esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 8 Challenges  
As was stated in the response to research question No. 5, implementing SHAs involves a number of challenges. In the following section, these challenges are described in more detail with possible solutions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Technologies** & **Methods** & **Simulation Tools** & **Datasets** \\ \hline
[55] & Resource allocation & Bandwidth allocation, QoE & Java platform & Wearable device datasets \\ \hline
[46] & Machine learning predictive analytics & Prioritization & MATLAB & EEG dataset \\ \hline
[47] & Machine learning predictive analytics & Load balancing & Wireless brain monitoring system & N/A \\ \hline
[49] & Resource allocation & Prioritization & N/A & Wearable device datasets \\ \hline
[50] & Machine learning predictive analytics & Load balancing, QoE & MATLAB & Electronic medical records (EMR) dataset \\ \hline
[48] & Traffic management & Error correction & MATLAB, C++/C\# & Wearable device datasets \\ \hline
[51] & Machine learning predictive analytics & Load balancing & Not mentioned & Wearable device datasets \\ \hline
[52] & Resource allocation & Load balancing & MATLAB & Electronic medical records (EMR) dataset \\ \hline
[53] & Resource allocation & Load balancing & NA & Wearable device datasets \\ \hline
[54] & Machine learning predictive analytics & QoE measurement & NetLogo &
\begin{tabular}{c} Electronic medical records (EMR) dataset \\ mHealth (mobile health) \\ \end{tabular} \\ \hline
[56] & Quality of experience & Prioritization & CloudSim & Wearable device datasets \\ \hline
[64] & Security tools & QoE measurement & C++/Java & Electronic medical records (EMR) dataset \\ \hline
[59] & Traffic management & Traffic shaping & TensorFlow &
\begin{tabular}{c} Electronic medical records (EMR) dataset \\ X-ray and ultrasound \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 5: Selected papers to show different technologies, techniques, tools, and datasets.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
## 2 Theoretical Analysis of the Coupling Loss Mechanism
### Axial Deviation  
Figure 7 shows the schematic diagram of the axial deviation when the collimator is aligned. Usually, when Gaussian beams are transmitted in the same medium, the electric field magnitude distribution decreases smoothly from the center to the periphery in the axial deviation of the Gaussian curve. Equation (7) is the coupling efficiency \(\eta\) when the radius of the spots at the end face of the two collimators is different from the beam waist radius.  
\[\frac{4}{\eta}=\bigg{(}\frac{\omega_{1}^{\prime}}{\omega_{1}}+\frac{\omega_{1 }}{\omega_{1}^{\prime}}\bigg{)}^{2}+\frac{\pi\omega_{0}\omega_{0}^{\prime}}{ \lambda}\bigg{(}\frac{1}{R_{0}}-\frac{1}{R_{0}^{\prime}}\bigg{)}^{2} \tag{7}\]  
where \(\omega_{1}\) is the spot radius at the end of the collimator, and \(\omega_{1}^{\prime}\) is the one after \(Z\), whose relationship is shown in Equation (8); \(R_{0}\) and \({R_{0}}^{\prime}\) are the initial radius of the beam wavefront and the one after \(Z\). When \(R_{0}=\infty\), \({R_{0}}^{\prime}\) could be expressed by Equation (9).  
\[\omega_{1}^{\prime}=\omega_{1}\Bigg{[}1+\bigg{(}\frac{Z\lambda}{n_{0}\pi \omega_{1}^{2}}\bigg{)}^{2}\Bigg{]}^{\frac{1}{2}} \tag{8}\]  
\[R_{0}^{\prime}=Z\Bigg{[}1+\bigg{(}\frac{n_{0}\pi\omega_{1}^{2}}{Z\lambda} \bigg{)}^{2}\Bigg{]} \tag{9}\]  
Figure 5: Angular deviation.  
Figure 6: Coupling loss due to angular deviation.  
Substituting Equations (4), (8) and (9) into Equation (7), when \(\lambda\) = 1550 nm, \(\sqrt{A}\) = 0.326, and \(n_{0}\) = 1.5946, the coupling loss due to axial deviation is obtained as shown in Equation (10). Figure 8 shows the relationship between the axial deviation and coupling loss when \(\omega_{0}\) is different. The larger the mode field diameter of the tail fiber, the higher the axial deviation loss under the same axial tolerance is.  
\[\eta_{Z}=4.434\ln\left[1+\left(\frac{\pi n_{0}A\omega_{0}^{2}Z}{2\lambda} \right)^{2}\right] \tag{10}\]
## 4 Simulation Results and Performance Analysis
### Experimental Results  
In this section, experimental results are presented to verify the feasibility of the proposed method to reduce acquisition time. Because P-code is currently a military code, the signal cannot be acquired in the actual environment. Thus, the acquisition is conducted only in terms of GPS signal in this experiment, where the IF is 4.092 MHz and sampling frequency is 16.368 Hz. The data samples from antennae are stored and, afterwards, post-processed for signal acquisition analysis. The results have been obtained for PRN-8, the location of which is at an elevation of 30 degrees at the time of data collection. Table 2 depicts experimental results of GC, utilizing adaptive and non-adaptive scaling schemes, as well as the results of a traditional method. The table demonstrates that the proposed method can achieve better signal parameter precision within a shorter time, as opposed to traditional methods, where one must narrow frequency search step size to enhance parameter precision. This consumes large amounts of signal search time. The table reveals that the average convergence iteration number of the proposed method is below 17 times (except for PRN-8 and PRN-4), which greatly saves a lot of hardware computation time. In addition, the high PSNR can cause the GC process to converge more rapidly in order to speed up the computation time. Note that the iteration numbers of the proposed scheme in the signal acquisition of PRN-8 and PRN-4 are 29 and 30, respectively, which are higher than that of other satellites. The reason is that the estimate parameter converges to a local optimal solution and thus the signal acquisition engine repeats the search process, which increases the iteration number. The same scenario also happens to the GC method without an adaptive scaling scheme in the case of PRN-8 and PRN-4. Figure 6a,b depicts the acquisition parameter search curve of frequency shift and code delay _versus_ iteration number, respectively. Figure 6a shows that the proposed scheme converges to a local optimal solution in the 18 th iteration and thus restarts search process. In the 29 th iteration the proposed scheme converges to a global optimal solution and successfully acquires a signal parameter. Table 2 compares the performances of the proposed method with a traditional (serial search) method [1] for PRN-8. This table shows that the proposed method can successfully acquire the satellite signal in the sky. Note that search times for PRN-8 and PRN-4 are longer because the signal power of the satellite is weaker.
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 4 Experimental Results and Discussions
For each pixel, this search requires only basic integer operationsand a minimal comparison loop. As such, the algorithm runs in constant time per pixel, resulting in an overall time complexity of \(\mathcal{O}(N)\) for an image with \(N\) pixels. To empirically validate this efficiency, we measured the execution time of the full embedding and extraction process on a \(512\times 512\) grayscale image using Matlab R2019b on a system equipped with an Intel Core i5-8250U processor (1.60 GHz) and 8 GB RAM. The total runtime including all encoding, decoding, and pixel reconstruction steps was approximately 0.97 s. These results demonstrate that the proposed method is computationally lightweight and suitable for low-latency applications. However, we acknowledge that strict real-time constraints in high-resolution or embedded systems may require additional platform-specific optimization, which is a topic for future research. Although the proposed method provides excellent performance in terms of embedding capacity and image quality, it inherits the common limitation of LSB-based RDH schemes in that it is not robust against lossy image compression such as JPEG. Because the embedded data reside in the least significant bits, such transformations tend to remove or distort the hidden content. This tradeoff reflects a key difference between RDH and robust watermarking, and points toward a promising future research direction in transform-domain RDH.
## 5 Conclusions and Future Studies  
The main purpose of this study is to see the applicability of the method called TPO/MA to more general structures than those it was previously applied to. In fact, TPO/MA has been applied until now to trusses and truss-like structures as cables and tensegoric structures. The current study has shown that plates can be also analyzed using TPO/MA.  
According to the No-Free-Lunch theorem [39], an algorithm may outperform another one in an example, while another one outperforms that algorithm in a second example. For that reason, several metaheuristic algorithms, such as HS, FPA, TLBO, and JA, were tested with the proposed novel hybrid method in the study. While HS has slightly failed to find the minimum energy level, the proposed  
Figure 8: Nodal displacements for Structure 3.  
Figure 7: Structure 2: A plate with the hole in the middle.  
HHS reached to minimum energy level quickly than the other algorithms. The methodology using HSS is effective on TPO/MA on solving plane stress problems. The processing time of the all iterations is in an acceptable value. By using TPO/MA, 1000 iterations of three examples were solved in 1.5 s, 2.5 s, and 8 s, respectively. Compared to other approaches, HHS can nearly find the global optimum results by half and 1/3 of the number of function evaluations for the first two examples and third example, respectively.  
The analysis results found according to the HHS approach is effective to find the same results with the FEM method. Only minor differences (last than 1%) can be seen, but the total potential energy values are not affected.  
Based on these considerations, it can be easily foreseen that this study will be followed by applications of TPO/MA on plane strain problems, plates with three-dimensional deformations, those with nonlinear constitutive equations, and those with other nonlinearities.  
[PERSON] and [PERSON] generated the analyses codes. [PERSON] developed theory background and formulations of the methodology. The text of paper was formed by [PERSON], [PERSON] and [PERSON]. The figures were drawn by [PERSON] and [PERSON], [PERSON] supervised the research direction. All authors have read and approved the final manuscript.  
This research was supported by the Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073164).  
The authors declare no conflict of interest.
# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 3 Results
### Burn Scar Mapping Machine-Learning Algorithms  
The process of determining the burnt area is carried out using a machine learning algorithm on the composite data from four variables. The burnt area classification process in this research uses three types of classifiers, namely: SVM, random forest, and CART with the parameters defined in Table 1. For the SVM classifier, the radial basis function (RBF) or the Gaussian kernel, is the most widely used kernel because of its high accuracy value. Usually used for datasets that are not linearly separated [51].  
Figure 7 shows the differences in the classification results of the three classification algorithms and the distribution of the hotspots. The results of the classification using the CART classifier still show a lot of noise that appears as a burn area. While the SVM and random forest classifiers show the maximum noise reduction results and the burn area distribution pattern matches the hotspot distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameter** & **Value** \\ \hline CART & Leaf Node & 10 \\ Random Forest & Tree Number & 10 \\ Support Vector Machine & Gamma & 0.5 \\  & Cost & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the classifiers.  
Figure 7: The difference in the burn area of the three types of classifiers, compared to the Hotspot data.  
Based on the significant burn area detected, we selected three locations as the area of interest, as indicated in Figure 7, to measure the model accuracy. Comparative data used to measure the accuracy of the model was the field data on the burnt land in the same time period. This data was obtained from integration of the Hotspot data, the fire extinguishing points, and the manual delineation of the Landsat-8 and Sentinel-2 optical images.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 4 Results
### Test Cases and Results
#### 4.2.1 Effect of Adding Appliance Status into the Initial Dictionary  
Figure 7 shows the electricity consumption of a family on a random week (24-30 June 2013). The load began to increase at 06:00, achieved a small peak at approximately 08:00, and typically reached the peak for one day at 18:00-20:00. The load profiles were consistent with the daily living habits of residents. For example, the washing and breakfast time of citizens was at 08:00 after getting up at 06:00. Upon returning home at 18:00, residents may take a bath, cook dinner, or engage in some form of entertainment. These activities considerably impacted the load profile and exhibited certain regularity.  
Using the appliance load as the initial dictionary of the K-SVD algorithm tended to make the extracted UBPs consistent with customer behavior and ensured sparsity. To achieve a fair assessment, sparse coding with and without appliance load scenarios were performed 100 times with the number of iterations K = 60.  
Table 1 compares the performance of the proposed edge deep K-SVD algorithm with appliance load data with the traditional deep K-SVD algorithm. Notably, MAPE of the 3-layer K-SVD algorithm was already small, so the improvement was not obvious. However, as illustrated, the proposed algorithm maintained a significant improvement in RMSE and MAE in all three tested compression ratios.  
Figure 7: Daily load profiles of a single residential customer for one week.
## 2 Calculation Model
### Boundary Condition  
To determine the accuracy of numerical simulation, the inlet condition of the vortex pump is set as the mass flow boundary according to the law of conservation of mass. The inlet velocity \(V=Q/A\) is calculated by the given mass flow rate \(Q\) and the geometric dimensions \(A\) of the inlet channel section. Meanwhile, the turbulence intensity is calculated according to the Reynolds number \(R_{e}\) at the inlet, and the inlet diameter is used as the hydraulic diameter. The initial pressure is set to standard atmospheric pressure with a value of 1.01325 \(\times\) 10\({}^{5}\) Pa, which is uniformly distributed over the inlet section. Considering that the pressure and velocity on the outlet boundary are both unknown, free outflow is adopted as the outlet boundary condition. This type of boundary condition is applicable to situations where the pressure or velocity on the outflow boundary is unknown and  
Figure 3: Numerical calculation mesh: (**a**) fluid domain model, (**b**) mesh model, and (**c**) enlarged area.  
is calculated internally by FLUENT software without the need for special specification. Incompressible pure water is selected as the fluid medium, and a no-slip condition (relative velocity is zero) is adopted in the flow channel and the wall of the tongue area in the fluid domain. Impeller wall is defined as a moving wall condition with standard roughness models to more accurately simulate friction and resistance effects in actual flows. The fluid domain and impeller wall are set to the same rotation direction and rotation center to ensure consistency during the simulation process. To correct the underdeveloped area of turbulent flow, the second type of boundary condition based on pressure iteration calculation is adopted. The SIMPLE pressure-velocity coupling algorithm with fast convergence velocity is used to numerically simulate the vortex pump, in which the convergence standard is set at \(1\times 10^{-5}\).
# Management Platforms and Protocols for Internet of Things: A Survey
## 5 IoT Network Management Platforms
### Zabbix  
Zabbix is an open-source tool distributed under the GNU GPLv2 license for network management. It monitors the network services status as well as servers or other hardware. As described in [61], it is characterized as being a centralized management system with semi-distributed monitoring. In Figure 15, the organization can be divided into three main modules.  
The platform architecture is distributed and consists of a central server in charge of administering the system and dealing with the interaction between the other two main components: (i) the \"Zabbix Agent\" to monitor local resources and applications and send them to the server, and (ii) the \"Zabbix Proxy\" is an optional part of the Zabbix configuration essential for distributed monitoring [62]. Zabbix proxy collects the data from the hosts and stores them in a database of their own to avoid loss of information if there is a problem with the communication with the server. The alert system includes three channels for sending notifications via email, SMS, and jabber (currently called XMPP--Extensible Messaging and Presence Protocol).  
_Pros: Mature platform and has a greater number of management metrics._  
_Cons: No basic security and only monitoring the networks._
## 3 Temporal Variations in and Correlations between PM and Meteorological Factors  
### Trend Analysis of Hourly Data from 2020 to 2022 and Daily Average Data from 2015 to 2023  
To verify the trend similarity, the hourly PM2.5 and PM10 data from 2020 to 2022 were converted into daily averages by calculating the mean over each 24 h period, ensuring that the data were on a consistent time scale. As shown in Figure 1, trend lines for the daily data over the years from 2015 to 2023 are plotted alongside the daily average trend lines for the years from 2020 to 2022. These sets of trend lines are displayed in overlay for a visual comparison of the similarity in data trends, thereby demonstrating the usability of the hourly data. The trends of PM2.5 and PM10 between the hourly data from 2020 to 2022 and the daily average data from 2015 to 2023 were indicated to have a high degree of consistency, with Pearson coefficients being 0.884 and 0.868, respectively (Figure 1). This substantiates the reliability of the hourly dataset, indicating that hourly data can effectively reflect long-term trends and can be utilized for further research and analysis.  
A comprehensive analysis of the daily average data from 2015 to 2023 was conducted by calculating the daily average PM2.5 and PM10 levels for each year to study the fluctuations in air quality throughout the year and to determine if there were any significant changes during specific periods. As depicted in Figure 2, the average concentrations of PM2.5 and PM10 for each month and each quarter were calculated and presented in bar graphs to further understand the variations in air quality between different seasons. Additionally, composite time series graphs were utilized to reveal the trends and potential seasonal patterns in PM2.5 and PM10 concentrations. It was found that PM2.5 and PM10 concentrations exhibited distinct seasonal patterns, as demonstrated in Figure 2, with higher concentrations in winter and lower concentrations in summer [19].  
Figure 1: The daily average concentrations (in \(\mathrm{\mu g/m^{3}}\) units) of PM10 (**a**) and PM2.5 (**b**) from 2015 to 2023 (solid line) and from 2020 to 2022 (bar chart).  
Figure 2: Concentration (in \(\mathrm{\mu g/m^{3}}\) units) variations for PM10 (**a**) and PM2.5 (**b**) from 2020 to 2022 on a daily basis (solid line), and from 2015 to 2023 on a monthly (red bars chart) and seasonal (gray bars chart) basis.
# Predicting Electromagnetic Performance Under Wrinkling in Thin-Film Phased Arrays
## 3 Structural-Electromagnetic Coupling Model
### Patch Electro-Magnetic Performance Simulation
This behavior shows that the film's vertical position alters the antenna's effective dielectric constant and electric-field distribution, thereby changing the resonance and matching. Figure 10: Effect of patch translation on S11 and gain (\(d_{pch}\)). As shown in the gain radiation patterns in Figure 11, at \(d_{zm}=0\) mm, the main lobe gain is highest and sidelobe levels are relatively low, indicating optimal radiation performance. As \(d_{zm}\) increases, the pattern symmetry degrades, exhibiting a double peak near the main lobe, while sidelobe levels remain largely unchanged. This reduces the beamforming resolution. In summary, translation of the film (\(d_{zm}\)) significantly affects the antenna's resonant frequency and matching and influences the symmetry and sidelobe characteristics of the gain pattern. In phased array applications, large variations in \(d_{zm}\) between elements can lead to pattern distortion and increased beam scan errors. Therefore, during design and fabrication, film positioning must be tightly controlled to ensure consistent element matching and beamforming accuracy, preventing performance degradation due to film displacement.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Ablation Study  
To evaluate the contribution of each architectural component in the proposed model, we conducted an ablation study comparing three configurations:  
* Baseline ResNet: The original ResNet-18 architecture without any modifications.
* ResNet + MSRB: A variant in which the standard residual blocks are replaced with multiscale residual blocks (MSRBs) to capture spatial patterns across multiple receptive fields.
* Enhanced ResNet (MSRB + SE): The full version combining MSRB with Squeeze-and-Excitation (SE) modules to introduce channel-wise spectral attention.  
Table 3 presents the evaluation results using RMSE, MAE, and \(R^{2}\) on the test set. All models were trained under identical experimental conditions to ensure a fair and consistent comparison. The results demonstrate that both architectural enhancements significantly improve performance. The introduction of MSRB alone leads to a 4.05 Mg/ha reduction in RMSE compared to the baseline, confirming the importance of multiscale spatial feature extraction in heterogeneous forest environments. Incorporating SE modules provides additional gains by adaptively reweighting spectral channels, particularly enhancing the influence of informative bands such as red-edge and SWIR.  
Figure 7: Performance comparison of different models across RMSE, MAE, and \(R^{2}\) metrics.