the search space, the probability of target presence in each raster is represented by a target probability map model. The probability value depends on the initial probability distribution of the target and the detection information of the on-board sensors. The UAV formulates a corresponding search strategy based on the initial target  
Figure 2: Simulation of the field of view range of the on-board sensors.  
probability map and, at the same time, dynamically updates the target probability map as the search process continues, including prediction updates based on the target motion model and detection updates based on the UAV's sensors. In the prediction update phase, the target probability map is recursive over time based on the target motion model. From Section 2.1, the single-step transfer probability of the target at the time \(t-1\) is \(P[x_{t}|x_{t-1}]\); then, at the time \(t\), the predicted updated target probability map is:  
\[\widetilde{\rho}(x_{t})=\sum_{x_{t-1}\in S}P(x_{t}|x_{t-1})\rho(x_{t-1}) \tag{7}\]  
where \(\widetilde{\rho}(x_{t})\) represents the target probability map for predicting the position of the target motion at time \(t\), and \(P(x_{t}|x_{t-1})\) denotes the conditional probability that the target moves from the position at time \(t\) to the position at time \(t-1\).  
During the detection update phase, the target probability map is revised by the integration of detection and observation information acquired from the onboard sensors of the UAVs. Suppose that the sequence of observations received by the sensor in \(t\) time steps is \(O_{1:t},O_{1:t}=\{O_{1},\ldots,O_{t}\}\); then, the probability that the target is detected at location \(x_{t}\) at time \(t\) is \(P(O_{t}|x_{t})\). The target probability map after the detection event is updated based on the Bayesian formula as follows:  
\[\rho(x_{t})=\gamma_{t}P(O_{t}|x_{t})\widetilde{\rho}(x_{t}) \tag{8}\]  
where \(\gamma_{t}\) is a normalization factor. It makes the probability of the existence of the target in each grid cell sum to 1. The formula is as follows:  
\[\gamma_{t}=1/\sum_{x_{t}\in S}P(O_{t}|x_{t})\,\widetilde{\rho}(x_{t}) \tag{9}\]
## 2 Mathematical Formulation of OPF Problem
### OPF Equality Constraints  
OPF has two equality constraints, that are real (MW) and reactive power (MVAr) balances in the network. The total real and reactive power produced must compensate for the total power loss and demand in the network.  
\[P_{Gi}-P_{Di}-V_{i}\sum_{j=1}^{N_{\mathrm{B}}}V_{j}[G_{ij}\cos(\delta_{ij})+ B_{ij}\sin(\delta_{ij})]=0\forall_{i}\in N_{B} \tag{13}\]\[Q_{Gi}-Q_{Di}-V_{i}\sum_{j=1}^{N_{B}}V_{j}[G_{ij}\sin(\delta_{ij})-B_{ij}\cos( \delta_{ij})]=0\forall_{i}\in N_{B} \tag{14}\]  
Here, \(N_{B}\) represents the total number of buses while \(B_{ij}\) and \(G_{ij}\) represent the susceptance and transfer conductance between bus \(i\) and \(j\), respectively.
## 4 Method
### Trustworthiness Model  
The term trustworthiness is used here to measure the trust level a follower agent has in the intelligent agent in making a correct decision about the next target. An agent is considered trustworthy if he/she has a high probability of performing a particular action about the next goal [12]. Particularly, before agent \(a\) decides to follow the actions taken by intelligent agent \(p\), agent \(a\) needs to evaluate the trustworthiness probability about the decision \(q\) provided by agent \(p\). The trustworthiness probability about the decision \(q\) is calculated as follows:  
\[P(q)=\xi\ \sum_{i=1}^{N}\sum_{j=1}^{2}P(q|a_{i}^{j})P(a_{i}^{j}), \tag{4}\]  
where:  
\(\xi\) is the normalizing factor,  
\(P(q|a_{i})\) is the certainty factor that an agent \(a_{i}\) has on decision \(q\). That means an agent \(a_{i}\) thinks \(q\) is correct with probability \(P(q|a_{i})\),  
\(p(a_{i}^{j})\) is the reliability factor of an agent \(a_{i}\).  
Therefore, the agent's probability of following a decision process is based on the greatest certainty value of \(P(q)\). In other words, agent \(a\) trusts intelligent agent \(p\) only if trustworthiness probability value is greater than or equal to a predefined threshold. Thus, the trustworthiness model helps to decide which information source to consider in case of receiving conflicting information.  
The first stage (sub-model) of the MLMS-ABM routing modelling system is the design of the rules that governs the decision-making process for agents to choose an intermediate target to reach in order to reroute around the accident. The model automates the process of extracting decision rules by adapting gene expression programming to find optimal decision rules from objective behaviors. A more detailed analysis of this sub-model was carried out in [12].  
All proposed stages and sub-models of the three levels of the MLMS-ABM model are performed in the microscopic layer because they are implementation components of the layer. However, when the agent status upgrading sub-model is performed, the intelligent agents are generated, and the macroscopic layer is thusly generated. The communication and trustworthiness evaluation stage is performed in both the microscopic and macroscopic layers.
## 1 Introduction
[32], 44.0 \({}^{\circ}\)C by [PERSON] et al. [34], and 37.8 \({}^{\circ}\)C and 39.6 \({}^{\circ}\)C by [PERSON] et al. [30; 33] have been found. However, results from the literature come from DSC experiments performed on a very small sample (a few milligrams of PCM) with a fast cooling rate (a few degrees per minute). It has been shown that fast cooling rates increase the supercooling degree [12; 39; 41] and large samples reduce the supercooling degree [43; 44]. Therefore, the cooling behavior of a real-scale DHW storage might be different than for DSC results, and it appears necessary to characterize PEG6000 on a large sample with various thermal loads. This study presents an experimental comparison of three characterization methods for two PCM suitable for DHW storage. Two methods, DSC and STEP, are carried out on a calorimeter. The third characterization is an FM method performed on a fluxmeter bench. The two PCM selected for the experimental characterizations are paraffin RT58 and polymer PEG6000. Paraffin RT58, from Rubitherm, is a well-known material for DHW storage. PEG6000 is widely used in the medical and cosmetic sectors, however, despite a lower cost than paraffin, this material is less common for DHW storage with PCM. The objective of this study is to investigate and compare, with the three characterization methods, the thermal performances of a well known paraffin-based material (RT58) with a less common polymer (PEG6000), which has a great potential for DHW storage. To the best knowledge of the authors, no scientific study as yet compares the experimental results of DSC, STEP and FM methods for two PCM with different thermal behavior, one with no supercooling (RT58) and the other with supercooling (PEG6000). Moreover, the authors did not identify any study directly comparing the thermal performances of a paraffin-based material and polymer PEG6000 with the same experimental set-up and the same experimental protocol. The first section of this study details the experimental set-up and the experimental protocol for the three characterization methods. The second section presents the results from the DSC and STEP methods for the two PCM. The third section focuses on the results of the experimental characterization with the FM method. The last section compares the results obtained with the three characterization methods and discusses the advantages and drawbacks of each method.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 3 Properties of NSPs
### Cross-Linking  
Under oxidizing conditions and in the presence of free radicals, adjacent AX molecules can form a covalent bond between ferulic acid residues, becoming cross-linked [142,143]. Cross-linking of AX is most often carried out using agents that generate free radicals: the peroxidase/hydrogen peroxide system (both of these components occur naturally in flour and yeast) or the enzyme lactose [143]. During the enzymatic coupling reaction, monomeric ferulic acid is oxidized and converted into isomers of dehydrodrierulic acid or dehydrotriferulic acid [144]. The cross-linking efficiency of AX is affected by their molecular structure, i.e., molar mass, the degree of substitution of the xylan skeleton with arabinose residues, and the content and distribution of ferulic acid in the molecules. The greater number of ferulic acid residues in the polysaccharide backbone, the more covalent cross-links can be generated. The total content of ferulic acid in AX molecules from cereal grains is estimated to be 0.4-450 mg/100 g [9; 145; 146; 147; 148; 149; 150]. As a result of cross-linking, NNPs of high molar mass and, consequently, high viscosity is obtained [151]. The weight average molar mass of AX cross-linked with horseradish peroxidase and hydrogen peroxide ranges from 191,000 g/mol to 720,000 g/mol [9; 83; 110; 9; 85; 101], and the weight average molar mass of lactose cross-linked AX ranges from 159,000 g/mol to 508,000 g/mol [84; 85; 87]. However, presented values of molar mass (Table 2) seem to be tentative and underestimated because problems with the solubility of large molecules formed after cross-linking are reported [9].  
Paramylon, which are \(\beta\)-glucans extracted from euglenin cells, and kurdlan, which are \(\beta\)-glucans produced by a strain of the bacteria _Alcaligenes faecalis_ strain, were cross-linked using ethylene glycol diglycidyl ether (EDGE) in the presence of 4% sodium hydroxide, yielding compounds with greater hydrophilic properties (greater ability to form a gel). Cross-linked \(\beta\)-glucans are obtained by breaking the hydrogen bonds in the chains of the molecules, so new hydroxyl groups are created [88].  
So far, data concerning cross-linking of arabinadactans, xyloglucans, and pectin polysaccharides of cereal origin have not been reported.