esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 2 SON Operation Rationale, Use Cases, Standardization, Architectures and Dimensioning  
### Manual RAN Planning and Optimization Activities Replaced by SON  
As already mentioned, SON applications were developed to provide network automation and fully replace the RAN optimization process as a main aim and the planning process as a secondary aim, in terms of initial RAN nodes and core/backhaul network configuration, since some steps of network planning such as site location selection and coverage planning cannot be fully automated. The basic steps of the planning lifecycle are depicted in Figure 2[22; 23; 28].  
The following figure describes the planning process from the early stage of detecting the need for a new site to the activation of an LTE site. As a very first step, the need of a new site is recognized based on known coverage holes or the capacity advancement needs for specific areas. After the radio propagation environment is examined and simulated and exact capacity needs are identified, a coverage simulation is deployed and the initial RAN parameters are designed by taking into account any neighboring sites. A configuration file is created and planned to be committed by the OSS to the RAN, and then the activation takes place. As a last step, real-time network KPIs are monitored and drive testing takes place so that the whole coverage area is examined and a clear outcome about any possible optimization actions needed is extracted. Apart from the initial steps of understanding the need for a new site due to poor coverage or capacity needs and coverage simulation, the configuration procedure can be substituted by SON self-configuration applications that are able to eliminate human errors since the interaction with neighboring sites is what creates complexity.  
SON system intervention, no matter the technology (2G/3G/4G/5G), starts from the configuration phase (preoperational state of a node/cell) and expands to network optimization, which is the basic field of SON applications as well as healing. Optimization and healing take place in the operational state, while both preoperational and operational states can migrate from a manual to fully automated status, as depicted in Figure 3, through a SON orchestrator that monitors and organizes SON applications.  
Healing activities regard the automated maintenance handling in case nodes fail and existing SON systems follow a reactive approach; however, a proactive approach is more efficient. Typical healing cases that lead to a reduction in operational expenses include interference mitigation, the detection of KPI degradation, outage detection, diagnosis and compensation [31].
## 3 Results and Discussion  
### Total Phenolic and Profile of Phenolic Compounds  
#### 3.1.1 Total Phenolic Content  
One of the most popular methods of determining polyphenols is the spectrophotometric method using the Folin-Ciocalteu reagent to determine the total polyphenol content. The raw vegetables were subjected to technological processes, including steaming and sous-vide cooking at three different temperatures. The highest total polyphenol content (TPC) was in red pepper, while the lowest was observed in green pepper [Table 2]. The effect of technological processes on the total polyphenol content varied and depended mainly on the cooking conditions [9; 30; 31]. Vegetables such as betroot and red pepper were characterised by a lower total polyphenol content after all culinary treatments, compared to their raw form. The smallest decrease in the total polyphenol content was observed after the sous-vide process at 85 \({}^{\circ}\)C, whereas vegetables such as red cabbage and green pepper had a higher total polyphenol content after all technological processes compared to their raw form (_p_ < 0.05). The highest TPC was observed in red cabbage, green pepper and kale after the sous-vide process at 85 \({}^{\circ}\)C. The most advantageous technological processes after which the TPC content in the tested vegetables was the highest or the decrease in this content was the smallest in relation to their raw form, and the remaining cooking technique was the sous-vide method at a temperature of 85 \({}^{\circ}\)C. The higher TPC in the vegetables tested after technological processes may be because the bioactive substances, including polyphenols, are not washed out by water through osmosis. Increased temperature also contributes to the loosening of the cell wall structure, facilitating the extraction of bioactive substances [10; 16]. Slightly higher temperature may contribute to a greater availability of polyphenic substances, e.g., steamed green pepper--718 mg GAE/100 g of freeze-dried vegetables vs. raw form--91.9 mg GAE/100 g of freeze-dried vegetables, which is an almost 7-fold increase in TPC.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 3 Preliminaries: Warp-and-Correct in Video Codecs  
Warping is an efficient operation for frame estimation. Given the preceding frame \(I_{t-1}\) and the motion vectors of the current frame \(Mv_{t}\), the current frame \(\hat{I}_{t}\) can be estimated by  
\[\hat{I}_{t}=\text{warp}(I_{t-1},Mv_{t}). \tag{1}\]  
However, biases always exist between the warped image and the real one. Modern video codecs add a correction step after image warping. (Figure 1(a)). Specifically, the codec performs pixel-wise addition between the warped image \(\hat{I}_{t}\) and the residual map \(ResI_{t}\). Each point in \(ResI_{t}\) is a three-dimensional vector, \((\Delta r,\Delta g,\Delta b)\), which describes the color differences between the warped pixel and the expected one. The overall inter-frame prediction process is described as  
\[I_{t}=\hat{I}_{t}+ResI_{t}. \tag{2}\]  
Inspired by this, we propose to learn the residual term in feature space (Figure 1(b)) to reduce errors incurred by warping.  
Recent codecs such as HEVC (H.265) and VCC (H.266) share the same motion vector and residual design as MPEG-4 part 2 (H.263) and MPEG-4 part 10 (H.264). More accurate motion vectors alleviate the error incurred by warping, whereas they also require more time on decoding, making the whole system run slower. No matter how sophisticated the motion compensation is, they still need correction in the compressed domain. It is expected that our method, which does correction in feature space, will still work for these codecs.  
Figure 2: Illustration of “warp-and-correct”. (**a**) Video codecs warp the preceding frame to the current one and then add the compressed-domain residuals. (**b**) We propose to learn the residuals in feature space to rectify the warped features.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 7 Discussion and Future Research Directions
Use of GIS mapping and satellite imaging will help to identify the most affected disaster regions. The GIS mapping and satellite imaging information will help to set priorities for the disaster regions. Additionally, any blockages or disruptions of the transportation routes will be analysed in a more effective way to find the best feasible route for transportation. Any update on the disruption of the route can be incorporated to redefine the transportation route that is re-optimising the travel route if needed. Monitoring real-time updates on demographic information with the inclusion of these techniques will provide better mobilisation of resources at disaster regions. It can be useful for vehicle location information that can be applied to make the distribution model more dynamic. Use of GIS and satellite imaging can be used at any stage of disaster management, starting from the preparedness to the post-disaster management. Another component that can be added to this research is the use of social media as a data source. Use of social media has increased over the years. The information available through social media will be very useful to understand the severity of the disaster. The data from social media such as Facebook and Twitter can be analysed using text mining or image mining to extract knowledge about the disaster. This knowledge can be implemented to identify disaster region priority and route conditions. Furthermore, as a future enhancement of this dynamic distribution model, IoT devices can be integrated into this dynamic relief items distribution system. The real-time update on disaster information using IoT devices can be implemented in developing the distribution schedule. The inclusion of these techniques will further enhance the effectiveness of the distribution model with the accurate and timely collection of data and making relief item distribution more effective. B.K.M. : conceptualisation, methodology, modelling, simulation, validation, formal analysis, investigation, writing--review and editing, K.D. : conceptualisation, methodology, validation, formal analysis, investigation, writing--review and editing, Z.P. : conceptualisation, methodology, validation, formal analysis, investigation, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
## 3 System Model
### Computational Model  
We studied four computing models for devices to choose from under the MEC network environment: local computing, D2D peer devices computing, edge computing, and migration computing. The computation delay and energy consumption for each model are as follows.
## 2 Materials and Methods
### Multipath System-Level Models
RF propagation in mobile channels is a very complex matter and several channel models have been developed, based on a statistical approach [5]. Narrowband channel emulators implementation have been proposed [6] and low-complexity architectures have been developed dealing with delays that are not integer multiples of the sampling rate [7] or solutions where massive calculations are demanded to a GPU (Graphical Processing Unit) using native highly parallelized architecture [8].
## 4 Results and Discussion
### Impact of Double Rayleigh Backscattering
To demonstrate this point, the SNRs of the signals were calculated using the proposed theoretical model with the DRB included, and the results were compared with the case in which only spontaneous Raman scattering was considered, as shown in Figure 8. From Figure 8, we can see that when the DRB was considered, the proposed model had good accuracy. When the pre-compensation length was less than 60 km, the two curves were almost coincident. With the pre-compensation length increasing over 60 km, a difference appeared and the SNR of the signal including DRB was lower than the SNR of the signal without DRB. This is because the NSNI caused by the DRB in the near-transmitter section increased. Still, the difference is small, which illustrates that the DRB has a much smaller influence on the performance of the system than spontaneous Raman scattering. Figure 8: SNR vs. The pre-compensation length, with and without DRB. Figure 7: OSNR vs. distance, with only spontaneous Raman scattering (spRs) and with both spRs and DRB.
# Gamification of Upper Limb Rehabilitation in Mixed-Reality Environment
## 4 Event Sequence
### Upper Extremity Joint Measurement  
A C# script uses the positions of the user's headset and hands and input from the player on their body dimensions to calculate the DoFs of the shoulder and elbow joints. The total number of DoFs measured is four. Three are from the shoulder (vertical flexion/extension, abduction/adduction, and internal/external rotation) and one is from the elbow (flexion/extension). The first step in this process was to create a user interface (UI) for players to enter information about their bodies. This was accomplished through the MRTK-provided slider UI shown in Figure 4, which allows participants to enter a value in meters (m) ranging from 0.00 m to 1.00 m for their forearm length, upper arm length, shoulder width, and the distance between their eye level and clavicle bone. This value was chosen and then accessed by methods used to determine the positions of various joints.  
Figure 3: Game levels shown in the Microsoft HoloLens 2 AR environment with its spatial awareness feature (i.e., recognizing and including the room details) (**a**) Picking cabbages in the garden. (**b**) Picking apples from a tree. (**c**) Pouring a cup of tea.  
The second step in this process was to locate the shoulder and elbow joints and determine the different DoFs. It is important to note that the x-axis in Unity represents the horizontal direction, whereas moving to the right is positive. Furthermore, the y-axis represents the vertical direction, with upward movement being positive, and the z-axis represents the depth, with forward movement being positive. The user inputs were used to determine the shoulder position. A new game object would then be instantiated at the modified x and y positions while retaining the same z position, as we assumed that the z position would remain constant due to the headset and shoulder being in the same plane, given that the user's neck would have to be straight to interact with the game appropriately.  
The elbow position, on the other hand, was calculated using the hand's position and the forearm length input. This was done using the Ray class [40] within the UnityEngine namespace. The origin point and ray's intended path must be determined before the ray can be built. Both were discovered using the MRTK and the HoloLens 2's hand-tracking capabilities in Unity. After the ray was constructed, the elbow position was determined using the Ray class's GetPoint method. Based on user input, the parameter distance was set to the distance between the wrist and the middle knuckle plus the forearm length. A new elbow game object was created at the specified position. This method, however, only determines an accurate elbow position when the top of the hand is parallel to the forearm. To control this factor, subjects were instructed to wear a wrist brace to straighten their wrists.
# Reconfigurable Antennas: Switching Techniques--A Survey
## 6 Comparison between Different Reconfiguration Techniques  
Electrical reconfiguration is the most common technique of tuning antenna characteristics using active switches. Electrically reconfigurable antennas are the best solution when size and efficiency are important issues. However, the power handling capability and the lifetime of these switches are important issues. Compared with other reconfiguration techniques, the implementation of such reconfigurable antenna with switching elements is easy. Nevertheless, it requires a complex biasing system [74; 75]. Unlike the electrical reconfiguration, the photoconductive approach does not require the use of bias lines and can lie in the antenna mainboard without adding a complex system to modify the radiating structure. In addition, the activation/deactivation of the switch does not generate harmonics and intermodulation distortion. However, in contrast with active switches, the optical switches are less popular due to lossy behavior and the need of a complex activation mechanism [76; 77; 78]. Table 2 compares the characteristics of the different switching techniques which have been mainly used on electrically and optically reconfigurable antennas [79; 80].  
The main advantage of the physical reconfiguration technique is that there is no requirement for active elements, biasing systems or optical fibers. Despite all these advantages, the flexibility of this type of antenna is limited, and it is difficult to provide multi-function reconfigurable characteristics. In addition, its performance has a slow response and requires a power source with complex integration [81; 82]. For the antenna systems which do not require fast reconfiguration, this approach is an attractive technique. The reconfigurable antennas with smart materials might have the advantage of having a low profile, but the common disadvantage is its low-efficiency characteristic [83; 84; 85]. In addition, compared with other reconfiguration techniques, the applications of smart-material-based reconfigurable antennas are limited. The advantages and disadvantages of different reconfiguration techniques are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Switch Types** & **Voltage [V]** & **Current [mA]** & **Power [mW]** & **Isolation** & **Loss [dB]** & **Speed [\(\thickapprox\)**]** \\ \hline
**PIN Diode** & 3–5 & 3–20 & 5–100 & High & 0.3–1.2 & 1–100 \(\times 10^{-6}\) \\
**MEMS** & 20–100 & 0 & 0.05–0.1 & Very High & 0.05-0.2 & 1–200 \\
**Optical (Si)** & 1.8–1.9 & 0–87 & 0–50 & High & 0.5–1.5 & 3–9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties for various switches [75; 76; 77; 78; 79; 80].  
Figure 10: (**a**) Configuration, (**b**) prototype sample and (**c**) frequency response of the reconfigurable antenna (redrawn from [73]).
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C
#### 3.1.1 Material and Measurements  
The measurements of the hydrogen diffusion in Zry-4 (Zr, 1.5% Sn, 0.21% Fe, 0.10% Cr) were performed using solid cylinders made of this material. The diameter was 12 mm, the height 20 mm. These cylinders were oxidized for 30 min at 1200 \({}^{\circ}\)C in an argon/oxygen atmosphere, producing an approximately 120 \(\upmu\)m thick oxide layer, which prevents hydrogen pickup. The oxide scale was removed at one base plane of the cylinder by mechanical grinding and polishing to allow hydrogen penetrating into the sample through this plane.  
The in situ neutron radiography investigations were performed at the ICON facility at SINQ (Paul Scherrer Institut, Villigen, Switzerland) at temperatures of 900, 1000, 1100, 1200, and 1300 \({}^{\circ}\)C. The furnace was mounted at the table for large samples. A collimation of about 600 in terms of L/D was applied. The thermal neutron flux at this beamline position was 3.9 \(\times\) 10\({}^{6}\) n cm\({}^{-2}\) s\({}^{-1}\). The illumination time per image was 19 s. Together with the readout time, this resulted in a frame repetition rate of 0.05 Hz. The pixels had a size of about 58 \(\upmu\)m, and the field of view was 464 mm \(\times\) 464 mm.  
The annealings were performed in a flowing argon atmosphere (50 L/h). After the collection of images for the initial state during the first 3 min, a hydrogen injection of 8 L/h into the flowing gaseous atmosphere was started. The images were referenced to the first images taken at the test temperature before the hydrogen injection was started. The resulting images show only the changes in the sample transmission to the initial state. This means that only the hydrogen distribution in the sample is displayed.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 3 Results and Discussion
### Comparison of Model 1 with Model 2  
In order to compare the performance of both models, the remaining 20% of the dataset not used to train and validate the models was used.  
For this comparison, the classification algorithms that showed best performance for each one of the two models were used: as shown in Sections 3.2 and 3.3, the SVM Quadratic algorithm for Model 1, while for Model 2 is used the Logistic Regression algorithm, for stage 1 (binary classification), and the SVM Quadratic algorithm for stage 2 (multi classification).  
The results of the performance obtained with both models are presented in Table 6. To compare them, as before, the average of the values of each metric, presented in the _Average_ row of Table 6.  
As only 20% of the data were used to perform the comparison between the two models, which results in a low amount of thermograms per class, it appears that the results of Table 6 are lower when compared to those presented in Table 5 (results of stage 2 in Model 2). However, it must be noted that Model 2 is composed of two stages, and the errors in the binary classification of stage 1 harm the global performance of the model.  
Nevertheless, Model 2 performs better than Model 1, for all of the metrics evaluated. It can be verified that the values of the metrics for Classes 2 and 3 are the same for both models, while for the Classes 1 and Healthy the performance is better with Model 2. This result suggests that Model 2 is the one that is able to best discriminate between Healthy individuals and those with slight signs of pathology (Class 1). Additionally, on average, F-score is almost 10% higher for Model 2 and Accuracy 5%; thus, this approach outperforms Model 1 and allows for a more accurate performance classification concerning the healthy category and the first class of diabetic individuals.
## 2 Related Works
Economic models play a crucial role in P2P energy trading systems, determining pricing mechanisms, cost allocation, and incentives for participants [43]. Dynamic pricing based on supply and demand, time-of-use tariffs, and incentive-based schemes are commonly employed to optimize resource utilization and encourage renewable energy generation. However, thanks to blockchain technology and smart contracts, P2P energy trading systems were made possible and easy to implement [44]. This concept aims to create a more efficient and sustainable energy ecosystem by directly empowering individuals to participate in energy production, consumption, and transactions, fostering local energy communities. Nevertheless, despite its potential benefits, P2P energy trading faces several challenges, including regulatory hurdles, technical complexities, market design issues, and privacy concerns. Scalability, interoperability, and grid stability are significant challenges that need to be addressed for the widespread adoption of P2P trading models. The challenges and limitations of P2P energy trading are detailed in [45]. This article investigates the application of blockchain-based P2P energy trading as a financing mechanism for DERs. With the growing adoption of DERs such as solar PV systems and battery storage, there is a need for innovative financing solutions to overcome barriers to deployment and maximize their economic viability. Traditional financing models often face challenges related to uncompensated excess energy, monopolized energy tariffs, time of energy injection into the grid, and transaction costs. In this study, we explore how blockchain technology can facilitate decentralized energy trading among prosumers, enabling direct transactions and value exchange within a distributed energy network. We examine the potential benefits of blockchain-based P2P energy trading, including open market rate negotiation, fair energy compensation, higher asset revenue, and increased autonomy for energy consumers. Overall, this article contributes to providing insights into the potential of blockchain-based P2P energy trading as an alternative billing mechanism to NEM and FiT, especially for rural and isolated microgrids, as well as a financing mechanism for DER development, thus advancing the transition to a more sustainable and resilient energy future.