esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The feasibility and advantages of a wireless power transfer charging station using dynamic matching and power supply with energy dosing have been introduced. Based on the analysis carried out, an IPT with compound ferrite cores having a 50-200 mm air gap was designed, which transfered the required electrical power (35 kW) to the battery. The equivalent circuit analysis and characteristics of the IPT system were presented. It has been shown that the magnetic coupling is an important parameter for IPT systems, as it determines the maximum transferred power and can have a great impact on system efficiency. Experiments were carried out to test the ability of the system with different air gaps and misalignments. The results showed that the system can transfer the specified electrical power with about 82-92% efficiency and that the IPT exhibited a high degree of stability under a misaligned (x-y-z) condition.  
As an HF power supply, an AI with ED was designed and implemented to verify the validity of the developed operating mode of energy dosing and control algorithm. The obtained expressions formulate the law for keeping the output power constant when the IPT and battery parameters are changed. It has been confirmed in energy dosing mode algorithm that the charging converter works most efficiently and maintains maximum charging current and/or output power.  
It was analyzed, tested, and concluded that IPT matching plays an important role when maximizing the transferred power and efficiency. For this reason, electronic switches with series capacitors were used, and an analysis of the dynamic matching was performed. A manner of changing capacitors values with three steps is defined for different scenarios, which are the result of specified x-y-z deviations (50-200 mm in vertical and \(\pm\)200 mm in horizontal direction).  
The obtained results and the drawn conclusions show that the proposed AI with ED and dynamic matching can be used as charging power supply sources characterized by flexibility and the possibility of working with a wide range of IPT and battery parameters.  
[PERSON], and [PERSON] were involved in the full process of producing this paper including conceptualization, methodology, modelling, validation, visualization, and preparing the manuscript. The carried-out research is realized in the frames of the project \"Model based design of power electronic devices with guaranteed parameters\",,L[H07/06/15.12.2016, Bulgarian National Scientific Fund. The authors declare no conflict of interest.
# Online Adaptive Parameter Estimation for Quadrotors
## 4 Experimental Verification  
In this section, experiments are conducted to validate the proposed method. The experimental equipment is shown in Figure 5. In this case, the wireless serial port communication module and the serial port receiving terminal of the host computer are used to collect the attitude angle data. The main hardware components of the quadrotor include: a microcontroller (flying control board), a brushless motor, electrical adjustment (ESC), a propeller, a lithium battery, an MPU6050 (an acceleration sensor, a gyroscope, and a barometer, produced by InvenSense, Sunnyvale, CA, USA) receiver, a rack, and other components. The diagram of the structure of the adopted quadrotor is given in Figure 6.  
In this experimental study, we collected 977 groups of data for the control actions of the vertical direction \(U_{1}\), roll direction \(U_{2}\), pitch direction \(U_{3}\), and the yaw direction \(U_{4}\) to validate the proposed method. The test environment's temperature is \(20\pm 2\)\({}^{\circ}\)C, and the environment humidity is about 30%. The sampling time is 0.005 ms. The parameters that need to be estimated are the same as those shown in Table 1.  
We can obtain the vertical direction, roll direction, pitch direction, and the yaw direction data via the wireless serial port communication module and the serial port receiving terminal of the host computer. Then, the obtained data is input into the proposed estimator to conduct the parameter estimation. The response of the estimated matrix \(\hat{Z}\) with the adaptive law (12) is given in Figure 7, which shows that the estimated parameter converges to the true value \(Z\). In order to further verify the effectiveness of the proposed adaptive law, the estimation error \(\widetilde{Z}\) of \(Z\) is given in Figure 8, which indicates that the estimation error \(\widetilde{Z}\) converges to zero in about 1.2 s. Nevertheless, the flight trajectory of the quadrotor is given in Figure 9, which indicates that the quadrotor is taking-off.  
Figure 5: The quadrotor used in the experiments.  
Figure 6: The diagram of the structure of the quadrotor.
###### Abstract  
Skeletal muscle-derived stem cells (MDSCs) are the key modulators of muscle regeneration. An inappropriate cellular microenvironment can reduce the regenerative capacity of MDSCs. This study evaluates the effect of microenvironmental alterations on the cell differentiation capacity using either mechanical fluid shear stress (FSS) or hypoxic conditions. C2C12 mouse myoblasts were differentiated under cyclic FSS (CFSS), periodic FSS (PFSS) for one hour, and hypoxia (3% O\({}_{2}\)) for up to seven days. Cell proliferation and myogenic differentiation capacities were evaluated using cell viability assays, immunohistochemical staining, and morphometric analysis. The expression of MyoD, myogenin, myosin heavy chain, nitric oxide, hypoxia-inducible factor 1 alpha (HIF1\(\alpha\)), vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) was quantified by means of RT-qPCR. The data showed that FSS conditions altered cell morphology and increased cell viability and cell distribution compared to static conditions. MyoD and myogenicin expression was upregulated under both FSS conditions. CFSS induction improved myogenic differentiation parameters including myotube number, size and fusion capacity. Although hypoxia enhanced cell viability compared to normoxia, it reduced differentiation capacity, as indicated by the downregulation of myogenin and mTOR expression, as well as reducing myotube formation. Under hypoxic conditions, increased nitric oxide production and upregulation of VEGF expression were detected for up to 72 h. The data suggest an improved myogenic differentiation capacity under mechanical FSS; in contrast, the cell differentiation capacity was impaired under hypoxic conditions. The data point out that optimizing the biomechanical and oxidative stressors in the cellular microenvironment could improve stem cell transplantation and enhance their regenerative potential in the context of cell-based therapies.  
skeletal muscle; stem cells; fluid shear stress; hypoxia; myogenic differentiation +
Footnote †: journal: Applied Mechanics and Mechanics
## 3 Performance Analysis
### Ergodic Capacity  
The ergodic capacity of intensity-modulated FSO systems can be calculated based on [37] [Equation (25)] and [38] [Equation (42)]:  
\[\bar{C}=\frac{1}{\log(2)}\int_{0}^{\infty}\log(1+\lambda\gamma)f_{\gamma_{t2e}} (\gamma)d\gamma, \tag{17}\]  
where \(\lambda=\frac{\exp(1)}{2\pi}\) is a constant term for intensity modulation/direct detection technique-based FSO systems [39], and \(f_{\gamma_{t2e}}(\gamma)\) is the PDF expression for the e2e instantaneous SNR.  
**Lemma 2**.: _The PDF of \(\gamma_{t2e}\) is calculated as_  
\[f_{\gamma_{t2e}}(z) =\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\frac{\Phi_{1}\phi_{ 2R}}{\mu_{0}^{\phi_{1}}}z^{-1}G_{3,7}^{7,0}\Bigg{(}\frac{\psi_{1R}^{2}}{2^{4} \mu_{R}}\Bigg{|}\begin{matrix}\kappa_{1R},1+\varrho_{1}\\ \varrho_{1},\kappa_{2R}\end{matrix}\Bigg{)}+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^ {\beta_{D}}\sum_{n=0}^{N}\frac{\Phi_{2}\phi_{2R}}{\mu_{0}^{\phi_{2}}}z^{-1}G_ {3,7}^{7,0}\Bigg{(}\frac{\psi_{1R}^{2}z}{2^{4}\mu_{R}}\Bigg{|}\begin{matrix} \kappa_{1R},1+\varrho_{2}\\ \varrho_{2},\kappa_{2R}\end{matrix}\Bigg{)}\] \[+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\sum_{n=0}^{N}\frac {\Phi_{3}\phi_{2R}}{\mu_{0}^{\phi_{3}}}z^{-1}G_{3,7}^{7,0}\Bigg{(}\frac{\psi_ {1R}^{2}}{2^{4}\mu_{R}}\Bigg{|}\begin{matrix}\kappa_{1R},1+\varrho_{3}\\ \varrho_{3},\kappa_{2R}\end{matrix}\Bigg{)}+\sum_{k=1}^{\beta_{R}}\Big{(}1-F_ {\gamma_{D}^{\prime}}(1)\Big{)}\phi_{2R}z^{-1}G_{2,6}^{6,0}\Bigg{(}\frac{\psi _{1R}^{2}}{2^{4}\mu_{R}}\Bigg{|}\begin{matrix}\kappa_{1R}\\ \kappa_{2R}\end{matrix}\Bigg{)}. \tag{18}\]  
Proof.: The PDF of e2e SNR, \(f_{\gamma_{t2e}}(z)\), is derived by differentiating (15), using [40] [Equation (07.34.20.0017.02)], as given in (18).  
Now, rewriting the log function in (17) as \(\log(1+\lambda\gamma)=G_{2,2}^{1,2}\Bigg{(}\lambda\gamma\Bigg{|}\begin{matrix} 1,1\\ 1,0\end{matrix}\Bigg{)}\) and using (18) in (17), we can write  
\[\bar{C}=\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\frac{\Phi_{1}\phi_{2R  
\[\mathcal{I}_{1} = G_{5,9}^{9,1}\Bigg{(}\frac{\psi_{1R}^{2}}{\lambda 2^{4}\mu_{R}} \begin{vmatrix}0,1,\kappa_{1R},1+\varrho_{1}\\ \varrho_{1},\kappa_{2R},0,0\end{vmatrix}\Bigg{)};\] \[\mathcal{I}_{2} = G_{5,9}^{9,1}\Bigg{(}\frac{\psi_{1R}^{2}}{\lambda 2^{4}\mu_{R}} \begin{vmatrix}0,1,\kappa_{1R},1+\varrho_{2}\\ \varrho_{2},\kappa_{2R},0,0\end{vmatrix}\Bigg{)};\] \[\mathcal{I}_{3} = G_{5,9}^{9,1}\Bigg{(}\frac{\psi_{1R}^{2}}{\lambda 2^{4}\mu_{R}} \begin{vmatrix}0,1,\kappa_{1R},1+\varrho_{3}\\ \varrho_{3},\kappa_{2R},0,0\end{vmatrix}\Bigg{)};\] \[\mathcal{I}_{4} = G_{4,8}^{8,1}\Bigg{(}\frac{\psi_{1R}^{2}}{\lambda 2^{4}\mu_{R}} \begin{vmatrix}0,1,\kappa_{1R}\\ \kappa_{2R},0,0\end{vmatrix}\Bigg{)}. \tag{21}\]  
The ergodic capacity expression is, therefore, obtained by using (21) in (19).
###### Abstract  
Attention has been paid to bioflocculants production because of their effectiveness, innocuusness and environmental friendliness. This study aimed to characterise a bioflocculant from _Bacillus megaterium_ BMBF and apply it in wastewater treatment. The proteins, carbohydrates and uronic acid were calculated using the Bradford, phenol-sulphuric acid and carbazole assays, respectively. An energy-dispersive X-ray (EDQ) and infrared spectrometry were employed for the identification of the elemental composition and effective units, respectively. Cytotoxicity was carried out against Vero (African green monkey kidney) and bovine dermis cells using a colorimetric cytotoxicity assay. The reduction in chemical oxygen demand (COD) and biological oxygen demand (BOD) in domestic and coal mine wastewater was studied using the Jar test. The flocculant was composed of 12% protein, 27% carbohydrates and 61% uronic acid. Infrared spectrometry indicated hydroxyl, carboxyl and amino groups. EDX indicated C (61%) and O (17.5%) as the main elements. The bioflocculant revealed the mean inhibition concentration of 59 ug/mL against bovine dermis and 240 ug/mL on Vero cells. Maximum COD and BOD removal percentages of 97% and 99.3% were recorded on coal mine wastewater treatment and about 99.2% (COD) and 93% (BOD) on domestic wastewater. In conclusion, the bioflocculant from _B. megaterium_ has potential industrial utility.  
Bacillus megaterium BMBF; flocculation; reduction efficiency; wastewater +
Footnote †: journal: Applied  
Nevertheless, the toxicity and nondegradation nature of their monomers have been major hindrances, as they impose a threat to humans, aquatic life and the environment [13]. Owing to ever-increasing environmental and health concerns, biological flocculants have been recently viewed as promising substitutes.  
Microbial flocculants are biomolecules secreted by fungi, actinomycetes and bacteria during their growth and environmental interactions. They are in the form of lipids, proteins, polysaccharides and glycoproteins [13]. Microbial flocculants have gained attention due to their biodegradability, nontoxic nature and effectiveness [14]. The genus _Bacillus_ of the family _Bacillusae_ consists of diverse bacterial strains known to secrete effective biofilocounts in larger quantities [15]. For instance, the biofiloculants from _Bacillus salmonaya_ 139 SI-7 have been used in the reduction of wastewater pollutants [16], whereas the biofiloculants from _Bacillus licheniformis_ NJ3 were utilised to reduce turbidity in water [17]. Moreover, the biofiloculants from _Bacillus megaterium_ PL8 and _Bacillus megaterium_ SP1 were efficient in remediating dye and pollutants in wastewater [18; 19]. Therefore, in our previous study, we isolated _Bacillus megaterium_ BMBF from the intestines of the marine fish from Mthunzine Beach, KwaZulu-Natal, South Africa, and its culture parameters were optimised for effective biofoculant production (unpublished).  
This study aimed to characterise the biofoculant from _Bacillus megaterium_ BMBF and apply it in the remediation of domestic and coal mine wastewater.
## 2 System Modelling
### Modelling of PV Generation  
Recently, solar PV panels have been widely used in many types of buildings and are likely to be one of the key renewable energy supplies in smart MGs. However, the generated electricity performance greatly depends on seasonal and climatic conditions. The unpredictability of the climate influences generated power and negatively impacts the grid's stability, dependability, and operation. As a result, an accurate PV generation forecast is a critical requirement for ensuring the smart MG's stability and dependability [55]. The following formula (Equation (11)) is used to determine a building's solar PV power generation at each time interval [56]:  
\[Pr_{pv,t}=a_{pv}\times f_{pv}r(t) \tag{11}\]  
\(a_{pv}\), \(f_{pv}\), and \(r_{t}\) indicate the area of the solar panels, PV efficiency, and PV irradiance, respectively. There are three different possibilities depending on the value of the net power (Equation (11)) [38]:  
* \(\bar{Pr}_{pv}\) = 0: Renewables completely meet the load, and no electricity from SB, EV, or utility is required;
* \(\bar{Pr}_{pv}\) \(\leq\) 0: SB, utilities, and EVs are necessary to compensate for the shortage of renewables (Equation (12)). The \(SOC_{SB}\), the utility prices, and the availability of EVs determine the utilization of each of these resources;
* \(\bar{Pr}_{pv}\) \(\geq\) 0: Overproduction is stored in an SB-free capacity. Excess power is transferred to the grid if the battery is fully charged or the demand exceeds the maximum power limit. However, the EV battery is a backup power source if the EV is available or the grid is insufficient.  
\[\bar{Pr}_{pv}=Pr_{comp}-(Pr_{PV}+Pr_{SB}+Pr_{EV}) \tag{12}\]  
The SB is the first to intervene when there is a renewable energy shortage (\(\bar{Pr}_{pv}\)\(\leq\)0). It can supply the power and maintain the balance between supply and demand when it is fully charged and the demand is below its maximum discharge power limits. However,the SB supplies its maximum power while the grid supplies the remaining power if the discharge power exceeds the maximum SB discharge power limits. Although the grid is a major energy supply, its power might be restricted due to reliability and security concerns. Therefore, the grid provides the load if the demand satisfies the grid power constraints. Otherwise, the EV serves as a backup to provide an uninterrupted power supply. Finally, load shedding can be used if the EV maximum power limits are exceeded or the EV battery is prohibited due to the minimum \(SOC_{EV}\).  
In contrast, the SB is used to store the surplus generated power during the instance where there is overproduction (\(\bar{Pr}_{pv}{\geq}0\)). Moreover, the utility grid interferes and maintains the power supply and demand balance if the SB is completely charged or the demand exceeds the SB maximum charge power limits. This restriction on grid power aims to increase smart MG stability and autonomy. Thus, EV stores a portion of the generated power when the grid's maximum power has been reached.
## 4 Experimental Results and Analysis
### Image Preprocessing for Input  
In traditional sliding window methods, the window is typically split using a fixed window size and stride, without considering the category or results of the previous detection sub-image. The window size determines the area covered by the sliding window at each step, usually chosen as a fixed value, such as \(50\times 50\) or \(100\times 100\), depending on the detection task and the characteristics of the object. The window size must be large enough to contain the key information of the target object, but not so large as to process too many irrelevant areas. The stride determines the step size by which the window slides across the image, typically set to the width of the window or some proportion of it. In traditional sliding window methods, the stride is usually fixed, sliding across the entire image with the same interval. The choice of stride directly affects the level of detail in detection: a smaller stride makes the window cover a more detailed area, improving detection accuracy but also increasing the computational load; a larger stride reduces the computational load but may miss some tiny defects.  
Since defects in our dataset are sparsely distributed across each 4K image, and most images are defect-free, this characteristic of the dataset results in defect-free regions occupy  
Figure 14: System flowchart.  
Figure 13: Partial defect diagram.  
ing the majority of the image. Therefore, when using traditional sliding window methods, many defect-free areas are repeatedly calculated, increasing the computational burden. To address this, we propose a dynamic sliding window method. The current detection sub-image is determined based on the previous detection sub-image's category and the current sliding window position. When the previous detection sub-image is defect-free, the stride is set to the window width, with no need to consider the overlapping region. If the previous candidate image shows partial defects, the stride is set to the window width minus the width of the overlapping region. If the previous candidate image is detected as defective, the system determines whether to end the inspection or continue with a stride of window width minus the overlapping region width. Given that defect-free images far outnumber the other two categories, this strategy significantly reduces the number of detection sub-images, thus improving the number of detected sub-images on the processed workpiece surface under resource-limited conditions. The sliding window diagram is shown in Figure 15.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 1 Background and Motivation
### Research Contributions  
To address these gaps, this paper makes the following contributions:  
(1) We propose a comprehensive evaluation index system that quantifies the trustworthiness of forecasts from different provinces by considering three key dimensions: forecast history reliability, regional impact significance, and forecasting complexity.  
(2) We develop a novel data fusion methodology based on principal component analysis (PCA) that dynamically weights provincial forecasts according to their evaluated reliability and significance, effectively addressing the heterogeneity challenge in multi-source grid-level forecasting.  
(3) We implement and validate a complete weighted fusion framework that integrates with existing forecasting systems without requiring provincial centers to modify their current methodologies.  
(4) We corroborate through case studies that our weighted fusion approach significantly outperforms direct accumulation methods, achieving a 2.33% average relative error in multi-regional forecasting.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 2 Methods  
### Overall Architecture  
High-quality edge information reflects both the geometric shape and location of buildings, aiding in their localization and detection. The encoder-decoder structure, through multiple downsampling steps, obtains high-level semantic features. However, this process irreversibly leads to a significant loss of image detail, affecting change detection performance, especially in the accuracy of edge segmentation.  
To solve the above problems and improve the localization accuracy of the edge region, this study proposes an edge-guided building change detection algorithm, termed Edge-Guided Hierarchical Network (EGHNet). The overall framework of the algorithm includes Encoder-Decoder Backbone, edge-extracted module (EEM), and edge-guided module (EGM). The overall structure is shown in Figure 1.  
For given bi-temporal images, these images are input to the encoder of the neural network to extract features, which include both deep semantic information and shallow detail information. The edge-extracted module utilizes both deep and shallow features to capture the edge structure of the changing feature, thus effectively extracting an accurate edge map of the changing feature. The edge-guided module fuses the extracted edge information with different levels of features, and the detail information is re-injected into the decoding process through the guidance of the edges, which effectively avoids the loss of detail information in the traditional coding and decoding structure in order to enhance the representation of features in the decoding process and to utilize the edge information to guide the generation of change detection results.  
Figure 1: Structure of Edge Guided Network (EGHNet). The green downward arrow denotes down-sampling. The yellow upward arrow denotes upsampling. The white hollow rightward arrow indicates output after the Sigmoid activation function.
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Reinforcement Learning Integration (Same as Previous Work)  
In this research, we _retain_ the core RL mechanism from our prior work (as the main goal is to compare two approaches for the efficiency of adaptive parameter selection in the performance of the model), which targets the optimization of the BC/DNB ratio rather than the shape of the membership functions. Specifically, the reinforcement learning agent continually evaluates whether the current proportion of nodes choosing to broadcast (BC) versus those opting for do to broadcast (DNB) remains optimal for the network's Coverage and Overhead requirements.