esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion
### Assessment of Planet Imagery
#### 5.1.2 Limitations and Challenges
Furthermore, a PlanetScope surface reflectance product produced from tandem observations with other sensors is now being tested and available in Beta as of 1 October 2017 and may allow more rigorous analyses [33]. Despite the above issues, specific methodological considerations can overcome some of them to enable robust hydrological analyses. First, water classifications based on a band ratio index such as NDWI is better suited for Planet analyses as it precludes the need for atmospheric correction [51]. We also advise against the use of global/stable thresholds due to cross-sensor variability. Perhaps the most critical methodological step is object-based time series analysis. By assigning the water extent change as an attribute to the lake polygon, and ignoring small temporal changes in lake location, problems of small image offsets and mismatched water edge boundaries are avoided. This approach is particularly important for Planet analyses due to geolocation uncertainty, the use of multiple sensors and varying resolution and swath coverage of the imagery. We therefore still conclude that Planet imagery is valuable for tracking rapid, fine scale changes in surface water extent, particularly as observation frequencies reach the company's near-daily imaging capacity [33].
# Each-Phase Metering with Gas-Liquid Stratified Flow Based on the Multi-Frequency Coriolis Principle
## 3 Methodology
### The Influence of GVF on Vibration Frequency Response of the Measuring Tube  
Without considering the influences of flow patterns and other factors, it is only necessary to change GVF to analyze its impact on the vibration frequency responses of a Coriolis flowmeter under different modes. The results of extracting the first six modes are shown in Table 3.  
The simulation results of the modal analysis show that with the increase in the GVF entrained in the measured fluid, the mixing density of two-phase fluid flowing through flowmeter decreases, which leads to the weakening of fluid-structure coupling between fluid and pipeline, so the vibration frequency of the tube increases in the natural state. This shows that the vibration frequencies of the measuring tube with flowing fluid depend on the mixing density of the measured fluid and fluid-pipeline interaction. It also shows that the GVF has a certain impact on the vibration response output of measuring tube. The vibration frequency of each order of measuring tube increases with decrease in mixing density of fluid [22]. With an increase in mode, the vibration frequencies also increase by multiples.  
The flow patterns in gas-liquid two-phase flow are complex and variable. The uncertainty of flow conditions is also one of critical factors that affect the accuracy in gas-liquid phase-separation metering. Therefore, the influence of flow patterns on the vibration frequencies of a single-frequency Coriolis flowmeter is analyzed in the next section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gas Volume Fraction (\%)** & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) & \(f_{5}\) & \(f_{6}\) \\ \hline
1 & 344.3 & 1045 & 1672 & 2388 & 3029 & 3148 \\
2 & 346.2 & 1051 & 1681 & 2401 & 3045 & 3162 \\
3 & 348.8 & 1058 & 1693 & 2418 & 3067 & 3183 \\
4 & 352.0 & 1068 & 1708 & 2440 & 3093 & 3209 \\
5 & 354.7 & 1076 & 1721 & 2458 & 3116 & 3231 \\
6 & 358.5 & 1088 & 1739 & 2484 & 3148 & 3263 \\
7 & 362.3 & 1099 & 1758 & 2510 & 3181 & 3294 \\
8 & 367.2 & 1114 & 1781 & 2543 & 3222 & 3335 \\
9 & 373.1 & 1132 & 1809 & 2583 & 3272 & 3386 \\
10 & 379.9 & 1153 & 1842 & 2630 & 3331 & 3445 \\ \hline \hline \end{tabular} Where \(f_{i}\) is the vibration frequencies of the measuring tube, Hz.  
\end{table}
Table 3: The simulation results of gas-phase content relative to the first six order mode vibration frequencies of the measuring tube.
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 4 Experimental Result
### Complexity and Computational Time  
The major components of the proposed data hiding and compression method in this paper consisted of DCT, the multi-bit SS mapping, singular value decomposition, and the CS acquisition process in the embedding and multi-bit SS de-mapping, SVD reconstruction, and audio decoding via CS reconstruction and IDCT. Each component had a different complexity. The SVD process to obtain \(\mathbf{U}\in\mathbb{R}^{M\times M}\), \(\mathbf{S}\in\mathbb{R}^{M\times M}\), and \(\mathbf{V}\in\mathbb{R}^{M\times M}\) from \(\mathbf{X}\in\mathbb{R}^{M\times M}\) had a complexity of \(O(M^{3})\)[28]. When we needed to get \(\mathbf{X}\) from \(\mathbf{U}\), \(\mathbf{S}\), and \(\mathbf{V}\) as (3), its complexity was \(O(M^{2.37})\)[29]. DCT and IDCT described in (8) and (9) had a complexity of \(O(N_{p}^{2})\) where \(N_{p}\) is the number of the DCT points, and \(N_{p}=M\) in this case. The CS acquisition in (4), which was also the multi-bit SS embedding, had a complexity of \(O(pr^{2})\). The multi-bit SS detection, as described in (22), had a complexity of \(O(r^{3})\). Finally, the audio reconstruction by OMP approach in (40) had a complexity of \(O(p^{2}r)\). Due to the relation \(p\leq r<M\), the highest computational cost was found in the singular value decomposition, i.e., \(O(M^{3})\); thus, the overall complexity of the components was dominated by the SVD. This finding confirmed the reason to use the lower \(M\) value. However, we still needed to check the computational time by the simulation to find out a proper \(M\) value to avoid a very long processing time.  
We applied the simulation to find out the computational time, which should represent the complexity of the embedding and the detection stage. In the simulation, we applied parameter \(M\) from 16 to 1024 with multiples of a power of two, parameter \(r=0.125M\), \(r=0.25M\), and \(r=0.5M\). We used 10 clips in the simulation, and we averaged the time processing result. The result is displayed in Figure 4(a). The processing time exponentially increased when \(M\) rose. Parameter \(r/M\) had no significant impact on the computational time. From this figure, lower \(M\) was recommended due to the low computational time. Moreover, as confirmed in Section 4.1, the lower \(M\) had a significant impact on the reconstructed audio quality.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 3 Joint Optimization Algorithm
### Subproblem 3: User Computation and Communication Resources
#### 3.3.2 Subproblem 3-1: \(\mathbf{b}\) Optimization Problem  
When \(\mathbf{f}\), \(\mathbf{t}\), \(\mathbf{p}\) are determined, the \(\mathbf{b}\) optimization problem can be expressed as  
\[ \begin{array}{rl}\text{P7-2}:&\min\limits_{\mathbf{b}}\sum\limits_{i\in \mathcal{N}_{m}}^{|\mathcal{N}_{m}|}\frac{b_{i}t_{i}N_{0}}{Si}\left(2^{\frac{s }{i\delta_{n}}}-1\right)\\ &\text{s.t.}\ \text{C1}:b_{i}\geq 0,\forall i\in\mathcal{N}_{m}\\ &\text{C2}:\sum\limits_{i\in\mathcal{N}_{m}}^{N_{m}}b_{i}=B,\forall i\in \mathcal{N}_{m}\end{array}  \tag{32}\]  
We can use the Karush-Kuhn-Tucker conditions (KKT) to solve (32), the optimal bandwidth can be expressed as  
\[b_{i}^{*}=\frac{s\ln 2}{t_{i}\left[1+\mathcal{W}\left(\frac{\delta_{i}\mu^{*}-B_{i }N_{0}}{Bi_{i}N_{0}e}\right)\right]} \tag{33}\]where \(\mathcal{W}(\bullet)\) is the Lambert \(W\)-function. The Lagrange multiplier vector \(\mu^{*}\) should satisfy the conditions:  
\[k(\mu^{*})=\sum_{i\in\mathcal{N}_{m}}^{|\mathcal{N}_{m}|}\frac{s\ln 2}{t_{i} \Big{[}1+\mathcal{W}\Big{(}\frac{Si\mu^{*}-Bt_{i}N_{0}}{Bt_{i}N_{0}\epsilon} \Big{)}\Big{]}}=B \tag{34}\]  
**Proof:** Since the P7-2 is a convex problem, it can be solved by using the KKT conditions. The Lagrange function is  
\[L(\textbf{b},\mu)=\sum_{i\in\mathcal{N}_{m}}^{|\mathcal{N}_{m}|}\frac{b_{i}t_{ i}N_{0}}{g_{i}}\Big{(}2^{\frac{s}{t_{i}}}-1\Big{)}+\mu\Bigg{(}\sum_{i\in \mathcal{N}_{m}}^{|\mathcal{N}_{m}|}b_{i}-B\Bigg{)} \tag{35}\]  
where \(\mu^{*}\) is the Lagrange multiplier associated with constraint C2. The first order derivative of (35) with respect \(b_{i}\) to is  
\[\frac{\partial L(\textbf{b},\mu)}{\partial b_{i}}=\frac{N_{0}}{g_{i}t_{i}} \bigg{(}\frac{(\ln 2)s}{t_{i}^{2}t_{i}}-1-\frac{\ln(2)s}{t_{i}b_{i}}e^{ \frac{(\ln 2)s}{t_{i}^{2}t_{i}}}\bigg{)}+\mu \tag{36}\]  
we can obtain \(b_{i}^{*}\) as the unique solution to \(\partial L(\textbf{b},\mu)/\partial b_{i}=0\). \(\square\)  
Since the Lambert \(W\)-function is an increase function, as a result, \(k(\mu^{*})\) is a decrease function of \(\mu^{*}\). So, we can use the bisection method to find \(\mu^{*}\), and then obtain \(\textbf{b}^{*}\). The details are shown in Algorithm 4. ```
1:Input: \(\mu^{\text{min}}=0\), \(\mu^{\text{max}}\), \(\textbf{t}^{*}\)
2:Output: \(\mu^{*}\), \(\textbf{b}^{*}\)
3:repeat
4: Set \(\mu=\big{(}\mu^{\text{min}}+\mu^{\text{max}}\big{)}/2\)
5:if\(k(\mu^{*})=B\)
6:\(\mu^{*}=\mu\)
7:return\(\mu^{*}\)
8:elseif\(k(\mu^{*})>B\)
9:\(\mu^{\text{min}}=\mu\)
10:elseif\(k(\mu^{*})<B\)
11:\(\mu^{\text{max}}=\mu\)
12:endif
13:endwhile
```  
**Algorithm 4** Solve The Subproblem 3-2: \(\textbf{b}\)  
Because the optimization of subproblem 3-2 will cause the change in bandwidth **b**, which will affect **f**, **f**, **p** in subproblem 3-1, it is necessary to iterate between subproblem 3-1 and subproblem 3-2. The details are shown in Algorithm 5.
## 4 Experiment and Analysis
### Grid DEM Data  
1. Evaluation dataset: A simulated DEM was generated by the computer as the initial data. The 50 rows and 50 columns on the left side of the DEM had an elevation value of 0, and the 50 rows and 50 columns on the right side had an elevation value of 1. The experimental data were processed using the following iterative algorithm to generate the datasets needed for subsequent evaluation experiments: (1) The size of the initial image is determined to be 100 \(\times\) 100, which serves as the output for iteration 0. (2) From the previous iteration, (100 \(\times\) 100)/2 pixels are randomly selected from the result image.  
3. The positions of each selected pixel and a randomly chosen neighboring pixel are swapped. 4. The result image of the current mixed iteration is output. 5. We return to Step 2 and continue until the number of iterations reaches a specified threshold. The initial image was iterated 10,000 times following the above steps, and some of the iteration results are shown in Figure 5. 2. Twelve simulated DEMs of different complexities of 12.5 m resolution were generated using the computer with a window size of 30 \(\times\) 30 (Figure 6). In Figure 6, (a-c) represent a flat terrain, a simple slope, and a single peak, respectively. (d-l) represent terrains with increasing complexity, which were simulated using sine functions, cosine functions, and superimposed random noise. 3. A grid DEM of 12.5 m resolution was selected from a region, and twelve real DEMs with varying complexity and a window size of 30 \(\times\) 30 were extracted (Figure 7). In Figure 6, (a-d) are DEMs extracted from flat areas, with minimal elevation variations and few elevation categories. The difference between the maximum and minimum elevations does not exceed 5 m. (e-h) are DEMs extracted from hilly areas, with more elevation categories, and the difference between the maximum and minimum elevations is within 10 m. (i-l) are DEMs extracted from mountainous areas with  
Figure 5: Partial images of the evaluation dataset. The displayed images are outputs of iterations 0 (a); 20 (b); 40 (c); 60 (d); 80 (e); 100 (f); 200 (g); 600 (h); 1000 (i); 2000 (j); 6000 (k); and 10,000 (l).  
the most complex terrain types and the highest number of elevation categories. The difference between the maximum and minimum elevations is greater than 10 m.
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 3 Result and Discussion
### Computational Complexity Analysis  
To provide a more comprehensive analysis of the lightweight characteristics of the proposed GSL-YOLOv8n, we conduct a theoretical computational complexity assessment. For the original YOLOv8n model, the core convolution operations have a time complexity of O(N'2 * K2 * C_in * C_out), where N is the input feature map size, K is the kernel size, and C_in and C_out represent the input and output channels, respectively. The improved GSL-YOLOv8n incorporates Ghost convolutions, reducing redundancy and lowering the complexity to O(N'2 * K'2 * C_in * C_out/r), where r is the compression ratio of the Ghost module.  
Additionally, the lightweight shared convolution detection head (LSCDH) used in GSL-YOLOv8n further enhances computational efficiency. The LSCDH module allows for multi-scale feature fusion with a time complexity of O(N'2 * C_in * C_out), significantly reducing repeated calculations compared to traditional detection heads. The integration of SimAM provides enhanced feature extraction with negligible computational overhead.  
As indicated in the results, GSL-YOLOv8n maintains a near-equivalent FPS to YOLOv8n (238.9 vs. 239.6) while drastically reducing model size, parameter count, and computational load by 66.7%, 67.4%, and 58.0%, respectively. These improvements highlight the effectiveness of the proposed method in optimizing computational complexity while preserving high detection accuracy.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
###### Abstract
* Comprehensive testing on a large-scale real-world dataset proves that SLIT outperforms contemporary methods in both short-term and long-term travel time predictions. Notably, SLIT exhibits significant enhancement in short-term prediction, achieving improvements of up to 9.67%, 9.2%, and 8.66% in terms of MAE, RMSE, and SMAPE, respectively. * Across a wide range of road complexity conditions, SLIT consistently achieves notable results. Such performance demonstrates SLIT's capability to handle varied road conditions effectively, proving its adaptability and robustness across multiple traffic environments. The rest of this paper is organized as follows: Section 2 provides a review of the relevant literature. Section 3 details the proposed framework. Following this, Sections 4 and 5 present the experimental setup and the experimental results and discussions, respectively. Finally, Section 6 concludes with a summary of our study.
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 2 Key Algorithms Based on Spectral Analysis  
In this section, we describe in detail the digital integrator, the envelope spectrum, and cepstrum. Then, a fault prognosis algorithm is given by exploiting these spectral analyses. Throughout this paper, uppercase letters denote frequency-domain variables, lowercase letters denote time-domain variables, and bold letters denote vectors or sequences.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 4 System Architecture
### System Framework  
A framework of the proposed system is shown in Figure 2. The system has three main components:  
1. The first component includes the wireless monitoring nodes. The wireless monitoring nodes are equipped with a low energy processing unit, wired soil and moisture sensors and a wireless  
Figure 1: System overview.  
communication unit. Each node has four soil moisture and four soil temperature sensors. The nodes are powered through a rechargeable battery. The battery is charged through a solar panel. The sensor data are passed to the processing unit for initial filtering and to form data packets. The data packets are passed to the wireless communication unit to forward them wirelessly to the relay nodes.
2. The second important component is the wireless network that forwards all the sensor data from the monitoring nodes to the control room. The network is composed of relay nodes. The relay nodes have only the necessary communication unit to forward the data as well as similar energy harvesting capabilities with the monitoring nodes. However, the relay nodes do not have any sensors. Each relay node is able to receive and to forward the data packets towards the control room.
3. The third component is the control room which consists of a computer and a UAV. At the control room, all the data are collected and processed. If the collected data exceeds a predefined threshold, the control room sends a notification to the UAV. The UAV then can maneuver to the points of interest as determined by the sensor nodes.
## 4 Conclusions  
Based on our findings, exogenous melatonin application significantly improved the quality parameters of sun-dried apricots, with 100 \(\upmu\)M emerging as the optimal concentration. The pre-drying application of 100 \(\upmu\)M melatonin was found to be particularly effective. The treatment notably enhanced color retention by increasing L*, a*, and b* values, while effectively reducing oxidative stress markers (MDA and H\({}_{2}\)O\({}_{2}\)). The 100 \(\upmu\)M melatonin treatment yielded the highest \(\beta\)-carotene content and optimal sugar composition, including glucose, fructose, and sucrose. Multivariate analysis revealed strong positive correlations between color values and sugar content, suggesting coordinated physiological responses to melatonin treatment. These findings have significant implications for both producers and consumers. For producers, melatonin treatment offers a practical, non-toxic solution for preserving dried apricot quality during processing, potentially leading to improved product marketability and reduced post-harvest losses. Indeed, the compound can be applied through dipping or spraying solutions, and both methods show promise for large-scale applications. The identified optimal concentration (100 \(\upmu\)M) provides clear guidance for commercial applications. Due to its safety and non-toxic nature, it has high potential for use in post-harvest fruit supply chains. For consumers, the enhanced preservation of natural colors, sugars, and antioxidants means access to higher quality dried apricots with better nutritional value and sensory attributes. However, this study's limitation lies in the rapid degradation of melatonin during the drying process, as evidenced by its undetectable levels in the final product. Additionally, there is no standardization for effective dose and application duration for the post-harvest use of the melatonin molecule. Future research should focus on developing more stable melatonin formulations or alternative application methods to extend its protective effects throughout the drying process. Studies are needed to reveal the effects of temperature and light on melatonin degradation kinetics in solutions to interpret melatonin stability in aqueous solutions used for fruit dipping. Additionally, investigating the molecular mechanisms behind melatonin's lasting effects and conducting sensory evaluations would provide valuable insights for optimizing treatment protocols.  
Conceptualization, R.Z. and S.E.; methodology, Y.U. and S.E.; formal analysis, R.Z., Y.L., H.H.-V. and Y.U.; investigation, R.Z., O.K. and Y.L.; data curation, R.Z., O.K., H.H.-V. and S.E.; original draft preparation, R.Z., Y.U. and O.K.; writing--review and editing O.K. All authors have read and agreed to the published version of the manuscript.  
No funding was received to support this research or manuscript.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 3 Method and Materials
### Structure from Motion  
The SfM method, belonging to the passive data acquisition technology, allows to create digital 3D models based on many two-dimensional images (photos). To generate a three-dimensional model, one needs a series of images of an area or object with a large overlap (about 80%), taken at different angles and at different distances, Figure 1 [41]. An average quality camera is sufficient to take pictures, and now even a camera built into a smartphone, making this method a low-cost solution. The smartphone is considered a device of common use, because its price in relation to the many different possibilities it offers to each owner is relatively low.  
In the SfM method, a series of photos is a set of data that is imported into an appropriate computer program dedicated to this method. This software allows to generate 3D models and textures. Today, there are many programs available, both free and open-source, such as Meshroom [42; 43], Colmap [44], VisualSFM [45], and commercial: Agisoft PhotoScan, Capturing RealityCapture [2].  
The general process of incremental SfM operations is shown in Figure 2[44]. It can be broken down into two main modules: correspondence search and incremental reconstruction. The first module aims to search for complete and diffuse matches specific to two images. It has three sub-steps: feature extraction, feature matching, and geometric verification. The second module aims to estimate the alignment of images and three-dimensional scene points. It is possible thanks to the following steps: initialisation (selection of the best pair of images), image registration (for orientation), triangulation (calculation of 3D points), adjustment of the beam (local and global improvement) and removal of outliers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Artec Eva Scanner** & **Artec Spider Scanner** \\ \hline Texture capture capability & yes & yes \\ \hline Scanning technology & structured white light &
\begin{tabular}{c} flashing light, blue light \\ (not laser) \\ \end{tabular} \\ \hline Resolution 3D, up to & 0.2 mm & 0.1 mm \\ \hline Point precision 3D, up to & 0.1 mm & 0.05 mm \\ \hline
3D precision at a distance, up to & 0.03\%/100 cm & 0.03\%/100 cm \\ \hline Linear field of view, & 214 mm \(\times\) 148 mm & 90 mm \(\times\) 70 mm \\ height \(\times\) width & to 536 mm \(\times\) 371 mm & to 180 mm \(\times\) 140 mm \\ \hline Working distance & 0.4–1 m & 0.17–0.35 m \\ \hline Weight & 0.85 kg & 0.85 kg \\ \hline Minimum requirements of the computer cooperating with the scanner & 15 (recommended 17), 8–12 GB & 15 (recommended 17), 8–12 GB \\ \hline RAM, NVIDIA GeForce & RAM, NVIDIA GeForce & RAM, NVIDIA GeForce \\ scanner & 400 series & 400 series \\ \hline \hline \end{tabular}  
* The data come from the manufacturer, but the actual values do not differ from those given, which has been confirmed by our many years of use of this equipment.  
\end{table}
Table 1: Specification of Artec Eva and Spider scanners *.  
Figure 1: Taking a series of photos in the Structure from Motion method.