esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Effects of the Second Phases on Properties  
### Cr\({}_{23}\)C\({}_{6}\) Phase  
Cr\({}_{23}\)C\({}_{6}\) phase is an important factor affecting the corrosion resistance and high-temperature creep characteristics of AISI 321 stainless steel. The formation of Cr\({}_{23}\)C\({}_{6}\) phase usually occurs close to the grain boundaries, where an amount of chromium present in the ferrite readily precipitates, leading to the redistribution at the austenite grain boundaries (Figure 4) [16]. It is a linear relationship between the susceptibility of AISI 321 stainless steel to intergranular corrosion and the volume of precipitated Cr\({}_{23}\)C\({}_{6}\) phase, and it is on account of the chromium-depleted zones resulting from Cr\({}_{23}\)C\({}_{6}\) precipitation that initiate intergranular corrosion [23]. Furthermore, [PERSON] examined the microstructure of AISI 321 stainless steel at mid-creep (around half its endurance limit) and found that dislocations were anchored by Cr\({}_{23}\)C\({}_{6}\) particles of about 40 nm. As creep progressed further, nearing the end of its lifetime, the Cr\({}_{23}\)C\({}_{6}\) near dislocations grew to 600 nm, with finely dispersed Cr\({}_{23}\)C\({}_{6}\) playing a key role in pinning dislocations and grain boundaries, which is instrumental in enhancing the high-temperature characteristics of the material [24].  
Hence, when contemplating the corrosion resistance characteristics of 321 stainless steel, endeavors can be directed towards the precipitation of a higher number of finer Cr\({}_{23}\)C\({}_{6}\) particles, thereby augmenting its creep resistance at elevated temperatures.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 2 Related Work
### Conclusions from literature  
As can be seen from the above sections, a clear distinction can be made between what is considered an acceptable amount of latency in a VR system depending on whether a single-user or multi-user system is considered. Although values around 100 ms are often reported, these are shown to be more of an upper bound for single-user experiences when the set of related studies is surveyed. The opposite is true for multi-user systems though, with multiple studies indicating a latency between 300 to 500 ms to be acceptable from a subjective point-of-view. Note that the latter mainly refers to user perception and acceptance as other objective and subjective dimensions, such as user performance, measurements of presence, simultaneity, and cybersickness lead to other boundaries with regard to subjective evaluation. Unfortunately, few investigations have yet been completed to concurrently assess, evaluate, and compare these aspects in a single study. As such, the multi-user aspect of VR seems to make end-users somewhat more accepting of latency impairments. From the presented research, this also seems to relate to the presence of a gamification and/or collaborative aspect to the use case [5; 15; 16], as other use cases tend to show more stringent requirements [19; 20].  
In addition, it is also worth noting that all the studies mentioned primarily focus on traditional latency, i.e., delaying every network packet with a certain amount of time. However, burst latency (in which the communication channel becomes saturated and, therefore, blocked for a given time interval) is another common network distortion besides traditional latency that often arises in multi-user VR traffic.  
Taking the above observations into consideration, we believe, therefore, that this work represents a valuable contribution to the existing literature by providing an experimental study evaluating the influence of uniform and burst latency on the end-user QoE (i.e., perception of latency, jerkiness, and de-synchronizations), performance and collaboration, subjective time perception, and the occurrence of cybersickness in a gamified, multi-user collaborative virtual environment.
## 4 Results and Discussion
### Compressive Strength
#### Regression Function:
The compressive strength for mortar samples with a GO content changed sharply from the usage level of 0.01 wt% up to 0.03 wt%, reaching the highest level. It is worth noting that when increasing the GO content to more than 0.04 wt% and 0.05 wt%, the compressive strength does not increase but tends to decrease. The results of this study are similar to other studies around the world, such as [22; 23; 24; 25]. * For samples with both GO and fly ash added to the mixture, the amount of fly ash used also significantly affects the strength when this amount increased too much (specifically, in this study, 30 wt%), which again leads to a decrease in the sample's intensity. However, with a reasonable usage content (15 wt%), fly ash has shown its effectiveness, not only improving workability but also the C-S-H pozzolanic reaction between SiO\({}_{2}\) and Ca(OH)\({}_{2}\) components, which is related to the pore-filling mechanism. Additionally, the structure of the mixture is tighter, contributing to significantly improving the strength of the mortar. Many studies around the world have confirmed the existence of fly ash. In addition, at 28 days, this effect is even more apparent; fly ash has displayed its advantageous features. * The mortar sample containing 0.03 wt% GO and varying amounts of fly ash had the highest compressive strength compared to the other samples. When 15 wt% fly ash was added, the maximum value of the compressive strength at 28 days was 63.99 MPa (an increase of 26.81% more than the control sample), and compared to the sample using zero fly ash, it only reached 60.15 MPa. In contrast, when fly ash content was used up to 30 wt% in the mixture, the compressive strength decreased sharply to only 52.08 MPa (a slight increase of 3.21% compared to the control sample). Therefore, the addition of GO has dramatically enhanced the compressive strength of the sample. This improvement can be attributed to the chemical interaction that occurs between the cation combination of GO and calcium, which improves the contact between GO and cement mortar. As a result, the interfacial transition zone becomes denser.
## 5 SD-CFN Routing  
SD-CFN architecture's routing adopts the Q-Learning algorithm. Q-learning is a classic reinforcement learning algorithm used to solve Markov Decision Process (MDP) problems. Its core idea is to learn a state-action value function \(Q_{t}(S_{t},A_{t})\), which evaluates the long-term expected return for taking a specific action in a given state. This algorithm is a model-free algorithm, so it does not require knowledge of the potential returns for taking specific actions in specific states [46]. For the state-action value function \(Q_{t}(S_{t},A_{t})\), its update rule can be expressed as:  
\[Q_{t}(S_{t},A_{t})=(1-\alpha)Q_{t}(S_{t},A_{t})+\alpha(r+\gamma max_{A_{t+1}}Q _{t+1}(S_{t+1},A_{t+1})) \tag{16}\]  
Here, \(Q_{t}(S_{t},A_{t})\) is the estimated value (Q-value) of selecting action \(A_{t}\) in state \(S_{t}\), \(\alpha\) is the learning rate, \(r\) is the immediate reward obtained after selecting action \(A_{t}\) in state \(S_{t}\), \(\gamma\) is the discount factor, and \(max_{A_{t+1}}Q_{t+1}(S_{t+1},A_{t+1})\) represents the maximum estimated value after selecting the optimal action \(A_{t+1}\) in the next state \(S_{t+1}\).  
In this paper, the learning of the agent involves a series of steps transitioning from the initial state to the target state (the computing task requester and the target computing node). Each step includes selecting and executing actions, changing the state (transitioning from one state to another), and receiving rewards. The updated Q-function values are based on the fundamental rewards obtained by taking action in the state, providing the optimal reward. Next, we will provide a detailed introduction to the RL agent and the RL routing algorithm.
## 5 Potential Use Cases
### Services Exploiting TSN over 5G
#### 5.3.1 Autonomous Mobile Robots Fleet Management  
The first use case is dedicated to the benefits arising from the introduction of the TSN concept to manage autonomous mobile robots (AMRs) within a construction site. AMRs move flexibly in a hostile environment such as construction site, recognizing and avoiding obstacles at high safety levels and are easily reprogrammable. This allows AMR movement inside the whole construction in order to perform daily or specific actions. AMRs are quite useful for construction because the system can work around people and machines at high levels of safety and reduce labour needs. In addition, accident risks are reduced by avoiding risky jobs or heavy tools transport. AMRs need to trade-off operational efficiency (uptime, speed, accuracy) with safety while achieving their aim inside the construction.
## 3 Methodology
### Sampling Error in Packet Loss Ratio  
In this paper, we assume that PLR is a random process that follows a Bernoulli distribution as reported in [7,11], i.e., loss is modelled as a simple random process. The probability for the packet to be dropped is p and the probability for the packet being successfully being received is \(q=(1-p)\). Since the whole population of the packets is not available, and the measurements are taken by sampling, the asymptotic standard error of \(X\sim B(1,p)\) is used, see Equation (1).  
\[\sigma_{PLR}=\sqrt{\frac{p(1-p)}{n}} \tag{1}\]  
where, \(p=\) packet loss probability and n is the number of samples.
## 3 Results of Findings
### Key Benefits Score Rate of IoTs on Customer's Base on Metrics Score and Behavior Score  
The benefits of IoTs are determined based on behavior scores (Table 1). The score rate range from poor, fair, good, very good, and excellent. The behavior score defined the gradient of the key benefits of IoTs below.  
* They enable users of IoTs to ensure smooth data security with tried and trusted solutions for education, healthcare, and businesses.
* They enable users of IoTs to enhance efficient centralized management systems that uphold higher standards and procedures needed for IoTs services.
* They enable users of IoTs to ensure cost-effective solutions with maximum support and help from existing challenges and services
* They enable effective use of available and predictable data with the help and support of edge computing, NLP, and AI.
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 5 Conclusions  
This study examines the three-coil PT-WPT system by employing circuit theory. The system's transmission efficiency, output power, and transmission gain were derived and compared with those of a two-coil PT-WPT system, demonstrating a notable enhancement in the PT-exact symmetry region. Furthermore, a novel control strategy for maintaining the system's rated output power utilizing solely transmitter information is proposed based on the transmission characteristics of the three-coil PT-WPT system. This strategy enables the system to retain stability at predefined values, even when the load undergoes alterations. Additionally, a reflection impedance detection method is introduced to discern the system's operation within the PT-exact region. The prototype design specifically employs a multi-layer coil method for winding relay and receiver coils, and effectively reduces the spatial footprint of the system. The experimental results validate that the proposed control strategy can sustain a transmission efficiency exceeding 90%, aligned with the defined reference performance, while maintaining output performance errors below 5%. In summary, this method offers a substantial enhancement to PT-symmetric systems without complicating control mechanisms, thus indicating a wide range of potential applications.  
Conceptualization, J.Y. and Y.Y.; methodology, J.Y.; software, R.L.; validation, J.Y., Y.Y. and R.L.; formal analysis, J.Y.; investigation, J.Y.; resources, Y.Y.; data curation, R.L.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y.; visualization, J.Y.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Municipal Natural Science Foundation (No. 4204093) and the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (No. X20038, X20087).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results  
### Downscaled SIF Performance  
The spatial distributions of the 2020 and 2021 growing seasons' downscaled SIF averages are shown in Figure 4. Notably, both the downscaled TROPOSIF and eSIF exhibited similar spatial patterns, but the values of TROPOSIF are slightly higher than those of eSIF. This is because TROPOSIF amplifies instantaneous SIF under clear-sky conditions to daily averages, while eSIF considers radiation amplification to daily averages under all weather changes, thus representing clear-sky TROPOSIF downscaled higher than eSIF considering all weather conditions. Additionally, the average downscaled eSIF values for the study area in 2020 and 2021 are both 0.32 mw m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) and are also spatially similar (Figure 4a,b). The mean downscaled TROPOSIF value in 2021 (0.40 mw m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) was greater than that in 2020 (0.37 mw m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)), and in most regions spatially, the values in 2021 were greater than those in 2020. This is because TROPOSIF represents SIF under clear-sky conditions, while eSIF represents SIF under all weather conditions, and the seasonal patterns of SIF under different weather conditions differ (Figure A3). The PAR in the study area during the summer of 2021 was significantly lower than that of 2020 (Figure A4), indicating that there may have been more cloudy days in 2021. Therefore, the production of SIF data should consider all weather conditions, as they may not only cause biases in seasonal variations in SIF but also errors in interannual variations.  
In the Saihanba region, a strong linear relationship was observed between the downscaled SIF and the SIF simulated by the SCOPE model at the two flux sites (Figure 5, R\({}^{2}\) ranging from 0.86 to 0.93). The relationship between the downscaled SIF and the simulated SIF at the SHB1 site was superior to that at the SHB2 site, and the relationship between the downscaled TROPOSIF and the simulated SIF at both sites was better than that with the eSIF. It is worth noting that the relationship between the downscaled eSIF and the simulated SIF tended to be closer to a 1:1 linear relationship. Additionally, the RMSE between the downscaled eSIF and the simulated SIF was 0.06 mw m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) at both sites, which is highly satisfactory. In conclusion, the strong relationship between the downscaled SIF and the modeled SIF indicates the high accuracy of the downscaled SIF results.  
Figure 4: Spatial distributions of mean downscaled SIF every 8 days for the 2020 and 2021 growing seasons (May-September). (**a–d**) represent the downscaled eSF values for 2020, the downscaled eSF values for 2021, the downscaled TROPOSIF values for 2020, and the downscaled TROPOSIF values for 2021, respectively.  
Figure 5: Comparing downscaled SIF and SCOPE model-simulated SIF at two sites: Downscaled eSF and simulated SIF at SHB1 (**a**) and SHB2 (**c**), downscaled TROPOSIF and simulated SIF at SHB1 (**b**) and at SHB2 (**d**). The red dashed lines represent the 1:1 line, and the black solid lines represent the regression line.
# Facial Recognition Method Based on Thin-Film Solar Cells
## 2 Experimental Design  
### Fabricating the Facial Recognition System Based on Thin-Film Solar Cells  
Twelve SP4.2-37 silicon thin-film solar panels (PowerFilm Inc., Ames, IA, USA) were arranged in a 4 \(\times\) 3 array (Figure 1). Each panel was 84.0 mm \(\times\) 36.5 mm and could generate 21 mW of power under 1/4 sun. In the top row, the solar cells from right to left were numbered as SC01 to SC04, respectively. The middle row comprised cells SC05 to SC08, and the bottom row comprised cells SC09 to SC12 (Figure 1). This array served as the sensor for facial recognition and was also part of the system's power source. When the system was not in operation, the array generated electricity to be utilized during operation. Arduino MEGA2560 microcontroller board ([[https://store-usa.arduino.cc/](https://store-usa.arduino.cc/)]([https://store-usa.arduino.cc/](https://store-usa.arduino.cc/))) (1 December 2021) was used as the system's core for data processing (Figure 2), which reads the voltage generated by each solar cell through 12 analog inputs. The lithium battery stores electricity generated by the solar cells and supplies it to the system and LEDs, distinguishing this system from other biometric technologies requiring external power supplies.  
Figure 1: Appearance of the facial recognition system.  
The facial recognition mechanism of this system used two commercially available LED light strips on the left and right sides of the thin-film solar cell array to illuminate the face of the tester at 45\({}^{\circ}\). The Arduino MEGA2560 read the voltage of the twelve thin-film solar cells in the array. The voltage values were affected by the intensity of the reflected light after the LEDs illuminated the face of the user, which served as the basis for facial feature identification. The detection process is demonstrated in Figure 3. We first established a user database and then selected the user's data as a reference. The system could be unlocked if the tester and the reference user were recognized as the same person. When a tester reached the correct physical position for facial recognition, the LEDs were activated for auxiliary illumination. As facial features and skin colors differ between individuals, the intensity of the reflected light entering the thin-film solar cell array after the LED illumination also differ. Accordingly, the voltage values read by the Arduino MEGA2560 differed. The measured voltage values were compared with the documented reference data. If the two sets of values matched, the tester and the reference user were recognized as the same person, and the system was unlocked. Alternatively, if the tester was recognized as a different person, the system could not be unlocked.
## 5 Discussion
In the \"source code size\" aspect, the level of agreement inside student groups reached the same level (based on [PERSON] kappa values interpretation) as in professional groups (i.e., \"Almost perfect agreement\"). The lowest level of agreement was seen in the \"cohesion\" and \"coupling\" aspects. This brings us to the final discussion on research question 2: \"Are the levels of agreement between students and level of agreement between professionals comparable?\" Based on the interpretation of [PERSON]'s kappa values (see Tables 7) we can report important differences in the majority of aspects. The exception is, when participants judged the \"source code size\" aspect. This is why we cannot easily answer research question 2--the level is comparable only in certain aspects, for others it is not. This is why we can the distinct quality aspects while answering research question 2a: \"In which aspects of source code quality assessment is the level of agreement comparable?\". When we observe the \"size\" and \"complexity\" aspects, students performed the best in terms of converging to the same answer. Our interpretation of this result is, that those aspects are an example of simple questions, where experience does not play an important role (\"Do you find this source code to be an appropriate size?\"/\"Do you find this source code to be of appropriate complexity?\"). However, to judge more complex aspects, such as \"overall quality\", \"coupling\" and, even more apparent, \"cohesion\", the experience seems to be necessary, thus students are not an appropriate substitution while experimenting. To sum up: our research is detailed in its finding that students are an appropriate substitution for professional participants in experiments in the source code quality research domain only when the experiment is dealing with simple aspects (e.g., source code size, source code complexity). When dealing with complex aspects, participants should show a certain level of professional experience in order to keep the experiment sound and relevant in terms of external validity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Exports**} \\ \hline  & Overall quality & Size & Complexity & Cohesion & Coupling \\ \hline \(\kappa\) & 1.00 & 1.00 & 1.00 & 0.80 & 1.00 \\ \hline  & \multicolumn{4}{c}{**Students**} \\ \hline  & Overall quality & Size & Complexity & Cohesion & Coupling \\ \hline \(\kappa\) & 0.61 & 0.81 & 0.65 & 0.50 & 0.53 \\ \hline \end{tabular}
\end{table} 
Table 7: Values of Fleiss’s kappa for the measured quality aspects.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 5 Multiple Reconfigurable Features in Antennas
### Frequency and Pattern Reconfigurability  
The proposed antenna in [111] consists of two symmetrical radiating elements, with a shared feedline and a ground plane which is fabricated on a Rogers5880 substrate. Each radiation element comprises a circular monopole branch, a regular hexagonal open ring, and two other branches added to the ring. The frequency of the antenna reconfigures between two bandwidths 1.84-2.00 GHz and 2.27-2.49 GHz. It is able to steer the beam in two directions (60\({}^{\circ}\) and 300\({}^{\circ}\) for the lower band and 34\({}^{\circ}\) and 326\({}^{\circ}\) for the higher band) by employing 8 PIN diodes loaded on the symmetric hexagonal split ring and monopole branches. An ultra-wideband flexible frequency and pattern reconfigurable antenna that uses two fabrication methods of conductive thread embroidery on the textile substrate and cutting the thin sheet of copper with and sticking it on the substrate is presented in [112]. The frequency and pattern of the antenna are reconfigured for six frequency bands in a range between 2 GHz to 10 GHz and three radiation patterns modes by using 10 stubs on the front part and two semi-elliptical stubs located on the back part, respectively. In Reference [113], the antenna with both frequency and pattern reconfigurability is proposed. To achieve the pattern reconfigurability in this structure which can be changed between a straight line and helix with 1.1-turn, liquid crystalline elastomers and heating effects are employed on their shape. This material phase transition to an isotropic state happens through heating. By using the behavior of liquid crystalline elastomers and adhering thin metal, a reconfigurable antenna is designed. At 30 \({}^{\circ}\)C, the antenna is straight (Figure 10a) and has an omnidirectional pattern at 4.9 GHz. The frequency and pattern are shifted to 14.04 GHz and directional by increasing the temperature to 92 \({}^{\circ}\)C and having 1.1-turn helix (Figure 10b). This antenna has a capability of reconfiguring the frequency from 12 GHz to 10.7 GHz as well by changing the shape of antenna from a 0.5-turn loop to a 1.5-turn helix.  
Figure 9: The fabricated frequency-reconfigurable antenna based on textile in [1]: (**a**) front view; (**b**) back view.  
Most of the origami-based antennas reconfigure frequency due to the nature of mechanical tuning which is based on changing antenna's dimension. The Nojima wrapping for origami-based structure was proposed for the first time in [114]. It provides the various models based on the shape of central hub, which is a square shaped in this paper, and different angles between the segments as well. In Reference [115], a dual-mode origami Nojima antenna which reconfigures between directional mode at 1.61 GHz for folded state and omnidirectional mode at 0.66 GHz for unfolded state is presented.