esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### SPME Procedure  
All extractions were conducted using direct immersion SPME mode. The treated TPB-DMTP-GS-SSF was directly immersed into a 20 mL working solution in an extraction  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Etched Methods** & **Etched Solution** & **Time** & **Coating** & **Analytics** & **Ref.** \\ \hline Chemical etching & 1 M NaOH & 30 min & Carbon-aerogel-based & OPs & [48] \\ \hline Chemical etching & HF (40\%) & 20 min & Ionic liquids & Alkylphenols & [49] \\ \hline Chemical etching & HF (40\%) & - & Carbonized MXene-polyvinylpyrrolidone & PAHs & [50] \\ \hline \multirow{2}{*}{Polished} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & Poly(1-Hexyl-3-Vinylimidazolium) & \multirow{2}{*}{PCBs} & [51] \\  & & & Bromide & & \\ \hline \multirow{2}{*}{Chemical etching} & 0.1 M NaOH and 0.1 M & \multirow{2}{*}{Washed} & Ionic liquids & OPEs & [52] \\  & HCl & & & & \\ \hline Chemical etching & H\({}_{2}\)SO\({}_{4}\) & - & \begin{tabular}{c} MOF/PANI \\ nanocomposites \\ \end{tabular} & \begin{tabular}{c} Aromatic \\ compounds \\ \end{tabular} & [53] \\ \hline Chemical etching & \begin{tabular}{c} FeCl\({}_{2}\)/HCl solution \\ (20\% \(w/v\)) \\ \end{tabular} & 15 min & - & PAHs & [54] \\ \hline Chemical etching & Aqua regia/water (1:1) & 30 min & \begin{tabular}{c} Montmorillonite/ionic \\ liquid composite \\ \end{tabular} & \multirow{2}{*}{Phenolic compounds} & [55] \\ \hline \multirow{2}{*}{Chemical etching} & \multirow{2}{*}{37\% HCl} & \multirow{2}{*}{30 min} & Multiple-helix cobalt & \multirow{2}{*}{Chlorophenol and \multirow{2}{*}{nitrophenols}} & \multirow{2}{*}{[56]} \\  & & & (II)-based & & \\ \cline{1-1} \cline{5-5}  & & & Metal-organic & & \\ \cline{1-1} \cline{5-5}  & & & nanotube & & \\ \hline Electrochemical etching & \
\begin{tabular}{c} (CH\({}_{4}\)OH\({}_{2}\))/HCl\({}_{4}\) \\ (\(v/v\), 9:1) \\ \end{tabular} & 50 s & COF-TpBD & PAHs & [20] \\ \hline Electrochemical etching &
\begin{tabular}{c} (CH\({}_{2}\)OH\({}_{2}\)/HCl\({}_{4}\) \\ (\(v/v\), 9:1);) \\ 0.1 M (CH\({}_{2}\)OH\({}_{2}\))/\({}_{6}\) of NH\({}_{4}\)F \\ \end{tabular} & 6 min & COF-TPB-DMTP & PAEs & This work \\ \hline \end{tabular}
\end{table}
Table 1: The comparison between different etching methods for the preparation of the SPME fibers.  
Figure 1: The schematic diagram of the fabrication process for the TPB-DMTP-GS-SSF.  
vial for the 30 min extraction. Then, the fiber was removed from the extraction vial rapidly and immediately transferred to the GC injection port for desorption at 280 \({}^{\circ}\)C for 2 min.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 3 Results and Discussions
### Experimental Results
The same conclusion can be found for DFT-S-OFDM-DM-IM, as shown in Figure 8b, which agrees well with the findings in Figure 6.  
Figure 9 shows the measured average BER versus transmission distance for different schemes. As shown in Figure 9a, the maximum distance below BER = \(3.8\times 10^{-3}\) that can be achieved by OFDM-SM-IM without interleaving is 48.3 cm, which is slightly increased to 53.2 cm when interleaving is performed. The maximum distances below BER = \(3.8\times 10^{-3}\) for DFT-S-OFDM-SM-IM without and with interleaving are 51.2 and 60.8 cm, respectively. Figure 8: Measured BER vs. distance for **(a)** DFT-S-OFDM-SM-IM without and with interleaving and **(b)** DFT-S-OFDM-DM-IM without and with interleaving (w/o: without, w/: with, interl. : interleaving). Figure 7: Average received SNR vs. IFFT/FFT length of the DFT-S-OFDM signal (w/o: without, w/: with, interl. : interleaving). Hence, an distance extension of 14.24% is obtained by DFT-S-OFDM-SM-IM with interleaving in comparison to that without interleaving. Moreover, it can be seen from Figure 9b that a significant distance extension of 40.2% is obtained for DFT-S-OFDM-DM-IM with interleaving when compared with OFDM-DM-IM with interleaving, while DFT-S-OFDM-DM-IM with interleaving also achieves a distance extension of 19.1% in comparison to that without interleaving. The results shown in Figure 9a,b indicate that it is beneficial to perform DFT spreading and interleaving in OSDMA-based practical MU-VLC systems. In addition, the corresponding received constellation diagrams are depicted in Figure 10a-h.
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 2 Materials and Methods
### Log Periodic Antenna Arrays  
The microstrip antenna elements in essence are not very directive, presenting low gain and very small bandwidth. In order to increase the gain, several microstrip elements were associated in arrays using a series feeding structure as it allows greater compactness. Additionally, a log-periodic configuration was applied in the construction of the array to greatly increase the bandwidth [32].  
Antennas with a log-periodic configuration belong to the class of frequency-independent antennas, which theoretically have no bandwidth limitations [32]. In practice the behavior is not independent of the operating frequency, but rather dependent on the logarithm of the frequency. These structures can be seen as a set of adjacent cells, whose dimensions are progressively changed, scaled by a fixed scalability factor, \(\tau\).  
Taking that into consideration the dimensions of the elementary patch antenna, namely its length (\(L_{n}\)), the width (\(W_{n}\)), the inset feed line and the reentrance (\(I_{n}\) and \(y_{n}\)), the different sizes of the various elements of the array can be estimated through the scalability factor \(\tau\) as:  
\[\tau=\frac{L_{n+1}}{L_{n}}=\frac{W_{n+1}}{W_{n}}=\frac{I_{n+1}}{I_{n}} \tag{1}\]  
These properties imply that the factor of \(\tau\) relates all operating frequencies  
\[f_{m+1}=\tau f_{m}=\tau^{2}f_{m-1} \tag{2}\]  
Considering \(f_{1}\) as a starting frequency, the relationship is as follows, proving the existence of a logarithmic relationship between frequencies.  
\[f_{2}=\tau\times f_{1}\ \dots\ f_{3}=\tau^{2}\times f_{1}\ \Leftrightarrow\ \ln\frac{f_{2}}{f_{1}}=\ln\tau\ \dots\ \ln\frac{f_{3}}{f_{1}}=2\times\ln\tau \tag{3}\]
## 2 System Model
### PU Spectrum Occupancy Model  
We assume that the PU is always authorized to use the channel. The PU traffic across the channel is modeled by the discrete-time semi-Markov (two-state) process [22; 23; 24], as shown in Figure 1. Without loss of generality, it is assumed that the durations of the \(OFF\) and \(ON\) states are represented by the random variables (RVs) \(A\) and \(B\), respectively. The RVs \(A\) and \(B\) follow exponential distribution with mean durations \(R\) and \(S\), respectively. These distributions are to be independent. At any time, the probabilities that the PU is in \(ON\) or \(OFF\) state are given by \(P_{ON}\) = \(S/S+R\), and \(P_{OFF}\) = \(R/R+S\), respectively.
## 4 Test Results and Discussion  
### Test Condition  
The experimental evaluation of the RAG system in this study was conducted on Inspur server with the following hardware configuration:  
* CPU: Intel Xeon Gold 6226R processor (Cascade Lake architecture, 2.9 GHz base frequency);
* GPU: NVIDIA RTX A5000 with 24 GB GDDR6 memory;
* System memory: 512 GB DDR4-2933 ECC RAM;
* Windows 10 Professional Edition operating system.  
The software environment was configured with Ollama v0.3.9 framework, implementing the Qwen 2.5 series large language model for retrieval-augmented generation tasks.  
In this test, the standard \"Technical specification for inspecting of concrete compressive strength by rebound method\" (JGJ/T 23-2011) [52] was selected as the external reference material. This standard consists of 7 chapters, appendices A to F, and explanatory notes. Multiple experts, based on common situations encountered in their work, have proposed 101 questions and provided answers according to the corresponding sections of the aforementioned standard.  
These 101 test questions can be divided into three categories:  
Figure 5: Prompt with instruction optimization and example.  
1. Accurately answerable questions that allow direct judgment of correctness, such as: \"What should be the maximum area of a test area in square meters?\". These questions have standardized numerical answers and are scored as either correct or incorrect.
2. Descriptive questions, such as: \"What is a test area?\". The answers to these questions are evaluated through similarity assessments to assign a quantitative score.
3. Hybrid questions that combine descriptive elements with standardized numerical values. These require evaluating both the numerical accuracy and textual description. The proportion distribution of the three question types is shown in Figure 6.  
This study employs the nomic-embed-text model for the embedded vector representation. Nomic-embed-text model is an open-source, high-performing text embedding model that achieves state-of-the-art performance on the MTEB benchmark while supporting context lengths up to 8192 tokens, offering transparency and reproducibility [53; 54].
## 6 Discussion and Future Works  
First, a more efficient 360-degree video technique with the combination of the techniques in Section 3 is plausible. With the combination of the strengths of DASH, tiling and ML, a better streaming approach with low bandwidth, high video quality and high QoE could be produced. However, the comparison of the particular techniques still needs further discussion which includes the comparison of the tiling schemes, ML algorithms, DASH encoded schemes to filter the best among them. Moreover, the other factors such as areas of projection, encoding and bitrate adaption need to be considered as well. This is very challenging, as for now, a unified, standardized assessment has not existed to measure the QoE and QoS 360-degree video streaming. For instance, Ref. [35] proposed the survey-based 360 JND model whereas [36] used the ffmpeg360 tool to evaluate the quality of the videos. Thus, a standardized assessment is required to have a better and more accurate evaluation of the QoE and QoS of 360-degree video services. Quantitative and qualitative measurements are required to perform the comparison between the QoE assessment model.  
On the other hand, the improvement of networks such as 5G and 6G is optimistic for the development of the 360-degree video. 5G and 6G networks will play a main role to overcome the delay and compensate for the high bandwidth requirements of 360-degree video by increasing the network availability along with the other optimized network streaming approaches such as edge computing, network caching and MEC which bring the content nearer to the client and solve the intensive computing requirement on the client-side. With the fast evolution because of the fast evolution of the network in recent years, a deep fake virtual world, \"Metaverse\" with high immersion and conducive for daily life working, learning and socializing are feasible. Moreover, there are more emerging research studies that implement the combination of the bandwidth reduction techniques in Section 3 and network approaches in Section 4 to deliver 360-degree video streaming more efficiently. For instance, [PERSON]. [77; 78] combine the strength of MEC, ML and tiling to improve the efficiency in 360-degree video streaming in terms of tile selection and reduce the computational requirement with the MEC server.  
All the research studies mentioned in this paper strive to improve the QoE and QoS of 360-degree video, AR and VR. For a good user experience, immersion of users is the key. With the promotion of big corporates such as Microsoft and Meta, a day where people highly depend on these technologies to carry out daily activities in the virtual world seems to be around the corner.
## 5 Experiments and Discussion
### Comparison of Feature-Detecting Algorithms  
Different algorithms including SIFT, SURF, ORB, and MinEigen were selected and compared with AKAZE. The _FPDD_ results where the spool was at zero position (\(\alpha=0^{\circ}\)) are shown in Figure 10. It was found that SURF detected the fewest feature points, ORB detected more but was still insufficient for FPDD, SIFT performed better in number of feature points but the distribution location was away from the rib plates, and the MinEigen shows great feature-point density and good matching of the rib plates.  
However, as shown in Figure 10f, when the actual drive rod position was \(\alpha=7^{\circ}\), the MinEigen feature points distributed in two major groups, which were opposite to each other on the bore.  
Figure 10: _FPDD_ results of (**a**) SIFT, (**b**) SURF, (**c**) ORB and (**d**) MinEigen (\(\alpha=0^{\circ}\)), and comparison of feature point distribution near drive bore for (**e**) AKAZE and (**f**) MinEigen (\(\alpha=7^{\circ}\)).  
Therefore, two clusters were generated and the pre-located position was far from the actual position. When taking a closer look at the feature points, it was found that there were far fewer MinEigen feature points compared to AKAZE in the designated area, where the arc-shaped edge generated by the contrast between the bore and shadow was found, as shown in Figure 10e. The reason for fewer MinEigen feature points is that, similarly to [PERSON] (1986) using Equation (7), [PERSON] (1986) detects corner features using Equation (8), which eliminates edge features that show a big difference between \(\lambda_{1}\) and \(\lambda_{2}\).  
\[R=\lambda_{1}\lambda_{2}-k(\lambda_{1}+\lambda_{2}) \tag{7}\]  
\[R=min(\lambda_{1},\lambda_{2}) \tag{8}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are two Eigenvalues of matrix \(M\) in the intensity change Equation (9).  
\[E(x,y)=(x,y)M(x,y)^{T} \tag{9}\]  
where \(E(x,y)\) is the change of intensity when the corner of the detector window shifts with increment \((x,y)\).  
Both KAZE and AKAZE showed blurring in images at multiple scale levels while retaining the boundaries of regions by non-linear diffusion filtering. The KAZE detector applies a standard nonlinear diffusion formula, as shown in Equation (10), and AKAZE adopts a computationally efficient framework called fast explicit diffusion (FED).  
\[\frac{\partial L}{\partial t}=div(c(x,y,t)\cdot\
abla L) \tag{10}\]  
where _div_ and \(\
abla\) are, respectively, the divergence and gradient operators, and \(c\) is a conductivity function to make the diffusion adaptive to the local image structure.
# The Development of a Soft Robot Hand with Pin-Array Structure
## 5 The Prototype and the Experiments
### Experiments with the Exploration of Grab Performance
#### 5.3.1 The Internal Pressure of CTSA-II Hand
To qualitatively investigate the effect of internal pressure on the grasping force, three CTSA-II hand prototypes were used to grab cubes with a size of 10 mm (length) \(\times\) 10 mm (width) \(\times\) 10 mm (height) under different pressure conditions and measure the grasping force. The way to measure the grasping force is as shown in Figure 21: grasp the object with CTSA-II hand firstly, and then use the dynamometer to pull the object slowly out of the CTSA-II hand; the peak of the dynamometer during the pulling process was used as the grasping force. In order to ensure the rationality, the measurement was performed 3 times and then the average of the 3 times was taken. For the grasping force measurement in this way, the most ideal method of applying force is as follows: increase the value of the dynamometer slowly enough, so that the pulling force that the cube is subjected to gradually increases from 0 until the cube moves, then slowly pull the cube out of the CTSA and the peak value in the process is the true grasping force. When using manual pull, a certain error will be formed. However, since the experimenters in this paper were all conducted by the same person, the experimental conditions were equivalent, and the average value was measured by three measurements. Therefore, most of the errors caused by the previous force measurement method on the exploration of the overall performance can be ignored. Finally, the relationships between the pressure in the CTSA-II hand and the grasping force during the grasping process of the three types of prototypes are shown in Figure 22. It can be concluded from Figure 22 that: (1) The smaller the pressure in the CTSA-II hand, the larger the grasping force, which is in line with the theoretical derivation in Section 4. (2) When the pressure in the CTSA-II hand is not small enough, it is easy to cause grab failure. (3) When the pressure (relative) is \(-\)0.15 units of atmospheres, all 3 prototypes can achieve stable crawling. (4) Comparing the three graphs in Figure 22, the CTSA-II hand with 8 pins fails when the pressure (relative) is \(-\)0.12 units of atmospheres or so, while the CTSA-II hand with 10 pins and 12 pins fail when the pressure (relative) is about \(-\)0.05 atmospheres, so the grasping performances of the 10 and 12 CTSA-II grips are better. Figure 21: The experiment of measuring the grasping force.
## 4 Algorithm for Rail Irregularity Estimation  
After the calculation of the transfer function of the axlebox acceleration measurement system, an algorithm for rail roughness estimation is proposed, which is based on the calculation of the power spectrum of the rail irregularity.  
To this aim, the acceleration signals are divided into independent time bins of duration \(T_{bin}\). The power spectrum of the acceleration signal within the \(k\)-th time bins is estimated  
Figure 9: Transfer functions of the axlebox acceleration measurement system, derived with point mass and flexible wheelset models.  
using the Welch method [24], in order to reduce the effect of noise on the acceleration measurements. The time bin is sub-divided into sub-windows of duration \(T_{win}<T_{bin}\). A user-defined overlap of the sub-windows (of time \(T_{over}\)) is used. A Hanning window function is applied to the sub-windows, in order to force the signal to be periodic. Finally, the power spectrum \(P_{acc,k}(f)\) in the \(k\)-th time bin is calculated by averaging the power spectra of the sub-windows.  
As a general comment, the proposed methodology is needed on account of the fact that acceleration data cannot be compared directly, because they are not always acquired at the same speed.  
The power spectra are in the frequency domain, but they can be expressed in the wavelength domain thanks to the speed signal measured with the encoder. In particular, the average speed in each time bin is used to switch from frequency to wavelength by means of Equation (3). By approximating the speed in the time bin as constant, a small error is committed when switching from the frequency to the wavelength domain. The lower the length of time bins \(T_{bin}\) the lower the error. However, shorter time bins make the power spectra more sensible for finding local defects on the rail.  
Another important parameter of the algorithm is the length of the sub-windows \(T_{win}\) used for the estimation of the power spectra of the acceleration signal. The shorter the sub-windows the higher the number of power spectra used in the averaging operation, and thus the higher the filtering of noise that affects the measurements. However, having short sub-windows leads to a low-frequency resolution. A trade-off between the frequency resolution and the noise filtering is found. The parameters used in the estimation of the irregularity power spectra are listed in Table 3, which are selected in order to find a compromise among the aforementioned aspects.  
The power spectra of the rail irregularities are calculated by considering the transfer function \(H_{i}(f)\) of the axlebox accelerometer measurement system:  
\[P_{irr,k}(f)=\left|H_{i}(f)\right|^{2}P_{acc,k}(f), \tag{23}\]
## 5 Conclusions and Future Studies  
This study aims to apply ML forecast models to provide a relevant, reliable, and accurate tool for tactical and strategic use in competitive sailing. This study demonstrated that ML models can be efficiently used for the postprocessing of numerical weather prediction models with their default configuration, thereby eliminating the necessity for optimizing the model parameters. The site used as a case study in this research is unique in its properties and introduces a challenging problem for prediction. Atmospheric models are known to produce high-accuracy predictions; however, the low performance of the atmospheric model in our research (WRF) is due to the decision to use the default WRF configuration and avoid the tedious process of model configuration optimization. Nevertheless, we successfully developed a categorical wind predictor with five categories for both CNN and MLP, with accuracy rates of 90% and above for most forecasts. In addition, we generated a preliminary wind velocity and direction forecast based on a CNN customized for sailors using wind speed units (kts).  
We suggest further investigations in the following areas:  
1. Further optimization of the number of categories used by the postprocessing ML mode.
2. Using spatial wind measurements conducted by synthetic aperture radar (SAR) to further enhance the accuracy and robustness of the processing of the ML model forecast.
3. Further exploration into using ML models to enhance the accuracy of the GFS with 13 km resolution. Such a model is accessible online and will eliminate the need for numerical modeling.  
Lastly, this research presents promising directions for advancing the area of integrated weather forecasts with ML models and will serve as a foundation for future developments in this field.  
Conceptualization, S.B., Y.S. and F.G.; methodology, S.B., Y.S. and F.G.; software, S.B., Y.S. and F.G.; validation, S.B., Y.S. and F.G.; formal analysis, S.B., Y.S. and F.G.; investigation, S.B., Y.S. and F.G.; resources, F.G. and Y.S.; data curation, S.B. and Y.S.; writing--original draft preparation, S.B., Y.S. and F.G.; writing--review and editing, S.B., Y.S. and F.G.; visualization, S.B. and Y.S.; supervision, Y.S. and F.G.; project administration, Y.S. and F.G.; funding acquisition, Y.S. and F.G. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Best CNN prediction of (**a**) wind velocity and (**b**) direction versus measurement. The colors represent data-point density (from dark to light), and the regression line equation, \(r^{2}\), and \(p\) value are included. The red line stands for the 1:1 regression line.