esa-sceva/satcom-chunk-collection huggingface dataset:
# Gathering Big Data in Wireless Sensor Networks by Drone +
###### Abstract  
The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone's energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node's radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics' performance in finding drone tours in this type of scenario.  
wireless sensor network; WSN; mobile sink; path planning; UAV +
Footnote †: The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone's energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node's radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics' performance in finding drone tours in this type of scenario.
## 1 Introduction
### HIPAA Privacy/Security Regulations
#### 1.2.2 Security Regulations  
Patients' understanding: The patient has the right to know how their health information will be used and preserved. Digital signatures can be used to protect patient health information.  
Confidentiality: Confidentiality concerns protections associated with the use of software. Patient health information must be encrypted and protected in both storage and transmission to ensure confidentiality. Encryption is the most effective way to achieve the confidentiality of information.  
Patients' control: Patients can control access to their own information by using generated and issued encryption and decryption keys.  
Data Integrity: The integrity of e-health information must be ensured. Medically negligent use, tampering and unauthorized destruction of patients' health information are prohibited.  
Consent Exception: When an emergency or special circumstance arises, the disclosure of a patient's medical records and health information without the patient's authorization is permitted. When this exception is used, the patient is not directly involved, so other methods of decrypting the ciphertext must be designed.
## 4 Conclusions  
New cream formulations, in which nanoparticles containing zinc complexes have been incorporated, were prepared. The nanoparticles were obtained through a simple, eco-friendly and economical method using a grapevine leaf aqueous extract as a natural source of ligands. The concentration of the nanoparticles incorporated in the formulated creams varied between 2.5-12.5 mg ZnNPs/g cream. The formulated creams, wherein the nanoparticles containing zinc complexes were incorporated, have a brown reddish, are homogenous and did not exhibit any tendency of phase separation in the 12-month period during which they were investigated. For the samples S3, S4, S5, and S6, the pH values are comprised between 4.97 and 5.25, the viscosities values are comprised between 13.75 Pa\(\cdot\)s and 16.50 Pa\(\cdot\)s and the spreading diameters are comprised between 5.23 cm and 5.76 cm, which indicates that these formulations are suitable for topical applications. The skin of the subjects that were treated with the obtained creams did not show any sign of redness, edema, inflammation, or irritation during the sensitivity test, indicating that these formulations are safe to use. The creams wherein the nanoparticles containing zinc complexes were incorporated exhibited antimicrobial activity against the tested microorganisms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & \\ \cline{3-8}  & & & & & & & & \\ \cline{3-8}  & & & & & & & & \\ \cline{3-8} Gram-positive & & & & & & & & \\ Bacteria & & & & & & & & \\ \cline{3-8} Bacteria & & & & & & & & \\ \cline{3-8}  & & & & & & & & \\ \hline Gram-negative & & & & & & & & \\ Bacteria & & & & & & & & \\ \cline{3-8} Yeast & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean value and standard deviation (SD) of the inhibition zones (mm) obtained for antimicrobial testing of cream samples using the diffusion method in a solid culture medium.  
**Author Contributions:** Conceptualization, E.I.M.; methodology, E.I.M., A.P., C.C., R.C., C.E.H. and C.M.R.; software, E.I.M. and B.I.; validation, E.I.M. and C.M.R.; formal analysis, E.I.M., R.C., B.I. and C.M.R., investigation, E.I.M., A.P., C.C., R.C., C.E.H. and C.M.R.; resources, E.I.M., C.E.H. and C.M.R.; data curation, E.I.M., A.P., C.C., R.C., C.E.H. and C.M.R.; writing--original draft preparation, E.I.M.; writing--review and editing, E.I.M. and C.M.R.; visualization, E.I.M. and C.M.R.; supervision, E.I.M.; funding acquisition, C.M.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Aspects of Cyber Security in Autonomous and Connected Vehicles
## 7 Result of Analysis  
Table 4 is the result of the review of possible attacks and defenses of autonomous and connected vehicles. Attacks on safety systems are due to vulnerabilities in sensors, and attacks on connectivity are because of vulnerabilities present in networks, and communication protocols. Attacks on diagnostics are due to low security at the OBD port and fewer security layers of the firmware. Implementing the available defenses and ensuring countermeasures increases the security level of the autonomous and connected vehicle.  
\begin{table}
\begin{tabular}{c c c} \hline
**Key Element Classification** & **Type of Division** & **Attacks** & **Defenses** \\ \hline \multirow{8}{*}{Safety} & Spoofing of radar [51] & Filter method [96], STCR [97] \\ \cline{2-3}  & Disruptive attack and spoofing attack & PyCRA [101] \\ \cline{2-3}  & on WSS [52] & Eavesdropping attack on TPMS [53] & Anonymity encryption [100] \\ \cline{2-3}  & GPS location spoofing [54] & Signal encryption [98] \\ \cline{2-3}  & Lidar replay attack [55] & \\ \cline{2-3}  & Lidar relay attack [18] & Side channel approach [102] \\ \cline{2-3}  & Lidar blinding attack [56] & \\ \cline{2-3}  & AsS & Lidar DoS attack [50] & Increasing objects [18] \\ \cline{2-3}  & Blind spot exploitation attacks on & \\ \cline{2-3}  & ultrasonic sensor [57] & \\ \cline{2-3}  & Sensor interference attacks on & \\ \cline{2-3}  & ultrasonic sensor [57] & \\ \cline{2-3}  & Blinding attack on camera [18] & \\ \cline{2-3}  & Phantom attack on camera [75;76] & Model to avoid phantoms [75] \\ \hline \end{tabular}
\end{table}
Table 4: Result of the review.  
\begin{table}
\begin{tabular}{c c c} \hline
**Autonomous and Connected Vehicle Classification** & **Sub-Systems** & **Defenses or Countermeasures** \\ \hline \multirow{4}{*}{Safety systems} & \multirow{4}{*}{ASS} & Filter method [96], STCR [97], PyCRA [101], anonymity encryption [100], signal encryption [98], side channel approach [102], increasing objects [18], camera integration [102], inferred light filters [18], photochromic lenses [18] \\ \hline \multirow{4}{*}{Connectivity} & \multirow{4}{*}{V2X} & 2 FLIP [77], SAKMP [80], GSIS [85], novel IDS [89], TACKs [83], AECFV [91], PACP [78], domain aware IDS [92], ECDSA [79], PPGCV [82], TARI [84], \\ \cline{2-3}  & Smart features & Multifactor authentication [93], cryptography [16] \\ \cline{2-3}  & In-vehicle & AES encryption [68], domain aware IDS [92], CNN IDS [69], secure ECU [94], gateway firewall [95] \\ \hline \multirow{2}{*}{Diagnostics} & OBD port & RBAC [47], balanced approach [103] \\ \cline{2-3}  & FOTA & secure protocol [104], ECU self-verification [105] \\ \hline \end{tabular}
\end{table}
Table 3: Defenses for attacks on autonomous and connected vehicles.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 2 Related Works  
From the existing works, routing protocols and schemes in wireless sensor networks (WSNs) are referred with brief descriptions of their features, advantages and limitations in the current section. Besides, the routing protocols related to the proposed schemes are also reviewed at the end of this section. For the sake of simplicity, related works are summarized in Table 1.  
Routing strategies implemented in terrestrial WSNs (TWSNs) are unable to perform effectively in the detrimental acoustic environment even if the metrics are well configured [3]. The major reason behind the lack of performance of the terrestrial-based routing mechanism is the unique nature of the acoustic channel, like the limited bandwidth, high delays, the mobility of sensing devices due to water currents, etc. [4]. For the sake of simplicity and better understanding, some of the TWSN routing protocols are discussed as two categories: proactive routing and reactive routing. Unfortunately, neither of the categories is suitable for the dynamic underwater environment due to the large overhead of control messages exchanged at the setup of the network and whenever the topology changes due to water currents. Due to the high exchange of control packets in proactive routing, nodes' energy dissipates quickly, resulting in void holes. Optimized link state routing protocol OSLR [5] and destination sequenced distance vector protocol DSDV [6] are proactive routing protocols having a high energy consumption due to large overhead, which ultimately degrades the network performance. Similarly, the use of reactive routing protocols is relatively better than proactive strategy, but the delay is very high as the acoustic channel already suffers from high delay. That is accepted for a dynamic situation when the established links between the source and destination are symmetric. Due to the high dynamic nature of the acoustic channel, asymmetric links are setup. Therefore, reactive routing protocols (Ad hoc on-demand distance vector AODV [7], Dynamic source routing DSR [8], etc.) yield high latency and are not well suited for the acoustic medium. The difference between the UWSN and TWSN protocols is given in Table 2
# Laser Cavitation Peening: A Review
## 5 Improvement of Fatigue Properties of Metals via Laser Cavitation Peening
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Fatigue Test**} & \multicolumn{3}{c}{**Fatigue Strength at 10\({}^{7}\)**} & \multirow{2}{*}{**References**} \\ \cline{3-3} \cline{5-6}  & & **Non-Peened** & & & **Laser Cavitation Peening** & **Shot Peening** \\ \hline AM (EBM) titanium alloy Ti6 Al4V & Plane bending & 169 MPa & 317 MPa & 335 MPa & [7] \\ \hline AM (DMLS) titanium alloy Ti6 Al4V & Plane bending & 185 MPa & 357 MPa & 355 MPa & [64] \\ \hline AM (DMLS) titanium alloy Ti6 Al4V & Torsion & 217 MPa & 361 MPa & 285 MPa & [65] \\ \hline Magnesium alloy AZ31 & Plane bending & 97 MPa & 151 MPa & 112 MPa & [66] \\ \hline Stainless-steel 316L & Plane bending & 279 MPa & 272 MPa & 305 MPa & [20] \\ \hline Stainless-steel 316L (wededed) & Plane bending & 188 MPa & 300 MPa & 260 MPa & [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Improvement in fatigue life of metallic materials via laser cavitation peening compared with non-peened and shot peening. Figure 24: Improvement in fatigue strength of metallic materials via laser cavitation peening compared with shot peening. the treatment methods [67; 68; 69; 70; 71]. It was reported that the relaxation of compressive residual stress introduced by cavitation peening is less than that of shot peening [72]. This would be another reason why the fatigue performance of cavitation peening is better than that of shot peening.
## 4 Results and Discussion
#### 4.1.2 Interpretability  
Linear Regression and Random Forest models are highly interpretable due to their straightforward structure, making it easy to trace decision paths and understand feature importance. In contrast, models like Artificial Neural Networks (ANNs) and LSTM operate as \"black boxes\", offering limited interpretability despite their moderate predictive capabilities.
## 4 Results and Analysis
### Discussions  
The system's performance in different educational settings (e.g., online vs. in-person classes) may yield different results. Both settings benefit from the system's ability to analyze verbal and textual engagement. Online classes can leverage its compatibility with virtual platforms, while in-person settings excel with direct audio capture and immediate interaction. However, online performance might suffer from network instability or noisier audio inputs, whereas in-person settings avoid these issues but require physical presence. The in-person environment can be influenced by external noises such as multiple students talking at once, background music, and simultaneous conversations between teachers and  
Figure 5: Positive emotions word cloud.  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Positive** & & **Negative** & \\ \hline
**Keywords** & **Count** & **Keywords** & **Count** \\ \hline
1. Reflections & 18 & 1. Question & 30 \\ \hline
2. Logic & 17 & 2. Error & 18 \\ \hline
3. Easy & 16 & 3. Fault & 10 \\ \hline
4. Actual & 13 & 4. Mistake & 10 \\ \hline
5. Interesting & 11 & & \\ \hline \end{tabular}
\end{table}
Table 5: Keyword analysis.  
Figure 6: Negative emotions word cloud.  
students. Therefore, using post-production manual labeling might be necessary to achieve better results. The rule-based emotion scoring and small dataset limit adaptability in both settings, although manual labeling is more manageable in person with teacher oversight.  
The sentiment analysis model's biases originate from its static dictionary, manual processes, limited sample size, and insufficient contextual depth. Mitigation strategies encompass refining the dictionary, enhancing natural language processing sophistication, standardizing labeling procedures, and expanding data diversity. While the current system is suitable for the manuscript's STEAM context, these proposed improvements would enhance its reliability across various educational settings, balancing feasibility with the study's resource constraints, such as mid-range hardware capabilities.  
Adapting the system for non-STEAM subjects involves reconfiguring input prompts, updating the sentiment dictionary, refining keywords, adjusting scoring, and aligning with classroom dynamics. These changes leverage its existing architecture\(-\)voice/text analysis and emotion scoring\(-\)making it versatile for assessing engagement in subjects such as literature, history, or languages, provided that subject-specific tweaks are applied. Further pilot studies in these areas would validate its effectiveness beyond STEAM.
## 4 Discussion
### Effect of Crop Growth, LAI and Air Temperature on GPP  
The rate of GPP was significantly higher from booting to heading due to increased daytime CO\({}_{2}\) uptake, resulting in higher NEE values of \(-\)6.54 and \(-\)8.10 at booting and \(-\)7.12 and \(-\)10.43 g C m\({}^{-2}\) d\({}^{-1}\) at heading in rice and wheat, respectively, due to increases in LAI which increased the canopy light interception efficiency. The GPP increased with LAI up to heading, but the GPP per unit leaf area later decreased because of increased shading of the lower leaves in the canopy.  
The GPP was significantly correlated to AT in both rice (0.129, \(p<\) 0.05) and wheat (0.443, \(p<\) 0.01). Maximum CO\({}_{2}\) uptake was observed at 26-31 \({}^{\circ}\)C and 10-26 \({}^{\circ}\)C in rice and wheat, respectively. The AT started to decrease after the reproductive stage in rice, thereby reducing the plant maintenance respiration, which produced a greater abundance of assimilates available for growth and yield [45]. The rate of GPP decreased after ripening in wheat. In wheat, higher temperatures after the reproductive stage stimulated photorespiration and the wheat plants reduced their photosynthetic activity, thereby lowering the GPP after ripening. Similar to our results, higher GPP levels from the vegetative to reproductive peri ods were reported in rice by [PERSON] et al. (2021) and higher net assimilation fluxes ranging between \(-\)9 and \(-\)13 g C m\({}^{-2}\) d\({}^{-1}\) were reported in winter wheat by [PERSON] et al. (2019). Plants may reduce their photosynthetic activity, thereby lowering CO\({}_{2}\) uptake at higher temperature due to stomatal closure in response to increased transpiration losses.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 2 Background  
### Privacy Safeguarding  
Data confidentiality encompasses several attributes, including identity privacy (keeping users' identities secret from unauthorized entities), data privacy (maintaining the confidentiality of data for authorized users only), and usage privacy (ensuring the secrecy of user activities against unauthorized parties) [17]. As the internet and informatics domains have rapidly expanded in recent years, the importance of privacy safeguards in cyberspace has gained significant attention [18]. The growing concern for privacy in the industry has spurred the exploration of innovative solutions by experts [19]. Initially, efforts focused on understanding data preservation and defining privacy-preserving techniques [20]. Cloud data failure can result from various factors, such as power outages or hardware and network issues. To protect data privacy, recommendations like randomization, data condensing, and micro-aggregation have been proposed [6]. These systems require a unique approach that incorporates essential privacy attributes [7]. The pixel-related system proposed by [PERSON] and [PERSON] [21] offers another example, designed to ensure privacy in email communication. It employs a pixelated user interface created with an online application programming language. This architecture permits the users to establish a secure link with the server for safeguarding their data [8].  
In the authors' opinion, the ongoing efforts to address privacy concerns in the industry are commendable. It is essential to delve deeper into data preservation and establish clear definitions for privacy-preserving techniques to effectively protect sensitive information. The recommended approaches, such as randomization and micro-aggregation, provide valuable insights into safeguarding data streams in cloud storage. Furthermore, the incorporation of essential privacy attributes, as demonstrated by the four-component model and the pixelated system, showcases the importance of a comprehensive approach. These innovative solutions contribute significantly to enhancing data privacy and fostering a more secure digital environment.
###### Abstract
In [16], the presence of acoustic events is included in the analysis, by considering their impact merged with RTN levels through the intermittency ratio (IR). Other studies, such as [17,18] also consider \(L_{Aeq}\) curves with different analysis window sizes, together with other parameters, such as traffic speed data, to complement the equivalent noise levels and complete the classification of the monitored acoustic environments. However, these studies focus on high-level features (e.g., \(L_{Aeq}\) and IR) to analyse the acoustic environments of interest. As an alternative to the categorization of acoustic environments based on their spectro-temporal behaviour, in [19], a preliminary proposal was described through the analysis of WASN-based raw acoustic data. The research showed the viability of the proposal, as well as the potential existence of two clusters in the urban pilot area of the DYNAMAP project. However, this conclusion was tentative as it was obtained through a straightforward visual inspection. Later, in [20], a further analysis was conducted by means of selecting two representative locations from each pilot area (Rome and Milan) on both weekdays and weekend days [21]. The analysis paid special attention to the low frequency range, which contains the main frequency components of RTN, as well as the sensor locations and day and night characteristics. The results showed a high dependence on the analysed environment and encouraged the authors to extend the study by considering all the sensors of both WASNs to characterize the spectro-temporal behaviour of the RTN across all the available locations. Following the authors' previous studies [19,20], this article introduces an analysis and clustering methodology to group road traffic noise spectro-temporal profiles, which are simultaneously collected across an area of interest during a given period of time. The approach, which is designed to determine the optimal number of clusters, as well as to interpret the results through expert-based post-processing, is evaluated using around 250 h of RTN-labelled data from the two pilot areas of the DYNAMAP project that were collected during a weekday and a weekend day. The remaining sections of the paper are organized as follows. Section 2 describes the most relevant studies related to the clustering of acoustic environments. Section 3 describes the proposed analysis and clustering methodology on the spectro-temporal distributions of road traffic noise collected in different locations of an area of interest simultaneously, Next, Section 4 describes the experiments conducted, as well as the results obtained for the data collected from the urban and suburban pilot areas of the DYNAMAP project, during a weekday and a weekend day. Finally, Sections 5 and 6 discuss the results obtained and present the conclusions of this research and several future research lines, respectively.