esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method  
The proposed fingerprint-based indoor positioning method was divided into two phases: offline (data collection) and online (test). In the offline phase, a hardware setup and a mobile application were used to collect Wi-Fi signal strengths and room-to-room transition times, respectively, across three different residential environments. The floor plan shown in Figure 1 corresponds to the house used in Dataset 2, while additional datasets were collected in two other houses with distinct spatial layouts. In the online phase, different ML models were trained and tested using these datasets, which contained Wi-Fi signal strengths and transition times between rooms. These phases are explained in the following subsections.  
The proposed system is based on active localization, as it relies on the use of wireless devices such as a smartphone and ESP32 module in the offline phase for localization. Passive or device-free localization systems differ from active localization systems in terms of practicality and scalability as they incur higher infrastructure costs, such as the use of Radio Frequency (RF) sensing (e.g., Wi-Fi Channel State Information [CSI]), vision-based systems, infrared sensors, or Light Spectral Information (LSI) [38; 39; 40; 41; 42].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Sensors** & **Feature Types** & **Methods** & **Accuracy** \\ \hline [PERSON] et al. [36] & magnetic, light sensors & fusion of magnetic field data and light intensity data & LSTM & mean error: 1.39 m \\ \hline [PERSON] et al. [32] & accelerometer, gyroscope, gravimeter, magnetometer & signal fusion & RNN, CNN & RMSE \(<\) 1.2 m \\ \hline [PERSON] et al. [30] & Bluetooth, Wi-Fi, inertial camera & LTE signal, Bluetooth, camera image, Wi-Fi radio image & SVM, CNN & region (92\%) \\ \hline [PERSON] and [PERSON] [37] & magnetometer, gyroscope & magnetic fields, iBeacon radio, RSSI & k-NN & RMSE: 1.4 m \\ \hline [PERSON] et al. [27] & Wi-Fi, magnetometer & CSI and magnetic field strength & Multi Module Data k-NN & avg error \(<\) 0.5 m \\ \hline [PERSON] et al. [33] & Bluetooth, accelerometer, gyroscope, barometer, magnetometer & signal fusion & MLA-MFL & avg error: 0.72 m \\ \hline [PERSON] et al. [35] & GNSS & spatial geometry distribution, time sequence, and statistical features of GNSS measurement & stacking model with HMM & 94.53–92.80\% \\ \hline This study & ESP32 Wi-Fi module and clock time of smartphone & RSSI and room-to-room transition time & Wide Neural Network & room-level (94.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature and method comparisons of the proposed approach and state-of-the-art studies (LSTM: Long Short-Term Memory, RNN: Recurrent Neural Network, CNN: Convolutional Neural Network, SVM: Support Vector Machine, k-NN: k-Nearest Neighbor, MLA-MFL: Map Location Anchors with Multisensor Fusion Location, HMM: Hidden Markov Model).
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 2 Materials and Methods
### Hyperspectral Imaging and Data Analysis
#### 2.3.2 Image Processing for Hyperspectral Data  
HSAnalyzer (EBA Japan Co., Ltd.) software was employed to preprocess the hyperspectral data, whose format was NIR and which were based on the band interleaved by the line data arrangement format. Further processing and data analysis were conducted using a MATLAB (MathWorks) program, which was developed for this study. Each leaf image was extracted from the hyperspectral data by image processing with MATLAB. To determine the leaf morphological characteristics, a region of interest (ROI) was selected, and a single wavelength of 1390 nm was used to construct a two-dimensional image (Figure 2).  
To produce a red-green-blue (RGB) format image file, wavelengths of 1000 nm, 1430 nm, and 1530 nm were respectively chosen as the special wavelengths to produce the individual fake colors of red, green, and blue. Then, the combined RGB image was transformed into a Lab (CIELAB) color space image to reduce the noise by threshold filtering. Subsequently, the grayscale image (Lab) was converted into a binary image to obtain binary statistics, which were used to calculate the LA (Figure 3).  
Figure 2: Hyperspectral image at a 1390 nm wavelength.
## 3 Experiment Results and Analysis of Dynamic Characteristics
### Analysis of Influence of Reinforced Layer on Dynamic Characteristics of Gravel Soil
#### 3.1.3 Effect of Number of Reinforcement Layers on Excess Pore Water Pressure  
Figure 9 compares the variation of excess pore water pressure \(u_{d}\) (the peak excess pore water pressure of each cycle) with loading cycles. The figure shows that, unlike accumulative strain, there is no significant transition stage for excess pore water pressure. Excess pore water pressure in the samples increase almost at logarithm scale with the loading cycles, then stabilizes. The number of cycles reaching the stabilization stage differs, but not very much among the samples, for example, about 800 cycles for the unreinforced sample, and 300 cycles for the sample with 2 layers of reinforcement. After 1000 cycles, excess pore water pressure in all samples stabilized, and the value is the highest in the unreinforced sample, which is about 16 kPa, and the lowest in the sample with two layers of reinforcement at a value of about 11 kPa. This indicates that the inclusion of geogrid may be beneficial in terms of excess pore water pressure dissipation.
## 2 Related Work
It can be observed in the paper that \(PP\) outperforms \(PS\) for a lower number of rounds, but \(PS\) performs better for a greater number of rounds. The variance is greater in the case of \(PS\). The authors of [33] identified that \(PS\) outperforms \(PP\) in terms of the mean least absolute percent error. Moreover, they showed that \(PS\) achieves worse performance only in large-scale networks with a low mean degree. \(PS\) is better than \(PP\) also in terms of the mean number of rounds except for large-scale networks with a high mean degree. It is furthermore shown in the paper that \(PS\) outperforms \(PP\) in terms of the mean number of wasted rounds in each analyzed scenario. In terms of the number of sent messages, \(PS\) achieves a greater performance than \(PP\) in all the executed scenarios. In [34], the authors introduced a distributed algorithm based on Extrema Propagation, which is general considered to be a fast and fault-tolerant approach for estimating the network size. In the presented algorithm, each agent generates a vector containing random numbers (generated data have a known probability distribution). Then, the data are aggregated (an obtained value has a distribution dependent on the network size) over the whole network by applying a pointwise minimum, an idempotent operation. Afterward, the resulting vector as a sample is used to infer the number of the nodes. In contrast to this approach, \(PP\) is used to estimate the arithmetic mean, from which the network size is then estimated (it is equal to the inverse value of the arithmetic mean estimate). In this algorithm, the initial inner states are equal to either \"1\" or \"0\", and one of the agents has to be selected as the leader. Thus, in [34], it is shown that there are also other ways to estimate the network size. As shown in [35; 36], network size estimation is an important process in many areas such as charging electric vehicles, social Internet of vehicles, etc. Thus, as seen in the provided literature review, the papers addressing distributed consensus gossip algorithms are primarily focused on the problem of distributed averaging. Thus, there is a lack of papers concerned with an analysis of \(RG\), \(GG\), and \(BG\) for network size estimation. The two other chosen algorithms (i.e., \(PS\) and \(PP\)) for this purpose are briefly discussed in [30; 34; 37; 38; 39; 40; 41; 42]. However, in none of these papers, a comprehensive analysis of these algorithms applied to estimating the network size is provided. Besides, a deep comparison of all the five algorithms is not found in the literature.
# Root Cause Analysis of a Collapse in a Hydropower Tunnel
## 2 Geological Observations and Interpretation
### Downstream Observations  
The debris pile from the collapse zone extended at least 500 m downstream, and during excavation, to clear the tunnel downstream of the collapse, clear evidence was obtained regarding several discrete deposits of debris, varying between 1 and 2 m in thickness, with traceable boundaries (bedding) between individual deposits. This indicated that deposition occurred in a series of steps, and lobes were deposited sequentially, specifically one on top of the other after each event during sequential collapse events in the FDZ.  
Observations downstream of the main collapse zone (for about 200 m downstream of the second collapse, noting that the tunnel was not accessible between the main and second collapse zones for safety reasons--see Figure 1) also show that the spalling of shotcrete preferentially occurred in the 10 o'clock to 12 o'clock position (when facing downstream). This coincides with \(\sigma_{1}\) orientated in the 1 o'clock to 2 o'clock position (when facing downstream). This statement is also supported in Figure 4, which shows a frequency plot of the spalled shotcrete area and the approximate location in the tunnel profile.  
Figure 5 shows the proposed mechanism of spalling downstream of the collapse, which is consistent with the mechanism and observations described by [20]. Once the shotcrete shear strength is exceeded, failures propagate in tunnel rock along maximum principal stress trajectories and form thin plates parallel to the tunnel boundary, as shown in Figure 5. An inspection of the exposed rock behind the spalled areas showed that flakes of rock had broken off in areas of shotcrete detachment. The rock flakes varied in size, from 10 to 20 cm and occasionally up to a metre in diameter. The rock flakes appeared to be plate-like, with the smallest dimension of the flakes typically in the 5 cm to 10 cm range. The flakes seem to either break off sub-parallel to the tunnel sidewall or break off sub-parallel to zeolite-filled veins (see Figure 5).  
This type of spalling is believed to be consistent with stress-induced fracturing of the rock (downstream of the collapse) as a result of relatively high in situ stresses in the rock, as shown in Figure 5, noting that stress levels are discussed more fully in Section 3.3. The observations at these downstream locations suggest that the shotcrete spalling is not related to swelling rock, although there are occasional indications of this in the HART rock upstream of the collapse that showed minor shotcrete cracking and spalling but not collapse. This effect is more likely related to swelling pressures in the HART.  
Figure 4: Total area of shotcrete detachment vs. tunnel mapping “Clock Time” facing downstream. (**A**) Chart and (**B**) mapping clock time legend, inset B.  
Figure 5: Tunnel section downstream of the collapse in AA rocks showing stress-induced failure initiation of zeolite-filled veins. (**A**) Section showing interpreted stress orientations; (**B**) close up showing dilation in rapid dewatering condition.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 3 Improved Gray Wolf Optimization Algorithm  
### Piecewise Chaotic Mapping  
This study employs a piecewise chaotic mapping strategy to enhance population diversity and augment the optimization process's early-stage exploration capability, mitigatingthe issues of insufficient population diversity and low-quality initialization in conventional GWO algorithms. Chaotic mapping exhibits ergodicity and regularity [33], producing a random sequence that can cover a more expansive search space, allowing the initial population to search from a broader range of positions. Standard chaotic mappings include the Logistic, Tent, Sinusoidal, and Piecewise chaotic mappings. The piecewise chaotic mapping is simple and efficient, resulting in a more uniform distribution of the initial gray wolf population, as shown in Formula (12):  
\[x(t+1)=\begin{cases}\frac{x(t)}{p},&0\leq x(t)<p\\ \frac{x(t)-p}{0.5-p},&p\leq x(t)<0.5\\ \frac{1-p-x(t)}{0.5-p},&0.5\leq x(t)<1-p\\ \frac{1-x(t)}{p},&1-p\leq x(t)<1\end{cases} \tag{12}\]  
where \(x(t)\) represents a random iteration value, \(x(1)\) is a random number in the range [0, 1]; \(p\) is a control parameter with values within the open interval (0, 0.5), meaning that \(p\) cannot be 0 or 0.5, as this would lead to undefined results in the piecewise chaotic mapping function, and in this paper, the value of \(p\) is set to 0.3.  
Piecewise chaotic mapping operates by defining different functional relationships across distinct segments, ensuring that each generated initial position is determined by more than just a single random distribution. This leads to a more uniformly distributed initial population, effectively improving the quality of the initial gray wolf population and enhancing the algorithm's early-stage exploration capability.
## 2 Materials and Methods
### Synthesis of MOF  
The synthesis of copper terephthalate took place in a medium containing a surfactant (\(n\)-octanol) to give it a bulk structure. A total of 2.4 g (0.06 mol) of sodium hydroxide was dissolved in 150 mL of hot water, and 5 g (0.03 mol) of terephthalatic acid was added, achieving the complete dissolution of the latter. In another vessel, a solution was prepared from 120 mL of hot water and 15 g of CuSO\({}_{4}\cdot\)5H\({}_{2}\)O. Both solutions were filtered using the hot filtration method. Then, 15 mL of \(n\)-octanol was added to a solution of sodium terephthalate with constant stirring, and at the same time, the number of revolutions of the magnetic stirrer was increased to form an emulsion. Next, a hot solution of CuSO\({}_{4}\cdot\)5H\({}_{2}\)O was added dropwise, while the solution was stirred. As a result of this reaction, a voluminous precipitate of a bright blue color formed. The crystals were filtered on a porous glass plate and dried in air and then at 60 \({}^{\circ}\)C for 6 h. This method allowed us to obtain 6.25 g of an almost colorless substance with a slight bluish tint, which corresponds to a yield of 91%, calculated as terephthalatic acid. Elemental analysis: Found (%) C--28.9; H--2.5; Cu--40.2. Calculated for Cu(BDC) \(\cdot\)2H\({}_{2}\)O (%): C--29.26; H--2.4; Cu--39.
# Check for special characters and punctuation
## 5 Discussion
### Computational Efficiency  
The performance scalability analysis, shown in Figure 6, reveals superior scaling characteristics with a scalability factor of 1.29, significantly better than the 1.66-1.73 range observed in competing methods. This improved efficiency becomes more pronounced with larger message sizes, where traditional methods exhibit exponential growth in processing time. Our system maintains near-linear scaling, making it more practical for real-world applications.  
Figure 6: Performance scalability: X-axis—message size; Y-axis—processing time; includes scalability factor; shows DeepStego’s superior scaling with message size.
## 4 Conclusions  
This study presents an advanced dielectric heating system as a novel approach for the selective thermal targeting of parasite-associated regions in _Cirrhinus microlepis_ using a 2.45 GHz horn antenna. The results demonstrate that dielectric heating can effectively elevate the temperature of fish scales, where parasites predominantly reside, to a lethal threshold while minimizing thermal effects on underlying tissues. The dielectric properties of fish tissues were characterized to optimize energy absorption and distribution, ensuring efficient and targeted heating. Through simulation and experimental validation, the optimal antenna-to-sample distance was determined to be between six and nine centimeters, allowing for uniform energy distribution and effective thermal localization. The findings indicate that dielectric heating provides a promising alternative to conventional parasite control methods, which often rely on chemical treatments or freezing. This technology offers a non-chemical, environmentally sustainable, and energy-efficient solution for aquaculture applications.  
Future research should focus on scaling up this technology for commercial aquaculture operations and refining the system to enhance efficiency and adaptability across different fish species. Additionally, integrating real-time monitoring systems could further improve precision and ensure the safety and effectiveness of dielectric heating in targeted thermal applications for parasite management. The insights from this study contribute to the development of innovative and sustainable solutions for aquaculture health management, aligning with the increasing demand for safer and more efficient food production technologies.  
Conceptualization, S.K., S.P., C.T., P.K., N.S. and S.S.; methodology, S.K., C.T., P.K., N.S. and S.S.; validation, S.K., S.P., C.T., N.S. and S.S.; formal analysis, S.K., S.P., C.T., P.K., N.S. and S.S.; investigation, S.K., S.P., C.T., P.K., N.S. and S.S.; data curation, S.K., S.P. and S.S.; writing--original draft preparation, S.K., S.P. and S.S.; writing--review and editing, S.K., C.T., P.K., N.S. and S.S.; visualization, S.K.; supervision, C.T. and S.S.; project administration, C.T. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Suranaree University of Technology (SUT), Thailand Science Research and Innovation (TSRI), and National Science Research and Innovation Fund (NSRF) (NRIS no. 160360).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** This work was supported by Suranaree University of Technology, Thailand.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Unit** & **532 nm** & **633 nm** & **1550 nm** & **4500 nm** \\ \hline The refractive index & - & 2.4744 & 2.4635 & 2.4573 & 2.4619 \\ The measured deflection angle at 25 \({}^{\circ}\)C & mrad.mm/kV & 63.35 & 61.6 & 54.88 & 8.68 \\ The measured deflection angle at 40 \({}^{\circ}\)C & mrad.mm/kV & 62.66 & 60.96 & 54.37 & 8.59 \\ The measured halfwave electric field at 25 \({}^{\circ}\)C & V/um & 0.124 & 0.136 & 0.228 & NA \\ The measured halfwave electric field at 40 \({}^{\circ}\)C & V/um & 0.128 & 0.141 & 0.232 & NA \\ Number of resolvable spots at 25 \({}^{\circ}\)C & - & 1403 & 1146 & 417 & 23 \\ Number of resolvable spots at 40 \({}^{\circ}\)C & - & 1388 & 1135 & 413 & 22 \\ The EO coefficient (s\({}_{11}\)) at 25 \({}^{\circ}\)C & m\({}^{2}\)/V\({}^{2}\) & \(1.57\times 10^{-15}\) & \(1.55\times 10^{-15}\) & \(1.39\times 10^{-15}\) & \(2.18\times 10^{-16}\) \\ The EO coefficient (s\({}_{11}\)) at 40 \({}^{\circ}\)C & m\({}^{2}\)/V\({}^{2}\) & \(1.55\times 10^{-15}\) & \(1.53\times 10^{-15}\) & \(1.37\times 10^{-15}\) & \(2.16\times 10^{-16}\) \\ The calculated halfwave electric field at 25 \({}^{\circ}\)C & V/um & 0.122 & 0.135 & 0.224 & 0.960 \\ The calculated halfwave electric field at 40 \({}^{\circ}\)C & V/um & 0.123 & 0.136 & 0.225 & 0.965 \\ The calculated deflection angle at 25 \({}^{\circ}\)C & mrad.mm/kV & 65.85 & 63.51 & 56.85 & 8.91 \\ The calculated deflection angle at 40 \({}^{\circ}\)C & mrad.mm/kV & 64.91 & 62.96 & 55.96 & 8.78 \\ \hline \end{tabular}  
* The optical path was 1.5 mm.  
\end{table} 
Table 6: The measured and calculated wavelength and temperature-dependent parameters of the EO beam steerer based on polycrystalline PMN-PT. Figure 22: The deflection angle simulation of the EO steerer based on PMN-PT ceramic under 1 kV/mm at 25 \({}^{\circ}\)C using a COMSOL model for (**a**) 532 nm, (**b**) 633 nm, (**c**) 1550 nm, and (**d**) 4500 nm. Table 7 compares the visible light (633 nm) and near-IR light (1.55 \(\upmu\)m) deflections associated with polycrystalline PMN-PT measured in this study to the ones measured for LiTaO\({}_{3}\) by [PERSON] et al. [12; 17]. As seen, polycrystalline PMN-PT performs about seven times better than LiTaO\({}_{3}\) at those two wavelengths, and yet, as shown, PMN-PT ceramic is polarization-independent and almost temperature-independent.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 3 Results  
### Effectiveness of Sparse Representing  
The foundation of our proposed method is that each hyperspectral curve can be sparsely represented by predefined atoms. The simulated dataset is used to verify this hypothesis, because it is theoretically noiseless. In detail, we decomposed the simulated dataset using the OMP algorithm and recovered the spectra using the sparse coefficients and the dictionary. The SNR indices between the original spectra and recovered spectra per band are shown in Figure 3. For an ideal recovery, its signal should be the same as original spectral and the SNR value is infinite.  
We calculated the SNR histogram of each of the spectral bands (Figure 3). These histograms have two high density regions. The first one is around the SNR and equals 70 for all of the bands. This means that the proposed method can equally represent all of the spectra in the range of 400 to 900 nm. Two SR-processed spectra with a SNR of 77.41 (Figure 4a) and 67.73 (Figure 4b) indicate that such an error level (the SNR is about 70) is negligible visually. The second high density region that appears in the SNR equals 100. In fact, for these samples, the SR and original signals are numerically indistinguishable. The SNR values are infinite. In order to better show the overall characters of the SNR distribution, we set the maximum SNR as 100 in Figure 3. In conclusion, the proposed algorithm can effectively represent the hyperspectral data of the inland water.