esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### MOO Evaluation
#### 4.1.1 Set Coverage  
As shown in Figure 3, the domination of our developed algorithm with those of each of NSGA-II and NSGA-III is presented. It was observed that the set coverage of SC-MOPSO was 0.06 over NSGA-II compared with only a domination of 0.04 of NSGA-II over SC-MOPSO.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameters** & **SC-MOPSO** & **NSGA II** & **NSGA III** \\ \hline Lower boundary positions & X = 0, y = 0 & X = 0, y = 0 & X = 0, y = 0 \\ \hline Coverage radius & 100 & 100 & 100 \\ \hline Higher boundary positions & X = 1000, y = 1000 & X = 1000, y = 1000 & X = 1000, y = 1000 \\ \hline Lower boundary dimensions & [1,2,2] & [1,2,2] & [1,2,2] & [1,2,2] \\ \hline Higher boundary dimensions & [3,4,6] & [3,4,6] & [3,4,6] & [3,4,6] \\ \hline Velocity min & [0,0,0] & N/A & N/A \\ \hline Velocity max & [200,200,2] & N/A & N/A \\ \hline Number of iterations & 100 & 100 & 100 \\ \hline Repository size & 200 & N/A & N/A \\ \hline Percentage mutation & 1 & N/A & N/A \\ \hline Mutated ration & 0.5 & 0.5 & N/A \\ \hline Number of grids & 7 & N/A & N/A \\ \hline Alpha & 0.1 & N/A & N/A \\ \hline Weight & 0.5 & N/A & N/A \\ \hline Scale & 0.1 & 0.1 & 0.1 \\ \hline Shrink & 0.5 & 0.5 & 0.5 \\ \hline Fraction & N/A & 0.5 & 0.5 \\ \hline Min No. of particles & 3 & N/A & N/A \\ \hline Coverage radius & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters values of SC-MOPSO and the experimental results.  
The set coverage of SC-MOPSO with NSGA-II is presented in Figure 3. There is a blue rectangle between Q1 and Q3. The maximum and minimum are represented in black. The median is represented in red line. The results show more domination of SC-MOPSO over NSGA-II. The domination percentage in terms of median value is almost 0.06. On the other side, the domination of NSGA-II over SC-MOPSO is approximately 0.025 in terms of the median value. Similarly, we present the set coverage of SC-MOPSO with NSGA-III in Figure 4. The results show equivalent domination in terms of the median value with value close to 0.01. This provides that the domination is almost the same between the two algorithms. However, there is more interquartile range for the domination of NSGA-III over SC-MOPSO due to the higher distance between minimum and maximum.
# An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios
## 3 Materials and Methods
### RFID Gate with Antenna in Symmetrical Configuration  
By referring to Figure 8, we consider a bi-dimensional scenario in which the forklift moves mainly along the \(x\)-axis with a constant speed \(v\); such a hypothesis is plausible in a few-second interval, when considering the forklift weight and inertia. When the forklift performs an \(IN\) action, it moves towards the positive direction of the \(x\)-axis with positive speed, whereas when performing an \(OUT\) action, it moves towards the negative direction with a negative speed. The tag is placed on the forklift top, at a height \(h_{tag}\). The gate antenna is placed in \([x_{ant},y_{ant},z_{ant}]^{T}=[0,0,h_{ant}]^{T}\), and it is facing the floor in such a way that its coverage area is symmetrical in the xy-plane with respect to the \(z\)-axis. The coverage area on the tag plane is determined by the antenna \(HPBW\) through the following equation:  
\[l=\Delta h\arctan(HPBW/2) \tag{6}\]  
where \(\Delta h\) is the height difference between the antenna and the tag: \(\Delta h=h_{ant}-h_{tag}\). This means that the tag is detectable when the forklift is inside the region \(|x|<l\).  
Let us suppose the forklift is performing an \(IN\) action. The time variation of the \(x\)-coordinate is:  
\[x(t)=-l+vt \tag{7}\]  
being \(t\geq 0\). By considering a constant sampling time \(T\), the acquisition time steps \(t_{n}\) can be written as \(t_{n}=nT\). By denoting \(x[n]=x(t_{n})=x(nT)\), we can also derive the normalized unwrapped phase sequence \(\phi^{n}[n]\) with (4) as follows:  
\[\phi^{n}[n]=\frac{-4\pi}{\lambda}\Big{(}\sqrt{(-l+vnT)^{2}+\Delta h^{2}}-\sqrt {(-l)^{2}+\Delta h^{2}}\Big{)} \tag{8}\]  
where we neglected the effect of the multipath for simplicity. Let us consider an RFID gate operating at the frequency \(f=865.7\) MHz. The unwrapped normalized phase \(\phi^{n}[n]\), is depicted in Figure 9 when \(l=3\) m, \(v=2\) m/s, \(\Delta h=2.5\) m, and \(T=50\) ms, for both \(IN\) and \(OUT\) actions. As expected, during an \(IN\) action, the normalized unwrapped phase decreases when the forklift (tag) is approaching the antenna in the region \(x\leq 0\), while it increases once the forklift (tag) has crossed the gate and gets further from the antenna in the region \(x>0\). For the \(OUT\) action, instead, the normalized unwrapped phase decreases when the forklift (tag) is approaching the antenna in the region \(x\geq 0\), and increases once the forklift (tag) has crossed the gate and gets further from the antenna in the region \(x\leq 0\). It appears straightforward that the time behavior of \(\phi^{n}[n]\) is the same for both \(IN\) and \(OUT\) actions, as the antenna coverage area is symmetrical. Therefore, it is not possible to discriminate between the two actions by using this gate configuration.  
Figure 8: Sketch of the symmetrical configuration of the RFID Smart Gate.
# Nanoindentation of Historic and Artists' Paints
## 4 Characterization of Artists' Paints
### Free Film Paint Samples
#### 4.1.1 Testing Area  
From the viewpoint of nanoindentation testing, the main advantage of a free film sample is its large surface area that enables multiple indentation tests to be performed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Specific Geometry** & **Benefits** & **Limitations** & \multicolumn{2}{c}{**Studies on Mock-ups or Original Historic/Artists’ Paints**} \\ \cline{5-5}  & & & & **Quasi-Static Method** & **Dynamic Method** \\ \hline \multirow{5}{*}{_Sharp_} & \multirow{5}{*}{Three-sided} & Manufacturable with & & [5] & [14] \\  & & high precision; & Underestimated & [2] & [6] \\  & & constant strain and & contact radius due to & [10] & [11] \\  & pyramid— & constant strain rate during & ‘pile-up’, hence overestimated modulus & [16] & [17] \\  & & the test & & [8] & [16] \\  & & & & [15] & \\ \hline \multirow{3}{*}{_Flat punch_} & \multirow{3}{*}{Column with flat & Well defined and & Small deformation & & \\  & & constant contact & assumption; & & \\  & circular base & radius when in & pronounced & & \\  & & full contact & substrate effect & & \\ \hline \multirow{3}{*}{_Spherical_} & \multirow{3}{*}{Sphere} & \multicolumn{2}{c}{Solely elastic} & Non-scalable strain & [21] \\  & & deformation at initial & field, hence ’pile-up’ & [13] \\ \cline{1-1}  & & contact at a & with depth & [20] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about probe types and measuring schema applied in nanoindentation studies of artists’ paints (RED—studies of original historic materials; BLUE—studies of mock-ups and model systems; GREEN—treatment/aging studies on mock-ups).  
Since many paints are strongly heterogeneous, it is advantageous to perform tests at a number of locations on the sample surface, and to analyze the results statistically. Such nanoindentation tests may show how strongly the presence of pigment particles (which may have different stiffnesses and sizes ranging from tenths of nanometers to tenths of micrometers) influences the local mechanical properties and whether the average properties of the constituent materials can be deduced statistically. The limitation of nanoindentation in testing highly pigmented, granular, and porous laboratory prepared gesso and historic distemper paint has been explored [15]. Multiple quasi-static indentation tests successfully informed the average properties of the laboratory sample, but not of the significantly more porous historic one. The difference in porosity of these samples was confirmed by X ray micro-CT scans.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 3 Sensor Introduction
### Sensor Calibration Test  
To characterize the working performance of the FBG strain hoop sensor, the calibration tests were conducted on a steel pipe model with the outer diameter 219 mm and wall thickness 6 mm, which is the same as the actual pipeline used for experiment. The test model is presented in Figure 4. The internal pressure was loaded by a water pump with a maximum pressure of 1.5 MPa. During the test, the pipe model was loaded step by step continuously from 0 kPa to 500 KPa at an interval of 100 KPa.  
The sensitive coefficient, reflecting the relationship between the pressure and hoop strain, is a key parameter for the FBG strain hoop sensor. As shown in Figure 5, the sensitivity coefficient of the FBG strain hoop sensors is 9.79 KPa/\(\mu\epsilon\) and the hoop strain measured by the FBG strain hoop sensor is linear to the inner pressure from the first to the fifth load step, demonstrating that the FBG strain hoop sensor is sensitive to the pressure variation. Therefore, leakage monitoring is viable based on an FBG strain hoop sensor. Fourteen FBG strain hoop sensors' sensitive coefficients were extracted and shown in Figure 6. Obviously, the sensitive coefficients fluctuate around the value of 10 KPa/\(\mu\epsilon\), indicating that the FBG strain hoop sensor has stable performance since the packaging mechanism of the sensor has no adverse effect on the hoop strain measurement.  
Figure 4: A pipeline model used for a calibration test.  
Figure 3: Photo of pre-tension system.  
Figure 2: Photo of FBG strain hoop sensor.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 4 LoRaWAN v1.1 Security Structure Analysis
### The Post-Operational Stage  
Key expiry and updating schemes are not fully specified in the LoRaWAN v1.1. Since all the secret keys are essential to protect the confidentiality of the message, it must be stored in a secret way. However, attackers keep finding these keys through the network traffic analysis, side channel analysis and other methods. Hence, the key updating is an effective way of avoiding the key exploration by attackers. Periodical updating is essential, also in the event of unusual behavior happening in the network. Hence, a secure key updating policy is paramount to the network security.
## 2 Materials and Methods
### Pigment Productivity  
Microalgal cells were collected after cultivation for 4 and 8 days, and the harvested biomass was freeze-dried using a freeze dryer (PVTFD20R, Ilshin Lab, Suwon, Republic of Korea). For carotenoid extraction, 10 mg of the freeze-dried biomass was weighed and suspended in 1.5 mL of MeOH. The mixture was sonicated for 90 min at a resonance frequency of 40 Hz in an ultrasonic bath (Bransonic CPX5800H-E, Danbury, CT, USA). After sonication, the samples were centrifuged at 16,022\(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C, and the supernatant was carefully collected. This supernatant was dried using a rotary evaporator (IKA RV, Staufen, Germany), and the resulting residue was dissolved in 1.5 mL of acetone before being filtered through a 0.2 \(\upmu\)m membrane filter (Minisart syringe filter, Sartorius Stedim Biotech, Gottingen, Germany) in preparation for high-performance liquid chromatography (HPLC) analysis. Caroenoid quantification was performed using an Agilent 1200 series gradient HPLC system (Agilent Technologies, Palo Alto, CA, USA) equipped with a diode array detector and a YMC C30 carotenoid column (250 mm \(\times\) 4.6 mm \(\times\) 5 \(\upmu\)m; YMC, Kyoto, Japan). The mobile phase consisted of 92% MeOH with 10 mM ammonium acetate (solvent A) and pure methyl tert-butyl ether (solvent B). The system operated at a flow rate of 1 mL min\({}^{-1}\), with the column temperature maintained at 45 \({}^{\circ}\)C. A 10 \(\upmu\)L aliquot of each sample was injected, and carotenoid absorbance was measured at 450 nm. The carotenoid content was quantified by comparing the chromatographic peak areas with those of standard solutions, including astaxanthin, lutein, canthaxanthin, zeaxanthin, and beta-carotene, which were obtained from Sigma-Aldrich (St. Louis, MO, USA).
## 5 Results: ROC-Based Measures
### Other Points on the ROC-curve  
Since the EER was so stable across gallery size, we thought it important to check other points on the ROC-curve. In Figure 8, (Top), for synthetic data, we present the false rejection rate (FRR) when the false acceptance rate (FAR) = 0.01% for 5, 10, 15 and 20 features. Note the stability across gallery size.  
In Figure 8, (Bottom), for face recognition data, we present similar results for FRR@FAR = 0.001% for our face recognition data set. We analyzed 5, 10, 15, and 20 PCA components. This error rate was quite stable across levels of gallery size.  
Neither EER or FRR@FAR = x% metrics appear to change systematically with gallery size. Therefore, we hypothesized that the entire ROC-curve was also not changing with gallery size. This was tested below.  
Figure 7: **(Top)**: Relationship between mean EER (30 repetitions) and gallery size for synthetic data (Band 8). Data shown for 5, 10, 15 and 20 features. The average EERs (shown to the right of the last dot) across gallery size are illustrated with dotted lines. Mean EER was highly stable with increases in gallery size. **(Bottom)**: Relationship between mean EER (30 repetitions) and gallery size (1000 to 10,000 subjects in 1000 subject steps) for FaceNet PCA components. Data shown for the first 5, 10, 15 and 20 PCA components. The average EERs (shown to the right of the last dot) across gallery size are illustrated with dotted lines. Mean EER does not change as a function of gallery size.
## 3 Result and Discussion
### Engine Performance
#### 3.6.2 Specific Fuel Consumption (SFC)  
The specific fuel consumption (SFC) for all fuel blends in full load operating condition shown in Figure 6. The specific fuel consumption of the diesel-biodiesel blends is lower than pure diesel fuel. This phenomenon is due to the result of biodiesel addition, which promotes combustion [72]. The lowest SFC observed as 355.34 g/kW-h for the B20 blend, whereas it is 415 g/kW-h for neat diesel (Figure 6).  
Figure 5: Results of brake power for different fuel blends at a constant speed of 2100 rpm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Test Method** & **Limits** & **Units** & **Measured Property** \\ \hline Water and Sediment & ASTM D2709 & 0.05 max & \%volume & \textless{}0.004 \\ Density at 15 \({}^{\circ}\)C & ASTM D4052 & 0.86-0.90 & g/cm\({}^{3}\) & 0.883 \\ Kinematic Viscosity @ 40 \({}^{\circ}\)C & ASTM D445 & 1.9-6.0 & mm\({}^{2}\)/s & 4.01 \\ Methanol Content & EN 14110 & 0.20 max & \%volume & 0 \textless{}0.01 \\ Flash Point, Closed Cup & D93 & 130 min & \({}^{\circ}\)C & 154 \\ Cloud point & ASTM D6751 & \(-\)3 to 12 & \({}^{\circ}\)C & \(-\)2 \\ Pour point & ASTM D6751 & \(-\)15 to 10 & \({}^{\circ}\)C & \(-\)6 \\ Cetane Number & ASTM D613 & & & 46 \\ Carbon Residue & ASTM D4530 & 0.05 max & \%mass & 0.02 \\ Acid Number & ASTM D664 & 0.50 max & mg & mg \\  & & & KOH/g & 0.27 \\ Total Glycerin & ASTM D6584 & 0.24 & \%mass & 0.175 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The produced biodiesel properties in comparison with the ASTM D6751 standard.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 2 Related Works
### Synchronous and Asynchronous Communication  
While prevailing messaging protocols and platforms such as Paho [31], AMQP [32], RabbitMQ [33], and Apache Kafka [34] are built upon the publish-subscribe model that predominantly features asynchronous communication, they also incorporate provision for synchronous communication. However, it is important to note that the existing protocols and platforms primarily cater to synchronous communication between a publisher and a broker.  
To address the scalability problem associated with synchronous communication, [PERSON] et al. [35] proposed Nucleus, which presents a container architecture comprising stateless brokers. [PERSON] [39] proposed a strategy for merging synchronous and asynchronous communication models in industrial IoT environments by integrating MODBUS TCP and MQTT to suit specific scenarios. [PERSON] et al. [36] undertook a performance analysis of RabbitMQ and Apache Kafka within fog computing environments, given that synchronous communication models like HTTP can result in performance degradation in such contexts. [PERSON] et al. [37] tackled the issue of synchronous communication in thedomain of public transportation monitoring systems, achieving this through development of an asynchronous message broker middleware. [PERSON] et al. [38] conducted a comparative assessment of the synchronous and asynchronous models of interprocess communication within distributed microservices. [PERSON] et al. [40] introduced a hybrid approach encompassing both synchronous and asynchronous communication mechanisms in event-based microservices, aimed at detecting vehicle information from real-time streamed video sources.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 5 Serverless-Based Decentralized Simulation Development Model
### The Decentralized Adaptive Development Model  
The development of the 6G satellite twin network needs to consider the support and connection between the communication system, the twin system, and the computing system. The global control and management of the communication-twin-computing integrated network are achieved through the grid-based management of resources and the logical hierarchy of functions. Thus, various requirements in the integrated network can be further developed and described by abstracting them into virtualized functions. Based on the analysis, we propose a new decentralized adaptive development model, as shown in Figure 5.  
As shown in Figure 5, the proposed model includes an ontology layer, a twin layer, and a computing layer. The ontology of the simulation can be abstracted into behaviors and functions. The ontology layer of the 6G satellite twin network in this paper can be abstracted into communication behaviors such as access, handover, and routing. The communication behaviors are implemented logically through communication functions such as topology, constellation, and protocol to simulate the communication capability. The twin layer is the control and mapping of the twin network. The control and management functions such as configuration, interface, and cache in the twin network are represented by functions. The optimal allocation of functions and resources is calculated according to the resource requirements of all functions to achieve the optimal utilization of resources in the 6G satellite twin network, that is, on the basis that the resources consumed by the functions do not exceed the resource capacity provided by all servers, as many simulation tasks as possible are completed, and the tasks allocated in each docker container are balanced more effectively to achieve the optimal simulation time or optimal utilization of resources. In the computing layer, functions are deployed according to the optimal allocation of resources and functions to meet the execution of simulation tasks.  
Figure 4: The three-dimensional hierarchical simulation framework.  
The development idea of the model is to establish the mapping relationship among functions and resources by sensing the correlation among ontology function models. Based on the mapping relationships, the function-resource adaptation relationships are calculated and the functions are deployed into the corresponding docker containers.
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 5 Evaluation
### Results  
In this experiment group, the number of destinations is fixed to 50 and the number of drones varies from 50 to 250. Figure 2(a) shows that in terms of data delivery ratio, HFPP performs better than the comparable methods in low and high node density. As shown, by increasing the node density, Epidemic improves the delivery ratio logarithmically. This is because drones will have a higher chance to meet each other. Consequently, delays sharply reduce in high node density scenario (See Figure 2(b)). It is also clear that HFPP achieves minimum delay as shorter paths deliver the data packets quicker.  
In terms of overhead, Figure 4 illustrates that HFPP has a low ratio, however, by increasing the number of nodes, the number of contact increases and the data packets will have more chance to be exchanged and become closer to the destination. Figure 5a shows that HFPP reduces the consignment delivery delay when the number of drones increases. This is because data packets will be quickly hosted at the best drone which is closer to the destination. Hence, drones will less likely modify their path frequently. This argument is proved by considering Figure 5b that shows in high node density, the average energy inefficiency sharply drops which is due to the establishing optimized path when data packets can be quickly hosted in the closest drone to the destination.
## 2 System Model and Formulation
### Signal Transmission Block Time Period  
Observing Figure 1, the IoT network needs two time slots to complete a signal propagation from the transmitter (IoT hub \(S\)) to the receivers (IoT device \(D_{n}\) for \(n=\{N,\ldots,1\}\)) through coupled IoT relays \(R_{1}\) and \(R_{2}\). For clarity, the study designed a novel transmission block time period diagram as shown in Figure 2. By adopting the SWIPT technique (PS protocol), the IoT hub \(S\) transmits including simultaneous energy by power domain \(\lambda_{R_{r}}P\) for EH and superimposed information by remaining power domain \((1-\lambda_{R_{r}})P\) for information processing at the IoT relay \(R_{r}\), where \(r=\{1,2\}\), \(\lambda_{R_{r}}\) is PS factor and \(P\) is power domain at IoT hub \(S\). By the RS strategy as shown in Algorithm 1, only the selected IoT  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Studies** & **Number of Relays** & **RS** & **Number of Devices** & **SWIPT** & **Jamming Signal** \\ \hline
[21] & 0 & no & 2 & no & no \\
[18] & 1 & no & 2 & yes & no \\
[25] & 0 & no & \(K\) & no & yes \\
[20] & 1 & no & 2 & no & yes \\
[33] & 1 & no & 1 & no & yes \\
[35] & \(K\) & yes & 1 & yes & no \\ This study & 2 & yes & \(N\) & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison to the other studies on secrecy performance.  
Figure 1: The green-cooperative IoT wireless network.  
Figure 2: Power resource allocation with PS factor \(\lambda\) and PA factors \(\alpha_{i}\) for \(i=\{1,\ldots,N\}\).  
relay is used for forwarding signal to legitimate IoT devices while the other IoT relay is used for transmitting the jamming signal to eavesdropper \(E\). In the second transmission block time, Algorithm 1 switches the selected relay, where variable \(flag\) is zero and repeats the signal transmission period.  
```
0:\(R_{1}\gets 0\); \(R_{2}\gets 1\); \(time\_period\gets random(1:9)\); \(selected\_relay\gets random(R_{1},R_{2})\); \(flag\gets time\_period\);
0: The selected relay forwarded legitimate signals while the other relay transmitted jamming signals.
1:while true do
2:if\(flag!=0\)then
3: Function_Information_Processing(\(selected\_relay\));
4: Function_Forwarding_Signal(\(selected\_relay\));
5: Function_Jamming_Signal(\(selected\_relay\));
6:\(flag\gets flag-1\);
7:else
8:\(flag\gets time\_period\);
9:\(selected\_relay\leftarrow\)_selected_relay;
10:endif
11:endwhile
```  
**Algorithm 1** Algorithm for switching relay selection