esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Changes in Different LULC Products  
To analyse how the areas of different LULC types change with the scale, the area of each LULC type at each scale is calculated in this paper, yielding numerous area change trend charts (Figure 3). As shown in Figure 3, there are differences between the ESA and RES LULC data: the area of grassland (GL) exhibits the largest difference, with an average difference of 534,207.99 km\({}^{2}\), while that of construction land (CO) has the smallest difference, with an average difference of 936.13 km\({}^{2}\). With increasing scale, between the two LULC products, the differences in the area of woodland (WO), water bodies (WA), CO and wetland (WL) tend to decrease and the difference value decreases by 24,425.55 km\({}^{2}\), 4690.35 km\({}^{2}\), 97 km\({}^{2}\) and 3065.85 km\({}^{2}\), respectively, while those of GL and unused land (UL) tend to increase, and that of cropland (CL) first increases and then decreases, and the difference increased by 21,055.50 km\({}^{2}\) and 110,847.60 km\({}^{2}\), respectively. Moreover, the LULC types that display change trends of similar appearance with increasing scale include CL, WO, WA, CO and WL; among them, CL has an upward trend, while the others all have a downward trend. In addition, from the perspective of stability, as the scale changes, ESA CL and RES WA change the most, indicating that these two LULC types are not stable, whereas the change trends of the other LULC types are relatively stable. Figure 3h shows the overall area change trend of both datasets, revealing little variation in the overall area. With an increase in the scale, the overall land use area also increases, increasing by 1644.75 km\({}^{2}\); however, this accounts for only 0.06% of the total area, indicating that an increase in the scale has little influence on the overall area.  
The degree of LULC change was further analyzed. For this purpose, the dynamic degree of LULC K was calculated (Figure 4). The LULC data at spatial scales of 300 m and 3900 m were used. For RES, the change amplitude of CO is the largest with a K value of 52.01, while the change amplitude of GL is the smallest with a K value of 2.84. For ESA, CL has the largest change with a K value of 91.65, and WO has the smallest change with a K value of 1.86. In general, the scale appears to have a considerable influence on the areas of GL, CO and CL.  
Figure 2: Acquisition of the difference in EVs of LULC at different scales.  
Figure 3: The areas of CL (**a**), WO (**b**), GL (**c**), WA (**d**), CO (**e**), UL (**f**), and WL (**g**) and the overall areas of RES and ESA (**h**) at different scales.
## 4 Review of Microgrid Control and Design
### Tertiary Control Level  
The higher level of MGs control is called the tertiary control level and focuses on tasks such as economic optimization, energy management, and demand response programs. It operates on longer timescales (from minutes to hours) and interacts with the main grid for energy exchange information. Its objectives include minimizing energy costs, ensuring energy balance, and coordinating demand response [109]. Using optimization algorithms, decision-making tools, and communication protocols, it interacts with secondary and primary control layers to optimize system performance. The process involves data collection, processing, decision-making, command generation, and continuous monitoring for adaptation. The benefits include economic efficiency, improved energy management, optimized demand response, and resilient operation. Challenges include computational complexity, dealing with uncertainties, and effective communication. Future efforts aim to improve the level of tertiary control by developing more efficient algorithms, incorporating advanced forecasting techniques, and implementing distributed control strategies for scalability and resilience in environments with limited communications [110].
## 5 Simulation Experiments and Results Analysis
### Results and Analysis of the Experiment
#### 5.2.3 Improvement of Load Balance  
The definition of the load balance is as follows: Once service scheduling is completed, during the simulation of service processing within the cluster, if the resource availability in  
Figure 10: Comparison of delay reduction rate of scheduling. (**a**) Comparison of different architectures under the same subjective weighting method (BWM). (**b**) Comparison of this paper and CFN architecture under subjective weighting method (BWM) and objective weighting method (EWM).  
any dimension of a cluster falls below 10%, that cluster is considered to be in an overloaded state. The load balance is defined as the percentage of not overloaded clusters out of the total number of clusters allocated with requests.  
From Figure 11a, it can be seen that the load balancing performance of the proposed solution in this paper was the best. Initially, the CFN performed slightly worse than the DNS, but as the system gradually saturated, it approached a stable value that was better than the DNS. This paper enhanced the load balancing between clusters through the domain division mechanism and the stateful programmable data plane mechanism scheduling strategy. The DNS also provided a certain load balancing by weighted random selection, but in the CFN, the mechanism of selecting only a single optimal node made the load balancing rate the worst. It is noteworthy that in the CFN when the number of requests increases and approached saturation, the optimal node remained unchanged. Even after overload, no other clusters were selected, leading to a stable load balancing rate. In contrast, the DNS employed global random selection, causing the load balancing rate to continuously decline with the increase in requests, ultimately falling below that of the CFN.  
As can be seen from Figure 11b, under the architecture of this paper, when the number of requests was moderate, the difference between the two was not large. However, as the system requests increased further, the BWM demonstrated better adaptability and stability. That is because the EWM performed weighting based entirely on the data distribution. When the number of requests was small, and the cluster resources were sufficient, it performed slightly better than the BWM. However, the EWM required a large amount of data and was easily affected by extreme values. It was not suitable for the domain division mechanism in this paper. When the number of requests increased, the advantages of the BWM gradually became prominent. Consistent with the results of the first two experiments, the performance of the proposed architecture still surpassed that of the CFN in terms of the load balancing rate.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 3 USVs for Natural Disasters  
This section presents the current USV research focused on specific natural disasters, namely: Tsunamis & Earthquakes (T. & E., Section 3.1); Hurricanes (Section 3.2); Floods (Section 3.3); Landslides & Erosion Risks (L. & E., Section 3.4). Table 2 summarizes the surveyed papers that deal with the application of USVs to natural disasters, where _NR_ means that the authors did not report the field in the corresponding paper.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**USV Related Terms** & **Disaster-Related Terms** \\ \hline unmanned vessel, unmanned boat, autonomous vessel, autonomous boat, autonomous craft, unmanned craft, Unmanned Surface Vehicles (USVs), Autonomous Surface Vehicles (ASVs), Unmanned Marine Vehicles (UMVs), Unmanned Surface Crafts (USCs), Autonomous Surface Crafts (ASCs), Unmanned Surface Vessel, Autonomous Surface Vessel, Underactuated surface vessel, micro unmanned surface vessels \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of keywords used in research databases.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Task** & **Reference** & **Year** & **USV Name** & **USV Type** & **Test Location** \\ \hline \multirow{8}{*}{T. \& E.} & [53; 54] & 2012 & _NR_ & Buoy & Tonakai segment, Nankai Trough, Japan \\  & [55] & 2013 & _NR_ & Monohull vessel & Japan Trench \\  & [56] & 2015 & MERAID & Argo floats & Mediterranean Sea \\  & [57; 58] & 2016 & _NR_ & Wave Glider & _NR_ \\  & [59] & 2014 & _NR_ & Wave Glider & Gulf of Mexico and US eastern seabard \\  & [60] & 2016 & SV2 & Wave Glider & Loch Ness, Scotland \\ \hline \multirow{8}{*}{Hurricanes} & [61] & 2008 & AEOS-1 & Twin-hull catamaran & Marco Island, USA \\  & [62; 63; 64] & 2009 & Sea-RAI & Twin-hull catamaran & Galveston, Texas, USA \\  & [65; 66] & 2012 & EMILY & Monohull vessel & Simulator \\  & [67] & 2014 & Benjamin & Wave Glider & New Caledonia \\  & [68] & 2015 & SV2 & Wave Glider & Caribbean Sea \\  & [69] & 2016 & _NR_ & Wave Glider & Okinawa, Japan \\ \hline \multirow{4}{*}{Floods} & [70; 71] & 2012 & _NR_ & Airboat & Philippines and New York, USA \\  & [8; 72] & 2015 & _NR_ & Monohull vessel & Province of Ancona, Italy \\ \cline{1-1}  & [73; 74] & 2016 & _NR_ & Monohull vessel & _NR_ \\ \cline{1-1}  & [75] & 2018 & _NR_ & Monohull vessel & Port Fourchon, Louisiana, USA \\ \hline \multirow{3}{*}{L. \& E.} & [76] & 2009 & ROAZ II & Twin-hull catamaran & Portuguese Tua River \\ \cline{1-1}  & [77] & 2008 & ROAZ II & Twin-hull catamaran & Douro estuary sand spit and \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & &
## 3 Results
### Molecular Weight Analysis of AGB and AGP Polysaccharides by SEC-MALLS  
The molecular mass distribution of extracted polysaccharides was determined by High-Performance Size-Exclusion Chromatography coupled with on-line Multi-angle Laser Light Scattering (HPSEC-MALLS). AGB and AGP were characterized by Mn of 711 \(\times\) 10\({}^{3}\) g\(\cdot\)mol\({}^{-1}\) and 1295 \(\times\) 10\({}^{3}\) g\(\cdot\)mol\({}^{-1}\), respectively, and M\({}_{\rm w}\) of 2173 \(\times\) 10\({}^{3}\) g\(\cdot\)mol\({}^{-1}\) for the crude fraction and 2042 \(\times\) 10\({}^{3}\) g\(\cdot\)mol\({}^{-1}\) for the purified polysaccharides (Table 2).  
The dispersion characterization of molar masses in polysaccharide samples exhibited a polydispersity index of 3.1 for AGB and 1.6 for AGP. This indicated that the purified fractions are monodisperse polymers, meaning that the samples are composed of a single macromolecular species with slight degradation during the extraction and purification processes. However, the crude extracts seemed to be a polydisperse polymer with D > 2, indicating the presence of macromolecular species of different molar masses. The measurement of the intrinsic viscosity revealed that AGB represented a [n] value of 266.7 mL\(\cdot\)g\({}^{-1}\) and 560.6 mL\(\cdot\)g\({}^{-1}\) for AGP (Table 2).
## 3 Results and Discussion
### CILTAG and Hydroalcoholic Extract Analysis
#### 3.2.2 The Compositional Analysis  
Several bioactive compounds were separated from the _Medicaginis herba_, _Trifolii pratense flores_, _Ginkgo bilobae folium_, and _Myrtilli fructus_ hydroalcoholic extractive solutions and are highlighted in Table 5.  
From Table 5, it can be observed that the identified compounds were flavonoid derivatives and polyphenolcarboxylic acids, which, due to their structural characteristics, exhibit antioxidant activity.  
The HPLC test was also conducted for the _Cynosbati fructus_ extractive solution, but we could not identify the chromatographic peaks. By using the UV-Vis spectrophotometric method, the spectrum was obtained and is presented in Figure S4 in the Supplementary Materials File.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Vegetable** & **Flavones/g\%** & **ODPs/g\%** & **Catechin-Type** & **Proantocyan** & **Moisture** & **Water-Soluble** & **Alcohol-Soluble** \\
**Product** & **(Rutoside)** & **(Caffeic Acid)** & **Tannins/g\%** & **L.A.\%/100 mL** & **Content, g\%** & **Substances/g\%** & **Substances/g\%** \\ \hline _Medicaginis_ & & & & & & & \\ _herba_ & 0.195–0.213 & 0.233–0.284 & 0.18–0.19 & - & 9.13–10.04 & 12.84–13.05 & 11.08–11.73 \\ _Trifoli pratense_ & & & & & & & \\ _foros_ & 0.223–0.275 & 0.274–0.301 & 0.23–0.31 & 0.093–0.105 & 8.15–9.03 & 14.93–15.73 & 14.05–14.83 \\ _Ginkgo bilobae folium_ & & & & & & & \\ _folium_ & 0.387–0.412 & 0.213–0.273 & 0.10–0.21 & - & 10.14–11.27 & 13.05–14.22 & 12.93–13.13 \\ _Cynosbati_ & & & & & & & \\ _fuctus_ & 0.198–0.203 & 0.487–0.503 & 0.27–0.30 & 0.323–0.373 & 9.27–10.33 & 11.87–12.27 & 12.87–13.12 \\ _Myrtilli fructus_ & - & - & 0.33–0.41 & 0.387–0.401 & 12.23–13.04 & 12.87–13.23 & 11.43–12.07 \\ \hline \end{tabular}
\end{table}
Table 4: The results of the quantitative determination of flavones, ODPs, catechin tannins, and proanthocyanins and the preliminary quantitative determination.  
By HPLC studies on the _Medicaginis herba, Trifolini pratense flouns, Ginkgo bilobae folium_, and _Myrtilli fruct_s hydroalcoholic extractive solutions, and UV-Vis spectrophotometry performed on the _Cynosbati fruct_s hydroalcoholic extractive solution, the presence of bioactive compounds with antioxidant activity was confirmed in their composition.  
From the quantitative point of view, we noted an increase in the concentration of flavones in the hydroalcoholic extract for _Trifolini pratense flores_, an increase in the concentration of ODPs in the hydroalcoholic extract for _Cynosbati fruct_s, and a lower concentration of tannins in the hydroalcoholic extract for _Ginkgo bilobae folium_.  
Based on the obtained results, the qualitative and quantitative composition of the CLITAG pharmaceutical product was found to be: 201.43-223.13 mg% rutoside, 317.39-333.29 mg% caffe acid, 215.39-223.14 mg% catechin-type tannins, 229.83-238.14 mg% leucanthocyanines, and 8.38 mg% anthocyanines.
## 4 Experiments and Performance Evaluation
### Performance on Skyline Queries  
We used the naive approach of the skyline query. Generally, the naive approach of the skyline query takes too much time. However, in the Spark framework, we did not face such a problem. We also compared our work with the MapReduce framework. However, the time difference was huge. This is shown in Figure 4. Here, we also see that the time delay remains constant for the total data volume from 50K to 500K. The processing time would also vary here when the data volume increases.  
Figure 4: Performance on skyline queries.  
Figure 3: Performance on score calculation.
# Cutting-Edge Trajectory Optimization through Quantum Annealing
## 2 Quantum Annealing Optimization
One company that has made significant contributions in this area is D-Wave Systems, which is a leading company specializing in QA. Their quantum processors, known as quantum processing units (QPUs), are designed to perform QA computations with fully quantum systems (for more details, please refers to the following link: [[https://www.dwavesys.com/media/htglccey/advantage_dataset_v10.pdf](https://www.dwavesys.com/media/htglccey/advantage_dataset_v10.pdf)]([https://www.dwavesys.com/media/htglccey/advantage_dataset_v10.pdf](https://www.dwavesys.com/media/htglccey/advantage_dataset_v10.pdf)), accessed on 6 November 2023). D-Wave's QPUs are used in various research and commercial applications to tackle real-world optimization challenges. D-Wave solvers are constantly being improved to address the limitations of quantum computing, and future advancements will likely include more sophisticated algorithms and improved processing capabilities to enable more efficient optimization.
###### Abstract
Figure 1: Schematic map showing the location of core ARA09C-St03 in the present study and other sediment records used for comparison in the present study (PC04, [21]; 03M03, [23]) in the western Arctic. The circulation paths (arrow) are modified [14; 17]. BG: Beaufort Gyre. ESCC: East Siberian Coastal Current. TPD: Transpolar Drift. AO+ and AO- indicate the paths of currents in the positive and negative phases of the Arctic Oscillation, respectively. Distribution of various terrigenous components in the Arctic sediment records conveys information on sediment sources and depositional environments and thus paleocirculation and changes in paleoclimate conditions, such as connection to other oceans and build-up/disintegration of ice sheets [9; 14; 25; 26]. The Chukchi Rise to Chukchi Basin areas are a sensitive region not only for oceanic currents such as Beaufort Gyre and coastal currents but also for the development of ice sheets, and thus these areas can provide useful records for understanding the evolution of ice and current patterns. However, previous studies in this area have mainly focused on the sedimentation patterns and marine environment [9; 21; 23; 27] and have not dealt with detailed sediment provenance changes. Furthermore, the sediment cores in the Chukchi Basin have limited records during the MIS 2 due to low sedimentation rate [9; 23; 27]. In this study, the core ARA09C-St03 on the inner slope of the Chukchi Rise is used to provide a continuous sedimentary record containing the MIS 2 period (Figure 2). We analyzed the bulk and clay mineral compositions and Nd isotope of the sediment core for the identification of sediment provenance. The main objectives are to reconstruct the paleoenvironmental change including the sediment transport mechanisms and oceanic currents based on the determined sediment provenance in the inner slope of the Chukchi Rise. Figure 2: Vertical lithology profile of core ARA09C-St03, marked with color cycles, IRD contents, units, and marine isotope stages (MIS). Correlation of core 03M03 in the Chukchi Basin [23] and core PC04 in the Chukchi Rise [21] based upon color cycles, IRD contents. Micro Mn nodules for images and EDS pattern are taken from Unit 4 below the B2 in core ARA09C-St03. Blue circles at the left of the graph for IRD content in core ARA09C-St03 indicate the positions of subsamples to Nd isotope analyses.
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Sixth-Generation Network's Footprint
#### 3.1.3 Economic and Environmental Impact
Furthermore, terahertz frequencies are vulnerable to blockages from small-sized objects, such as home furniture or moving humans [49], which further reduces their applicability and usage. However, this is not the case for all terahertz frequencies; for example, the work in [50] demonstrated that the spectral windows at 1.0 THz, 4.5 THz, and 9.1 THz are able to minimize this effect and enhance the coverage, especially when terahertz frequencies are combined with ultra-dense base stations, beamforming antennas with a small beamwidth, and a low density of omnidirectional nanosensors.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control in UAV Development
#### 5.2.5 Modern Era and Advanced Techniques  
With the advent of advanced sensing, computing, and communication technologies, UAVs became more versatile and capable. Nonlinear control methods, along with machine learning techniques, were applied to handle complex tasks such as autonomous navigation, obstacle avoidance, and cooperative control of UAV swarms [99].
## 4 Blackout Analysis
### Preconditions
#### 4.1.3 Dependencies between Regions (PREC_3)  
The extent and speed of the blackout can be significantly affected by supply dependencies between regions of a connected network. To put it another way, when a significant disruption occurs, regions are frequently unplugged to stop its spread. Power generation shortages or surpluses, however, may result in a collapse in areas with high reliance on the supply of electricity, leaving those areas in an unrecoverable state. If imported power exceeds 20% of total consumption in the region or is dangerously equal to the import capacity limitation, we consider the region to have high dependency, and thus, it is at risk. For example, before the blackout on 31 March 2015, the Eastern part of Turkey exported a great amount of hydroelectricity (42% of its total production), with the majority to be transferred to the Western region, which imported 21% of its load demand.
# Improving Adversarial Robustness via Distillation-Based Purification
## 3 Method
### Base Model: Convolutional Autoencoders  
In our work, the two teacher models and the student model in the knowledge distillation framework are based on the same Convolutional Autoencoder structure (see Figure 1). An Autoencoder is an encoder-decoder neural structure that compresses the input through the encoder and restores it to its original dimension through the decoder. The bottleneck layer between the encoder and decoder has a low-dimensional latent representation that retains important features of the original input. The decoder aims to produce an output that is as close as possible to the original input based on this latent. Autoencoders have been widely used for tasks such as data generation, super resolution, and data restoration. We believe that autoencoders are also well suited to the task of purification, which is the task of restoring images by removing noise from AEs.  
Furthermore, Convolutional Autoencoders are specialized in dealing with image data. Instead of a fully connected network (FCN), a Convolutional layer with local connections is mainly utilized, which can better learn the spatial features of images. Here, the encoder consists of a series of Convolution, batch normalization, and Rectified Linear Unit (ReLU) layers in one block, for a total of 15 blocks. The Convolution layer extracts various features, colors, textures, etc., from images, while the batch normalization layer keeps the distributions within a batch consistent for stable learning. The ReLU activation function mitigates the problem of gradient vanishing. The decoder also consists of 15 blocks, each of which consists of a series of Convolutional Transpose, batch normalization, and ReLU operations. A tangent hyperbolic (Tanh) operation is added to the end of the last block. The latent  
Figure 1: The structure of the Convolutional Autoencoder used in our work, inspired by [34]. The colors of the layers have the following meanings. Blue: Convolutional layer, green: batch normalization layers, orange: ReLU operations, gray: transposed Convolutional layers, yellow: Hyperbolic tangent operations.  
representation is upsampled by the Convolutional Transpose operation to decode as close to the input image as possible.  
Our Convolutional Autoencoder is quite deep, with a total of 30 blocks. This has the advantage of learning a good quality of latent representation, but because of its depth, there is a risk that the gradient may vanish or explode during backpropagation. To avoid this, we make skip connections at the encoder and decoder to convey the gradient flow directly. This also has the effect of helping the decoder to reconstruct images by preventing the loss of information or details that are useful for reconstruction. As a result, the network structure of this study is similar to that of U-net [36].
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.12 BSS Color  
It is an innovative scheme to increase throughput of dense WLAN networks, where each BSS is assigned a specific color (in-terms of bits designated in LSIG field of physical header). A station upon receiving frames from neighboring BSS, can abandon the reception process assuming the channel idle during that transmission and thus increasing the transmission opportunities.