esa-sceva/satcom-chunk-collection huggingface dataset:
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 7 Conclusions  
With the growing harmful effects of F-H around China, this paper has employed the WNN prediction model to forecast photovoltaic output and power load based on the analysis of the influence of F-H. On that basis, the concept of the VPP is adopted to handle the dispatch problem of multiple DERs connecting to the power grid, and a multi-objective optimal dispatch model of a power system containing VPPs, with the goals of energy conservation and emission reduction under F-H weather, is constructed based on traditional unit commitment. Several conclusions have been drawn from the above study:  
(1) The influence of F-H on the photovoltaic output and load forecasts cannot be ignored, and the prediction accuracy could be improved effectively by selecting similar days of F-H to account for the influence of F-H.  
Figure 14: Variations of cost and emission under different values of \(\alpha\).  
(2) F-H has a great influence on the power system dispatch with the goals of energy conservation and emission reduction. If the AQI factor is considered, VPPs would generate more electricity, and thermal power units would be restricted, based on the comprehensive consideration of the overall benefit of the power system and macro policies, such as emission punishment, financial subsidies and emergency warning measures. Therefore, clean DERs would be fully used, and air pollution would be controlled to some degree.  
(3) The influence of F-H on the output characteristics and control strategies of DERs inside VPPs has not been discussed in this paper; this will be studied in later papers.  
The authors would like to acknowledge the financial support from the National High Technology Research and Development Program of China (No. 2015 AA050104) and the Fundamental Research Funds for the Central Universities.  
[PERSON] proposed the original idea and analyzed and double-checked the results and the whole manuscript. [PERSON] carried out the main research tasks and wrote and translated the full manuscript. [PERSON] helped to collect data and to translate the original manuscript. [PERSON] and [PERSON] contributed to the interpretation of the results.  
The authors declare no conflict of interest.
## 3 An Automated Framework for DSM
### Hybrid Model for DSM  
The variation in the material properties of the dam can be well reflected using the mechanistic model, as it can reduce the effect of noise. However, it is difficult to respond  
Figure 4: Structure of the LSTM unit.  
to external environmental changes on time. The data-driven model is capable of dynamic learning and self-optimization based on current data and is highly time-sensitive and accurate. However, it cannot predict data for the parts of the dam where sensors are not installed. Also, the data model is susceptible to noise, which is frequently collected during the operation of the sensors.  
A combination of the mechanism model and the data-driven model was used to build a more accurate model in order to achieve the complementary strengths of both. The process of creating the hybrid model is shown in Figure 5. First, the material parameters were obtained using dynamic inversion. In the second step, the water level with a rate of change higher than the threshold was applied as a load and boundary constraint to the calculation. Next, the monitored data in the selected date were preprocessed and then divided into training and test sets to be input into LSTM for fitting and prediction. Finally, the simulated value was used to replace the predicted data where the rate of change in the water level exceeded the threshold to generate the complete time series. \(p_{l}\), \(p_{m}\), and \(m_{e}\) represent the predicted values of the data-driven model, the predicted values of the mechanistic model, and the monitored data of environmental variables, respectively; \(p_{l,t}\), \(p_{m,t}\), and \(m_{e,t}\) are the monitored values at time \(t\) (\(1\leq t\leq n\)); \(\delta_{l}\) is the threshold value of the data variation rate; and \(d_{p}\) is the predicted value of the hybrid model, which is a combination of the simulated and predicted values.
###### Abstract  
This pilot study evaluated the effectiveness of ice boots in cooling the metacarpal and coronary regions of a horse after training over 8 days (\(n=8\)). Background: While cryotherapy is effective in managing exertional heat illness, stress fractures, and laminitis in horses, conventional methods are often costly and impractical. This pilot study assessed the efficacy of ice boots as an accessible alternative for cooling the metacarpal and coronary regions post-training. Methods: A four-year-old Warmblood mare was trained on a treadmill over 8 days. An ice boot was applied to the right thoracic limb for 20 min post-exercise. Thermographic images were captured at six time points from pre-training to 30 min post-cooling. Mean temperatures in four regions were analyzed using the FLIR Tools software v6.4.18039.1003. Results: Post-training, metacarpal temperatures increased by 10.97 \(\pm\) 0.46 \({}^{\circ}\)C (\(p=0.000\)). Ice boot application reduced metacarpal temperature by 20.27 \(\pm\) 0.22 \({}^{\circ}\)C (\(p=0.001\)) and coronary band temperature by 5.28 \(\pm\) 0.30 \({}^{\circ}\)C (\(p=0.001\)), with altered thermal patterns visible on the imaging. Treated regions returned to baseline within 30 min, while the control limb took 50 min. Conclusions: Ice boots provide rapid, effective cooling and distinctive thermal pattern changes, offering a practical cryotherapy alternative for equine limb care post-training. These initial findings lay the groundwork for larger studies involving more horses under varied conditions, which will be necessary to confirm the results and establish clear guidelines for the clinical use of ice boots in equine practice.  
Keywords:horse; training; ice boot pack; thermography +
Footnote †: journal: Applied sciences
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 4 Discussion  
Based on the almost linear relationships between NRCSs of VV and RV polarizations, the C-band RV-polarized wind retrieval model has been simply proposed using the C-band VV-polarized wind retrieval model [11; 12]. However, there are two factors should be further discussed: (1) the VV-polarized wind retrieval models, routinely CMODs, are developed for scatterometer but not SAR, and (2) the relationships between the two polarized NRCSs are almost linear but not accurately. Therefore, this study aims to improve the CP mode wind retrieval accurate by tuning each parameter in the CMOD frame. To reduce the large computations, we employ the sensitivity analysis. [PERSON] et al. (2018) developed a new wind retrieval model for C-band VV-polarization [19]. In the CMOD series, including CMOD4, CMOD5, CMOD5.N, CMOD6, CMOD7 and et al., the equations are the same but with different parameters [20; 21]. We compare the results from CoVe-Pol models here and the method proposed by [PERSON] et al. (2015) based on the first data group [11], which is shown in Figure 11. It is obvious that the new model CoVe-POL present better wind retrieval results.  
Figure 10: Wind speed retrieved by CoHo-Pol, the RH polarization mode model, vs. reserved wind speeds observed by buoys; the test data subset.  
[PERSON] et al. (2018) proposed a semi-empirical ocean surface model for RCM CP mode and the simulated results suggest that the RV-polarization has a better potential capability for the ocean wind retrieval than the RH-polarization [22]. This result is consistent with the wind retrieval results here. As indicated by [PERSON] et al. (2018) [22], the noise floor should be a challenge for sea surface wind retrieval. As shown in Figure 9a, the NRCSs are under-estimated by the new proposed wind retrieval model when the values are around \(-25\) dB. On one hand, this may be due to the errors of the model. However, on the other hand, this should be caused by the noise floor which is designed as \(-25\) dB in the RCM. Therefore, the low wind retrieval from RCM RV and RH polarizations will be a challenge. Moreover, the sea surface ice or oil spill can be detected by SAR due to covering the ocean surface and changing the backscattering features [22]. The determination of oil spill from low wind condition is a current issue from the existing SAR observations. As the noise floor for RADARSAT-2 is much lower than the RCM, this would also be a problem for the sea ice or oil spill study using RCM in the future.
## 4 Workplace for Experiment to Measure Shielding Effectiveness
The R&S HF907 is a broadband directional antenna with linear polarization, featuring a frequency range of 0.8 GHz to 18 GHz. It is designed for use in electromagnetic compati  
Figure 4: RF spin DRH18-E antenna. Figure 5: Rohde & Schwarz HF 907 antenna. bility measurements of components. Distinguishing itself from typical broadband antennas, its radiation pattern contains only one main lobe across the entire frequency range, with no significant side lobes [23]. The measurement setup further included the Agilent N5181A pulse generator (Agilent, Hong Kong, China), which powered the RF spin DRH18-E transmitting antenna. Between the antennas, the measured shielding was placed. The output from the R&S HF907 receiving antenna led into the Agilent N9038A MXE EMI spectrum analyzer. Both antennas were connected to the measuring instruments through feedthroughs and a network filter located in the EMC chamber. The Agilent N9038A MXE EMI spectrum analyzer is a standard receiver for electromagnetic interference (noise) and a diagnostic signal analyzer, built on an expandable platform. In the laboratory, it provides the precision, repeatability, and reliability necessary for each measurement. This device offers a wide frequency range from 20 Hz to 44 GHz. As standard with Agilent devices, it communicates via GPIB bus and also includes LAN and USB. The Agilent N5181A pulse generator, with its high reliability and performance, is optimized to provide accurate and repeatable reference signals. It further enhances throughput and maximum operational capability and saves space in the rack. Its wide scalability in RF power ensures easy configuration to meet specific measurement needs. The device offers a wide frequency range from 100 kHz to 20 GHz. Naturally, it communicates via the GPIB bus and also includes LAN and USB connections. By measuring the shielding effectiveness (_SE_)--Figure 6, it is possible to determine how effective shielding materials are within the measured spectrum. Different materials can have varying shielding capabilities against electromagnetic waves. Based on the relationships for determining the shielding effectiveness (_SE_) and the reflection (_R_) of the electromagnetic waves, it can be concluded that the magnitudes of these quantities depend on the physical properties of the shielding material such as permeability or the specific conductivity of the shielding material. From this, it is also possible to determine the contribution of reflection (_R_) and absorption (_A_) of the electromagnetic wave to the overall shielding effectiveness. The values of _SE_, \(R\), and \(A\) also depend on the space through which the electromagnetic wave propagates. However, this work described measurements that took place in an anechoic chamber and, therefore, the electromagnetic wave propagated through air.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Antimicrobial Activity of the Biosynthesisized ZnO-NPs  
The ZnO-NPs biosynthesized using crude extracts of _U. lactuca_, _U. intestinalis_, and _S. muticum_ were evaluated against two pathogenic bacterial species, Escherichia coli and Klebsiella pneumonia, and two fungal species, _Aspergillus niger_ and _Candida albicans_, using the disk diffusion method (Table 1). The diameter of the inhibitory zones ranged from (11 \(\pm\) 0.21 mm to 21.3 \(\pm\) 1.02 mm) for different ZnO-NP concentrations and other treatments. The highest inhibition zone of the _Ulva lactuca_ ZnO-NPs was observed for the  
Figure 6: Zeta potential analysis of ZnO-NPs biosynthesized using different seaweeds: _Ulva lactuca_ (**A**), _Ulva intestinalis_ (**B**), and _Sargassum muticum_ (**C**).  
60 mg/mL treatment against _A. niger_ (21.3 \(\pm\) 1.02 mm), followed by _E. coli_ (20.7 \(\pm\) 0.42 mm), _C. albicans_ (19.3 \(\pm\) 0.36 mm), and then _K. pneumoniae_ (17.8 \(\pm\) 0.02 mm). These results were followed by the treatment using 30 mg/mL, and finally, for the 15 mg/mL treatment as shown in Table 1.  
The highest inhibition zone of the _S. moticum_ ZnO-NPs was observed for the 60 mg/mL treatment against _E. coli_ (19.1 \(\pm\) 0.39 mm), followed by _C. albicans_ (18.6 \(\pm\) 0.36 mm), then _A. niger_ (17.7 \(\pm\) 1.05 mm), and finally _K. pneumoniae_ (15.1 \(\pm\) 0.02 mm). These results were followed by the treatment using 30 mg/mL, and finally, with 15 mg/mL treatment.  
On the other hand, the highest inhibition zone of the _U. intestinalis_ ZnO-NPs was observed in the 60 mg/mL treatment against _K. pneumoniae_ (17.6 \(\pm\) 0.23 mm), followed by _E. coli_ (16.9 \(\pm\) 0.75 mm), then _A. niger_ (16.2 \(\pm\) 0.24 mm), and finally _C. albicans_ (15.8 \(\pm\) 0.00 mm). These results were followed by the treatment using 30 mg/mL, and finally, with the 15 mg/mL treatment. Thus, by increasing the concentration of ZnO-NPs, the diameter of the inhibition zone also increased. The inhibition values of the standard antibiotics fluctuated depending on the bacterial and fungal strains.
# Interoperability Analyses of BIM Platforms for Construction Management
###### Abstract  
It is incontrovertible that an exchange of files is essentially required at several stages of the workflow in the architecture, engineering, and construction (AEC) industry. Therefore, investigating and detecting the capabilities/inabilities of building information modeling (BIM) software packages with respect to interoperability can be informative to stakeholders who exchange data between various BIM packages. The work presented in this paper includes a discussion on the interoperability of different software platforms commonly used in the AEC industry. Although, in theory, flawless interoperability of some types of files between different BIM platforms is ensured, in practical applications, this is not always the case. Hence, this research aims to identify faults in data exchange by assessing different possible scenarios where a sample Industry Foundation Classes (IFC) four-dimensions (4D) BIM model and related Gantt charts are exchanged. Throughout the interoperability analysis of both IFC file and Gantt charts, the following checks were carried out: geometrical and nongeometrical information exchange through IFC files, 4D information correct readability, and presence of missing schedule information in Gantt charts after their import/export procedure. The results show that interoperability between the analyzed platforms is not always ensured, providing useful insight into realistic scenarios.  
BIM; interoperability; IFC; project management; Gantt chart +
Footnote †: journal: Applied Sciences
# Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
## 5 Experimental Results and Analysis  
### Loss Functions and Evaluation Indicators  
The study presented in this paper belongs to the multi-category image segmentation problem, which are subject, text, and graphics. Therefore, the categorical cross-entropy function is chosen as the loss function of the image segmentation model, which is calculated as follows:  
\[Loss=\frac{1}{N}\sum_{i=1}^{N}-[o_{i}ln(p_{i})+(1-o_{i})\cdot ln(1-p_{i})] \tag{3}\]  
where \(N\) is the number of samples, \(o_{i}\) is the true label value of the sample, and \(p_{i}\) is the probability of the predicted value of the sample. The loss value is the difference between the actual output probability and the expected output probability. The smaller the value of the cross entropy is, the closer the two probability distributions are.  
This loss function is particularly suitable for multi-class pixel-wise classification tasks such as semantic segmentation, and enables stable training with differentiable loss signals.  
Firstly, 2070 movie poster datasets and corresponding labeled graphs are imported into four image semantic segmentation models for training, of which 1610 images are in the training set and 460 images are in the validation set, and the changes in the loss function in the training and validation sets are observed during the training of the networks. As shown in Figure 7, it shows the change in loss function during the training process of the three models, where the horizontal coordinate represents the number of training rounds, and the vertical coordinate represents the loss function value. The first 50 rounds of all three models were trained by freezing, and the last 150 rounds were trained by unfreezing. A comparison of the loss change curves of the three models shows that the gap between the training loss and the validation loss of the improved model in this paper is small, and the training loss and the validation loss as a whole tends to decrease during the whole training process, and the loss gradually tends to flatten out after 150 rounds of iteration, which indicates that the model has reached the optimal state. It can be seen that the improved model in this study is normal throughout the training process, there is no overfitting or underfitting phenomenon, and the model is in good condition.  
After the network training is completed, 230 test set images are fed into the trained model. Despite the fact that movie posters have complex graphical backgrounds and fonts of various colors and sizes, they still perform well on the improved segmentation model in this paper.  
In this paper, Mean Intersection over Union (MIoU) is used as an evaluation index, which is calculated as follows:  
\[MIoU=\frac{1}{n}\sum_{i}\frac{n_{ii}}{\sum_{j}n_{ij}+\sum_{j}n_{ij}-n_{ii}} \tag{4}\]  
where \(n_{ij}\) denotes the number of pixels in which category \(i\) is predicted to be category \(j\), \(n_{ii}\) denotes the number of pixels that are predicted accurately, and \(n\) is the number of target categories (containing background, theme, text, and graphics).
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 2 Literature Review and Paper Contributions
### Paper Contribution  
To the best of our knowledge, this paper is the first work that addresses poisoning attacks against 2C task classification. Our paper is different because, first of all, it sets the whole problem in the edge-computing scenario and shows the implications of these attacks and methods in wireless communication and edge computing. Second, in contrast to the other works that use optimization methods for 2C task offloading, we use more practical, ML-based tools. Moreover, the existing mitigation techniques such as anomaly detection [39] and adversarial training [40] abstract from the specifics of wireless communication and 2C task classification problems. In this paper, we investigate how data poisoning attacks impact 2C task classification and offloading and how they can be mitigated.  
The work starts with an introduction of our considered system model. Then, a hybrid ML technique is proposed to assist the near-optimal 2C classification. Next, two radio communication-specific poisoning attacks are considered and described. The effects of these attacks on 2C task classification are analyzed. In the last step, poisoning mitigation techniques are considered that can detect these attacks. Their effects on the classification system are shown.
# Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems
###### Abstract  
This paper consists of evaluating the performance of a vibro-acoustic model in the presence of uncertainties in the geometric and material parameters of the model using Monte Carlo simulations (MCS). The purpose of using a meta-model is to reduce the computational cost of finite element simulations. Uncertainty analysis requires a large sample of MCS to predict the effect of uncertain parameters on the system response. So, if this study is done through the finite element method (FEM), then the computational cost will be very important. Furthermore, for that, we use meta-models to be able to conduct an efficient uncertainty analysis more quickly. In the present contribution, the approximated meta-model is verified and validated using error measures and cross-validation (CV). Then, the uncertainty analysis is performed by Monte Carlo simulations using the computed Kriging meta-model. The developed methodology has been applied in two vibro-acoustic models. In these two models, the covariance of uncertainty of geometric and physical (elasticity and density) parameters are equal to 2% and 5% respectively. The obtained results prove that the suggested methodology of uncertainty propagation based on the Kriging meta-model can be considered as a very efficient and sufficiently accurate approach for the quantification of uncertainties in acoustic-structural systems.  
vibro-acoustic models; uncertainty analysis; surrogate model; Kriging; Monte Carlo simulations +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Photoluminescence (PL)
For instance, the SA1a sample, which was very soluble, displayed IRL at the edges of the writing line, and no IRL in the middle of the written \"A\" letter with a black appearance. The luminescence observed at the edges of the stroke can be attributed to the diffusion of the solvent carrying the highly concentrated crystal violet dye, where the mixture of phthalocyanine dye and crystal violet in the middle of the line resulted in luminescence quenching and caused the black color. Furthermore, when writing with a dilute solution of TO3 and ZR1a samples, their obtained IRL resembled that of crystal violet. These two samples had poor solubility, which further suggests the presence of D4 as a phthalocyanine dye with limited solubility. The limited solubility of the phthalocyanine dye resulted in low concentrations, thus limiting the quenching effect. Concerning the PL spectra, the combination of the two dyes causes a red shift in the emitted light wavelength. For instance, the RO4 and RX1 samples, containing crystal violet and D1 dyes, exhibited PL at 660 nm and 672 nm under the excitation range (400-585 nm). The emission maxima of the ink strokes of these samples were positioned around 705 nm under excitation using the first filter, around 735 nm using the second to sixth filters, and around 750 nm under the seventh excitation filter. This red shift of the PL may result from the conjugation of the present dyes, in addition to the Stokes shift. The extent of the observed red shift varied between different ink samples with different dye compositions, leading to the sub-differentiation of samples of both subgroups I and II. These results show that PL spectra measured using the VSC can provide an important tool for ink examination, while higher discriminatory power can be achieved. The IRL properties of the blue BP pen inks have been assigned mainly to the presence of the crystal violet dye [38]. Our results confirm that crystal violet constitutes the main dye component causing IRL, along with contributions from some other dyes such as the triarylmethane dyes. Interestingly, results show that some triarylmethane dyes exhibited even stronger PL compared to crystal violet. Moreover, their IRL behavior has a significant influence on the overall IRL characteristics obtained from the studied ink stroke samples.