esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study of the Oval-Square-Shaped SRR Structure  
### Different Type of the Substrate  
Every dielectric substrate material has different properties. In this study, the metamaterial structure was simulated using several substrate materials such as Flame Retardant 4 (FR-4), Rogers RO4003C, Rogers RT5880, and Rogers RT5870. The value of thickness, dielectric constant, and tangent loss of the substrates are different. The properties of the substrate materials were as shown in Table 2.  
Figure 8: The comparison of the S-parameter results for \(2\times 2\), \(3\times 3\) and \(4\times 4\) arrays.  
Figure 9: Comparison of the S-parameters for the unit cell metamaterial structure and \(4\times 4\) array structure.  
The simulated result was plotted as illustrated in Figure 10. The FR-4 substrate showed that it only exhibited C-band with a frequency ranged between 4.09 GHz and 5.12 GHz, and the magnitude of S\({}_{21}\) was \(-\)29.38 dB. Meanwhile, the Rogers RO4003C substrate produced C- and X-band frequencies ranging from 4.98 GHz to 5.92 GHz and 8.76 GHz to 8.86 GHz, respectively. The magnitudes of S\({}_{21}\) were \(-\)43.03 and \(-\)17.19 dB. C- and X-band frequencies ranged between 5.4 GHz to 6.42 GHz and 9.98 GHz to 10.07 GHz, 5.27 GHz to 6.28 GHz, and 9.72 GHz to 9.80 GHz, respectively, on Rogers RT5880 and Rogers RT5870 substrates. The magnitudes of S\({}_{21}\) for Rogers RT5880 were \(-\)44.05 and \(-\)18.35 dB, whereas for Rogers RT5870, they were \(-\)42.76 and \(-\)18.15 dB. A comparison of the S-parameter results for both substrates is shown in Table 3. All the substrates manifested dual frequency bands except FR-4, which manifested only a single resonance frequency. Generally, the dielectric constant is defined by the ratio of the electric permittivity of the material to the electric permittivity of free space, such as vacuum. The dielectric constant can also be called the electric permittivity. From the properties of the substrates, it can be understood that FR-4 has the highest dielectric constant. Hence, the shifting in the value of resonance frequency occurred because of the different values of the dielectric constant of the substrate. Overall, from the graph, it can be observed that the resonance frequency increases with the lower value of the dielectric constant. It can also be concluded that a low dielectric constant can be used to achieve a higher resonance frequency. Rogers RO4003C has low loss, and the dielectric constant is higher when compared to Rogers RT5880 and Rogers RT5870. It also exhibits dual frequency bands when compared to FR-4, so it was chosen for further investigation.
## 7 Conclusions  
This study presents a novel MIMO antenna system optimized for full-screen smartphones operating in the 5G NR sub-6 GHz bands. The antenna was integrated into the metallic frame via a two-step process that effectively addressed key challenges posed by compact smartphone antenna design, including mutual coupling and the need to reduce correlation. The proposed two-port configuration was selected over an initial four-port approach to achieve a more reliable balance among impedance stability, isolation, and manufacturability under real-world constraints. The antenna exhibits wideband impedance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Nominal Value** & **Variation Applied** &
\begin{tabular}{c} **Estimated Frequency** \\ **Shift [MHz]** \\ \end{tabular} \\ \hline Substrate permittivity & 4.3 & \(\pm\)0.2 & \(\pm\)60 \\ Loss tangent & 0.025 & \(\pm\)0.005 & \(\pm\)15 \\ Copper thickness & 35 \(\mu\)m & \(\pm\)5 \(\mu\)m & \(\pm\)10 \\ Coupling gap & 0.6 mm & \(\pm\)0.1 mm & \(\pm\)90 \\ width (\(\xi_{\mathrm{g}}\)) & 0.6 mm & \(\pm\)0.1 mm & \(\pm\)90 \\ Stub width (\(\xi_{\mathrm{W}}\)) & 6.5 mm & \(\pm\)0.2 mm & \(\pm\)55 \\ SMA connector & Ideal port vs. real SMA & N/A & +120 \\ Parasitic & & & \\ \hline \hline \end{tabular} Note. Estimated frequency shifts are approximate and derived from single-parameter perturbations while holding other parameters constant.  
\end{table}
Table 4: Sensitivity analysis of resonance frequency shift versus key parameter variations.  
matching over the n77/n78 bands, as well as low ECC and good isolation, both of which are critical metrics for robust MIMO operation. Both the simulated and measured data validate the efficacy of the design and confirm that practical implementation is feasible in next-generation mobile devices. It is important to note that the absence of empirical hand phantom measurements represents a limitation of this study. Based on the recent literature, hand proximity and grip scenarios are expected to induce impedance detuning, reduce radiation efficiency by approximately 20-40%, and increase ECC above 0.2, potentially degrading isolation by 3-6 dB. To mitigate these effects, future work will incorporate comprehensive full-wave simulations and measurements with standardized hand phantoms, as well as antenna selection and reconfigurable switching techniques to maintain performance in various usage conditions. Overall, the proposed shared coupling architecture provides a promising foundation for scalable and practical MIMO antenna integration in modern smartphones.  
Conceptualization, C.Y., Y.L. and W.S.; methodology, W.K.; validation, C.Y., Y.L. and W.K.; writing--original draft preparation, C.Y.; writing--review and editing, C.Y. and W.K.; visualization, W.S.; supervision, C.Y. and W.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Flexural Strength and Acoustic Damage Characteristics of Steel Bar Reactive Powder Concrete
## 3 Results and Discussion
### AE Indirect Caculation Analysis  
Figure 9 shows the RA and AF curves during the SBRPC failure. The RA, as well as AF, magnitudes change dynamically, implying the transformation for failure types of SBRPC. During the specimen failure process, the order for cracks initiation in the concrete matrix is considered to start from tensile cracks, and the shear cracks are gradually active as the cracks expand and fiber drawing events occur [30,31]. Figure 8 shows that the specimen in the failure stage would have larger RA values or smaller AF values with respect to the initial ones, indicating that when the SBRPC specimen is damaged, the shear mode is in an important position. In the period of crack development, RA and AF are in a dynamic fluctuation condition, and this indicates tensile cracks and shear cracks coexist at this stage. It can be seen from Figure 9a,b that RPC and SBRPC-1 have a lower initial RA value or a higher initial AF value, so there will be more shear cracks in the process of specimen failure or shear cracks dominate the failure of the specimen. As shown in Figure 9c,d, SBRPC-3 and SBRPC-5 have a larger initial RA or a smaller initial AF. Therefore, compared with RPC and SBRPC-1, there will be more tensile cracks in the process, implying a combined effect of steel bars and RPC could affect the failure mode of the specimen.  
Figure 8: The distribution of AE amplitude: (**a**) RPC; (**b**) SBRPC-1; (**c**) SBRPC-3; (**d**) SBRPC-5.  
Figure 9: _Cont._Figure 9: The variation curve of RA and AF: (**a**) RPC; (**b**) SRRPC-1; (**c**) SRRPC-3; (**d**) SBRPC-5.  
To clearly characterize the transformation of the crack mode of the SBRPC during the fracture process, a parameter RA and AF ratio (RF) is defined in this paper. The calculation formula of RF is as follows:  
\[\text{RF}= \frac{\text{RA}}{\text{RA}_{\text{initial}}}-\frac{\text{AF}}{\text{ AF}_{\text{initial}}} \tag{4}\]  
The variation curve of the load level and defined parameter RF is plotted in Figure 10. Figure 10a,b implies that the main crack mode to the specimen during the failure process is shear cracks, accounting for more than 80% of the crack amount. This is related to the RPC failure mode, and the pulling and friction events of the fiber cause the shear mode to be more active. Figure 10c,d indicates that during the concrete matrix failure on its right side of the loading point of SBRPC-3 and SBRPC-5, more tensile cracks occurred compared with RPC and SBRPC-1. This is related to the combination of steel bars and RPC; the RPC near the steel bars undergoes tensile failure along with the tensile strain of the steel bars.  
Figure 10: _Cont._
## 4 Parametric Analysis
### Axial Compression Ratio \(\mu\)  
The axial compression ratio is selected as the main parameter by considering the influence of vertical load on the lateral bearing capacity. The axial compression ratio is the ratio of the axial load to the design strength of the column, and the formula is \(\mu\) = N/fy. Under the condition that the basic parameter of concrete is C30 and the thickness of steel plate is 2.5 mm, the axial compression ratio with the values of 0, 0.2, 0.3, 0.4, 0.5 and 0.6 are used in the finite element analysis. Specimen numbers and parameters are shown in Table 8.  
It can be found that the ultimate bearing capacities at point A and B change little with the increase of the axial compression ratio from load-displacement curves, as shown in Figure 23. However, the influence of ultimate strength is slightly larger at point C. The bearing capacity is gradually reduced with the increase of the axial compression ratio. Comparing the axial compression ratio of 0 and 0.6, the ultimate bearing capacity of the former is 14.4% higher than that of the later, and the difference is about 120 KN, which is shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimens Number** & \begin{tabular}{c} **SABC+SCDA Area** \\ **(kN-mm)** \\ \end{tabular} & \
\begin{tabular}{c} **SOBE+SODF Area** \\ **(kN-mm)** \\ \end{tabular} &
\begin{tabular}{c} **Energy Dissipation** \\ **Coefficient E** \\ \end{tabular} \\ \hline S1C30 & 128,510 & 33,600 & 3.82 \\ S1C40 & 129,120 & 33,780 & 3.82 \\ S1C50 & 129,740 & 33,850 & 3.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hysteretic Loop Envelope Area and Energy Dissipation Coefficient.  
Figure 21: The hysteretic curves with different grades of concrete.  
Figure 22: Accumulation Energy Consumption.  
Figure 24: The hysteretic curves with different axial compression ratio. (**a**) \(\mu=0\); (**b**) \(\mu=0.2\); (**c**) \(\mu=0.3\); (**d**) \(\mu=0.4\); (**e**) \(\mu=0.5\); (**f**) \(\mu=0.6\).  
Figure 25: Accumulated Energy Dissipation.
## 2 Damage Classification in SHM  
SHM covers several application areas and the assets monitored range from small components to huge civil structures and complex machines. Building SHM systems focus on measuring changes in the physical parameters to assess the current state of the structure and, in some cases, predict the building's response to future seismic excitations. To make these predictions, it is necessary to identify the natural frequencies of the buildings [5]. In the case of buildings, the structure is subjected to the effects of static and dynamic loads, so the complexity of the analysis presents a challenge in giving an accurate model that includes all these known and unknown effects.  
SHM systems are composed of several hardware and software elements. An overview of the main components of SHM systems as defined by [PERSON] and [PERSON] [6] are:  
1. Operational assessment: The aspect related to damage conceptualization and operational conditions.
2. Data acquisition: The sensor system design and data preprocessing.
3. Feature extraction: The selection of sensitive damage features according to the damage identification capabilities of the desired SHM system.
4. Statistical model development: The design and implementation of the physics-based or data-based model.  
[PERSON] et al. [1] explored the data acquisition aspect (1) of recent proposals for SHM systems, showing several features of accelerometer sensing systems that are attractive for the structural monitoring and evaluation of SHM systems. This study focuses on areas three and four of SHM systems, analyzing proposals of the physics-based models and the data-based models presented in the literature that belong to these areas. Figure 1 shows these areas in SHM systems, and the methods, techniques and algorithms involved.  
Figure 1: Diagram of structural health monitoring systems areas.  
The core of SHM systems is their ability to perform damage identification. Damage is the change in the material's physical properties due to progressive deterioration or as a result of a single event on a structure. This change can detract from the behavior or integrity of a structure. Damage characterization can be conceived in several ways depending on the objectives of the SHM system, and damage states can be defined in terms of extent, severity, remaining operational time, thresholds, and damage index standards. [PERSON] [7] also presents a damage identification classification in SHM systems, as shown in Table 1.
# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 1 Introduction
### Taguchi Orthogonal Arrays  
This paper aims to present a new, improved UCP model constructed using artificial neural networks based on [PERSON]'s orthogonal vector plans. ANNs represent a tremendous artificial intelligence tool and are often used in combination with parametric methods [18; 19]. Using various ANN architectures, we can arrive at a fast, accurate, and reliable estimate of the effort and cost required to develop a project. Each ANN architecture is based on robust design methods, i.e., Taguchi methods of orthogonal vector plans. A robust method design implies meeting the prescribed criteria when planning and implementing software. [PERSON]'s orthogonal vector plans are based on a unique set of Latin Squares [20; 21]. The discovery of orthogonal vector plans and their application minimizes crucial parameters for the project's successful development. The impact parameters are not duplicated, which achieves a much faster estimation of the efforts and costs of a particular project. This design method is based on a factorial experiment realized only with all possible experimental combinations of parameter values. The construction of, e.g., the artificial neural ANN-L36 prim architecture using [PERSON]'s orthogonal plan achieves a higher convergence rate, reducing the time and number of iterations required to achieve the minimum MMRE. The number of iterations required for the implementation of the Full Factorial Plan (FFP) within a robustly designed experiment is N = L\({}^{\text{P}}\) (for example, when three levels with 16 parameters are used according to FFP, it is necessary to execute N = 3112431 = 8,503,056.00 experiments). Using the Taguchi orthogonal vector plan with 16 parameters (weight coefficients) on three levels, only 36 experiments are necessary. The Taguchi method of robust design reduces experiments by 99.99% (0.9999957662 \(\ldots\) = 1 \(-\) (36/8 503 056)). It is expected that the new, improved UCP approach constructed based on different ANN architectures that are in line with the Taguchi Orthogonal Plans will give better results than the previously proposed UCP model.  
This article is structured as follows: Section 2 provides an overview of previous studies that applied UCP for effort estimation in software projects. Section 3 explains the new, improved UCP model with the methodology used. Section 4 discusses the obtained results. The concluding remarks are given in the last section.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 2 Dataset Description
### Reference Sea Ice Data  
The sea ice type (SIT) provided by the OSISAF [44] is adopted as the reference data to train the sea ice classification model and validate the results. The OSISAF provides daily SIT maps with a spatial resolution of 10 km in the polar stereographic projection. The SIT products discriminate OW, FYI, and MYI through analyzing multi-sensor data. The data with a confidence level above 3 are adopted to avoid using low-quality data [44]. In addition, the SIC products generated from the SSMIS measurements provided by OSISAF are also used as reference data to analyze the impacts of SIC on sea ice classification. The reference SIC products are mapped on a grid with a size of 10 km \(\times\) 10 km in the polar stereographic projection. The TDS-1 data is matched with the SIC maps through the SP location and data collection date, which are available in the TDS-1 dataset.  
Figure 2a presents the GNSS-R ground tracks over OW, FYI, and MYI on 26 November 2015. The TDS-1 DDMs over OW-FYI and FYI-MYI transitions are shown in the figure. Meanwhile, the GNSS-R ground tracks are mapped against the SIC map in Figure 2b. In general, the SIC of MYI is higher than that of FYI.
# A Hybrid Soft Actuator Inspired by Grass-Spike: Design Approach, Dynamic Model, and Applications
## 6 Conclusions  
In this paper, we propose a hybrid soft actuator (HSA) inspired by a grass-spike, as well as a hybrid soft actuator module (HSAM). The design approach and dynamic models are presented for both the HSA and HSAM. Experiments are performed to estimate the friction coefficient and the damping effect by the system identification process. The estimated parameters are employed to replicate the real measurement by simulation, and the simulation results confirm that they can be replicated successfully. Two potential applications--an elbow manipulator and a robot hand grasper--are introduced to demonstrate the feasibility of the HSA and the HSAM, and both are performed successfully.  
One of the future works should be refining our design approach. Specifically, the diameter of the HSA needs to be reduced, and the maximum force needs to be measured for the size-reduced version. If it generates enough force, then we need to check the possibility of encasing it. The system identification process to estimate the friction coefficient, as well as the damping effect should be refined to yield consistent values even under various motor revolution speeds. Lastly, for the encased HSAM, the control scheme should be modularized as well to be able to perform dexterous manipulations such as pencil gripping, pinch gripping, hand maneuvers, and so on.  
Figure 13: The actuator module is applied for robot hand grasping. The two actuator module grasped and released an object successfully. The total weight of the five fingers was \(M=70\) g, approximately, but there existed energy loss caused by the silicone ligaments between finger joints; therefore, we applied two actuator modules. \(\Omega=160\) rev/s was applied.  
**Author Contributions:** Conceptualization, H.-U.Y.; methodology, C.-W.L. and H.-U.Y.; software, C.-W.L. and H.-U.Y.; validation, D.-W.C., C.-W.L., D.-Y.L., D.-W.L. and H.-U.Y.; formal analysis, C.-W.L. and H.-U.Y.; investigation, H.-U.Y.; resources, C.-W.L. and H.-U.Y.; data curation, C.-W.L. and D.-Y.L.; writing, original draft preparation, D.-W.C., C.-W.L. and H.-U.Y.; writing, review and editing, D.-W.C. and H.-U.Y.; visualization, C.-W.L. and H.-U.Y.; supervision, H.-U.Y.; project administration, D.-W.L. and H.-U.Y.; funding acquisition, D.-W.L. and H.-U.Y. All authors read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Korea Institute of Industrial Technology as under \"Development of Soft Robotics Technology for Human-Robot Coexistence Care Robots (Grant Number: KITECH EO200005).\"  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Encoder-Decoder Based Approaches
#### 3.1.6 Transformer-Based Encoder-Decoder Models  
Transformer models, initially designed for natural language processing, have recently gained traction in the field of video captioning due to their exceptional capacity to capture long-range dependencies. [PERSON] et al. [29] pioneered the use of a Transformer-based model for video captioning, which outperformed traditional RNN-based approaches. Building on this success, [PERSON] et al. [30] further advanced the field by presenting an end-to-end Transformer model for dense video captioning. This model utilizes an encoder for video feature extraction and a decoder for caption generation, significantly enhancing efficiency through parallel processing.  
More recently, the introduction of Vid2 Seq by [PERSON] et al. [31] has marked a new milestone in video captioning. As depicted in Figure 7, Vid2 Seq is a pretrained Transformer-based encoder-decoder model specifically designed for narrated videos. It takes video frames \(x=\{x_{i}\}_{i=1}^{F}\) and transcribed speech sequence \(y=\{y_{j}\}_{j=1}^{S}\) as inputs, and outputs an event sequence \(z=\{z_{k}\}_{k=1}^{L}\), where each event includes a textual description and corresponding timestamps in the video. Despite its top performance across multiple benchmarks, it is important to note that Transformer models require substantial data for training, are computationally intensive, and often exhibit less interpretability than RNN-based models due to their reliance on self-attention mechanisms.  
Figure 6: Overview of the proposed MAN architecture for video captioning (redrawn by the authors, adapted from [28]).  
Figure 7: Transformer-based Encoder-Decoder Models for video captioning (redrawn by the authors, adapted from [31]). This approach frames dense event captioning as a sequence-to-sequence task, utilizing special time tokens to enable the model to comprehend and produce token sequences containing both textual semantics and temporal localization details, thereby anchoring each sentence to the corresponding video segment.
## 3 Results
### Detectability Enhancement with Coarse Measurement Grid
(**a–c**) The 4 mm flaw with 8 mm grid point distance, (**d–f**) the 5 mm flaw with 10 mm grid point distance, (**g–i**) the 10 mm flaw with 14 mm grid point distance, and (**j–l**) the 20 mm flaw with 18 mm grid point distance. In the first column, with the default measuring grid position. In the second column, with a 20 mm shifted measuring grid and in the third column with a 40 mm shifted measuring grid. On the basis of this data, we determined a function that represents the limit of the detectable flaw size relative to the detectability limit with the flaw influenced area in the measuring plane relative to the measuring grid point distance. This is shown in Figure 11:1 The function is the following equation:  
Figure 11:2  
It was created as an interpolation function of the specific maximum detectability. This is depicted in Figure 17 as the blue curve. The red crosses are the maximum measuring grid point distance associated with the flaw sizes. The maximum detectability with the re-radiation method of \(\frac{\lambda}{2}\) is there at 1. This function asymptotically drops to the detectability limit but never reaches it. The values above the curve can thus be used for good identification of flaws with this method. To know if the desired measuring grid is sufficient to determine flaws up to a certain diameter, one could calculate the range of influence of the smallest expected flaw size with Equation (7) and divide it by the area of one square of the measuring grid. For values/data points above the blue line in Figure 17, this method is able to reliably identify flaws, regardless of the position of the measuring grid relative to the flaw. For the special case presented here, Figure 18 depicts the flaw size with the measuring grid size. The whole area above the blue curve can be easily evaluated with this method. The red crosses are the measurement data used for this purpose. It can be clearly recognized that, for flaws that are \(<\)\(\lambda\), measuring grid point distances of \(>\)\(\frac{\lambda}{3}\) can be used. Figure 11:3 Determinable flaw ratio in relation to the area ratio. Figure 11:4 Determinable flaw size in relation to the grid point distance.