esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 4 CAV Driving Risk Quantification Model  
### Time-to-Collision Adjustment  
Surrogate safety measures (SSMs) are used to identify near-collision or collision-like events [10]. One of the most commonly used SSMs for rear-end collisions is the \(TTC\). The \(TTC\) is defined as the remaining time before two vehicles collide, assuming their current speeds and trajectories remain unchanged throughout the process [11].  
\[TTC=\frac{X}{v_{h}-v_{q}},v_{h}>v_{q} \tag{10}\]  
where \(X=x_{q}-x_{h}-l_{q}\), \(\Delta v=v_{h}-v_{q},x_{q},v_{q}\), and \(\mathrm{l_{q}}\) are the position, speed, and length of the lead vehicle, respectively; while \(\mathrm{x_{h}}\) and \(\mathrm{v_{h}}\) are the position and speed of the following vehicle.  
The TTC is only meaningful when the following vehicle is moving faster than the lead vehicle. To address this limitation, [PERSON] et al. [12] proposed the modified time to collision (MTTC), which takes into account the accelerations of both the lead and following vehicles, as well as their relative speed.  
\[t_{1}t_{2}=\frac{-\Delta v\pm\sqrt{\Delta v^{2}+2\Delta aX}}{\Delta a},\Delta a\
eq 0 \tag{11}\]  
\[MTTC=\left\{\begin{array}{ccc}\min(t_{1},t_{2})&,t_{1}>0,t_{2}>0\\ \max(t_{1},t_{2})&,t_{1}\cdot t_{2}<0\\ \frac{X}{\Delta v}&,\Delta a=0\end{array}\right\} \tag{12}\]  
where \(\Delta a=a_{h}-a_{q}\) and \(t_{1}\) and \(\mathrm{t_{2}}\) are the two roots of Equation (11).
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 2 Related Works
### Deep Learning-Based Log Anomaly Detection
#### 2.2.2 Pre-Processing and Feature Extraction  
Parsed log sequences must be encoded into numerical features to be input into deep learning. Thus, the parsed logs are first grouped into sequences using the fixed window, sliding window, and session window grouping methods, as illustrated in Figure 1. The fixed window method groups them by the time stamp(\(\Delta t\)) of each log. Sliding windows are grouped by time stamp(\(\Delta T\)) and the transmission interval. In this case, \"windows\" means a fixed range, and the occurrence time range is set according to the length of the set windows. However, if the windows are too short, deep learning models have difficulty detecting anomalies that span multiple sequences, and if they are too long, the sequence may contain multiple anomalies, confusing the detection scheme [12]. Finally, unlike the other two methods, the session window method groups them according to identifiers rather than time order [5]. For example, the session window method groups event templates based on identifiers, a unique variable in each log.  
Finally, the grouped log sequence is encoded using the term frequency-inverse document frequency (TF-IDF) method. The TF-IDF is primarily used to calculate the similarity of a search system or document and is a method of weighting the target word in that document through the frequency and importance of the target word [13].  
The TF-IDF is obtained through the document(\(d\)), word(\(t\)), and total number of documents(\(n\)). When the total frequency of target words in the document is \(f(t,d)\), the equation for the word frequency \(tf\) is presented in (1). In this case, \(max\{f(w,d):w\in d\}\) indicates the number of all words appearing in the document:  
\[tf(t,d)=0.5+\frac{0.5\times f(t,d)}{max\{f(w,d):w\in d\}} \tag{1}\]  
Figure 1: Deep learning-based log anomaly detection framework.  
Then, \(idf\), a value indicating how often one word appears in common across a set of documents, can be obtained by dividing the total number of documents by the number of documents that include the target word and converting it to a logarithm. In this case, \(|D|\) denotes the number of all documents, and \(|d\in D\colon t\in d|\) denotes the number of documents that include the target word. The formula is presented in (2):  
\[idf(t,D)=log(\frac{|D|}{|d\in D\colon t\in d|}) \tag{2}\]  
Finally, \(tfidf\) is expressed as (3) through (1) and (2):  
\[tfidf(t,d,D)=tf(t,d)\times idf(t,D) \tag{3}\]  
Log sequences encoded through the corresponding method are input into the deep learning model to perform learning for log anomaly detection.
# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 3 Proposed Methodology
#### 3.1.1 Ipca  
Principal Component Analysis (PCA) is a commonly used method and a topic that has received extensive scholarly attention. It is a method for reducing a set of correlated variables' data dimensions. Numerous concepts and data mining activities are represented by a significant amount of data, so the feature reduction technique, PCA, was developed. As a result, PCA conveniently applies to the analysis of statistical data. This indicates that such a method is typically used as an offline batch procedure. However, PCA might be helpful when used with incrementally accessible data. Using an incremental algorithm with PCA can be applied to challenges involving pattern recognition. When it is practical for image features, IPCA is meant to maintain the restricted number of variables and alter its dimensionality. Using memory that is unrelated to the quantity of input size, IPCA creates a low-rank estimate for the input image.
## 5 Experimental Results and Discussion
### Test Scenario  
We consider an indoor testbed at the premises of the Department of Information Engineering and Mathematical Science (DIISM) of the University of Siena. The environment is made of several rooms at the same level and includes the presence of machinery and obstacles, movements of objects and people. In this setting, we deployed several EDs and GWs inside the building for different test scenarios. In particular, we have conducted a preliminary set of tests to evaluate the PLR in the presence of a single ED transmitting in UNCONF mode. In this case, we have verified that the PLR is always well below the required limit of 0.1% even in the SF7 case. These results are in line with the LoRa coverage expectations and are not reported here for the sake of brevity. Hence, we focus on the results obtained in three different experimental setup characterized by the presence of possible collisions and of concurrent DL transmissions. In all the reported results, we consider only the PLR of UP packets, since RPs are not critical in the considered scenario. We report the parameter settings for the three different scenarios in Table 1 where \(N_{ED}\), \(N_{GW}\), \(N_{UP}\), \(\Delta t_{RP}\), and \(\Delta t_{UP}\) denote the number of EDs, GW, and UPs, and the RPs and UPs inter-packet arrival times, respectively. In order to achieve consistent statistics and reduce the experimental time, we have kept the RPs inter-packet arrival times to relatively low values. Moreover, the UPs are generated by forcing the triggering of gas sensors at random intervals. In the following, we report the description of each scenario, rationale for performing the particular test, the results obtained and the corresponding discussion.
# Designing a Leveled Conversational Teachable Agent for English Language Learners
## 2 Designing a Leveled Interactive Teachable Agent  
### Leveled Interactive Teachable Agent: Concept and Modeling  
The leveled interactive teachable agent proposed in this study is based on a speech-based interface. Typically, speech-based English language learning systems require learners to listen to and follow the words and sentences spoken by the agent. However, in our study, we designed a model where learners teach English sentences to the agents. The agent adapts according to the learner's level, unlike existing interactive agents that initiate the teaching and learning of English sentences (see Figure 1), we set up an agent that acts as a professor to teach the correct pronunciation of sentences or words that learners may not know. This is particularly important for young or inexperienced English language learners who have limited vocabulary and struggle to form sentences and, therefore, require additional support to learn English effectively.  
In our proposed method, the learner first speaks some sample sentences into the microphone, and the voice data of the sentences are converted into text through the Google speech-to-text API [16] and sent to a server for processing. The server analyzes the voice data to evaluate the learner's pronunciation and intonation. To do so, it compares the input with the speech data from more than 1000 native speakers for the sample sentence, and the speech data are converted into mel-frequency cepstral coefficients (MFCCs) [17] and the similarities with the existing native speaker speech data are calculated. The learner's skill level is determined according to the results of the similarity analysis, and the teachable agent is then trained to reflect the learner's skill level as the learner improves.  
In addition, learners can view the waveforms generated by a fast Fourier transform (FFT) algorithm [18] for the sentences they speak, and the waveforms of existing native speakers' speech data are also presented to help learners recognize whether their pronunciation is correct. In addition, errors in the sentence recognition function of the speech recognition API are corrected using a dictionary of children's English mispronunciations constructed for this purpose.  
The learner's utterance score and level are applied to the teachable agent, which is implemented as a character. The human study then begins basic learning with leveled sentences provided by the AI teaching model. The learner provides several spoken sentences to the computational agent. After repeatedly processing these sentences, the teachable agent constructs a knowledge database for that user. When the agent has collected sufficient data, it can create new dialogues with the learner and use a leveled dialogue generation system, which allows the agent to generate leveled dialogues similar to the dialogues it has learned and the sentences it has accumulated. To perform this function, we first collected data to train a machine learning model, which included dialogues of various levels of linguistic complexity.
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
###### Abstract  
Optimization of the transmission power and rate allocation is a significant problem in wireless networks with mobile nodes. Due to mobility, the vehicles establishing wireless networks may exhibit severe fluctuations of their link quality, affecting their connection reliability and throughput. In Vehicular Ad-hoc Networks (VANETS), the IEEE 802.11\(p\) standard provides a practical metric for the Packet Reception Ratio (PRR), which is related with the transmission power and rate. Finding a global strategy for optimizing PRR for all mobile nodes can be treated as a potential game where each vehicle is considered as a selfish player, aiming to maximise its transmission reliability while rate constraints are satisfied. To this end, we propose a game-theoretic approach that converges to a Nash equilibrium. The main contributions of this work include: (i) identification of the best case equilibrium, for two cases of interference: diminished or kept stable, and (ii) verification of the equilibrium optimality, by showing that the value of stability is 1. Moreover, numerical results exhibiting the ease of the utility function calculation are provided, especially after an SINR level, whereby the utility function is concave and can be solved efficiently in polynomial time.  
utility function; potential game; rate allocation; transmission power; price of stability; DSRC +
Footnote †: journal: Journal of Information Theory  
0
Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Simulation Configuration  
In this study, all the simulations were carried out using CFD software ANSYS Fluent-19.0. The details of the tank configurations corresponding to the computational domain is shown in Figure 1.  
Around 350,000 total computational nodes were created to ensure grid independency. The fluid motion produced by the stirring was modeled with the multiple reference frame model, a widely used method for simulating the fluid flows of stirred tank reactors [7; 30]. The inlet boundary condition was specified as an inlet velocity of 0.1 m/s for bubbles located along the circumference of a round shape sparger of 0.01 m diameter. A total of 11 groups of bubbles with diameters from 0.3 to 9.6 mm were taken into account. The diameters of each of the 11 groups were 0.3, 0.4, 0.6, 0.8, 1.2, 1.6, 2.4, 3.3, 4.8, 6.7, and 9.6 mm, respectively. The bubble group with diameter 2.4 mm was set for inlet size. In this study, carbon monoxide was used as an inlet gas. Therefore, the diffusion coefficient for the liquid phase carbon monoxide was used as 2.03 \(\times\) 10\({}^{-9}\) m\({}^{2}\)s\({}^{-1}\)[31]. The saturation concentration of the dissolved carbon monoxide was calculated by [PERSON]'s law:  
\[\phi_{l}\)* = \(y_{g}P_{g}/K_{H} \tag{26}\]  
Figure 1: Schematic illustration of a (**a**) stirred tank and (**b**) Rushton turbine.  
where \(y_{g}\), \(P_{g}\), and \(K_{H}\) are the molar fraction of carbon monoxide in the gas phase, total pressure of the gas phase, and [PERSON]'s constant, respectively. [PERSON]'s constant was determined by the Van't Hoff correlation:  
\[K_{H}=K_{H}^{0}\exp\left(-\frac{\Delta_{soln}H}{R}\left(\frac{1}{T}-\frac{1}{T_ {ref}}\right)\right) \tag{27}\]  
where \(\Delta_{soln}H\) is the enthalpy of the solution and \(K_{H}^{0}\) is [PERSON]'s constant at the reference temperature, \(T_{ref}\). The values \(9.5\times 10^{-4}\) M/atm and 1300 K were used for \(K_{H}^{0}\) and \(T_{ref}\), respectively for carbon monoxide in water at 298.15 K [32]. For the other conditions, the tank walls were set as no-slip conditions. The tank was filled with water and the top free surface was defined to have a degassing boundary condition which allows the gas phase to escape but not the liquid phase  
The second-order upwind spatial discretization was applied for convection terms in equations for the turbulent kinetic energy, turbulence dissipation rate, momentum, volume fraction, and pressure. A pressure-velocity-coupled algorithm was used to solve the equations of momentum and continuity [24]. The convergence criteria used in all the simulations was \(10^{-4}\).
## 2 Problem Formulation  
### Transactive DR Framework  
Figure 1 is the proposed transactive DR framework for high renewable penetrated multi-energy prosumer aggregators, which includes the distribution system operator, ancillary service center, and prosumer aggregators. With the integration of energy storage and renewable energy sources, more and more passive consumers are transforming into prosumers and changing towards aggregated systems for proactive DR participation. In addition to demand-side power flexible and time flexible loads, a prosumer aggregator includes various flexible energy production, conversion, and storage equipment. In a prosumer aggregator, 100% of the renewable energy is firstly converted via wind turbine (WT), photovoltaic thermal (PVT), and geothermal generators into different energy carriers; these are conditioned via various energy conversion and storage equipment, including combined heat and power (CHP) and power to gas (P2G), in order to embrace the needs of users [16]. As proactive end-users, prosumer aggregators act as price-takers to buy or sell electricity from or to the distribution system operator [4].  
In the distribution system, all flexible multi-energy resources within prosumer aggregators can participate in the regulation of energy supply and demand. DR can make full use of the adjustable potential of flexibility resources in order to improve the economy of the distribution network. In this framework, the DR program is integrated with a flexibility potential quantification, which allows one to calculate the maximum possible deviation of every flexible resource from the cost-optimal operation. Once the resource flexibility is quantified and sent to the ancillary service center, it is then priced to calculate the profitability of the prosumer aggregators in an ancillary service. After interacting with the  
Figure 1: Transactive DR framework in the context of a smart gird.  
distribution system operator, the DR program of prosumer aggregators can be optimally and flexibly coordinated.
## 3 Results
### FMR Does Not Modify the Associations Between MC and PFI  
Figure 3 depicts the association between PFI and MC. As can be seen, the association is not modified by FMR levels, since in both groups, the association is statistically significant in children with high FMRs (r = 0.526; \(p\) < 0.01) and in the children with low FMRs ( r = 0.549; \(p\) < 0.01).  
However, the \(t\)-test (Table 4) and histogram (Figure 4) showed that children with higher FMRs have lower MC (\(p\) = 0.01) and PFI (\(p\) < 0.01). With regard to, the diminished PFI in children with higher FMRs was only maintained in boys (\(p\) = 0.01).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Low FMR**} & \multicolumn{2}{c}{**High FMR**} \\  & & **n = 74** & **n = 82** & \(p\) \\ \hline \multirow{2}{*}{All children} & MC & 15.55 \(\pm\) 4.52 & 13.71 \(\pm\) 3.66 & **0.01** \\ \cline{2-5}  & PFI & 0.82 \(\pm\) 2.64 & \(-\)1.08 \(\pm\) 2.51 & **<0.01** \\ \hline \multirow{2}{*}{Boys} & MC & 16.10 \(\pm\) 4.60 & 15.73 \(\pm\) 4.18 & 0.71 \\ \cline{2-5}  & PFI & 2.04 \(\pm\) 2.17 & \(-\)0.40 \(\pm\) 2.50 & **0.01** \\ \hline \multirow{2}{*}{Girls} & MC & 13.29 \(\pm\) 3.78 & 13.30 \(\pm\) 3.42 & 0.99 \\ \cline{2-5}  & PFI & \(-\)0.92 \(\pm\) 2.35 & \(-\)1.61 \(\pm\) 2.54 & 0.21 \\ \hline \hline \end{tabular} Differences between FMR groups were examined by Student’s \(t\)-test. The significance level (\(p\)-values) is set at 0.05, and significant values are marked in bold. Abbreviations: FMR = fat to muscle ratio; MC = motor competence; PFI = physical fitness index.  
\end{table}
Table 4: \(t\)-test for motor competence and physical fitness index according to FMR level and sex.  
Figure 3: Scatter plots for MC and PFI. Dotted and dashed lines depict children with high FMRs (above p50) while squares and straight lines depict children with low FMRs (below p50). \(p\) values are from [PERSON]’s correlation.
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 1 Introduction
Apart from a few commercially available blockchain-based copyright protection platforms [16; 17], one can find only a handful of blockchain-based copyright protection schemes in the literature. Recently, the authors in [18] investigated the use of blockchain technology in diverse online multimedia applications, such as music and advertising industries, healthcare, social media, and content delivery networks. Though the descriptive study analyzes the characteristics (target market, underlying platform technologies, consensus protocols and reward system) of the existing blockchain-based online media platforms, the research direction is not focused on addressing the problems related to integration of copyright protection mechanisms with the blockchain technology. Through this research work, we attempt to investigate how copyright infringement-related problems can be resolved using the blockchain technology. The key contribution of this paper is to provide a holistic survey of multimedia content protection applications that use the blockchain technology. A taxonomy is developed to classify these applications based on technical blockchain characteristics, content protection mechanisms, and performance criteria. To the best of our knowledge, no systematic taxonomy has been defined in the literature to characterize the state-of-the-art of blockchain-based copyright protection schemes. The proposed taxonomy integrates technical aspects and application knowledge to address the challenges with feasible solutions and identify the possible research gaps in blockchain-based copyright protection applications. We believe that the current moment is opportune to present this survey due to recent emergence of copyright protection systems based on blockchain. The rest of the paper is organized as follows. Section 2 presents the taxonomy of blockchain-based multimedia content protection applications. Section 3 surveys recent work on copyright protection schemes based on the blockchain technology. Additionally, we compared the schemes w.r.t. the attributes defined in the taxonomy. Section 4 provides an overall discussion and reveals a number of insights into the field of research. In addition, the future research directions are outlined. Finally, we present the conclusions of this research work in Section 5.
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 2 Materials and Methods
### The Combination of TLS\({}_{\text{sf}}\) and TLS\({}_{\text{sf}}\)  
After obtaining TLS\({}_{\text{ef}}\) and TLS\({}_{\text{sf}}\) through two CNNs, their scores must be combined to determine the final irrigation classification. Simple MLP is used for this and its architecture is shown in Figure 6. This MLP has three hidden layers, with 1024, 512, and 256 neurons in each layer. The activation functions of these three layers are all ReLU. The output layer contains three neurons representing three water stress conditions and uses softmax as the activation function.
## 3 Results and Discussion
### Effects on the Pyrolysis Behavior of OS
#### 3.1.2 TG/DTG Analysis  
Generally, the TG curve of OS can be divided into three stages. Stage I (\(<\)250 \({}^{\circ}\)C) consists of the evaporation of water, including the interlayer water of clay minerals. Stage II (250-600 \({}^{\circ}\)C) is mainly the pyrolysis of kerogen, while stage III (\(>\)600 \({}^{\circ}\)C) involves the decomposition of clay minerals including carbonate minerals [7; 8]. Figure 3 shows the TG/DTG curves and weight losses at different stages of OS and various mixed samples. As shown in Figure 3, the weight losses in stage I of the mixed sample are increased compared to OS. The catalyst in wet-mixed samples may crystallize during the drying process, releasing a large amount of crystallization water during heating, which mainly leads to an increase in weight loss. Afterwards, the weight losses in stage II of the mixed samples corresponding to MnCl\({}_{2}\cdot\)4H\({}_{2}\)O, AlCl\({}_{3}\cdot\)6H\({}_{2}\)O, and CrCl\({}_{3}\cdot\)6H\({}_{2}\)O also show significant increases compared to OS. According to Figure 2 and Equations (2)-(9), this may be due to the continuous decomposition of calcite in acidic environments and thermal decomposition of the generated substances, mainly MnCO\({}_{3}\), Al(OH)\({}_{3}\), and Cr\({}_{2}\)(CO\({}_{3}\))\({}_{2}\), etc. Finally, Figure 3a shows that the peak in stage III on the DTG curve almost disappeared, and Figure 3b indicates significant reductions in weight loss of the mixed samples compared to OS. This proves that most of the carbonates in the OS have been removed during wet mixing or previous heating processes.