esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Influence Parameter Analysis
### Gas Film Clearance  
Bearing film clearance has a great influence on the formation of wedge-shaped clearance, which directly affects bearing performance. The effects of different values of film clearance on bearing performance are calculated, while other parameters remain unchanged, as shown in Table 1. The load capacity and the attitude angle of hydrogen gas bearings with different film clearances are shown in Figure 12. The bearing load capacity increases with smaller values of gas film clearance. In addition, the difference in load capacity under different gas film clearance becomes greater at higher rotational speeds. Decreasing the gas film clearance is very effective for enhancing the bearing load capacity. Simultaneously, with decreasing gas film clearance, the bearing attitude angle exhibits a gradual decrease. The decrement of attitude angle increases with rotational speed.  
The friction torque of hydrogen gas bearings with film clearance is shown in Figure 13. At larger values of gas film clearance, the friction torque of hydrogen gas bearings diminishes. Therefore, in the design stage, a compromise between load capacity and friction loss should be carefully considered.  
Figure 11: Critical mass of hydrogen gas bearings with ambient pressure.  
Figure 10: Friction torque of hydrogen gas bearings with ambient pressure.  
The critical mass of hydrogen gas bearings with film clearance is shown in Figure 14. With decreasing film clearance, the critical mass of the hydrogen-lubricated bearings increases, improving the dynamic stability of the bearing.  
Figure 14: Critical mass of hydrogen gas bearings with gas film clearance.  
Figure 12: (**a**) Bearing load capacity and (**b**) attitude angle of hydrogen gas bearings with gas film clearance.  
Figure 13: Friction torque of hydrogen gas bearings with gas film clearance.
## 2 Model for Attitude, Antenna PCO/PCV and Its POD Strategies
### Jason-3 POD Models and Strategies  
For new models or methods used in POD, including the combined attitude model, the most effective and practical evaluation method is to evaluate the accuracy performance of orbits obtained from them. We took the POD for example to analyze and validate the above models. The LEO POD usually adopts an undifferenced reduced-dynamic method to eliminate the error of ionospheric first-order term by forming the dual-frequency ionosphere-free combination [21; 33; 34]. The settings of model correction and parameters for perturbative force, parameter estimation and strategies in the POD are shown in Table 1.  
Currently, there are no reference paper about the precise orbits of Jason-3 satellite to show its orbital precision. But, the Jason-3 Products Handbook [1] (Page 42, Section 5.1. Precise Orbits) mentioned that \"CNES (the French Space Research Center) has the responsibility for producing the orbit ephemerides for the Jason-3 data products. The Jason-3 OGDRs provide a navigator orbit that has radial accuracies better than 5 cm (RMS), the Jason-3 IGDRs provide a preliminary orbit that has radial accuracies better than 2.5 cm (RMS), while the GDRs provide a precise orbit that has radial accuracies better than 1.5 cm (RMS).\" In this paper, we use the GDR-E precise orbits ([[ftp://cddis.gsfc.nasa.gov/doris/products/orbits/ssa/ja3/GDR-E/](ftp://cddis.gsfc.nasa.gov/doris/products/orbits/ssa/ja3/GDR-E/)]([ftp://cddis.gsfc.nasa.gov/doris/products/orbits/ssa/ja3/GDR-E/](ftp://cddis.gsfc.nasa.gov/doris/products/orbits/ssa/ja3/GDR-E/))) provided by CNES for Jason-3 as a reference. Thus, the radial accuracies of CNES reference orbits are better than 1.5 cm (RMS) according to the Jason-3 Products Handbook. Meanwhile, the POD strategies about CNES orbits in the above link. CNES orbits are obtained by joint orbit determination using GPS, DORIS and SLR technologies. In its POD strategies, the attitude model of CNES precise orbits applied quaternions and solar panel orientation from control center, completed by nominal yaw steering law when necessary. The GPS receiver PCO and PCV by CNES precise orbits are consistent with constellation orbits and clocks (IGS08 ANTEX) and pre-launch GPS receiver phase map, respectively. That is, for GPS receiver PCV of Jason-3, CNES GDR-E orbits did not in-flight adjust GPS receiver phase map; it is different from our strategies, which is based on in-flight residuals modeling.
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 4 Experimental Results and Discussions
### Compact Dual-Band Planar Monopole Antenna Design  
Antennas are widely used in mobile communications, radar, and satellite communications as they play an important role in the transmitting and receiving of signals. Since antenna performance determine the quality of a wireless communication system and different antenna configurations have different antenna performances, the optimization of the antenna configurations has attracted a lot of attention. Traditional antenna optimization starts from a given initial geometric structure and only adjusts the size of the relevant geometric parameters. However, this kind of optimization method is not conducive to exploring the possibility of novel antenna structures. The topology optimization used in this paper, also called pixel optimization, is a technique that can automatically generate an antenna topology suitable for the design requirements in a given design area. It divides the design space into several pixels represented by a matrix with \"1\" (conductor) and \"0\" (air). Through some stochastic optimization techniques, we can find the optimal pixel distribution mode (i.e., the optimal antenna topology) that satisfies the design requirements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Alg.** & **Best** & **Worst** & **Mean** & **std** & **Time** & **D/Opt** \\ \hline \multirow{4}{*}{\(k_{11}\)} & BPSO & 69,899.29 & 65,823.05 & 67,998.33 & 960.01 & **0.7627** & \\  & BBA & 70,880.25 & 65,925.82 & 68,010.64 & 931.82 & 2.9429 & \\  & AMBA & 70,142.52 & 66,377.51 & 68,827.49 & 815.61 & 1.5098 & \\  & A-AMBA & 70,001.73 & 67,426.10 & 68,670.86 & **658.35** & 1.5353 & \\  & P-AMBA & **71,349.91** & **67,815.82** & **68,984.79** & 870.12 & 1.5722 & \\ \hline \multirow{4}{*}{\(k_{12}\)} & BPSO & 88,635.44 & 84,595.14 & 86,473.41 & 1108.52 & **0.9212** & \\  & BBA & 89,195.16 & 84,320.84 & 86,486.67 & 1350.48 & 3.6654 & \\ \cline{1-1}  & AMBA & 89,304.33 & 84,346.38 & 87,014.87 & 1138.13 & 1.8777 & \\ \cline{1-1}  & A-AMBA & 88,575.85 & **85,092.06** & 86,678.28 & **979.82** & 1.9048 & \\ \cline{1-1}  & P-AMBA & **89,408.59** & 84,120.74 & **87,074.01** & 1115.51 & 1.9664 & \\ \hline \hline \end{tabular} \({}^{1}\) Bold data is the optimal result.  
\end{table}
Table 4: _Cont._Figure 5: Average convergence curves of BPSO, BBA, AMBA, A-AMBA, and P-AMBA on some selected large-scale problems over 30 independent runs.
# Synergistic Effect of Nonthermal Plasma and ZnO Nanoparticles on Organic Dye Degradation
## 5 Conclusions  
In summary, this study investigated the synergetic effect of an atmospheric-pressure plasma treatment and the photocatalytic activity of synthesized ZnO nanoparticles on the degradation of methylene blue dye. Therefore, the first part of this paper presented the synthesis of ZnO nanoparticles using the hydrothermal route, and their physicochemical characteristics were assessed via SEM, XRD, Raman, XPS, and UV-visible techniques. The synthesized ZnO had nanoparticles with a size of 30-50 nm, was crystallite with a hexagonal lattice structure, and the E\({}_{\text{g}}\) = 3.17 eV. The atmospheric-pressure plasma was generated at a fixed discharge voltage and flow rate. Reactive species, such as excited/metastable Ar, hydroxyl (OH), N\({}^{\uparrow}\)\({}_{2}\), and atomic oxygen OI, could be detected via optical emission spectroscopy. The plasma treated methylene blue remotely; the plasma species interacted with the surrounding air, generated various radicals diffused in the MB solution, and played a crucial role in the degradation of the MB dye. Adding ZnO nanoparticles to the MB solution under plasma treatment influenced the plasma degradation efficiency; the MB degradation efficiency of plasma improved in the presence of 0.2 gL\({}^{-1}\) ZnO, while it decreased when the loading ZnO increased to 0.4 gL\({}^{-1}\) ZnO. The improvements in plasma performance in the case of 0.2 gL\({}^{-1}\) ZnO were probably due to the increased generation of OH radicals, while the higher 0.4 gL\({}^{-1}\) ZnO probably caused turbidity in the MB solution and, hence, limited UV-photons and other plasma species reaching the ZnO NP surface.  
**Author Contributions:** [PERSON] fabricated the plasma jet and performed OES measurements; [PERSON] and [PERSON] conducted experiments and analysis. [PERSON]-[PERSON] wrote the paper; [PERSON] reviewed the draft; [PERSON] secured the funding for project. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the deanship of scientific research at Prince Sattam Bin Abdulaziz University, grant number (PSAU-2022/01/22237).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm
## 4 Prototype of a Generative AI-Enabled Facility Layout Design Platform  
### Implementation Process of System Development  
Following the proposed architecture of FLD 4.0, as illustrated in the Figure 2, as a proof of concept, we further demonstrate a potential implementation method of developing a generative-AI-enabled facility layout generation system. Figure 4 illustrates the technical route.  
First, over ten thousand 3D models are collected into a centralized repository. These models represent industrial robots, automated guided vehicles, conveyors, machine tools, storage facilities, sensory devices, and other equipment. The AAS is then utilized to digitally represent these physical assets, with submodels outlining core attributes such as basic information, operational parameters, functionality, CAD models, and asset dependencies through standardized semantic references [80]. Ontology-based knowledge maps are developed by parsing AAS datasets to extract entities like submodels and properties, which are then mapped as nodes in a knowledge graph, with relationships identified and stored as triples in a Neo4j graph database. This methodology facilitates semantic connections that support knowledge inference, analysis, and optimization, particularly in facility selection, asset interconnections, and layout generation. However, the AAS-based repository may lack the essential contextual knowledge, real-world scenarios, and specific task orientations necessary for effective facility layout design. That is, a large number of causal relationships still exist, which cannot be represented by the AAS-based ontology alone [81]. To address this gap, LLMs can analyze textual data and extract valuable insights from extensive information sources, including the best practices, case studies, and contextual frameworks absent in the AAS repository, assisting in generating tailored recommendations and layout knowledge based on workflow efficiency, safety regulations, and technological integration.  
Integrating knowledge maps based on AAS and LLMs forms a mixed knowledge map tailored for complex, scene-oriented facility layout design. This approach facilitates end-to-end, multi-scale semantic information integration, connecting diverse data sources  
Figure 4: AI-driven end-to-end generation of shop-floor facility layout.  
to the planner's design intentions. By continuously incorporating new knowledge through user interaction and model fine-tuning, the system evolves to address dynamic operational requirements. Finally, convolutional knowledge graph embedding (ConvE) model is employed for graph embedding transformations, converting complex relationships in shop-floor layout design into actionable low-dimensional vector representations [82], which enable chain forecasting tasks for the specific selection and generation of workshop layouts; the resulting layout generation is then showcased through a visualization engine. The main implementation details are introduced in the following subsections.
## 3 Single-Image Destriping Method
### Multi-Level Unidirectional Total Variation Stripe Noise Removal Model
Sub-question about \(d_{ly}\):  
\[\min_{d_{ly}}\ \ \lambda_{1}\|d_{ly}\|_{1}+\frac{\alpha}{2}\|d_{ly}-D_{y}(u-f)-b_{ly} \|_{2}^{2}. \tag{14}\]  
Equation (14) can be solved by the soft-shrinkage operator, and the following equation can be obtained:  
\[d_{ly}^{k+1}=shrink(D_{y}(u^{k}-f)+b_{ly}^{k},\lambda_{1}/\alpha). \tag{15}\]  
Finally, update the Bregman variable \(b_{lx}\), \(b_{ly}\) as follows. \[ \begin{array}{l}b_{ly}^{k+1}=b_{ly}^{k}+D_{y}(u^{k+1}-f)-d_{ly}^{k+1}\\ b_{lx}^{k+1}=b_{lx}^{k}+D_{x}u^{k+1}-d_{lx}^{k+1}\end{array} . \tag{16}\]  
However, only using the parameter \(\lambda\) does not completely guarantee the details, which will result in a loss of data. Therefore, the multi-level iterative method is used to improve. First, the image after adapted moment matching\(f\) is used as the source image for (8)-(16) correction, and the difference between the source image\(f\) and the corrected result \(u_{1}\) is taken as the source image of the second iteration. After multiple iterations, all the results are summed as the corrected image \(u_{sum}\). Among them, in each iteration, \(\lambda_{2}\) should decrease as the number of iterations increases. In general, the first iteration is to remove most of the stripes, and the latter iteration is to restore the image details.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Duty Cycle
#### 3.5.2 Sleep Duration  
In ODYSSE, nodes are not synchronized. They select a random sleep period within the interval I given by:  
\[I=[MIN\_SLEEP\_PERIOD,\alpha\times ACTIVE\_PERIOD] \tag{1}\]  
where \(MIN\_SLEEP\_PERIOD\) and \(ACTIVE\_PERIOD\) are some of ODYSSE parameters. We will vary \(\alpha\) in experimentations. The random sleep period is actually adjusted in some cases, according to two application scenarios:  
1. Infrequent events: This classical WSNs scenario is characterized by low volumes of data generated either periodic, regular, rare or exceptional. With this assumption, router nodes have just to choose a sleep period within the I interval. This is because in this scenario, a current packet is rarely a predictor for an additional incoming packet. This algorithm will be denoted INFR.
2. Multimedia flows: This scenario considers a bulk data transfer of still images from the source. For this scenario, we define two algorithms: 1. MED_ADAP (MultimEDia ADAPtive): routers adapt their duty cycle by assuming existing traffic and shortening their duty cycle. Indeed, when any router transmits a packet, instead of turning back to the sleep state for a random period, it rather sleeps for the minimum period MIN_SLEEP_PERIOD for a predefined number of times (SHORT_SLEEP_COUNT). That is, before SHORT_SLEEP_COUNT packets transmitted, the node sleeps a minimum period of time between two transmissions. After that, it returns to the normal behavior (the sleep period is a random period in the interval I). With this adaptive strategy, the network will face the congestion resulting from the bulk transfer. We will show in Section 5.4 that this method significantly reduces end-to-end delays without compromising the energy saving. 2. MED_N_ADAP (MultimEDia Non ADAPtive): This scenario is the same as the MED_ADAP, but nodes do not adapt their duty cycle, they select a random sleep period within the I interval.
## 3 Experimental and Numerical Methods
### Experimental  
Figure 3 illustrates the experimental arrangement. An aluminum alloy plate specimen was mounted on a test machine for a tensile load. An optical interferometric technique, known as the Electronic Speckle-Pattern Interferometry (ESPI), was used to record the differential displacement field. Two sets of optical interferometers were configured. The first set was sensitive to the in-plane displacement component perpendicular to the tensile axis and the second set is sensitive to the in-plane displacement component parallel to the tensile axis. The laser beam (wavelength 660 nm) was split into two beams by a 50% transmissive beam splitter for each interferometer and the two beams were recombined on the specimen surface. The recombined image (the interferogram) was a superposition of two speckle fields formed by the respective beams. The ESPI setup recorded the interferogram continuously with a preset time interval. The CCD camera captured the interferogram of the perpendicular and parallel components individually in order to separate the perpendicular and parallel components. The captured images were sent to computer memory where the image taken at a time step was subtracted from the image taken at a later time step. The subtracted image frame exhibits the so-called fringe patterns that represent contours of differential displacement components. More details about this ESPI technique can be found elsewhere [33; 34; 35].
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 2 System Model
### Information Transmission  
The second stage is the information transmission stage, where in the first time interval \((1-\rho)T/2\), \(R\) receives information from \(S\), and in the rest time interval \((1-\rho)T/2\), \(R\) forwards information to \(D\) by decoding and forwarding. According to Equation (1), the available power for \(R\) to forward information to \(D\) is  
\[P_{\text{R}}=\frac{E}{(1-\rho)T/2}=\varphi\sum_{j=1}^{N_{\text{S}}}P_{\text{S} }\Big{|}h_{\text{S},\text{R}}\Big{|}^{2}d_{\text{SR}}^{-m}, \tag{2}\]  
where \(\varphi=2\eta\rho/(1-\rho)\). According to Equation (2), the signal-to-noise-ratio (SNR) of the first-hop link and second-hop link can be calculated by  
\[\gamma_{\text{R}} =\frac{\sum_{j=1}^{N_{\text{S}}}P_{\text{S}}\Big{|}h_{\text{S}, \text{R}}\Big{|}^{2}d_{\text{SR}}^{-m}}{N_{0}}=\gamma_{0}\sum_{j=1}^{N_{\text {S}}}\Big{|}h_{\text{S},\text{R}}\Big{|}^{2}d_{\text{SR}}^{-m} \tag{3}\] \[=\gamma_{0}Yd_{\text{SR}}^{-m},\]  
and  
\[\gamma_{\text{D}} =\frac{P_{\text{R}}|h_{\text{RD}}|^{2}d_{\text{RD}}^{-m}}{N_{0}}= \varphi\gamma_{0}|h_{\text{RD}}|^{2}\sum_{j=1}^{N_{\text{S}}}\Big{|}h_{\text{ S},\text{R}}\Big{|}^{2}d_{\text{SR}}^{-m}d_{\text{RD}}^{-m} \tag{4}\] \[=\varphi\gamma_{0}XYd_{\text{SR}}^{-m}d_{\text{RD}}^{-m},\]  
respectively, where \(\gamma_{0}=P_{\text{S}}/N_{0}\) is the SINR of each antenna in the source node, and \(h_{\text{RD}}\) is the channel coefficients from \(R\) to \(D\). \(d_{\text{RD}}\) is the distance from \(R\) to destination node \(D\), \(X=|h_{\text{RD}}|^{2}\), and \(Y=\sum_{j=1}^{N_{\text{S}}}\Big{|}h_{\text{S},\text{R}}\Big{|}^{2}\).
## 3 Trajectory Optimization Design
### Adaptive Genetic Algorithm
#### 3.3.3 Fitness Function  
The fitness function is mapped from the objective function. In order to convert the minimum value of running time of the pickup manipulator to the maximum value of the fitness function, the fitness function is designed as:  
\[f=\left\{\begin{array}{ll}\frac{1}{h_{j}},&\max\left\{\left|\hat{\theta}_{i}(t) \right|\right\}\leq\hat{\theta}_{\max}\;\;\text{and}\;\;max\left\{\left|\bar{ \theta}_{i}(t)\right|\right\}\leq\bar{\theta}_{\max}\\ \frac{1}{h_{j}^{\max}},&\text{Others}\end{array}\right., \tag{18}\]  
In the formula, \(h_{j}^{\max}\) represents the maximum value within the time limit of the time individual \(j\) that does not meet the motion constraint. \(\max\left\{\left|\hat{\theta}_{i}(t)\right|\right\}\) is the maximum angular velocity of joint \(i\) in the time range of \(h_{j}\), \(\max\left\{\left|\bar{\theta}_{i}(t)\right|\right\}\) is the maximum angular acceleration of joint \(i\) in the time range of \(h_{j}\).
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 9 Conclusions  
This paper investigated methodologies to minimize the physical aperture of real-time acoustic cameras for improved mobility.  
Acoustic systems rely on an array of microphones to perform directional processing. Reducing the physical aperture of the array typically decreases the angular resolution and introduces grating lobes. However, making the assumption of using higher audio harmonics while maintaining intelligibility without the fundamental frequencies and with careful selection of design parameters, compact arrays may still achieve wideband performance on par with larger arrays. This work investigates this premise through simulation of a current acoustic system and proposed compact prototype. A case study of the _Dual Cam_ prototype that utilizes a 0.5 \(\times\) 0.5 m\({}^{2}\), 128\(-\)microphone planar array to generate acoustic field visualizations in real time revealed limitations around size, weight, power, and computational complexity that restrict widespread adoption. To transform such cameras into portable devices, we proposed co-optimizing the array layout and beamforming filters through simulations to concentrate directional sensitivity and minimize artefacts. Analyses quantified that a 32\(-\)element 0.21 \(\times\) 0.21 m\({}^{2}\) array optimized for the bandwidth [2; 6.4] kHz operation could theoretically achieve better directivity than the full-scale _Dual Cam_ prototype up to 4 kHz, confirming substantial miniaturization is viable with tolerable performance trade-offs. Ongoing efforts are focused on constructing miniature microphone arrays guided by these numerical optimizations to develop hand-held acoustic cameras that interface with tablets and smartphones for easy deployment. Realizing compact, real-time acoustic imaging devices could expand applications in structural health monitoring, urban noise mapping, VR/AR audio rendering, and other fields currently constrained by large form factors. This paper provided an array of signal processing insights to guide physical prototype development towards transforming acoustic imaging capabilities from constrained lab settings into widely accessible mobile platforms. With further progress in microphone technologies, embedded computing, and calibration techniques, ubiquitous acoustic imaging could become viable--providing uniquely valuable spatial and semantic context across applications ranging from the industrial internet of things to smart city sound monitoring (Figure 26).