esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Method of Swirling Flame Lean Blowout Based on Flame Image Morphological Features
## 4 Results and Discussion
### Discussion about Influence of Time Series Information on Prediction Accuracy  
Considering that the time series information is used in the prediction model to expand the data features during the construction process, and how long the time series information is used to multiply the data features is an important factor affecting the model accuracy and construction efficiency, the influence of time series information on prediction accuracy is estimated in this section. The data set used is similar to Model (d). Figure 9 exhibits how the length of the time series information affects the accuracy of Model (d) by adjusting the length of the time series information \(k\) and calculating corresponding model accuracy quantities \(R^{2}\) and RMSE.  
It is clear from Figure 9 that in general, with the increase in the length of the time series information \(k\), the accuracy of the model is continuously improving. When \(k\) is less than 100, especially when \(k\) is less than 50, the model accuracy will improve significantly with the increase of \(k\). However, if \(k\) is greater than 100, although there is still a certain improvement, it is minimal: the \(R^{2}\) and the RMSE are basically stable at about 0.98 and 3.3, respectively.  
Figure 9: The effect of the length of the time series information used by the LBO prediction model on the accuracy of Model (d). The red line with circles shows the \(R^{2}\) results with the left scale. The green line with diamonds shows the RMSE results with the right scale.  
This means that, for the LBO prediction model proposed, the image morphological features of the past 50 frames (or 100 \(\mu\)s in time) are crucial for effective prediction, and the features of the past 50 th to 100 th frames are also helpful for improving the prediction accuracy, but the time series information image features of more than 100 frames are not much helpful for the prediction accuracy of the LBO. Considering that with the increase in the length \(k\) of the time series information, the input dimension of the model also increases simultaneously, which in turn requires more time for model construction, so taking hyperparameter \(k\) as 100 is a good choice that takes into account the model construction accuracy and efficiency.
## 5 Results
### Mechanical Response  
In this section, the mechanical response of plasmonic nanoparticles was investigated detecting the forward scattering as this setup is simpler, less susceptible to alignment errors, and it gives higher to signal-to-noise ratio with fewer averages because the optical throughput is higher. As the time to acquire an image is much longer than the previous section where the same points were compared, air surrounding media is used to avoid any defects such as the formation of air bubbles inside the gasket cell.  
The mechanical response of plasmonic nanoparticles was investigated using three different sizes of gold nanoparticles: 150, 200 and 250 nm (samples S1-S3, respectively). Their theoretical breathing mode frequencies are 21.6, 16.2 and 12.9 GHz, respectively. However, this will vary depending on the actual size of the particle, allowing size characterisation.  
Figure 4 shows the experimental data taken from a sample containing a mixture of gold nanoparticles of different sizes. The sample is scanned over an area of 50 \(\times\) 50 \(\upmu\)m\({}^{2}\) with a step size of 1 \(\upmu\)m (the same size as the spot of the pump laser). In this case, the probe laser wavelength is 780 nm where their optical response shows similar behaviour. The optical picture of the scanned sample shows several nanoparticles with different apparent sizes (Figure 4a). After signal processing, the breathing mode frequency for each particle is obtained, as shown in Figure 4b. Each frequency can be related to the actual size of the particle (Equation (9)), giving a study of the population of sizes (Figure 4c) or an ultrasonic picture in nm (Figure 4d).  
Our experimental setup allows us to scan specific area with finer step size, marked as the solid and dashed square areas in Figure 4. These scanned areas are 5 \(\times\) 5 \(\upmu\)m\({}^{2}\) with 100 nm step size. Both optical pictures show a single dip in intensity and shape (Figure 4e,g); however, the ultrasonic pictures given by the pump-probe system are slightly different. Figure 4f shows a single frequency where we can assume that there is a single gold nanoparticle with size \(\sim\)250 nm. On the other hand, Figure 4h shows three different frequencies. The highest frequency is given by the top particle (\(\sim\)150 nm) that is only just visible in the optical picture. The other two frequency components are given by the middle blob. This means that two particles might be present inside the same point spread function. This will be discussed in more detail in Section 5.3. Changing the size provides a way to generate different vibrational frequencies. Additionally, this might be a useful technique to characterise the size of plasmonic nanoparticles because their vibrational modes only depend on the mechanical properties of the nanostructures (Section 2.4). However, multilayered nanostructures such as core-shell nanoparticles cannot be characterised by such a simple approach, as there are complex boundary conditions that depend on the core, shell, medium and particle fabrication parameters [33].
## 4 Experimental Results
### Evaluation of Feature Extraction and Matching Modules
#### 4.3.1 Accuracy  
As mentioned, we calculated the accuracy of three classifiers for the assessment of the verification task. The accuracy can be defined by the following formula:  
\[\text{Accuracy}=\frac{\text{TP+TN}}{\text{TP+FP+TN+FN}} \tag{5}\]  
Although the main focus was on evaluating our proposed model, we also validated the verification performance for different training sample sizes.  
The impact of different classification methods is illustrated in Figures 13-15 related to the matching stage. As can be seen in Figure 13, linear SVM outperformed other classification methods and achieved a verification accuracy of 1 for the contact-based HKPU database. Additionally, SVM showed the best performance in terms of verification accuracy for 2D contact-free HKPU and IITD touchless databases.  
Tables 1-3 show the accuracy results obtained using SVM, KNN, and RF classifiers with the input of extracted deep features on three databases. The overall trend shows that the larger the training sample size, the higher the accuracy. It can be realized that the classifiers in our proposed model required low training samples to achieve good performance.  
Figure 12: Visualization of the extracted deep features for two individuals.  
Figure 14: Accuracy curves of SVM, RF, and KNN classifiers in the 2D contact-free HKPU database.  
Figure 13: Accuracy curves of support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN) classifiers in the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database.  
Figure 15: Accuracy curves of SVM, RF, and KNN classifiers in the IITD touchless database.
## 3 Control Design for Transition Phase
### Control Law Design
#### 3.2.2 Pitch Angle Rate Control  
The pitch angle rate loop is responsible for controlling the pitch angular rates, as the main control objectives. It is controlled using INDI+ESO [30]. The overall control structure is shown in Figure 7.  
The control structure consists of four key components: reference model, error controller, ESO, and control allocation.  
The gain in the control law can be determined by adjusting parameters, which requires sufficient experimentation and iteration. We chose to fine tune based on previous relevant research [30] and determined that the gains \(K_{r}\), \(K_{e}\), and \(K_{h}\) are 7, 70, and 0.11, respectively.  
For the control assignment section, how the pitch rate command \(\hat{q}_{c}\) is assigned to the elevator command \(\delta_{cc}\) and the rotary-wing pitch command \(\Omega_{pitch}\) is determined by the control strategy which will be explained in Section 3.3.  
The structure of ESO is shown in Figure 8.  
where the on-board model input \(\hat{q}_{m}\) can be calculated as  
\[\begin{split}\vdots\\ \hat{q}_{m}=\bar{M}_{\delta_{c}}\delta_{e}+\bar{M}_{q}q_{s}\end{split} \tag{16}\]  
The gain parameters \(K_{P}\) and \(K_{I}\) are determined as 4 and 4, also by tuning parameters. The purpose is to enable the ESO to evaluate the angular acceleration of the current timestamp.
# Weighted Constraint Iterative Algorithm for Phase Hologram Generation
## 2 Method
During the iteration process, the amplitude of the incident plane wave in the hologram plane is calculated as:  
\[E_{\text{target}}=\iint\big{|}A_{\text{target}}(u,v)\big{|}^{2}dudv \tag{4}\]  
\[E_{\text{holo}}=E_{\text{target}} \tag{5}\]  
\[|A_{\text{holo}}|=\sqrt{\frac{E_{\text{holo}}}{H}} \tag{6}\]  
where \(E_{\text{target}}\) represents the total energy of the target image, \(E_{\text{holo}}\) represents the total energy in the hologram plane, \(H\) is the area of the hologram, and \(|A_{\text{holo}}|\) is the enforced amplitude constraint in the hologram plane. During the iterations, the amplitude of the incident plane wave in the hologram plane is replaced by \(|A_{\text{holo}}|\). According to Equations (3)-(6), the following formula can be deduced:  
\[\iint\big{|}A^{\prime}_{k}(u,v)\big{|}^{2}dudv=\iint\big{|}A_{\text{target}}( u,v)\big{|}^{2}dudv \tag{7}\]  
In this way, the total energy in the image plane is constrained indirectly by controlling the total energy in the hologram plane. Meanwhile, the amplitude distribution of the signal region is constrained directly during the iteration process. Through the iterations, more and more energy is gathered in the signal region so that the noise of the non-signal region is suppressed effectively. The above operation provides a larger optimization space of the algorithm than introducing the noise suppression parameter to directly constrain the non-signal region [26] while maintaining overall and effective constraint in the image plane.
## 5 Modeling and Simulating Si-SC Structures
### Modeling and Simulation of Si-SC with Continuous MLARC  
The optical performance of continuous MLARC configurations comprising SiO\({}_{2}\)/ NQD/ LiF and Si\({}_{3}\)N\({}_{4}\)/NQD/LiF was investigated using Lumerical FDTD simulations. The boundary conditions of the solver are set as periodic in the \(x\) and \(y\) directions to enable light propagation along the \(z\)-axis. Optimal layer thicknesses were determined through parametric studies, resulting in configurations of 40 nm SiO\({}_{2}\), 40 nm NQD, and 100 nm LiF for the first structure, and 110 nm Si\({}_{3}\)N\({}_{4}\), 50 nm NQD, and 70 nm LiF for the second.  
Figure 7a compares the reflectivity of these MLARCs with the reference Si-SC structure. The SiO\({}_{2}\)/NQD/LiF MLARC demonstrated a significant reduction in reflectivity, particularly in the longer-wavelength region. While the Si\({}_{3}\)N\({}_{4}\)/NQD/LiF MLARC exhibited lower reflectivity across the entire spectrum, it showed a more pronounced reduction in the visible range. The implementation of continuous MLARC configurations yielded a substantial reduction in overall optical reflectivity compared to SLARC structures, underscoring the enhanced light-trapping capabilities of the multi-layer design.  
To assess the impact of these MLARC configurations on Si-SC performance, IQE and EQE were calculated. Table 5 presents a comparative analysis of these parameters. Both MLARC configurations exhibited improved EQE compared to the SLARC structures, particularly at shorter wavelengths, attributed to the enhanced light harvesting capabilities of the NQD layer, facilitated by its downshifting effect. However, the Si\({}_{3}\)N\({}_{4}\)/NQD/LiF configuration showed a decrease in EQE at longer wavelengths, due to the change in the reflectivity.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Structure** & \(\mathbf{V_{oc}}\) **(V)** & \(\mathbf{J_{sc}}\) **(mA/cm\({}^{2}\))** & **FF** & **PCE (\%)** \\ \hline Si-SC (no ARC) & 0.59 & 25.18 & 0.82 & 12.27 \\ Si-SC with SiO\({}_{2}\) SLARC & 0.60 & 33.67 & 0.82 & 16.68 \\ Si-SC with Si\({}_{3}\)N\({}_{4}\) SLARC & 0.60 & 37.11 & 0.82 & 18.47 \\ \hline \end{tabular}
\end{table}
Table 4: Electrical characteristics of Si-SC structures with and without SLARCs of SiO\({}_{2}\) (101 nm) and Si\({}_{3}\)N\({}_{4}\) (73 nm).  
Figure 6: Current–voltage (J-V) characteristics of Si-SC structures with and without SLARC of SiO\({}_{2}\) (101 nm) and Si\({}_{3}\)N\({}_{4}\) (73 nm).  
The electrical performance of Si-SCs with continuous MLARCs was evaluated through J-V characteristic simulations, as shown in Figure 7b. In addition, Table 6 summarizes the key electrical performance parameters for Si-SC structures with and without MLARCs. The calculated \(J_{sc}\) values for the SiO\({}_{2}\)/NQD/LiF and Si\({}_{3}\)N\({}_{4}\)/NQD/LiF configurations were 34.1 mA/cm\({}^{2}\) and 35.54 mA/cm\({}^{2}\), respectively, representing improvements over the SLARC-based Si-SCs. Corresponding PCE values of 17.26% and 18.02% were obtained.
# Investigation of In Situ and Ex Situ Passivation of Pyrophoric Uranium-Niobium Alloy Powder
Several techniques exist to reduce the reactivity of bulk metals to oxygen, such as chemical coatings and anodization. However, these methods are not always applicable to powders. Passivation is a well-known technique to make powders safer to handle [16; 17] through the deliberate formation of a thin protective oxide layer on the surface of a metal, which slows or prevents further reaction with oxygen, reducing the risk of spontaneous combustion [18; 19]. The effectiveness of passivation is dependent on how the metal oxidizes and the structure and properties of the oxide layer. For example, aluminum forms a tightly adhering oxide layer that effectively prevents further oxidation [20], whereas iron forms a loosely adhering oxide in humid air, which can even accelerate further oxidation [21; 22]. For metals that do not naturally form protective oxide layers, alloying with specific elements can substantially improve their corrosion resistance. Uranium metal is known to readily form uranium oxide in air as a loosely adhered black powder [23]. However, uranium alloyed with six-percent niobium by weight (U-6 Nb) in its \(\alpha^{\prime\prime}\) phase is known for its oxygen corrosion resistance [24; 25; 26]. The \(\alpha^{\prime\prime}\) phase is a metastable martensitic structure characterized by uniform niobium supersaturation, which is achieved by rapidly quenching the alloy from the high-temperature \(\gamma\)-phase [27]. In our previous work [28], we demonstrated an alternative route to produce 20-75 \(\upmu\)m spherical U-6 Nb powders via the hydride-dehydride (HDH) process followed by plasma spheroidization; however, the powders produced were found to be pyprohoric. This can be attributed to two factors. First, the metal powders produced were found not to be in the \(\alpha^{\prime\prime}\) phase but in a combination of distorted \(\alpha\) and \(\gamma\) phases. Second, the HDH-prepared powders were found to have thin oxide layers (as thin as 5 nm) when compared to powders synthesized via other methods, which can have oxide layers over 50 nm thick [2; 29; 30]. Passivation by increasing the oxide layer thickness is an attractive method to mitigate pyprohoricity. In this study, two passivation methods were applied to U-6 Nb powders processed via the HDH-plasma spheroidization technique. The first method, in situ passivation, introduced a controlled amount of oxygen during the spheroidization process, allowing oxide formation to occur at elevated temperatures as the powder melts and resolidifies in the plasma chamber. The second method, ex situ passivation, was performed by exposing the already spheroidized powder to oxygen at ambient temperature over an extended period. Each method was evaluated based on the impact on material reactivity, as determined by pyprohoricity testing, as well as the uniformity and thickness of the oxide layers, as assessed by microscopy.
## 4 Conclusions  
In conclusion, this work has presented a novel method involving the use of a small capacity and relatively less expensive fuel cell onboard a drone to cool Li-ion batteries. Temperature rise in batteries is known to cause battery degradation, eventually leading to thermal runaway and explosions. Thus, battery cooling is essential. However, drones are starved for weight, and so mounting an additional cooling system which draws power from a drone's battery, can shorten its flight duration. Thus, this work used a power source (a PEM fuel cell stack), which, when powered by a solid fuel stick, offered dual benefits, i.e., primarily it showed a decrease in battery temperature, and as a side effect, it elongated the flight time a little compared to the case that used only a battery. Further, the above results were verified across several different capacity batteries from different manufacturers.  
The work above opens doors to several possibilities for future research. The cooling system enclosure design can be optimized both with respect to thermal and mechanical efficiency. Different options for an onboard fuel cell, and a hydrogen fuel stick, can be tried to find a combination that offers the most benefits, i.e., longer flight time, less add-on weight, and largest cooling effect. Enhancing flight time, while maintaining optimal battery temperature so that thermal failures of batteries and battery degradation can be reduced, is a very challenging and important direction for research. This is because it can definitely help future generations of delivery drones by reducing their overall operating costs related to frequent replacement of batteries due to thermal failure. Further, the cooling effect reported in this paper can also be applied to other applications where overheating may be an issue.  
[PERSON] conceived the idea, the system, and the experiments and also supervised the experiments. [PERSON] mainly handled the writing of this manuscript with some initial assistance from [PERSON] and [PERSON]. Further, [PERSON] and [PERSON] designed the system, and performed initial experiments, [PERSON] performed a later batch of experiments and analyzed the experimental data.  
**Acknowledgments:** Parts of this work were funded by a seed grant provided by the department of electrical engineering at the American University of Sharjah, UAE. The authors also acknowledge [PERSON] for help with a field test.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: (**a**) Flight test 6—temperature of fuel stick before take-off; (**b**) Flight test 6—temperature of fuel stick after landing. Flight test 6—fuel stick temperature profiles. Images captured by thermal imaging device 2.
## 5 Forest Fire Risk Prediction in China
### The Fire Spread Behavior Model  
Forest fire spread is basically determined by the fuel property, meteorological factors and terrain. The process of forest fire spread contains the self-replicating feature, and forest fire behavior can be described by the fractal geometry. [PERSON] _et al._ simulated the fire spread based on remotely-sensed data (Landsat TM and AVHRR imagery) and a geographic information system [94]. A method of limited spreading lumping was adopted to describe the growing phenomena and to simulate the dynamic process of the forest fire spread. The result of fire simulation was adjusted by a scale rule. The simulated fire and the actual fire manifest the self-similarity in their spreading shapes as well as the quantitative similarity in their areas.  
[PERSON] _et al._ indicated that forest fire spread is an example of self-organized criticality (SOC) behavior [95]. [PERSON] _et al._ improved the traditional forest-fire spread cellular-automata model, and considered the effect of tree species, meteorological conditions and human activities on the forest fire spread [96]. It was concluded that forest fires occurrence and spread showed a significant SOC behavior in China for the period from 1950 to 1989, and simulation result using the improved model was better with the actual forest fire occurrence and spread data than those of the traditional model.
## 4 Evaluation Results
### Effect on Evacuation Time  
The standard operating procedure for earthquakes also tells people to first evacuate to a safe place if they are on a lower floor [28, 29]. They should stay away from buildings and trees to avoid potential injury. On the other hand, people on a higher floor should first find protection in a doorway or crouch under a desk or table. They then should evacuate to a safe place after the shaking becomes small. ADRS can play an important role in both scenarios to help people evacuate.  
In this experiment, we investigated the effect of ADRS on evacuation time. We first asked several volunteers to evacuate from different floors inside a building to a safe place outside the building. The experiment was conducted at the Engineering Building V of National Yunlin University of Science and Technology, which is a four-floor building. The floor plan of each floor is similar to that of the first floor, which is shown in Figure 14. In our experiment, we repeated three rounds for each floor. For each round, we asked a volunteer to follow a certain path to evacuate from a floor to a safe place outside the building. Volunteers on the third or the fourth floor were asked to shut down a computer, open windows and an escape door. On the other hand, volunteers on the first and second floors needed to open windows and an escape door only. We measured the time taken by volunteers to evacuate from different floors and present the average time in Figure 15. We found that ADRS can reduce evacuation  
Figure 14: Experiment environment.  
Figure 13: The operation time of each task.  
time from 6 to 10 s, which is long enough for faculty or students to get under sturdy furniture, or to evacuate from the second floor to the first floor, or to run more than 50 m.