esa-sceva/satcom-chunk-collection huggingface dataset:
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
###### Abstract  
The simultaneous processing of orbital angular momentum (OAM) and polarization has recently acquired particular importance and interest in a wide range of fields ranging from telecommunications to high-dimensional quantum cryptography. Due to their inherently polarization-sensitive optical behavior, Pancharatnam-Berry optical elements (PBOEs), acting on the geometric phase, have proven to be useful for the manipulation of complex light beams with orthogonal polarization states using a single optical element. In this work, different PBOEs have been computed, realized, and optically analyzed for the sorting of beams with orthogonal OAM and polarization states at the telecom wavelength of 1310 nm. The geometric-phase control is obtained by inducing a spatially-dependent form birefringence on a silicon substrate, patterned with properly-oriented subwavelength gratings. The digital grating structure is generated with high-resolution electron beam lithography on a resist mask and transferred to the silicon substrate using inductively coupled plasma-reactive ion etching. The optical characterization of the fabricated samples confirms the expected capability to detect circularly-polarized optical vortices with different handedness and orbital angular momentum.  
P ancharatnam-Berry optical elements; silicon metasurfaces; mode division multiplexing; orbital angular momentum; polarization division multiplexing; electron beam lithography; subwavelength digital gratings; nanofabrication; reactive ion etching +
Footnote †: journal: Applied Physics Letters
## 2 Results and Discussion
### Ultrafast X-ray Diffraction Measurements
In the second pump-probe experiment, the sample is composed of a 105 nm thick (110) SrRuO\({}_{3}\) (SRO) film epitaxially grown on a (110) SrTiO\({}_{3}\) (STO) substrate. We excited the SRO layer with 800 nm femtosecond laser pulses. The penetration depth is about 52 nm [28]. The focal spot diameter is 2 mm on the sample with an excitation fluence of 13 mJ/cm\({}^{2}\). Unlike Au film, imperfect crystal growth and a smaller cross-section in X-ray diffraction lead to the broadening of diffraction peaks and a decrease in diffraction efficiency during our data collection process. As a result, a total of fifteen-cycle acquisitions are required to achieve a good signal-to-noise ratio in this experiment. The acquisition time of an image in each cycle is 30 s.  
The photoinduced dynamics are represented by the transient 0-20 scans shown in Figure 4a. The Bragg peak becomes much broader/weaker and moves to a lower angle following the optical excitation [29]. The absorption of the pump pulse leads to a quasi-instantaneous temperature jump, resulting in inhomogeneous heating of the film and strains, which modify the interplanar spacing. Such transient changes in lattice geometry give rise to changes in the X-ray diffraction pattern. As shown in Figure 4b, the observed maximum change in the intensity corresponds to the maximum peak shift to a smaller angle, indicatingthe maximum lattice expansion of \(\Delta d/d\approx 0.4\%\). The characteristic timescale is determined by \(\tau_{ac}=d/v_{s}=16.7\) ps, where \(d\) is the layer thickness (105 nm) and vs. = 6.3 km/s is the longitudinal sound velocity [4, 28, 30]. Thereafter, the peak intensity is followed by a slight backshift and then reaches a new equilibrium position, which indicates a monotonical decrease of strain until the equilibrium state is reached. This process is determined by the pure thermal expansion and will continue until it relaxes to ambient temperature via heat diffusion into the substrate on a nanosecond or even microsecond timescale [31, 32].
## 2 Materials and Methods  
### Case Study Description  
AGV-based transportation systems are commonly utilized to deliver materials to individual production facilities or workstations along production lines. These systems necessitate timely and precise preparation of materials for processing in the required quantities. Minimizing excessive inventory is crucial, ideally achieving Just-In-Time (JIT) deliveries. When production is stable and uniform, supplying workstations is relatively straightforward. In that case, AGVs can follow a predefined path or move freely among fixed points. However, in flexible production systems where operations vary in sequence or duration, or if the numbers and locations of serviced workstations change, optimization of workstation supply becomes a much more complex issue.  
The solution to the problem of servicing the abovementioned flexible manufacturing system is represented using a fictitious case study. This production system is designed for manufacturing two distinct products, referred to as Part 1 and Part 2, and consists of ten workstations (Figure 1). For better clarity within the model, the products Part 1 and Part 2 are color-coded (Part 1--red; Part 2--black). Four different operations are performed by ten devices organized into four machine groups (MG1-MG4) based on the operations conducted.  
Each product undergoes three operations across three different machines. The black part (Part 1) is processed sequentially on machines from groups MG1, MG2, and MG4, while the red part (Part 2) is machined on machines from groups MG1, MG3, and MG4. Each group may contain a varying number of machines, each with different processingtimes. Transport between machines is facilitated by an appropriate number of AGVs, which are dispatched based on machine requests.  
The AGV transport system ensures not only the delivery of materials to individual workstations but also the transfer of products between workstations according to the technological process and the final transfer of finished products to the warehouse. For modeling and simulation purposes, we selected the SIEMENS Tecnomatix Plant Simulation program (version 2302). For the subsequent optimization of the AGV system, the optimization tool Genetic Algorithm is used, which is part of the Tecnomatix 2402 software.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 2 Background
### Space Spectrum Estimation  
Spatial spectrum estimation is an important part of array signal processing. The spectrum represents the energy distribution of the signal in each frequency, while the \"spatial spectrum\" represents the energy distribution of the signal in each direction. Therefore, if we can get the \"spatial spectrum\" of signal, we can get the direction of arrival (DOA). Therefore, the spatial spectrum estimation is often called \"DOA estimation\" [29]. The direction finding technology of spatial spectrum estimation can achieve the simultaneous direction finding of multiple targets (including coherent signal and incoherent signal), and under the condition of low signal-to-noise ratio, the direction finding accuracy is very high, which can be fully used in the direction finding of radiation sources in complex electromagnetic environment. There are two basic algorithms, one is MUSIC (multiple signal classification) [30] algorithm. The principle of the algorithm is to decompose the autocorrelation matrix into subspaces, obtain the noise subspace and signal subspace of the matrix, and then use the orthogonal relationship between the noise subspace and the direction vector to estimate the incident direction of the signal. Another method is ESPRIT [31] (estimating signal parameter via rotational invariance techniques), which is based on the rotation invariant subspace of signal subspace class. It estimates the direction of incoming wave through the acquired signal subspace. At present, the WJ-9010 detection system (Leonardo DRS: Arlington, VA, USA) and RAF-5100 detection system (Rafael Advanced Defense Systems: Haifa, Israel) are both receivers using spatial spectrum direction finding technology. The measurement accuracy of the direction finding system can reach within 1\({}^{\circ}\) in the military field, and it is also used in the civil field such as auto driving. For the consideration of cost and other factors, the equipment model used in this paper is a small civil space spectrum direction finding radar, which has no interference and occlusion, so as to ensure that the accuracy of the target can reach within 10\({}^{\circ}\) in the range of angle. In this paper, the reference space spectrum DF radar model is shown in Figure 2.
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 4 Experiment
### Results and Analysis  
The experimental results are shown in Table 2. It can be observed that MGKGR achieves the best performance across all metrics. This demonstrates that multimodal semantic information effectively enriches the feature representation of geographic knowledge graphs, thereby enhancing the model's performance in geographic attribute prediction.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**\#Entity**} & \multicolumn{2}{c}{**\#Relation**} & \multicolumn{2}{c}{**\#Triple**} \\ \cline{2-7}  & **\#Attr\_Entity** & **\#Geo\_Entity** & **\#Attr\_Relation** & **\#Adj\_Relation** & **\#Attr\_Triple** & **\#Adj\_Triple** \\ \hline PA-30K & 218 & 31,267 & 87 & 514 & 223,987 & 186,049 \\ FL-25K & 222 & 24,037 & 87 & 360 & 249,222 & 76,326 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Statistics.  
In traditional KRL models, TransE, ComplEx and RGCN achieved a relatively strong performance on two datasets. This can be attributed to the fact that TransE aims to minimize the distance between entities, a direct optimization objective that helps the model more accurately capture relation features within the knowledge graph. ComplEx is highly effective in modeling asymmetric relations and demonstrates strong performance in capturing rich-semantic adjacency relations. The design of the RGCN model is inherently capable of handling complex relationships, which enables it to comprehensively understand the information in the knowledge graph. Therefore, these two models are able to effectively leverage the enriched adjacency information in the training set, resulting in better feature representations. In contrast, models such as ConvE and CompGCN struggle to capture the connections between adjacency relations and attribute relations. The abundance of adjacency information even acts as noise for attribute prediction, leading to lower performance.  
The multimodal model MKGFormer [35] achieved the second highest results in multiple metrics, which validates the importance of multimodal data for geographic knowledge graph representation. However, since it does not model the structural information of the knowledge graph, it still has certain limitations. In contrast, our proposed model MGKGR captures the structural features of the knowledge graph using the RGCN, and by integrating these with multimodal semantic features, MGKGR obtains a more comprehensive feature representation. The experimental results show that our approach achieves optimal performance across all metrics. This demonstrates that the two-stage multimodal feature fusion method proposed in this paper can effectively mitigate the impact of heterogeneity across different modalities. Additionally, it enhances the model's ability to represent the knowledge graph by integrating features from multiple modalities.
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 3 Background  
### Target Estimation: Bayesian Filtering  
We use Bayesian inference to recursively estimate target state \(x\) through sequential observations from each of \(n\) agents, \(y^{1:n}\) in a probabilistic manner. This inference model aims to predict the posterior distribution of target position at time \(k\), namely \(p(x_{k})\). Bayesian filtering uses a prediction stage and a correction stage with incoming sensing information. Assuming that the prior distribution \(p(x_{k-1})\) is available at time \(k-1\), the prediction step attempts to estimate \(P(x_{k}|y^{1:n}_{1:k-1})\) from previous observations as follows.  
\[p(x_{k}|y^{1:n}_{1:k-1})=\int p(x_{k}|x_{k-1})p(x_{k-1}|y^{1:n}_{1:k-1})dx_{k- 1}, \tag{1}\]  
where \(p(x_{k}|x_{k-1})\) is the target's motion model based on a first-order Markov process. Then, when the measurement \(y^{1:n}_{k}\) is available, the estimated state can be updated as  
\[p(x_{k}|y^{1:n}_{k})=\frac{p(y^{1:n}_{k}|x_{k})p(x_{k}|y^{1:n}_{1:k-1})}{p(y^{ 1:n}_{k}|y^{1:n}_{1:k-1})} \tag{2}\]  
where \(p(y^{1:n}_{k}|y^{1:n}_{1:k-1})=\int p(y^{1:n}_{k}|x_{k})p(x_{k}|y^{1:n}_{k-1} )dx_{k}\) and \(p(y^{i:n}_{k}|x_{k})\) is a sensing model for a multi-agent system that can also be decomposed to \(i\)-th agent's sensing model \(p(y^{i}|x)\). For the correction stage, the measurements of all agents are used to modify the prior estimate, leading to the target belief. If a static target is assumed, the target prediction can be described as \(p(x_{k}|x_{k-1})=\mathcal{N}(x_{k-1};x_{k},\Sigma)\), only containing a noise term with the previous target state.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 3 Model Establishment and Parameter Calibration
### Calculation of Particle Coarsening Treatment Method  
Due to the small scale of the direct shear and Marsh funnel test models, the corresponding discrete element simulations require limited computational resources. However, when these mesoscopic particles are applied to the overburden grouting model, the number of computational elements increases exponentially. To balance computational accuracy and efficiency, a multi-scale particle size optimization strategy is implemented based on a stepwise amplification method [23]. Coarse graining is not merely a matter of increasing particle size; it typically requires corresponding adjustments to material parameters--such as contact stiffness, damping coefficients, and friction coefficients--to ensure the rationality  
Figure 4: Direct shear test results of overburden. (**a**) ZJ1 direct shear tester. (**b**) Direct shear test results.  
Figure 5: Cement fluidity test. (**a**) Marsh funnel test. (**b**) Numerical simulation test of Marsh funnel.  
of particle behavior. The core objective of particle coarsening is to enable efficient simulations at larger spatial and temporal scales while preserving the macroscopic physical behavior of the system. This is achieved by reducing computational complexity without compromising the fidelity of the simulation results. The implementation process involves the following steps: (1) Initially, direct shear tests and Marsh funnel tests were performed using the same model scale and a smaller particle radius, consistent with laboratory conditions. Through iterative trial-and-error calibration, the mechanical parameters derived from the numerical simulations were aligned with the experimental results, thereby yielding a set of appropriate PFC parameters. (2) Similar grading: Maintain the original particle gradation of the overburden while proportionally enlarging the model's geometric dimensions. (3) Parameter equivalence correction was performed by applying particle size scaling and mesoscopic parameter inversion to ensure that the macroscopic mechanical response of the system remained consistent. Based on the calibrated parameters from the initial step, a larger-scale model was subsequently constructed to assess its mechanical performance. This approach enables model upscaling while preserving the fidelity of particle interaction characteristics. (4) Iterative convergence control: Repeat the first two steps until the desired model scale is achieved. After grading and amplification, the particle size of the overburden and cement particles is enlarged by factors of 50 and 20, respectively. As a result, the minimum particle size of the overburden is 0.01 m and that of the cement is 0.001 m in the grouting simulation, ensuring that the groutability ratio exceeds 10. After recalibrating the contact constitutive parameters, the particle contact properties used in the grouting simulation are listed in Table 2.
## 2 Materials and Methods
### Numerical Calculation Method and Verification
#### 2.4.2 Grid Independence Analysis  
This study analyzes the complex interaction of multiple physical phenomena, including interphase heat transfer, fluid flow, and phase change, by simulating the freezing process in ballast tanks. Consequently, the selection of the model mesh size is crucial for obtaining accurate numerical results. To enhance the precision of the numerical calculations, this paper examines three different grid sizes, namely 0.1 m, 0.06 m, and 0.03 m, and evaluates grid independence when calculating the temperature and ice volume fraction across these varying grid sizes. Table 2 presents the temperature and ice volume fraction for each grid size.  
It can be observed from Table 2 that as the mesh size decreases from 0.1 m to 0.06 m, the range of temperature change is small, and the ice phase volume fraction increases from  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Grid Size/m**} & \multicolumn{2}{c}{**Temperature/\({}^{\prime}\)C**} & \multicolumn{2}{c}{**Volume of**} \\  & **a** & **b** & **Ice Phase/\%** \\ \hline
0.1 & \(-\)1.009 & \(-\)1.036 & 68.470 \\
0.06 & \(-\)1.014 & \(-\)1.042 & 69.049 \\
0.03 & \(-\)1.015 & \(-\)1.043 & 69.336 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature and ice volume fraction of different mesh sizes.  
Figure 4: Numerical analysis process.  
69.470% to 69.049%, resulting in a change of 0.84%. When the mesh size further decreases from 0.06 m to 0.03 m, the ice phase volume fraction increases from 69.049% to 69.336%, with a change of 0.41%. Therefore, the mesh size has a negligible effect on the calculation results. Considering both the calculation rate and accuracy, this paper employs a structured grid division method to model the evolution of ice shape within the ballast tanks, while utilizing an unstructured grid to depict the evolution of ice shape in the curved section at the bottom of the ballast tank. Figure 5 illustrates the grid models of ballast tanks with varying sizes. Among them, the upper green area is the air domain, and the lower brown area is the ballast water.
## 1 Introduction
Many HEAs have been and are being studied nowadays in order to obtain better properties or increase their application range, such as MoNbTiVTaW [18], Fe20 Mn15 Cr15V10 Al10C2.5 [19], CoCrFeMnNi [20], TiNbTaZrMoHfWCr [21], Fe20 Co30 Ni10 Cr20 Mn20 [22] and CoCrFeNiMo [23; 24; 25]. CoCrFeNiMo HEA has been doped with silicium [26], zirconium [27], aluminum [28] or titanium [29], as well as used as a coating [30; 31], which enhance the HEA's versatility in different engineering applications. The effect of Mo has proven to be crucial not just on its mechanical properties, such as high hardness [32], but in the formation of a passive layer in the marine environment with the creation of Cr\({}_{2}\)O\({}_{3}\) and MoO\({}_{3}\), which increases the HEA's corrosion resistance [31; 33]. On the other hand, the addition of Ni and Cr on the HEA contributes to the formation of a resistant protective oxide film over the outer layer of the material, implying also an improvement in corrosion resistance [34; 35]. This paper aims to investigate the mechanical and electrochemical properties of B4C materials doped with 0.5% and 1% CoCrFeNiMo HEA and compare the results with those of B4C doped with 2% and 3% CoCrFeNiMo [36; 37]. Thus, in this study, the impact of CoCrFeNiMo HEA doping on the electrochemical and mechanical properties of B4C ceramic has been investigated. Electrochemical tests were conducted in an artificial seawater environment, a 3.5% volume of NaCl solution. Additionally, scanning electron microscopy (SEM) and a microhardness statistical anal ysis were conducted. The study focuses on evaluating and comparing these two new compositions against the previously analyzed compounds under a marine and corrosive environment. By expanding the scope of the investigation, this work seeks to provide a deeper understanding of how CoCrFeNiMo HEA doping affects B4C ceramic and which of these compounds have the optimal composition, with an emphasis on applications where factors such as durability, corrosion resistance and enhanced hardness are essential.
## 2 Materials and Methods
### Genotyping and SNP Identification  
Blood samples were drawn from the metatarsal vein using standard venipuncture and collected in vacuum tubes. Genomic DNA was extracted using a QIAampR DNA Blood Mini Kit (QIAGEN, Tian Gen Biotech (Beijing) Co., Ltd., China). Whole-genome resequencing was performed on the DNBSEQ-T7 platform with 150 bp paired-end reads, achieving an average depth of over 2\(\times\) per generation. For subsequent analysis, reads were aligned to the mallard duck reference genome (ASM874695v1) [16] using BWA (v0.7.10) [17]. Following alignment, SNPs were identified using GATK HaplotypeCaller (v4.1) [18], with parameters set to default except for -stand_call_conf being set to 30. Subsequently, the individual gvct files were merged, and autosomal quality control was performed using VCFtools (v0.1.16) [19] with the parameters -remove-indels, -minQ 30, -min-alleles 2, and -max-alleles 2 to filter SNP sites for genotype imputation. After this, STITCH (v1.6.10) [20] was used for genotype imputation. Post-imputation, quality control was conducted using BCFtools (v1.8) [21] with INFO_SCORE > 0.4. Then, PLINK (v 1.90) [22] was used for further quality control based on -maf 0.01, -geno 0.05, and -mind 0.05. Following this, linkage disequilibrium pruning was performed with the parameters 50 5 0.2 to extract independent SNP sites. Finally, 365,860 SNPs were retained for subsequent analysis (Figure S1).
## 4 Experimental Setup
### Evaluation Metrics  
The goal of validating the quality of clustering methods is the assessment of the similarity of objects in the same cluster and, at the same time, the dissimilarity of objects in different clusters by using a pairwise difference of between- and within-cluster distances [62; 63]. We evaluate our models using the following clustering evaluation methods: \(F_{1}\) score (weighted), silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin index.