esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Problem Statement  
The development of a secure service provisioning mechanism for IoT became a reality with the advent of blockchain technology. In IoT, many electronic devices are connected together to form a network. Due to the rapid increase in IoT devices, huge amounts of data are being generated. To handle this huge volume of data efficiently and securely, a decentralized data control mechanism is required [8]. Authors in [9] proposed a blockchain-based secure service provisioning mechanism for IoT using blockchain. The service codes are protected from untrustworthy edge servers involved in edge transparent computing network, using blockchain technology. The effectiveness of the system is evaluated for resource constraint devices through simulation results. However, neither a service charging mechanism nor a cryptographic mechanism for secure communication is considered. In [10], authors proposed an edge transparent computing network for smart transportation, in which both centralized and distributed architectures are used for efficient and cost-effective communication. Edge servers are placed in a distributed manner to provide essential services and to achieve localization. However, efficient deployment of edge servers, enabling caching techniques and data monetization among IoT are not considered. With the increase in population, smart vehicles are increasing with every passing day. In [7], the authors proposed a blockchain-based VN, which allows the development of a distributed network for large scale vehicles in an efficient and effective manner. However, for reliable communication among vehicles and for efficient data storage, there is a need of a trust management and distributed file system. In V2V communication, vehicle's ID number is used normally for communication purpose. However, it leads to the privacy leakage issue. To tackle the biasness issue and to encourage users, an incentive mechanism is required. However, no incentive mechanism for vehicles is introduced in [7].
## 3 The Use of the Lozi Map in Electrical Devices, Memristors, and Computers
### Chaotic Interleaver for Turbo Codes  
In [53], Sahnoune and Berkani presented a new method to design a deterministic interleaver with random-like behavior based on the Lozi map. The interleaver is a fundamental part of turbo code design and plays a critical role in the performance of turbo coding. The basic role of the interleaver is to construct a random code and spread out burst errors. The interleaver provides \"scrambled\" information data to the second component encoder for constructing random codes, and decorrelates the inputs to the two component decoders; hence, the convergence of the iterative decoding algorithm improves.  
An interleaver is a vector \(\pi\), where \(\pi(n)\) is the position in the information sequence that is interleaved to position \(n\). Most proposed deterministic interleavers are linear interleavers with a designed index function provided by  
\[\pi(n)=kn+u\quad modN,\quad 0\leq n\leq N, \tag{24}\]  
where \(k\) and \(n\) are fixed integers and \(k\) is prime relative to \(N\).  
The chaotic interleaver based on the Lozi map (with \(a=1.7\) and \(b=0.5\)) is defined as follows:  
* Step 1: the integer sequence \(\{n\}_{n=1}^{N}\in\mathbb{Z}\) is mapped to a real number sequence \(\{x(n)\}_{n=1}^{N}\in\mathbb{R}\) using (3).
* Step 2: \(\{x(n)\}_{n=1}^{N}\) is mapped to another integer sequence \(\{s(n)\}_{n=1}^{N}\in\mathbb{Z}\) by designing \(s(n)\) as the position of index of \(x(n)\) in the sort ascending of \(\{x(Urrea et al. [54] compared the performances of a chaotic interleaver with short block lengths to a new chaotic interleaver based on the Duffing map, which showed slightly better performance.
# SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems
## 4 Proposed Solutions for Locality Problem in P2P Cloud System
### Hypercube Based Overlay Network
As mentioned above, the routing algorithm in Hypercube is optimal. In this algorithm, the least significant bits of the source and destination addresses are compared. If the bits are different, the message is routed in the dimension of that bit. The same procedure is done for the all bits, one by one, until the all bits are checked and the message is delivered to the destination. It is further worthy to mention that, unlike most of the well-known P2P networks, there is not any cost for leaving the network in the proposed solution, due to the repetitive checks of nodes to their neighbors.
## 1 Introduction
[19] compared the SBSs and ARIs amongst 3D custom-printed resin brackets with different retention but all the same base size, a micro-retentive group and a macro-retentive group and a conventional metal bracket group, and a 3D-printed bracket group with a larger base size. They reported a higher shear bond strength value for the metal bracket group than for the other groups, and there were significant differences between the 3D-printed bracket groups. Also, regarding the ARI score, there were significant differences in the micro-retentive group compared to the other groups. New and unique 3D custom-printed polycrystalline alumina brackets (Lightforce(r), Cambridge, MA, USA) were recently introduced to orthodontic practice. Adequate SBS and ARI studies for this bracket have not been performed. Thus, the present study pursued this evidence to assist clinicians in bonding decision-making. Purpose:  
1. To determine and compare the SBSs [20] amongst 3D custom-printed polycrystalline alumina brackets (3 DBs), ceramic brackets (CBs), and metal brackets (MBs). 2. To determine the ARI [21] as an assessment of failure at the adhesive-bracket interface amongst the three noted bracket types. Hypotheses:  
**Hypothesis 1. **_Primary null hypothesis: no significant difference exists in SBSs among the 3 DB, CB, and MB groups._  
**Hypothesis 2**. : _Secondary null hypothesis: no significant difference exists in the ARI amongst the 3 DB, CB, and MB groups._
## 5 Performance Evaluation of EMOR
### Simulation Environment
Moreover, the traditional data forwarding algorithms and schemes do not incorporate OCRG in calculating the optimal path towards the sensor nodes, so an algorithm is needed which utilizes the concept of OCRG, highlights the link quality aspect, and also demonstrates adherence to accomplish the real-time and non-real-time requirements of SGs NAN while efficiently managing the energy resources. Therefore, we propose an energy efficient multipath opportunistic routing protocol for smart grids NAN. The performance comparison of EMOR with that of ExOR, POFA, RPRDC, and DCBONC is completed with reference to performance metrics like link connectivity, PDR, energy consumption, network lifetime, and average end-end delay.
## 2 Materials and Methods
### Evaluation Metrics  
The performance of the proposed framework is evaluated using accuracy, precision, recall, and F1-score for both overall performance and individual classes. Area under the curve--receiver-operating characteristic curve (AUC-ROC) was used to assess the model's ability to distinguish between classes, particularly the minority class. Visualisations of Koopman eigenfunctions and metastable clusters provide insights into the connectivity patterns.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 3 Cavitation and Vortex Dynamics Analysis of Pump-Jet Propeller  
### Tip Vortex and Monitoring Points Positioning  
In the rotor system of a pump-jet propeller, due to the tip clearance between the blade tip and the inner wall of the duct, vortices including the tip leakage vortices (TLV) and the tip separation vortices (TSV), occur among this area when the pump-jet propeller rotates at high speeds as shown in Figure 6. The formation of TLV is mainly influenced by two factors: pressure difference between pressure side and suction side of blades and relative movement between blade and wall. The formation of TSV is mainly influenced by the tip and wall boundary layer, so the track of TSV core is basically in the gap area of the tip while its position is close to the tip. The starting point of the TLV is the same as that of the TSV, but its trajectory gradually diffuses from the starting point of the TSV to the direction away from the blade wall, since the motion of the main flow field drives TLV to the main flow channel.  
The research focus of this paper is on the analysis of the dynamic characteristics of the tip vortices, so it is an effective means to set monitoring points to measure the unsteady characteristics of the tip vortices. Based on the principle of minimum pressure [49], vortex cores in different axial positions of TLV and TSV are selected to monitor their pressure. The position of the vortex core is shown in Figure 7. The horizontal axis \(\lambda\) represents the projection of the chord length of the blade tip on the coordinate axis and the vertical axis represents the axial distance in the absolute coordinate system. Five and seven monitor points are taken from TLV and TSV, respectively. The monitor points of TLV are prefixed by LP while the monitor points of TSV are prefixed by SP.  
Figure 6: TLV and TSV schematics at \(J=1.02\): (**a**) streamline of Tip Leakage Vortex (TLV) (**b**) streamline of Tip Separation Vortex (TSV).  
In order to clearly compare the influence of cavitation on tip vortices before and after cavitation, the rotational speeds selected in this paper are 1250 rpm and 1750 rpm, corresponding \(J=1.02\) and \(J=0.73\), respectively. As shown in the Figure 8, peak propulsion efficiency with cavitation occurs at \(J=1.02\), and then the curve of efficiency begins to decrease. When the rotational speed increases to \(J=0.73\), the efficiency of cavitation condition is much lower than non-cavitation which is regarded as a severe cavitation scenario.
# Detecting Suspicious Texts Using Machine Learning Techniques
## 1 Introduction
Therefore, the research question addressing in this paper is--\"RQ: How can we effectively classify potential Bengali texts into suspicious and non-suspicious categories?\"  
To address this research question in this work, we first develop a dataset of suspicious and non-suspicious texts considering a number of well-known Bengali data sources, such as Facebook posts, blogs, websites, and newspapers. In order to process the textual data, we take into account unigram, bigram, trigram features using tf-idf and a bag of words feature extraction technique. Once the feature extraction has been done, we employ the most popular machine learning classifiers (i.e., logistic regression, naive Bayes, random forest, decision tree, and stochastic gradient descent) to classify whether a given text is suspicious or not. We have also performed a comparative analysis of these machine learning models utilizing our collected datasets. The key contributions of our work are illustrated in the following:  
* Develop a corpus containing 7000 text documents labelled as suspicious or non-suspicious. * Design a classifier model to classify Bengali text documents into suspicious or non-suspicious categories on developed corpus by exploring different feature combination. * Compare the performance of the proposed classifier with various machine learning techniques as well as the existing method. * Analyze the performance of the proposed classifier on different distributions of the developed dataset. * Exhibits a performance comparison between human expert (i.e., baseline) and machine learning algorithms. We expect that the work presented in this paper will play a pioneering role in the development of Bengali suspicious text detection systems. The rest of the paper organized as follows: Section 2 presents related work. In Section 3, a brief description of the development of suspicious Bengali corpus and its several properties have explained. Section 4 explained the proposed Bengali suspicious text document classification system and its significant constituents. Section 5 described the evaluation techniques used to assess the performance of the proposed approach. Results of the experiments are also presented in this section. Finally, in Section 6, we concluded the paper with a summary and discussed the future scopes.
###### Abstract  
Ionospheric scintillation often occurs in the polar and equator regions, and it can affect the signals of the Global Navigation Satellite System (GNSS). Therefore, the ionospheric scintillation detection applied to the polar and equator regions is of vital importance for improving the performance of satellite navigation. GNSS radio occultation is a remote sensing technique that primarily utilizes GNSS signals to study the Earth's atmosphere, but its measurement results are susceptible to the effects of ionospheric scintillation. In this study, we propose an ionospheric scintillation detection algorithm based on the Sparrow-Search-Algorithm-optimized Extreme Gradient Boosting model (SSA-XGBoost), which uses power spectral densities of the raw signal intensities from GNSS occultation data as input features to train the algorithm model. To assess the performance of the proposed algorithm, we compare it with other machine learning algorithms such as XGBoost and a Support Vector Machine (SVM) using historical ionospheric scintillation data. The results show that the SSA-XGBoost method performs much better compared to the SVM and XGBoost models, with an overall accuracy of 97.8% in classifying scintillation events and a miss detection rate of only 12.9% for scintillation events with an unbalanced GNSS RO dataset. This paper can provide valuable insights for designing more robust GNSS receivers.  
polar and equator ionospheric scintillation; GNSS radio occultation; remote sensing; power spectral density; SSA-XGBoost +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 5 Simulation Experiments and Analysis
### Analysis of the Performance  
In this section, the simulating data of initial velocity-error and leading vehicle deceleration is listed in Figure 10, including all situations and policies. According to the performance, we will analyze the practical significance of the proposed method and pay attention to the following error and stable time. We may find that the initial errors are different in each vehicles because of the difference in driving state of neighbors. In aforementioned control methods, the following errors are critical to the driving process and should be strictly managed. However, the following errors in the proposed method will be the fundament of the motion controller and reveal the changing tendency of the velocity. Besides, the normal spacing policy and the \(AL\) value both give good performance in average stable time. It is a strong proof that they have a great ability to prevent disturbance. In Figure 10b, the leading vehicle was assumed to deaccelerate from the stable state which means that the initial errors are equal to zeros. The deecelerating progress will not stop until the vehicles reach the static state.
## 4 Development and Simulation of a Predictive S&OP Methodology
### Statistical Treatment of Demand for a Defined Case Study
#### 4.2.3 Random Number Generation for Future Expected Demand  
Based on the statistical treatment, there are three different demand patterns involving three states over the four years of the simulation model. In this context, the generation of random numbers for the expected demand is performed, following the functions and characteristics shown in Table 6:  
Random number generation is performed for the next twelve working days for each working day of the simulation period. This means that on day 102, demand values are generated for the days from day 103 to day 114.  
Figure 13: Correlogram of the residuals of the ARIMA model (25, 0, 0) (13, 0, 0)\({}_{1}\) (own elaboration).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Value** & **CI 80\%** & **CI 95\%** \\ \hline
19,443 & 17,518 & 21,531 & 16,540 & 22,681 \\
18,742 & 16,640 & 21,011 & 15,499 & 22,438 \\
18,286 & 16,382 & 20,569 & 15,330 & 21,549 \\
20,452 & 18,316 & 22,698 & 17,155 & 24,060 \\
15,472 & 13,965 & 17,491 & 13,127 & 18,459 \\
20,954 & 18,413 & 23,265 & 17,306 & 25,200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction extracts and their confidence intervals at 80% and 95%.
## 5 Research Challenges and Future Directions
### Mobility  
Most of the schemes utilized for passive wake-up radios assume all the sensor nodes to be stationary. However, mobility is needed in some IoT applications, such as large and sparsely deployed sensor nodes environments, where data aggregation by a central node may be challenging. Some of the passive WuR schemes are adapting to the mobility environment while attempt to improve energy efficiency, such as [79], which uses a mobile sink for an initial wake-up in the system. For example, a mobile car, working as a base station, drives along the road to wake-up each node and collect data. Therefore, there is an opportunity to develop robust protocols that can adapt to the rapid topology change in mobile networks.
# A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas
## 1 Introduction
Also in the visible (VIS) and near-infrared (NIR) regions of the electromagnetic spectrum it is possible to detect the presence of an oil layer on the sea surface thanks to the different reflectance of the two elements [37; 38; 22; 39; 40; 41; 42]. The main limits of these methodologies are related to their applicability only to particular geometries of observation [32], and their poor level of automation due to the need for several interventions of an operator (e.g., region of interest identification and visual recognition) [43; 44; 45; 33]. Among the methodologies working in the optical band, the one specifically applied to oil detection based on the general robust satellite technique (RST), named RST-OIL [20; 26; 27], has already been successfully applied to Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) TIR data [20; 27], showing excellent performances with a high level of reliability (no false alarm). In this work, the same approach has been extended to MODIS visible bands to exploit their higher spatial resolution (250 m) than the thermal channel (1 km) to improve the detection sensitivity towards small spills such as in the wakes of ships or illicit vessel discharge. Such a better spatial resolution combined with a high revisit frequency may offer frequent and detailed mapping of oil-affected areas. The oil spill event studied in this paper is an illicit vessel discharge from 18 June 2007 off the south coast of Cyprus, reported by the European Maritime Safety Agency (EMSA) [46], which was undetectable by RST-OIL due to the non-adequate spatial resolution of the MODIS TIR data used for this event. The area affected by the event is shown in Figure 1, together with a Radarsat image collected on the same day. Three clear, elongated dark areas are recognizable (green ellipses in Figure 1) and seem to be connected, indicating the movement of a vessel along a triangular path. Several dark areas with a non-uniform shape are also present in the northern part of the scene, close to the Cyprus coastline, probably due to the sea current, representing a typical example of SAR look-alikes.
## 4 Experimental Results
### PDR Testing  
The PDR is the ratio of successfully delivered data packets to the total packets transmitted within a network. The observation of the PDR for Babel, BATMAN, and OLSR when the number of nodes varied from 2 to 11 can be seen in Figure 8.  
As seen in Figure 8, BATMAN exhibits the best performance, with a PDR of 100% at two nodes, a slight decrease to 96% at five nodes, an improvement to 98.3% at eight nodes, and a drop to 42.8% at eleven nodes. OLSR follows, starting at 100% and experiencing a less pronounced decline, while Babel shows the greatest decline in the PDR, starting at 100% and dropping to 28.7% at eleven nodes, indicating the highest sensitivity to network size.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 3 Methodology
### Phase 2: Generation of the Object Detection Model
#### 3.2.2 Objects Labeling  
Before the labeling process, a combination of characteristics or requirements is established based on rules [40]. The rules represent the expert's knowledge to classify the two classes of objects of interest. An object is considered a ship if the cutout of the object of interest is positive, and will be labeled \"plot\". Otherwise, if the clipping of the object is harmful, then it is not a ship and will be labeled \"no\". This is represented as (12):  
\[box=\left\{\begin{array}{ll}``plot^{\prime\prime}&\text{this is a contact}\sim ship\\ ``no^{\prime\prime}&\text{this is not a contact}\end{array}\right\} \tag{12}\]  
For an object to be considered a \"plot\", the rules established by the radar expert include persistence and completeness.  
Persistence in three consecutive turns means that the object appearing in the current round must be present in the previous and next round. This translates as follows:  
_The object is present in the current round AND the same object is present in the previous round AND it is also present in the next round._  
Completeness of the object. This translates as follows:  
_The object must contain a single echo AND the echo must be perfectly framed in the object cutout._  
Likewise, for an object to be considered \"no\", the following must occur:  
_The object is not persistent OR the object is not complete OR the object cutout partially contains the object OR the object contains overlapping elements OR any other type of ambiguous elements._  
To carry out the labeling more efficiently, a proprietary labeling interface is developed with functionalities that make the process easier. This interface allows for visualization of the complete image and shows the clipping area and the clippings of objects of interest of three consecutive laps with their corresponding coordinates. The labeling process is carried out by a team of experts who apply the previously established rules.  
In our previous works [41; 42], the labeling process was performed with the tool LabelImg [43]. The time it took us to label an image of 50 objects was 60 min. With the developed labeling interface, it has been possible to visualize in parallel the images of the previous and next round, facilitating the application of the radar expert's criteria. It has allowed to reduce the labeling time from 60 to 7 min, this means from 1.2 min to 8.4 s. The labeling time per radar image has been reduced by 88%. Figure 12 shows the labeling interface designed based on the rules of the radar expert.  
Figure 11: Images of cutouts of objects.  
The labeling ends with the generation of the object of interest clippings and a text file with the coordinates of the bounding boxes of the objects of interest together with the class labels \"plot\" or \"no\".