esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 2 Literature Review
### Deep Learning Models for Recommendation Systems and Autonomous Systems  
Deep learning models have significantly transformed recommendation systems and autonomous systems by employing intricate algorithms to deliver precise and tailored user experiences, hence augmenting the capabilities of autonomous vehicles and other systems.  
Deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers have greatly enhanced the accuracy of recommendation systems. They achieve this by effectively capturing intricate user interaction patterns and preferences. A novel approach has been suggested to enhance the quality of recommendations by tackling the challenges of data sparsity and scalability. This approach integrates collaborative filtering with deep learning techniques [12]. The DLRM, a cutting-edge deep learning recommendation model, efficiently manages categorical features and optimizes computation [13]. Deep neural networks may efficiently integrate user and item characteristics to enhance the relevance of suggestions [14]. In addition, a hybrid approach that combines Hidden Markov Models and Artificial Neural Networks has been proposed to enhance the accuracy and resilience of recommendations [15].  
Deep learning has played a vital role in the advancement of models for tasks such as navigation, object detection, and decision-making in autonomous systems. The efficacy of Convolutional Neural Networks (CNNs) in delivering dependable visual perception for autonomous vehicles, a crucial requirement for ensuring resilient driving capabilities, has been substantiated [16]. A survey has been conducted on the incorporation of reinforcement learning (RL) and deep reinforcement learning (DRL) in autonomous Internet of Things (IoT) systems. The survey highlights the potential of RL and DRL in decision-making processes [17]. Researchers have developed methodologies to provide accurate uncertainty estimations in autonomous driving, which are crucial for ensuring safety in high-risk conditions [18]. Researchers have investigated methods to optimize models in deep learning for autonomous driving, resulting in improved performance and economy [19].  
These achievements demonstrate the wide range of uses for deep learning in improving both recommendation systems and autonomous systems, resulting in substantial enhancements in user experience and operational efficiency.
## 1 Introduction
### Review Methodology  
#### 1.2.1 Literature Search Strategy  
This narrative review employed a multi-database literature search to identify relevant studies on new generation urban traffic control systems. The primary databases searched included Web of Science Core Collection, IEEE Xplore Digital Library, ScienceDirect, Engineering Index (EI Compendex), and CNKI (China National Knowledge Infrastructure) to ensure comprehensive coverage of both international and Chinese literature. Additional sources included conference proceedings from major transportation conferences (TRB Annual Meeting, IEEE Intelligent Transportation Systems Conference) and technical reports from research institutions.
# Application of Ozone Therapy in Paediatric Density
## 2 Literature Review: Methodology and Results  
PubMed-Medline, Embase, Web of Knowledge, Scopus, Cochrane Library, and Google Scholar were used as databases for this literature review.  
At first, in October 2022, the operators conducted a search, ranging from 2012 to present, combining the following mesh terms or keyword topics \"ozone therapy\" with \"primary teeth\" or \"paediatric dentistry\" or \"paediatric oral pathology\" or \"children\" and \"dentistry\". No other filters were added.  
A total of 51 papers were found. Reading the title and abstract, 17 studies were excluded: 11 were duplicates, 3 were not about pediatric dentistry, and 3 were off topic. Analyzing the papers, the fields of application already investigated in the literature were collected [1; 2; 4; 6; 9; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. In Table 1, the applications of ozone therapy in paediatric dentistry, resulting from the first phase of the literature review, and the main related references are shown.  
A second search was performed in October 2022, meshing \"ozone\" and \"dentistry\", filtering for the reviews, ranging from 2012 to present. Starting from 12 reviews, only 6 were included [2; 3; 4; 25; 26; 27] because they were about the plethora of uses of ozone.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Uses of Ozone in Paediatric Dentistry** & **References** \\ \hline Dental caries management & [1; 2; 4; 6; 8; 9; 15; 26] \\ \hline Early childhood caries (ECC) & [12; 13] \\ \hline Molar incisor hypo-mineralization (MIH) & [1; 40] \\ \hline Root canal therapy & [2; 4; 9; 14; 15; 17; 19; 26; 28; 39] \\ \hline Remineralization & [1; 2; 4; 9; 41] \\ \hline Dental hygiene and prophylaxis & [2; 4; 9; 26; 29] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Application of ozone therapy in paediatric dentistry and the main related references.  
The results of the first and second phases were compared, looking for fields of application already investigated or not in a pediatric population [1; 9; 16; 30; 31; 32; 33; 34; 35; 36; 37]. In Table 2, the main results of the comparison of the literature are shown.  
The references of the studies were also analyzed to find other articles to be included.  
The authors chose to perform a narrative literature review because of the heterogeneity or insufficiency of the results.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms
#### 4.2.2 Intrusion Detection  
Intrusion detection mechanisms are to detect, identify and isolate malicious attackers from the network, including internal or external intruders. However, intrusion detection mechanisms usually work after the malicious attacks take effect and have been discovered. It is difficult to detect malicious intruders the first time that attacks occur. Hence real-time detection mechanisms need to be researched and improved. Alternatively, intrusion tolerance mechanisms can be used to protect networks while allowing the existence of malicious intruders, which is considered to be an efficient security mechanism. Moreover, algorithms, technologies and IDS have been proposed to further improve UWSNs security.  
_Standalone IDS_: there is no data exchanged between nodes; each node runs IDS and detects attacks independently.  
_Distributed and Cooperative IDS_: every node participates in intrusion detection by having local and global detection decision-making.  
_Hierarchical IDS_: this is suitable for multilayered UWSNs. Cluster head nodes in clusters perform the task of IDS and act as checkpoints such as routers in wired networks.  
Among these types of IDS, the hierarchical IDS is suitable for UWSNs which are based on a cluster structure.
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 5 Conclusions  
This paper demonstrated two signal-processing approaches to transducer motion estimation within a constrained sweep that enabled swept synthetic aperture imaging without external motion tracking. The constrained SSA motion problem is extremely well suited to high-precision estimates given uniform motion of the field within the imaging plane, the ability to use large kernels for estimation and high-frame-rate tracking. This approach may greatly reduce the cost and complexity of an SSA imaging system and is a step towards full freehand motion estimation. The use of this system for in vivo imaging to improve the resolution and target conspicuity at depth without the need for large, expensive imaging arrays will be explored in future work. This work was funded by NIH R03-EB032090 from the National Institute of Biomedical Imaging and Bioengineering. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Code for speckle tracking and channel correlation using the weighted least squares approach, with a sample simulated data set, can be accessed at [[https://github.com/bottenuslab/lateral_transducer_tracking](https://github.com/bottenuslab/lateral_transducer_tracking)]([https://github.com/bottenuslab/lateral_transducer_tracking](https://github.com/bottenuslab/lateral_transducer_tracking)) ([[https://doi.org/10.5281/zenodo.7682384](https://doi.org/10.5281/zenodo.7682384)]([https://doi.org/10.5281/zenodo.7682384](https://doi.org/10.5281/zenodo.7682384))). The author would like to thank [PERSON], [PERSON] and [PERSON] for contributions to early versions of this work, including 3-D scanning of the P4-2v transducer, speckle tracking code and general discussions. Thanks to [PERSON] for assistance in acquiring the ex vivo tissue sweep data. The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript: \begin{tabular}{l l} FWHM & Full width at half maximum \\ gCNR & Generalized contrast-to-noise ratio \\ HCC & Hepatocellular carcinoma \\ PSF & Point spread function \\ RF & Radio frequency \\ ROI & Region of interest \\ SSA & Swept synthetic aperture \\ \end{tabular}
# 3-D Point Cloud Registration Using Convolutional Neural Networks
###### Abstract  
This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.  
onvolutional neural networks (CNNs); Iterative Closest Point (ICP); point cloud; RANdom SAmple Consensus (RANSAC); 3-D registration +
Footnote †: journal: Applied  
_Article_  
3-D Point Cloud Registration Using Convolutional Neural Networks
## 8 Evaluating AI Agents in Data Annotation
### Empirical and Quantitative Validation  
Empirical studies reveal significant performance variations across different agent architectures. Single-agent systems demonstrate remarkable efficiency gains, with ActiveLLM achieving 17-24% points accuracy improvement over traditional active learning methods while maintaining seconds-level runtime [66]. In contrast, multi-agent collaboration frameworks show superior quality outcomes but at increased computational cost. For instance, federated agent approaches achieve higher Macro-F1 scores than majority voting across 8 NLP tasks, though requiring coordinated processing overhead [64]. The comparative analysis of HITL versus fully autonomous agent systems reveals critical trade-offs. HITL frameworks like CoAnnotating demonstrate optimal efficiency-accuracy balance, reducing annotation time by up to 74% while matching human-level quality through strategic human-agent work allocation [65; 107]. Conversely, fully autonomous systems achieve superior scalability, with platforms processing millions of instances at costs as low as CNY (Chinese Yuan) 0.00006 per annotation call compared to CNY 0.082 for traditional methods [112].  
Empirical validation studies employ standardized metrics enabling direct performance comparison. SR emerges as a primary effectiveness indicator, with state-of-the-art agent systems achieving 85-95% success rates across benchmark tasks compared to 60-75% for baseline methods [109]. Element accuracy and operation F1 scores provide granular performance insights, revealing that agent adaptability contributes 4-7 percentage point improvements in overall task completion [110]. Economic validation demonstrates substantial cost-effectiveness advantages. Industry deployments report 50% reduction in annotation expenses and completion time compared to fully manual approaches, with some specialized systems achieving up to 80% cost reduction through intelligent human-agent collaboration [124]. Processing speed metrics show orders-of-magnitude improvements, with agent systems handling thousands of annotations per hour compared to dozens for human annotators, while maintaining comparable or superior quality metrics. These quantitative findings are further validated through extensive real-world deployments detailed in Section 9, where case studies demonstrate consistent performance improvements across healthcare, financial services, content moderation, and humanitarian applications, with documented cost reductions of 50-80% and throughput improvements of 3-5x over traditional annotation methods.
## 2 Test Cases and Data Sets  
The wind farm of interest features six Vestas V52 wind turbines, installed in the year 2007, and it is sited in Italy on mountainous terrain.  
The SCADA data sets that were used have ten minutes of sampling time and go from 1 January 2017 to 31 December 2018. At the beginning of March 2018, the target wind turbine (named Tar in this study) experienced electrical damage at the generator, in consequence of which the generator had to be replaced. In [21], a study was devoted to the analysis of the performance of the Tar wind turbine before and after the generator replacement, in comparison to the other wind turbines in the farm. The objective of that study was the assessment of the impact of the generator aging on wind turbine performance. The result was that after the generator replacement the performance of Tar slightly recovered, while the performance of the rest of the wind farm kept slightly worsening due to the effect of aging. In [21], only the main operation variables were analyzed.  
The present study is instead devoted to the diagnosis of generator damage before it occurs and, for this purpose, the data set used also included the most important electrical parameters. The measurements used for this study were:  
* Wind speed \(v\) (m/s);
* Power \(P\) (kW);
* Blade pitch angle \(\beta\) (\({}^{\circ}\));
* Rotor speed \(\omega\) (rpm);
* Generator speed \(\Omega\) (rpm);
* Gear bearing temperature \(T_{b}\) (K);
* Generator Phase 1 temperature \(T_{1}\) (K);
* Generator Phase 2 temperature \(T_{2}\) (K);
* Generator Phase 3 temperature \(T_{3}\) (K);
* Current Phase 1 \(I_{1}\) (A);
* Current Phase 2 \(I_{2}\) (A);
* Current Phase 3 \(I_{3}\) (A);
* Voltage Phase 1 \(V_{1}\) (V);
* Voltage Phase 2 \(V_{2}\) (V);
* Voltage Phase 3 \(V_{3}\) (V);  
Section 3.3 features a detailed discussion about how the data sets were arranged for the diagnosis of the damage at the Tar wind turbine and for the comparison against a healthy reference (named Ref). For completeness, Tar and Ref correspond to ITA4 and ITA3 according to the nomenclature of [21].  
As regards the data pre-processing, in general it should be noted that the operation of wind turbines in industrial farms is affected by curtailments dictated by grid requirements and it is therefore necessary to filter the data appropriately. This was done as follows (similarly to [22]):  
* filter using the run-time counter, requesting production for 600 s out 600;
* filter below rated power (approximately \(v\leq 13\));
* the data corresponding to operation under grid curtailment are filtered out by automatic data clustering through the random forest algorithm [23].  
An example of the scattered power curve before and after data pre-processing is reported in Figure 1.
## 4 Measurement Results
### Measurements Related to Under-Sleeper Pads
The shaker measurements are better for the high-frequency resonance and reduction effects than the train measurements in Figures 4c, 5c and 6 which show weaker resonance amplifications and weaker reductions that start at frequencies between 50 and 100 Hz in agreement with the calculations in Figure 2b with similar pad stiffnesses. Under-sleeper pads yield a high-frequency reduction which depends primarily on the stiffness of the rail pad and secondarily on the mass of the sleeper. Figure 5: Under-sleeper pads at the Lengnau site; measured ground vibration at distances of \(\square\) 8, \(\Circle\) 10, \(\triangle\) 12, + 16 m from (**a**) the reference track, (**b**) a track with under-sleeper pads, and (**c**) average ground vibration reduction for two different track sections. Figure 6: Ground vibration reductions of under-sleeper pads measured at 8 m distance at different sites. \(\triangle\) Lengnau [19], \(\square\) Timelkam 1, \(\Circle\) Timelkam 2 [20], + Sempach [21].
## 3 Methodology
### Research Map  
This section describes the research framework (Figure 2) for indoor localization of mobile nodes, divided into three phases: data collection, training, and testing.  
The data collection phase consists of two stages: gathering RSSI data using a TurtleBot3 mobile robot and LA66 LoRaWAN modules within the indoor environment and generating  
Figure 2: Research map.  
a radio map. The training phase is also divided into two stages: data preprocessing and model training. This study introduces an innovative preprocessing technique for indoor localization based on sector division and evaluates it against existing data preprocessing approaches. Data collection is conducted using four fixed receiver nodes alongside a mobile node mounted on the TurtleBot3 robot. The radio map visualization is presented and described in more detail in Section 5.1. In the next step, for comparison purposes, we perform preprocessing using the Kalman filter and the sectorization method. As a control measure, a separate copy of the data is sent to the training phase without any preprocessing. During the final step of the second phase, ML is applied using algorithms such as k-nearest neighbors (kNN), support vector regression (SVR), random forests (RF), Gated Recurrent Units (GRU), Multilayer Perceptrons (MLP), Back-Propagation Neural Networks (BPNN) and Random Neural Networks (RNN). A detailed description of each algorithm and the selected hyperparameters is provided in Section 3.4. The metrics used to evaluate the performance of the machine learning algorithms are presented in Section 3.5. Finally, in the testing phase, three distinct movement scenarios for the mobile node are proposed. A comprehensive explanation of these testing scenarios is available in Section 4, titled _Experimental Setup_. The testing dataset is independent from the training dataset and is not included in the generated radio map. It is a separate dataset, which represents three scenarios for the trajectory of a mobile node in a given room, each varying in complexity.
## 4 DPI Applied to Honeynet Traffic and Attacks
### Layer 5 to 7 Payload Analysis
#### 4.4.1 Traffic Analysis of Port 23  
Recently, the code of Mirai botnet was made available to the public. Mirai can be considered a kind of malware that infects IoT devices running BusyBox. Regarding its capabilities as a botnet, information about it emerged in September 2016 when it was used in a Distributed Denial of Service (DDoS) attack against a security blog [72] as one of the first targets. One of the main characteristics of Mirai is that it works by brute forcing, over Telnet, weak and default credentials on devices. Once it gains controls of the device, it reports the infection to a command and control server and the device is now part of a botnet. Once many owners of devices directly and indirectly connected to the Internet never change the default user and passwords, Mirai could infect over 380 million devices and then it was used in one of the largest DDoS attacks known so far, generating at least 1.1 Tbps on OVH (Roubaix, France), a French cloud computing company [72].  
The attack commands usually follow the sequence below:  
{username} and {password}, then enable or system or shell, or sh, then  
Figure 9: Some of the IP addresses that sent four packets to the Honeynet (inner chart); protocol used (middle chart) and their corresponding TCP flags (outer chart).  
/bin/busybox MIRAI, where _[username]_ and _[password]_ are those present in the Mirai dictionary, and the following commands are used to detect if the target is not a router or common honeypot, like Cowrie.  
If the login succeeds, it runs the following commands in order to download the malware's payloads and then scan for new targets:  
'busybox tftp' -r [MalwareFileName] -g [IPsource] 'busybox tftp' -g -l 'dvrHelper' -r [MalwareFileName] [IPsource].  
After execution, the malware is self-deleted to avoid early detection and leaves the process running in memory of the compromised device.  
Exploring the Telnet protocol payload, it was possible to read and analyze data to determine which were the most guessed credentials that appear in the _data_ field of the protocol. The most guessed usernames and passwords are shown in Figure 10a, from which can be noted a prevalence of the username _root_. Together with 14 other guessed usernames, analysis shows they correspond to 98.86% of usernames, thus implying a concentration and a little variety of usernames guessed. This analysis may be used to create signatures used by perimeter defense mechanisms.  
Regarding the prevalence of a password, there is a bigger variety. Fifteen passwords correspond to 76.06% of the data and the distribution of the guesses is closer to a uniform view as seen in Figure 10b. Table 8 contains more detail about the number of times each username and password has been guessed by Mirai infected devices towards the Honeynet.  
The usernames and passwords displayed in Figure 10 are hardcoded in Mirai source code and are either commonly used credentials in general devices, such as root and 123456, or are default credentials defined by popular IoT devices manufacturers, as xc3511 and xmhdipc.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment
### Summary of Wildfire Manager Needs  
In order to maximize the value of a satellite system for wildfire management, certain features were highlighted through additional comments provided during the end-user consultation. These features included: Fast and consistent data delivery, mapping of active and inactive wildfire areas, smoke and air quality information, wildfire behavior, and threat estimates, as well as detection in remote regions.