esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Methods
### Film Calibrations and Scanning  
To assess the DPP independence of the MOS_kin_ within the constraints discussed above, a suitable form of reference dosimetry is required. Gafchromic EBT-XD film was used for FLASH investigations at clinical energies [60]. The EBT-XD film used in this experiment was calibrated at the Imaging and Medical Beamline (IMBL) at the ANSTO Australian Synchrotron, adjacent to PEER. A 31022 PinPoint ionisation chamber (PTW Freiburg, Breisgau, Germany) was used to confirm the dose to water at reference conditions of 2 cm depth within a 10 cm \(\times\) 10 cm \(\times\) 10 cm cube of Solid Water(r) HE (Gammex-RMI, Middleton, WI, USA) using a beam with a mean energy of 137.63 keV and a dose rate of 110 Gy/s. The EBT-XD films were then placed at the same 2 cm depth and exposed to doses from 0.8 Gy, to 40 Gy. After 24 h, the films were scanned using the central region of an Epson V850 at 150 DPI, as per manufacturer recommendations. The pixel values for each dose were then extracted, and curve fitting was performed using the manufacturer's recommended function \(D=a+\frac{b}{x-c}\), where \(x\) is the red channel pixel value, \(D\) is the dose, and \(a\), \(b\) and \(c\) are fitting constants. Previous research has shown that for EBT3 and EBT4 film, calibrations at low keV energies may lead to misrepresentation of the dose delivered by MeV beams by as high as 12% [85; 86]. To confirm this effect is not present for EBT-XD film calibrated at IMBL, a second calibration was performed at the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) using 15 MeV electrons. Using Plastic Water (CIRS, Norfolk, VA, USA), the doses delivered with a 14 cm \(\times\) 14 cm electron applicator were first confirmed at a 3.4 cm depth using a 34001 Roos(r) (PTW Freiburg, Germany) ionisation chamber. Once the monitor units required for a range of doses up to 40 Gy had been confirmed, the film was placed at the same depth and irradiated. Scanning and curve fitting was performed in the same manner as the IMBL calibrations.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 4 Discussion
Though such models are of undeniable robustness, designing task-specific DL topologies and training them from scratch would potentially exhibit better results and reveal more significant regions of interest. For example, integrating an attention mechanism may enhance the model's ability to seek important features in vital areas of the image. Moreover, designing a three-component Siamese network [26], which aggregates the distances of the three input images and computes the gradients based on a carefully designed loss function, may improve the results further. Moreover, extensive fine-tuning has not been performed for all networks. The selection of VGG19 was based on the performance of all networks under the same setup (i.e., with approximately 3-4 million trainable parameters). Some networks, however, may perform better under more in-depth fine-tuning. Experimenting with various optimisers is also a future research opportunity. Secondly, the study used the experts' diagnostic yield as the ground truth. This limitation constrains the horizons of the experiments because we can only measure the agreement with the experts and not the prediction's precision compared to surgical and histopathologic results. On the other hand, surgical results can provide a minimal number of negative cases, which poses severe limitations for the training purposes of every DL model. Thirdly, the study uses only planar views for the diagnosis. Future work should consider more views when available. Finally, more data could aid in the re-assessment of the proposed method. These limitations cannot degrade the importance of the findings. With the absence of related works that use the same image source, this study is the first attempt to introduce DL approaches for localising PGs in parathyroid scintigraphy with 99 mTc-secstanibi (MIBI) studies. It is demonstrated that DL can at least compete with human expertise in the specific task, which is very desirable when developing medical decision support systems.
# Optimization of Organic Content Removal from Aqueous Solutions by Fenton-Ozonation
## 4 Conclusions  
The degradation and mineralization of 2,4-D by the Fenton processes were studied at laboratory scale, and ozonation and coupling advanced oxidation processes were investigated at pilot scale. The catalyst used is developed from a commercial AC (L27) that has been enriched with iron. In order to determine the quantities of H\({}_{2}\)O\({}_{2}\) and L27-Fe favorable for efficient mineralization, an experimental plant and a statistical study were carried out. These show an ideal dose of Fe-L27 of 730 mg\(\cdot\)L\({}^{-1}\) and an H\({}_{2}\)O\({}_{2}\) dose of 224 mg\(\cdot\)L\({}^{-1}\). The application of ozonation processes alone has been shown to be effective for the complete degradation of 2,4-D, but is limited in terms of mineralization. The addition of H\({}_{2}\)O\({}_{2}\) then allowed for the increase of the generation of hydroxyl radicals, resulting in a significant improvement of mineralization. The introduction of an activated carbon (L27) enhanced this performance in terms of mineralization, reflecting a favorable interaction between the surface of the AC and oxidizing species. The incorporation of iron as a catalyst (Fe-L27) led to the best performance, achieving complete mineralization after 60 min, accompanied by the highest kinetic constants. The catalytic support role of L27 in the system (Fe-L27) is evidenced by the absence of a desorption peak of 2,4-D on the TGA thermogram.  
These results confirm the potential of the catalytic system Fe-L27 coupled to ozonation in the presence and absence of H\({}_{2}\)O\({}_{2}\) as an effective solution for the treatment of organic pollutants in water. Future investigations will allow us to evaluate its stability in treatment cycles, its efficiency in complex matrices, and to study other processes of elaboration of Fe-AC systems like the ionothermal carbonization (ITC).  
Figure 8: Thermogravimetric analysis. (**a**) Activated carbon before and after 2,4-D adsorption and (**b**) activated carbon after 2,4-D treatment by advanced oxidation processes (AOPs).  
**Author Contributions:** P.F.S.: methodology, formal analysis, investigation, and writing--original draft preparation; M.S.S.: conceptualization, methodology, formal analysis, data curation, validation, writing--review and editing, and publication funding acquisition; S.S.: methodology, formal analysis, investigation, validation, and writing--review and editing; B.C.: conceptualization, methodology, validation, and writing--review and editing, resources, supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Region Centre Val de Loire.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Acknowledgments:** The authors thank Jacobi Carbon for supplying the activated carbons and [PERSON] for the XPS analyses.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Conclusions  
In this study, we analyzed the error characteristics of the offset fields obtained from RapidEye images and proposed correction methods. We developed an attitude jitter distortion model that considered the differences among other push-broom satellites, fully accounting for the gradient trend perpendicular to the flight direction. The polynomial fitting method effectively removed the satellite attitude jitter distortions and accommodated errors with constant characteristics or linear trends. Our investigation identified decorrelation noise, orbit error, and satellite attitude jitter distortions as the primary errors. We propose corresponding correction methods for each of these errors. Additionally, we presented a comprehensive processing workflow for monitoring surface deformation using RapidEye based on the SPC technique. Following the workflow, we obtained the 2D displacement field of the 2019 California earthquake. After error correction, fault traces of the earthquakes were effectively identified, and the deformation characteristics of the earthquake were extracted from the offset fields. The 2D offset field showed good consistency with GNSS observations in terms of magnitude and direction. In the future, improvements can be made to the error correction methods of RapidEye to enhance the automation processing. Strengthening data fusion with other monitoring methods, such as GNSS and SAR images, will enable more accurate and real-time monitoring and early warning of geological disasters such as earthquakes and landslides, thereby providing greater support for disaster prevention and control.  
Conceptualization, W.L. and G.F.; Methodology, W.L., Q.A. and G.F.; Software, W.L., Q.A. and Z.X.; Validation, W.L. and G.F.; Formal analysis, L.H., Y.W. and H.J.; Resources, G.F. and L.H.; Data curation, Z.X.; Writing--original draft, W.L. and Q.A.; Writing--review & editing, W.L., G.F., Z.X., Y.W., H.J., X.W., N.L. and W.W.; Funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (No. 42174039) and the Natural Science Foundation of Hunan Province (No. 2021 JJ0807).  
Figure 8: The 3D offset field of the 2019 Ridgecrest earthquake sequence.
## 5 Conclusions  
Convolutional codes and the Viterbi algorithm are two efficient methods used for error correction in satellite communication systems, particularly in GPS systems. Convolutional codes are effective error-correcting codes that can work at high data rates and demand relatively few resources, thus making them a suitable choice for employment in GPS systems. In contrast, the Viterbi algorithm is computationally efficient and can estimate the most probable transmission sequence from a received signal in real-time, making it an appropriate choice for GPS systems. Other efficient coding methods, such as LDPC codes and Turbo codes, which have a high computational complexity and memory requirements, may not be compatible with the limited processing power and low-cost devices used in commercial applications, making them less well-suited for GPS systems.  
Overall, the advantage of Convolutional codes is that they can achieve high coding gains with relatively low decoding complexity. However, the performance of Convolutional codes can be limited by their constraint length (the number of bits that each input bit is dependent on), and they may not be able to correct bursts of errors as effectively as other error-correcting codes. This article contributes to the existing knowledge by offering a comprehensive understanding of why convolutional codes are suitable for GPS systems. The discoveries act as a valuable asset for researchers, engineers, and professionals in satellite communication, facilitating the progression and refining of GPS system designs.  
Methodology, N.H.S.; Validation, A.L.; Formal analysis, N.H.S.; Investigation, N.H.S.; Resources, N.H.S.; Data curation, N.H.S.; Writing--original draft, N.H.S.; Writing--review & editing, A.L.; Supervision, A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded bythe Ministry of Science and Higher Education of the Russian 128 Science Foundation (Project \"Goszaadanie\" No 075-01024-21-02 from 29 September 2021, FSEE-2021-0015).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
LDPC codes  
BCH codes  
EUMETSAT  
Low-Density Parity Check (LDPC) codes  
The Bose, Chaudhuri, and Hocquenghem (BCH) codes  
European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)
## 4 Discussion
### Strategies for Real-World Application  
Adaptation strategies to the challenges discussed can be implemented, such as robust data augmentation, simulating noise in clinical data during the training, and domain adaptation techniques to transfer learning from a controlled dataset to a clinical dataset with the use of fine tuning. These strategies have been implemented in the models. Finally, collaboration with healthcare institutions is necessary in order to acquire clinical data from psychiatric institutions, as well as active learning within an active feedback loop, where human experts can monitor and decide on the validity of the results.
# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 3 Proposed Methodology
### Swarm-Based Ensembler
#### 3.5.2 DenseNet Mechanism  
The distinguishing essence of DenseNet lies in its foundational concept of feed-forward networks, bestowing upon it a prominent edge over other network architectures. DenseNet  
Figure 3: DenseNet model.  
offers a plethora of enthralling advantages. First and foremost, DenseNet helps to mitigate the vanishing-gradient issue. Furthermore, DenseNet exhibits its prowess by intensifying the propagation of features, fostering their regeneration, and effectively reducing the parameter count. It is achieved through the unique mechanism of a dense layer, which elegantly consolidates the outputs from preceding layers by concatenating them along the depth dimension. Figure 4 depicts the function of a DenseNet process, which combines transition layers and dense blocks to categorize an input review depending on its content. When a text is supplied as input to the DenseNet, the text is processed through many dense blocks. Each layer's feature maps remain the same from layer to layer except for the number of filters, which change from layer to layer within a single dense block. Upon traversing a dense block, data moving within DenseNet progress to the next stage, the transition layer.  
Within the architectural framework of DenseNet, the transition layer assumes the crucial responsibility of executing two fundamental operations: convolution and pooling. These operations are diligently performed in the downsampling procedures strategically positioned outside the dense blocks. To ensure the harmonization of feature map lengths within the dense block before concatenation, a convolution layer can be incorporated before each convolution. In the realm of the DenseNet architecture, the transition layer encompasses diverse components, including batch normalization (BN), as well as other essential elements like average pooling layers and convolution layers, all working together to optimize computational efficiency and to minimize the number of input feature maps.  
Figure 4 depicts the DenseNet flow chart. The DenseNet design's dense block comprises a layer of BN, ReLU activation, and convolution, all accompanied by convolution. After the last dense block, an average global pooling layer is performed, and the results are sent to a Softmax classifier.
## 2 Materials
### MODIS Data  
The MCD43A1 V6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is a daily product with a 500 m resolution and a 16-day composite period. It offers parameter values that pertain to isotropic scattering, volumetric scattering, and geometric-optical scattering across MODIS bands 1 through 7, which correspond to the wavelengths of 645 nm, 859 nm, 469 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively. Additionally, the dataset encompasses three broad spectral bands, specified as 300-700 nm, 700-5000 nm, and 300-5000 nm. In the context of this paper, MCD43A1 data synchronized with the dates of the Jilin-1 GP01/02 PMS were secured. The concurrent MCD43A2 dataset was utilized for the quality control of BRDF parameters. Subsequently, a selection criterion was applied, wherein only BRDF parameters associated with pixels of the highest quality and good quality were deemed suitable for inclusion in the correction process. This study aimed to employ the MODIS BRDF parameters of band 1 (645 nm), band 2 (859 nm), and band 4 (555 nm) for the reflectance correction of Jilin-1 GP01/02 PMS green, red, and NIR bands.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Benefits of ITSs
#### 5.1.1 Environmental Impact  
ITSs can significantly reduce greenhouse gas emissions. Public transport has much lower emissions than individual motorized transport. This makes urban mobility account for around 40% of CO\({}_{2}\) emissions. ITSs encourage the use of public transport and reduce dependence on fossil fuels, thus reducing air pollution [48]. For example, a report by the European Commission [49] shows that the implementation of intelligent traffic management systems has reduced fuel consumption by 11%.
# 3D Printed Microfluid Separators for Solid/Liquid Suspensions
## 2 Materials and Methods
### Characterization  
The particle size distribution of each powder sample was determined before and after the separation process. Prior to separation, an analysis was performed to determine the particle size range and distribution within the inlet stream. Following separation, methods for particle size characterization of the powder from the outlet streams were used to investigate the correlation between particle size and separation efficiency.  
The sieve analysis was used to determine the particle size distribution in different size intervals. Seven wire sieves with aperture sizes of 850 \(\upmu\)m, 710 \(\upmu\)m, 355 \(\upmu\)m, 180 \(\upmu\)m, 125 \(\upmu\)m, 90 \(\upmu\)m, and 63 \(\upmu\)m were used. After separating the particles into size intervals, the samples are weighed to obtain the cumulative particle size distribution.  
The particle size distributions (PSDs) of baby powder, calcium carbonate and quartz sand were determined in wet mode (the samples were previously suspended in demineralized water) by applying a laser diffraction method (Shimadzu, Kyoto, Japan, model SALD 3101). In the case of the baby powder particles, 10% isopropyl alcohol was used to enhance  
Figure 2: 3D-printed separators: SLA (**left**) and FFF with added blue color for easier channel visualization (**right**).  
dispersion. Diffraction measurements were performed five times under identical process conditions. Each sample was scanned five times under identical process conditions in order to test the reproducibility and to gain more representative size distribution data for each tested sample. The net size distribution data were provided by averaging those five size distribution data for each size interval. The mean PSD is expressed on the basis of volume, with characteristic diameters \(d_{50}\) as the median diameter, \(d_{\text{mode}}\) as the modus diameter (dominant size in a population), and the diameter mean, \(d_{3,2}\) and reported via differential distribution function, \(\text{dQ}_{3}(d)\).  
The morphology of the individual powder sample was analyzed using a Tescan Vega 3 scanning electron microscope (SEM) (Brno, Czechia). Prior to analysis, the samples were coated with a conductive layer of gold and platinum in argon plasma under a high vacuum of 0.1 mbar at 17 mA for 60 s. A secondary electron (SE) detector and a voltage of 10 kV were employed for this analysis.
## 2 Materials and Methods  
### Theoretical Computation Equations of Line Losses  
Theoretical equations of line losses are proposed to compute technical line losses, which are based on the equivalent resistance method. The method supposes that there is an equivalent resistance at the head of line, where the energy loss of three-phase three-wire and three-phase four-wire systems can be formulated as [30]:  
\[\Delta A_{b}=NK^{2}l_{av}^{2}R_{eq}T\times 10^{-3}(\mathrm{kWh}), \tag{1}\]  
where \(\Delta A_{b}\) refers to the theoretical line loss with balanced three-phase load. \(N\) is the structure coefficient, which is equal to 3 at a three-phase three-wire system and equal to 3.5 at three-phase four-wire system. \(K\), \(I_{av}\), \(R_{eq}\) and \(T\) denote the shape coefficient of load curves, the average current at the head of line (A), the equivalent resistance of conductors (\(\Omega\)) and the operating time (h), respectively. Furthermore, \(R_{eq}\) can be computed under the following equation:  
\[R_{eq}=\frac{\sum_{i}N_{i}A_{i}^{2}R_{i}}{N\Big{(}\sum_{j}A_{j}\Big{)}^{2}}, \tag{2}\]where \(N_{i}\), \(A_{i}\) and \(R_{i}\) are the structure coefficient, the metered electricity power and the resistance of the \(i\)th line segment, respectively. \(A_{j}\) denotes the electricity power collected from the \(j\)th power meter. For a system with balanced three-phase load, the theoretical line loss should be corrected as:  
\[\Delta A_{ub}=\Delta A_{b}\times K_{ub}, \tag{3}\]  
where \(K_{ub}\) represents the correction coefficient that can be defined as:  
\[K_{ub}=1+k\delta_{I}^{2}, \tag{4}\]  
where \(k=2\) with one phase heavy load and two phase light loads. If there are two phase heavy loads, \(k=8\). \(\delta_{I}\) denotes the unbalance level of three-phase load, which can be calculated as follows:  
\[\delta_{I}=\frac{I_{max}-I_{av}}{I_{av}}, \tag{5}\]  
where \(I_{max}\) is the current from the phase with the maximum load. Thus, the theoretical line loss defined above is an unavoidable energy loss, which is so-called technical line loss. However, the non-technical line loss caused by electricity theft is also concerned by power grid operators. As those non-technical loss situations can lead to abnormal values in the metered daily line loss rates, it is of necessity to calculate reasonable intervals for outlier discrimination, which is one of the purposes in this study.
## 3 Results and Discussion
### Primary and Secondary VOC Emissions
#### 3.2.2 From Synthetic Fibers  
The \(F_{i}\) values of the detected target VOCs are summarized in Figure 6a,b for the synthetic fibers. Full data on emission factors of detected VOCs from synthetic fibers can also be found in Table S2.  
Figure 6: Primary and secondary emissions between the ozone and synthetic fibers. (**a**) Aromatics, esters, and aliphatic compounds. (**b**) Aldehydes and ketones.  
According to Figure 6a,b the emission behaviors of VOCs from the specimens of synthetic fibers under ozone exposure were visibly varied. On the one hand, the emissions of aldehydes and ketones were all small from weaved leather, microfiber lining, and carpet. However, formaldehyde emissions were significantly high from flannel, with \(F_{i}\) the lowest at 55.4 \(\upmu\)g\(\cdot\)m\({}^{-2}\)\(\cdot\)h\({}^{-1}\) when \(y_{\text{ozone,in}}\) was at 100 ppb, and highest at 150.8 \(\upmu\)g\(\cdot\)m\({}^{-2}\)\(\cdot\)h\({}^{-1}\) when no detectable ozone was introduced, indicating that formaldehyde-contained adhesive was probably used in the specimen of the flannel. When the given concentration of ozone was as high as 300 ppb, formaldehyde emission was approximately 125.5 \(\upmu\)g\(\cdot\)m\({}^{-2}\)\(\cdot\)h\({}^{-1}\). One hypothesis was that formaldehyde was both the reactant and product of the chemical reaction(s) in the chamber, which depended on the reaction rate(s) and yield(s) from the ozone-initiated reaction(s).  
On the other hand, when \(y_{\text{ozone,in}}\) was less than 10 ppb, no targeted aromatics, esters, and aliphatic compounds were observed at notable amounts from the leather and the lining. However, when \(y_{\text{ozone,in}}\) was as high as 100 ppb or 300 ppb, a large number of compounds, mostly benzene and toluene, were emitted from all the synthetic materials. Even though the carpet was not as popular in Chinese societies as in Western countries, an alarm was raised when high emissions (35.6-91.5 \(\upmu\)g\(\cdot\)m\({}^{-2}\)\(\cdot\)h\({}^{-1}\)) of toluene were observed from the carpet specimen when exposed to high concentrations of ozone.