esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Incidence Management in 5G Based on Situal Awareness
## 1 Introduction
[15] propose a single network function that monitors radio and access elements but doesn't take into account other 5G components such as virtualization or application layers. Meanwhile, the 5G-Ensure Project is intended to cover security requirements in 5G Networks. The proposed architecture will provide a trustworthy 5G system, offering reliable security services to customers by means of the development of a set of non-intrusive security enablers such as privacy, security network management, and trust, among others [17]. As part of its proposal, 5G-Ensure defines a Risk Assessment and Mitigation methodology in order to evaluate security concerns in 5G systems, based on NIST-SP-800-30 and ISO 27005 standards [18]. Although 5G-Ensure covers a wide range of security issues on 5G Networks, this project is still at an early stage and does not keep in mind the concept of actuators, which have been introduced in our proposal in order to mitigate possible risks and deploy corrective measures. This article introduces a novel architecture for incident management on 5G Mobile networks, which combines the foundations of the traditional risk management guidelines with the Situational Awareness model published by [PERSON]. It covers all layers of information processing in 5G networks, from the infrastructure to the actuators responsible for implementing mitigation actions. The basis for the identification, monitoring, analysis, decision-making, prediction and countermeasure tracking are also introduced. The paper is divided into six sections, the first section being this introduction. The risk management systems, Situ Awareness and the related work are described in Section 2. The challenges posed by 5G networking related to risk management are explained in Section 3. An architecture that combines traditional risk management and Situational Awareness for 5G is detailed in Section 4. The componets to carry out analysis and decision-making processes are described in Section 5. Finally, conclusions and future work are presented in Section 6.
## 3 The Deployment Models
### Inspection without Decryption  
The main motivation of this approach is that TLS-encrypted traffic itself exposes unencrypted metadata, and is equipped with other measurable properties (for example, packet length sequence and inter-arrival time sequence of a flow) that can be used to infer certain information related to the encrypted content.  
While [PERSON] and [PERSON] [73] propose that the packet length sequence can be used in exact signature matching for encrypted traffic, and a few exact pattern matching-based NTA methods can be found especially in TLS fingerprinting (Section 4.4), a majority of the solutions in this category adopt graphical, statistical, or machine learning algorithms.  
A representative example in graphical methods is graphlet in BLINC [74]. Graphlet is a transport layer interaction pattern between hosts represented by a graph with the intent to identify the network application. In BLINC, heuristics are used for application classification with graphlet. Statistical methods have been discussed in the context of application protocol classification. For example, [PERSON] et al. [75] compared flow-based, packet-based, and byte-based statistics and observes that flow-based statistics are more stable than others. While statistical methods have established a wide range of literature, the majority of the recent works discussed in malware detection and family classification employ machine learning techniques. For the machine learning-based methods, we have discussed their solutions in Section 4.  
To avoid potential network performance degradation due to the on-the-path inspection using complex machine learning-based algorithms, various studies in this category implicitly assume that encrypted traffic is inspected off-the-path (for example, by traffic sniffing with switch port mirroring [76] or network taps, or flow record collection through NetFlow sensors [8; 77]).  
However, we should note that in general, the lightweight inspection without decryption can be deployed on-the-path, as shown in Figure 1. For instance, there are near real-time protocol identification solutions without decryption, such as iPoque's (acquired by Rohde and Schwarz) protocol and application classification engine (PACE) [78] and nDPI [79]. Similarly, there are several studies that identify application layer protocol with the first few packets only [80; 81], even with encrypted traffic [82]. Nevertheless, such solutions primarily focus on protocol identification in the middlebox to forward network traffic to a protocol-specific proxy (e.g., TLS proxy in Section 3.1) for interception.
# Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson's Disease
## 1 Introduction
In neuroscience, they are considered a powerful analytic tool to study dynamics of neuronal systems and to discriminate between healthy and pathological states within neural dynamics [23]. Wavelets are specific to analyze non-lineal, spatially non-homogeneous and non-stationary power time series, such as EEG, and to transform a timescale signal in the time-frequency spaces by means of a multi-resolution analysis. In addition, a cross wavelet product, needed to generate a covariance analysis from Wavelet data, can remark intrinsic properties of a time series and identify common features when more than two sets of data are analyzed together [24; 25; 26]. Accordingly, the aim of this study was to apply the covariance wavelet analysis to EEG from women diagnosed with PD. The search for information in the time-frequency space, not previously reported by the classical models used, and which could identify new electroencephalographic abnormalities in the brain activity of PD patients, can be achieved through this methodology. The application of wavelet analyses in this study has advantages in examining finite-length time series, limiting the edge effects by means of expanding with sufficient zeros to bring the total length up to the next higher power of two. In this research, we also apply a wavelet-based filter because it can isolate single events that have a broad power spectrum or events that have varying frequency, which is useful in processing heterogeneous signals such as EEG. Patterns obtained by means of this methodology show major differences between EEG from control and PD patient groups. This approach could be applied under several physiological and pathological conditions, growing the potential of brainwave analysis obtained by standard EEG.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 4 Experimental Setup
### Dataset  
For SV, we used the VoxCeleb dataset [43; 44], which contains large-scale celebrity interviews or shows, including VoxCeleb1 and VoxCeleb2. VoxCeleb1 contains over 100,000 utterances for 1251 celebrities, while VoxCeleb2 contains over 1 million utterances for over 6000 celebrities extracted from videos uploaded to YouTube. The utterances were extracted from YouTube videos, and the speech data was collected in a completely realistic environment that contains a variety of noisy scenes that can be used for speaker identification and verification. VoxCeleb1 is split into development (dev) and test sets. There are no overlapping speakers between them.  
Our network is trained using only the VoxCeleb1 dev dataset, which contains a total of 1211 speakers with a total of 297,286 voices. Performance of all systems are evaluated on the test set of VoxCeleb1, which contains 4874 utterances from 40 speakers. All speakers with names starting with \"E\" are reserved for testing, since this gives a good balance of male and female speakers. All test set lists can be found on the VoxCeleb website ([[https://www.robots.ox.ac.uk/~vgg/data/voxceleb/](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)]([https://www.robots.ox.ac.uk/~vgg/data/voxceleb/](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)), accessed on 15 July 2022).
# New Isolated Autochthonous Strains of _S. cerevisiae_ for
## 3 Results and Discussion
### Determination of Selected Organic Acids
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strain Symbol**} & **Citric Acid** & **Tartaric Acid** & **Malic Acid** & **Lactic Acid** & **Acetic Acid** & **Fumaric Acid** \\  & **[g/L]** & **[g/L]** & **[g/L]** & **[g/L]** & **[g/L]** & **[mg/L]** \\ \hline Grape pulp & \textless{}0.10 \({}^{\text{a}}\) & 1.95 \(\pm\) 0.08 \({}^{\text{c}}\) & 2.55 \(\pm\) 0.14 \({}^{\text{e}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.65 \({}^{\text{a}}\) \\ \hline
1\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.79 \(\pm\) 0.14 \({}^{\text{ab}}\) & 1.60 \(\pm\) 0.01 \({}^{\text{bcd}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & 5.55 \(\pm\) 0.07 \({}^{\text{efg}}\) \\ \hline
2\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.69 \(\pm\) 0.14 \({}^{\text{ab}}\) & 1.68 \(\pm\) 0.01 \({}^{\text{bcd}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & 5.22 \(\pm\) 0.02 \({}^{\text{def}}\) \\ \hline
3\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.74 \(\pm\) 0.09 \({}^{\text{ab}}\) & 1.52 \(\pm\) 0.22 \({}^{\text{abcd}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & 4.95 \(\pm\) 0.23 \({}^{\text{de}}\) \\ \hline
4\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.70 \(\pm\) 0.00 \({}^{\text{ab}}\) & 1.86 \(\pm\) 0.06 \({}^{\text{cd}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & 5.49 \(\pm\) 0.37 \({}^{\text{efg}}\) \\ \hline
5\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.45 \(\pm\) 0.06 \({}^{\text{a}}\) & 1.64 \(\pm\) 0.02 \({}^{\text{bcd}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & 5.83 \(\pm\) 0.10 \({}^{\text{fgh}}\) \\ \hline
6\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.87 \(\pm\) 0.22 \({}^{\text{ab}}\) & 1.09 \(\pm\) 0.08 \({}^{\text{a}}\) & 0.23 \(\pm\) 0.13 \({}^{\text{b}}\) & 0.11 \(\pm\) 0.01 \({}^{\text{ab}}\) & 4.61 \(\pm\) 0.35 \({}^{\text{d}}\) \\ \hline
7\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 1.01 \(\pm\) 0.27 \({}^{\text{ab}}\) & 1.58 \(\pm\) 0.02 \({}^{\text{bcd}}\) & 2.95 \(\pm\) 0.08 \({}^{\text{c}}\) & \textless{}0.10 \({}^{\text{a}}\) & 5.91 \(\pm\) 0.17 \({}^{\text{gh}}\) \\ \hline
8\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 0.64 \(\pm\) 0.05 \({}^{\text{ab}}\) & 1.60 \(\pm\) 0.00 \({}^{\text{bcd}}\) & \textless{}0.10 \({}^{\text{a}}\) & \textless{}0.10 \({}^{\text{a}}\) & 6.38 \(\pm\) 0.02 \({}^{\text{h}}\) \\ \hline
9\_NCS & \textless{}0.10 \({}^{\text{a}}\) & 1.22 \(\pm\) 0.26 \({}^{\text{b}}\) & 1.65 \(\pm\) 0.06 \({}^{\text{bcd}}\) & \textless{}0.10 \({}^{\text{a}}\) & 0.16 \(\pm\) 0.08 \({}^{\text{bc}}\) & 2.83 \(\pm\) 0.11 \({}^{\text{b}}\) \\ \hline
1\_CS & \textless{}0.10 \({}^{\text{a}}\) & 0.93 \(\pm\) 0.03 \({}^{\text{ab}}\) & 1.44 \(\pm\) 0.15 \({}^{\text{ab}}\) & \textless{}0.10 \({}^{\text{a}}\) & 0.18 \(\pm\) 0.04 \({}^{\text{bc}}\) & 3.62 \(\pm\) 0.08 \({}^{\text{c}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of selected organic acids in red wines.
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
###### Abstract  
The Internet of Things (IoT) provides robust services to connected sensors in a distributed manner, and maintains real-time communication using wireless standards. The smart network has offered many autonomous smart systems to collect information from remote nodes, and share it by exploring the network layer. Researchers have recently offered a variety of ways to increase the effectiveness of emerging applications using trustworthy relaying systems. However, there are still many issues with route reformulation due to frequent disconnections of mobile devices and resource limitations. Furthermore, most of the existing methods for IoT systems are unable to utilize network resources, which lowers the performance of green networks. Thus, providing a foolproof solution for the autonomous system with energy efficiency is a challenging task. Therefore, this paper presents an algorithm for the mobile network using fog computing to reduce network disconnectivity. Furthermore, using security services, the proposed algorithm efficiently explores the characteristics of the device, and avoids malicious traffic to drain the additional energy consumption of the network. The main aspects of the proposed algorithm are as follows: (i) using the adjustable transmission power, the proposed algorithm offers a fault-tolerant solution to transmit the aggregated data over the unpredictable wireless system; (ii) with the support of fog nodes, the data load is reduced among devices with the offering of a secured authentication scheme. Using simulations, the proposed algorithm is tested, and its significance is demonstrated against other related studies.  
energy systems; fog computing; distributed services; Mobile-IoT; next generation Article
# Investigation of OFDM-Based HS-PON Using Front-End LiFiSystem for 5G Networks
A hybrid HS-PON/OFDM with a LiFi system provides the ability to transmit data over high-bandwidth fiber-LiFi links (\"50 Gbps\" per channel) [17]. 1. This paper proposes and investigates the performance of an HS-TWDM PON/OFDM system that utilizes LiFi. Data transmission over fiber-LiFi links based on 5G is addressed using our proposed system [18], serving as the front-end communication network for deployed broadband access networks. The effects of fiber attenuation, linear and non-linear impairments, dispersion, noise in LiFi channels, and misalignment are considered. 2. We analyze the system's performance in terms of the bit error rate (BER), received spectra, receiver sensitivity, and optical signal to noise ratio (OSNR). 3. We compare our results with those of prior established studies in order to verify the results we obtained. An overview of the proposed model is presented in Section 2 with a conceptual diagram and mathematical description. As well as illustrating the obtained results, Section 3 analyzes the proposed model. The conclusion is presented in Section 4 along with future prospects.
# Decentralized Inner-Product Encryption with Constant-Size Ciphertext
## 2 Preliminaries
### Definition of Decentralized Inner Product Encryption
#### 2.3.1 System Model  
A DIPE scheme contains three roles, i.e., sender, receiver and authorities. A sender is a participant of the system who transfers the encrypted data to the receiver. The data are encrypted by an attribute vector before delivered to receiver. Authorities are responsible for issuing partial keys for receivers who make a request to obtain partial keys. The authorities will issue partial keys according to the predicate vector of the receiver. A receiver is a participant who wants to receive encrypted data. After a receiver receives all the partial keys from the authorities, the receiver will perform a decryption procedure to recover the data.
# HydroLab: A Module for the Investigation of Fertigation Strategies in Hydroponics
## 2 State-of-the-Art
[21] conducted a study to investigate the cultivation of paprika plants under different fertigation frequencies based on solar radiation. The plants were grown on rockwood slabs using the vertical trellis \"V\" technique. Fertigation was initiated when the cumulative solar radiation reached a specific set point. Two different set points were defined to assess their impacts on transpiration, growth, fruit yield, and water use efficiency. It is important to note that, in this study, the focus is on investigating different fertigation frequencies based on solar radiation, rather than comparing different types of scheduling events, such as time-based, substrate monitoring, or climate monitoring strategies. A summary of the reviewed articles is provided in Table 1. It is evident that no test bench capable of exploring various types of _fertigation strategies_ has been proposed thus far. Additionally, the analyzed studies do not employ _Internet of Things_ (IoT) technologies, which could enhance system monitoring and control [22]. These two aspects form the novel contributions proposed in this work. \begin{table}
\begin{tabular}{l l l l l} \hline  & \begin{tabular}{l} **Controlled** \\ **Environment** \\ \end{tabular} & \begin{tabular}{l} **Fertigation or** \\ **Irrigation** \\ \end{tabular} & \
\begin{tabular}{l} **Scheduling Event** \\ \end{tabular} &
\begin{tabular}{l} **Treatments** \\ \end{tabular} \\ \hline [PERSON]. [PERSON] et al. (2014) [16] & \(\times\) & Fertigation & Time-based & Substrate \\ [PERSON] et al. (2018) [17] & ✓ & Irrigation & Time-based & Drip depth \\ [PERSON] \& [PERSON] (2006) [18] & ✓ & Irrigation & Substrate monitoring & Volumetric water content \\ [PERSON] et al. (2021) [19] & ✓ & Irrigation & Time vs. substrate monit. & Irrigation strategy \\ [PERSON] et al. (2017) [20] & ✓ & Fertigation & Time-based & Substrate \\ [PERSON] et al. (2012) [21] & ✓ & Fertigation & Climate monitoring & Solar radiation \\ \hline \end{tabular}
\end{table} 
Table 1: Classification of the works studied in the state-of-the-art analysis.
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 5 Computational Results
(2011) adopted the coding method with a tree structure. A chromosome represented a feasible multicast tree. The GA has the following parameter values: crossover probability \(p_{c}\)\(\in\)[0.8, 1], mutation probability \(p_{m}\)\(\in\)[0.1, 0.5], the maximum number of iterations \(CN_{max}\) = 1000, population number \(TreeNum\) = 30. A randomized algorithm based on depth-first search was used for building a random Steiner tree to construct the initial population. Then, the multicast tree changed through adopting crossover and mutation. Since ACO and GA methods are both random algorithms, the same procedure in the same instance may generate different results due to different random numbers in each run. We thus repetitively executed each instance 20 times and the average values over these times are depicted in Figure 9. Figure 9 shows the illustration of comparison of the cost attained by ACO&CM, ACO, and GA against different numbers of nodes. It also can be shown from the curves that in nearly all scales of topology ACO&CM outperforms ACO and GA in an extremely important way. Besides, ACO yields less total cost than GA, so ACO shows a significantly better performance than GA. The computational results also prove that ACO&CM outperforms ACO, and the multicast trees obtained are feasible if these results meet the QoS constraints.
## 5 Model Compilation Using Deep Learning Methods
### Classifiers  
Unlike machine learning models, deep learning networks do not directly perform feature extraction and classification. Without involving a third-party researcher, the deep learning network's hidden layers tacitly carry out all of these tasks. Below is a brief explanation of the deep learning networks used.  
1. **Convolutional neural network (CNN):** This is an improvised variant of the multi-layer perceptron. A CNN typically consists of an input layer, an output layer, and numerous concealed layers. Convolutional, pooling, and fully connected layers usually comprise a CNN's hidden layers.
2. **Long short-term memory (LSTM):** The units are a unique type of recurrent neural network (RNN) construction block. Long short-term memory refers to the fact that it is capable of holding information for a long period of time. An LSTM network can analyze, categorize, and predict temporal data patterns with time lags of any length, and typically consists of memory, input, output, and ignore gates. The memory in an LSTM network can retain numbers for any duration of time. The three gates are variations of neurons that calculate the activation function of a weighted sum. They are referred to as gates because they regulate the flow of values through LSTM levels. The exploding and vanishing gradient problem is a crucial challenge that LSTM addresses.
3. **Deep bidirectional LSTMs (BiLSTM):** These are an extension of LSTM models in which two LSTMs are applied to the input data. An LSTM is used on the input sequence in the first round (i.e., forward layer). In the next round, the LSTM model is given the input sequence's reverse form (i.e., backward layer). By using the LSTM twice, long-term dependencies become clearer, which enhances the model's accuracy.
4. **Gated recurrent unit (GRU:** This is another type of recurrent neural network that is similar to LSTM but has fewer parameters. GRU has been shown to be effective for sequence classification tasks, especially in cases where long-term dependencies are not as important. In our project, we used GRU to classify the Arabic text data based on the features generated by the different feature extraction techniques.
5. **Hybrid networks (CNN-LSTM)/ (CNN-BiLSTM) / (CNN+GRU):** In hybrid networks, only the convolutional layer is applied as a feature extraction layer with max-pooling to generate output for the next layer (LSTM, BiLSTM, or GRU).
## 3 Results
The range of variation in the three indices was very similar, with NDVI ranging from 0.51 to 0.96, SAVI ranging from 0.24 to 0.62 and DVI from 0.11 to 0.39. Correlation coefficients between LAI and the vegetation indices showed a significant positive relationship, with very little difference between the indices (Table 3). The implication that could be drawn from this is that the vegetation indices could be used to estimate LAI for the Risley Moss study plot. As SAVI had the strongest relationship with LAI, it was used in the leave-one-out cross-validation regression procedure to determine independent estimates of LAI for each date. The relationship between the LAI values estimated using the DMC-derived SAVI and the measured LAI values are shown in Figure 3. There was clearly a strong linear relationship between the predicted and the actual LAI values. For the study plot, Figure 3 indicates that there was some overestimation, where actual LAI is around one, and some underestimation for values around three. The over- and under-estimates generated a root mean square error of 0.47. Figure 2d shows the seasonal variation in the measured LAI and the LAI values estimated from the regression analysis based on SAVI. The general seasonal pattern of the estimated values matched the measured values. The estimated value for DOY 109 in 2005 showed the largest error (+1.2 LAI), but all other estimates were less than 0.9 LAI. When LAI values were at their highest, between DOY 150 to DOY 192 in 2005, there was a close association between estimated and measured LAI. \begin{table}
\begin{tabular}{c c} \hline \hline
**Vegetation Index** & _r (n = 9)_ \\ \hline Normalised difference vegetation index (NDVI) & 0.92 \\ Soil adjusted vegetation index (SAVI) & 0.94 \\ Difference Vegetation Index (DVI) & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Linear correlation coefficients between measured LAI and vegetation indices (VI); all correlation coefficients were statistically significant (\(p<0.01\)).