esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Urban and Rural Ecosystems in the Context of Suitability for Honeybees
## 3 Results
### The Impact of Geographic Location on the Functional and Biological Characteristics of Bee Colonies
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Traits or Measurements *} & \multicolumn{3}{c}{**Location ***} & \multicolumn{3}{c}{**Statistical Test **} & \multicolumn{1}{c}{\(p\)-Value**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Warsow (Mean \(\pm\) SE)**} & \multicolumn{1}{c}{**Lublin (Mean \(\pm\) SE)**} & \multicolumn{1}{c}{**Pulawy**} & \multicolumn{1}{c}{**Cracow (Mean \(\pm\) SE)**} & \multicolumn{1}{c}{**Tychy**} & \multicolumn{1}{c}{**Statistical Test **} & \multicolumn{1}{c}{\(p\)-Value**} \\ \hline B1 & 4.8 \(\pm\) 0.07 a & 7.4 \(\pm\) 0.19 c & 5.8 \(\pm\) 0.15 b & 5.9 \(\pm\) 0.12 b & 5.7 \(\pm\) 0.09 b & \(\mathrm{H}=91.37\), df = 4, & \(\leq\)0.01 \\ B2 & 4.3 \(\pm\) 0.17 a & 6.1 \(\pm\) 0.22 c & 4.8 \(\pm\) 0.20 ab & 5.5 \(\pm\) 0.10 bc & 5.1 \(\pm\) 0.10 b & \(\mathrm{H}=45.93\), df = 4, & \(\leq\)0.01 \\ OIndex & 0.60 \(\pm\) 0.11 a & 1.55 \(\pm\) 0.24 b & 0.95 \(\pm\) 0.15 & 0.34 \(\pm\) 0.09 a & 0.53 \(\pm\) 0.11 & \(\mathrm{H}=19.06\), df =4, & \(\leq\)0.01 \\ BC0 (dm\({}^{2}\)) & 25.25 \(\pm\) 1.34 a & 39.15 \(\pm\) 2.93 & 28.71 \(\pm\) 2.64 a & 20.83 \(\pm\) 1.74 a & 29.72 \(\pm\) 1.42 & \(\mathrm{F}=7.4\), df = 4 & \(\leq\)0.01 \\ BC1 (dm\({}^{2}\)) & 56.5 \(\pm\) 3.02 a & 72.0 \(\pm\) 9.98 b & 53.8 \(\pm\) 3.33 a & 63.5 \(\pm\) 4.93 & 48.6 \(\pm\) 1.85 a & \(\mathrm{F}=5.7\), df = 4 & \(\leq\)0.01 \\ BGD (\%) & 226.1 \(\pm\) 5.98 & 210.0 \(\pm\) 16.67 & 227.1 \(\pm\) 19.80 & 336.7 \(\pm\) 34.32 & 166.6 \(\pm\) 5.95 a & \(\mathrm{H}=32.02\), df = 4, & \(\leq\)0.01 \\ BC2 (dm\({}^{2}\)) & 65.0 \(\pm\) 2.87 b & 71.5 \(\pm\) 3.28 b & 60.0 \(\pm\) 3.25 b & 40.4 \(\pm\) 3.70 a & 69.9 \(\pm\) 1.31 b & Hartley’s = 12.22, & \(\leq\)0.01 \\ BC3 (dm\({}^{2}\)) & 1.16 \(\pm\) 0.47 & 2.79 \(\pm\) 0.85 & & 0.35 \(\pm\) 0.37 & \(\mathrm{H}=14.19\), df = 4, & \(\leq\)0.01 \\ BC3 (dm\({}^{2}\)) & ab & ab & 3.04 \(\pm\) 0.76 b & 0.03 \(\pm\) 0.02 a & 0.35 \(\pm\) 0.37 & \(\mathrm{H}=14.19\), df = 4, & \(\leq\)0.01 \\ SC1 (number) & 6759.4 \(\pm\) & 13,767.5 \(\pm\) & 7409.1 \(\pm\) & 9996.2 \(\pm\) & 6162.5 \(\pm\) & \(\mathrm{H}=61.38\), df = 4, & \(\leq\)0.01 \\ SC2 (number) & 304.2 a & 873.5 c & 357.0 a & 541.2 b & 133.6 a & \(\mathrm{N}=160\) & \(\leq\)0.01 \\ SC2 (number) & 15,241.3 \(\pm\) & 14,074.1 \(\pm\) & 1322.3 \(\pm\) & 10,100.0 \(\pm\) & 15,695.0 \(\pm\) & \(\mathrm{H}=29.97\), df = 4, & \(\leq\)0.01 \\  & 1130.6 b & 695.0 b & 503.9 b & 373.3 a & 13.2 b & \(\mathrm{N}=155\) & \(\leq\)0.01 \\ \hline \hline \end{tabular}  
\end{table} 
Table 3: Effects of apary location on the utility and biological characteristics of bee colonies.
## 5 Data Evaluation
### Comparison to Previous Work
#### 5.4.2 Triangle Counting  
We compared the performance of our method for estimating the number of triangles with the state-of-the-art method \(\text{gSH}_{T}\)[43]. To compare Mosar and \(\text{gSH}_{T}\) under the same computational cost, we set the parameters of \(\text{gSH}_{T}\) as \(\text{gSH}_{T}(p,p)\). As alluded to, the runtime of Mosar is then almost same as \(\text{gSH}_{T}(p,p)\), and the probabilities of observing a triangle (sampled as a closed or unclosed wedge) are \(p_{T}\approx 3p^{2}q\) and \(p_{T}\approx p^{2}\) for Mosar and \(\text{gSH}_{T}\), respectively. Let \(n_{T}\) be the number of triangles and \(\hat{n}_{T}\) be an estimate of \(n_{T}\); then, the variance of \(\hat{n}_{T}\) is nearly \(\frac{n_{T}}{p_{T}}\). Thus, the variance of Mosar is up to three times larger than \(\text{gSH}_{T}\). This is consistent with the results shown in Figure 12, where \(p=0.01\). We can see that the NRMSE of Mosar is nearly 1.7 times smaller than \(\text{gSH}_{T}\).
## 2 Wireless Multimedia Sensor Networks
### Battery-Powered
#### 2.1.1 Energy-Efficient Computation  
In traditional WSNs, a well-known fact is that the level of computational energy consumptions can be neglected. However, it is shown in [5] that the energy used for computation in WMSNs is intrinsically high. For example, in a simple vehicle tracking application, the energy for frame  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristics** & **Requirements** & **Approaches** \\ \hline \multirow{8}{*}{Battery-powered} & & Energy-efficient computation \\  & & \(\bullet\) Image compression algorithms: [26; 27; 28; 29; 30; 31] \\  & & \(\bullet\) Video compression algorithms: [32; 33; 34] \\ \cline{3-3}  & & Dynamic power management: [35; 36; 37] \\ \cline{3-3}  & & Energy-efficient communication \\  & & \(\bullet\) Transport protocols: [38] \\  & & \(\bullet\) MAC protocols: [39; 40; 41; 42; 43; 44; 45] \\  & & \(\bullet\) Routing protocols: [46; 47; 48; 49; 50; 51] \\ \hline \multirow{8}{*}{Real-time video data} & & Delay guarantee \\  & & \(\bullet\) MAC protocols: [39; 40; 41; 42; 43; 44; 45] \\  & & \(\bullet\) Routing protocols: [47; 51] \\ \cline{3-3}  & & Reliability guarantee: [13; 52; 53] \\ \cline{3-3}  & & \(\bullet\) MAC protocols: [41; 42; 44] \\ \cline{3-3}  & & \(\bullet\) Routing protocols: [46; 47] \\ \cline{3-3}  & & Prioritization and service differentiation: [4; 54] \\ \cline{3-3}  & & \(\bullet\) MAC protocols: [39; 40; 41; 42; 43; 44; 45] \\ \cline{3-3}  & & \(\bullet\) Routing protocols: [47] \\ \cline{3-3}  & & Quality of Experience & [55; 56; 57; 58] \\ \hline \multirow{8}{*}{Large volume of multimedia data} & & Local processing: [59; 60; 61; 62] \\ \cline{3-3}  & & \(\bullet\) Multimedia in-network processing \\ \cline{3-3}  & & Reducing data & \(\bullet\) Multimedia data fusion: [63; 64] \\ \cline{3-3}  & & redundancy & \(\bullet\) Multi-view video summarization [65] \\ \cline{3-3}  & & & Distributed source coding: [66; 67; 27; 30; 34; 68] \\ \cline{3-3}  & & & In-network data storage and query processing: \\ \cline{3-3}  & & \(\bullet\) Multi-channel MAC protocols: [74; 75; 76] \\ \cline{3-3}  & & \(\bullet\) Multi-channel MAC protocols: [74; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86;  
capture and processing can reach up to 12% of the overall energy consumption in the detection of an event [61]. Consequently, WMSNs must try to utilize the more energy-efficient vision processing algorithms, such as energy-efficient image processing [26; 27; 28; 29; 30; 31] and energy-efficient video compressing [32; 33; 34]. Moreover, whilst pursuing low level of computational energy usage, a tradeoff between energy consumption and image quality must also be addressed [106; 107].
## 2 Materials and Methods
### Classification and Accuracy Assessment
Analogous to the initial classification step, 54 different classification settings were tested and are summarized in the results section (Table 5). In contrast to the previous step, spectral similarities are much higher between broad-leaved tree species (compare Figure 6). Thus, a multitemporal approach as well as dimension reduction using Principal Component Analysis (PCA) was used to improve results. The main goal of using principal components is reducing noise and extracting information from data. The first principal component captures the largest possible variance within a dataset while further PCs have less contribution [35]. PCA was computed in ArcGIS Pro for each input dataset. Furthermore, a vegetation index (NDVI) was included in the classification. For the object-based approach, we first evaluated 30 different segmentation settings to generate an optimal segmented image as input for the ML algorithms. In total, we tested 54 different classification settings that are summarized in Table 5. This approach allowed to first evaluate segmentation settings and then to optimize classification by comparing the two ML algorithms in an OBIA and PB approach on different bands, with or without additional variables such as PCs, NDVI. A total of 15 training areas and five reference areas for the creation of training and test samples were available for this second step. Samples were created as described in the previous step. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Name** & **Broad-Leaved-Forest** & **Coniferous Forest** & **Total** & **UserAccuracy** & **Kappa** \\ \hline
**Broad-Leaved Forest** & 35 & 0 & 35 & 1 & 0 \\
**Coniferous Forest** & 3 & 54 & 57 & 0.95 & 0 \\
**Total** & 38 & 54 & 92 & 0 & 0 \\
**ProducerAccuracy** & 0.92 & 1 & 0 & 0.97 & 0 \\
**Kappa** & 0 & 0 & 0 & 0 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix based on independent test samples for the first classification step (Ebersberger Forest). Results are shown for the classification setting highlighted in Table 3.  
Figure 5: Spectral profile for a multitemporal analysis of three Sentinel-2 images for four tree groups. (Inventory Data@Bayerische Staatsforsten).
# Embedding Equality Constraints of Optimization Problems into a Quantum Annealer +
## 9 Using the Constraint Embeddings for Solving Constrained Optimization Problems  
In the previous sections, we focused on finding an embedding of a single constraint in the Chimera graph. An interesting question is whether the proposed solution can be combined with an embedding of an objective function and possibly with other constraints in the Chimera graph in order to solve quadratic binary constrained problems. The difficulty is that the placement of the problem variables \(x\) in the constraints and in the objective function embeddings may be inconsistent with each other. Connecting different copies of the same variable by chains in order to force them to take the same value is likely to be very expensive in terms of qubit usage and may even be impossible. To resolve this issue, we offer a different approach that makes use of the constructed Ising program implementing the constraint, but ignores the mapping of variables to specific vertices of the Chimera graph details. Although we are not using the mapping, we are still making use of the sparsity of the Chimera graph, which is preserved in the Ising program of the embedding.  
The proposed approach involves the following three steps. (i) Find an Ising program for the constraint using the method described in the previous sections. The only difference is that one can use now a complete graph instead of a bipartite one for the unit cell or change the size of the cells, in order to get a better gap or to use fewer variables. (ii) Add together the Ising program of the constraint(s) and the Ising program of the objective, using appropriate weights. (iii) Embed the resulting Ising problem onto the Chimera graph using any embedder, e.g., the one offered by D-Wave. While we lose the advantage of having the Ising program of the constraint already embedded onto the target hardware, the other two advantages, sparsity of the Ising program and a bigger gap, still remain. Fine-tuning the parameters and experimentally analyzing such an approach can be a subject of further research.  
Figure 18: In this figure one can see how the dimension of \(x\) affects the accuracy of the embedding of the constraint, i.e., the number of \(+1\) s in the resulting vector.
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 5 Experiments
### Correlation between LST and Air Temperature Values  
The LST values were derived from the MOD11A1 products and the air temperature measurements from the ground stations from 1 May to 31 December 2021. Due to cloud coverage and bad weather conditions, the LST observations were limited in the study area during the observed period while the Tair observations were average from 10:00 to 11:00 a.m., corresponding to the MODIS Terra acquisition time. After filtering, these observations are described in Table 4 and Figure 9. The correlation between LST and Tair observations is presented as Equation (11), using the linear regression with the correlation coefficient of 0.816. Moreover, it also presents the coefficient of determination of 0.668 and the RMSE of 3.697 \({}^{\circ}\)C.  
Figure 8: The spatial distribution of the estimated mass concentrations of PM2.5 and PM10 on 9 June 2022 in the Dong Thap Province.  
Figure 9: Relationships between the LST and Tair observations from 1 May to 31 December 2022.
# Organic Acid-Assisted Hydrothermal Leading of Silver from End-of-Life Photovoltaic Panels
## 4 Conclusions  
Recycling end-of-life PV panels is challenging due to the complex structure and the small amounts of various elements that coexist in their numerous components. In this study, hydrothermal leaching experiments with organic acids were conducted using a Box-Behnken design and response surface technique. The objective of hydrothermal Ag leaching was to develop a rapid and green method that does not require oxidants and can use organic acids. The organic acids employed in this study are inexpensive and safe for the environment. Oxalic acid leaching proved to be more effective than citric acid in Ag leaching. In fact, the highest yield achieved with citric acid leaching was only 30% of that achieved with oxalic acid. The optimized parameters for citric acid leaching were 210 \({}^{\circ}\)C, 2M, and a time of 95 min, while for oxalic acid, a concentration of 1 M, time of 60 min, and temperature of 210 \({}^{\circ}\)C. However, the strong inorganic acid (65% HNO\({}_{3}\)) proved far more effective than the examined organic acids; therefore, more research is needed to establish efficient leaching routes when utilizing organic acids.  
Figure 11: Effect of oxalic acid concentration on Ag leaching (180 \({}^{\circ}\)C, 150 min).  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15126383/s1](https://www.mdpi.com/article/10.3390/app15126383/s1)]([https://www.mdpi.com/article/10.3390/app15126383/s1](https://www.mdpi.com/article/10.3390/app15126383/s1)), Figure S1: Interaction plot for parameters during citric acid leaching; Figure S2: Surface plot of Ag vs. temperature (oC) and time (min) during citric acid leaching; Figure S3: Surface plot of Ag vs. time (min) and citric acid concentration (M); Figure S4. Optimization for Ag leaching with CA; Table S1: Analysis of Variance for CA experiments; Figure S5: Residuals versus order for CA leaching; Table S2: Analysis of Variance for OA experiments; Figure S6: Residuals versus order for OA leaching.  
**Author Contributions:** Conceptualization E.K.; methodology, E.K. and A.G.; validation, R.A. and A.K.; formal analysis, E.K., A.K. and R.A.; investigation, E.K.; resources, E.K. and A.G.; data curation, E.K., R.A. and A.K.; writing--original draft preparation, E.K., R.A. and A.K.; writing--review and editing, E.K.; supervision, E.K. and A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**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.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 6 Challenges and Future Recommendations
### Other Challenges  
* Providing real-time feedback to users about their posture and encouraging corrective actions can be a challenge. Designing user-friendly interfaces and feedback mechanisms is essential for user engagement and behavior modification.
* Integrating sensor data with machine learning algorithms and feedback mechanisms in a seamless system requires coordination across different components. Ensuring compatibility and synchronization between hardware and software components is essential.
* Balancing the cost of implementing the intelligent correction seat system with its effectiveness and benefits can be a challenge, especially in ensuring affordability for widespread adoption.
* Achieving high accuracy in recognizing a wide range of sitting postures across different individuals and scenarios can be challenging. Ensuring that the system can generalize well to various body types, postures, and environmental conditions may have been a challenge [59].  
* Encouraging user acceptance and compliance with the system for long-term monitoring of sitting postures in a workplace setting can be challenging. Ensuring that the system is user-friendly, non-intrusive, and provides meaningful feedback to users may have been a consideration.
* Addressing the diverse range of user heights, weights, and sitting preferences poses a challenge in creating a universal correction solution that caters to the needs of different individuals.
* The creation of useful sitting posture monitoring systems with pressure sensors is severely hampered by the intrinsic complexity of human behavior [58].
* Many existing solutions, such as video-based approaches, are impractical due to privacy concerns and reliance on specific sensor devices, highlighting the challenge of developing non-intrusive and privacy-preserving systems [102].  
Addressing these challenges through robust system design, algorithm optimization, user-centered design principles, and continuous testing and refinement can lead to the development of effective sitting posture monitoring systems that promote better posture habits and overall well-being.
# A New Period-Sequential Index Forecasting Algorithm for Time Series Data
## 4 Results and Discussion
### Periodic Recognition and Prediction on Cumulative Electricity Output (CEO) Dataset  
For the CEO dataset, a time series subset of cumulative electricity output in China [25] is used as an example to show the correlation coefficient detection and prediction results. For the large-scale reference historical time series dataset from January 2016 to December 2017, there is an obvious similarity periodicity in the CEO dataset with a length of 12 months (one year), and shows an upward trend from January to December. Based on the detected periodic model from January 2016 to December 2017, we predict the CEO for the next periodic (the whole year of 2018), as shown on the right side of Figure 6. We can see that the predicted values are very close to the actual values when using PSI method, while there are large deviations when using other methods, especially in January 2018.  
Figure 5: Time series pattern and prediction on the Volkswagen sales (VS) dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Prediction Model**} & \multicolumn{3}{c}{**Error Indicators**} \\ \cline{2-4}  & _MAPE (\%)_ & _RMSE (Sets)_ & _MAE (Sets)_ \\ \hline SPNN-PSI & 9.94 & 32,148 & 24,970 \\ MA & 12.33 & 41,610 & 29,998 \\ ES & 14.38 & 46,810 & 33,500 \\ ARIMA & 15.13 & 48,211 & 35,702 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error indicators of each prediction model on the VS dataset.  
Then, we evaluate the forecasting accuracy of the SPNN-PSI method by comparing the actual values and the predicted values, shown in Table 4. Because the correlation coefficient for reference historical dataset achieves a very high value (equal to 1.0, shown in Table 1), the MAPE, RMSE, and MAE by SPNN-PSI method are lower and equal 5.23%, \(2912.23\times 10^{8}\), and 2374.38 KWH, which indicates a smaller difference between the prediction value and actual value. By contrast, the higher _MAPE_, _RMSE_, and _MAE_ by other forecasting methods are also given in Table 4. Their _MAPE_ values are all more than 50%, which indicates a relatively large fluctuation of prediction error.  
In a sum, based on the above error indicators, the proposed SPNN-PSI algorithm achieves very high accuracy and quality in time series prediction on the CEO dataset with its very high correlation coefficient value of 1.0.
## Appendix A 3-D FDTD Simulation Conditions  
In 3-D FDTD simulation, the radius of the half sphere of PSS and its periodicity was fixed at 2 \(\upmu\)m and 4.5 \(\upmu\)m, respectively. Here, we assumed that the nano-pillars arranged periodically with the hexagonal lattice. According to the FE-SEM images as shown in Figure 2, the radius of the nano-pillar was 100 nm and the periodicity and the height of the pillar was set as 300 nm and 1 \(\upmu\)m, respectively. Also, we assumed that the SiO\({}_{2}\) spheres with the 50 nm radius were closely packed and filled in the air-void between the nano-pillars. The volume ratio of the materials in the nano-pillar layer is 57:31:68 (air:GaN:SiO\({}_{2}\)). The total thickness of the GaN layer including the nano-pillar was fixed at 12.1 \(\upmu\)m and the center of the 1 \(\upmu\)m thick nano-pillar was located at 5.6 \(\upmu\)m from the top surface. In order to consider the limited computational memory, the lateral size of the total calculation domain is limited by \(4.5\upmu\times 4.5\upmu\)m, the sapphire thickness was set as 2.5 \(\upmu\)m. In consideration of the size of the fabricated sample, the periodic boundary conditions were used in the horizontal direction, and the perfectly matched layer was used as an absorbing layer in the vertical direction. The refractive indices of the GaN, SiO\({}_{2}\), and sapphire were set as 2.5, 1.5, and 1.8, respectively. In order to consider the c-plane sapphire substrate, the 100 \(E_{x}\) and \(E_{y}\) dipole sources (l = 460 nm \(\pm\) 30 nm) were generated at 100 nm from the top surface with random phase and position. The light extraction efficiency was obtained by dividing the output power flux toward top or bottom with the total dipole radiation power.
## 1 Introduction and Literature Survey
### Background
The main contributions of the presented study are as follows:  
* TopicDiff-LDA, an original text segmentation method has been developed. The method was experimentally evaluated on a benchmark dataset. Performance was better (\(p<0.001\)) than other popular unsupervised TS algorithms tested on the same data. * A new TS-enhanced topic modeling approach to automated multiple-label text annotation has been proposed. The approach was evaluated in classifier model development experiments. Overall, it demonstrated a better performance than a state-of-the-art semi-supervised annotation method powered by Multilabel Topic Model (MLTM). The rest of this paper is organized as follows. The next subsection briefly surveys related studies. Section 2 provides for an overview of the proposed approach to data annotation and introduces TopicDiff-LDA, a method for unsupervised text segmentation. Text segmentation experiments are described in Section 3. Section 4 presents a case study of a classifier model for tourism reviews, in which TopicDiff-LDA is deployed to classify and label the training dataset. Experimental results obtained and limitations of the approach are discussed in Section 5. Finally, Section 6 formulates the conclusions.