esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
###### Abstract  
The power consumption model can be represented in multiple dimensions, and it is proliferating to include structured and unstructured data. Dealing with such heterogeneous data and analyzing it in real-time is an ongoing challenge in the energy sector. Moreover, converting these data into useful information remains an open research area. This study focuses on modeling realistic and efficient power consumption data management in the heterogeneous environment for the Iraq energy sector and suggested a novel hybrid load forecasting model. The proposed system is named the Power Consumption Information and Analytics System (PIAS), which can perform various roles such as data acquisition from mechanical and smart meters, data federation, data management, data visualization, data analysis, and load forecasting. The proposed system has a four-tier framework (Data, Analytics, Application, and Presentation). Each layer is discussed in detail in this study to overcome the anticipated challenges. Furthermore, this study discusses the proposed system by applying two case studies. The first case study discusses power consumption data management, while the second introduces a novel hybrid load forecasting model using Fuzzy C-Means clustering, Auto Regressive Integrated Moving Average (ARIMA), and Gradient Boosted Tree Learner. The dataset used in this forecasting is based on a 1-year duration dated 1 January 2019 to 31 December 2019, on an hourly basis (365 * 24) for the Baghdad governorate. The results showed high accuracy in load forecasting with improved error rates (MAPE, MAE, and RMSE) achievements in comparison with other evaluated models such as standalone ARIMA and Gradient Boosted Trees methods.  
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Footnote †: journal: Applied sciences
# Insights into Membrane Damage by \(\alpha\)-Helical and \(\beta\)-Sheet Peptides
## 3 Results
### Experimental Investigation
#### 3.3.2 Leakage Assays  
A modified leakage assay, in which a fluorophore and quencher pair were encapsulated within the liposomes [64], was used to experimentally probe the effect of M03 and M04 peptides on model membranes, to compare with the computational models. In the assay, large unilamellar vesicles (LUVs) were made using a 7:3 ratio of POPC-POPG lipids, giving the membranes an overall negative charge due to the negatively charged POPG lipids, which could facilitate electrostatic interactions between the membrane and the positively charged amino acid residues of the peptides. To confirm that the vesicles were unilamellar after extrusion, cryo-EM images of the liposome solution were obtained, revealing that approximately 90% of the solution contained uniform, unilamellar vesicles (Figure S4). A fluorophore and quencher pair (ANTS/DPX) were encapsulated within the interior of the vesicles, leading to quenching of the fluorophore due to their close proximity. Damage to the membrane was assessed by measuring the percent leakage of the fluorophore and quencher from the interior of the vesicles. Percent leakage was calculated from the increase in fluorescence observed as the fluorophore/ quencher pair diffused away from each other in free space after escaping the confines of the LUVs. Maximum release, or maximum fluorescence, was determined by adding reduced Triton(tm) X-100 detergent to the samples. Percent leakage was calculated using the following equation:  
\[\text{Percent Leakage}=\frac{F-F_{0}}{F_{\text{lysed}}-F_{0}}\times 100, \tag{1}\]  
where \(F\) is the relative fluorescence (RF) at time \(t\), \(F_{0}\) is the RF at \(t=0\), and \(F_{\text{lysed}}\) is the RF after the addition of the detergent.  
Leakage experiments for peptides M03 and M04 were performed at peptide-lipid (P:L) ratios of 1:50, 2:50, 1:10, 1:5, and 2:5, and the results of these leakage studies are shown in Figure 6A. The percentage of leakage was greater than 80% at all P:L ratios for M03 peptides, suggesting that it can damage lipid membranes even at low concentrations. In contrast, there was no leakage at the lowest ratio (1:50) studied for M04 peptides, and at the second lowest ratio (2:50), the percentage of damage was only 21%. For the ratios tested, the P:L ratio had to be at least 1:10 for M04 peptides to produce a level of leakage that was comparable to M03 at any of the ratios. This corroborates the results from the MD simulations, showing that M03 peptides exhibited a higher tendency to damage lipid membranes than M04. In addition, these experiments suggest that, if simulations of M04 peptides are performed at higher concentrations, they can cause significant membrane damage.
## 4 Experimental Results
### Ablation Study  
For the ablation study, we exhibit the quantitative metrics of the proposed networks with different amounts and complexities of ingredients in Tables 7 and 8. The simulation results show that our simplest architecture of the decoder, i.e., Solo-SimpleUp, achieves comparable performance on NYU Depth V2 with incredibly fast inference speed, making it more suitable to be implemented on mobile devices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Decoder** & **Bins Estimator** & **\#Bins** & **Weights (M)** & **MAC (G)** & **FPS** \\ \hline SimpleUp & AdaBins & 256 & 4.52 & 4.43 & 430 \\ UpCSPN3 & AdaBins & 256 & 45.10 & 11.97 & 280 \\ UpCSPN5 & AdaBins & 256 & 45.39 & 12.20 & 270 \\ UpCSPN7 & AdaBins & 256 & 45.81 & 12.54 & 240 \\ SimpleUp & AdaDRBins & 32 & **2.28** & **1.15** & **560** \\ UpCSPN3 & AdaDRBins & 32 & 42.86 & 8.70 & 340 \\ UpCSPN5 & AdaDRBins & 32 & 43.14 & 8.92 & 330 \\ UpCSPN7 & AdaDRBins & 32 & 43.57 & 9.26 & 310 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The complexity comparisons of 32-bin AdaDRBins and 256-bin AdaBins [12] used in the proposed models for generating high-precision 224 \(\times\) 224 depth maps.  
Figure 12: Qualitative results achieved by the proposed networks that the model with the UpCSPN-7 + AdaDRBins decoder achieves the best results, specially marked by white dash-line rectangles which highlight the clearer depth edges.  
Although the combinations of UpCSPN-k and AdaDRBins have lower inference speeds, their performances are much higher than the model with the solo-SimpleUp decoder. As Table 7 shows, the model with UpCSPN-7 and AdaDRBins can make the generated depth maps acquire very high precision with an RMSE of 0.185, which is lower the RMSEs achieved by existing AdaBin-based networks [12, 21, 22, 23, 24, 25], i.e., with RMSEs higher than 0.19 for various task-specific testing images.
## 3 Results
### Sensitivity of Different Geographic Regions and Vegetation Types to Extreme WUE Variations  
The dominant factors driving extreme WUE variations may shift under the influence of climate change. The sensitivity of these factors to climate change differs across regions and vegetation types [35; 64]. Therefore, the study data's time series was divided into 2001-2010 and 2011-2020 to analyze the dominant factors driving extreme WUE variations in each period. The results indicate that the impact of climate change on extreme WUE variations shows significant regional differences across China's seven geographic divisions (Figure 5). When WUE reaches maximum (WUE\({}_{\text{max}}\)), in NE, extreme WUE variation shifted from being controlled by ET to being controlled by GPP, and collectively, by GPP and ET, which led to a 21.86% decrease in the area controlled by ET, a 9.14% increase in the area controlled by GPP, and a 12.72% increase in the area collectively controlled by GPP and ET (Figure 5a,c). When WUE reaches a minimum (WUE\({}_{\text{min}}\)), the area controlled by GPP for extreme WUE variations decreased by 18.05%, while the areas collectively controlled by GPP and ET and solely controlled by ET increased by 8.14% and 9.91%, respectively, in EC (Figure 5b,d). SC shows high sensitivity to both types of extreme WUE variations, with the area collectively controlled by GPP and ET increasing by an average of 20.65%, and the areas solely controlled by GPP and ET decreasing by an average of 17.82% and 2.83%, respectively (Figure 5). In NE, EC, and SC, climate change has caused a shift in the dominant factors of extreme WUE variations from single-factor dominance to dual-factor dominance by GPP and ET. SC demonstrates the highest sensitivity of extreme WUE to climate change.  
In this study, to investigate the differences in the sensitivity of dominant factors driving WUE extreme variations to climate change across different vegetation types, land cover data from 2005 and 2015 were used to represent the general distribution of vegetation for the periods 2001-2010 and 2011-2020. It was found that the dominant factors driving extreme WUE variations changed in 7.40% of the vegetation-c  
Figure 5: Regional statistics of the dominant factor of extreme variations in vegetation WUE in China. (a) WUE\({}_{\text{max}}\), 2001–2010; (c) WUE\({}_{\text{max}}\), 2011–2020; (b) WUE\({}_{\text{min}}\), 2001–2010; (d) WUE\({}_{\text{min}}\), 2011–2020.  
the changes were predominantly observed in grassland, forest, and cropland areas. WSN, SN, and GL collectively account for an average of 66.24% of the vegetation experiencing changes in dominant factors. ENF, EBF, DBF, and MXF collectively account for an average of 15.45%, and CL accounts for an average of 18.32% (Table 2). In a comparative analysis across various vegetation types, grasslands exhibit the highest sensitivity of extreme WUE to climate change, with their proportions being 4.29 times that of forests and 3.62 times that of croplands.
## 2 Proposed Method  
The proposed method incorporates the complimentary information captured from airborne color images and noisy airborne LiDAR data to accurately detect traffic signs in highway areas. To this end, it first utilizes geo-referenced color images to remove non-road points and keep candidate road points as the search space, which significantly reduces the time spent in searching for traffic sign candidates. It then maps the reduced search space in color images to its corresponding reduced search space in LiDAR data to quickly detect traffic sign candidates using the height information. Finally, it maps traffic sign candidates in LiDAR data to their counterparts in color images, employs the deep learning technique to automatically extract deep features, and uses the deep features in the optimization model to classify traffic sign candidates as positive or negative. These three major steps are summarized in the diagram as presented in Figure 1, where we name each step as road extraction, traffic sign candidate detection, and traffic sign classification, respectively. Detailed information about each step is presented in Sections 2.1-2.3.  
Figure 1: The overview of the proposed method.
## 3 Winding Simulation at Strands Level
### Stranded Wires Configuration  
In pursuit of minimizing AC losses, an alternative winding configuration employing stranded conductors within the same slot geometry was thoroughly investigated. Typically, such windings are positioned randomly within the stator slot, making it challenging to determine the exact position of each strand. To address this practical concern, the strands were transposed to different locations within the slot for a more realistic simulation. A 10-strand coil, maintaining the same number of turns as the flat solid conductor case, is simulated, as depicted in Figure 8.  
Figure 6: Transient power loss variation in the windings under sinusoidal and PWM currents.  
Figure 7: Variation of the maximum current density with time in the windings under sinusoidal and PWM currents.  
Furthermore, a comprehensive comparison between both configurations, focusing on the maximum current density, is presented in Table 3 at a frequency of 100 Hz. At this frequency level, the flat solid coil exhibits a more favorable current density distribution solely under a pure sinusoidal current waveform. However, when high-frequency harmonics are introduced, the stranded coil demonstrates a relatively superior current density profile.  
The effect of the PWM current on copper losses is shown in Figure 9 at different frequency levels. As can be seen, the losses increase remarkably as the frequency increases. For instance, at 1 kHz, the losses are more than twice their value under sinusoidal excitation, while at 2 kHz, the losses are more than three times their value.  
Figure 8: Modeling of the stranded windings in the two-slot model and the current density distribution under PWM current at 100 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Peak Current Density [A/mm\({}^{2}\)]**} \\ \hline  & **Flat Solid** & \multicolumn{2}{c}{**Stranded**} \\ \hline Sin. & PWM & Sin. & PWM \\
6.37 & 12.6 & 5.18 & 11.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak current density at 100 Hz.  
The iron losses are also investigated for the E-shape core under different current waveforms. The core loss and its components are shown in Figure 10, including hysteresis, eddy, and excess losses. As can be noticed, the eddy current losses are the most affected component, especially at high frequency levels. As a result, the core losses increase significantly under PWM current.
Green synthesis of nanoparticles employs processes using natural materials and is more innovative and cheaper than previous strategies [13,14]. The widespread availability of biological resources and the simplicity of the methodology are key benefits promoting the popular use of green chemistry for metal nanoparticle synthesis [15,16]. Presently, hyptonanotechnology has been presented as a new avenue for nanoparticle synthesis with applications in the biomedical and environmental fields [17]. The low toxicity, potential antimicrobial activity, and opportunity for green synthesis of nanoparticles herald a new era of their production. Natural components for AgNP production with improved antimicrobial properties have recently been proposed, with one example being chitosan [18, 19, 20]. Chitosan is a natural polysaccharide biopolymer which consists of two monosaccharides, GlcNAc and D-glucosamine (GlcN), linked together by \(\beta\)-(1\(\rightarrow\)4) glycosidic bonds [21]. Researchers have determined that chitosan from sweet cherry (_Prunus avium_ L.) can inhibit the growth of microorganisms [22,23]. The main properties of chitosan are its nontoxicity, biocompatibility, biodegradability, and hemostatic properties. Due to these effects, polyelectrolytes such as chitosan have been used in various ways to stabilize nanoparticles and to increase their antimicrobial properties upon successful formation of polymer-metal ion complexes [24]. Previous studies have shown that chitosan can stabilize nanoparticle surfaces and thereby modulate cytotoxicity. Moreover, AgNPs coated with chitosan demonstrated high efficiency in killing common Gram-positive and Gram-negative bacteria as well as some fungi [25]. Considering the importance of metal nanoparticle synthesis, especially those of silver synthesized using different plants, the aim of this study was to develop a method for green synthesis of silver nanoparticles (AgNPs) with stable chitosan-coated silver nanoparticles(CH-AgNPs) using _Prunus cerasus_ leaf extracts. Investigation of their antimicrobial activity has focused on different Gram-positive and Gram-negative strains of bacteria, paving the way for the possibility for their translation into clinical settings as an alternative antimicrobial strategy.
## 3 Results
### PHCs Removal and Degradation in Soil  
The remaining concentration of PHCs was examined after 90 days of experiment and it showed that in the control treatment (the soil with 5000 mg kg\({}^{-1}\) initial concentration of PHCs but without plants or any amendments) 42.2% reduction in PHCs was observed as compared to the initial concentration of PHCs (i.e., 5000 mg kg\({}^{-1}\)) (Figure 5). The individual application of biochar and _Enterobacter_ sp. MN17 and their combination in the absence of plants could remove 27.9%, 32.3% and 38.9% higher amounts of PHCs than their respective unplanted control. Meanwhile, mungbean plants could remove 26.8% higher amounts of PHCs than their respective unplanted controls (without any amendments). Similarly, plants in the presence of biochar or strain MN17 could remove 33.1% and 39.9% higher PHCs than the control. Interestingly, plants in the presence of both biochar and strain MN17 could remove substantial amounts (50.5%) of PHCs that were higher than control as well as any other treatments (Figure 5), suggesting that the co-application of biochar and _Enterobacter_ sp. MN17 could significantly improve the phytoremediation potential of mungbean plants for PHCs.  
Figure 5: Effects of _Enterobacter_ sp. MN17 and biochar on the removal of PHCs by mungbean grown in  
PHC-contaminated soil. Values shown here were taken after 90 d of sowing. Columns and bars represent means and standard deviation, respectively, of triplicate values. Means with different letters differ significantly according to the LSD test at \(p\leq 0.05\). (_Control_, Uncontaminated soil with plants; \(E\), Contaminated soil with _Enterobacter_ sp. MN17; \(B\), Contaminated soil with _biochar_; _E+B_, Contaminated soil with _Enterobacter_ sp. MN17 and _biochar_; \(P\), Contaminated soil with plants; _P+B_, Contaminated soil with plants and _Enterobacter_ sp. MN17; _P+E+B_, Contaminated soil with plants, _Enterobacter_ sp. MN17 and _biochar_.
## 2 Materials and Methods
### Experimental Procedure
#### 2.2.2 Isothermal Adsorption Tests  
Coal exhibits significant adsorption capacity owing to its highly developed pore structure. To investigate the adsorption characteristics of collected coal samples and derive critical parameters for subsequent analyses, isothermal adsorption tests were conducted. The isothermal adsorption behavior of gases in porous coal mass is governed by the Langmuir theory [56], which postulates a dynamic equilibrium between adsorption and desorption processes. Specifically, gas molecules adhere to coal pore surfaces through adsorption, while simultaneously desorbing back into the gaseous phase, resulting in equal  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Coal Type** & **Visual Density (g/cm\({}^{3}\))** & **Real Density (g/cm\({}^{3}\))** & **Porosity (\%)** & **A\({}_{\text{ad}}\) (\%)** & **V\({}_{\text{dat}}\) (\%)** & **M\({}_{\text{ad}}\) (\%)** & **FC\({}_{\text{ad}}\) (\%)** \\ \hline Bituminous coal & 1.405 & 1.485 & 9.16 & 14.93 & 17.28 & 1.10 & 66.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate analysis results for coal samples.  
adsorption and desorption rates under constant temperature conditions. This equilibrium state is expressed as  
\[\frac{\theta}{1-\theta}=\frac{k_{a}}{k_{d}}p, \tag{1}\]  
where \(\theta\) is the surface coverage of porous media (\(\theta=V/V_{m}\)), defined as the ratio of monolayer adsorption amount \(V\) (mmol/g) to the saturation adsorption capacity \(V_{m}\) (mmol/g); \(k_{a}\) and \(k_{d}\) denote the adsorption and desorption rate constants, respectively; \(p\) (MPa) is the adsorption equilibrium pressure under isothermal condition.  
Through algebraic rearrangement, Equation (1) can be rewritten as the relationship between the adsorbed gas amount (\(V\)) and equilibrium pressure (\(p\)):  
\[V=\frac{V_{m}Kp}{1+Kp}, \tag{2}\]  
where \(K\) (MPa\({}^{-1}\)) represents the Langmuir equilibrium constant quantifying the adsorption energy, valued as the reciprocal of the pressure at half-saturation (\(p_{1/2}\)), i.e., \(K=\frac{k_{a}}{k_{d}}=\frac{1}{p_{1/2}}\).  
In engineering practice, the Langmuir adsorption constants \(V_{m}\) and \(K\) are widely adopted as key indicators of coal's adsorption behavior. The parameter \(V_{m}\) quantifies the theoretical maximum gas storage capacity, while \(K\) proportionately indicates the adsorption affinity under specific thermodynamic conditions. Therefore, to evaluate the adsorbability of collected coal samples, Isothermal Adsorption Tests were conducted using an automated gravimetric analyzer ([PERSON] IGA-100, Cheshire, UK). This system employs a magnetic suspension microbalance to quantify adsorbed gas mass by tracking sample mass changes with a resolution of 0.1 \(\upmu\)g. Coal particles (100 g, 50-80 mesh) were prepared for testing. In order to investigate the permeability difference in adsorbable gases in coal's porous structure, the adsorption behaviors of samples exposed to three gases (N\({}_{2}\), CO\({}_{2}\), and CH\({}_{4}\)) were systematically compared.
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 4 Discussion  
### Comparison of Multiple Models  
In order to make the comparison among the constructed models more convenient, the average absolute percentage error (_MAPE_), the average absolute error (_MAE_), and the root mean square error (_RMSE_) were introduced to evaluate the prediction error [47; 48]. Besides, the accuracy ratio (_A_) and correlation coefficient (_R_) were also introduced to measure the correctness and reliability of the model. The specific calculation formulas of indicators are as follows:  
\[e_{MAPE}=\frac{1}{n}\underset{t=1}{\overset{n}{\underset{t=1}{\sum}}}\Big{|} \frac{y_{t}-\hat{y}_{t}}{y_{t}}\Big{|}\times 100\% \tag{15}\]  
\[e_{MAE}=\frac{1}{n}\underset{t=1}{\overset{n}{\underset{t=1}{\sum}}}\big{|}y_ {t}-\hat{y}_{t}\big{|} \tag{16}\]  
\[e_{RMSE}=\sqrt{\frac{1}{n}\underset{t=1}{\overset{n}{\underset{t=1}{\sum}}} \big{(}y_{t}-\hat{y}_{t}\big{)}^{2}} \tag{17}\]  
\[A=1-\frac{\sqrt{\underset{t=1}{\overset{n}{\underset{t=1}{\sum}}}\big{(}y_{t} -\hat{y}_{t}\big{)}^{2}}}{\sqrt{\underset{t=1}{\overset{n}{\underset{t=1}{ \sum}}}\big{(}y_{t}\big{)}^{2}}} \tag{18}\]  
\[R=\frac{Cov(y_{t},\hat{y}_{t})}{\sqrt{Var[y_{t}]\cdot Var[\hat{y}_{t}]}} \tag{19}\]  
The calculated error indicators are shown in Table 4. Figures 10 and 11 show that the SARIMA-NAR combination model 1 had the highest accuracy. The MAPE of the fitted and predicted series were as low as 5.77% and 5.89%, respectively, and the accuracy values were as high as 0.93 and 0.92, respectively. In addition, most of the indicators showed that the fitting effect was better than the prediction effect.  
Figure 11: Accuracy index statistics of fitting and prediction. (**a**) Accuracy (A), (**b**) Correlation coefficient (R).  
Figure 10: Error statistics of fitting and prediction. (**a**) MAPE, (**b**) MAE, (**c**) RMSE.
## 2 Materials and Methods
### Method Validation
#### 2.4.3 Accuracy and Precision  
The method's accuracy was validated through spike recovery experiments conducted across five feed matrices at three concentration levels each, reflecting expected residue ranges in different feed types: complete feed and vitamin premix (0.05, 0.5, 5.0 \(\upmu\)g/g), concentrated feed and bovine supplement (0.1, 1.0, 10.0 \(\upmu\)g/g), and premix feed (0.5, 5.0, 50.0 \(\upmu\)g/g). For each concentration level, five replicate samples and corresponding blank controls were prepared following the national standard GB 5009.295-2023 [5] for food safety analysis method validation. Following the established sample preparation protocol, spiked samples were analyzed by LC-MS/MS. Measured concentrations were determined using matrix-matched calibration curves, with recovery rates calculated as follows: Recovery (%) = (measured concentration/spiked concentration) \(\times\) 100%. The method's precision was evaluated through both intra-day (n = 6 replicates within one analytical batch) and inter-day (n = 6 replicates across three consecutive days) repeatability studies at all concentration levels. The precision was expressed as relative standard deviation (RSD, %) of the recovered concentrations.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 4 Proposed MAC for LLDN  
In this section, for collisions due to simultaneous transmissions, a new MAC protocol is designed. Below, we first give the network model, as well as our assumptions and then present our detailed MAC protocol.