esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Integrating Machine Learning into Edge Sliced Networks  
To analyse large amounts of data and obtain useful information for detection, classification, and prediction of future events, machine learning techniques are often applied in a lot of different applications and services. As it enables fast inference and autonomy within the networks, it is also envisioned to be an important component of 5G networks and beyond. An example of how ML algorithms can be integrated into edge-enabled network slicing architecture is presented in Figure 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Methodology** & **Objective** & **E2E** &
\begin{tabular}{c} **Slice** \\ **Isolation** \\ \end{tabular} & **Outcome** \\ \hline
[27] & NFV & Scalable E2E service slicing & Yes & No & Lower latency for various web services \\ SDN \& & Resource utilization and load & No & No & Up to 48\% higher resource utilization than SOTA algorithms \\ NFV & balancing & No & No & Up to 50\% higher network throughput than SOTA algorithm \\ SDN \& & Flexible and intelligent traffic steering & No & No &
\begin{tabular}{c} \\ \end{tabular} \\
[31] - & Maximize operator’s average revenue & No & No & Flexible balance between the revenue and the average delay \\
[32] & NFV & Improve network slice acceptance & Yes & No & Improved load balancing and reduced load of edge data centers \\
[23] - & Efficient and scalable network slicing & No & Yes & Instantiates slices 6 times more efficiently than SOTA algorithms \\
[34] & NFV & Isolation of low latency slices & No & Yes & Maintains the same minimal latency with a competing low-latency slice \\
[35] - & Efficient resource allocation & No & Yes & Nearly optimal bandwidth allocation \\
[36] & NFV & Automated E2E slicing & No & Yes & Lower E2E instantiation times for mMTC slices \\
[37] & NFV & Automated and flexible dynamic resource allocation & Yes & Yes & Guaranteed latency \\
[38] & SDN\& NFV & Application-aware E2E slices on demand & Yes & Yes & Guaranteed E2E bandwidth \\
[39] & NFV & Flexible and dynamic placement of micro-services & Yes & Yes & Guaranteed QoS for migrating application flows \\
[33] - & Achieve QoS goals of slices & Yes & - & Assigns the necessary number of C-RAN and MEC cores \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of edge-enabled slicing studies mentioned in Section 2.  
Figure 2: Integration of ML into wireless network architecture.
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
## 2 Materials and Methods
### Characterization of Porous Chitosan Microspheres
#### 2.3.4 Fourier Transform Infrared Spectra (FTIR)  
In order to study the adsorption mechanism of porous chitosan microspheres for Cr(VI), Fourier transform infrared spectra (FTIR) of the chitosan powder and porous chitosan microspheres (with or without Cr(VI)) were recorded using a FTIR spectrometer (IRAffinity-1 spectrophotometer, Shimadzu, Japan) at a resolution of 4 cm\({}^{-1}\) and 32 scans in the range 500\(-\)4000 cm\({}^{-1}\). The dry samples were ground with KBr and then pressed into pellets.
# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
## 4 Conclusions  
Predicting sediment transport is essential for effective river basin planning and management. Among them, the simulation of river sediment transport is highly variable and nonlinear in nature, hence the difficulty of the runoff-sediment production hydrological process remains challenging in terms of sediment prediction. Additionally, sediment load data are highly dependent on runoff, while runoff is directly related to rainfall intensity. In this study, based on wavelet transform analysis and neural network principle, the rainfall-runoff-sediment model was established, so as to improve the accuracy of sediment prediction.  
Using the wavelet transform, this article obtains the study of interannual cycle variation of rainfall and runoff in the study area, both of which have the first main cycle of 28 years, and the main period data were decomposed to obtain the detail signals (D1, D2... D4, D5 and D6) of high-frequency components and approximate signal (A) on time series. Then, the monthly average wavelet decomposition of rainfall and runoff was coupled into the neural network training set as the input layer and the monthly average sediment is used as the output quantity for predicting the monthly average sediment quantity. The Root Mean Squared Error (RMSE), the fit rating (S) and the Correlation Coefficient (r) were used to compare the observed and predicted monthly average sediment transport values; the results show that the RMSE, the S and the r were 0.05, 0.98 and 0.99, respectively. The model establishes the relationship between rainfall, runoff and sediment in river basins, which effectively improves the accuracy of the predicted sediment model and can enhance the recognition of river systems, thus providing a reference for the establishment of small information sediment prediction in river areas, and provide a reference basis for sediment prediction and improve the utilization rate of hydropower energy.  
Conceptualization, Z.L.; Data curation, W.X.; Funding acquisition, Z.S. and H.Z.; Methodology, Z.L. and H.D.; Project administration, Z.S., L.S. and H.Z.; Visualization, J.L.; Writing-original draft, J.L. All authors have read and agreed to the published version of the manuscript.  
The article was supported by was supported by the National Natural Science Foundation of China (Grant No. 91647209) and the Public Welfare Project of Zhoushan City (No. 2022C31046).  
Not applicable.  
Data Availability Statement: The data presented in the present study are available on request from the corresponding author.
## 3 Methodology
### Logic Architecture  
Before delving into the development of the algorithms for both modules, it is important to understand the general logic of the system and how both modules are related and communicate with each other.  
Initially, the system was designed without fixed cameras, only a PTZ camera, so the SDR module was the first step in detection. When it detected a signal that could be coming from a UAV, it would alert the PTZ camera module, which would then begin sweeping the entire area until the UAV was located.  
However, the system has been improved with the addition of fixed cameras. Now, when two fixed cameras simultaneously detect the UAV, they send the exact position to the PTZ camera, and, once it detects the UAV, it begins to track it. The main objective of the system is for the PTZ camera to identify the UAV and continuously follow it, allowing the security company using this system to always know the UAV's location. For this reason, the PTZ camera can detect the UAV through two methods:  
1. Through sweeping. This situation occurs either when the SDR module detects the UAV or when it is detected by only one fixed camera. In both cases, the UAV's position is unknown, so the PTZ camera begins to sweep its surroundings to detect it.
2. After being located by two fixed cameras. If two or more fixed cameras detect the UAV, they send its position directly to the PTZ camera, which then moves directly to the UAV's location without needing to sweep.  
Furthermore, once the PTZ camera locates the UAV, it tracks it. However, if the UAV disappears from the camera's view at any point because it has flown away or moved too quickly for the camera to follow, the PTZ camera begins a post-loss sweeping period to relocate it. If the UAV is not found after the sweeping period and no alarm is received from another camera or the SDR, the UAV is considered to have left the area.  
Finally, when the UAV is detected, an alert is sent to the security company's ARC so they are aware of the presence of a UAV in the protected area. Once the UAV is located and tracked by the PTZ camera, videos of the UAV will be periodically sent to the ARC, along with a message when the UAV has disappeared.  
Communication with the ARC is conducted through the Data Transport Layer Security (DTLS) protocol, which creates a secure and robust communication link between the detection system, acting as the client, and the ARC, acting as the server. The alarms and videos themselves are sent using the SIA DC-09-2021 protocol (Security Industry Association, Silver Spring, MD, USA). On the other hand, communication between the two modules is carried out via sockets programmed in Python, just like the rest of the system.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 5 Discussions  
Our experimental results empirically demonstrated that DL reconstruction in certain situations is superior to the conventional iterative reconstructions, especially when it deals with signals compromised by strong noise. The test on the sound reflectors suggests a great potential of DL-based methods for removing reflection artifacts in the PAT image formation.  
However, there are some limitations in this study. First, some of the parameters in these iterative algorithms, such as the number of iterations and the value of the regularization parameter, are not specifically optimized. Varying these parameters may result in somewhat improved performance of the iterative algorithms. However, finding the optimal values of such parameters are highly empirical, and there is no universal approach in general. Second, we empirically used the combination of MSE and SSIM as the loss function to optimize the network, and evaluated the image quality by PSNR and SSIM. The choice of the loss function and image metrics may not be the optimal to capture the visual quality for PAT imaging. Third, we only studied the reflection artifacts under different noise levels and a simple variable sound model, other more complicated conditions such as limited views and more complicated variable sounds can be surely addressed by extending the proposed network.  
Figure 5: Distribution of the sound speed.  
Figure 6: The box plots of the image quality evaluated by PSNR and SSIM on the testing set in a heterogeneous medium. From top row to bottom row: noise increases from 0 to 0.4. Diamonds (\(\diamond\)) indicate outliers.
# Growing Neural Gas with Different Topologies for 3D Space Perception
## 1 Introduction
Figure 2c shows an example result of GNG with the weighted distance measurement. By using the weighted distance measurement, the topological structure represents the original 3D environmental space of the point cloud and learns the color information simultaneously. In particular, the algorithm of GNGA includes the normalization of each feature vector in the input vector for solving the scalability problem in the 2D image. However, it is difficult to normalize the input vector of the 3D point cloud with the other properties if the point cloud is composed of the increasing environmental map. In addition, the number of clusters generated from the topological structure is only one since it is difficult to cut the edges between each objects from the floor surface (in the green circles of Figure 2c) by designing the appropriate weights from the unknown data distribution composed of the multiple properties' vector. Therefore, the weighted distance-based methods need a cutting algorithm of the edges for clustering the 2D/3D point cloud data according to the property after generating the topological structure. In this way, the learning method of the 2D/3D point cloud with additional information that can generate the topological structure composed of the multiple properties and preserve the position space simultaneously is not realized. In this paper, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for addressing all of these issues, which were not incorporated in the previous work. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. The main contributions of this paper are listed as follows:  
1. GNG-DT can preserve 2D/3D position spaces with additional feature information from the point cloud by using the distance measurement of only the position information. 2. GNG-DT can give multiple clustering results by utilizing multiple topologies within the framework of online learning. 3. GNG-DT is a robust learning algorithm for scale variance of the input vector composed of point cloud with additional properties. This paper is organized as follows. Section 2 proposes growing neural gas with different topologies for the 3D space perception. Section 3 shows several experimental results by using 2D and 3D simulation data and RGB-D data. Finally, we summarize this paper and discuss the future direction to realize the 3D space perception.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 5 Results
### Analysis of Models' Features  
In addition to the model's accuracy characteristic, the stability feature is equally important. Stability ensures that the model performs consistently over time, avoiding fluctuations, which can reduce trust in its reliability.  
Evaluation of the stability aspect was carried out in three different ways: analyzing model MAE and RMSE values for short-term (24 h) and long-term (72 h) forecasts, as well as assessing the model's prediction performance in different seasons.  
Assessing model performance at the daily level reduces the impact of short-term fluctuations and biases in hourly data. The choice of daily (24 h) and three-day (72 h) hourly frequencies provides a standardized and consistent time interval, which is commonly used in the NordPool market. Furthermore, NordPool allows participants to trade electricity across multiple European countries through a centralized, transparent platform. Power generation companies are required to submit day-ahead price forecasts. However, in practice, these forecasts are typically made 2-3 days in advance, optimally 72 h, and are subsequently adjusted daily based on new predictions. Models' performance across seasons displays their ability to adapt to different weather fluctuations, since each season has specific weather patterns. Long-term observations of meteorological elements, including wind speed values and their fluctuations, in Lithuania showed that the strongest and most gusty winds are observed in January, November, and December [58].  
For all 3 experiments, the full dataset of 8760 h was used. This solution was chosen to keep time intervals within complex forecasting conditions. Also, using a full dataset ensures the continuity of the 72-h sequence, which would otherwise be compromised.  
For the second model stability experiment (Section 5.3.2), the entire dataset was split into 3 parts--training, validation, and test datasets. The training dataset contained 220 days (5280 h), and the validation and testing datasets contained 72 days (1728 h) of information. Errors in validation and test datasets reveal the model's ability to perform on unseen data.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction  
### Ad Hoc Network System Composition  
Ship ad hoc network system is developed from the related systems of traditional ships. For a complete set of ship internet, its structure should include a shore-based integrated management center, information service platform, signal base station, buoy and other intelligent navigation aids, satellites, ships, etc., for ship ad hoc network, we focus on the ship itself, as shown in Figure 1, including ship sensing system, ship positioning equipment, shipborne communication system, etc., including various radars, AIS, Gyrocompasses, cameras, etc. It can obtain the location information of the ship and the surrounding object information. The communication system is responsible for sending and receiving relevant information. In this way, a connected mobile ad hoc network is formed between ships.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 3 Existing Methods for Vbda
### Vary Beam Divergence by Adjusting the Focal Length F of the Collimating Lens
#### 3.2.2 Inserting Parallel Plate  
The schematic diagram in Figure 18 illustrates the method. A motor drives a parallel plate glass to insert into the optical path, thereby altering the optical path length between the light source and the collimating lens. This enables the switch between small and large divergence angles. The advantages of this method are its simplicity in principle and ability to greatly relax the requirement for lateral positioning accuracy. Additionally, it does not require precise location determination, so the mechanical properties of the motor driver can be simple and economical. However, the method cannot continuously change the divergence angle, and switching between large and small divergence angles introduces scintillation, which causes the link to experience an outage during insertion time.  
[PERSON] [66] applied this method to pickup and camera systems, enabling quick and easy focusing between different film surfaces by moving in and out of parallel plate glass. In the context of laser communication systems, this method can be implemented using a group of parallel plates of varying thickness. The plates can be selected and moved into the optical path by electric actuators, allowing for different beam divergence angles to be achieved by using plates of different thickness or by simultaneously selecting multiple plates.
# The Application of Mulberry Elements into a Novel Form of Easy-to-Prepare Dried Smoothie
## 3 Results and Discussion
### Antioxidant Activity of Smoothies  
The authors chose to use raw materials with high nutritional value, known, among other things, for the presence of polyphenols, for the smoothie. The quantification of the phenolics content of each extract was achieved through the utilization of the Folin-Ciocalteu reagent with methanolic extracts of the dried smoothes. The quantification relied on a calibration curve generated from gallic acid standards ranging from 0 to 250 ug/mL, with a regression equation of y = 4.1323x - 0.0041 and a coefficient of determination (R\({}^{2}\)) of 0.999. The analysis revealed that the total phenolics content ranged from 1.095 to  
Figure 3: Proposition of food labels for smoothies: (**a**) fresh smoothie with chia seeds, (**b**) fresh smoothie without chia seeds. 3.434 mg GAE/g d.m., indicating an approximate 3-fold variation. Notably, the DC2 and DB1 samples exhibited the highest phenolic compounds content: 3.434 \(\pm\) 0.200 mg GAE/g and 3.227 \(\pm\) 0.115 mg GAE/g, respectively. Conversely, the lowest TPC concentrations were observed in the DC1 sample (1.095 \(\pm\) 0.181 mg GAE/g). When comparing the results of the designed smoothies with other authors' medicinal plant-based smoothies [36], the TPC was quite close to other designed mulberry-licorice-based smoothies, and in honeybackled berry-based smoothies, it ranged from 3.469 to 4.268 mg GAE/g (Table 4, Figure 4). Such results suggested that the smoothie product serves as a rich source of phenolic compounds, which have the potential to alleviate oxidative stress within the body. The reducing compounds present in the smoothies were classified into phenolic compounds and flavonoids based on their chemical structure. The contents of phenolic acids across the four types of smoothies elucidated notable variations between benzoic acid derivatives and cinnamic acid derivatives. Product DB2 exhibited the highest concentration of cinnamic acid derivative phenolics (0.459 mg/g d.m. ), followed by DC1 (0.422 mg/g d.m.). Additionally, the concentration of analyzed selected benzoic acid derivatives appeared to surpass that of analyzed cinnamic acid derivatives. Flavonoids, which are phytountrients found in plants with antioxidant properties, play a pivotal role in reducing oxidative stress and imparting color. The contents of flavonoids varied among the four variants of smoothie, with the highest subgroup being flavanols (mainly rutin), followed by flavones, while flavonones exhibited the lowest levels, from those analyzed in studies. Samples DB2 and DC1 demonstrated the highest levels of
## 5 Discussion
### Limitation of the Autocorrelation Function Method  
As presented in Section 3.3, the principle behind the autocorrelation function method is to quantify the similarity of two waveforms at different times \(\tau\), using correlation functions. It can be observed that stress or strain time histories are highly correlated at different \(\tau\), when the applied strain amplitude is relatively small since the ignorable influence of the number of cyclic loading \(N\), as noted in Figure 6a,b. However, at large strains, the similarity gradually diminishes with increasing \(N\), the stress time histories in particular. Although the previous study used the second cycle to calculate the \(G\) for the other three determination methods, it is the entire stress and strain time histories that are used for the autocorrelation function method, rather than a single loading cycle (i.e., one cycle is impossible to compute the autocorrelation function).  
Therefore, the aforementioned means that the autocorrelation function method discards the influence of \(N\) on both \(G\) and \(G/G_{\text{max}}\). As an example, Figure 16 compares the \(G/G_{\text{max}}\) results of \(N=2\), \(3\), and \(4\) for various determination methods, corresponding to \(D_{\text{r}}=30\%\) and \(\sigma_{\text{c}}=50\) kPa. It can be observed that the autocorrelation function method predicts identical \(G/G_{\text{max}}\) curves, and illustrates the average result of the five hysteresis loops. Both the hysteresis loop method and the least square method indicate an increased non-linearity for high \(N\). This leads to the \(G/G_{\text{max}}\) curves of these two methods which are on the top for \(N=2\) whereas these two curves move to the downsides for \(N=4\). Additionally, Figure 16 further demonstrates the influence of \(N\) on the uncertainty quantification of determination methods, particularly in the large strain range. For instance, the percentage change of the least square method tends to decrease with increasing \(N\), with a maximum \(\delta\) of around \(5\%\) for \(N=4\).