esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Since the presentation of the Internet in 1969, from the Advanced Research Projects Agency Network (ARPANET) military errand, the advancement of framework has been upheld and predicted, with a goal of around 5 billion connections by the end of 2019 [1, 2]. It is definitely not a top secret for specialists who organize the framework system in dissimilar associations and affiliations. Once created, it is important to keep up perfect working and check for issues such as the development of contraptions and interoperability, among others [3]. Instead, there has been a necessity to ensure the system wherever key and crucial material is taken care of for associations and affiliations, principally in game plans with sophisticated datasets of clients and suppliers. In this sense, perfect models have been developed, for instance, Cloud Computing, that have empowered these data to be taken care of and set away remotely by the highest availability, protection, and validity, and abusing the advantages of the framework [1].  
The idea of an software defined network (SDN) depends on the model made out of the three layers--application, control, and information plane--that ideally repeat and robotize the system framework [3]. Engineering expects to integrate the control and information planes in organized gadgets, for example, in routers and switches [1]. The control plane is answerable for choices in regard to the traffic which associates through any gadget in system, although the information plane performs the assignments of shipping information parcels. The design of programming characterized systems is reasonably made out of the three layers: foundation, control, and application [1]. Additionally, the application program interface (API) that conveys these three planes is the northbound API and the southbound API, which are contingent upon the correspondence discourse regarding the SDN-based controller. Inside the SDN condition, the advancement of the open source APIs is advanced, which provides greater adaptability and dynamism [3].  
As a result, a setting for this system worldview will be given, centered on current mechanical patterns and their point of view. The applications, modes, controllers, and architectures of SDNs can be seen in Figure 1.
## 4 Results and Discussion
### Environment and Datasets  
The environment of this experiment and the statistics of the datasets are shown in Table 1 and Table 2, respectively.  
In the conducted experiments, two real-world social network datasets published by Stanford were utilized. Among these, the Wiki-Vote network stands out as a voting dataset. In this network, the nodes symbolize Wikipedia users, and a directed edge from node \(i\) to node \(j\) indicates the voting behavior of user \(i\) towards user \(j\). The Facebook dataset discussed in the study represents a social friendship network. It comprises users and their friendship relations extracted from Facebook, the largest social networking platform globally.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Node** & **Edge** & **Average Clustering Coefficient** & **The Longest Shortest Path** \\ \hline Wiki-Vote & 7115 & 103,689 & 0.1409 & 7 \\ Facebook & 4039 & 88,234 & 0.6055 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical characteristics of the Wiki-Vote and Facebook datasets, along with their distributions, are shown in Figures 13 and 14.  
Figure 14: Facebook dataset. The different colors represent different communities within the Facebook dataset, with each color indicating a distinct cluster of nodes.  
Figure 13: Wiki-Vote dataset. The different colors represent different communities within the Wiki-Vote dataset, with each color indicating a distinct cluster of nodes.
## 5 Implementation and Deployment
### Traffic Simulation Software  
Traffic simulation software can fulfil the requirements of a scenario simulator for vehicle motion scenario simulations. We selected the current mainstream simulation software, SUMO. The simulation platform [32] of MCity 2.0 was established based on SUMO. Veins [33], and the Internet of Vehicle (IoV) simulation platform combining the existing network simulation software and traffic simulation software also selected SUMO to support its traffic simulations.  
Eclipse SUMO [34] is a free and open-source traffic simulation suite. It has been available since 2001 and allows for the modeling of intermodal traffic systems, including road vehicles, public transport, and pedestrians. SUMO includes a wealth of supporting tools that automate core tasks in the creation, execution, and evaluation of traffic simulations, such as network import, route calculations, visualization, and emission calculations. SUMO can be enhanced with custom models and provides various APIs to control the simulation remotely.
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 6 Numerical Results
### Performance Comparison  
We set four algorithms (benchmarks) to compare the performance and advantage of COPP-DDPG, including binary offloading policy with DDPG (BOP-DDPG), offloading all tasks to MEC (OT-MEC), offloading all tasks to VEC (OT-VEC), and local execution all tasks by ICV's processor (LE-VP).  
1. BOP-DDPG: Although we adopt the DDPG algorithm, BOP-DDPG does not consider the situation of partial offloading, but only offloading all tasks to the MEC server, VEC, or local execution. However, the neural network has the same structure as each network as COPP-DDPG and the allocated computational resources for the vehicles from all RSUs are the same as COPP-DDPG.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Size of the first hidden layer for actor and critic & 300 \\ Size of the second hidden layer for actor and critic & 300 \\ Learning rate of actor and critic \(\alpha^{\prime}/\alpha\) & 0.0001/0.001 \\ Size of experience memory \(B\) & 20,000 \\ Parameters for OU noise \(\theta,\ \mu,\sigma\) & 0.15, 0.15, 0.10 \\ Discount factor \(\gamma\) & 0.95 \\ Penalty for failed tasks execution \(P\) & 8 \\ Total number of all episodes \(K\) & 1000 \\ Total time periods of one episode \(T\) & 110 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main hyperparameters of the De-DDPG.  
2. OT-MEC: The vehicular tasks generated by the ICVs are all offloaded and executed to the MEC server through their linked RSUs. The VEC is not considered in the simulation environment.
3. OT-VEC: The vehicular tasks generated by the ICVs are all offloaded and calculated to VEC with spare computational resources. The MEC offloading strategy is not considered in the simulation.
4. LE-VP: All computation tasks are executed by the local processor of ICV. VEC and the MEC server are not applicable for offloading tasks.
## 4 Experiments
### Implementation and Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **ACC** & **PR** & **Recall** & **Weighted F1** & **Macro F1** & **MCC** & **FM** \\ \hline
**Manual Augmentation Only** & & & & & & & \\ \hline Decision Tree & 95.66 & 0.92 & 0.95 & 0.93 & 0.90 & 0.93 & 0.94 \\ \hline Random Forest & 91.00 & 0.84 & 0.91 & 0.87 & 0.80 & 0.86 & 0.87 \\ \hline AdaBoosted DT & 95.00 & 0.93 & 0.95 & 0.93 & 0.91 & 0.93 & 0.91 \\ \hline OLA & 90.33 & 0.84 & 0.90 & 0.86 & 0.79 & 0.86 & 0.88 \\ \hline LCA & 87.33 & 0.80 & 0.87 & 0.82 & 0.74 & 0.81 & 0.83 \\ \hline KNORA-U & 89.33 & 0.83 & 0.89 & 0.68 & 0.78 & 0.84 & 0.85 \\ \hline KNORA-E & 91.33 & 0.85 & 0.91 & 0.87 & 0.81 & 0.87 & 0.88 \\ \hline META-DES & 91.66 & 0.86 & 0.91 & 0.88 & 0.81 & 0.88 & 0.89 \\ \hline
**Manual Augmentation + Augmentation with SMOTE** & & & & & & \\ \hline Decision Tree & 94.44 & 0.95 & 0.94 & 0.93 & 0.93 & 0.91 & 0.92 \\ \hline Random Forest & 94.44 & 0.95 & 0.94 & 0.93 & 0.84 & 0.91 & 0.92 \\ \hline AdaBoosted DT & _97.66_ & 0.97 & 0.97 & 0.97 & 0.97 & 0.96 & 0.94 \\ \hline OLA & 92.66 & 0.90 & 0.92 & 0.90 & 0.85 & 0.89 & 0.89 \\ \hline LCA & 91.33 & 0.89 & 0.91 & 0.89 & 0.93 & 0.87 & 0.87 \\ \hline KNORA-U & 94.99 & 0.94 & 0.95 & 0.94 & 0.89 & 0.93 & 0.94 \\ \hline KNORA-E & 93.66 & 0.91 & 0.93 & 0.92 & 0.86 & 0.90 & 0.91 \\ \hline META-DES & 93.33 & 0.92 & 0.93 & 0.92 & 0.87 & 0.90 & 0.89 \\ \hline
**Manual Augmentation + Augmentation with Borderline-SMOTE** & & & & & & \\ \hline Decision Tree & 96.00 & 0.94 & 0.96 & 0.94 & 0.92 & 0.94 & 0.93 \\ \hline Random Forest & 93.33 & 0.90 & 0.93 & 0.91 & 0.87 & 0.90 & 0.89 \\ \hline AdaBoosted DT & 95.00 & 0.93 & 0.95 & 0.93 & 0.91 & 0.93 & 0.91 \\ \hline OLA & 92.33 & 0.91 & 0.92 & 0.91 & 0.85 & 0.89 & 0.88 \\ \hline LCA & 91.33 & 0.89 & 0.91 & 0.89 & 0.84 & 0.88 & 0.85 \\ \hline KNORA-U & 94.33 & 0.93 & 0.94 & 0.93 & 0.88 & 0.92 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the multiple stratified CV method for the ensemble models implemented based on the framework proposed by [PERSON] et al. [23].
## 1 Introduction
tested several different pore sizes and shapes to separate H\({}_{2}\)/N\({}_{2}\) mixtures. The smallest pores are reported to allow the H\({}_{2}\) permeation because of steric effects. Conversely, for bigger pores, N\({}_{2}\) is reported to cross the membrane more frequently. The authors explained the observed trend highlighting that Van der Waals interactions between the gas and the membrane are stronger in the case of nitrogen atoms [21]. Experimentally, the gas separation of H\({}_{2}\), CO\({}_{2}\), Ar, N\({}_{2}\) and CH\({}_{4}\) mixtures with graphene was assessed with pores generated by ultraviolet etching. After the pore generation only H\({}_{2}\) and CO\({}_{2}\) are reported to cross the membrane [22]. Mixture of H\({}_{2}\)S and CH\({}_{4}\) were instead reported to be separated by means of carbon-based membranes, specifically carbon nanotubes. An inner diameter of 23 nm was necessary for the task [23]. Different aspects influencing gas permeation through porous graphene are suggested in literature, however the parameters that mainly affect selectivity and permeability in gas-gas separation are not clear yet. Steric hindrance is known to be important but it is not expected to be the only effect influencing filtration. For instance, different chemistries at the pore edges are likely to play an important role as it is known from first principles simulations that gas molecules adsorb with different strength on B, N, Al, and S doped graphene [24]. As proof of principle, a theoretical study showed that hydrophilic pore passivation with fluorine atoms improves water permeability at low pressures during desalination through nanoporous graphene membranes [25]. In this work we focused on understanding the features that the graphene pores must possess to accomplish CO\({}_{2}\)/CH\({}_{4}\) and H\({}_{2}\)/CH\({}_{4}\) separation during natural gas purification to enhance graphene membrane performances. By means of molecular dynamics simulations we demonstrate how pore shape, dimension and chemical functionalization determine the graphene membrane permeance and selectivity.
## 3 Results and Discussion
### Pearson Correlation between Parameters  
A significant correlation was observed among the methods used to determine the biological activities (Table 4 (A,B)). RSADPPH and TP (\(r\) = 0.881 and 0.951 for 2019 and 2020, respectively) were strongly correlated and the same pattern was observed for RSADPPH and esterified catechins (\(r\) = 0.871 and 0.911 for 2019 and 2020, respectively). A strong positive correlation was also observed between RSADPPH and FIC activity (\(r\) = 0.743) for 2019; however, a strong negative correlation was found for 2020 (\(r\) = \(-\)0.781). For RSADPPH and TF, a strong negative correlation was observed in 2019 (\(r\) = \(-\)0.922), but a weak negative correlation was detected in 2020 (\(r\) = \(-\)0.271). Concerning the correlation between FIC activity and other parameters, the results present moderate variability, showing both positive and negative correlations. For TP and TF, a negative correlation was observed in both years, showing a strong correlation in 2019 (\(r\) = \(-\)0.884) and a moderate correlation in 2020 (\(r\) = \(-\)0.500). For TP and esterified catechins, a positive moderate correlation was noted in 2019 (\(r\) = 0.577), while a very strong correlation was observed in 2020 (\(r\) = 0.925). In relation to TF and esterified catechins, a strong negative correlation was observed in 2019 (\(r\) = \(-\)0.819), while a weak negative correlation was found in 2020 (\(r\) = \(-\)0.838).  
These results highlight that polyphenols were the compounds that contributed the most to the antioxidant activities in all samples, with catechins being the major contributor, in particular, for the 2020 samples. Regarding the FIC activity, the correlations observed in the present study may suggest that other compounds, besides polyphenols, are also recognized as effective chelating agents and may also contribute to the moderate correlation.
# Improving Decodability of Polar Codes by Adding Noise
## 3 Conventional Schemes
### PP-SSC Decoding Algorithm  
The PP-SSC decoding algorithm [35] has been proposed to improve the performance of polar codes in data storage systems, where the GA procedure is employed to enhance the iteration process by producing perturbation vectors that are inherently better than the directed perturbed ones.  
The fitness function of an individual is evaluated as  
\[F_{c}=1\,/\sqrt{\sum_{k\in A}\left[d(k)\!-\!d_{p}(k)\right]^{2}/|A|}, \tag{2}\]  
where \(d\) and \(d_{p}\) are the decoded sequences of the received signal and the perturbed signal, respectively, and \(A\) is the information set of the polar code.  
Selection operations in the GA are used to select the parents of the next offspring at each evolution stage, where fitter individuals are forwarded as parents for the upcoming offspring. Then, the surviving individuals will then encounter evolutionary transformations, namely, mutations and crossovers, to generate offspring which would represent the new population. The perturbed vector generated by the GA process in the PP-SSC decoding algorithm will be added to the received signal \(y^{N}\) again for decoding, until a valid codeword is obtained or the prescribed maximum number of generations \(T_{2}\) is reached. We refer the readers to [31] for further details on the PP-SSC decoding algorithm.
###### Abstract
In this article, the underlay approach of the inband category is adopted to share the spectrum with all UEs connected to the D2D pair in each slice. We used the same data traffic model as in [1]. In summary, the main contributions of this paper are the following:  
* The proposal of the DDPG-KRP algorithm applied to the problem of limited resource allocation from BS to UEs. The radio resource allocation problem is modeled through state transitions considering a mobile network with NS and D2D communication. * A reward function is proposed in an augmented action space scenario where the agent learning process occurs without prior knowledge of a closed-form mathematical expression and system statistics, such as packet arrivals and channel quality of UEs. The proposed reward function generates a compromise between meeting the SLA requirements and maximizing the throughput and packet delivery ratio (PDR) in each slice while efficiently consuming the least amount of power possible. Reward penalty assigns power equal to zero to actions that do not satisfying the SLA constraints of each service. * The action elimination strategy is integrated into the deep-Q network (DQN) and double DQN (DDQN) agent training to eliminate the combination of undesirable actions exceeding the total available resource capacity. Agent exploration is directed toward desirable actions, improving the chances of making the optimal resource allocation decision and increasing the training convergence speed. * The DDPG-KRP algorithm performance is validated through extensive simulations. The results show that this algorithm significantly outperforms the other considered DRL-based methods due to better learning in high-dimensional action spaces. The remainder of this paper is structured as follows: Section 2 presents the main works related to this study; Section 3 provides the formulation of the radio resource allocation problem in a network slicing scenario based on two service categories (i.e., URLLC and video), where each slice has a probability distribution representing its network traffic. This section also presents a reward function for the agent's learning process; Section 4 begins with some reinforcement learning (RL) characteristics, then describes the motivation for the evolution of RL to DRL and introduces the proposed DDPG-KRP algorithm to solve the resource allocation problem considering NS and D2D and the strategy to reduce the high dimensionality of the action space; Section 5 discusses the results obtained from the DRL agents that learn through the reward function for resource allocation considering NS and D2D; and Section 6 summarizes the conclusions obtained.
###### Abstract  
A primary energy consumption and CO\({}_{2}\) emission source stems from buildings and infrastructures due to rapid urbanisation and social development. An accurate method to forecast energy consumption in a building is thus critically needed to enable successful management of adaptive energy consumption and ease the level of CO\({}_{2}\) emission. However, energy forecasting for buildings, especially residential buildings, has several challenges, such as significant variations in energy usage patterns due to unpredicted demands of the residences and some intricate factors, which can randomly affect the patterns. Traditional forecasting approaches require a tremendous number of inputs needed for building physic models and variations often exist between as-built and as-designed buildings in reality. Most recent studies have adopted only ambient weather conditions, building components, and the occupant's behaviours. As a result, in order to take into account the complexity of factors that can affect the building energy model development and its computation, we develop advanced machine learning models driven by the inherent electricity consumption pattern associated with the day and time. In this study, we demonstrate benchmarking results derived from three different machine learning algorithms, namely SVR, XGBoost, and LSTM, trained by using 1-year datasets with sub-hourly (30 min) temporal granularity to determine the outperformed predictor. Ultimately, the machine learning model robustness and performance on a basis of the coefficient of variation (CV) obtained by the SVR is benchmarked across XGBoost and LSTM trained by the same datasets containing attributes related to the building type, data size, and temporal granularity. The insight stemming from this study indicates that the suitable choice of the machine learning models for building energy forecasts largely depends on the natural characteristics of building energy data. Hyperparameter tuning or mathematical modification within an algorithm may not be sufficient to attain the most accurate machine learning model for building energy forecast.  
AI model; CO\({}_{2}\) emissions; energy consumption; machine learning; building energy; smart cities +
Footnote †: journal: Applied Sciences
### Potential Challenges in Underground Mines
#### 2.2.3 Gaseous Hazards and Limited-Line-of-Sight Problems  
One of the main factors that causes interruption of communication signals is the eruption of flammable gases. A risk of gas blast increases when the concentration of such gases exceeds a particular threshold. Therefore, continuous evacuation is required to decrease the probability of any disaster. Moreover, the explosion or any disaster can affect the propagation characteristics of EM signals and leads to a complete link failure [21]. LOS links in such a critical environment improve the quality of wireless communication because waves can propagate directly towards the receiver, rather than around the corner or through materials, as both cases produce high attenuation [22]. However, in underground mines, a congested structure of room-and-pillar and tunnel blockages restrict LOS communication.
## 8 Future Directions
### Utilizing Three-Dimensional Representations to Mitigate Image Acquisition Challenges  
Previous palmprint research has predominantly focused on two-dimensional (2D) images, which are susceptible to environmental factors. Recognition methodologies, categorized into line, texture, subspace, and coding approaches, often compromise accuracy due to the lack of depth information in 2D representations. To address this limitation, three-dimensional (3D) palmprints offer promising biometric identification with qualities of uniqueness, stability, and universality [95; 96; 97; 98]. However, adopting 3D palmprints introduces challenges in data volume and computational complexity. Sparse point clouds from 3D sensors impact mesh resolution and identification performance. Prolonged pre-processing time and compatibility issues with 3D recognition algorithms further complicate matters. Advanced research is crucial for optimized 3D sensors, addressing time efficiency and data volume challenges in palmprint-based recognition. Enhancing 3D sensor capabilities is imperative for efficient and accurate palmprint recognition.