esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 5 Experimental Results and Discussion
### Watermark Imperceptibility  
Considering 1000 color images extracted from COCO dataset [30], and a binary watermark of \(L=3  
Figure 5 the comparative one-to-one show in almost all images a gain of approximately 1.5-2 dB, meanwhile for few images there is a small loss in dB. This behavior is related to the trade-off between imperceptibility-robustness of digital watermarking. The second one, and the most relevant, as is known in the literature, is that the VIF value reflects perceptual distortions more precisely than peak signal to noise ratio (PSNR) according to reference [29]. The range of VIF is [0, 1] where the closer value to 1 represents the better fidelity respect to the original image. In this way, the imperceptibility results in terms of VIF are shown in Figure 6.  
From Figure 6 we show that the average VIF obtained employing PSO is 0.9086 meanwhile not using PSO it is 0.8659. In terms of average VIF the gain is hardly of 0.0427. The comparative one-to-one show in almost all images is a gain of approximately 0.05-0.08, meanwhile for a few images there is a small loss in VIF value. Again, this is a trade-off between the imperceptibility and robustness of digital watermarking. With illustrative purposes, Figure 7 shows a resultant image employing and non-employing PSO algorithm, considering a binary watermark of \(L=64\) bits.  
As mentioned above, we show that although in quantitative form the imperceptibility results using PSO outperform slightly conventional DFT, the visual quality regarding to the original image is remained when the PSO is employed, as shown in Figure 7. The major benefit in terms of visual quality is obtained in flat regions, where usually the 2D DFT-based invisible watermarking causes a noise effect, as shown in Figure 7.  
Considering a watermark length \(L=32\), the optimized values using PSO algorithm for the pair of radiuses \(r_{1}\) and \(r_{2}\), as well as for the value of the watermark strength \(\alpha\); and the PSNR and VIF values obtained by PSO-DFT and conventional DFT watermarking are shown in Table 4. Additionally, for illustrative purposes, the PSO convergence history of the proposed method for a couple of images are shown in Figures 8 and 9 respectively. Figure 10 shows PSO convergence of 50 images from the COCO dataset.
## 3 RF-EH Techniques  
Contrary to other energy-harvesting sources, such as wind, solar, and vibrations, the RF-EH is characterized as follows:  
1. Ability to regulate and provide constant energy transfer over a long distance.
2. The harvested energy is relatively stable and predictable for long-term performance to the fixed distance in an RF-EHN setup.
3. The different locations of network nodes exhibit a substantial difference in the RF-RH since the total RF-EH relies on how far the dedicated RF source is from the ambient RF source.
## 1 Introduction
[PERSON] and co-workers (2007) showed that the drying time for the banana fruits using a solar hybrid drying was lower compared to open-air or artificial drying [30]. Furthermore, fruits color, aroma, and texture were far better when dried in hybrid dryer system than under open-air sun drying [31]. Similar findings were found by [PERSON] et al., (2018) when studying the hybrid-dried peppermint [32]. They also, reported a dryer efficiency of 31% with a net carbon dioxide (CO2) mitigation of 32 tons over the life span of the hybrid dryer. Similar findings were reported on other crops such pineapples [33], mushrooms [34], cashew nuts [35], and sweet cherries [36]. In order to limit these post-harvest losses of arbutus fruits, and in particular, in Morocco, solar drying is considered a viable option as it is a clean and less expensive source of energy for small farmers. This allows local farmers to dry their strawberry fruits by shortening the drying time, and extend their shelf life while preserving their qualities. The application of this method is an alternative and promising method, especially in Morocco where sunshine is abundant (3000 h of sunshine per year, or 5.3 kWh/m\({}^{2}\)/day) [37]. To reduce post-harvest losses and properly valorize the Moroccan _Arbutus unedo_ L. fruits production, this research aimed to investigate the possibility of adopting forced convection solar drying as an eco-friendly method under Moroccan climate conditions. Also, evaluating its efficiency in converting these highly perishable fruits into more stabilized dried fruits while maintaining their quality and extending their shelf life by storing them under a minimal controlled environment. We hypothesize that results of this study as well of other published data on the use of hybrid solar drying on various crops can persuade local farmers to adopt it over the traditional open sun drying technique  
The objectives of this study are:  
* Investigate the drying kinetics of _Arbuttus unedo_ L. fruits in a convective solar dryer;
* Study the effect of both temperature and flow rate of the drying air on the drying kinetics of the _Arbuttus unedo_ L. fruits;
* Describe the characteristic drying curve (CDC) of _Arbuttus unedo_ L. fruits;
* Calculate, at different temperatures, the effective moisture diffusivity as well as the corresponding energy of activation;
* Estimate both the energy efficiency and the total energy consumption of the solar drying process;
* Select the best model that could describe the drying characteristics of the _Arbuttus unedo_ L. fruits;
* Investigate the effects of the three drying temperatures (60 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C) on phenolic compounds by liquid chromatography analysis.
## 3 Methodology
### Feature Selection
#### 3.3.4 Lasso  
The famous least absolute shrinkage and selection operator (Lasso), proposed by [PERSON] [24], is very popular because of its variable selection property and has been used in many fields of statistics. This method shrinks values of some coefficients to zero by a constraint on the sum of absolute values of regression coefficients so that Lasso can serve as a tool for variable selection. The substantial difference between Lasso and the subset selection procedures or the information criteria is that Lasso selects variables, estimates the coefficients simultaneously, and retains good subset selection and ridge regression features. Lasso is a regularisation and variable selection algorithm that performs mostly better than other methods. Suppose we have a selected subset of features with size k, denoted by \(\{s_{1},s_{2},\ldots,s_{k}\}\). \(x_{i}=\left(x_{i}^{(s_{1})},x_{i}^{(s_{2})},\ldots,x_{i}^{(s_{k})}\right)^{T}\) is the vector of selected features for individual \(i\), and \(\beta_{0}\) is the intercept, and \(\beta=\left[\beta_{(s_{1})}^{T},\beta_{(s_{2})^{\prime}}^{T},\ldots,\beta_{(s _{k})}^{T}\right]^{T}\) is the parameter vector. The simple logistic regression of the selected features is explained through Equation (9):  
\[P_{r}(y_{i}=1)=\frac{e^{\beta_{0}}+x_{i}^{T}\beta}{1+e^{\beta_{0}}+x_{i}^{T} \beta} \tag{9}\]  
We can estimate \(\beta\) by minimising the negative log-likelihood via Equation (10):  
\[l(\beta_{0},\beta)=-\frac{1}{n}\sum\limits_{i=1}^{n}\Bigl{(}y_{i}\Bigl{(}\beta _{0}+x_{i}^{T}\beta\Bigr{)}-\log\Bigl{(}1+e^{\beta_{0}+x_{i}^{T}\beta}\Bigr{)} \Bigr{)} \tag{10}\]  
We add L1 Lasso (Least absolute shrinkage and selection operator) penalty for obtaining sparse solutions and enhancing predictive performance. The Lasso estimator is obtained from the penalised minus log-likelihood using Equation (11):  
\[\hat{\beta}_{LASSO}(\lambda_{1})=argmin_{\beta_{0},\beta}\,l(\beta_{0},\beta) +\lambda_{1}\,||\beta\,||_{1} \tag{11}\]  
where \(||\beta||_{1}=\sum\limits_{j=1}^{p}\left|\beta_{0}\right|\), \(p\) is the total number of dummy variables of selected features, and \(\lambda_{1}\) is the tuning parameter. Note that the intercept is not included in the penalty term. Lasso penalty corresponds to a Laplace before Bayesian inference. Hence, it will obtain a subset of important features with non-zero coefficients and shrink the reset to zero. Increasing \(\lambda_{1}\) will shrink more coefficients to zero by adding a heavier penalty. Because this optimisation problem is convex, it can be solved efficiently for large data. There are several algorithms for calculating the Lasso estimator, among which coordinate descent performs the best. Coordinate descent optimises each parameter separately while holding all others fixed. Feature selection reduced the data dimensions by reducing the number of features. Initially, there were 108 attributes in our data set. Fourteen most-contributing attributes were selected for school performance measurement through various feature selection methods, as shown in Table 2.
###### Abstract  
With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry-property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.  
BIM watermarking; element perturbation; invisible character; data security 2023
## 4 Discussion
### Influence of Remotely Sensed \(i_{o}\) and fAPAR on the Estimation SIF Escape Probability
However, fAPAR for whole canopy (i.e., fAPAR_canopy_) generally can be divided into fAPAR for photosynthetic elements (i.e., fAPAR_leaf_) and fAPAR for non-photosynthetic elements (i.e., fAPAR_wood_), respectively [46; 47]. The impact of fAPAR_wood_ is neglected in most satellite-retrieved fAPAR products [48]. As a result, the direct use of satellite-retrieved fAPAR products (mostly fAPAR_canopy_) to estimate SIF escape probability still needs further investigation [12; 49]. Our simulation results illustrated that using fAPAR_canopy_ will induce an underestimation of SIF escape probability when compared to fAPAR_leaf_ (Figure 8). This underestimation can be a potential advantage for the sparse forest where the SIF escape probabilities are generally overestimated due to the contamination of background, providing an additional mechanism behind the contamination compensation effect. Still, more measurement data from stand to global scale are needed to explore this finding and develop a robust method to separate the various canopy layers.
# Modifications of Wound Dressings with Bioactive Agents to Achieve Improved Pro-Healing Properties
## 4 Patented and Commercial Bioactive Dressings
Due to its composition, the dressing has antimicrobial properties, and thanks to the Ringer's fluid that is released into the wound bed, it stimulates the healing process [88]. The British company Advancis Medical offers an Algivon alginate dressing soaked in Manuka honey. Antibacterial properties are ensured by the glucose oxidase occurring in the honey. This enzyme enables the formation of hydrogen peroxide in the wound bed with an antiseptic effect and inhibits biofilm formation. In addition, biomaterial ensures the moist environment and accelerates the healing process. Algivon is dedicated to all types of wounds, especially for necrotic wounds [89]. A large group of commercial medical dressings contain silver particles, dedicated to combating a wide range of microorganisms in the wound bed. An example of commercial antimicrobial dressings containing silver particles is the  
Figure 4: Foam-like agarose/curdlan dressing materials with different shapes and sizes produced according to Polish patent no. PL 236367.  
product of the Molnycke company, which offers Mepilex Border Ag foam dressing. This wound care silicone foam dressing contains activated carbon, silver sulphate, and a Safetac wound-contact layer. It is dedicated to moderately and highly exuding wounds, which include partial-thickness burns, surgical and traumatic wounds, pressure ulcers, or leg and foot ulcers [90]. Another dressing containing homogeneously distributed silver in its structure is called Biatain Ag. This foam-like wound care product provides a stable release of silver for up to 7 days upon contact with exudates. The conducted studies confirm its effectiveness in combating bacterial infections [91]. The Coloplast company also offers the highly absorbent Biatain(r)Alginate Ag dressing made of carboxymethyl cellulose, calcium alginate, and an ionic silver complex. Apart from antibacterial effect, this wound dressing also provides a hemostatic effect at the wound site [92]. Other silver-nanoparticle-based commercial dressings commonly used for the treatment of infected wounds include ACTICOAT(tm), PolyMem WIC Silver(r), Suprasorb(r) A + Ag, and Atrauman(r) [92; 93].
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Integration with Electronic Health Records
#### 5.8.2 Existing Solutions  
Using APIs is crucial for integrating AI systems with EHR systems. APIs facilitate communication and data exchange between the two systems, enabling seamless sharing and access to data. With APIs, AI systems can directly connect to EHR systems to access patients' medical records and health information. This integration allows AI systems to retrieve real-time patient data and use it for analysis, prediction, and decision support [147]. Effective integration of AI systems with EHR systems involves several key aspects. Firstly, developing machine learning (ML) models tailored for specific purposes is crucial. For example, when integrating AI systems for predictive analytics, the models must seamlessly fit into EHR workflows. An example of this is the Mayo Clinic, where AI models are customized to align with the EHR system, allowing for real-time prediction of patient readmission risks based on patient data. Secondly, understanding the technical constraints of EHR systems is necessary. For instance, Epic EHR systems might require data to be processed in specific formats, so hospitals must develop data conversion tools to ensure compatibility with these technical limitations [148]. Governance and regulatory requirements are also important considerations, such as complying with the Health Insurance Portability and Accountability Act (HIPAA) to ensure data privacy and security, as demonstrated by the detailed audits and compliance checks during the implementation of AI tools by the NHS [149]. Evaluating the long-term deployment scope involves planning for the impact and scalability of AI systems, such as the strategic implementation of IBM Watson Oncology across multiple hospitals, ensuring consistent performance and benefits. Additionally, addressing the challenges of changing clinical behavior is critical. For example, the Cleveland Clinic provides training and support to help clinicians adapt to AI systems, ensuring successful integration. Finally, evaluating ML models and their impact on health equity is essential. For example, the University of California, San Francisco, rigorously assesses AI-driven clinical decision support systems to ensure fairness in model predictions across different patient groups. Providing detailed descriptions of EHR interventions is also important, as seen with Johns Hopkins University, which documents how their AI image analysis tool interacts with and interprets imaging data within the EHR system, ensuring transparency and clarity in the operational process [150].  
Figure 6: The overview of the AI integration with EHR.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 3 Results
### EEQ of the Yangtze and Yellow River Basins
#### 3.3.2. Monitoring of the EEQ of the Yellow River Basin  
The Yellow River basin, similar to the Yangtze River basin, serves as a vital economic belt and ecological safeguard for China [50]. However, extensive resource exploitation and agricultural reclamation have led to various ecological and environmental challenges in the Yellow River basin [51], such as the collapse of the overflow well in Luanchuan and excessive benzene content in Lanzhou's drinking water. Consequently, there is an urgent need to monitor the EEQ of the Yellow River basin and comprehensively study and analyze the ecological environment changes using scientific methods, aiming to establish a green economic zone along the Yellow River.  
As illustrated in Figure 7, the distribution of EEQ in the Yellow River Basin remained similar in 2000, 2008, and 2017, exhibiting the characteristic pattern of \"worse in the east and better in the west\". Among the three years, Qinghai Province consistently displayed the highest EEQ, followed by Ningxia Hui Autonomous Region and Gansu Province. Conversely, Shaanxi Province, Shanxi Province, and certain regions of Inner Mongolia Autonomous Region consistently demonstrated lower EEQ throughout the same period.  
Figures 9 and 10 illustrate that the changes in EEQ during the period of 2000-2017 were predominantly concentrated in the Inner Mongolia Autonomous Region, Shaanxi Province, and certain regions of Shanxi Province. These areas were characterized by a transition from a bad to poor EEQ rating. Additionally, the reduction in EEQ was minimal, suggesting a consistent improvement in the EEQ of the Yellow River basin over this period.  
In Table 6, the majority of the Yellow River basin, encompassing an area of 879,400 km\({}^{2}\), accounting for 74.80% of the total basin area, exhibited an unchanged EEQ from 2000 to 2017. The area where the EEQ had decreased was 10,000 km\({}^{2}\), representing 0.86% of the total basin area. Conversely, the area where the EEQ had increased spanned 286,100 km\({}^{2}\), accounting for 24.34% of the total basin area. Overall, the EEQ of the Yellow River basin showed a consistent trend of improvement.
# Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy
## 3 Jellyfish Search Algorithm for the Problem
### Jellyfish Algorithm (JS)  
Similar to the flower pollination algorithm [51], JSA also uses two techniques, called exploration and exploitation, to search new solutions. However, only one out of the two methods is used for each iteration based on the comparison between a decision parameter at the current iteration and a balance parameter. For simplicity, the balance parameter can be fixed at 0.5 for simplicity, whereas the decision parameter is dependent on the current iteration and maximum iteration as follows:  
\[CPG_{pre}=\left(1-\frac{G_{pre}}{G_{max}}\right)\times(dr_{0-2}-1) \tag{17}\]  
where \(CPG_{pre}\) is the decision parameter at the present iteration \(G_{pre}\). Equation (17) indicates that \(CPG_{pre}\) has one value only at each iteration. So, the comparison of (\(CPG_{pre}\) and 0.5) leads to one result only and only one method is used for each iteration.  
If \(CPG_{pre}\) is smaller than 0.5, the whole population is updated based on the exploration technique, as shown in the following formula:  
\[SN_{j}^{new}=SN_{j}+dr_{1}.(SN_{*}-3 dr_{2}.SN_{mid}),\ j=1,\ \ldots,\ S_{size} \tag{18}\]  
For other case (i.e., 0.5 < \(CPG_{pre}\)), exploitation technique is used to update the solution at the present iteration as the following equation:  
\[SN_{j}^{new}=\left\{\begin{array}{ll}SN_{j}+\Delta_{j},&\ \variables--three from wind farms and four others from other electric components. They are listed as follows:  
1. Location of wind turbines: \(L_{WPm}\) where \(m=1,\ \ldots,\ N_{WPs}\). Each wind turbine can be located at the smallest node number (node 1) and the highest node number (Node \(N_{nodes}\)). So, Node 1 and Node \(N_{nodes}\) are, respectively, the minimum and maximum locations, which are represented by \(L_{WPm}^{Min}\) and \(L_{WPm}^{Max}\).
2. Rated power of wind turbines: \(PW_{m}\ (\ m=1,\ \ldots,\ N_{WPs})\).
3. Power factor of wind turbines: \(PF_{WPm}\ (m=1,\ \ldots,\ N_{WPs})\).
4. Active power generation of all thermal units excluding that in slack node: \(PT_{k}\) (\(k=2,\ldots,\ N_{TUs}\)). It is supposed that the first thermal unit (i.e., \(k=1\)) is located at the slack node.
5. Tap of transformers: \(Tp_{n}\ \ (n=1,\ldots,\ N_{TF}\)).
6. Reactive power generation of all capacitors: \(QC_{c}\ (c=1,\ \ldots,\ N_{Caps})\).
7. Voltage magnitude of all thermal units: \(Vol_{k}\ (k=1,\ldots,\ N_{TUs})\).
## 4 Discussion
### Validation of Fault Detection Measurement Results
When \(R_{1}\) increases abruptly, however, Coulomb friction can no longer be ignored (which is consistent with Equation (2)). Hence, simplified SIA and SWA models based on (6) and (8) that omit Coulomb friction lose accuracy. Also, the Coulomb friction value \(M_{\text{c}}=0.33\)\(\,\)mNm, which yields the best fit, appears to be more accurate than the value reported in [45]. However, the agreement between the measured system output and the output of the linearized model (2), with \(M_{\text{c}}=0.33\) mNm, does not exceed 80% for any fault magnitude in \(R_{1}\), indicating that the system can only be linearized approximately.