esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Opportunities in O-RAN
### Satellite NTN  
One of the challenges of Satellite NTN low-latency communications is long latency due to the significant distance between the terrestrial UE and the satellite [71; 72; 73]. Using a distributed computing model, O-RAN can help a satellite solve its latency drawback. In this model, the O-RAN intelligent controller can be deployed closer to the end-user (for example, at the satellite gateway or user terminal) to reduce the round-trip time for control and management signals. The controller can also use advanced algorithms to intelligently allocate radio resources, reducing the need for frequent signaling between the user terminal and the satellite. Another way O-RAN can help reduce latency is through the use of edge computing. Edge computing involves moving tasks closer to the user or device, reducing the amount of data that must be sent back and forth between the user and the satellite. This approach can be used to run applications such as video streaming, gaming, or IoT applications, which require low-latency and high-bandwidth connections.  
For massive devices involved in NTN, ML-empowered O-RAN architecture can play an essential role in optimizing the performance, e.g., throughput, coverage probability, latency, and energy efficiency. Firstly, for capacity optimization, ML can help predict the capacity requirements of the NTN and optimize the allocation of resources such as bandwidth, power, and antenna coverage. Secondly, ML can help identify and mitigate network interference sources, such as adjacent satellite interference, co-channel interference, and inter-system interference. Thirdly, ML can optimize the energy consumption of the O-RAN-assisted NTN, such as by reducing the power consumption of individual network elements or adjusting the power levels based on traffic demand. Finally, as latency remains one of the main challenges in Satellite NTN, reducing latency is important for Satellite NTN to support a wide range of applications. Some recent papers introduce the use of generative AI and digital twin in the communication system to deal with bandwidth limitation and, particularly, the latency [74]. As O-RAN offers convenient hosting of ML models, ML models can be pervasively deployed in O-RAN to encourage use of generative AI and digital twin in satellite communications.
# Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS
## 2 The Suppression Mechanism of the New Method
### The Structure of the Damping Busbar  
The conventional GIS busbar was hollowed out into a spiral tube, and the busbar conductor was changed into a series circuit with a multiturn hollow inductance coil and a multiturn gap; then, we paralleled the damping resistance with the spiral tube inductance circuit. This method increases the active losses of the busbar by changing the wave impedance of the busbar. As a result, the transient energy is consumed, and the amplitude of the VFTO is reduced. Thus, the main components of the spiral tube damping busbar are (a) a spiral tube damping busbar made by screwing out the conventional busbar to a spiral slotted solenoid busbar; (b) solid metal oxide resistors with an antipulse voltage function as the noninductive damping resistors, as shown in Figure 2; and (c) electrical connection tools.  
Figure 1: The new damping busbar installed in a gas insulated substations (GIS): (**a**) general view of the damping busbar with its components; (**b**) the placement of the damping busbar.
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
## 1 Introduction
The main motivation behind this work is that there are conflicting interests of players/vehicles, which aim to maximise their profits by not cooperating. Thus, a methodology will be suggested whereby the conflicting members will not lose throughput by employing the best transmission power level. In summary, the contributions of this paper are listed below:  
* We devise a new metric that pushes the PRR further down the wireless stack, in particular the PHY layer. * We form a potential game of the joint power and rate control. * We identify the best case Nash equilibrium for two cases, namely no or stable interference. * We show that the price of stability is 1. * We show that with a stable delay, the utility function is maximised because the rate increases. * We show that the utility function is concave, and it can be solved in polynomial time. * We indicate the number of time slots and time in seconds that are required for the transmission with and without the duty cycling mechanism. * We show results that we can accomplish a maximum rate and PRR by raising the transmission power, which with duty cycling means increasing the signal-to-interference-plus-noise ratio (SINR). The remainder of this paper is as follows: Section 2 gives the related work, Section 3 gives the system model, Section 4 gives the results and Section 5 describes the conclusions.
## 2 Materials and Methods
### \({}^{13}\)Co\({}_{2}\) Sampling, Analysis, and Calculations  
For determination of \(\delta^{13}\)C of soil-emitted CO\({}_{2}\), samples were flushed into 12 mL Exetainer\({}^{\text{\textregistered}}\) septum-capped vials (Labco, High Wycombe, UK). Samples were taken twice a day for the first 5 days, daily for the next 12 days, every second day for the next 14 days, and every 3 days until day 43. Samples were introduced by a Combi-Pal autosampler (CTC-Analytics, Zwingen, Switzerland) to a GC (GC-Box, Thermo Fisher Scientific, Bremen, Germany) coupled to an isotope ratio mass spectrometer (Delta plus XP, Thermo Fisher Scientific, Bremen, Germany) via a Conflo III Interface (Thermo Fisher Scientific, Bremen, Germany). The fractions of CO\({}_{2}\) derived from litter (f\({}_{\text{filter}}\)) and SOM (f\({}_{\text{SOM}}\)) were calculated using Equations (1) and (2):  
\[\text{f}_{\text{filter}}=(\delta^{13}\text{C}_{\text{treatment}}-\delta^{13} \text{C}_{\text{Control}})/(\delta^{13}\text{C}_{\text{filter}}-\delta^{13} \text{C}_{\text{Control}}) \tag{1}\]  
\[\text{f}_{\text{filter}}+\text{f}_{\text{SOM}}=1 \tag{2}\]  
where \(\delta^{13}\)C\({}_{\text{treatment}}\) is the measured \(\delta^{13}\)C (%) of CO\({}_{2}\) from litter treatment, \(\delta^{13}\)C\({}_{\text{Control}}\) is the measured \(\delta^{13}\)C (%) of CO\({}_{2}\) from control treatment without litter addition, and \(\delta^{13}\)C\({}_{\text{Filter}}\) is the mean measured \(\delta^{13}\)C (%) of CO\({}_{2}\) lost from maize litter (Leaf: \(-\)7.91 \(\delta\)%, Root: \(-\)7.50 %, Staw: \(-\)9.33 \(\delta\)%, see supplement for details, Figure S2). For each treatment, the priming effect (\(PE\)) was calculated as the difference between SOM-derived CO\({}_{2}\)-C (\(C_{\text{SOM}}\)) and CO\({}_{2}\)-C from control treatment without litter (C\({}_{\text{Control}}\)) (3):  
\[\text{PE}=\text{C}_{\text{SOM}}-\text{C}_{\text{Control}} \tag{3}\]
## 1 Introduction
A control system based on [PERSON]'s algorithm from graph theory is presented in [21] to synchronize and separate DN into multiple island systems to keep the system balanced. In [22], a bi-level optimization problem is presented to transform an active DN in multiple MGs using spectral graph theory to define the maximum numbers of sub-networks through a mixed-integer nonlinear problem (MINLP), and a second lower-level problem is presented to measure the reliability of the MGs formed by the upper-level. An optimal scheduling model is presented in [23] to address dynamic MG reconfiguration, where a linear version of the AC-OPF is proposed and uses the bender decomposition method to decompose and coordinate the grid-connected system and isolated operation through a master problem and sub-problem, respectively. The existing literature sets out that the techniques used until now for partition and dynamic MG reconfiguration are based on mostly spectral graph theory and hierarchical and community detection algorithms. Nevertheless, to the best of our knowledge, there is not a single mathematical optimization model which provides the DN dynamic reconfiguration scheduling, based on demand side response (DSR)and load curtailment, such that it prevents widespread blackouts as a result of low power generation or network failures, for DN with high penetration of DG and BESS. In order to extend the literature, the proposed model addresses this issue and provides additional information for the distribution system operations (DSO). The main contributions of this article are summarized as follows:  
* A new mathematical optimization model is presented which provides the virtual microgrid topology scheduling of an ADN considering the load curtailment and its flexibility response as the main criteria to partition an unbalanced system into several VMs balanced, with a computational time short enough to ease its adoption by DSO in their usual operational procedures. * We have extended the literature related to the partitioning methods in order to obtain VMs with dynamic boundaries, leaving the algorithms based on hierarchical or graph theory, and providing an optimization linear model. * A quantification of the weaker nodes in terms of the probability of the node to not meet the load under consecutive scenarios, and the size of the energy not supplied which is a useful value in flexibility markets. The rest of the paper is organized as follows. Section 2 describes the problematic where the mathematical model is situated, and the methodology follows to validate the model under different scenarios. Section 3 presents the mathematical model with the nonlinear constraints already linearized. In Section 4, the model proposed is tested in a case of study and the results are mentioned and discussed. In addition, finally, the conclusions are presented in Section 5.
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 5 Optimization Techniques
### Stoa  
A novel bio-inspired optimization algorithm called Sooty Tern Optimization Algorithm (STOA) has been created by [PERSON] and [PERSON] [42] to address the constraints of the industrial issues. The movement and attacking habits of the sea bird sooty term in nature are the key motivations for modeling the STOA technique. STOA was validated using 44 benchmark test functions and compared it with nine well-known optimization techniques in terms of performance. The results of CEC 2005 and CEC 2015 standard test functions prove that the STOA is capable of addressing difficult and high dimensionality bound constrained actual situations. The pseudo-code of the STOA technique is indicated in Algorithm 3.  
```
1. Initialize the population \(\mathrm{X}_{1}^{\mathrm{P}}=\ (\mathrm{X}_{1}^{\mathrm{P}},\ \mathrm{X}_{2}^{\mathrm{P}},\ \mathrm{X}_{3}^{\mathrm{P}},\ldots\ldots,\ \mathrm{X}_{N}^{ \mathrm{P}}\) within the limits \(\mathrm{X}_{1}^{\mathrm{min}}\leq\mathrm{X}_{1}^{\mathrm{P}}\leq\mathrm{X}_{1}^ {\mathrm{max}}\)
2. Initialize parameters \(\mathrm{D}_{\mathrm{A}}\) and \(\mathrm{C}_{\mathrm{B}}\)
3. Evaluate the fitness of whole population
4. Best search agent \(\rightarrow\)\(\mathrm{X}_{\mathrm{best}}\)
5. While (it < \(\mathrm{Max}_{\mathrm{it}}\))
6. for(i = 1: N) do
7. Update the position of the current search agent
8. endfor
9. Initialize parameters \(\mathrm{D}_{\mathrm{A}}\) and \(\mathrm{C}_{\mathrm{B}}\)
10. Evaluate the fitness of whole population
11. Update \(\mathrm{X}_{\mathrm{best}}\)
12. it = it +1
13. endwhile
14. return\(\mathrm{X}_{\mathrm{best}}\)
```  
**Algorithm 3** STOA [42]
## 1 Introduction
In recent years, scholars have proposed many measures for reducing the micro-vibration caused by the flywheel and control moment gyros [19; 20; 21]. However, the structure of the sun-tracking solar arrays makes it hard to test them by these methods. Therefore, experimental research on the disturbance produced by the SADA is still in its infancy. Two main constraints limit the experimental research on the disturbance of solar arrays: (1) Solar arrays are characterized by low stiffness and low damping, which make it risky to operate them in a ground test; (2) Gravity and air resistance have a great influence on the ground disturbance test of the solar array, and the influence is hard to avoid. For the reasons above, there is a lack of experimental studies on the micro-vibration, which also leads to the difficulty in verifying the accuracy of the disturbance torque model of the sun-tracking solar array. In summary, the sun-tracking solar array is one of the sources of disturbance on spacecrafts. However, there has not been a comprehensive disturbance torque model that takes both the friction and flexibility of the solar array into consideration. Moreover, due to the structural characteristics of the solar array, it is difficult to conduct ground tests to verify the accuracy of a model. Therefore, this paper studies the disturbance torque of the solar array from the modeling and experimental perspectives. Firstly, a model of the disturbance torque was proposed, which took the structural friction and the flexibility of the solar array into consideration. Then, to verify the model by ground experiment, a solar array simulator (SAS) with an adjustable moment of inertia and torsional mode frequency was designed to replace the solar array for the ground tests. At the same time, as the moment of inertia and the modal frequency of the SAS were changed, their influences on the disturbance moment were investigated. Finally, the effectiveness of the model was verified by comparing the simulation results with the experimental results.
## 3 Materials and Methods
### K-Medoids Clustering Algorithm  
The proposed clustering algorithm is based on k-medoids (KCA) to obtain a general clustering method to improve the overall QoS performance. First, we collect the nodes' coordinates and remaining energy information, and then compute the group number k. We optimized the k-medoids algorithm by computing the central circle's midpoint and residual energy to reduce iteration time [32; 33].  
Figure 1: Clustering in WSN.  
Set-Up Phase  
All nodes are divided into appropriate groups during the setup phase, and CH nodes are identified. Let k be the number of groups defined by Equation (3).  
\[\text{k}=\sqrt{\frac{N}{2}} \tag{3}\]  
where N represents the number of nodes. Let L be the node's central position. Equation (4) can be used to calculate L.  
\[\text{L}=\frac{\sum_{i=1}^{N}X_{n}}{N} \tag{4}\]  
where X\({}_{\text{n}}\) is the sensor I coordinate and d represents the average distance between the node's central position, L, and all SNs. Equation (5) can be used to calculate d.  
\[\text{d}=\frac{\sum_{i=1}^{N}X_{n}-L}{N} \tag{5}\]  
A central circle can be computed using L and d. We randomly choose k points on the central circle as the initial mean point l_i, which can be calculated using Equation (6).  
\[\begin{cases}I_{ix}=d\times\cos\bigl{(}\frac{360}{k}\times(i-1)\times\frac{\pi} {180}\bigr{)}+L_{x}\\ I_{iy}=d\times\sin\bigl{(}\frac{360}{k}\times(i-1)\times\frac{\pi}{180}\bigr{)} +L_{x}\end{cases} \tag{6}\]  
where \(I_{ix}\) and \(I_{iy}\) denote the mean point coordinates of cluster I. Since the k-medoids algorithm's initial midpoints are chosen at random, when two or more midpoints are very near, we often obtain incorrect results, resulting in a huge amount of iteration time. Equation (7) accurately represents the absolute-error criterion, E.  
\[\text{E}=\sum_{i=1}^{k}\sum_{x\in C_{i}}dist\;(x,r_{i} \tag{7}\]  
where x is a common node in cluster \(C_{i}\) and \(C_{i}\) represents cluster i. E is the sum of the distances between all represent nodes and common nodes. The replace method is illustrated in Equation (8).  
\[I^{(t+1)}=\begin{cases}I^{*},\;E^{*}-E^{(t)}<0\\ I^{(t)},\;otherwise\end{cases} \tag{8}\]  
Equation (7) and new clusters can be used to calculate the new absolute-error criterion, \(E^{*}\). If \(E^{*}\) is less than \(E^{(t)}\), \(I^{(t+1)}\) will be replaced by \(I^{*}\).
## 3 Conceptual Design
### Security and Privacy
The IIP provides additional functionalities, as shown in Figure 2b, for managing access to information channels. Information consumers are provided with resources for listing information channels (information channel catalog service), requesting access to information channels and listing their pending access requests to information channels. Information providers, on the other hand, are provided with resources for authorizing access to information channels that belong to them (adding allowed users to their allowed user lists), deauthorizing access to information channels that belong to them (removing allowed users from their allowed user lists) and listing pending access requests to their information channels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Usernames** & **Channel\_1** & **Channel\_2** & \(\ldots\) & **Channel\_n** \\ \hline
**username\_1** & YES & YES & NO \\
**username\_2** & NO & YES & YES \\ \(\vdots\) & & & \\
**username\_m** & NO & NO & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Access control matrix for read access to information channels. The identity-based access control to different information channels is meant to be used by applications (both information providers and information consumers). User identities in this case are application identities, not users of the applications (e.g., patients, nurses). These identities are used for controlling what information different applications can or cannot use, where the information is generated by other applications. However, these identities, which are used in the IIP, can also be directly mapped to user identities in the information consumer applications if required (e.g., a patient ID as a username in the IIP). Finer degrees of access control can be implemented by information consumer applications, for example, utilizing a role-based access control (RBAC) to group individual users of the applications.
# Abstractive Summarizers Become Emotional on News Summarization
## 6 Emotions in Summarization Systems
### Emotional Coherence and Bias
#### 6.2.2 Emotional Bias  
Emotional bias measures how the emotion ratios for an emotion \(e\) in the generated summaries correlate with the emotion ratios for \(e\) in the reference summary. Hence, it quantifies the strength and direction of the relation between the emphasis, regarding the source article, placed on an emotion \(e\) in a generated summary and the emphasis placed on that emotion in the reference summary. The emotional bias for an emotion \(e\) is computed as the Pearson correlation between the emotion ratios in the reference summaries \(y=\{ER(e,s_{1},a_{1}),\ldots,ER(e,s_{N},a_{N})\}\) and in the generated summaries \(\hat{y}=\{ER(e,s_{1},a_{1}),\ldots,ER(e,s_{N},a_{N})\}\). To compute the emotional bias, we discard all those examples where the emotion ratio is undefined (when \(ED(e,a)=0\)).  
Figure 3 shows the emotional bias between reference summaries and summaries generated by each model for all the emotions and corpora. In almost all the cases, the emotional biases are higher than 0, suggesting positive relationships between emotion ratios. The strength of the correlations is notably lower than in the emotional coherence (Figure 2). It suggests it is more difficult to approximate the emotion ratios than the emotion densities.  
The abstractive systems show higher emotional bias in Xsum than in Cnn/Dailymail. In Xsum, T5 is the abstractive model with the lowest emotional bias. Pegasus shows the highest emotional bias for almost all emotions in Cnn/Dailymail and Xsum. All the abstractive systems show, in Xsum, the lowest emotional bias for _anger_ and _surprise_, and the highest emotional bias for _sadness_, _fear_, and _trust_. The emotional biases of abstractive systems are similar for all the emotions in Cnn/Dailymail.  
Figure 2: Emotional coherence of each model for each emotion in Cnn/Dailymail and Xsum. Correlations are statistically significant (\(p\)-value is 0 in all the cases).  
Baseline models, Lead and Random, show a low emotional bias in Cnn/Dailymail and a negligible one (close to 0) in XSum. The low emotional bias of Lead indicates that the first sentences of the source articles do not show the expected emotion ratios in the summaries neither of Cnn/Dailymail nor XSum. In Cnn/Dailymail, Lead shows a slightly lower emotional bias than abstractive models for all the emotions, but in XSum, the difference concerning abstractive models is high. All the systems show higher emotional bias than Random.  
The oracle shows the highest emotional bias in Cnn/Dailymail but not in XSum, where the abstractive models stand out. It could suggest again that abstractive models could increase their emotional bias in Cnn/Dailymail if they focus on better sentences from the source article (in terms of Rouge with respect to the reference summary) but not in XSum.
## 1 Introduction
In unsaturated soils, the level of matric suction caused less uniform water pore pressure dissipation, with differences of up to 21% in the Soft Soil models compared to experimental values. As vertical stress increased, effective saturation rose, and suction decreased, achieving full saturation in soils with initial saturation of 80% and 99% earlier than in those with 60%. Saturated soils exhibited uniform dissipation behavior, while unsaturated soils showed different behavior due to matric suction. In summary, the Soft Soil and Hardening Soil constitutive models, in both saturated and unsaturated conditions, effectively simulate the reduction in the void ratio under a given load magnitude. However, discrepancies arise in the recovery of the void ratio between the experimental and numerical models during the unloading phase. These differences are due to the Soft Soil model prioritizing plastic compression, which limits expansion after unloading, whereas the Hardening Soil model adjusts stiffness with the load, allowing for greater volumetric recovery [10; 15; 16]. The importance of the results lies in providing a basis for understanding and modeling the hydromechanical behavior of compressible soils under variable saturation conditions. However, there are limitations due to the variability in pore pressure dissipation, which is influenced by the boundary conditions imposed in the numerical model. In real scenarios, the variability of soil permeability and drainage conditions can lead to responses that are different from those simulated. Additionally, the high variability in the soil-water characteristic curve (SWCC) adjustment and its dependence on loading history can introduce uncertainty in predicting soil behavior. While this study represents a useful first step for 2D simulations, transitioning to 3D models may require additional parameter adjustments to better capture three-dimensional effects, especially under complex loading conditions. Finally, is important to highlight that the methodology proposed in this study allows for a better understanding of the effects of saturation on soil consolidation, providing a foundation for selecting an appropriate constitutive model based on soil characteristics and saturation degree.