esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experimental Results  
To comprehensively evaluate the effectiveness of the proposed deep water body segmentation network, we compare it with a range of representative models on the publicly available GLH-water dataset. These include classic semantic segmentation networks such as DeepLabV3+, FCN8s, HRNet-48, PSPNet, and STDC-1446, which are widely used in natural scene segmentation tasks; water body-oriented models like MSResNet, MECNet, and MSCENet, which are specifically designed to capture spectral and structural features of water regions in remote sensing imagery; high-resolution segmentation methods such as MagNet, FCL, and ISDNet, which focus on preserving fine-grained boundaries in very high-resolution images; and PCL, which introduces a pyramid consistency loss to address visual inconsistency and topological discontinuity in ultra-large-scale satellite imagery. All models are trained under the same settings, and their performance is evaluated using IoU and F1-Score. The experimental results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **IoU (\%)** & **F1-Score** \\ \hline MECNet [30] & 44.67 & 61.75 \\ ISDNet [53] & 53.04 & - \\ MagNet [54] & 62.77 & - \\ MSResNet [55] & 69.76 & 82.18 \\ FCN8s [16] & 73.66 & 84.83 \\ FCLL [56] & 74.92 & 85.66 \\ MSCENet [23] & 74.81 & 85.58 \\ PSP-Net [18] & 75.19 & 85.84 \\ STDC-1446 [57] & 75.82 & 86.25 \\ HRNet-48 [22] & 78.6 & 88.01 \\ DeepLab-V3+ [21] & 79.8 & 88.76 \\ PCL [44] & 82.26 & 90.27 \\ Our Deep Network & 88.86 & 94.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different segmentation models in terms of IoU and F1-Score on GLH-water dataset [44].  
Among them, the proposed deep water body segmentation network achieves 88.86% in IoU and 94.10% in F1-Score. Compared with other methods, this represents an improvement of 6.6-44.19% in IoU and 3.83-32.35% in F1-Score. These results clearly validate the effectiveness of our architectural design in suppressing noise and enhancing segmentation accuracy under complex environmental conditions.
## 4 Discussion
### Influence of Bridge Layer  
In Figures 21 and 22, the tensile load-displacement response curves of samples with bridge layers are compared to the control samples, which do not have bridge layers.  
The features of the compared samples and the relative experimental results can be found in Table 6.  
Samples 17a and 17b and 18a and 18b were built using a \"bridge layer\", which was put between the mastic and elastomer layer. Compared to the control samples (samples 14a and 14b), samples 17a and 17b show almost the same behavior (Figure 21), both in terms of strength and elongation capacity. Samples 18a and 18b show a little less strength, but greater elongation capacity (Figure 22) in comparison with their control samples (samples 16a and 16b).  
Hence, since the observations are ambiguous, the only certainty is that the use of bridge layers does not negatively influence the adherence between steel and elastomer, and it may improve the elongation capacity, i.e., the ductility, and consequently, the dissipative capacity of the isolator.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 3 State of the Art in Linear Computation Coding
### Multidimensional Functions
#### 3.2.2 The CORDIC Algorithm  
The CORDIC algorithm [37] is typically used to compute scalar nonlinear, typically trigonometric functions. Since these functions occur in rotation matrices, they can be computed by a suitable coordinate transform of the input. Thus, computing the nonlinear function is reduced to calculating a linear function in multiple, typically two dimensions.  
To save computations, the coordinate transform is approximated by multiple rotations, such that each rotation matrix contains only zeros or signed powers of two. Thus, the CORDIC algorithm also performs a matrix factorization as in (6). For rotations in two dimensions, e.g., that results in the following approximation of a rotation by the angle \(\phi\):  
\[\begin{bmatrix}\cos\phi&-\sin\phi\\ \sin\phi&\cos\phi\end{bmatrix}\approx\prod_{b=0}^{L-1}\frac{1}{\sqrt{1+4^{-b}} }\begin{bmatrix}1&-c_{b}2^{-b}\\ c_{b}2^{-b}&1\end{bmatrix} \tag{7}\]  
with appropriate choices of the signs \(c_{b}\in\{-1,+1\}\) and accuracy growing with the number of factors \(L\). However, the algorithm is limited to rotation matrices and not suited for general linear functions. Furthermore, the reduction to multiple rotations implies that all involved matrices must be square.  
While we also propose a multiplicative decomposition into matrices containing only zeros and powers of two, we show that a decomposition into only square matrices is a very bad choice. In fact, for low approximation error, at least one of the matrix factors should be either very wide (as in the mailman algorithm) or very tall.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 3 Results and Discussion
### Simulation Results and Analysis
#### 3.1.4 Rotation Speed  
Rotation directly influences deposition efficiency and spot morphology by altering the airflow field distribution and particle transport behavior. Among these factors, rotation speed is the most critical control parameter affecting deposition uniformity, efficiency, and spot morphology. To enhance deposition uniformity while maintaining a certain level of deposition efficiency, precise control of wafer rotation speed is required during the deposition process. Studies indicate that as the rotation speed increases, the gradient of the rotational shear stress difference from the wafer center to the edge also increases, resulting in position-dependent forces acting on the wafer surface. In general, higher rotational shear stress leads to lower deposition efficiency, meaning that deposition efficiency at the wafer center is lower than at the wafer edge.  
As shown in Figure 7, particle deposition patterns were compared at four different rotation speeds. The results reveal that as the rotation speed increases, the morphology of the deposition spots becomes increasingly unstable, exhibiting an outward diffusion trend. At the highest rotation speed (31.4 rad/s), particles mainly accumulate at the wafer  
Figure 6: Deposition distribution contours for different deposition sizes and relative positions.  
edge. This pattern aligns with the expected relationship between shear force and deposition efficiency. As shear force increases, deposition efficiency decreases toward the wafer center.  
Additionally, high rotation speeds intensify turbulence and vortex effects in the airflow, which influence deposition in two opposing ways: on one hand, turbulence-induced particle entrainment can enhance deposition efficiency, but on the other hand, the increased rotational shear stress reduces deposition efficiency. Overall, as the rotation speed increases, the reduction in deposition efficiency due to shear stress outweighs the enhancement from turbulence effects. Consequently, a moderate reduction in rotation speed helps mitigate shear stress gradients, maintain uniform particle distribution, and prevent excessive particle accumulation at the wafer edge.  
Based on these findings, this study selects a rotation speed of 0.314 rad/s to achieve a balance between deposition efficiency and spot uniformity. At this speed, both the concentration distribution and uniformity of deposition spots are well maintained.
# Green Analytical Chemistry--Recent Innovations
## 3 Innovations in Green Analytical Techniques
### Ionic Liquids and Eutectic Solvents
Eutectic solvents, particularly natural deep eutectic solvents (NADES), have emerged as a greener alternative to ILs. Formed by mixing inexpensive and biodegradable components, such as sugars, amino acids, and organic acids, ESs exhibit low toxicity and high biodegradability. These solvents are increasingly used in sample preparation techniques like liquid-liquid extraction, solid-phase extraction, and microextraction due to their ability to replace hazardous solvents while maintaining or improving extraction efficiency. Additionally, their biocompatibility makes them ideal for applications in food analysis, pharmaceutical quality control, and environmental monitoring. Furthermore, [PERSON] and [PERSON] demonstrated the effectiveness of using hydrophobic natural deep eutectic solvents (NADES), such as those formed by thymol and butyric acid, in dispersive liquid-liquid microextraction (DLLME) for the analysis of multiclass emerging contaminants in surface water. Their study optimized parameters such as pH, temperature, and solvent volume, achieving recoveries between 70% and 120%, with relative standard deviations below 20%. This innovative approach not only replaces traditional toxic chlorinated solvents but also ensures environmentally sustainable and efficient extraction of contaminants across diverse water matrices [60]. The integration of ILs and ESs into analytical workflows has also enhanced the sensitivity and selectivity of various analytical methods, such as gas chromatography (GC), liquid chromatography (LC), and mass spectrometry (MS). For example, ILs have been successfully used as stationary phases in GC [61] and as additives in LC mobile phases [62], improving separation performance. Similarly, ESs have been employed in the preconcentration of trace analytes, reducing matrix interference and improving detection limits. Despite their numerous advantages, challenges remain, such as the high cost of some ILs and the need for a deeper understanding of their long-term environmental impact. However, ongoing research continues to address these issues, with efforts focusing on synthesizing low-cost, biodegradable ILs and exploring new combinations of ES components. The adoption of these green solvents represents a significant step toward more sustainable analytical chemistry practices, offering a balance between performance and environmental responsibility. As innovations in ILs and ESs advance, their role in analytical chemistry is set to expand, driving the transition toward safer and more eco-friendly methodologies.
## 1 Introduction
### Methodology
Specifically, the study has examined the following:
* Transaction speed, evaluating the processing efficiency of different storage approaches;
* Storage cost, comparing economic feasibility across on-chain, off-chain, and hybrid methods. By evaluating these performance metrics, the study provides insights into how various blockchain storage models impact real-world implementations. * _Systematic Presentation of Findings_: To ensure clarity and accessibility, the findings of the study have been systematically visualized using tables and diagrams. * On-chain, off-chain, and hybrid storage methods have been comprehensively compared in terms of security, scalability, and cost efficiency. * The comparative analysis has been structured to highlight key advantages and limitations of each approach, supporting future research directions in blockchain-based storage.
## 3 Results and Discussion
### SEM Analysis  
The morphology of 5%, 15% and 25% in composition cellulose pulp fiber composites were observed under SEM. The fractured surfaces of samples used for tensile testing were examined to study the interfacial boding between pulp fibers and PA6 matrix sequentially. All the fractured samples were allowed to reach equilibrium weight at room temperature and atmospheric conditions prior to SEM analysis. The SEM micrographs for 25% cellulose pulp-PA6 composite are shown in Figure 11.  
The SEM images reveal that there was good wetting of cellulose pulp fibers within the PA6 matrix, denoting that the cellulose fibril surfaces are saturated by the PA6 matrix. Simultaneously in Figure 10(a), it is observed that pulp fiber (micro fibers) were well dispersed and no agglomerations were observed. At higher magnifications (Figure 10(b)-d), the adhesion between the polymer and the pulp fibers might be due to the chemical affinity between both components favoring the interface compatibility. The hydrophilic nature of PA6 makes the adhesion between pulp fibers and PA6 matrix strong [7; 51]. The attraction between PA6 and cellulose fibers makes dispersion and distribution more  
Figure 11: SEM micrographs of fracture surfaces of 25% cellulose pulp composite tested under tensile load at different magnification levels.  
effective, as it was validated by the improvements of tensile and flexural properties of the composites. Some authors have explained the chemical interaction between PA6 matrix and cellulose fibers in the partial hydrolysis of PA6 in presence of moisture and high temperature. The moisture content prior to compounding process in (non-dried) fibers aid in interfacial bonding, due to the partial hydrolysis of PA6 caused by moisture at high temperature, generating carboxylic acid end groups which are compatible with the (-OH) groups forming ester bonds [45]. The reinforcing effect of cellulose materials is determined by the stress transfer through the interfacial adhesion [14]. The PA6 matrix showed surface distortion characteristic of ductile deformation after reaching to a level that the interfacial adhesion cannot resist and the fibers were broken at the surface. No fiber pullout was observed as seen from Figure 11c.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 6 Hardware Model Using Finite State Machines
### FSM Optimizations  
In very large and complex systems, the composition of states often has an exponential growth of the size of the system state space leading to a problem known as _state explosion_. The drawback of this problem is a significant waste of hardware resources which eventually lead to hardware system failure. For example, in Xilinx ISE, this common error _\"ERROR: Portability:3 - This Xilinx application has run out of memory or has encountered a memory conflict...\"_ is reported after FPGA synthesis failure as a result of FSMs that are too big. To avoid this problem, we exploit some characteristics of the FSM \(M\) in order to reduce too many state variables while also optimizing for significant cut-down of used hardware resources. Below we propose four types of optimizations with results trade-off between resource use and performance that can be used by the designer to explore the best solution space that meets the application requirements.
## 3 Preliminaries
### Cryptographic Primitives
#### 3.4.1 Hash Functions  
A cryptographic hash function \(H:\{0,1\}^{*}\rightarrow\{0,1\}^{l}\) maps an input of arbitrary length to a fixed-length output string, known as a hash value or message digest [5; 8]. For a hash function to be cryptographically secure, as detailed in [1; 2; 5], it must satisfy the following:  
* Pre-image resistance (one-wayness): Given a hash value \(y\), it is computationally infeasible to find any input \(x\) such that \(H(x)=y\).
* Second pre-image resistance (weak collision resistance): Given an input \(x\), it is computationally infeasible to find a different input \(x^{\prime}\
eq x\) such that \(H(x^{\prime})=H(x)\).
* Collision resistance (strong collision resistance): It is computationally infeasible to find any two distinct inputs \(x\) and \(x^{\prime}\) such that \(H(x)=H(x^{\prime})\).  
Our protocol assumes the use of a standard secure hash algorithm such as SHA-256 (Secure Hash Algorithm 256-bit), as recommended in NIST FIPS PUB 180-4 [8]. In formal security models like the BPR model, hash functions are often modeled as random oracles.
# Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates
## 4 Demonstration Case Studies
### Case I: Ring-Slot Antenna  
Figure 7 shows our first verification structure: a ring slot antenna, for which its circular ground plane featuring a slot with defected ground structure is excited through a microstrip line [67], whereas all the pertinent details are provided in Figure 8. We assumed independent uniformly distributed parameter deviations with maximum deviation \(\delta_{\max}=0.05\) mm. The yield optimization results are provided in Table 2 for the algorithm of Section 3.1 and the benchmark surrogate-assisted procedures of Table 1. For the discussed approach, the size of the metamodel domain was set to \(\delta_{c,k}=2\delta_{\max}\) (see Figure 3); the training data set for constructing the surrogate contained 35 samples; and its relative RMS error equals 0.5%.  
The details pertaining to the benchmark procedures are as follows. For Algorithm 1, the metamodel of relative RMS error 0.7% was constructed using 400 samples within the domain of size \(10\delta_{\max}\). For Algorithm 2, the surrogate models were built using 50 training data samples over the domain of size \(3d_{\max}\). Here, the first metamodel (of the domain focused around \(\mathbf{x}^{(0)}\)) featured a relative RMS error of 0.4%. In both cases, the aim was to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization Algorithm** & \multicolumn{2}{c}{**Initial Yield**} & \multicolumn{2}{c}{**Optimized Yield**} & \multirow{2}{*}{**CPU Cost \(\mathbf{\lx@sectionsign}\)**} \\  & **Estimated by Metamodel** & & & **Estimated by Metamodel** & & **EM-Based** \\ \hline Algorithm 1 & 81\% & 81\% & 92\% & 93\% & 400 \\ Algorithm 2 & 81\% & 81\% & 91\% & 91\% & 150 \# \\ Algorithm of Section 3.1 & 81\% & 81\% & 91\% & 91\% & 62 \# \\ \hline \hline \end{tabular}
\end{table}
Table 2: Yield optimization of the ring-slot antenna of Figure 7.  
Figure 8: Description of the ring slot antenna of Figure 7 [67].  
Figure 7: Geometry of the ring slot antenna with a microstrip feed (dashed line) [67].  
render the metamodels of similar accuracy to less than one percent to ensure the reliability of the yield estimation. The following yield maximizing design w rendered \(\mathbf{x}^{*}=[20.18\;6.43\) 0.21 11.85 2.95 6.78 7.90 2.31]\({}^{T}\). Figure 9 visualizes the results of Monte Carlo analysis at \(\mathbf{x}^{(0)}\) and at \(\mathbf{x}^{*}\) (carried out with the use of 500 samples).  
The results of Table 2 may be summarized as follows. The metamodel domain confinement according to the methodology described in Section 3.1. results in dramatic cost savings, which is mainly due to the decreased volume size. Still, as the domain is spanned over the significant directions of the design space (those representing the maximum variations of the antenna response), the yield optimization process may successfully be concluded in one stage.
# Modelling and Measuring Trust in Human-Robot Collaboration
## 4 Results
### Influence of Past Iteraction in Trust Dynamics  
The experimental process exposed participants to a series of interactions with an overall 75% reliability. Two experimental models were conceived to alter the sequence in which participants faced the test. Both models alternated between a fully reliable scenario and a chaotic sequence where only 50% of the readings were correct. Model-0 began with trustworthy iterations, while Model-1 presented chaotic iterations first. This setup allows us to investigate whether the order in which participants faced the test influences the propensity to trust.  
To integrate the interaction results, we counted the times participants chose to trust the sensor's reading on each experimental stage and divided the result by the number of interactions on that stage, obtaining the trust rate for each stage. These results were aggregated according to the experimental model they interacted with. Figure 6 shows the distribution of trust rates among the different models and experimental stages, along with the mean trust rate for each case.  
To verify the impact of past interactions on the conformation of trust, we decided to perform a statistical test on the trust rate distributions among the different experimental models for both the case of perfectly working sensors and for the randomly working sensor scenarios. Since Figure 6 shows that the trust rate distribution does not follow normality, we conducted the Mann-Whitney U-test to check the significance of the trust rate differences among experimental models. Table 4 shows the results of these tests, emphasizing those with statistical significance.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Stage** & **Segments** & **Statistic** & **Test** & \(p\)**-Value** \\ \hline Correct sensor & Model-0 (X0)\(-\)Model-1 (X1) & 2231.0 & X0 \(>\) X1 & 0.9979 \\  & & X0 \(<\) X1 & (*) 0.0020 \\ \hline Random sensor & Model-0 (X0)\(-\)Model-1 (X1) & 4692.0 & X0 \(>\) X1 & (**) 1.920 \(\times\) 10\({}^{-9}\) \\  & & X0 \(<\) X1 & 1.000 \\ \hline \end{tabular} (*) Results presenting statistical significance. (**) Results presenting very strong statistical significance.  
\end{table}
Table 4: Results of the Mann–Whitney U-test for trust dynamics between different models and experimental stages.  
Figure 6: Trust rate distribution among experimental model and stages.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 3 Calibration Mission Impact over Systematic Errors  
In this section, the impact of the proposed calibration system is analysed over three very important systematics, that can be clearly benefited by a calibration satellite : the polarization angle, pointing and beam pattern. For the optimal calibration of these effects, a point-like source should be viewed in the far-field of the telescope (see Table 1 for some example distances).  
While other systematic errors could be corrected in-flight without the requirement of an external source, however, it is important to note that the proposed calibration system allows also to characterise all those additional errors (gain, non-linearity, spectral or band-pass response, etc.) and takes into account the complete optical path of the CMB polarimeters. Thanks to the accurately characterized artificial source embarked in L2-CalSat, the calibration of fully integrated and cold receivers will be possible, allowing the characterization of any misalignment due to thermal contractions in operation at L2. Additionally, systematic errors from optical or mechanical systems (HWP, filters, etc.) can be characterized, taking into account the complete optical path. Note again, that by using the proposed calibration system, the assumptions on TB and EB signals will not be required, allowing an accurate determination of cosmological parameters and the measurement of CB, between others physical phenomena.  
The statistical characteristics of the CMB anisotropies are usually studied by means of the angular power spectrum. The variance of the spherical harmonic coefficients from temperature (TT) and polarization (EE, BB) fluctuations are represented, as a function of the multipoles l. Additionally, the correlation between temperature and polarization are also represented by the cross spectra TE, TB and EB. Overall, spurious polarization effects produce a power leakage from the T- and E-mode signals that result in a systematic error in the BB, TB and EB spectra.