esa-sceva/satcom-chunk-collection huggingface dataset:
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 5 Commercial Case Studies
### Aria  
Autonomous roadless intelligent array (ARIA) insights use artificial intelligence (AI) and drones to eliminate people from unpredictable circumstances. Smarter collection of data and machine learning allow decision-makers to solve a problem or conduct a task rapidly and efficiently, while ensuring that human lives are not placed at risk. Decision makers no longer have to spend hours recording and watching a video with sophisticated analytics. Instead, the robots from Aria insights can recognise information that is relevant, sound an alarm when new data are detected, and eventually link all the data to a digital 3D map [60].  
The new machine learning technologies of Aria insights require consumers in the public safety, oil and gas, and other commercial sectors to gather information and then transform it into actionable plans. Aria's drones can conduct fully autonomous missions fitted by AI capabilities to make it easier to recognize characteristics of interest in multiple use cases. For example, drones may help maintain enclosed spaces, such as oil tankers or pipelines, allowing operators to locate areas that need to be repaired without human exploration into potentially unsafe working conditions. Aria insights drones can also track the progress of a natural disaster and inform first responders about increased activity in a  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Case Study** & **Middleware Strategy** & **Data Sharing** & **Supported Applications and Services** \\ \hline Matternet & Cloud-based & Cloud-based & Healthcare and logistics \\ \hline Aria Insights & Service-based & Distributed & Oil and Gas \\ \hline Drone\$SmartX & Cloud-based & Cloud-based & Agriculture Energy Manufacturing Oil and Gas \\ \hline H3 Dynamics & Cloud-based & Cloud-based & Real State Mining Oil and Gas \\ \hline \hline \end{tabular}
\end{table}
Table 3: Commercial case studies.  
given area, as well as warn event organizers when sections of the crowd may require more supervision [60].
## 4 Discussion
### Lean Assembly for Industry 4.0  
The question related to Lean production is: \"Is Lean production the best starting ground for implementing Industry 4.0 assembly operations?\"  
Lean manufacturing offers an array of tools and techniques to deal with the increasing demand complexity and variability, and which could benefit assembly operations in the context of mass customization. While most authors consider Lean manufacturing as a valid approach for increased complexity of mass customization, others claim that Lean cannot be applied straightforwardly in the Industry 4.0 era. Lean might not be necessarily the best possible starting ground for smart assembly in every situation. However, it clearly has positive synergy with Industry 4.0 because new technologies can enhance Lean assembly, and Lean maturity supports the implementation of new technology. Moreover, both Industry 4.0 and Lean consider that people have a central role to play in assembly operations.
# Exploiting Data Similarity to Improve SSD Read Performance
## 4 Design
### Multi-Path Read Scheme
#### 4.3.2 I/O Scheduling Optimization  
Figure 8 illustrates the detailed process of reading data in DS-FTL. Similar to existing FTL works, DS-FTL handles read sub-requests in the following manner. When a read sub-request is received by the flash back-end, DS-FTL performs a lookup in the mapping table and the indirect mapping table to retrieve the associated PPNs. One or several PPNs may be obtained. DS-FTL then transfers this result to the I/O scheduler and selects an idle path to retrieve data from one of the resident pages. If a single PPN is obtained, this indicates that the data to be read are unique within the SSD. In this case, the read sub-request is directly inserted into the corresponding I/O request queue. However, if a set of PPNs is obtained, corresponding to multiple chip queues (e.g., chip A queue, chip B queue, and chip C queue), DS-FTL chooses the chip C queue with the lowest number of requests and more available idle slots, as depicted in Figure 9. This approach ensures maximum utilization of the SSD's parallelism and improves the efficiency of processing read requests. Furthermore, if there is a sub-request in the selected chip queue that reads similar data, DS-FTL can merge the two sub-requests to reduce duplicate read operations and unnecessary time overhead.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
The algorithm is evaluated on two real-world datasets and compared with several classic algorithms from the aspects of both feature extraction and model building. The main novelties of the proposed algorithm and contributions of this paper are listed as follows:  
* Traditional clustering-based prediction algorithms ignore trajectories between the clusters such as points along the road. The proposed STS feature extraction algorithm transforms the trajectory into location sequences with fixed and discrete road IDs. The method can take points along the road into account, which can meet the demand of specific applications. Moreover, the generated location sequences can be better used in the prediction model, which can achieve better prediction results. * The location sequences generated by the STS feature extraction algorithm might be very long. Traditional sequential models such as HMM and RNN may not perform well with long sequences. The LSTM-based prediction model is proposed to solve the problem. The model can take advantage of location sequences over a long period of time, which can make better predictions. Evaluation results prove that both the STS feature extraction algorithm and the LSTM based prediction model outperform traditional methods.
## 4 Properties of 3D Printed Pla-Metal Biocomposites
### Thermal Properties  
Researchers create PLA filaments with different amounts of Mg microparticles using a two-step extrusion process. The goal is to study the effects of treating the thermal degradation of these filaments. Researchers also conduct in vitro degradation experiments, with the Mg microparticles completely released after 84 days in phosphate-buffered saline media [119]. The primary objective is to obtain a usable filament for future 3D printing, so it makes sense that the product will be more scalable if the processing is simpler. Here, authors achieve microcomposites by means of the double-extrusion technique; the materials are preserved throughout, and the microparticles are evenly distributed throughout the PLA matrix. The microparticles are not subjected to any physical or chemical alterations [43]. Although the addition of Ti64 raised PLA's glass transition temperature, it had a slight negative impact on the filament's thermal stability, melting point, and crystallization point. By reducing the number of conductive fillers, conductive 3D printing could make it easier for the electronics manufacturing business to provide easily customizable parts [120]. In this situation, examiners create a silver-organic complex (SOC) to make a 3D-printed filament material that contains less than 3 wt. % of pre-conductive additives [121].  
Even when exposed to high heat during extrusion and 3D printing, the cage effect reduces thermal damage to 0.91 wt. % in a thermally stable (TS)-SOC that has chelating agents. In summary, the electrical properties might reach 55.7 S/cm, and the problems of short circuits from nozzle clogs in regular conductive filament could also be fixed. Researchers discovered that adding more PEO to PLA improved its thermal stability and flexibility while increasing its hydrophilicity. Total color difference and picture analysis verified the material's sensitivity and color variations [65]. Simulations indicate that the setup of a nickel-coated antenna and a silver-coated diplexer has a return loss greater than 10 dB and a high gain efficiency of 12 to 17 dBi in the RX band of 10.7 to 12.8 GHz and the TX band of 17.42 to 18.81 GHz. The measurement findings of the feed system verify these predictions. Additionally, the rejection stage between the TX and RX ports exceeds 60 dB. This low-cost production method for making prototypes or testing ideas for Ku-band systems is shown to be effective because it has a complicated design with many shapes inside, unlike the more expensive CNC-based metal manufacturing methods [122]. Figure 12 denotes the thermal stability curves of different PLA-metal 3D-printed biocomposites.
## 5 Analysis of the Evaluation Results and Countermeasures  
### Analysis of the Indicator Weights  
As shown in Table 5, the order of the ratio of the comprehensive weights of the first-level indicators is personnel factor > hazardous chemical factor > management factor > environmental factor > equipment factor > vehicle factor. The personnel factor in the road transport of hazardous chemicals accounted for 32.71% of the weight, the largest proportion, as the primary consideration in the transport process. The hazardous chemical factor is second only to the personnel factor, accounting for approximately 18.55%. The evaluation index weight of the vehicle factor is the smallest, accounting for approximately 9.6%, but the index remains a valuable component of the evaluation process, and is an influencing risk factor in transportation that cannot be ignored.  
The top five evaluation indicators with the greatest indicator weights in the secondary indicators are ranked as follows: psychological quality > carrying capacity of dangerous chemicals > professional and technical ability > safety education and training > safety responsibility awareness. Among them, the indicator weight of psychological quality is the largest, and it is the most critical risk factor in the road transportation of hazardous chemicals. Hazardous chemical transport enterprises need to focus on the psychological quality of operators. Among the secondary indicators, the density of people and property along the route, traffic conditions and road conditions accounted for the smallest weight. However, in the overall system of risk indicators of the road transport of dangerous chemicals, this weight is not negligible. The density of people and property along the road in the process of the road transport of dangerous chemicals accounted for the smallest proportion, but the density of people and property along the road is one of the risk factors that drivers must pay attention to in real time during the driving process. The driver can plan a suitable time or route for transport tasks to avoid areas with a dense population and dense property, thereby reducing the risk of transport accidents.
## 3 Results and Discussion
### SSIM Analysis  
SSIM is a structural similarity index describing image quality degradation during image processing in MATLAB. This degradation is mainly caused due to data processing and transmission. SSIM is a reference metric describing the correlation between the original and processed images. The SSIM value ranges from 0 to 1. The value describes the relatedness to the original image after processing. Analysis was performed after the colorization of the image by the proposed colorization algorithm in this study. Figure 11 describes the SSIM analysis of the images during the test phase.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 4 Experiment
### Results for Different Parameters  
In the process of anomaly detection of user behavior based on multi-source behavior deep clustering, the deep clustering model includes three main hyperparameters: the number of class labels \(k\) for user multi-source behavior sequences, the number of consecutive behavioral events \(l\) contained within the user multi-source behavior sequences, and the dimension \(h\) of the embedded feature vectors of the behavioral events' entities. In order to investigate the impact of different hyperparameters on deep clustering anomaly detection, this section adjusts the parameter values of different parameters and compares the anomaly detection results of different parameter values. In this section, AUC was used as the evaluation metric for all parameter values in the comparative experiments. In the experiment, the default parameter values for the three hyperparameters were set to \(k=10\), \(l=8\), and \(h=10\). When adjusting the parameter value of one parameter, the remaining parameters were kept at their default values.  
Figure 5 illustrates the impact of the length \(l\) of user multi-source behavior sequences on the deep clustering anomaly detection method. As the parameter value of \(l\) increases, the AUC values for detecting the first type (type 1) and the second type (type 2) of abnormal behaviors remain relatively stable, while the AUC value for detecting the third type (type 3) of abnormal behavior shows an increase.  
Figure 6 illustrates the impact of the number of class labels \(k\) in user multi-source behavior sequences on the deep clustering anomaly detection method. With an increase in the value of \(k\), the AUC value for detecting the second type (type 2) of abnormal behavior remains relatively stable. However, the AUC values for detecting the first type (type 1) and the third type (type 3) of abnormal behaviors initially rise and then fall. The maximum AUC value for detection is achieved when the number of class labels \(k\) ranges between 10 and 12.  
Figure 5: Different detection results when adjusting the length \(l\) of user multi-source behavior sequences.  
Figure 7 illustrates the impact of the dimension \(h\) of embedded feature vectors of user behavior events' entities on the deep clustering anomaly detection method. As the parameter value of \(h\) increases, the AUC value for detecting the second type (type 2) of abnormal behavior remains relatively stable. However, the AUC values for detecting the first type (type 1) and the third type (type 3) of abnormal behaviors fluctuate slightly.  
Based on the above experiments, we can conclude that among the three hyperparameters, the length \(l\) of user multi-source behavior sequences and the dimension \(h\) of the embedded feature vectors of user behavior event entities have a relatively minor impact on deep clustering anomaly detection. Conversely, the number of class labels \(k\) in user multi-source behavior sequences significantly affects the performance of deep clustering anomaly detection.
## 2 Materials and Methods  
### Bacterial Strains and Spore Preparation  
_Bacillus horneckiae_ SBP3 was isolated from a sample in June 2006 close to a shallow underwater hydrothermal vent on the island of Panarea (Eolian Islands, Italy), also known as Black Point (coordinates: 38\({}^{\circ}\)38\({}^{\prime}\)23\({}^{\prime}\) N-5\({}^{\circ}\)06\({}^{\prime}\)28\({}^{\prime}\) E, depth: 23 m). Temperature equal to 130 \({}^{\circ}\)C, pH value equal to 3.0 and conductivity equal to 46.20 mS/cm are the properties of the fluid emitted from Black Point [8]. _B. horneckiae_ DSM 23495T, the closest phylogenetic relative strain to _B. horneckiae_ SBP3, and _B. subtilis_ 168 DMS 402, the biodsimetry strain, were purchased from the German Collection of Microorganisms (DSMZ).  
In order to produce spores, the strains were plated onto Shaffer's medium (containing 0.1% KCl, 0.012% MgCl\({}_{2}\), 0.5 mM of CaCl\({}_{2}\), 0.01 mM of MnCl\({}_{2}\), 0.001 mM of FeSO\({}_{4}\) and 8 g of nutrient broth) modified by the addition of 1% NaCl and 2% agar. A single colony from each strain was used to inoculate the plate medium and followed by incubation at each optimal temperature: _B. horneckiae_ SBP3 (T = 45 \({}^{\circ}\)C) for two days; _B. horneckiae_ DSM 23495T (T = 30 \({}^{\circ}\)C) and _B. subtilis_ (T = 37 \({}^{\circ}\)C) for four days. To check for the presence of spores, each culture was observed under a phase contrast microscope (BX53, Olympus Scientific Solutions America). The spores were subsequently purified as described by [PERSON] et al. (2007). Briefly, the spores were washed from three to ten times with sterile distilled water to remove the vegetative cells and cell debris. The purified spores were suspended in sterile distilled water and stored at 4 \({}^{\circ}\)C.  
In particular, scanning electron microscopy (SEM) (Sigma Zeiss, Germany) was employed to observe the spores. The suspensions of spores were placed onto a support of aluminum and dried at room temperature for 24 h and then coated with a layer (18 \(\pm\) 0.2 nm) of Au-Pd alloy using a coating device (MED 020, Ba Tec AG, Tucson, AZ, USA).
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
## 2 Optimization Design of the Primary Mirror
### Topology Optimization of Primary Mirror
M is the upper quality limit of the design index of the primary mirror, a pattern grouping constraint is imposed in the design domain to generate a central symmetric configuration, \(G_{k}(X)\) represents the \(120^{\circ}\) circumferential cyclic symmetry constraint between the kth elements of the primary mirror, and \(\rho_{\min}\) is the lower limit of the relative density of the element set to avoid the singularity of the stiffness matrix during the optimization process. The weighted compliance of the observation angle gravity is used as the topology optimization objective, and sensitivity analysis is performed on the weighted structural compliance. When a rigid body rotates around an axis by a micro angle, the direction of the angular displacement vector follows that axis. Therefore, Equations (1)-(3) can be transformed into  
\[ \begin{array}{ll}\frac{\partial C_{\omega}}{\partial\rho_{i}}=&\alpha\Big{[} \Big{(}F_{0^{\circ}}^{T}-\hat{U}_{0^{\circ}}^{T}K\Big{)}\frac{\partial U_{0^{ \circ}}}{\partial\rho_{i}}-U_{0^{\circ}}^{T}\frac{\partial K}{\partial\rho_{i} }U_{0^{\circ}}\Big{]}+\beta\Big{[}\Big{(}F_{30^{\circ}}^{T}-\hat{U}_{30^{ \circ}}^{T}K\Big{)}\frac{\partial U_{30^{\circ}}}{\partial\rho_{i}}-U_{30^{ \circ}}^{T}\frac{\partial K}{\partial\rho_{i}}U_{30^{\circ}}\Big{]}\\ &\chi\Big{[}\Big{(}F_{60^{\circ}}^{T}-\hat{U}_{60^{\circ}}^{T}K\Big{)}\frac{ \partial U_{60^{\circ}}}{\partial\rho_{i}}-U_{60^{\circ}}^{T}\frac{\partial K }{\partial\rho_{i}}U_{60^{\circ}}\Big{]}+\gamma\Big{[}\Big{(}F_{90^{\circ}}^{ T}-\hat{U}_{90^{\circ}}^{T}K\Big{)}\frac{\partial U_{90^{\circ}}}{\partial \rho_{i}}-U_{90^{\circ}}^{T}\frac{\partial K}{\partial\rho_{i}}U_{90^{\circ}} \Big{]}\end{array}  \tag{11}\]  
\(KU=F\) according to the symmetry of \(K\), and the adjoint equations \(K\hat{U}=F\) and \(\hat{U}=U\) are established. Formula (11) can be expressed as follows:  
\[\frac{\partial C_{\omega}}{\partial\rho_{i}}=-\alpha\Big{[}U_{0^{\circ}}^{T} \frac{\partial K}{\partial\rho_{i}}U_{0^{\circ}}\Big{]}-\beta\Big{[}U_{30^{ \circ}}^{T}\frac{\partial K}{\partial\rho_{i}}U_{30^{\circ}}\Big{]}-\chi\Big{[} U_{60^{\circ}}^{T}\frac{\partial K}{\partial\rho_{i}}U_{60^{\circ}}\Big{]}-\gamma \Big{[}U_{90^{\circ}}^{T}\frac{\partial K}{\partial\rho_{i}}U_{90^{\circ}} \Big{]} \tag{12}\]  
The topology optimization results tend to converge after 179 iterations; Figure 4 shows the material distribution results of the topology optimization. The ribs can be easily grouped based on the topology optimization results. The grouping of the primary mirror variables is shown in Figure 5a,b. Considering that many parameters must be considered during the parametric design based on compromise planning theory, sensitivity analysis is performed on the primary mirror to improve the efficiency of the parametric design.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Residual Energy  
The residual energy for every round needs to be calculated to determine the energy effectiveness of route methods. The results show that this protocol's energy efficiency is superior to existing methods. The remaining energy from each round is shown in Figure 7. To aggregate the biological data with direct or CH biosensors, our proposed approach uses a multi-hop design in which each distant biosensor transmits the biological data to the receiver through an appropriate body sensor. Regarding simulation, the proposed protocol's topology uses the least energy within 60% of the simulation time. As a result, more nodes have sufficient capacity to send packets to their final target and send more packets.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Psychological Factors
#### 3.6.1 Perceived Risk  
Perceived risk refers to an individual's assessment regarding the potential negative outcomes or uncertainties associated with the situation. Perceived risk is influenced by personal beliefs, emotions, and individual perceptions of a situation.  
Perceived risk emerged as the most consistently significant determinant of the intention to use [21]. In a study conducted in Italy, fear of contracting the coronavirus was among (20.0%) the primary reasons for the contact-tracing app's uptake [127]. An Israeli study revealed that the perception of disease risk was positively associated with the use of contact-tracing apps [56]. A study conducted in France mentioned one of the main reasons for downloading the contact-tracing app: protecting one's family, others, and oneself from possible infection [100]. In Germany, across four waves of study, it was consistently observed that the majority of participants expressed equal concern about the risk of infection to themselves and others [118]. Another study reported that the top 10 reasons among individuals willing to use a contact-tracing app included the desire to control the spread of the virus, followed by efforts to mitigate risks for both others and oneself and to increase personal freedoms [61]. A Spanish study has also reported that perceived risk has a positive impact on the behavioural intention to use the contact-tracing apps [130]. Similarly, a study conducted in Australia mentioned the lack of perceived risk of COVID-19 as a reason for not downloading the contact-tracing app [101].  
A study in England reported no association between the fear of contracting COVID-19 and downloading the app. However, a weak negative correlation was observed between being in a high-risk group for COVID-19 and downloading the app. Notably, a strong negative relationship was identified between having concerns about the app and the actual act of downloading it [116].  
Contrary to the above findings, a study conducted in Ireland has reported that the perceived threat, specifically the concern about getting infected with COVID-19, did not significantly influence the adoption of the contact-tracing app [85]. Perceived severity and susceptibility were not found to be significant among the predictors of intention in Belgium [40]. Similar findings were reported in Portugal: The impact of COVID-19 was not a strong predictor of adoption [69]. In France, the intention to download the contact-tracing apps was reduced by the salient COVID-19 disease concerns. The same pattern was observed in Australia, and the reluctance was mediated by a rise in socially conservative views due to disease concerns. In the USA, the willingness to download the app decreased with salient COVID-19 concerns due to greater personal privacy concerns [132].