esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The author validates the proposed model result by evaluating the performance in terms of training and validation accuracy and has achieved a training accuracy of 96.22% and a validation accuracy of 92.73%. The NSL-KDD dataset was used for the performance evaluation. A MapReduce-based machine learning model was proposed in [25], in which intrusion in cyber networks is detected intelligently with multiple data classification tasks. The proposed model uses multiple network sources in real-time and predicts unknown test scenarios. The proposed scheme was validated using the NSL-KDD dataset and a training accuracy of 97.6% and validation accuracy of 95.7% were achieved. [PERSON] et al. [26] proposed a lightweight real-time Performance and Feature (P&F) algorithm to extract the feature of low-rate DDoS attack. P&F also tries to locate the source of the LR-DDoS attack and identify the victims according to the flow features using time-frequency analysis. P&F can be deployed on the SDN controller to achieve real-time detection with less system cost (in terms of time and memory). In the proposed model, the detection is performed using supervised learning by deploying the Gradient Boosted Decision Trees (GBDT) algorithm. The achieved results show that the P&F framework has achieved a 98.06% success mitigation rate. Kalman backpropagation neural network was adopted in the IoT dynamic environment to detect and mitigate the DDoS attack [27]. The author validates the framework through simulations using up to date CICDDoS2019 dataset and attains the 97.49% detection rate. The proposed mechanism is intelligent intrusion detection against the biggest threat to the data traffic in IoT networks. In [28], the author proposed the multi-layer recurrent neural network (ML-RNN) to secure the end user and IoT devices in Fog computing. The proposed model was validated using the NSL-KDD and two additional metrics are added to evaluate the performance of the proposed system namely, (i) Mathew correlation and (ii) Cohen's Kappa Coefficient. The proposed model was based on the two-layer of RNN, on the first layer, the model attains the detection accuracy of DoS attacks up to 97.8%, and on the second layer, it achieves the detection accuracy of 98.27%. The existing work has achieved an excellent milestone in the detection of LR-DDoS attacks; however, some areas still need true attention, which includes the support to the distributed system, network overhead consideration, distributed-based local devices capabilities as well as the interaction with the centralized global model. Our proposed work mainly deals with the support of distributed learning, which can provide many benefits and support to the existing issues of malicious attack detection systems.
## 1 Introduction
### Salt Tolerance Mechanism of Halophytes: Role of Secondary Metabolites
#### 1.3.7 Maintaining Biogenetics  
_Haloxylon salicornicum_ is a xero-halophyte found in saline and arid regions of the world. [PERSON] et al. [46] used GC-QTOF-MS and HPLC-DAD to analyze the expression of metabolites in shoot samples of \(H\). _salicornicum_ treated with 400 mM NaCl. Out of 56 identified metabolites, 47 were significantly changed in response to salt stress. These metabolites were mainly amino acids, organic acids, amines, sugar alcohols, sugars, fatty acids, alkaloids, and phytohormones. Some amino acids, e.g., alanine, phenylalanine, lysine, and tyramine, were upregulated. These metabolites upregulated the tricarboxylic (TCA) cycle by optimal production of coenzymes (NADH and FADH2) and ample quantity of ATP during stress conditions.  
_Carix pumila_ was treated with 200, 300, 400, and 500 mM NaCl for 60 h, and leaf samples were analyzed to identify metabolites involved in the regulation of glycolysis, the pentose phosphate pathway, and the TCA cycle [47]. Heat map of hierarchical cluster analysis revealed changes in 39 metabolites, including 16 organic acids (galactaric acid, succinic acid, benzoic acid, fumaric acid, glyceric acid, malonic acid, gluconic acid, glutaratic acid, malic acid, hexadecanoic acid, propanoic acid, octadecanoic acid, phosphoric acid, hexacosanoic acid, and hydroxycarbamic acid), 9 amino acids (alanine, proline, aspartic acid, valine, serine, asparagine, glycine, threonine, isoleucine), 9 kinds of sugars (glucose, mannopyranoside, galactopyranose, galactose, allose, sucrose, fructose, mannose), 3 sugar alcohols (glycerol, galactinol, myo-inositol), and 2 amines (ethanolamine, hydroxylamine) [48].
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
###### Abstract  
Climate change, awareness of energy efficiency, new trends in electricity markets, the obsolescence of the actual electricity model, and the gradual conversion of consumers to _prosumer_ profiles are the main agents of progressive change in electricity systems towards the _Smart Grid_ paradigm. The introduction of multiple distributed generation and storage resources, with a strong involvement of renewable energies, exposes the necessity of advanced metering or _Smart Metering_ systems, able to manage and control those distributed resources. Due to the heterogeneity of the _Smart Metering_ systems and the specific features of each grid, it is easy to find in the related literature a wide range of solutions with different features. This work describes the key elements in a _Smart Metering_ system and compiles the most employed technologies and standards as well as their main features. Since _Smart Metering_ systems can perform jointly with other activities, these growing initiatives are also addressed. Finally, a revision of the main trends in _Smart Metering_ uses and deployments worldwide is included.  
communication technologies; electricity grids; smart grid; _Smart Metering_
## 2 Methods  
### Feature Extraction Network Construction  
The first step in multi-view stereo matching reconstruction is to perform feature extraction on the image. Feature matching refers to the process of searching for feature points with the same name in the images captured by multiple viewpoints and multiple sensors and matching them together accurately. The main process is usually divided into feature extraction, feature matching, and mismatch point rejection. The feature matching algorithm with rotational invariance and scale invariance makes it possible to effectively improve the matching accuracy, but because the matching process is easily affected by factors such as light, noise, imaging angle, etc., the generation of mismatched points is unavoidable, and it is necessary to improve the process of feature matching to obtain high-precision matching results [23]. In image 3D reconstruction technology, the extraction and matching of feature points are crucial, and the use of feature extraction and matching algorithms that consider both accuracy and efficiency is the key to realizing high-precision image 3D reconstruction [24].  
In previous studies, the feature extraction steps are mostly processed in a serial time sequence, and there are problems such as feature loss due to downsampling to expand the sensory field, blurring of the edge structure of the final point cloud, and ineffective differentiation between the depth of the object and the background region, among other issues [25]. To address the above problems, the input multi-view image first undergoes a 2D convolution to capture simple features of the image (e.g., lines, color blocks, etc.) to help the model understand the image and provide raw information for 3D reconstruction. Next, another 2D convolution is performed so that the network can start learning to understand more complex features (e.g., shapes, textures, etc.) and use them to obtain depth information. Then, into the feature extraction network, this paper combines the composite U-Net and self-attention mechanism to improve the DRE-Net (Density Reception Expanded) structure, as shown in Figure 1. This structure can extract more semantic information and semantic segmentation.
## 2 Data and Methods
### Sensitivity Analysis Methods
#### 2.3.4 EFAST Analysis Method  
On the basis of FAST, [PERSON] et al. [27] proposed an improved extended Fourier amplitude sensitivity test (EFAST) combined with the variance decomposition theory of [PERSON]'s method. In this method, the spectrum of the Fourier series is obtained with the Fourier transform, and the variance of the model results caused by each parameter and their interaction is obtained with the spectrum curve.  
According to the appropriate search function, model \(y=f(X)\) can be converted into \(y=f(s)\) and obtained with Fourier transform [58; 59].  
\[f(s)=\sum_{-\infty}^{\infty}\bigl{(}A_{j}cosjs+B_{j}sinjs\bigr{)} \tag{15}\]  
where \(j\in Z^{0}=\{-\infty,\cdots,-1,1,\cdots,\infty\}\), \(A_{j}\) and \(B_{j}\) can be calculated with the following formula.  
\[A_{j}=\frac{1}{2\pi}\int_{-\pi}^{\pi}f(s)cosjsds\ B_{j}=\frac{1}{2\pi}\int_{- \pi}^{\pi}f(s)sinjsds \tag{16}\]  
The spectral curve of the Fourier series is defined as \(\Lambda_{j}=A_{j}^{2}+B_{j}^{2}\), \(A_{j}=A_{-j}\), \(B_{j}=B_{-j}\). The output variance caused by parameter \(x_{i}\) is  
\[V_{i}=\sum_{p\in Z^{0}}\Lambda p\omega_{i}=2\sum_{p=1}^{+\infty}\Lambda p \omega_{i} \tag{17}\]  
where \(p\) is the spectrum of the Fourier transform; the model's total variance can be obtained by summing the ranges of all frequencies.  
\[V=\sum_{j\in Z^{0}}\Lambda_{j}=2\sum_{j=1}^{+\infty}\Lambda_{j} \tag{18}\]  
Sampling \(s\) at equal intervals within the interval \([-\pi,\ \ \pi]\) and running the model many times can estimate the values of \(A_{j}\) and \(B_{j}\).  
\[A_{j}=\frac{1}{n}\sum_{m=1}^{n}f(s_{m})cos(js_{m})\ B_{j}=\frac{1}{n}\sum_{m=1 }^{n}f(s_{m})sin(js_{m}) \tag{19}\]  
where \(n\) is the number of samples. According to \(A_{j}\), \(B_{j}\), and the corresponding frequency \(\omega_{i}\) of parameter \(x_{i}\), variance \(V_{i}\) and total variance \(V\) caused by each parameter can be calculated, that is, the first-order sensitivity (\(S\)) of each parameter can be obtained. To calculate the total sensitivity parameter \(x_{i}\), frequency \(\omega_{i}\) needs to be set for \(x_{i}\), and a different frequency \(\omega_{i}^{\prime}\) needs to be set for other parameters. By calculating the spectral values of \(\omega_{i}^{\prime}\) on \(p\omega_{i}^{\prime}\), partial variance \(V_{-i}\) can be obtained that influences all parameters except parameter \(x_{i}\) and their interactions. The total sensitivity (\(S_{T}\)) of parameter \(x_{i}\) is calculated as follows.  
\[S_{T{i}}=\frac{V-V_{-i}}{V} \tag{20}\]  
Similar to [PERSON]'s analytical method, first-order sensitivity \(S_{i}\) and total sensitivity \(S_{T{i}}\) describe a single SWAT model parameter and the combination with model parameters on the SWAT model output, respectively.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### Channel Model  
The IEEE 802.15.6 standard document defines the channel models for the WBANs [10]. Figure 4 shows these channels (termed CM1-CM4). As mentioned previously, this paper only addresses the off-body communication, i.e., between the central node (on waist, see Figure 1) and the gateway (Figure 3). This channel is referred to as Channel Model 4 (CM4) in the IEEE standard document [10]. In WBANs, there are many factors which affect and deteriorate the signal quality, e.g., shadowing, reflections, differences, etc. Shadowing is a key factor which causes signal degradation due to the body environment; body movements and postures can also cause shadowing. Therefore, the node is either in direct line of sight (LOS) with the gateway or in non-line of sight (NLOS) at any instant. This also creates additional complications to create an accurate mathematical model for CM4. The IEEE standardization document presents the measured values for the channel model [10] but no concrete mathematical model is included. From these measured values, we have formulated a mathematical channel model for the CM 4 using MATLAB, as shown in Equation (1):  
\[\text{pathloss}=\text{ax}^{3}+\text{bx}^{2}+\text{cx}+\text{d}, \tag{1}\]  
which is a third degree polynomial equation modelling CM4. The identified values for the constants a, b, c and d are given in Table 2.  
Figure 4: IEEE 802.15.6 Channel Models.  
Figure 3: Network topology.  
Our mathematical model for CM4 takes the shadowing caused by body orientation as well as LOS and NLOS into consideration. This mathematical model gives realistic channel properties for CM4 and is in compliance with the values presented in [10].
## 5 Conclusions  
We demonstrated low loss, broadband, and fabrication-insensitive silicon photonic 3-dB power splitters based on adiabatically tapered rib waveguide. The shallow-etched rib waveguides were employed to make the critical dimensions of the designed device larger than 200 nm. The compact power splitter with the taper length of 40 \(\upmu\)m exhibited a low excess loss of 0.06 dB and a broad bandwidth over a 100-nm wavelength. The design compatibility with existing silicon photonics foundries and good fabrication tolerance enable the wide usage of the power splitter on a variety of silicon photonics applications. Thanks to its low loss, the proposed power splitter can favorably be integrated to demonstrate large-scale photonic integrated circuits.  
Design, V.H.N.; Simulation and Layout, V.H.N. and I.K.K.; writing--original draft preparation, V.H.N.; writing--review and editing, V.H.N. and T.J.S.; supervision, T.J.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2018R1C1B6005302); Nano-Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning. (2009-0082580); GIST Research Institute (GRI) grant funded by the GIST in 2020.  
The authors would like to thank [PERSON] for discussion on the fabrication process at NNFC.  
The authors declare no conflict of interest.
## 3 Findings for Each of the Three Layers
### Technologies Layer  
The technologies layer covers systems, technologies, and energy products and services that physically create a smart grid environment. In this section, the physical systems of smart grid will be analyzed from three perspectives; DER, demand side flexibility and resource side flexibility. These perspectives were to highlight smart grid flexibility, which is considered to be the key component for its integration into the electricity network [44].
## 5 Use Case of 6G-OPENSEC-Security
### Applying ML/DL and Security Policies  
From what has been discussed in Sections 3 and 4, it is necessary to disseminate where and how AI and security policies have been applied and enforced within the proposed Closed-Loop architecture. All the components within the SCL are essential for the continuous monitoring and security of deployed service-based slices; however, most of the  
Figure 8: A high-level overview of the interaction between these components.  
complexity within these processes is handled by two components, namely the Security Data Analytics and Security Decision components, which are where Deep Learning/Machine Learning models and security policies are enforced, respectively. The use of AI in the SCL closely resembles how it was used in the work of [89], wherein Deep Learning was applied in the network slicing infrastructure of 5G networks to detect and mitigate threats proactively. The way that policies were reinforced is also like how, in the ANASTACIA ([[http://www.anastacia-h2020.eu/](http://www.anastacia-h2020.eu/)]([http://www.anastacia-h2020.eu/](http://www.anastacia-h2020.eu/)) (accessed on 29 April 2024)) Project, policy conflicts are resolved by checking already applied policies to requested policies, and these are further enforced through the integration of security orchestrators that check their compliance throughout the network.  
However, in this project, the role of these security orchestrators is attributed to the Security Decision to mitigate detected threats by the Security Data Analytics, which, in a sense, showcases that the SCL combines principles from both these projects into one. The following are details of both key components of the SCL for a better understanding of their context within the SCL and their roles.
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
###### Abstract  
Network selection plays a pivotal role in ensuring efficient handover management. Some existing approaches for network selection may use one criterion, such as RSSI (Received Signal Strength Indicator) or SINR (Signal to Interference Noise Ratio). However, these approaches are reactive and may lead to incorrect decisions due to the limited information. Other multi-criteria-based approaches use techniques, such as statistical mathematics, heuristics methods, and neural networks, to optimize the network selection. However, these approaches have shortcomings related to their computational complexity and the unnecessary and frequent handovers. This paper introduces NetSel-RF, a multi-criteria model, based on supervised learning, for network selection in WiFi networks. Here, we describe the created dataset, the data preparation and the evaluation of diverse supervised learning techniques (Random Forest, Support Vector Machine, Adaptive Random Forest, Hoeffding Adaptive Tree, and Hoedding Tree techniques). Our evaluation results show that Random Forest outperforms other algorithms in terms of its accuracy and Matthews correlation coefficient. Additionally, NetSel-RF performs better than the Signal Strong First approach and behaves similarly to the Analytic Hierarchy Process-Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS) approach in terms of the number of handovers and throughput drops. Unlike the latter, NetSel-RF is proactive and therefore is more efficient regarding Quality of Services (QoS) and Quality of Experience (QoE) since the end-devices perform the handover before the network link quality degrades.  
network selection; multi-criteria; random forest; AHP-TOPSIS; strongest signal first +
Footnote †: journal: Applied Sciences
###### Abstract  
Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth's land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results.  
Crete; Mediterranean; Landsat; Big Data; time series; long-term; land degradation; phenology; reproducibility; replicability; semi-arid; vegetation decomposition +
Footnote †: journal: remote sensing  
022214597
## 4 Experimental Validation
### Results  
#### 4.3.1 Optimal Selection of Window Function Length  
The length of the window function is an optional parameter in the TFR frame. Generally, the sampling point is selected as the \(n^{\text{th}}\) power of 2 in order to ensure the convenience of the algorithm. In order to find an optimal window length, the time-frequency reassignment spectrogram \(\overline{P}_{a}^{S}(t,\omega)\) is calculated by different lengths, such as 64, 128, 256, and 512, corresponding to the sampling intervals 3.2 s, 6.4 s, 12.8 s, and 25.6 s, as shown in Figure 6. It is obvious to see from the figure that the time-frequency resolution of length 64 is very low, hard to extract the frequency along the time axis. The resolution becomes better when using 128 sampling points, but still represents a zigzag fluctuation in the time-frequency spectrogram, especially in the low-frequency region. Hence, the time-frequency resolutions of 256 and 512 are much preferable to the previous results. Therefore, the length of the window function is chosen as 256 in the time-frequency analysis.
## 3 Simulation Results
### Simulation Results According to the Variation in the Cardinality of the Sensor Network  
In this subsection, we deal with the dynamics of the sensor nodes. Table 1a shows the EDM and localization errors for a case where only \(P\) is changed while keeping \(N\) constant (\(N=30\)). Since the EDM measurements are independent of \(P\), only the denoising performance of each algorithm affects the EDM error. In the case of localization errors, it can be seen that the performance improves as \(P\) increases for all algorithms, and the two algorithms we propose show an excellent performance overall.  
Table 1b shows the impact of the change in the number of nodes after NN optimization is performed, with \(N\) equal to 30. In other words, this experiment serves to verify the flexibility of T-DAE-EDMR when the number of nodes, \(N\), changes between the training and test phases. For convenience, we will refer to the changed value of \(N\) as \(N_{new}\). In the undenoised scenario, as \(N_{new}\) increases, the possibility of cooperation between the nodes increases; consequently, it can be seen that the performance marginally improves. As expected, T-DAE-EDMR shows its denoising capability well when \(N_{new}\) is equal to \(N\). In addition, since the model is optimized based on the information for a situation where \(N\) is 30, the performance of T-DAE-EDMR deteriorates as the value of \(|N_{new}-N|\)  
Figure 3: Performance comparison according to the NLoS probability. (**a**) NMSE between ground-truth and denoised EDMs and (**b**) localization error in meters.  
increases. Nevertheless, our proposed T-DAE-EDMR can be considered an effectively designed algorithm that can adapt robustly to a change in the number of nodes between the training and test phases. Due to its potential, T-DAE-EDMR could be very useful in highly time-varying networks.
## 1 Introduction
In the next section, we provide a detailed technical description of a multimode silicon photonics micro-transceiver designed to operate in high-temperature environments with an integrated laser for lower overall power consumption that can meet the stringent cost targets of future 5G+ environments due to multimode packaging.