esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Relevant Published Research
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ord.** & \multirow{2}{*}{**Title of Paper**} & **Service/Application** & **Methods and** & **Observed** & **Comparative** \\
**Number** & & **Application** & **Models Used** & **Factors/Variables** & **Improvements Presented** \\  & & **Observed** & & **Affecting _QoE_ & **in This Paper** \\ \hline  & _QoE_ Modeling for Voice & & & & \\  & over IP: Simplified & & Objective simplified & & \\  & E-model Enhancement & & & & \\  & Utilizing the Subjective & & & & \\  & MOS Prediction Model: A & & & & \\  & Case of G.729 and Thai & & & & \\  & Users & & & & \\ \hline  & A holistic modeling for & & & & \\  & _QoE_ estimation in live & & & & \\  & video streaming & & & & \\  & applications over LTE & & & & \\  & Advanced technologies & & & & \\  & with Full and Non & & factor analysis & & \\  & Reference approaches & & & & \\ \hline  & & & & & \\  & Privacy Preserving _QoE_ & & & & \\  & Modeling using & & & & \\  & Collaborative Learning & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & Framework for & & & & \\  & Controlled & & streaming & & \\  & Experimentation and _QoE_ & & & & \\  & Modeling & & & & \\ \hline  & & & & & \\  & Scalable Ground-Truth & & & & \\  & Annotation for Video _QoE_ & & & & \\  & Modeling in Enterprise & & & & \\  & WiFi & & & & \\ \hline  & & & & & \\  & Modeling _QoE_ in & & Subjective method & & \\  & Dependable & & based on perceived & audience & \\  & Tele-immersive & & & reliability; & characteristics, \\  & Applications: A Case & & & & \\  & Study of World Opera & & & & \\  & Study of World Opera & & & & \\  & & & & \\ \hline  & & & & & \\  & & & & & \\  & & & & \\  & The Memory Effect and & & & & \\  & Its Implications on Web & & & & \\  & QoE Modeling & & & & \\  & & & & \\  & & & & \\  & & & & \\  & Quality of Experience for & & & system-related & \\  & Streaming Services: & & & influencing factors; & \\  & Measurements, & & objective methods; & & \\  & Challenges & & hybrid methods & & \\  & and Insights & & & \\  & & & & \\  & & & & \\  & & & & \\  & Evaluating _QoE_ in VoIP & & MOS model; PESQ & & \\  & networks with QoE & & & \\  & mapping and machine & & & & \\  & learning algorithms & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline  & & & & & \\  & Evaluating _QoE_ in VoIP & & MOS model; E-model; a & & \\  & networks with QoE & & single-layer artificial & & \\  & mapping and machine & & neural network & & \\  & learning algorithms & & model & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative overview of _QoE_ modeling in previous research with _QoE_ modeling in this paper. In addition to the code of each of the 11 references in the first column and the titles of papers in the second column, the third column of the table shows the services/applications observed, the fourth shows methods and models used, and the fifth column shows the factors affecting \(QoE\) as a research variable. In the sixth column, the comparative advantages of the solution in this paper are interpreted.
# Single-Class Data Descriptors for Mapping
## 4 Discussion
### McNemar's Test  
The four-cell confusion matrix is very intuitive to show the similarities and differences between the proportions (i.e., the true and false allocated parts) concerning two sets of specific labels. On the basis of the error table, we wish to achieve the statistical significance of the differences between the proportions using [PERSON]'s test [76] so as to assess two allocated results, which are obtained by two SCDDs, or just given, under a position-specific comparison. Here, [PERSON]'s test is specifically useful for comparing paired proportions derived from two sets of samples. In the formulas below, we use the notations:  
\[\begin{array}{l}\mathit{pdisc}=p_{F+}+p_{F-}\\ \mathit{pdiff}=p_{F+}-p_{F-}\end{array}, \tag{5}\]  
where the \(p_{F+}\) is the proportion of testing samples that the first SCDD is true, and the second is false; meanwhile, the \(p_{F-}\) denotes the proportion of testing samples that the first SCDD is false while the second is true. Thus, [PERSON]'s test focuses on the proportions of testing samples that one SCDD is true while another is false (and vice versa) [41].  
\[\begin{array}{l}\mathit{SE}_{p}=\sqrt{(\mathit{pdisc}-\mathit{pdiff}^{2})/ N},\end{array} \tag{6}\]  
where \(\mathit{SE}_{p}\) represents the standard error derived from the difference between the proportions, and \(N\) is the total number of the pairs of objects. [PERSON]'s test will perform the evaluation of the \(100(1-\alpha)\%\) confidence interval for comparing the difference between two accuracy values based on the differences (\(D_{\alpha}\)) between the proportions. Assuming a normal distribution \(z_{\alpha}\), the general expression of the confidence interval [76] can be expressed as:  
\[\begin{array}{l}D_{a}\pm z_{\alpha}\mathit{SE}_{p}.\end{array} \tag{7}\]  
For exploring in a straightforward manner, we split Equation (7) into a real image, then the image part may be more crucial to give the proper assessment with regard to a confusion matrix. In this way, the statistical assessment of the differences is carried out to determine if these are significantly different or not [76]. Since three kinds of error matrices are presented in this study, here we name them as CDt (i.e., the classifier-dependent using the test set), CIa (i.e., the classifier-independent using the reference map of the \"sa\"), and CIb (i.e., the classifier-independent using the reference map of the \"sb\").  
The difference between the accuracies yielded by the paired SCDDs (or two sets of labels) is \(D_{a}\), ranging from \(D_{a}-|z_{\alpha}|\mathit{SE}_{p}\) to \(D_{a}+|z_{\alpha}|\mathit{SE}_{p}\) at the \(100\ \times(1-0.05)\%\) confidence interval. In order to make all confidence intervals comparable, the one-sided absolute range (\(|z_{\alpha}|\mathit{SE}_{p}\)) around \(D_{a}\) is exhibited in Table 6. Two inferior SCDDs (i.e., the c5 and c9) have their appearances again. As for two slightly inferior SCDDs (i.e., the c2 and c6), only the c2 can be observed. Such results estimated by [PERSON]'s test provide a useful back-up to previous analysis.
## 3 Results
### Radiation Angular Distribution
The initial phase \(\phi_{0}\) only determines the initial amplitude of the laser electric field at the moment t = 0 but does not change the direction of polarization; therefore, the change of \(\phi_{0}\) affects the initial conditions of the electron oscillations, but the radiation bimodal peaks are always along the direction of the electric field and \(\psi\) do not change with it. Numerical simulations were performed for electron initial energies of 20.44, 30.66, 40.88, and 51.1 MeV (corresponding to \(\gamma=40,\ 60,\ 80,\ 100\)). The angular radiation distributions exhibit a characteristic bifurcation effect; as the initial phase varies, the polar angle \(\theta\) develops a double-peak structure at the maximum intensity position, while the azimuthal angle \(\psi\) remains constant. It is worth noting that when we use elliptically polarized light, the electrons are in three-dimensional helical motion, and the angular distribution of the radiation is single-peaked (or tilted elliptically distributed). Building upon these findings, Figure 4 presents the correlation between radiant energy and polar angle \(\theta\) for varying initial electron energies \(\gamma_{0}\), with the initial phase fixed at \(\phi_{0}=0\). As shown in Figure 4, we find that the bimodal polar angle \(\theta_{m}\) decreases with the increase of the initial energy \(\gamma_{0}\), but there exists a lower limit (determined by the laser intensity \(a\)), which \(\theta_{m}\) is stabilized at \(\pi-c/\gamma_{0}\). This property stems from the planar constraints of the electron motion and the stationarity of the acceleration direction. Therefore, this property of bimodal radiation produced by linearly polarized lasers can be well applied to polarization resolution for a precise measurement of physical parameters or tuning of specific functions. At the same time, the increase of the initial energy of the electron, also causes a superlinear growth of the maximum radiation, which is proportional to \(\gamma_{0}\)4, and this law provides a key basis for the design of controllable high-intensity radiation sources.
# SPCL: A Smart Access Control System That Supports Blockchain
## 3 The Proposed Scheme SPCL
### Authentication Phase  
In the authentication phase, the mobile client (\(MC\)) establishes authentication with the smart lock (\(SL\)).  
1. Requesting authentication. 1
2. \(MC\) picks a random value \(r_{MC}\in\mathbb{Z}_{n}^{*}\) and computes \(R_{MC}=r_{MC}\times G=(x_{MC},y_{MC})\). \(MC\) chooses the identifier of the smart lock you want to open. \(SL\) picks a random value \(r_{SL}\in\mathbb{Z}_{n}^{*}\) and computes \(R_{SL}=r_{SL}\times G=(x_{SL},y_{SL})\). 2
3. \(MC\) generates the current time-stamp \(T_{MC}\) and computes \(RID_{MC}=Enc_{P_{SL}}(ID_{MC}\)\(||N_{SL}||R_{MC})\). \(MC\) sends message \(M_{1}=(RID_{MC},T_{MC},R_{MC})\) to \(SL\) via wireless channels. \[MC\to SL:M_{1}=(RID_{MC},T_{MC},R_{MC})\]
4. Responding to the mobile client. 1 Upon receiving \(M_{1}\), \(SL\) verifies \(T_{MC}\) and decrypts \(RID_{MC}\) as \((ID_{MC}\)\(||N_{SL}||R_{MC})=Dec_{d_{SL}}(RID_{MC})\). \(SL\) computes \(t_{SL}=(d_{SL}+r_{SL})\)_mod_\(n\) and \(V=t_{SL}\times(P_{MC}+R_{MC})=(x_{V},y_{V})\). 2 After that, \(SL\) calculates \(S_{SL}=H_{1}(y_{V}||H_{1}(x_{V}||N_{SL}||N_{MC}||x_{SL}||y_{SL}||x_{MC}||y_{ MC}))\) and the current time-stamp \(T_{SL}\). 3 Then, \(SL\) sends message \(M_{2}=(T_{SL},R_{SL},S_{SL})\) to \(MC\). \[SL\to MC:M_{2}=(T_{SL},R_{SL},S_{SL})\]
5. Accepting the mobile client's session key. 1 Upon receiving \(M_{2}\), \(SL\) verifies \(T_{SL}\) and \(R_{SL}\). If the verification succeeds, \(MC\) calculates \(t_{MC}=(d_{MC}+r_{MC})\)_mod_\(n\) and \(U=t_{MC}\times(P_{SL}+R_{SL})=(x_{U},y_{U})\). 2 Then, \(MC\) calculates \(S_{1}=H_{1}(y_{V}||H_{1}(x_{V}||N_{SL}||N_{MC}||x_{SL}||y_{SL}||x_{MC}||y_{MC}))\) and checks whether \(S_{1}=S_{SL}\). If verification is successful, \(MC\) computes \(S_{MC}=H_{1}(y_{U}||H_{1}(x_{U}||N_{MC}||N_{SL}||x_{MC}||y_{MC}||x_{SL}||y_{ SL}))\) and the session key \(SK_{MC}=H_{2}(x_{U}||y_{U}||N_{MC}||N_{SL})\). Then, \(MC\) sends message \(M_{3}=(S_{MC})\) to \(SL\). \[MC\to SL:M_{3}=(S_{MC})\]
6. Accepting the smart lock's session key. 4 Accepting the smart lock's session key. Upon receiving \(M_{3}\), \(SL\) computes \(S_{2}=H_{1}(y_{V}||H_{1}(x_{V}||N_{MC}||N_{SL}||x_{MC}||y_{MC}||x_{SL}||y_{SL}))\). Then \(SL\) checks whether \(S_{2}=S_{MC}\). If verification is successful, \(SL\) calculates the session key \(SK_{SL}=H_{2}(x_{V}||y_{V}||N_{MC}||N_{SL})\). Thus, \(MC\) and \(SL\) successfully establish session keys.
## 4 Discussion
### Limitations of the Study and Future Plans  
Compared to other studies, this research has certain limitations.  
1. Temporal Resolution: The temporal resolution of Landsat 8 is relatively low, with images captured every 16 days, resulting in fewer available images compared to higher temporal resolution remote sensing data like Sentinel 2 [46]. Additionally, cloud cover further reduces the number of usable images. In 2016, there were only six images with cloud cover less than 10%. Therefore, the selected months in this study are limited to January, February, March, August, and December.  
2. Lack of Consideration for Other Variables: combing the spectral information obtained from Landsat 8 with salinity to establish a soil salinity monitoring inversion model. However, only spectral information was used and did not incorporate other variables such as elevation, soil pH, and water content [47]. Water and salt are highly correlated during the process of soil salinization, and salinity can change with variations in water content. Thus, soil salinity is likely to vary with changes in moisture. In salinity monitoring, it is essential to consider both salinity and moisture to further develop coordinated monitoring of water and salt, achieving high monitoring precision and effectiveness. The next step is to use Sentinel 2 in conjunction with Sentinel 1 and more variable information for salinity inversion in the Yellow River Delta.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 5 Discussion
### Detection Evaluation  
In terms of detection accuracy, the RF wrapper technique consistently outperforms other feature selection methods, whether the data is split into a 70:30 training-to-testing ratio or subjected to 10-fold cross-validation. For instance, the detection accuracy with RF as a feature selector is 98.15% for the 70:30 split and 98.04% for 10-fold cross-validation, whereas the detection accuracy with J48 as a feature selector is 96.80% for the 70:30 split and 96.77% for 10-fold cross-validation.  
Among the filter methods, InfoGain and Relieff perform better than chi-squared. For example, the detection accuracy with InfoGain as a feature selector is 97.12% for the 70:30 split and 97.47% for 10-fold cross-validation, while the accuracy using chi-squared as a feature selector is 96.47% for the 70:30 split and 96.59% for 10-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Classifier**}} & \multicolumn{6}{c}{**All Features included**} & \multicolumn{6}{c}{**Feature Selection**} & \multicolumn{6}{c}{**Filter Methods**} \\ \cline{2-13}  & & \multicolumn{6}{c}{**Wupper Methods**} & \multicolumn{2}{c}{**Follow Methods**} & \multicolumn{2}{c}{**Follow Criteria**} & \multicolumn{2}{c}{**Follow Methods**} & \multicolumn{2}{c}{**Follow Criteria**} & \multicolumn{2}{c}{**Follow Criteria**} & \multicolumn{2}{c}{**Follow Criteria**} \\ \cline{2-13}  & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** & **Random Forest** \\ \cline{2-13}  & **96.200** & **96.400** & **70.300** & **96.640** & **70.300** & **96.640** & **70.300**  
Figures 11 and 12 show the mean accuracy rate obtained by each combination using a different number of selected features ranging from 1 to the maximum number of features 38 in the dataset. The RF-SFSF approach outperformed the other five wrapper-based approaches, achieving the highest accuracy rate of 98.26% with 22 out of 38 features.  
Upon comparing the results of the filter-based and wrapper-based methods, we concluded that the RF-SFSF approach is the most effective feature selection method for this dataset. The final subset of features employed in our study is detailed in Table 5.
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 4 Validation and Discussion
### Simulation Setup  
To better evaluate the performance of the proposed protocol, we use the NS3 simulator and Wireshark tools to provide experimental results. The simulation environment is divided into five scenarios, i.e., consisting of 40, 80, 120, 160, 200 nodes in the range of 200 m \(\times\) 200 m with four clusters in the network. We design the topology of the WSN and set the intrusion scenarios of attack nodes. In this case, we take one cluster of them as an example. The nodes are deployed randomly in the network and multiple compromised nodes (such as the black hole, wormhole and Sybil attacks) are set in the network topology. According to the working principle of different protocols, they have diverse ways dealing with attacking events. Therefore, the routing performances of them are different. For the flooding protocol, when some nodes are attacked in the network, the flooding mechanism is to spread a packet to its surrounding nodes and perform this operation repeatedly until the target area receives the packet. This \"best effort\" approach is inefficient and energy-consuming. Moreover, the most important thing is that this method cannot sense the existence of attacks. For the two geographic routing protocols (GEAR and GPSR), although they can discover the compromised nodes, the routing performances are much worse than that of GIN protocol. In the simulation, the GIN protocol can detect the attacks as early as possible and dynamically select the next hop, which effectively bypasses  
Figure 8: The transmission process in GIN.  
the attack area and improves the network performances. The specific parameters are sent as shown in Table 3.
## 3 Results
### Analysis Using T-Patterns  
The search for T-patterns was conducted based on the following criteria: First, a search was carried out from an individual perspective. This perspective corresponds to the different categories of the observational instrument. In particular, the criterion \"technique\" includes all the techniques classified by [PERSON] (68 throws and 32 control techniques). This implies a high dispersion of data, which considerably reduces the possibility of detecting patterns. For this reason, secondly, a search for patterns was conducted from a grouped perspective. The 100 techniques were grouped according to the categories established by [PERSON]: arm techniques (te waza), leg techniques (ashi), hip techniques (koshi), sacrifice techniques (sutemi), hold-down techniques (osaekomi), strangulations (shime), and joint locks (kansetsu). With this strategy, the dispersion of the data was reduced from 100 possible patterns to 7, favoring the detection of patterns. In both cases, a search for patterns with a minimum of three occurrences in all weight categories was conducted. This search and grouping strategy has been employed in previous studies [29]. The results of these searches are presented in Table 4.  
From an individual perspective, several relevant observations can be made. Firstly, the number of detected scoring action patterns is limited. In the \(-\)90 kg category, no scoring action patterns were identified. In the \(-\)60, \(-\)66, \(-\)73, \(-\)81, and +100 kg categories, waza-ari patterns are more numerous than ippoon patterns; however, this is not the case in the \(-\)90 and \(-\)100 kg categories. From a grouped perspective of the techniques, this trend is further emphasized. It is observed that the heavier weight categories (\(-\)90, \(-\)100, and +100 kg) follow a different trend compared to the other categories. Regarding the type of technique, it is observed that the patterns of standing techniques are significantly more numerous than the patterns of ground techniques. In standing techniques, the most commonly used patterns involve leg and arm techniques. Patterns of hip techniques are only present in the \(-\)81 kg category (grouped perspective). Patterns of sacrifice techniques are particularly relevant in the \(-\)73 kg category (grouped perspective). This distribution may vary depending on the weight category; in the \(-\)81, \(-\)90, and \(-\)100 kg categories, patterns of scoring actions with arm techniques are absent. Additionally, it is noteworthy that in ground fighting, there are no patterns of scoring actions with joint lock techniques, with immobilizations being the most utilized.
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 1 Introduction
* Communication Layer: The communication layer facilitates the transmission of data between IoT devices and the blockchain network. It encompasses protocols, standards, and network infrastructure that enable secure and reliable communication. * Blockchain Layer: The blockchain layer is the core component of the system and comprises the distributed ledger and associated consensus mechanisms. It records and validates transactions, ensuring the integrity and immutability of the data stored on the blockchain. * Smart Contract Layer: This layer involves the implementation of smart contracts, which are self-executing contracts with predefined rules and conditions. Smart contracts enable automation, enforce business logic, and facilitate secure interactions between IoT devices and the blockchain network. * Application Layer: The application layer includes the various applications and services built on top of the blockchain-based IoT system. These applications leverage the secure and transparent nature of the underlying blockchain to provide functionalities such as data analytics, supply chain management, and decentralized control. Blockchain-based IoT systems can improve data integrity, transparency, accountability, and interoperability [15]. However, the deployment of blockchain-based IoT systems also presents unique cybersecurity challenges that need to be addressed [16]. These challenges encompass areas such as IoT device security, blockchain security, and the integration of IoT devices with the blockchain infrastructure [17]. Understanding and addressing these challenges are crucial for ensuring the secure and reliable deployment of blockchain-based IoT systems [18]. Continuous research and collaboration among different stakeholders are necessary to overcome these challenges and enable the widespread adoption of blockchain-based IoT systems in various industries [19]. This paper aims to provide a comprehensive review of the existing literature pertaining to cybersecurity challenges in blockchain-based IoT systems. In Section 4, the challenges are categorized into three main categories: (i) IoT device security, (ii) blockchain security, and (iii) integration of IoT devices with blockchain (network security). In Section 5, a detailed analysis is conducted for each challenge, covering the discussion on IoT security and blockchain, and the potential applications of blockchain for IoT in smart cities. Furthermore, existing solutions and their limitations are examined. Section 6 focuses on future research directions, outlining strategies to address these challenges and enhance the cybersecurity of blockchain-based IoT systems. By offering a comprehensive overview and discussing potential solutions, this paper makes a valuable contribution to the existing knowledge in this field.