esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Random Fiber Lasers and Their Sensing Application
## 2 RFLs Based on Various Optical Fibers
### AOF-Based RFLs  
Unlike SBS or SRS in optical fibers, rare-earth ions are real luminescent centers and can provide a greater gain for optical fibers. Therefore, the RFLs based on AOF can effectively reduce the threshold of the laser and shorten the length of the fiber. It constitutes an indispensable part of the field of the RFLs.  
The erbium-doped RFL is the most common AOF-based RFL [83; 84; 85; 86] because erbium ion has a corresponding energy level around at the wavelength of 1550 nm and a wide gain spectrum. In 2009, a low-threshold (3 mW) erbium-doped RFL was prepared by writing random grating in the erbium-doped fiber (EDF) [84]. In 2016, stable mode output of the erbium-doped RFL was performed  
Figure 3: Configuration of random fiber F-P resonator-based Brillouin random fiber laser (RFL).  
Figure 2: Principle scheme of random distributed feedback fiber laser operation.  
by heating FBGs at specific locations in EDF and changing the location of the hot spots, and schematic diagram of the experimental setup of the RFL based on EDF is shown in Figure 4[85]. In 2018, lasing with the line-width less than 10 kHz was obtained by adding EDF into the Brillouin RFL [86]. The length of EDF used in the RFL is shorter than that of SMF.  
Research of the erbium-doped RFL focuses on the C-band, while additional element-doped RFLs further extend the range of the optical band. For example, as the gain spectrum of ytterbium ion is relatively wide at the wavelength of 1060 nm, super-continuous (SC) spectrum from the visible to the near-infrared area was realized [58]. Figure 5 shows the experimental setup of the SC source. 1 km germanium-doped fiber (GDF) and 5 m ytterbium-doped fiber (YDF) are the hybrid gain medium, and 1 km GDF is the random feedback medium. Two 976 nm diodes are pump source with the power of 21.8 W and 22.9 W, respectively. The optical reflector provides all-optical feedback with the central wavelength of 1060 nm and the bandwidth of 40 nm, which could enhance the output of the SC spectrum. Moreover, the laser at the wavelength of 2000 nm was generated in 2016, in which an RFL at the center wavelength of 1173 nm was used to pump Tm-doped fiber [87]. There was no ASE emission or parasitic oscillation in the experimental results, due to the 1173 nm random distributed feedback fiber laser.
# A Comprehensive Survey on Knowledge-Defined Networking
## 3 Knowledge Plane
### Knowledge Generation Plane  
The knowledge generation plane extracts descriptive knowledge from raw data using a knowledge generation model that uses a learning technique that can be either a heuristic model-based method or a machine learning method. Knowledge generation effectively reduces the volume of information transmitted to the controller, as the descriptive knowledge produced from a large volume of data is much smaller. Different types of data will be  
Figure 3: Architecture of the knowledge plane and its interaction with the network.  
collected by the management and control planes, as is discussed in Sections 4.5.2 and 5.4.1, respectively, and can be fed as input for knowledge generation. For example, the input data to the knowledge creation plane can be the data traffic received at the controller, whereas the output knowledge can be the traffic class.  
The generated knowledge can be modeled using the Resource Description Framework (RDF) knowledge representation language. RDF represents knowledge as a set of triplets, which consist of a subject, predicate, and object. In RDF, subjects and objects are identified by Uniform Resource Identifiers (URIs), and predicates represent relationships between them. This structure makes RDF a flexible and extensible knowledge representation model that can represent diverse types of knowledge [64].
## 5 Experimental Tests
### Flight Tests and Discussions  
In this subsection, the positive effect of the proposed adaptive rotational speed control strategy on the flight performance of the MAV is comparatively demonstrated by a series of flight tests. The control scheme for the flight tests is shown in Figure 10, where we employ the existing adaptive controllers in [2] or PID controllers in the altitude loops, and the PID controller with or without the closed-loop rotational speed control structure in the altitude loop. For the MAV, the control software runs on the DSP platform, and the sensor units mounted on the top of the fuselage are provided with the measured states of the MAV. In order to ensure the flight test safety, the following steps are conducted: (i) vertical take-off to 20 m above the ground; (ii) holding altitude, as shown in Figure 11. During flight, the serial-based data links provide a link to the ground station computer that allows monitoring the real-time flight information and uploading remote control commands such as the altitude command signals from the manual control unit.  
Figure 10: The control scheme.  
Figure 14: The experimental results using the PID controllers in the altitude loops, and the PID controller without the closed-loop rotational speed control structure in the altitude loop: (**a**) \(\omega\); (**b**) \(h\); (**c**) \(\theta_{c}\) and \(\theta\); (**d**) \(e_{\theta}\).  
Figure 13: The experimental results using the existing adaptive controllers in the altitude loops, and the PID controller with the closed-loop rotational speed control structure in the altitude loop: (**a**) \(\omega\); (**b**) \(h_{t}\); (**c**) \(\theta_{c}\) and \(\theta\); (**d**) \(e_{\theta}\).  
It is finally noted that from the comparison of the experimental results, the proposed adaptive rotational speed control scheme can directly and significantly improve the control quality of the rotational speed of the propeller, which further results in the improvement of the control qualities of the flight height and altitude. It is therefore demonstrated both theoretically and experimentally that the proposed control scheme is effective and essential to further improving the overall flight performance of such MAVs.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 3 System Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(U_{i}\) & The \(i^{th}\) UAV \\ \hline \(V_{i}\) & The \(i^{th}\) vehicle \\ \hline \(K_{i}\) & The \(i^{th}\) task \\ \hline \(O_{i,V_{j}}\) & If \(V_{j}\) is the owner of \(K_{i}\), 1; otherwise, 0 \\ \hline \(\alpha_{i}\) & The priority of \(K_{i}\) \\ \hline \(D_{i}\) & The penalty point of \(K_{i}\) \\ \hline \(w_{i}\) & The weighted penalty point of \(K_{i}\) \\ \hline \(C_{i}\) & If \(K_{i}\) is completed, 1; otherwise, 0 \\ \hline \(p_{heavy}\) & The probability of the task offloaded to the cloud if the pool is heavily occupied \\ \hline \(p_{medium}\) & The probability of the task offloaded to the cloud if the pool is moderately occupied \\ \hline \(p_{light}\) & The probability of the task offloaded to the cloud if the pool is lightly occupied \\ \hline \(S_{K_{i}}\) & The size of \(K_{i}\) \\ \hline \(P_{i}\) & The \(i^{th}\) processor \\ \hline \(\beta_{i}\) & The processing speed of \(P_{i}\) \\ \hline \(\beta_{cloud}\) & The processing speed of cloud \\ \hline \(S_{Q_{i}}\) & The queue size of \(P_{i}\) \\ \hline \(Q_{i}\) & The queue of \(P_{i}\) \\ \hline \(N_{U}\) & The number of UAVs \\ \hline \(N_{K}\) & The number of tasks \\ \hline \(N_{P}\) & The number of processors \\ \hline \(z\) & The extra penalty point if the task deadline is missed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations in system modeling. location of vehicles, the orchestrator application listens for ARP packets. Additionally, access points notify the controller of vehicle departure or disconnection events at their associated stations periodically. The tasks created by the vehicles have an exponentially distributed inter-arrival time with rate \(\lambda\). If the task pool is not empty when a new task arrives, it may be offloaded/migrated to the cloud processing server with a probability based on the task pool's current occupancy at that time. Essentially, we consider minimizing average prioritization-based weighted penalty points for task offloading management as a QoS key performance indicator and thus propose algorithms to achieve that objective.
## 2 Materials and Methods
### Synthesis of Objects
#### 2.3.3 **MR@Org** and **MROrg@POMs** Synthesis [52]  
**MR@Org** synthesis: **MR** (1.4-1.6 mmol Cl/g, 4 g) was stirred with 1-methylimidazole (0.048 mol, 4 g) or pyridine (0.05 mol, 4 g) in DMF (20 mL) at 80 \({}^{\circ}\)C for 24 h, followed by being washed with dichloromethane to remove unreacted organic compounds. **MR@Imd** (4 g) and **MR@Pyr** (4 g) were obtained as a pale beige powder.  
**MR@Pyr**: IR (ATR, v(cm\({}^{-1}\))): 3050-3100 (C-H), 2910 (C-H), 1630 (C-N), 1484 (C=C), 700-800 (C-H aromatic). E.A: C: 40.27%, H: 8.8%, N: 2.26%.  
**MR@Imd**: IR (ATR, v(cm\({}^{-1}\))): 3050-3100 (C-H), 2910 (C-H), 1616 (N-H), 1332 (C-N) 1155 (C-H). E.A: C: 39.86%, H: 8.71%, N: 4.25%.  
**MROrg@POMs** synthesis: The functionalized **MR@Org** was grafted with POMs by mixing them in an aqueous solution.  
For **MRImd@PMo\({}_{12}\)O\({}_{40}\)** and **MRPyr@PMo\({}_{12}\)O\({}_{40}\)**, 2 g of **MR@Imd** or For **MRImd@PMo11 VO40** and **MRPyr@PMo11 VO40**, (2 g) of **MR@Imd** or **MR@Pyr** was mixed with 1.8 g of **H4 PMo11 VO40** (0.8 mmol) then filtered, washed with water and dichloromethane and finally dried under vacuum.  
IR (ATR, \(\
u\)(cm\({}^{-1}\))) for all grafted POMs: 1055-1100 (P-O), 1000-900 (M-O), 850-700 (M-O-M).  
**MRIMd@PMo12O40**: Olive-green color. EA: C: 33.13%, H: 2.63%, N: 3.49%, TGA (0-600 \({}^{\circ}\)C): 81%. \({}^{1}\)H MAS: 7.29, 3.37, 1.1. \({}^{13}\)C MAS: 146.14, 136.27, 128.74, 53.19, 45.56, 40.11, 36.77. \({}^{31}\)P MAS: \(-\)0.23, \(-\)1.95, \(-\)4.0 (major).  
**MRIMd@PMo11 VO40**: Sepia color. EA: C: 38.55%, H: 3.3%, N: 4.1%, TGA (0-600 \({}^{\circ}\)C): 80%. \({}^{1}\)H MAS: 7.25, 3.89, 1.13. \({}^{13}\)C MAS: 145.81, 128.50, 53.05, 43.89, 40.11, 36.53. \({}^{31}\)P MAS: \(-\)4.28.  
**MRPyr@PMo12O40**: Olive-green color. EA: C: 31.74%, H: 1.75%, N: 1.78%, TGA (0-600 \({}^{\circ}\)C): 77.8%. \({}^{1}\)H MAS: 8.12, 3.77. \({}^{13}\)C MAS: 144, 128.96, 64.82, 44.76, 40.27, 9.40. \({}^{31}\)P MAS: \(-\)0.22, \(-\)2.40, \(-\)4.1 (major).  
**MRPyr@PMo11 VO40**: Sepia color. EA: C: 37.02%, H: 2.39%, N: 2.04%, TGA (0-600 \({}^{\circ}\)C): 76.6%. \({}^{1}\)H MAS: 7.99, 3.55. \({}^{13}\)C MAS: 144.53, 128.98, 64.9, 43.93, 39.96, 9.44. \({}^{31}\)P MAS: \(-\)4.46.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 4 Discussion
As such, it represents a zoning of the hydrological risk of the urban areas that have been affected by the flood events under study, and provides an indication of risk should events of similar magnitude affect Lima again in the future. Some limitations were found during the study with regard to the following aspects:  
* The quality of archive satellite imagery available for the area was uneven. In addition to the ERS-1/2 scenes used for the analysis (see Section 2.2.1), 29 more ERS-1 images were downloaded from the EOLi-SA catalogue and pre-processed but, in the end, they could not be used due to issues affecting the raw data and their geolocation (due to Doppler Centroid values >2000 Hz), as reported by ESA's EO Help team. These images were not included in the input dataset catalogue, and therefore the amount of archive data that could be incorporated in the change detection analysis was reduced. * Information gaps (missing values) were found in the DEM obtained by the Shuttle Radar Topography Mission (SRTM; see Section 2.2.4). For the case of Lima, data from the ASTER Global DEM (GDEM v.2) was used to address this problem. Alternatively, to remedy the lack of information in the SRTM DEM, measurements taken directly in the field in areas of uncertainty, or by interpolation of level curves, could have been used, but this was outside the scope of the present research. * The lack of ancillary information with which to carry out a complete analysis. Specifically, the absence of updated information on urban land cover was a limit that had to be overcome at the beginning of this research by creating an _ad hoc_ spatial layer through the union of data from the Global Urban Footprint (GUF) and manual digitization of new urban areas, the latter based on very high-resolution optical satellite images accessed through the basemap service of QGIS and Google Earth. * The nominal revisit time of the SAR satellites used for this study, i.e., 35 days for the ERS-1/2 and 12 days for the Sentinel-1 (because since October 2016 the area is surveyed by Sentinel-1B satellite only, according to the constellation observation scenario currently operating [63]), which did not allow for acquiring data on the specific days when the flood events occurred, or with shorter site revisits. However, the multi-temporal approach based on multiple pre-, cross- and post-event SAR pairs has increased the possibility of identifying changes relevant to the analysis. * Both natural and anthropogenic changes have been identified in the SAR images used for this study. Therefore, an expert-led interpretation operation was needed to correctly classify the observed changes into the different categories of \"Flooding\" (F) and \"No Flooding\" (NF) and, therefore, to identify the occurred variations in the urban fabric that had some relevance for the purposes of flood risk zoning.
## 3 Results and Discussion
### Hydrogen Storage Measurements
The incremental wt.% in the hydrogen storage was calculated by the following mathematical equation: (Hydrogen storage after treatment or decoration--Hydrogen storage before treatment or decoration) \(\times\) 100/(Hydrogen storage before treatment)  
At cryogenic temperature, ammonia treatment improved the maximum hydrogen storage by an average of \(\sim\)4.0 wt.% for all treated samples. At the same temperature, the decoration correspondingly decreased the hydrogen storage by \(\sim\)8.0 wt.% for most of the decorated metals other than palladium. The lower hydrogen uptake of the transition metal-decorated samples suggested that the spillover effect at this temperature was negligible in comparison to the effect of blocking the pore by metal nanoparticles and their additional mass. Such result is reliable with the reported negative effect of metal decoration at cryogenic temperatures [28; 67]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hydrogen (H\({}_{2}\)) Excess Capacity (wt.\%)**} \\ \cline{2-6}
**Sample** & \multicolumn{2}{c}{**Incremental wt.\% after Ammonia Treatment at 77 k**} & \multicolumn{2}{c}{**Incremental wt.\% after Metal Decoration at 298 K**} & \multicolumn{2}{c}{**Incremental wt.\% after Ammonia Treatment at 298 K**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Treatment wt.\%**} & \multicolumn{2}{c}{**Decoration at 298 K**} & \multicolumn{2}{c}{**Incremental wt.\%**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Treatment wt.\%**} & \multicolumn{2}{c}{**Decoration at 298 K**} & \multicolumn{2}{c}{**Treatment at 298 K**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Treatment wt. At room temperature, the small hydrogen storage capacities could be ascribed to the low enthalpy of the adsorption of hydrogen at this temperature [28; 59]. At this temperature, ammonia treatment resulted in hydrogen storage enhancement of ~57 wt.% for the pristine AC, and between 8-42 wt.% for the metal-decorated ones. At the same temperature, metal decoration improved the hydrogen storage capacity of the pristine AC between 24-105 wt.% and between 12-48 wt.% for the ammonia-treated AC, suggesting that the spillover worked well at room temperature with the highest contribution belonging to iridium and palladium. No hysteresis, especially at cryogenic temperature below the experimental error, was observed, confirming the total reversibility of the hydrogen storage when removing the applied pressure. This reversibility was free of any storage capacity loss after many cycles, and without performing any adsorbent treatment between the cycles. Minor hysteresis may be observed, with complete desorption, confirming no hydride formations of the initially adsorbed hydrogen at ambient pressure due to the adsorption metastability and/or network effects at ambient temperatures [102].
## 3 Methods and Materials
### Pre-Processing
#### 3.2.1 sMRI Pre-Processing and Features Extraction  
For sMRI pre-processing and feature extraction, the well-known Recon-all pipeline from FreeSurfer V.5.3.0 software was employed. This pipeline addresses the challenges associated with developing and implementing pre-processing stages for neuroimaging data [40]. It improves the visual quality of the images, eliminates inter-subject variability caused by data gathering methods and artifacts, and enhances the reproducibility of the study [5; 41]. Recon-all consists of 31 steps for surface-based analysis and volumetric segmentation, including intensity normalization, skull-stripping, brain segmentation, region labeling based on Desikan-Killiany (DK) atlas, surface amplification, spheroid atlas scoring, and cortical surface parcellation [40]. These steps are illustrated in Figure 3.  
When executed on a personal computer, the Recon-all process generates substantial analytical volumes (approximately 400 MB) and takes around 10-20 h to complete. However, utilizing high-performance computers can significantly improve the performance of ML applications by efficiently managing large datasets and reducing computation time and resource demands [42]. To expedite the Recon-all pipeline, we utilized the Aziz Supercomputer, which allowed us to process data independently for each subject and in parallel, with \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **ASD\%** & **Male\%** & **Age (years)** & **Total Participants** \\ \hline ABIDE I & 47.7 & 82.7 & 6.4–10.9 & 220 \\ ABIDE II & 44.7 & 69.4 & 5.1–10.9 & 418 \\ KAU & 57.6 & 66.7 & 5.4–10.8 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Demographics of KAU, ABIDE I, and ABIDE II participants. 24 subjects using 24 CPUs. After completing all steps, the volume of subcortical structures (n = 66), surface area (SA) (n = 70), CTs (n = 70), mean curvatures (n = 68), and volumes (n = 68) of cortical structures were calculated. Descriptions of these morphological features can be found in Table 3 and further elaborated in Table S1 in the Supplementary Materials. Furthermore, Figure S1 in the Supplementary Material represents the difference between cortical and subcortical regions in the DK atlas.
# Optimizing Geospatial Data for ML/CV Applications:
## 1 Introduction
### Scope  
This study aims to address the lack of automation in data acquisition and processing, as well as introduce a concept for dataset optimization by filtering non-essential information, as presented in Figure 2. We present a Python-based method designed to address these optimization challenges and streamline geospatial data processing for road networks. The proposed solution leverages publicly available libraries and data sources, automating tasks such as data retrieval, tiling, masking, and image optimization. The focus is on extracting road parcels and eliminating non-essential information to create more efficient datasets.  
Figure 2: Highway aerial image clipped to the parcel boundary.  
This study addresses the primary challenge in satellite image processing for ML/CV applications: raw, high-resolution datasets are often too large and complex, leading to inefficient processing and higher computational demands. Our research is guided by the need to optimize these datasets by minimizing irrelevant information and structuring data for efficiency.
## 4 Model Results
### Parametric and Sensitivity Analyses  
In the quest to understand the influence of each separate input variable on the prediction of the punching shear strength utilizing the MSP-based method, a thorough sensitivity analysis was carried out. The sensitivity analysis comprehensive approach proved instrumental in uncovering the most significant factors that influenced the punching shear strength predictions. By systematically excluding input variables and analyzing the model performance, researchers were able to identify areas for improvement and refine the model  
Figure 10: Scatter plot of punching shear parameters vs. model inaccuracies.  
for more accurate results. The analysis involved methodically excluding individual input variables and using the remaining data for the development and evaluation of the predictive model. This approach enabled researchers to pinpoint the most significant factors that played a role in the prediction of the punching shear strength. Table 6 demonstrates the impact of individual input variables on the M5P model performance, considering the percentage changes in R\({}^{2}\), RMSE, and MAE values for both the training and testing sets when specific input variables are excluded. As anticipated, the most consequential variable among the inputs affecting the punching shear strength proved to be the effective depth of the slab (\(d\)). Excluding the effective depth (\(d\)) led to the most significant reductions in performance across all measures: R\({}^{2}\) decreased by 32.28% for the training set and 38.91% for the testing set, while RMSE increased by 252.7% and 218.0%, and MAE increased by 161.1% and 182.5% for the training and testing sets, respectively. These substantial reductions emphasize the crucial role of the effective depth of the slab (\(d\)) in the model. On the other hand, excluding the yield strength of reinforcement (\(f_{y}\)) had the least impact on performance: R\({}^{2}\) decreased by 1.87% for the training set and 2.18% for the testing set, while RMSE increased by 7.45% and 4.88%, and MAE increased by 0.77% and 2.12% for the training and testing sets, respectively. The relatively smaller changes in performance metrics indicate a lower influence of the yield strength of reinforcement (\(f_{y}\)) on the model compared to other variables. This consistency provided further insights into the relationship between the input variables and their influence on the prediction of the punching shear strength, thereby allowing researchers to better understand the underlying mechanisms and optimize the model accordingly.