esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The heterogeneous satellite system is the development trend of satellite communication. In order to effectively utilize GEO/LEO heterogeneous satellite network resources, the access and handover decision strategy of the network is particularly important. In this paper, a two-step access and handover strategy for GEO/LEO heterogeneous satellite networks is designed, considering signal quality, handover cost, and load balancing. Firstly, the GEO/LEO satellite network selection function is designed based on the utility function, and then we propose an Importance-TOPSIS scheme to choose an LEO satellite if the users select to access LEO satellites in the first step. Compared with the SINR-max, the original TOPSIS, and the Entropy-TOPSIS handover strategy, the strategy proposed in this paper  
Figure 8: The sensitivity analysis of Target Point. (**a**) The average throughput, (**b**) The average number of handovers.  
Figure 7: The sensitivity analysis of \(p\). (**a**) The average throughput, (**b**) The average number of handovers.  
performs better in average throughput, the average number of handovers, and the forced termination probability. The strategy has robustness in a certain degree, and the values of related parameters need to be tested further. We will consider using the fuzzy set and gray method to reduce the robustness of the system for future work.  
Conceptualization, L.Z. and S.W.; methodology, L.Z. and X.L.; software, L.Z. and J.J.; validation, L.Z., S.W. and J.J.; formal analysis, L.Z. and X.L.; investigation, S.W.; resources, L.Z.; data curation, L.Z. and S.W.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z.; visualization, L.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key Research and Development Program of China under Grant no. 2020 YFB1806403, and in part by the National Natural Science Foundation of China under Grant nos. 61871147, 62071141 and in part by the Shenzhen Municipal Science and Technology Plan under Grant no. GXWD20201230155427003-20200730122528002.  
Not applicable.  
The authors declare no conflict of interest.
# Study on the Ozone Gas Sensing Properties of rf-Sputtered Al-Doped NiO Films
## 2 Materials and Methods  
### Deposition Conditions  
A Nordiko RFG2500 rf sputtering system was employed to grow NiO:Al films using a Ni metal target and Ar-O\({}_{2}\) air mixture in plasma. Two (2) Al pellets were placed on the Ni target surface for Al-doping. Both pressure and sputtering power were kept constant during deposition, being 5 mTorr and 300 W, respectively. The films were deposited on three different substrates, namely the commercial (Metrohm/DropSens) InterDigitated Transducers (glass substrate, Pt electrodes, bands/gaps = 5 \(\upmu\)m) for sensing measurements, a piece of silicon wafer for structural characterization, and microscope glass for optical measurements. All depositions were done at room temperature (RT). The O\({}_{2}\) content in plasma was varied, being 2.0%, 2.8%, and 4.0% in order to examine its effect on the gas sensing characteristics of the films. After deposition, the films underwent thermal annealing at 400 \({}^{\circ}\)C under air for 24 h, in order to improve both their crystallinity and optical properties.
# Prediction of Blood Pressure after Induction of Anesthesia Using Deep Learning: A Feasibility Study
## 1 Introduction
Deep learning is one of solutions for the limitation as it automatically extracts arbitrary patterns (i.e., features) beneath the observed data. The deep learning is rooted from artificial neural network (ANN) [11], and it can be used for the regression problem by adopting a particular loss function (e.g., mean squared error). The deep learning is theoretically capable of modeling all non-linear patterns by stacking many layers. As it is discovered that stacking too many layers might cause worse outcome (e.g., low accuracy, high error) due to gradient vanishing [12], there have been several approaches to effectively building deeper layers: rectified linear unit (ReLU) [13], residual connection [14], shortcut connection [15], Inception module [16], and pretraining concept [17; 18]. Thanks to these studies, convolutional neural network (CNN), which mainly consists of convolutional layers and pooling layers, was widely used for detection and recognition problem [19; 20; 21]. The convolutional layer extracts latent local features, and the pooling layer picks the most meaningful feature among the extracted local features; the CNN effectively captures local patterns and makes a decision by summarizing the most meaningful patterns. On the other hand, recurrent neural network (RNN) [22] allows a layer to have a recursive connection to itself, so that the RNN effectively captures sequential patterns by memorizing previous inputs. Such property makes the RNN to be widely used for machine translation [23; 24] and real-time prediction for sequences [25; 26]. In this paper, we aim at real-time prediction of blood pressure between the induction of anesthesia and the beginning of operation. This is basically a problem of real-time regression for blood pressure. Please note that we do not predict the current blood pressure, but the blood pressure of future (e.g., three minutes later). We adopt the RNN to capture arbitrary features from the sequential vital signs, and it makes prediction based on the features. As far as we know, this is the first study of applying the RNN to the real-time prediction of future blood pressure. We believe that this might be helpful for preventing some patients from falling into a critical condition. This paper is structured as follows. Section 2 describes the characteristics of target data (e.g., vital signs) and how we preprocess the data. It also provides details of our proposed approach as well as the definition of input and output. Section 3 demonstrates the performance of our approach by experimental results, and Section 4 interprets some sample results and discuss about additional experiments with different settings. Finally, Section 5 summarizes and concludes this paper.
# Monitoring Forest Phenology in a Changing World
## 4 Monitoring Challenges to Be Addressed  
### Expanding the Phenological Monitoring Networks  
The next steps for phenological monitoring need to include collaborative, quantitative and technology driven studies that incorporate multiple monitoring methods and focus on long-term monitoring networks across a range of biomes. The majority of the long-term phenological studies have been established in northern hemisphere, temperate biomes [159] and there is a need for these long-term monitoring networks to continue expanding into comparatively understudied systems such as southern hemisphere, 'aseasonal' tropical, and arid/semi-arid water-limited environments (however, see [209; 210; 211; 212]).  
There are of course challenges in these systems that have prevented the effective long-term monitoring of phenology previously, such as the asynchronous, individual tree crown level patterns in tropical forest phenology. Yet, the emerging technological methods of monitoring can overcome many of these limitations, so integrating these new methods into existing networks should be a priority. There are, however, examples of linking technologies and networks that can act as starting points. For example, the integration of fixed cameras into citizen science networks has already occurred, with the partnership of the PhenoCam network and the USA-NPN [151] and similar set ups in the NEON network [159].  
Expanding further into more localities and more data-heavy monitoring methods will produce its own challenges with regard to 'big data', cross-platform support, the reconciliation of data from varied sources and environments, and the upscaling of phenological observations. Multi-mode, long term monitoring will require large cloud-based data repositories, along with efficient methods to extract phenological metrics and then archive large data files. Thus, a further step in phenological monitoring will need to include options to integrate, process and store the large cross-platform datasets produced by these networks. Developments in machine learning and high throughput processing power should hopefully offer solutions to this problem.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Synthetic Minority Oversampling Technique  
The Synthetic Minority Oversampling Technique (SMOTE) is used to handle the class imbalance problem while dealing with crop datasets. It identifies a minor class and then finds the nearest neighbors to generate synthetic samples to balance the minor class. With this approach, the problem of overfitting is avoided, which improves the overall performance of crop recommendations.  
When we train a model having a dataset with a minor class, its performance will become poor. If we randomize the observations here, it leads to overfitting. But, we can apply the SMOTE as follows:  
Choose a sample \(X\) and identify its nearest neighbor \(N\). Find the difference between the sample and nearest neighbor, i.e., \(D=(X-N)\).  
Consider a random number from \(0\) to \(1\). For example, the number is \(R_{n}\) and it is multiplied by the difference \(D\). So, it is (\(R_{n}*D\)).  
Add these results to the sample \(X\) to generate a new synthetic feature \(Y\) shown in Equation (23) as follows:  
\[Y=X+(R_{n}*D) \tag{23}\]
## 4 Review and Survey Articles
A QC-assisted and QML-based framework for 6G communication networks was proposed, where it outlined the infrastructure, edge, air interface, and user end. Final Group Energy Efficiency of 5G and 6G 5G and 6G analyses the most recent works that address energy efficiency. We offer an overview of 5G, 6G, enormous IoT, and it concentrates on recent works. Two articles summarise current work on the radio access and core of wireless networks and identify ongoing difficulties and opportunities. The following are paths for power optimization: using game theory and machine learning, as well as different energy-saving measures [71; 72]. This paper proposed that future cellular networks will have different system requirements, which include utilising energy-harvesting technologies and optimising tool sets [73]. Another article discusses the opportunities and problems given by 5G networks. This review discusses the latest progress on standardisation, possible architecture candidates, and the energy considerations in 5G networks [74]. The authors [75; 76] presented a panoramic perspective of the supporting technologies for 6G and 6G-capable applications, such as multi-sensory-extended reality, digital replica, and more. There are over ten new applications of the 6G technology that are only just starting to be implemented. Furthermore, explore the multi-faceted communication possibilities of 6G, which will bring about a drastic transformation in the corporate arena. In next-generation applications of mmWave and THz solutions, space-air-ground integrated networks, full-duplex techniques, and other sophisticated channel coding assisted system designs, where powerful machine learning algorithms are expected to make autonomous decisions about the best mode of operation with minimal human intervention, this intelligent Tripple-fold adaptivity offers significant benefits [77]. One article [78] described, as mentioned above, requirements were contradictory, such as high efficiency and high density. This combination eventually led to reduced energy efficiency. TL can alleviate practically every problem caused by 6G needs.
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 4 Big Stream Fog-Based e-Learning Architecture
### Workflow Management and Resource Lookup  
Two important aspects in distributed architectures, such as the one presented in this article, regard service and data discovery and retrieval, as well as workflow management. In the following, we detail these aspects with reference to the aforementioned scenario.  
Concerning service and data discovery, our proposal could be based on Distributed Hash Tables (DHT), not unlike what usually happens in structured peer-to-peer networks such as Chord or Kademlia [39]. The peers we consider in this case are fog nodes, endowed with at least the PaaS layer and capable of semi-permanent storage and dynamic source allocation and retrieval. In this way, fog gateways may be federated through the DHT overlay network across different domains, furnishing at the same time scalability, quick lookups and a global view as a legacy cloud architecture. In this light, those nodes featuring only spouts register by nodes endowed with upper layer bolts that in turn manage discovery, lookup and access operations according to a certain DHT algorithm, possibly enriched with trust and reputation capabilities [40], without relying on any centralized cloud-based functional entity.  
In the scenario we describe in this article, featuring many streams of data and fog layers, one should also carefully consider how to split the workload for those services aggregating information from nearby devices and how to guarantee consistency among shared data. From this perspective, a workflow management layer is shared across the bolts in the SaaS and PaaS layers as described in Figure 3, with the aim of enriching them with synchronization, locking, scheduling and caching capabilities. Caching plays a fundamental role in fine-tuning the transmission rate of data streams from the lower levels towards the same-layer or upper-layer logic components. Locking processes are triggered whenever multiple spouts try to write on the same stream of data, in order to allow for the sources to retain their own data. Scheduling takes place in defining thresholds either for data transmission and migration between different workflow states or for offloading the workload to the cloud layer, even if the process can happen both ways.
# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 4 Experiments and Evaluation
### Comparative Analysis of Applied Techniques
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Correctly Classified Instances** & \multicolumn{2}{c}{1185} & \multicolumn{2}{c}{98.7 \%} \\ \hline
**Incorrectly Classified Instances** & \multicolumn{2}{c}{15} & \multicolumn{2}{c}{1.24 \%} \\ \hline
**TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **Class** \\ \hline
0.997 & 0.008 & 0.976 & 0.997 & 0.986 & A+ \\ \hline
0.996 & 0.001 & 0.996 & 0.996 & 0.996 & B \\ \hline
0.983 & 0.001 & 0.994 & 0.983 & 0.988 & B+ \\ \hline
0.994 & 0.007 & 0.983 & 0.994 & 0.989 & A \\ \hline
0.969 & 0.000 & 1.000 & 0.969 & 0.984 & C \\ \hline
0.909 & 0.000 & 1.000 & 0.909 & 0.952 & F \\ \hline
0.943 & 0.000 & 1.000 & 0.988 & 0.971 & D \\ \hline \multicolumn{5}{c}{**Weighted Average**} \\ \hline
0.988 & 0.004 & 0.988 & 0.986 & 0.987 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result obtained for MB + MLP. Figure 9: Fusion-based ensemble models (MB + MLP) result. Statistical hypothesis testing is used to make a claim related to the distribution of data or whether a set of results vary from one another. The null hypothesis is essential in interpreting the results and ensuring the strength of the claim of model performance by some statistical analysis. An Analysis of Variance test (ANOVA) is used in this study where statistical hypothesis testing is performed by estimating the \(p\)-value which is used to interpret the results of a test to either reject or fail to reject the null hypothesis. The \(p\)-value is selected as 0.05 for this study. The activity is conducted by comparing the \(p\)-value to the pre-chosen threshold value called alpha. If the \(p\)-value < alpha, the null hypothesis would be rejected or we would fail to reject the null hypothesis in the \(p\)-value > alpha case. The issue is addressed as whether the single and ensemble-based classifiers' performances are similar to fusion-based ensemble classifiers or not. The performances are evaluated by the four performance evaluation metrics (accuracy, precision, recall and F-measure) with the use of a collected dataset. After testing, it was revealed that the four performance evaluation metrics are relatively higher for fusion-based ensemble classifiers models. By using the one-way ANOVA test, the \(p\)-value turned out to be 0.045 for single-based classifiers and 0.002 for ensemble-based classifiers, which is less than the significance value of alpha. Hence, the null hypothesis was rejected and the claim about the efficiency of the fusion-based ensemble model was strengthened.
# The DREAM Endstation at the Linac Coherent Light Source
## 4 Scientific Application
### Coltrims  
This technique developed in the late 1980s and early 1990s gives access to kinematically complete experiments on photoionization and photofragmentation processes, by enabling the coincident and momentum-resolved detection of recoiling target ions and emitted electrons [28,29]. Momentum spectroscopy is a technique extensively used in molecular (atomic) spectroscopy to detect the total multidimensional momentum of constituent particles (i.e., cations, anions, electrons, and photons) undergoing photo-reaction. The latter arises as a consequence of ionization processes such as electron-ion collision or Coulomb explosion (CE). The tools used for particle detection to resolve and reconstruct the full momentum are often called reaction microscopes (REMIs) because of their source-target interaction. The DREAM endstations with the delay line anode, combined with a cold atomic and molecular target, forms an ideal workbench to study fundamental atomic physics with this technique. Previous measurements demonstrated ultrafast manipulation of the weakly bound He\({}_{2}\) dimer potential through an external laser field [30], with the potential to investigate the superposition of the ground state and excited Efimov state of He\({}_{3}\) using 4-body momentum imaging, only possible through COLTRIMS technique [30,31]. Furthermore, COLTRIMS allows the study of the multi-particle fragmentation dynamics of molecules through simple yet powerful momentum gating methods. Through measurements with high position and temporal resolution, the momentum vectors of all emitted  
Figure 14: Trajectory simulation for electron-ion coincidence experiments, with electrons detected by the detector equipped to the short arm, and ions to the long arm. The electric field strength is 47 V/cm, and the magnetic flux density is 14.5 Gauss. The trajectories of electrons with kinetic energies of 65 eV and 130 eV are shown in green and brown, respectively. The trajectories of N\({}^{2+}\) with kinetic energy of 15 eV and N\({}^{3+}\) with kinetic energy of 30 eV are shown in orange and blue, respectively. The ejection angles relative to the spectrometer axis range from 0 to 360 degrees. (**a**) Side display of spectrometer equipotential lines (red) and particle trajectories. (**b**) Spectrometer equipotential lines (red) and particle trajectories along the potential energy surface (green).  
electrons and ionic fragments in the same event can be measured, revealing the entangled many-particle dynamics in the molecular system. In the multistep fragmentation process, experiments [32; 33] involving left and right circular polarized photons in molecules showed evidence of possible dynamically induced symmetry breaking between core-hole and Auger electrons. With LCLS-II and advanced modes of operation, such as multi-color, multi-pulse, polarization, and ultra-short sub-femtosecond duration [34; 35; 36], these symmetry breaking measurements with variable X-ray polarization would be possible with DREAM endstation.
## 2 Security and Privacy Challenges in IoE
### Fundamental Security Risks in IoE
#### 2.1.4 Layered Classification of IoE Security  
Various technologies are utilized to ensure security and privacy in IoE environments. Blockchain enhances security by providing decentralized and immutable records, while Edge Computing offers the advantage of low-latency, real-time data processing. On the other hand, Quantum Security provides future-proof solutions by offering encryption methods resistant to post-quantum threats. FL allows data to be processed without being sent to a central server, and Zero-Knowledge Proof (ZKP) provides a verification mechanism that prevents data leakage.  
However, each method has specific strengths and weaknesses. Therefore, IoE security strategies should be built upon hybrid solutions that integrate different security layers. In this context, Table 2 presents a structured classification that highlights key technologies, the security features they provide, and their fundamental limitations for each security layer.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
This can significantly deteriorate the efficiency and performance of routing algorithms due to outdated information, further worsening network conditions. Consequently, the inaccuracy of path state information should be considered when designing multicast routing algorithms. Additionally, this issue can be formulated as a delay-constrained minimum cost tree problem, also known as a constrained Steiner tree (CST) problem [13; 14], which is an NP-Complete problem [15]. Persistent issues remain despite introducing various heuristic algorithms [16] to find approximate solutions. Due to the challenges associated with acquiring real-time global network status and the inherent complexities in solving the CST problem, our approach is focused on seeking approximate optimal solutions rather than pursuing precise resolutions. Moreover, multicast is a point-to-multipoint distribution pattern, whereas IP was initially designed for point-to-point communication networks. Addressing distribution issues through point-to-point communication protocols is complex and error-prone. This complexity arises from the dual nature of current IP addresses, serving as host and location identifiers. Moreover, in addition to distribution challenges, scalability, mobility, security, and other issues have hindered the rapid progress of the Internet [17]. The Information-Centric Network (ICN) [18; 19] is an innovative network architecture following the 'Name and Address-Separation' paradigm. It is proposed to overcome the limitations of traditional IP networks and make content distribution within the network more efficient and reasonable. ICN has attracted extensive research attention for its potential to promote revolutionary network advances. It achieves identity and location separation by naming content and using these names instead of IP addresses as identifiers in network transmissions [20]. Within ICN, two content forward mechanisms exist, name-based routing and name-based lookup [21]. In name-based routing, requests are forwarded in the network based on information names, and content routers deliver the data from the content holder to requesting users by following the reverse path of the request [17; 22]. However, this approach necessitates content routers to cache substantial routing states [23]. In the name-based lookup approach [24; 25], a Name Resolution System (NRS) [26] maps names to Network Addresses (NAs) of the underlying network, such as IP addresses, and data are routed based on these NAs.