esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Environment Generator for UAV-Based Simulation
## 4 Artificial Intelligence Algorithms
### YOLO v4
#### 4.1.2 Training and Results  
Because of the varying size of the aerial training images, we valued a higher network resolution over slow training time for better quality results. We trained our images with a network resolution of \(832\times 832\), which is double squared the standard \(416\times 416\). We additionally increased the amount of subdivisions from 16 to 64 to be memory efficient on our singular GPU. Each of the three convolutional layers parses 8 filters over the three classes before the final YOLO v4 layer identifies the bounding boxes. In total, over 2k images were trained over 3k iterations, starting from pre-trained weights on MS COCO dataset [24], provided by YOLO v4 for custom object detection.  
After completing training and testing on the validation set, our model achieved a mean Average Precision (mAP) value of 37.99%, which is about half the accuracy obtained by most YOLO v4 detection models. However, the drop in accuracy can be attributed to both the complexity of our high-res satellite images and the insufficient structure of identifying roads with bounding boxes. To address our training set's complexity, some of our larger images had over 100 instances of objects to track between our three classes. For comparison, models that achieve greater than 75% mAP typically have a single class with no more than 10 to 20 instances per image [25]. The advantage of this is that our model can predict a large amount of instances at the expense of somewhat low confidence values in these predictions.  
The second obstacle was the slanted, curvy nature of most roads in satellite imagery. YOLO v4 does not segment the road from its nearby surroundings, instead opting for a unitary bounding box as a means of identification. This presented some problems, as highlighted by the low accuracy of roads (26.00%) in our presented graphic in Figure 9. For road identification specifically, our solution was to abandon YOLO v4 for a more accurate model--resulting in YOLO usage on only three classes: (1) buildings, (2) trees, and (3) roundabouts (see Figure 10). The segmentation of roads will be done using a separate algorithm, namely Mask R-CNN, which will be detailed in the next section.  
Figure 8: YOLO v4 architecture pipeline.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
### Our Contribution  
Based on the previous motivation, this work is an analysis to select emerging technologies and their application to the military field, with special emphasis on tactical scenarios, improving such scenarios, and therefore serving as a starting point for new scientists to open up new lines of research. The focus is placed on tactical scenarios because it is the medium that includes the greatest application of communications. The tactical scenario is a term or concept to encompass all actions, operations, and decisions during a mission. For this reason, it is the term that includes the most possibilities of renewal within the military field. This analysis is not merely a compilation but a critical basis in a tutorial mode for future work. The contribution lines are as follows:  
* We chose cutting-edge technologies advocated by both 5G and 6G that hold the potential to enhance tactical communications.
* We conducted a comprehensive overview of these technologies to pinpoint potential military applications. This description proves invaluable for field personnel in the military who may not be familiar with these technologies.
* We offer the reader a framework for comprehending how these technologies can be applied in the suggested tactical scenarios or use cases.
* Our work is a starting point for future lines of research that promote the use of civilian technology to help military development.  
The rest of this paper is organized as follows: Section 2 presents a brief panoramic view of the two main elements in tactical scenarios. Section 3 proposes a group of emerging technologies as a potential candidate in tactical communications, performing an overview to understand the main features. In Section 4, the use cases are presented. In Section 5, the future trend and challenges are discussed. Finally, the conclusions are summarized in Section 6.
## 4 Results and Discussions
### Influence of Working Parameters  
#### 4.3.1 Acquisition Flow Rate  
The velocity contours at different acquisition flow rates (\(Q_{a}\)) are shown in Figure 22. Furthermore, how the collection efficiency (\(\eta\)) and the power consumed by the collector (\(P\)) varied with \(L\) are plotted in Figure 23.  
Figure 22 clearly shows that, as the acquisition flow rate (\(Q_{a}\)) increased, the strength of the two acquisition jets increased. This also applied to the strength of the updraft formed after scouring the ground, thus resulting in an increase in the ability to lift the nodules. Most of the \(Q_{a}\) flowed into the conveying channel. Therefore, it can also be seen in Figure 22 that, as \(Q_{a}\) increases, the flow velocity in the conveying channel also increases, thus resulting in a smoother transport of the nodules in the channel. These above effects ultimately lead to an increase in collection efficiency (\(\eta\)) with increasing \(Q_{a}\), as shown by the black curve in Figure 23. Note that when \(Q_{a}\) is greater than or equal to 160 m\({}^{3}\)/h, \(\eta\) is already above 95% and the increase in \(\eta\) is already minimal. In contrast, the power consumed by the collector (\(P\)) increases dramatically with the increase in \(Q_{a}\), which is shown by the red curve in Figure 23. In light of the above discussion, it is more appropriate to set \(Q_{a}\) at 140-160 m\({}^{3}\)/h in order to achieve a balance between the collection efficiency and power consumption.  
Figure 21: Collection efficiency (\(\eta\)) vs. the slant angle of the acquisition nozzle (\(\beta\)).  
Figure 22: Velocity contours of the hydraulic collector at different acquisition flow rates (\(Q_{a}\)).
## 5 Performance Evaluation
### Real-Time Analysis  
Performance comparison of real-time data packets in a guaranteed network lifetime between the proposed scheme and EPGLT is discussed in this section. To evaluate the performance of packet delivery in Figure 8a, the simulation is performed on the average ratio of successful and missed end-to-end packets toward the sink. The proposed protocol selects the next hop relaying node through the fuzzy controller and adjusts extended transmission on real-time packets to achieve the end-to-end deadline in the network. To see the significance of the proposed scheme, we assign different deadline values \(\in\) (150 s, 200 s, 250 s, 300 s) to real-time packets. We run the simulation 100 times for different settings of real-time packet deadlines and plotted the average results in Figure 8a,b. Figure 8a further demonstrates both the fuzzy and EPGLT schemes using multi-hop routing techniques to route data packets to achieve soft real-time communication. Even though EPGLT is based on a mathematical model to select the next hop relaying node, its delivery ratio is lower than ours, since it assigns the same transmission power to forward a real-time packet toward the sink.  
It is discerned from Figure 8a that the proposed scheme improves the delivery ratio within the deadline for traffic of different priority levels as compared to our previous scheme and EPGLT. The proposed fuzzy controller scheme successfully forwards packets to the sink with a steady on-time delivery rate throughout the network lifetime. The proposed scheme also shows better performance on data packets' missed deadlines. The simulation results are based on 50 real-time packets generated and forwarded to the sink node in each round. Out of 50 real-time packets, in the Fuzzy scheme, less than five packets missed their deadlines in each round. On the other hand, the ratio of missed packets in EPGLT is less  
Figure 8: Packet delivery ratio. (**a**) Network lifetime; (**b**) packet ratio.  
than 10 in each round. The ratio of successful packets reaching the sink node within the deadline is 6% higher than EPGLT. Similarly, the ratio of packets that missed their deadline by reaching the sink node is 11% less than EPGLT.  
Furthermore, Figure 8b shows the end-to-end packet delivery ratio of guaranteed and missed packets using the _ETR_ and _NTR_ relay nodes for the whole communication during the simulation time. The simulation analysis is done for both techniques separately to check the delivery ratio in more detail. The simulation results showed that the fuzzy controller-based _ETR_ relay nodes' selection performed very well and delivered more than 93% of packets successfully.
# Beam Scanning Capabilities of a 3D-Printed Perforated Dielectric Transmitarray
## 2 Unit-Cell Analysis
If the Transmitarray is designed as a center-fed configuration, it is necessary to consider an additional phase error due to the difference between the required phase distribution needed when considering a different feed angle position and the required phase distribution for the center-fed case. Thus, the total phase error for scanning angles is the following:  
\[\psi_{error}(m,n)|_{scan}=[\psi_{req}(m,n)|_{scan}-\psi_{req}(m,n)]+\psi_{ error}(m,n), \tag{5}\]  
where \(\psi_{req}(m,n)\) is the required phase distribution for the center-fed case expressed by Equation (1), \(\psi_{req}(m,n)_{scan}\) is the required phase shift needed for scanning angle case and \(\psi_{error}(m,n)\) is the phase error related to the incidence angles by Equation (4). The second term of Equation (5) can be removed by choosing properly the values of the transmission phase \(\angle S_{21}(\theta_{f}(m,n))\). This method has been applied to find the optimal phase distribution that allows for reducing the phase error for all the considered scanning angles. The feed is a circular horn working in Ka-band: it has a gain of 17 dB at 30 GHz and its pattern can be modelled as \(cos^{q_{f}}(\theta)\) with \(q_{f}=12.5\). The focal distance between the horn and the dielectric structure is \(F=150\) mm (\(F/D=1\)). Since here the main interest is in investigating the scanning capabilities of the TA, the beam steering that is obtained with a relative rotation between the two  
Figure 4: Sketch of the TA configuration with the adopted coordinate system: (**a**) side view; (**b**) perspective view of the illuminated surface. parts of the antenna, i.e., the transmitting surface and the feed, and this is equivalent to moving the horn along a circular arc having the center coincident with that of the TA itself, has been sketched in Figure 6a. In this way, the offset angle \(\theta_{f}\) of the field impinging the TA changes, and consequently the direction of the main beam, is denoted with \(\theta_{b}\). A range of variation for \(\theta_{f}\) of \(60^{\circ}\) in the E-plane has been considered for the transmitarray proposed here. Figure 5: Matrix of the incidences angles \(\theta_{f}(m,n)\) and related phase error: (**a**) incident angles for a center-fed TA; (**b**) phase error distribution considering a normal incidence; (**c**) incident angles when the scanning angle is \(-30^{\circ}\); (**d**) phase error distribution considering a normal incidence. Figure 6: Pictorial view of the beam scanning mechanism (**a**) and of its actual implementation (**b**).
## 2 Experimental
### Characterizations  
Field-emission scanning electron microscopy (FE-SEM) and energy dispersive spectroscopy (EDS) of the samples were acquired on a JSM-6700 (JEOL) scanning electron microscope operating at 5 kV. Transmission electron microscopy (TEM) images of the samples were obtained on a Tecnai F20 (Philips) transmission electron microscope operating at 100 kV. Powder X-ray diffraction (XRD) patterns were obtained on a Miniflex 600 X-ray automatic diffractometer (Rigaku Corporation) with a Cu K\(\alpha\) (\(\lambda\) = 0.15406 nm) radiation source in an explored angular range of 10-65 degrees. Thermogravimetric analysis (TGA)/differential thermal analysis (DTA) was performed on a simultaneous TGA/DTA analyzer (NETZSCH STA449F3) from 30 to 800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\) in air. The nitrogen adsorption/desorption isotherms were measured at 77 K using an ASAP2020 (Micromeritics, Norcross, GA, USA) gas adsorption analyzer, and the samples were evacuated at 10\({}^{-4}\) Pa and 393 K for 2 h before measurement. The water vapor adsorption/desorption isotherms were tested at room temperature by an IGA100B (Hiden Isochema Ltd.) intelligent gravimetric sorption analyzer. The samples were evacuated at zero to a saturated vapor pressure and 393 K for 2 h before the measurement. The X-ray photoelectron spectroscopy (XPS) measurements were performed with an ESCALAB 250 Xi spectrometer using an Al K\(\alpha\) (1486.6 eV) radiation source to evaluate the chemical states of the elements on the surface of the samples. Raman spectra were obtained with a LabRAM HR system.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 5 Conclusions  
In this paper, an overview of the laser feedback technology is presented. Two major configurations, the laser feedback interferometer and the laser confocal feedback tomography, are introduced theoretically, and a series of applications are described respectively.  
The laser feedback interferometer has great potential because it does not need a retroreflector or corner prism set on the target. However, the stability and the performance indicators of the instrument are not as good as the He-Ne laser interferometer nowadays. Therefore, improving the performance of the laser feedback interferometer to be comparable with the traditional laser interferometer is a major research area, which includes speed improvement [44; 45] and common path compensation [46; 47; 48]. On the other hand, research on the application is an important part to solve the scientific or industrial problem utilizing the ultra-high sensitivity, such as the measurement of refractive index, thermal expansion coefficient, liquid evaporation rate, etc. [66; 72; 73; 74; 75; 76].  
The laser confocal feedback tomography is proposed to reach a greater imaging depth compared with the confocal microscopy or optical coherence tomography [38; 39]. It also shows great performance in the structure measurement of the micro-electro-mechanical system [80; 81; 82]. However, the system is also challenged to realize both high resolution and large penetration depth the same as the other optical methods. Therefore, combining other technologies with the advantages of the laser confocal feedback tomography is interesting research, such as the numerically reconstructing imaging to extend the DOF and ultrasound-modulated technology to improve the SNR [96; 97].  
In conclusion, the frequency-shifted optical feedback can be applied in a vast range of fields such as metrology, physical quantities measurement, imaging, microstructure measurement, and others. It is promising to be developed in further study, and in new fields combined with other technologies in future research.  
**Author Contributions:** [PERSON] initiated the manuscript. All authors participated in the revision of the manuscript. [PERSON] provided overall supervision.  
**Funding:** This research was funded by the National Science Fund for Excellent Young Scholars of China, grant number 51722506; the National Natural Science Foundation of China, grant number 61475082; and the National Natural Science Foundation of China, grant number 61775118.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Analysis of SMA-Compensated Torque Performance  
### Driving Force of the SMA Spring  
To compensate for the degradation in the performance of the MRF under high-temperature conditions and to maintain the stability of the transmission performance, SMA springs were used to output additional friction torque. Figure 14 shows a schematic diagram of the SMA spring. The structural parameters of the SMA (Ni51 Ti49 (at. %)) spring are a spring middle diameter \(D_{0}\) = 12.5 mm, a spring wire diameter \(d_{0}\) = 1.95 mm, an effective number of turns \(n_{\text{SMA}}\) = 6, and an inclination angle \(\Phi\) = 6\({}^{\circ}\). The material parameters of the spring are shown in Table 4.  
Figure 14: Schematic diagram of the SMA spring.  
Figure 13: Relationship between temperature and MR shear torque.  
\(M_{\rm S}\), \(M_{\rm F}\), \(A_{\rm S}\), and \(A_{\rm F}\) are the starting and finishing transformation temperatures of martensite and austenite, respectively, as shown in Table 4. \(G_{\rm M}\) and \(G_{\rm A}\) are the shear moduli of martensite and austenite, respectively.  
The SMA spring is restrained during the phase transformation, and the spring exerts a force on the restrained body, which is the restoring force of the SMA spring \(F_{r}\). When the temperature of the SMA spring is between the finishing temperature of the martensitic phase transformation \(M_{\rm F}\) and the finishing temperature of the austenitic phase transformation \(A_{\rm F}\), the relationship between the temperature and the restoring force is [22]  
\[F_{r}=\frac{G(T)d_{0}^{4}}{8D_{0}^{3}n_{\rm SMA}}\delta_{L} \tag{13}\]  
where \(\delta_{L}\) is the axial expansion of the spring when the temperature is lower than \(A_{\rm s}\).  
\(G(T)\) is the shear modulus of the SMA, which is temperature dependent and takes the value of  
\[G(T)=G_{\rm M}+\frac{G_{\rm A}-G_{\rm M}}{2}[1+\sin\psi(T-T_{m})] \tag{14}\]  
where \(T_{m}=(A_{\rm S}+A_{\rm F})/2\) and \(\psi=\pi/(A_{\rm F}-A_{\rm S})\).  
The restoring force of the SMA spring in the interval from 20 \({}^{\circ}\)C to 100 \({}^{\circ}\)C can be obtained from Equation (13) and was compared with the temperature-force data on the SMA measured in the previous experiment [21] as shown in Figure 15. It can be seen from the experimental values that the restoring force \(F_{\rm r}\) increases rapidly from 7.93 N to 63.66 N at temperatures from 40 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. When the temperature is greater than 80 \({}^{\circ}\)C, the high-temperature austenite phase transformation inside the SMA ends and the restoring force is generated by the high-temperature expansion and deformation of the material. The value remains basically constant, and the maximum restoring force is 67.28 N.
## 3 Results and Discussion
### Anti-Inflammatory Activity of Au-Ag Alloy NPs  
Figure 4 presents the results of Western blotting, indicating the levels of various inflammation-related proteins in LPS-stimulated Raw264.7 cells treated with various concentrations of Au-Ag alloy NPs. The concentration of Au-Ag alloy NPs was negatively correlated with the production of these proteins (Figure 4A). Thus, anti-inflammatory activity increased with increasing NP concentration (Figure 4B). The strongest anti-inflammatory activities were observed against NO, IL-6, TNF-\(\alpha\), COX-2, and LOX, corresponding to percent values of 90.2% \(\pm\) 3.2%, 68.1% \(\pm\) 1.5%, 96.3% \(\pm\) 3.8%, 95.8% \(\pm\) 0.4%, and 81.3% \(\pm\) 1.2%, respectively. The IC\({}_{50}\) values for the anti-inflammatory effects of Au-Ag alloy NPs on NO, IL-6, TNF-\(\alpha\), and LOX were 29.13, 35.41, 9.45, and 15.96 mg/L, respectively; corresponding values for the positive control, indomethacin (a potent nonsteroidal anti-inflammatory drug), were 22.47, 40.17, 10.21, and 8.28 mg/L, respectively. These findings underscore the anti-inflammatory potential of Au-Ag alloy NPs, with the most pronounced activity observed against COX-2, where a concentration of 20 mg/L achieved an anti-inflammatory activity of \(>\)95%.  
Figure 4: (**A**) Levels of IL-6, TNF-\(\alpha\), COX-2, and LOX proteins in LPS-stimulated Raw264.7 cells incubated with various concentrations of Au-Ag alloy NPs at 37 \({}^{\circ}\)C for 24 h. \(\beta\)-Actin was used as the internal control for Western blotting. (**B**) Effects of Au-Ag alloy NPs on the levels of NO, IL-6, TNF-\(\alpha\), COX-2, and LOX in LPS-treated Raw264.7 cells. Data are presented in terms of mean \(\pm\) standard deviation values (\(n=3\)). A \(p\) value of \(>\)0.05 indicates a nonsignificant between-group difference. Data are expressed as means and standard deviations of three independent experiments.  
Au-Ag alloy NPs synthesized using procyanidins exhibited significant anti-inflammatory effects and facilitated tissue repair in animal models of periodontal inflammation [49]. Conversely, Au-Ag alloy NPs synthesized using _A. racemosus_ root extract inhibited TNF-\(\alpha\) production but did not significantly affect IL-6 production in LPS-stimulated THP1 cells [46]. This discrepancy may be attributable to differences in bioactive plant extracts serving as reducing and capping agents during NP synthesis [50].
## 3 Results and Discussion
### Challenges That the Engineering Approach Initiates (RQ2)
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Category** & \multicolumn{2}{c}{**Observations/Solutions**} \\ \hline \multirow{6}{*}{Communication (1)} & Help and interaction possibilities should be offered more clearly. \\  & Expressing a clear intent to provide support. \\  & Presentation of opportunities early on, based on working examples. \\  & Realistic feedback on the projects’ challenges and opportunities. \\  & Providing means to discuss the issues. \\  & Providing help to students who find themselves in frustrating situations is essential. \\ \hline \multirow{6}{*}{Guidance (2)} & An open engineering task is challenging. \\  & Monitoring of students’ notes is required. \\  & Providing support by documenting the project’s progress. \\  & Spare parts available if something goes wrong in the assembly. \\  & Workshops for all basic engineering skills are important (e.g., soldering, coding, 3D printing, etc.). \\  & Providing a means to reach consensus when discussing open issues. \\  & Providing well-argumented and friendly pressure to help solve problems. \\ \hline \multirow{6}{*}{Learning (3)} & Adapt theory to previous knowledge and skills. \\  & Clear communication of goals is needed. \\  & Students lack of experience, demanded content knowledge, tacit and meta level knowledge. \\  & Steps in the design process must be explained. \\  & Providing pointers to relevant literature is needed. \\  & Lectures has to be introduced and timed in accordance with previous knowledge. \\  & Help with coding is required from instructors. \\  & More training on project management. \\  & Frequent live meetings to support progress. Live classes with hands-on experience increase understanding and improve synthesized concepts. \\  & Students need a clear working schedule about when instructors are available, and the range of communications channels. \\  & Step-by-step procedure for problem solving, e.g., a form. \\  & Providing basic terminology is needed. \\  & Providing help with mastering the basics of OS navigation helps reduce students’ frustration. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Categorized observations associated with the challenges that the engineering approach initiates (RQ2).