esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Applications
#### 2.2.2 Search and Rescue  
UAVs are employed in search and rescue during disaster situations to locate the survivors in the disaster-hit area and provide support. In [12], human wearable IoT devices establish connectivity through UAV mounted base stations in the absence of traditional network infrastructure to send messages for disaster rescue. A multi-UAVs system is considered to search and rescue people affected by natural disaster in [39]. The authors proposed a scheduling scheme to minimize the processing and data transfer time which are critical in search and rescue operations. In [10], a search and rescue system composed of multiple UAVs is studied and a centralized task distribution algorithm is proposed to locate maximum number of survivors in minimum time. Energy efficient mobility model and routing protocol for UAVs in disaster recovery operation are studied in [40] that can adapt to the topology changes while minimizing the network delay. A multiple UAVs autonomous evacuation guidance support system is investigated in [41]. The proposed system decides the evacuation route according to the disaster situation through cooperation between the UAVs.
## 3 The Proposed MMGAT Methods
### The Structure of the Proposed MMGAT
#### 3.2.2 The Introduction of the Auxiliary Classifier  
MMGAT integrates label augmentation into its training process. To enhance both recognition accuracy and generalization performance, the network concurrently performs the auxiliary classification of instrument groups and primary classification of instrument categories. The grouping strategies, based on onset types, are applied for the auxiliary classification task. By simultaneously carrying out both the auxiliary and primary tasks, our network operates in a multitask learning framework. These two tasks leverage shared information to produce different outputs. The structure of MMGAT is shown in Figure 1.  
In MMGAT, the used backbone is the commonly used VGGish.  
Figure 1: The MMGAT structure.
# A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water
## 2 Materials and Methods
### Dataset Description  
The proposed ANN model for predicting Lindane removal efficiency from contaminated water was developed from 49 datasets acquired from batch adsorption experiments. The effect of different parameters on the percentage removal of the lindane was investigated. The parameters are pH, initial concentration, temperature, contact time, and adsorbent dosage [36]. The experiments involve the adsorptive removal of lindane by seagrass powder from contaminated water. The computational tasks of the simulation and model-building processes presented in this research were performed utilizing the MATLAB computing environment. Before the developed model was put into use, the 49-dataset available for modeling was divided into two sets (training and testing sets), and 38 (77.5%) dataset was used as training. However, 22.5% of the data points (also known as the testing set) were used to test the model's performance using three performance-measuring tools. The tools are correlation coefficient (R\({}^{2}\)), root mean square error (RMSE), and mean absolute percentage error (MAPE).  
The input variables for the experiment are Dose (g), pH, Conc (mg/kg), Time (hrs), and Temp (C). However, we believe that the dataset used in this study has some limitations. The limitations involve the use of limited though common variables (5 parameters) as input to develop the models. Therefore, the model will only be valid to predict experiments that involve only these variables. Nevertheless, the limitations do not affect the performance of the model.  
The data correlation matrix shows the associations between each pair of variables, and the result is presented in Figure 4. The correlation between any two input variables ranges from \(-\)0.201 to 0.676. These low values indicate that the input variables are unrelated.
## 5 Prestress Loss Tests
### CWI Analysis Results
#### 5.3.1 Max. CC Values  
Figure 19 presents the max. _CC_ values on both the south and north sides of the bridge. The low-frequency wave propagation distances were 6 m across two Station pairs (1-2 on the south span and 3-4 on the north span), while the ultrasonic wave propagation distance was 1 m in the mid-span areas between A-B and D-E on both sides. Additionally, the low-frequency wave propagation distance of 18 m across Station pairs 1 and 4, covering most of the bridge structure, was also compared.  
The bridge temperature did not significantly affect the max. _CC_ values during short-term measurements. This conclusion is based on comparing the bridge temperatures between tests #1 to #5 and tests #7 to #11. Overall, there is a clear correlation between the max. _CC_ values and changes in prestressing: higher prestress levels correspond to higher max. _CC_ values. Assuming that existing cracks in the bridge under lower prestress levels were opened or extended, such structural changes lead to lower max. _CC_ values. While the quantification of cracks falls beyond the scope of this study, we acknowledge the potential of integrating our approach with surface Lamb wave techniques in future  
Figure 17: The change ratio (\(f/f_{ref}\)) of the first three modal frequencies.  
Figure 18: The impulse responses obtained from the stacked and normalized causal parts of the 40 CCFs on the south span in tests #1 and #2.  
research. Notably, [PERSON] et al. demonstrated the effectiveness of fundamental Lamb modes in detecting the depth of surface-breaking cracks [42], highlighting a promising direction for extending our methodology.  
The changes in the max. _CC_ values due to varying prestressing forces were significantly different depending on the wave propagation distances. The max. _CC_ value was more sensitive to shorter distances than longer ones. Based on this, the range of max. _CC_ values at the north mid-span over 1 m are similar to those over a distance of 6 m at the north span. This implies that the damage across the north span is uniform. On the other hand, at the south mid-span, the range of max. _CC_ values over 1 m are much lower than that over a distance of 6 m at the south span. This implies that the south span has a concentrated damage area in the 1-m mid-span.  
In summary, the max. _CC_ values can help localize damage but not quantify the damage level.
## 2 Materials and Methods
### Hybrid AI Models and Evaluation Metrics
#### 2.5.3 Statistical Accuracy Assessment  
In [32], the authors introduced a modified version of the performance score to rank the effectiveness of the applied ML and hybrid IFS-ML models. The performance score (\(\varphi\)), defined by Equation (2), is used for this evaluation, where higher values of \(\varphi\) indicate poorer model performance.  
\[\varphi\ =\ \text{rank}(\text{MBE})+\text{rank}(\text{RMSE})+\text{rank}( \text{MAPE})+\text{rank}(\sigma)+\text{rank}(\text{R}^{2}), \tag{2}\]  
The performance indicators in Equation (2) include the Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), standard deviation (\(\sigma\)), and the Coefficient of Determination (\(\text{R}^{2}\)). These indicators are detailed in Equations (3)-(7).  
\[\text{MBE}\ =\ \frac{1}{\text{K}}\text{$\sum$}\Big{(}\text{v}_{\text{p}}^{ \text{i}}-\text{v}_{\text{m}}^{\text{i}}\Big{)}, \tag{3}\]  
\[\text{RMSE}\ =\ \bigg{(}\frac{1}{\text{K}}\text{$\sum$}\Big{(}\text{v}_{ \text{p}}^{\text{i}}-\text{v}_{\text{m}}^{\text{i}}\Big{)}^{2}\bigg{)}^{\frac{ 1}{2}}, \tag{4}\]  
\[\sigma\ =\ \Bigg{[}\frac{\text{K}\Big{(}\text{RMSE}^{2}-\text{MBE}^{2} \Big{)}}{(\text{K}-1)}\Bigg{]}^{\frac{1}{2}}, \tag{5}\]  
Figure 3: Flowchart for the employed IFS method [31].  
\[\text{MAPE} = \frac{100}{\text{K}}\sum\Bigg{|}\frac{\left(\text{v}_{\text{p}}^{ \text{i}}-\text{v}_{\text{m}}^{\text{i}}\right)}{\text{v}_{\text{m}}^{\text{i}}} \Bigg{|}, \tag{6}\] \[\text{R}^{2} = 1-\frac{\sum\left(\text{v}_{\text{p}}^{\text{i}}-\text{v}_{\text {m}}^{\text{i}}\right)^{2}}{\sum\left(\text{v}_{\text{m}}^{\text{i}}-\overline {\text{v}_{\text{m}}}\right)^{2}}, \tag{7}\]  
Here, \(\text{v}_{\text{p}}^{l}\), \(\text{v}_{\text{m}}^{l}\), and \(\overline{v}\) represent the ith predicted value, the ith measured value, and the mean value, respectively, while K denotes the total number of measurements.  
Hence, this paper only gives brief information for each statistical indicator, and the readers are referred to [30] for more details.
## 4 Experiment and Analysis
### Implementation Details  
The segmentation labels of the MICCAI dataset are multi-atlas, including 15 different labels, such as amygdala, caudate, hippocampus, etc. However, in this work, only the labels of the hippocampus are useful. Thus, the segmentation labels of the hippocampus are first separated from the multi-atlas MRI images of the MICCAI. The processed MICCAI labels are displayed in Figure 8.  
To reduce computation and conserve resources, the MICCAI dataset and the HarP dataset are cropped to 64 \(\times\) 64 \(\times\) 96 pixels with the hippocampus preserved. Because the datasets are too small, some commonly used data augmentation strategies that do not cause MRI resolution change or MRI distortion, such as random flipping and random rotation, are used to expand the two datasets. Random flipping makes the model learn hippocampus features in a broader direction, and random rotation improves the recognition ability of the model for the hippocampus at different angles. The HarP dataset is expanded from 135 groups to 540 groups. Among them, 400 groups are used for training, and 140 groups are used for validation. The MICCAI dataset is expanded from 35 groups to 140 groups. Among them, 100 groups are used for training, and 40 groups are used for validation.  
In this research, the dice loss and the binary cross-entropy loss are combined for the loss function.The Adam optimizer with a weight decay of 0.0001 is utilized. The model is trained multiple times with different values of hyperparameters, and the results of each  
Figure 8: Three different dimensional slices of the hippocampus segmentation labels on the MICCAI dataset.  
training step are recorded. Finally, the hyperparameter settings with the best performance are obtained. The spatial dropout rate is 0.1, and the early stopping epoch is set to 20. The hyperparameters used in the HarP experiment include a learning rate of 0.01, a batch size of 16, and a total of 50 epochs. The learning rate for the MICCAI dataset is set to 0.005, the batch size is set to 4, and the number of epochs is set to 50. The proposed CADyUNet is based on Pytorch, and all experiments in this research were performed on two NVIDIA Tesla GPUs, each with 14.8 GB of memory.
## 2 Materials and Methods
### Characterization  
X-ray powder diffraction patterns were obtained by a Rigaku D/MAX-2000 H rotating anode diffractometer (CuK\(\alpha\) radiation) equipped with the secondary pyrolytic graphite monochromator operated at 40 kV and 80 mA over the 20 collection range of 10-80\({}^{\circ}\). The scan rate was 0.05\({}^{\circ}\) s\({}^{-1}\). The average crystallite size (D in nm) of nanorods was calculated from the line broadening of the X-ray diffraction peak according to the following Scherrer formula:  
\[\mathrm{D}=\mathrm{k}\lambda/\beta\mathrm{cos}\theta \tag{1}\]  
where k is the Scherrer constant (\(-0.9\)), \(\lambda\) is the wavelength of the X-ray radiation (1.54 A for CuK\(\alpha\)), \(\beta\) is the full width at half maximum (FWHM) of the diffraction peak measured at 20, and \(\theta\) is the Bragg angle. The morphology and microstructure analyses were performed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) on a JSM-6390 LV microscope. A conventional and transmission electron microscopy (HR-TEM) were used, using a LaB6 JEOL 2100 electron microscope, operating at an accelerating voltage of 200 kV. All the images were captured by the Gatan ORIUSTM SC 1000 CCD camera. For the purposes of the TEM analysis, a drop of a diluted samples with ethanol solution was deposited onto a carbon-coated copper TEM grid and the ethanol was allowed to evaporate. The UV-Vis spectra were measured by a Perkin-Elmer Lambda 950 UV-VIS-NIR Spectrometer. The photoluminescence (PL) emission spectra were recorded using a Perkin-Elmer Luminescence Spectrometer LS 50 B. The samples were excited with 330 nm wavelength light at room temperature and the emission was scanned between 300 and 800 nm. FT-IR spectra were recorded on a Thermo-Electron Nicolet 6700 FTIR optical spectrometer with a DTGS KBr detector at a resolution of 2 cm\({}^{-1}\). Raman measurements were performed at room temperature using a Nicolet Almega XR Raman spectrometer with a 473 nm blue laser as an excitation source. The laser power was 15 mW and the beam was focused on the sample through a confocal microscope equipped with a 50\(\times\) objective.  
Figure 1: Formation mechanism of metal titanate nanorods ATIO\({}_{3}\) (A = Mg, Co, Ni, Zn).
## 5 Conclusions  
This paper presents the conclusions drawn from model tests conducted on grouting and static load to study the single pile-bearing capacity under conditions of unloading and different grouting parameters. The main findings are summarized as follows:  
1. Increasing the grouting volume has a favorable impact on the ultimate bearing capacity for both pile tip, pile side, and combined grouting piles. Pile side grouting is more beneficial to control pile settlement. From a grouting efficiency perspective, combined grouting demonstrates superior reinforcement effects compared to the other two grouting methods. The influence of different grouting pressures on bearing characteristics is not significantly apparent.
2. Following grouting, a lateral friction resistance enhancement region is formed near the grouting outlet, extending to a certain height. Within this region, the pile's lateral friction resistance shows a negative correlation with the distance from the grouting outlet. Both pile lateral and tip resistance exhibit a positive correlation with the grouting volume.
3. Post-grouting results in the formation of a grout bubble at the outlet, with a compact diffusion zone on the inside and a split diffusion zone on the outside. The size of the grout bubble demonstrates a positive correlation with the grouting volume. Another portion of the grout diffuses vertically along the pile-soil interface from the grouting outlet, creating a reinforcement zone. Combined grouting piles exhibit the widest range of grouting fluid diffusion, enabling the connection of reinforcement areas at the pile tip and pile side. As grouting pressure increases from 0.5 MPa to 0.6 MPa and 0.7 MPa, the grout return height rises from 6.5 D to 7.4 D and 9.8 D. However, the relationship between pile side grouting height and pressure is less pronounced due to considerations of the unloading effect.
4. Two primary modes of pile-soil failure are observed. The first mode involves failure either within or outside the split diffusion zone of the grout body, typically occurring near the outlet. The second mode of failure occurs between the pile and the grout, generally at a location away from the grouting outlet. The ultimate pile lateral friction resistance under the first failure mode is significantly higher than that under the second failure mode.  
Conceptualization, J.Z. and C.Z.; methodology, J.Z. and Y.W.; software, J.Z.; validation, J.Z., C.Z., and Y.W.; formal analysis, J.Z.; investigation, J.Z.; resources, C.Z.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z., C.Z., and Y.W.; visualization, J.Z.; supervision, J.Z. and C.Z.; project administration, J.Z.; funding acquisition, C.Z. and Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Nos. 42377151), the Natural Science Foundation of Chongqing (CSTB2023 NSCQ-BHX0149), and the China Postdoctoral Science Foundation (2023 MD734112). Not applicable.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 3 Modified Rytov Theory for Gaussian Beam Waves
### Finite Inner and Finite Outer Scale Model (Modified Atmospheric Spectrum)
Equations (7)-(24) are employed in the calculation of the scintillation index values in the presence of small scale sized and large scale sized eddies. These values range from \(\sim\)0.083 to \(\sim\)0.936 based on the periods not exceeded 50%, 99%, 99.9%, and 99.99% of the time. It should be noted that the outer scale of turbulence has little effect on scintillation, it is the inner scale of turbulence that controls scintillation levels. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multirow{2}{*}{ \begin{tabular}{c} **Ground Wind** \\ **Speed (m/s)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **RMS Wind** \\ **Speed (m/s)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\(C_{n}^{2}\)** \\ **(m\({}^{-2/3}\))** \\ \end{tabular}  } & \multicolumn{6}{c}{**Propagation Length of 2 km**} \\  & & & & & **Zero Inner Scale and** & & & **Finite Inner and Finite** \\  & & & & & **Infinite Outer Scale Model** & & & **Outer Scale Model** \\  & & & & & & **Infinite Outer Scale Model** & & & \\  & & & & & & **Infinite Outer Scale Model** & & & \\  & & & &
## Evaluation and Results
### Random Network Formation  
#### 5.5.1 Significant Node Variance  
In the previous series of results, utilising the grid topology to be found in Figure 1, no matter the interference range of the nodes, the significant nodes were unchanged throughout. With the random topology, which can be seen in Figure 2, this is not the case. As the interference range is reduced as in the previous tests, as does the number of significant nodes reduce. This a result of some now being out of range of the circular path of the MSN as the interference range decreases. Results are still given in reference to significant nodes, however, which nodes are of significance for each scenario can be seen in Table 2.
## 3 Pathway Metabolic Engineering Strategies to Improve Glycerol-Based Bioprocesses
### Polyphenols  
Glycerol-based production of polyphenols such as resveratrol, a plant secondary metabolite with multiple health-beneficial properties, has also been studied using _Y. lipolytica_. However, the microbial production process requires laborious engineering to reach commercially viable levels. [PERSON] et al., constructed resveratrol producing _Y. lipolytica_ strains by overexpressing bacterial and plant-derived resveratrol biosynthesis genes. Resveratrol is produced either via tyrosine or phenylalanine; therefore, they tested both pathways. They reported that using glucose media, the phenylalanine pathway resulted in slightly higher production, although after supplementation with amino acids, the best producing strain was the one bearing two copies of the tyrosine pathway, reaching 0.085 g L\({}^{-1}\). Using glycerol instead of glucose, the highest titer (0.104 g L\({}^{-1}\)) was achieved with the engineered strain expressing both biosynthetic pathways. The strain with the best performance was then evaluated in a bioreactor where a production of 0.43 g L\({}^{-1}\) was reached, suggesting that _Y. lipolytica_ is a promising host for resveratrol production from glycerol [131]. In another study, the potential of _Y. lipolytica_ to produce not only resveratrol but also other sublimimate pathway-derived metabolites (_p_-cournaric acid, _cis_, _cis_-muconic acid, and salicylic acid) was explored. In this work, medium optimization was done using mineral medium, YNB and YP with either glucose (20/80 g L\({}^{-1}\)) or glycerol (20/80 g L\({}^{-1}\)) as carbon source. For all types of media and concentrations, when 80 g L\({}^{-1}\) of carbon source were added, only ca. 15-25 g L\({}^{-1}\) were consumed after 96 h cultivation, and glucose performed better than glycerol. Interestingly, when comparing media containing the same carbon source, cultivation in a mineral medium yielded 33-181% higher titers than in the complex YNB medium. In a small-scale cultivation, the _Y. lipolytica_ strain expressing a heterologous pathway produced 52.1 mg L\({}^{-1}\) resveratrol, whereas the titer increased to 409 mg L\({}^{-1}\) when the strain was further engineered with feedback-insensitive alleles of key genes in the shikimate pathway and with five additional copies of the heterologous biosynthetic genes. In controlled fed-batch bioreactor, the strain produced 12.4 g L\({}^{-1}\) resveratrol, the highest reported titer to date for _de novo_ resveratrol production, showing that _Y. lipolytica_ is an attractive host organism for the production of shikimate pathway-derived metabolites [132].
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Hazard
#### 2.3.1 Straight-Line Distance to the River  
The direct distance from a building to the nearest river serves as a key metric for assessing flood risk. Structures located near rivers face a higher likelihood of external flooding during a flood event, thereby elevating the risk of physical damage. To quantify this distance, a GIS analysis was performed to compute the shortest straight-line path from each building to the closest river. Employing this approach enables a more accurate evaluation of the potential for external flooding based on the building's geographic position.
###### Abstract  
The main characteristic of a reflective semiconductor optical amplifier chip (RSOA) is that it does not generate optical resonance under electric pumping and maintains the operation state of spontaneous emission. In this paper, a Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\)/Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) (four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\)) film system is employed as the coating material for the output facet of the RSOA. The 3 dB spectral width of the spontaneous emission spectrum from this RSOA reaches 79.4 nm, with a ripple of less than 1 dB occurring across this wavelength range. Notably, around the 1550 nm wavelength, the ripple is as low as 0.5 dB. This represents the best performance reported for this type of chip. The RSOA is packaged as a narrow-linewidth external cavity laser. Under test conditions of 25 \({}^{\circ}\)C and 180 mA, the external cavity laser produces an output power of 12.6 mW and achieves a linewidth of 299.8 Hz. Furthermore, by adjusting the Fabry-Perot (FP) standard cavity, filtering, and other external cavity parameters, the lasing spectrum of the narrow-linewidth external cavity laser based on the RSOA is tunable across a wavelength range from 1535.83 nm to 1561.42 nm, which shows the usability of the proposed ROSA for a C-band external cavity narrow-linewidth laser.  
RSOA; narrow-linewidth external cavity laser; ripple 2025  
27 January 2025  
Revised: 20 February 2025  
Accepted: 21 February 2025  
Published: 25 February 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Reflective Semiconductor Optical Amplifier Chip with Low Ripple for C-Band External Cavity Narrow-Linewidth Laser. _Pluonics_**2025,** 12, 193  
[[https://doi.org/10.3390/photonics12030193](https://doi.org/10.3390/photonics12030193)]([https://doi.org/10.3390/photonics12030193](https://doi.org/10.3390/photonics12030193))  
**Correction Statement:** This article has been republished with a minor change. The change does not affect the scientific content of the article and further details are available within the backscatter of the website version of this article.  
**Copyright:** (c) 2025 by the authors.  
Lioonsee MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).