esa-sceva/satcom-chunk-collection huggingface dataset:
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 4 Results and Discussion
### Impact of Multi-View Partitioning Strategies on Model Performance  
This experiment was conducted to examine the impact of different multi-view partitioning strategies on the performance of self-supervised representation learning. Specifically, three partitioning approaches were compared: random partitioning, information gain-based partitioning, and cluster-based partitioning. All other modules and training configurations were held constant, and performance was assessed using accuracy, F1-score, AUC, and normalized MSE.  
As shown in Table 7, the random partitioning strategy resulted in the worst model performance, suggesting that arbitrary splitting fails to preserve meaningful structural relationships among features. Partitioning based on the information gain resulted in improved performance by capturing subsets of features with higher relevance to the target labels. The best results were achieved using cluster-based partitioning, which consistently outperformed the other two strategies across all metrics. This advantage was particularly apparent in accuracy and F1-score, indicating superior capability in modeling feature dependencies. From a theoretical perspective, the performance differences observed across these partitioning strategies stem from their ability to retain informative structure within each subspace. Random partitioning ignores statistical and semantic correlations among features, leading to unstable distributions in the latent embeddings. In contrast, the information gain-based approach utilizes label-conditioned entropy to guide the inclusion of high-importance features in each view, but has limited effectiveness under weak supervision. The cluster-based strategy constructs high-similarity feature groups via unsupervised learning, which enhances both intra-subspace consistency and inter-subspace complementarity. This in turn leads to more cooperative and expressive view-specific embeddings. Given that the proposed method relies heavily on multi-view consistency learning, the structural quality of the subspaces directly influences the discriminative power of the latent space, which explains why cluster-based partitioning consistently yields superior results in both theoretical and empirical settings.
###### Abstract  
In this work, we report an optofluidic system for manipulation of orientation of zeolite crystals near the bottom of a rectangular cross-sectional, straight, quartz microfluidic channel. Manipulation is accomplished by using two computer-controlled syringe pumps that generate adequate hydrodynamic forces for translation and rotation of crystals. Rotation of a crystal around its longitudinal axis allows us to inspect its four major faces for defects. Coffin-shaped zeolite crystals have been studied by several authors by fixing them to a substrate, using two different crystals to assess the roof and gable orientations. The proposed system permits complete assessment of a single crystal by shifting it between roof and gable orientations; moreover the medium can be controlled. Computational fluid dynamics simulations show that crystals in free motion near the bottom of the channel should move faster than the velocity estimated from video. An opposing force, which prevents the crystals from moving freely, has been calculated in order to match translation velocities from simulations and experiments for three given flow rates. The reported optofluidic system is proposed as a novel tool that we believe will open new possibilities for individual zeolite crystal assessment by manipulation of its orientation and medium control.  
microfluidics; hydrodynamic force; zeolite crystal; manipulation Article
## 6 Simulation and Analysis
### Simulation Results and Analysis
The delay of GWOORAC is lower than that of GWOORANC, which proves that the inter-satellite cooperation reduces the delay effectively. The delay of GWOORAC is much lower than that of OLC, and the delay reduction becomes larger gradually. This demonstrates that a combination of suitable task offloading decisions and reasonable computing resource allocation can reduce the delay significantly. Figure 4: Delay vs. number of satellites. Figure 5: Delay vs. computing resources of each satellite. As shown in Figure 6, the delay of all schemes shows upward trends as the computation intensity, i.e., \(c_{i}\), increases, because of the increased task computation delay. The delay of GWOORAC is much lower than that of GWOORAC due to the optimal computing resource allocation. The delay of RandORAC is higher than that of GWOORAC, which validates that the GWO algorithm can obtain the proper task offloading decision, thus reducing the delay. Thanks to the inter-satellite cooperation, the delay of GWOORAC is lower than that of GWOORANC. The computing capacity of IoT devices is weaker than that of MEC servers, so the delay of OLC is higher than that of GWOORAC, and its growth speed is much faster than that of GWOORAC. When \(c_{i}\leq 400\) cycles/bit, the delay of GWOORAC and other schemes is fairly close. When \(c_{i}>400\) cycles/bit, the gap becomes larger, which validates that both the appropriate task offloading decision and the proper computing resource allocation are necessary. As shown in Figure 7, the data size of each task, i.e., \(a_{i}\) is assumed to be the same. As \(a_{i}\) increases, the delay of all schemes increases. The reason is that the data size is bigger, and the corresponding task transmission delay and task migration delay are higher. Additionally, the computing resources required by a task are proportional to its data size, which means that big data sizes bring high task computation delay. Compared with GWORandRAC, the delay of GWOORAC is much lower due to the optimal resource allocation. As can be seen from the fact that the delay of GWOORAC is lower than that of RandORAC, the task offloading decision obtained by the GWO algorithm can reduce the delay. The gap between the delay of GWOORAC and that of GWOORANC shows the contribution of the inter-satellite cooperation. The delay of OLC is much higher than that of GWOORAC, which validates the effectiveness of GWOORAC. As Figure 8 shows, the delay of both schemes increases with the increase of \(N\). The delay of GWOORAC is lower than that of PSOORAC. This is because the values of the parameters, such as \(\widetilde{A}\), are adaptive, allowing for a better balance between exploration and exploitation, thus more effectively avoiding falling into the local optimum. Figure 6: Delay vs. computation intensity.
## 2 Materials and Methods
### Quantitative Reverse Transcription PCR (RT-qPCR) Analysis  
Expression of _satP_, _focA_, _napF_, _glgP_ and _napD_ genes was evaluated by qPCR at the mRNA level. The cells were grown to an exponential phase (OD\({}_{600}\) = 0.5) and treated with ENR (sub-inhibitory concentration) over a period of 15 min. Total RNA extraction of _PmS_, _PmR_ and _PmHR_ was performed using an RNAiso Pure RNA Isolation Kit (TaKaRa), according to the manufacturer's instruction, and was reverse-transcribed to cDNA. The real-time PCR process was performed on the Applied Biosystems 7500 Real-Time PCR System (ABI, state abbr., USA). The primers of _satP_-F (CGCCAAAGCAAACGCCATACC) and _satP_-R (TACGCCAATCCGGTCCATTAGG) were designed. The specificity of the qRT PCR results was confirmed by agarose gel and melting curve analysis, and the data were analyzed by the Applied BiosystemsTM 7500 software. All qPCR reactions were performed with six replicates.
# New Trends in 4D Printing: A Critical Review
## 7 Application and Case Studies
### Biomedical  
Many biological applications, including drug delivery systems, self-expanding stents, implanted devices, catheters, guidewires, atrial occlusion, and thrombectomy devices, benefit from the features of SMMs [184]. 4 DP can boost the possibility of reimagining a customized medical treatment. Recent research has shown that 3D-printed components allow developing therapeutic and rehabilitative devices, since they can be customized for patients [105]. With the 4 DP integration, this customization can be enhanced by adding functionalities from the SMs for novel specialized therapies, e.g., compression treatment of edemas [118]. The future development of smart medical devices is strictly related to the enhanced functionalities given by the SMM, such as self-healing ability [162].  
Significant 4 DP biomedical examples are self-expanding stents [105] and scaffolds for tissue engineering [159; 161]. In Ref. [185], an extrusion-based deposition system was used to fabricate reinforced hydrogels, which can be used as a technique for printing artificial tendons. Auxetic materials with negative Poisson's ratios have important applications across a broad range of engineering areas, especially biomedical and tissue engineering. They can act as deployable systems that can be moved to inaccessible zones in the human body. For example, cardiovascular diseases can be treated using self-expandable vascular stents, particularly through the creation of vascular grafts, stents, and devices for left atrial appendage closure [92].  
Polymeric stents find good use in drug delivery systems. Curved shape-changing specimens with different lattice structures were designed and printed as drug delivery reconfigurable structures in [119]. In another example, magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles were employed to functionalize PLA-based Shape Memory Composite spiral intravascular stents as magnetic-response devices able to complete the unfolding in 10 s.  
4 DP can be used to create self-healing products. For example, the authors of [21] developed inks with a photocurable resin and a semicrystalline thermoplastic polymer to simplify DIW-based printing of semi-Interpenetrating Polymer Network elastomer composites. The ink can be utilized to 3 DP extremely stretchy, shape-memory and self-healing elastomers via UV light-assisted DIW. Urethane diacrylate and a linear semicrystalline polymer are combined to create an ink that can be used to 3D print an elastomer that can be stretched up to 600%.  
In an access-limited surgical space, SMA devices allow release of external loads; thus, they are used in the neurosurgical field as coils, stents, and micro-guidewires, mainly to treat cerebral aneurysms [4]. The TiN alloys have been successfully applied as biomaterials in orthodontic and orthopedic implants. Such devices are predicted to enhance pathological responses and enable the use of less invasive surgical methods and the insertion of implants into areas that would otherwise be inaccessible [92].
# New Method for Simultaneous Arsenic and Selenium Speciation Analysis in Seafood and Onion Samples
## 4 Conclusions  
The method presented here allowed for simultaneous speciation analysis of five analytical signals of arsenic forms, As(III), As(V), DMA, MMA, and AsB, and four analytical signals for selenium forms, Se(IV), Se(VI), Se-Met, and Se-cystine. Separation of the main forms of As, Cr, Sb, and Se was not possible during one single analytical run on one analytical column and mobile phase. The proposed method showed good selectivity, linearity, and sensitivity for arsenic and selenium species with the use of two analytical columns, Dionex IonPac AS22 + Dionex Ion Pac CG5A, and ammonium nitrate at pH = 9.0 as a mobile phase. The presented method was able to provide the necessary data for arsenic and selenium species condensed into one chromatogram. The new method developed allowed for the simultaneous determination of nine speciation forms, and among the analyzed onion samples, three forms were determined for one sample (white bio onion), and four for two seafood samples (red shrimp and squid). The iAs was obtained only in all onion samples (form As(III)), and the dominant form of organic As species in all seafood was AsB. In the case of all selenium, all forms were separated but for the one sample. The  
\begin{table}
\begin{tabular}{c c} \hline \hline Generator power [W] & 1200 \\ Argon flow—plasma [L/min] & 8.0 \\ Argon flow—nebulizer [L/min] & 1.1 \\ Argon flow—auxiliary [L/min] & 0.7 \\ Nebulizer &
\begin{tabular}{c} Concentric type “micro” \\ Torch \\ \end{tabular} \\ Spray chamber temperature [\({}^{\circ}\)C] & 5.0 \\ Collision gas—He [mL/min] & 6.0 \\ Voltage on octapole rods [V] & \(-\)21 \\ Energy filter [V] & 7.0 \\ Sampling deep [mm] & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ICP–MS parameters for As and Se analysis.  
domination of SeMet forms was observed in all samples, especially in onions. The data obtained in water extracts on arsenic and selenium by ICP-MS analysis were comparable with the results from speciation analysis. This new method can be successfully applied for the speciation analysis of samples with similar natures and matrices.  
Conceptualization, K.K. and M.F.; Methodology, K.K., A.Z.-F. and M.F.; Validation, K.K. and M.F.; Formal Analysis, K.K. and M.F.; Investigation, K.K.; Writing--Original Draft Preparation, K.K.; Writing--Review and Editing, K.K., A.Z.-F. and M.F.; Supervision, M.F.; Funding Acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
The work was supported by grant no. POWR.03.02.00-00-1023/17 cofinanced by the European Union through the European Social Fund under the Operational Program Knowledge Education Development.  
Not applicable.  
The authors confirm no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 4 Experimental Conditions and Results
### Fine Structures of the Level 4d\({}^{10}\)6d \({}^{2}\)D (J = 5/2 and J = 3/2)  
Figure 9 shows clearly the fine structure split of the Ag-level 4d\({}^{10}\)6d \({}^{2}\)D. The scan shows resonance peaks at 421.217 nm and 421.402 nm, corresponding to the transitions to J = 5/2 (left) and J = 3/2 (right), respectively. The fine structures show a split of roughly 186 \(\pm\) 2 pm, corresponding to 314 \(\pm\) 3 GHz, in accordance with the National Institute of Standards and Technology (NIST) database value of 185 pm, corresponding to 312.5 GHz [17].  
Qualitatively, the left peak had a higher intensity and could indicate higher transitional probability of the line. According to this measurement, the second-step transition to J = 5/2 would be the preferred choice for production of Ag\({}^{+}\) ions in the SPES facility. Quantitative comparison of the relative intensity of the two transitions can, unfortunately, not be provided as the stability of the ablation plume intensity was not determined sufficiently precisely.  
Figure 8: First-step Doppler broadening; scanned with second-step laser, operating as a multi-mode laser with central frequency @421.402 nm Scanning range = 50 pm (139.2 GHz).  
Figure 9: Fine structure of 4d\({}^{10}\)6d \({}^{2}\)D [J = 5/2 (left peak) and J = 3/2 (right peak)]. Scanning range = 330 pm (557.4 GHz).
## 4 Fault-Tolerance Control Strategy
### Differential Drive Control
#### 4.3.2 Yaw Moment Controller  
The PID controller, which is commonly used in industry, is selected for the yaw controller. The swing torque controller receives as input the difference between the ideal and intended yaw rates, and the output is the additional yaw torque required to stabilize the vehicle.  
\[\Delta T=K_{P}e(t)+K_{I}\int e(t)dt+K_{D}\frac{de(t)}{dt} \tag{25}\]  
In Equation (25), \(K_{P}\) is the ratio element, \(K_{I}\) is the integral element, \(K_{D}\) is the differential element, \(\Delta T\) is the additional yaw moment, and \(e(t)\) is the yaw rate difference.  
Figure 16 depicts the yaw torque controller's structure:  
After several simulation experiments to adjust the parameters, our PID scale coefficients are \(K_{P}=100\), \(K_{I}=0.001\), \(K_{D}=0.1\).  
Figure 16: Yaw moment controller.  
Figure 15: Linear two-degrees-of freedom model.
## 3 State of the Art
### Related Surveys
* Which communication patterns are utilized in the solutions they offer? * Do the proposed solutions operate in synchronous or asynchronous mode? * Which aggregation model do the proposed solutions utilize? * Which optimization algorithms do the proposed solutions utilize? * Are the proposed solutions designed to support real-time processing? * Are the proposed solutions designed to support imbalanced data distribution? * What is the impact of the implemented solutions on overhead costs? Based on the formulated questions, we considered the following criteria during our review of the papers to organize the information in a structured manner that allowed for easy comparison and understanding:  
* Year of publication;
* Datasets used;
* Attacks detected;
* ML models;
* Communication patterns;
* Communication synchronization;
* Evaluation metrics;
* Model aggregation algorithms;
* Optimization algorithms;
* Real-time considerations;
* Data distribution;
* Communication overhead. While FL-based IDSs for IoV are the primary focus of this paper, we did not conduct any experiments on the reviewed approaches to evaluate them. The study aimed to highlight open difficulties and research directions by considering the described factors. Table 9 provides a summary of the comparison's results. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
**Ref.Year Dataset** & **Attacks** & **ML** & **Communication** & **Communication** & **Evaluation** & **Model** & **Optimization** & **Real-Time** & **Data** & **Overhead** \\  & **Detected** & **Model** & **Patterns** & **Synchronization** & **Metics** & **Aggregation** & **Algorithms** & **Real-Time** & **Data** & **Overhead** \\ \hline
[90], 2022 Practical dataset & -Spoing attacks & \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & -The detection & - & - & - & Real-time \\  & -Energy attacks & \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & - & - & accuracy & - & - & - & processing & - & - \\  & -Dgo attacks & \begin{tabular}{} \end{tabular}  & \ \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & & & & & & & & & & \\ \hline
[60], 2023 Car Hacking & -Dgo attack & \ \begin{tabular}{} \end{tabular}  &
\begin{tabular
# Developmental History of Soil Concepts from a Scientific Perspective
###### Abstract  
Various soil concepts have emerged since the beginning of the twentieth century, with some shared similarities. These concepts have contributed to a rise in the awareness of protecting limited soil resources, but not every idea has equally gained widespread attention from scientists. The purpose of this study was to document the developmental history of 10 soil concepts from 1900 to 2018 and investigate their growth/decline. Articles containing words related to the selected soil concepts in titles, abstracts, or publication contents available in the Web of Science were examined. \"Soil production\" was the oldest concept, found in a paper published in 1910, followed chronologically in the literature by soil care, fertility, conservation, quality, health, protection, security, sustainability, and resilience. Most of the concepts were initially found in non-soil-science journals that predated publications in soil science journals, which implies slowness of the soil science community's adoption. The statistical publication trend for each concept over time was analyzed and interpreted based on diffusion of innovation theory. The results suggest that all of the soil concepts experienced a statistically positive/upward shift (\(p<0.01\)) over time. In particular, soil concepts cited in soil science journals tended to maintain their momentum and communal value over time in soil science research, except the soil care concept. Applications of soil concept research based on collaboration between scientists of different nationalities, affiliations, and research expertise would further increase the possibility of citation frequency and foster interdisciplinary and transdisciplinary collaboration.  
soil health; soil quality; soil security; soil conservation; soil fertility; soil sustainability; soil productivity; soil care; soil protection; soil resilience
## 4 Privacy and Security Challenges
### Privacy Challenges  
In the integration of Blockchain and federated learning in IoT, many privacy issues should be considered. Such challenges can be briefly explained as follows:  
1. Shared data (P1): Blockchain storage capacity is limited, which means it could be a challenge to manage the storage of the massive shared raw data [10].
2. Model gradients leakage (P2): Can be referred to as message spoofing, which is when an adversary manages to obtain shared model gradients and, in time, derive information about the training data [28].
3. Linking attack (P3): The DLT enables connected nodes to have complete access to the transaction logs. An adversary could apply linking algorithms and extract information from the federated learning procedures [12].
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method
### Design and Analysis  
#### 2.3.1 Coupler Analysis  
The most central component of the Butler matrix is the quadrature coupler, also known as the branchline hybrid network. The loaded short-ended stubs coupler was designed using square coaxial lines. The branch coupler's scattering parameters are  
\[[S]=-\frac{1}{\sqrt{2}}\begin{bmatrix}0&j&1&0\\ j&0&0&1\\ 1&0&0&j\\ 0&1&j&0\end{bmatrix} \tag{3}\]  
Its scattering matrix has symmetry, and the values at the diagonal are all zero to characterize that all four ports are matched. The design of the orthogonal coupler focuses on the following parameters: input reflection coefficient, isolation, transmission coefficients, output ports phase difference, bandwidth.  
When cascaded in the Butler matrix, the branch coupler is not sufficiently flat in its in-band output amplitude to narrow the available bandwidth. Short-ended stubs are loaded at the four ports of the branch coupler to extend the bandwidth of the branch coupler and improve isolation.  
Short-ended stubs [23] are used in Butler matrix components to broaden the bandwidth by 10%. Meanwhile, they help heat dissipation and prevent device burnout. Four pairs of short-ended stubs are placed to support the inner conductors, broadening the bandwidth [24; 25; 26] and not affecting the output magnitude. The couplers are optimized for impedance matching. The simulation model is shown in Figure 7. Ls = 21.7 mm, L_off = 19 mm. The simulation results are shown in Figure 8.