esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Phase 2: Diary-Interview  
In this phase, the diary-interview method was employed to investigate the positive and negative experiences of the users with the chest band and smart T-shirt during the use period of two weeks. The diary-interview method requires the participants keep a diary, then these diary records are examined and follow-up interviews about the collected data are conducted with the participants [57; 58]. The follow-up interview provides an important opportunity for the researcher to evaluate the diary records that the participants had difficulty in expressing or those that could be misinterpreted. Because the follow-up provided a prolonged engagement, it allowed us to understand the information on the purpose the products were used for and how the duration and frequency of use were verified. So, prolonged engagement is the best way to ensure credibility in a qualitative study [59]. Furthermore, diary studies are advantageous as participants report their experiences according to their own context and time [60]. Therefore, this method does not affect the user's experience.  
A total of 10 users, 5 women and 5 men, participated in the diary-interview for 2 weeks. Participants in this phase of the study were recruited from the survey respondents by using a snowball sampling method. Inclusion criteria were regular participation in fitness activities for 3-4 days a week, owning a smart phone, and wearing a medium size T-shirt. All participants were informed about the study protocol and a written consent form was obtained. Participants were asked to use the chest band and the smart T-shirt taking random turns for one week (3-4 times a week) during their daily exercise routines. Participants were expected to connect the provided wearable technology products to their smart phones and monitor the obtained data. After each use, participants were asked to convey their positive and negative experiences with each product about their wearability, usability, usefulness, emotions, social experiences, and visual aesthetics either as voice or text messages. The recorded daily experiences were examined by the researchers and follow-up interviews were conducted for the missing or incomprehensible parts.  
The qualitative data obtained as a result of the diary-interview method were analyzed using the NVivo software. Users' audio and text diaries and interviews were primarily transcribed. Afterwards, these transcripts were coded with the help of NVivo software. A total of 46 codes were created which were categorized under the titles of wearability, usability, usefulness, emotions, social experiences, and visual aesthetics, and classified as positive and negative experiences for the chest band and the smart T-Shirt.
# High Dielectric Design of Polymer Composites by Using Artificial Neural Network
## 3 Results & Discussions
### Influence of Variables on Dielectric Constant  
#### 3.3.1 Effect of Filler Dimension  
Figure 8 presents the predicted dielectric constant depends on three types of different filler's dimensions (0D, 1D, and 2D) as a function of filler volume fraction. The predicted \(\varepsilon_{r}\) of 0D nanoparticle and 1D nanowire increases with the volume fraction, while 2D nanosheet decreases with the volume fraction. It is derived from the different ceramic filler's shape (0D, 1D, and 2D). Compared to spherical nanoparticles (0D), nanofibers or nanowires (1D) can induce higher dielectric constants at much lower concentrations due to their large dipole moments as a result of their high aspect ratio [54]. On the other hand, BN(\(\varepsilon_{r}\sim 4\)) used in this study as the 2D nanosheet filler, has a lower dielectric constant than P(VDF-HFP) (\(\varepsilon_{r}\sim 8.4\)), used as a matrix, showing a tendency to decrease as the volume fraction increases.
## 2 Site Description and Techniques for Data Analyses
### Numerical Modelling
#### 2.4.1 Ship Emissions  
Ship emissions were calculated (in the frame of CESAPO project, and then updated in the MED POSEIDON project) following the methodology Tier 3 of the European methodology EMEP/EEA emission inventory guidebook [43], that takes into account gross tonnage and ship typology to calculate the specific fuel consumption. The emission inventory was built considering ship by ship, i.e., every ship was represented as a single point source characterized by its own emissions rates. Emission factors for CO, NOx, NMVOC, and PM were estimated as a function of the combination of engine type and fuel type. The considered engine types were: gas and steam turbines, diesel engines at low, medium, and high speed; for the fuel we considered: Bunker Fuel, Oil, BFO, Marine Diesel Oil, MDO, and Marine Gas Oil, MGO. We have used the statistical distribution of fuel and engine type of the ship derived from tables of world fleet data from Lloyd's [43], since more detailed information was not available in the database supplied by the Port Informer. Emissions factor (EF) for SO\({}_{2}\) were calculated as a function of the percentage of contentsulphur by mass (S) in the fuel, that is EF = 20*S. As for the percentage of content sulphur by mass in the fuel, we distinguished between manoeuvring and hotelling phase. For the manoeuvring phase, we considered a limit of sulphur in fuel equal to 1.5% for all passenger ships (including cruise ships), and a limit equal to 3.5% for all other ship typologies, according to the current legislation in force, the revised MARPOL Annex VI. For the hotelling phase, a limit equal to 0.1% was considered for all ship typologies. The fuel consumption was estimated according to [PERSON] et al. [20]. Ship data regarding ship movements (manoeuvring and hotelling phases) were available from the Brindisi Port Informer ([[http://www.porto.brit/bpi/index.php](http://www.porto.brit/bpi/index.php)]([http://www.porto.brit/bpi/index.php](http://www.porto.brit/bpi/index.php)) (accessed on 1 March 2021)). Emissions rates were then calculated by considering time spent by each ship in the different phases. The uncertainties related to emission factors of the different pollutants range between 20% and 50% [43]. Furthermore, inaccurate information about fuel consumption, engine typologies, and used fuel could increase the uncertainties of the estimated emissions fluxes.
## 2 Segment NURBS into Bezier Curves Based on Closed-Loop Chord Error Constraint
### Curve Segment Based on Closed-Loop Chord Error  
Although the knot insertion method can transform a NURBS curve into multiple Bezier curves by inserting new control points without altering the geometric type of the curve, the subdivided cubic Bezier curves have two types: C and S. Figure 1b shows the insertion results, where Bezier1 and Bezier3 are in type-C, and Bezier2 is in type-S. For type-C, there is only one extremum point, while type-S has two extremum points. The curve can be split into two type-Cs at the point where the curvature is zero. One simple method to solve this is by projecting the curve parameter u onto the x-axis, in which case the y-axis represents the distance from the curve to the line, which is the chord error. The curvature is defined as:  
\[\kappa=\frac{\left\|\ddot{B}(u)\times\ddot{B}(u)\right\|}{\left\|\ddot{B}(u) \right\|^{3}} \tag{10}\]  
where \(\dot{B}(u)\) and \(\ddot{B}(u)\) are the first and second derivatives of the curve, respectively, and the knot with zero curvature is obtained through Equation (10). Figure 2 shows the segmentation results.  
Finally, according to the set chord error \(\delta\), the new Bezier curves are continuously segmented into halves until the chord error of all Bezier curves meet the requirement. Figure 3 shows the second segmentation and subsequent iterative segmentation results. Details can be found in Algorithm 1.  
Figure 1: A sample NURBS: (**a**) NURBS curve with 6 control points; (**b**) using knot insertion method, segment NURBS curve into 3 Bezier curves and C.P.1-4 is control points.  
Figure 2: Segmentation results: (**a**) one type-S Bezier2 curve segmented into two type-C curves by zero curvature point; (**b**) the max value on y-axis of the curve is the chord error.  
The pseudo-code is proposed as follows to generate the chord error control method:  
```
0: Input NURBS curve parameters, Chord error \(\delta\)
0: Bezier curves that satisfy the \(\delta_{c}\)
1: knot insertion \(\rightarrow\) Bezier curve Bi;
2:for\(i=1\) to Bezier curve number do
3: calculate the parameter \(u\) with zero curvature in the type-S curve Bi.
4: calculate the \(\delta_{\max}\) in the type-C curve Bi.
5: If\(\delta_{\max}>\delta_{c}\)then
6: Bisection method \(\rightarrow\) segment curve \(Bi\to 1\) Bi, 2 Bi;
7: do algorithm 1 for 1 Bi;
8: do algorithm 1 for 2 Bi;
9: else
10: return 1 Bi, 2 Bi;
11: end
12: end
```  
**Algorithm 1**Segmentation of NURBS curve with chord error \(\delta_{c}\).
# Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm
###### Abstract  
Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max-min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 3 Applications and Use Cases of FANETs
### FANETs for Events  
We have seen several fascinating deployments of a fleet of UAVs in events to create drone light shows and provide 3D imagery (e.g., each drone as a \"pixel\") at important events. (For example, see [[https://skymagic.show/](https://skymagic.show/)]([https://skymagic.show/](https://skymagic.show/)) and [[https://agb.events/drone-skysh](https://agb.events/drone-skysh)]([https://agb.events/drone-skysh](https://agb.events/drone-skysh)) ow/ [last accessed on 15 July 2022]) However, this is only one way that a fleet of drones cansupport events. The ability to deploy or spawn a FANET over a large event, e.g., during Olympics events, a large festival in a regional area, or a large rally such as a New Year's Celebration event, opens up new possibilities for improving people's experiences of such events, and possibly new commercial event-drone services.  
For example, a FANET could support connectivity services for people on the ground in an event over areas under-served by 4G/5G networks (e.g., in a regional town). Expensive infrastructure or base stations cannot be deployed overnight for an event that would just take place over several hours, or even over a few days or weeks - instead, a FANET can be used to provide connectivity for the period of the event according to requirements. A higher density of more powerful UAVs could provide higher bandwidth and higher reliability connectivity if this is needed (e.g., high-quality video captures, 4K to 12K ultra HD) but an event that does not need it might only use a smaller fleet of UAVs that can support lower connectivity. Maintenance of such connectivity may involve UAVs replacing current UAVs to maintain the FANET connectivity services. UAVs can even be multi-functional, providing a light show at certain times and connectivity at others, or both at the same time, if their energy capacity allows. Such UAVs can complement any other network connectivity made available (e.g., WiFi stations on the ground). Such a FANET can also provide on-demand ad hoc fly-in and fly-out edge computing services, with powerful drones acting as servers [68].
# Study on the Load-Bearing Behavior of Underground Steel Structures with Pitting Corrosion Damage
## 1 Introduction
[13] proposed a relationship model between the extreme value of pitting depth and time based on experimental data. [PERSON] et al. [14; 15] demonstrated by using the random field method that among various types of corrosion, single-sided corrosion is the most harmful, and the surface irregularity of the corroded plate is the main reason for the decline in its mechanical properties. [PERSON] et al. [16] conducted an in-depth study on the residual load-bearing capacity of H-shaped steel after corrosion. Through parametric simulation and calculation analysis, they found that the nonlinear finite element method can provide highly accurate predictions when dealing with such corrosion problems. [PERSON] et al. [17] explored the influence of the depth of corrosion defects, as well as the circumferential and longitudinal extents of corrosion, on the failure pressure of pipelines using the nonlinear finite element method. [PERSON] et al. [18] investigated the effect of interacting corrosion defects of the same depth on the local buckling strength of X46 pipelines. The analysis revealed that the interaction of corrosion defects led to a reduction in the overall buckling strength of the pipeline. At present, studies on the corrosion of steel structures are mainly concentrated on the field of materials, and the effects of random pitting on components and structures are less frequently studied [19]. In order to study the influence of random pitting on the bearing performance of underground steel structures, samples with random pitting damage defects were designed and made in this paper, and compression and three-point bending tests were carried out to analyze the influence of pitting damage on the failure mode, bearing capacity and deformation capacity of the steel structure. By examining the influence of different corrosion damage types on the bearing capacity of the I-beam members under compression and three-point bending, the law of the influence of pitting corrosion damage on the bearing capacity of steel structure is explored in order to provide theoretical support for evaluating the durability and reliability of the underground steel structure.
## 3 Background Work
### Cryptographic Primitives Used in Proposed Work  
Elliptic curves over finite fields are the foundation of Elliptic Curve Cryptography (ECC), a technique for public key cryptography (PKC), which is used in this part to secure our suggested system. Compared to PKC, ECC employs more small keys while keeping the level of security the same. This enables encryption and decryption techniques to be used by \"Zigbee devices (ZDs)\" with little memory and computing capacity. In our proposed system, digital signatures and key negotiations are denoted by \"ECC\". The Zigbee coordinator will make \"the public-private key\" pair available to edge devices via ECC to let them sign messages and blocks published in the anticipated BCS. The suggested \"blockchain architecture\" employs the \"Elliptic Curve Digital Signature Algorithm (ECDSA)\", a PKC technique that guarantees the authenticity and integrity of communications sent from fog nodes to edge devices. Before adding blocks, the distributed ledger verifies them using a threshold digital signature technique.
# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## 4 Functional Requirements for PDCA 4.0
### Automatic Data Collection System (R1)  
As conventional practices depend heavily on the manual collection of data, which implies serious limitations in the process, an automatic data collection system is proposed for the CI team in order to have readily available data. This requirement is patent in conceptual approaches that mention that such systems are enabled by the application of Sens&Act to collect data [82], as well as IoT for the intelligent monitoring of the production and supply chain management functions [47]. Real-time data collection from the production system is also enabled by using MES [56; 57], this being the primary reason for its deployment [57].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{8}{c}{**PDCA 4.0’S Functional Requirements**} \\ \cline{2-13}  & & **R1** & **R2** & **R3** & **R4** & **R5** & **R6** & **R7** & **R8** & **R9** & **R10** & **R11** \\ \hline \multirow{5}{*}{**I4.0**} & IoT & X & X & X & X & X & X & X & X & X & X & X & X \\ \cline{2-13}  & CPS & & & X & X & & & & & & & & X & \\ \cline{2-13}  & Big Data & X & X & X & & & X & X & X & & & & & \\ \cline{2-13}  & Cloud & X & X & X & X & X & X & X & X & X & X & & X & \\ \cline{2-13}  & Sens\&Act & X & & & & & & & & & & & & \\ \cline{2-13}  & AutRob & & & & & & & & & & & & & \\ \cline{2-13}  & Sim\&Virt & X & & & X & X & X & & & & & X & \\ \cline{2-13}  & 3 DP & & & & & & & & & & & & & X \\ \cline{2-13}  & MES & X & & X & & & & X & & & & & \\ \hline \multicolumn{13}{c}{eV} & X & X & X & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technological concepts for PDCA 4.0’s functional requirements.  
Regarding data generated outside of the factory, eVC can increase connectivity and allow information exchange along the supply chain [43; 45]. After further refinement, the collected data can be stored in Cloud databases that constitute the heart of a factory digital twin [51]. Lastly, the concept of Big Data, as it is associated with the gathering of data from sensor readings [43], and because it is needed in order to establish a digital twin [43], is also associated with this requirement. Based on an empirical study, the use of digital sensors/interfaces facilitates the identification of abnormal product/operating conditions [62]. Additionally, IoT can be used as a supporting mechanism to interconnect products and processes [62]. Finally, this requirement is also present in practical use cases, such as a sensor-based data collection system that measures different machine parameters [63] and data collection by MES to support root cause problem solving [30].
## 2 Brief Literature Review and Research Gaps
### Metaheuristic and RL Solution Approach  
Recently, there has been a growing focus on using reinforcement learning (RL) [60], a class of ML algorithms, as an emerging intelligent optimization method for addressing complex combinatorial optimization problems. The RL algorithms have been successfully applied to solve a wide range of production scheduling problems [42; 61]. Ref. [62] proposed a Q-learning (QL) algorithm to solve the classical PFSP. The authors reported competitive results compared to the optimum values proposed in the literature. Ref. [63] applied the QL algorithm to the makespan hybrid flowshop scheduling problem. To validate the method, two real-life applications were made. Ref. [64] proposed a multi-agent reinforcement learning framework for the jobshop scheduling problem. The best results are returned by the approach using well-known benchmark instances.  
In the literature, integrating RL algorithms into metaheuristics has been proven to offer high performance in terms of search robustness, solution quality, and convergence speed [42]. Metaheuristics can rely on RL algorithms to calibrate their key parameters [31; 65] or support their search process [66; 67]. Despite extensive efforts to incorporate RL algorithms into metaheuristics for scheduling problem resolution, little research has addressed constraints such as maintenance, learning, and deteriorating effects [42]. Furthermore, despite the extensive literature on flowshop scheduling problems, to the best of our knowledge, references to the problem addressed in this paper, that is, the PFSPwith flexible maintenance under learning and deteriorating effects, are very scarce. Hence, this paper introduces several enhancements to the ABC algorithm based on the specific characteristics of the problem under investigation. These improvements, including the hybrid initialization method and QL-based neighborhood search, aim to improve the search capability and overall performance.
# Hardware Countermeasures Benchmarking against Fault Attacks
## 2 Fault Injection Attack Techniques
* Combined approaches: These attacks include, as their name suggests, combinations of several of the attacks described above. Physical attacks can be carried out simultaneously by altering the clock signal and the power supply voltage. In this way, it is possible to achieve more advantageous scenarios for the injection of faults. It is also possible to combine two or more attacks using, for example, power consumption and electromagnetic emanations simultaneously [33; 34]. The difference between this wide variety of attack types lies in the cost of their implementation and the necessary granularity of the attack, i.e., the range of fault types that can be achieved by the attacker. Attacks that allow the maximum fault insertion capacity (single-bit or set of bit faults desired in any part of the circuit) will be the costliest and, therefore, determine the most powerful attacker. The described attacks and their most important characteristics are summarized in Table 1.  
Each of these methods has its requirements and necessary control from the attacker. For example, by using a laser it is possible to inject single-bit faults, but it is also necessary to perform a circuit stripping and carry out a study to know its routing and component placement (and thus high level of knowledge and equipment is required). On the other hand, an attack performed by means of clock glitches does not require the manipulation of the circuit (requiring almost no knowledge of the circuit layout and very low cost of equipment), but it will not be possible to inject faults with the same precision as with a laser attack. Therefore, in this last example, the attacker will be less powerful when it comes to achieving the theoretical requirements to perform a DFA successfully. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Type** & **References** & **Fault Level** & **Requirements** & **Fault Control** \\ \hline Temperature & [22; 23] & byte & Control of the temperature limits & Low \\ White light & [24] & byte & Strip of the circuit and layout knowledge & Medium \\ Laser beam & [24; 25] & bit & Strip of the circuit and layout knowledge & High \\ Electromagnetic fault injection & [26] & bit & Knowledge of the circuit & Medium \\ Power supply & [27] & byte & Fine control of the power supply & Low \\ \hline Clock Glitches & [5; 29; 30; 31; 32] & bit & Clock cycle control and good signal generator & Medium \\ Combined approaches & [33; 34] & bit & Combination of the previous ones & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fault Injection Techniques Classification.