esa-sceva/satcom-chunk-collection huggingface dataset:
# Half-Elliptical Resonator Lowpass Filter with a Wide Stopband for Low Band 5G Communication Systems
## 4 Conclusions  
A new microstrip lowpass filter with a small occupied size (0.126 \(\lambda_{\text{g}}\)\(\times\) 0.055 \(\lambda_{\text{g}}\)) and ultra-wide stopband (1.48 to 20 GHz) is proposed. The proposed filter has an attenuation level higher than 24 dB in stopband and a very sharp response in the transition band with  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **fc** & \(\zeta\) & **NCS** & \multirow{2}{*}{**RSB**} & \multirow{2}{*}{**SF**} & **RL** & **IL** & **FOM** & **Substrate** \\  & **(GHz)** & **(dB/GHz)** & & & & & & & \\ \hline
[16] & 1.89 & 139 & 0.0177 & 1.69 & 2.5 & 10.3 & 1.8 & 33,179 & Rogers \\
[18] & 2.35 & 135 & 0.032 & 1.64 & 2.2 & 10.6 & 0.6 & 18,562 & RT/Duroid \\
[46] & 2.4 & 92.5 & 0.037 & 1.35 & 3 & 10 & 0.25 & 10,125 & RT/Duroid \\
[47] & 1.96 & 104 & 0.0228 & 1.8 & 2 & 12 & 0.6 & 18,063 & [PERSON] \\
[48] & 1.47 & 85.6 & 0.0076 & 1.68 & 2.2 & 12 & 0.4 & 41,628 & RT/Duroid \\
[49] & 2.44 & 67.27 & 0.038 & 1.39 & 2.2 & 14.8 & 0.6 & 5414 & FR \\
[50] & 3.01 & 58.6 & 0.0242 & 1.43 & 2.5 & 20 & 0.3 & 865.5 & RT/Duroid \\
[51] & 5.55 & 84 & 0.0857 & 0.66 & 1.5 & 20 & 0.7 & 116.5 & FR4 \\
[52] & 2.11 & 100 & 0.032 & 1.6 & 1.8 & 12 & 0.7 & 9000 & FR4 \\
[53] & 1.07 & 75 & 0.0088 & 1.66 & 2 & 20 & 0.4 & 2804 & DxClad \\
[54] & 1 & 77 & 0.0117 & 1.74 & 2.4 & 20 & 0.3 & 27,192 & [PERSON] \\
[55] & 2.49 & 37.5 & 0.0378 & 1.62 & 1.5 & 7.6 & 0.8 & 2410 & [PERSON] \\
[56] & 1.66 & 171 & 0.0176 & 1.75 & 2 & 12 & 0.5 & 34,000 & [PERSON] \\ This Work & 1.26 & 103.9 & 0.00693 & 1.73 & 2.4 & 12 & 0.3 & 62,520 & RT/Duroid \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the proposed filter and some published filters.  
a sharp roll-off rate higher than 103 dB/GHz. With these specifications, the implemented device can be used in modern wireless systems.  
Conceptualization, S.R., R.A. and A.L.; methodology, S.R., R.A. and M.H.M.; software, R.A. and S.R.; validation, A.L; formal analysis, A.N. and M.H.M.; investigation, A.L.; resources, A.N. and M.H.M.; writing--original draft preparation, R.A. and A.L.; writing--review and editing, S.R. and M.H.M.; visualization, R.A. and A.L.; supervision, S.R. and A.N.N.; project administration, S.R. and A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All the material conducted in the study is mentioned in the article.  
The authors declare no conflict of interest.
# Sensorless Posture Detection of Reluctance Spherical Motor Based on Mutual Inductance Voltage
## 3 Particle Swarm Optimization Algorithm for 3-DoF Attitude Recognition
### Experimental Verification
During the verification experiment, the rotor is first manually rotated to the preset position according to the reading of the contact sensor. Due to the need to meet the position requirements in three directions at the same time in the manual process, certain deviations would inevitably occur. In addition, in the process of measurement, the rotor would be slightly displaced due to the gravity of the additional mechanism and other factors, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Test Series** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline roll preset & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) & \(-10\) \\ roll recognized & \(-9.2\) & \(-9.9\) & \(-9.6\) & \(-10.6\) & \(-8.2\) & \(-8.2\) & \(-8.8\) & \(-8.9\) & \(-8\) & \(-9.4\) & \(-9.7\) \\ pitch preset & \(-10\) & \(-8\) & \(-6\) & \(-4\) & \(-2\) & \(0\) & \(2\) & \(4\) & \(6\) & \(8\) & \(10\) \\ pitch recognized & \(-12.4\) & \(-11.1\) & \(-8.5\) & \(-5.8\) & \(-5.5\) & \(-3.2\) & \(1\) & \(4.6\) & \(5.5\) & \(6.4\) & \(8.2\) \\ yaw preset & \(20\) & \(16\) & \(12\) & \(8\) & \(4\) & \(0\) & \(-4\) & \(-8\) & \(-12\) & \(-16\) & \(-20\) \\ yaw recognized & \(20.5\) & \(15.3\) & \(10.7\) & \(9.8\) & \(2.3\) & \(-1.3\) & \(-4.9\) & \(-7.4\) & \(-15.5\) & \(-17.2\) & \(-21.3\) \\ \hline bias of roll & \(-0.8\) & \(-0.1\) & \(-0.4\) & \(0.6\) & \(-1.8\) & \(-1.8\) & \(-1.2\) & \(-1.1\) & \(-2\) & \(-0.6\) & \(-0.3\) \\ bias of pitch & \(2.4\) & \(3.1\) & \(2.5\) & \(1.8\) & \(3.5\) & \(3.2\) & \(1\) & \(-0.6\) & \(0.5\) & \(1.6\) & \(1.8\) \\ bias of yaw & \(-0.5\) & \(0.7\) & \(1.3\) & \(-1.8\) & \(1.7\) & \(1.3\) & \(0.9\) & \(-0.6\) & \(3.5\) & \(1.2\) & \(1.3\) \\ \hline \multicolumn{8}{c}{population standard deviation} & & & & \multicolumn{1}{c}{\(1.7\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Preset; detection values; and deviations of roll, pitch, and yaw of experiment \(1\) (\({}^{\circ}\)). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Test Series** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline roll preset & \(15\) & \(12\) & \(9\) & \(6\) & \(3\) & \(0\) & \(-3\) & \(-6\) & \(-9\) & \(-12\) & \(-15\) \\ roll recognized & \(16.4\) & \(12.7\) & \(7.4\) & \(2.4\) & \(0.7\) & \(0.3\) & \(-1.1\) & \(-2.1\) & \(-5.3\) & \(-11.8\) & \(-which would also lead to the deviation between the measured angle and the preset angle.
## 3 Conclusions  
SAFs represent a primary tool for achieving net-zero emissions in the aviation industry by 2050. Their combustion performance must be capable of seamlessly replacing traditional aviation fuels, considering both usability and safety. Equally important is the requirement for SAFs to mitigate the adverse effects of aviation emissions on ecological environments and human health while reducing GHG emissions.  
From an LCA perspective SAFs have much lower CO\({}_{2}\) emissions than conventional aviation fuels. In an aeroengine, fuel combustion is a complex process in the combustor of the engine, which transfers chemical energy into heat energy. The completeness of combustion is related to a fuel's composition and properties, including spray, evaporation, and flammability. Therefore, engine performance is not only affected by fuel performance, but also interacts with engine structure and control laws. The SO\({}_{2}\) and PM emissions from SAFs have a clear advantage due to less sulfur and aromatic hydrocarbon content. CO/UHC emission parameters are controlled by both the engine and the fuel properties, while NO\({}_{\rm x}\) emission parameters are mainly influenced by the engine. To sum up, SAFs offer substantial advantages in CO\({}_{2}\), SO\({}_{2}\), and PM emissions compared with conventional petroleum-based aviation fuels.  
There are technical gaps in comparing the emission performance of SAFs and traditional aviation fuels. For instance, there is a need to intensify research on the emission performance of SAFs at the engine and even aircraft level. Comprehensive studies on the emissions performance of ASTM-approved and non-approved SAFs are lacking. The variability in SAF compositions due to different feedstocks and production processes complicates uniform analysis. Differences in experimental application engines, detection equipment, and human error prevent cross-comparison of results. Moreover, there is a paucity of research on the CO emission performance of SAFs.  
In the future, more research should be directed towards the physicochemical properties, atomization and combustion performance of SAFs and their components, as well as their performance in engines and aircraft levels. The development and application of SAF technology will be promoted to accelerate the process of realizing net-zero emissions in the aviation industry.  
Z.S.: writing--original draft, and methodology; [PERSON]. ([PERSON]): data analysis; [PERSON]. ([PERSON]): supervision, methodology. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Sino-Europe ALTERNATE Research Program-China (MJ-2020-D-09).  
The authors declare no conflicts of interest.
# Estimating Tail Probabilities of Random Sums of Phase-Type Scale Mixture Random Variables +
## 2 Preliminaries
### Phase-Type Distributions and Properties  
PH distributions have been used in stochastic modelling since being introduced in [12]. Apart from being mathematically tractable, PH distributions have the additional appealing feature of being dense in the class of non-negative distributions. That is, for any distribution on the positive real axis, there exists a sequence of PH distributions which converges weakly to the target distribution (see [2] for details). In other words, PH distributions may arbitrarily closely approximate any distribution with support on \([0,\infty)\).  
In order to define a PH distribution, we first consider a continuous-time Markov chain (CTMC) \(\{Y(t),\ t\geq 0\}\) on the finite state space \(E=\{1,2,\ldots,p\}\cup\{\triangle\}\), where states \(1,2,\ldots,p\) are transient and state \(\triangle\) is absorbing. Further, let the process have an initial probability of starting in any of the \(p\) transient phases given by the \(1\times p\) probability vector \(\mathbf{\alpha}\), with \(\alpha_{i}\geq 0\) and \(\sum_{i=1}^{p}\alpha_{i}\leq 1\). Hence, the process \(\{Y(t):t\geq 0\}\) has an intensity matrix (or infinitesimal generator) \(\mathbf{Q}\) of the form:  
\[\mathbf{Q}=\left(\begin{array}{cc}\mathbf{T}&\mathbf{t}\\ \mathbf{0}&0\end{array}\right),\]  
where \(\mathbf{T}\) is a \(p\times p\) sub-intensity matrix of transition rates between the transient states, \(\mathbf{t}\) is a \(p\times 1\) vector of transition rates to the absorbing state, and \(\mathbf{0}\) is a \(1\times p\) zero row vector.  
The (continuous) PH distribution is the distribution of time until absorption of \(\{Y(t):t\geq 0\}\). The 2-tuple \((\mathbf{\alpha},\mathbf{T})\) completely specifies the PH distribution, and is called a PH-representation. The CDF is given by \(F(y)=1-\mathbf{\alpha}\exp(\mathbf{T}\,y)\mathbf{1},\ y\geq 0\,,\) where \(\mathbf{1}\) is a column vector with all ones.  
Besides being dense in the non-negative distributions, the class of continuous PH distributions forms the smallest family of distributions on \(\mathbb{R}_{+}\) which contains the point mass at zero and all exponential distributions, and is closed under finite mixtures, convolutions, and infinite mixtures (among other interesting properties) (see [12]).
## 3 Results and Discussion
### Reuse of MFA@PDMS Sponge  
The experiment shown in Figure 11 was designed to characterize the reutilization performance of the sponge bionic material. The sponge material was put into n-hexane dyed with Sudan red II, and it was observed that its thickness increased from 0.6 cm to 1.2 cm after only 10 min. We then removed the sponge bionic material and weighed it to calculate its adsorption multiple (i.e., the weight of the material after absorbing the normal hexane/the weight of the material). The oil phase could be extruded by simple compression, and the material could be reused within 30 min after extrusion. Over eight adsorption-extrusion-drying tests, the oil absorption capacity did not decrease significantly, showing good reusability.  
An H5 KT-type static mechanical tester was used for our stress-strain performance analysis. The compressive stress-strain relationships under single and 20 cyclic quasi-static compressive loadings are shown in Figures 12 and 13, respectively (the compression rate was 50 mm/min).  
As shown in Figures 12 and 13, the maximum stress of the material was 446.9 kPa before adsorption on the sponge and 410 kPa afterwards, indicating that the material has good recyclability. The stress-strain curves of 20 compression cycles showed good overlap, and the maximum stress was basically unchanged, indicating that the sponge has good mechanical properties, elasticity, and fatigue resistance.  
Figure 11: Flexible deformation of materials during oil absorption-extrusion-drying process on MFA@PDMS sponge.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 4 Discussion
### Main Contributions and Limitations of PARIMC  
PARIMC proposes a number of novel ideas for the state of the art. To the best of the authors' knowledge, it is the first time geodata has been used to create a PAR physical model of an existing urban terrain. It is also the first time a PAR system has been controlled using a game engine with AI algorithms and a user interface accessible from a mobile device.  
[PERSON] et al. (2016) propose a PAR system to augment prototype buildings. But [PERSON]'s approach requires a number of different projectors. Their focus is on the buildings themselves. In that regard, [PERSON]'s approach is similar to [PERSON]'s (2017), who developed a PAR system for architectural design, as described in Section 1. PARIMC uses just one projector above the scene, so the setup and calibration of the system is very simple. Plus, there are fewer shadows using just one projector. In PARIMC, the 3D effect comes from a model of the terrain which must be printed using geodatadata from OSM or an equivalent database. The scale is also larger than in previous models, such as [PERSON] or [PERSON]'s models, which focus on small solids rather than large areas of terrain. In PARIMC, the changes expected in altitude are small and the surface of the terrain gradually changes over large areas, except for the height of the buildings, which represent sudden vertical changes.  
PARIMC also relies on a game engine for real time simulations. In the present prototype, Unity 3D was used, but other game engines may be used for control. The use of a mobile application to interact with the model is also a simple and effective innovation, which facilitates the user interface. PARIMC is, to the best of the authors' knowledge, the only model which is suitable for some specific applications. PARIMC's architecture makes it suitable, for example, for simulating and showing, in 3D, scenarios such as the advance of water in a flooding situation. Not only that, but the use of AI algorithms also confers on the system the ability to show clear and safe paths. It is also the only PAR model suitable to use for virtual visits or war games over a realistic model of the terrain.  
One limitation of the system is its vertical resolution. Using just one projector it is not possible to accurately show the facades of the buildings, for example. Given the scale of the model, it is not of paramount importance. Nonetheless, it is still one limitation which must be resolved for larger models with higher resolution.
# Vacuum Ultraviolet Single-Photon Positionization of Amino Acids
## 2 Experiment
The VUV laser beam passed through the metal mesh and reached the Pd plate. Photoelectrons generated through irradiation by VUV photons on the Pd plate were accelerated by the electric field toward the metal mesh, and the positive pulsed voltage generated in the Pd plate was directly measured using a fast oscilloscope (Infiniium DSO8104A, Agilent, Santa Clara, CA, USA). The Xe pressure was optimized to obtain the maximum number of VUV photons, as measured using the detector. All amino acids (\(\geq\)98%) were purchased from Aldrich (St. Louis, MO, USA), whereas 2,5-DHB (98%) and THAP (99%) were purchased from Acros (Waltham, MA, USA). All chemicals were directly used without additional purification. A matrix solution (100 umol/mL) was prepared in 75% acetonitrile and 25% deionized water, whereas an amino acid stock solution was prepared by dissolving 10 umol of dried analyte in 1 mL of deionized water. In an experiment detecting amino acids, the matrix and analyte solution were premixed in a 20:1 ratio in a vortex for several minutes to facilitate mixing. In quantitative analysis experiments, glycine/histidine or tryptophan/phenylalanine were premixed with different molar ratios, which were then mixed with matrix solution in a matrix-to-total-analyte ratio of 100:1. Each sample was dried in vacuum after pipetting 4 mL of a mixture onto a stainless-steel plate, which was then analyzed using the homemade MALDI/VUV postionization and TOF-MS system.
## 3 Results and Discussion
### Effect of BTH Treatment on the Activities of Key Enzymes of LOX Pathway
#### 3.4.2 C6 Alcohols  
C6 alcohols, such as E-2-hexenol, Z-3-hexenol, and hexanol, are direct products of ADH and are the immediate substrates for the formation of C6 esters in grapes. The ratio of Z-3-hexenol to E-2-hexenal can be used to determine the maturity level of grapes from an aroma point of view, with a decreasing trend observed as grapes ripen. Except for Z-3-hexenol, both E-2-hexenol and hexanol tended to increase during grape ripening. [PERSON] et al. [63] also reported that the Z-3-hexenol concentration in Monastrell grapes declined two weeks after veraison. BTH treatment resulted in a significant increase in AAT activity in mid-ripening grapes (Figure 3D). This increase in AAT activity led to the conversion of a higher amount of E-2-hexenol and hexanol into E-2-hexenyl acetate and hexyl acetate. Consequently, there were significant reductions of 36.74% and 33.44% in the levels of free E-2-hexenol and hexanol, respectively, in ripe BTH-treated grapes compared to the CK (Table S3). [PERSON] et al. [1] also found that AAT activity and C6 esters showed a very similar trend of elevation in Shine Muscat grapes near harvest, which supports our speculation. From the point of aroma maturation, BTH was found to promote aroma changes in Cabernet Gernischt grapes. However, in terms of TSSs and reducing sugar content, BTH was found to delay the technological ripening of these grapes. This indicates that aroma changes and the optimal technical ripening of grape berries may not occur at the same time.
## 3 Proposed Approach
### Fine-Tuning with Reinforcement Learning  
After histogram matching, the 12-bit EDR and HDR images have similar pixel intensity distributions. However, due to the limited capabilities of the histogram for image representation, some details may still be lacking. To improve the consistency of regional pixel intensities, reinforcement learning is used to fine-tune the image in the spatial domain. The resulting 12-bit HDR image is first converted to its 16-bit HDR representation by multiplying all pixel intensities by \(2^{4}\) for subsequent fine-tuning. The multi-agent reinforcement learning architecture, PixelRL, is used for image adjustment [20]. PixelRL modifies the asynchronous advantage actor critic (A3C) architecture into a fully convolutional form. It takes each pixel as an agent to gradually modify the image intensity through a pre-defined set of actions and find the optimal strategy that minimizes the squared error between the resulting image and the ground truth.  
We use Chainer [21] and ChainerRL [22] to construct network architectures for deep reinforcement learning. The 16-bit HDR representation converted from the 12-bit HDR image is used as the input image \(I\) (\(s^{(0)}\)), where \(s\) is the state of the agent and the corresponding16-bit HDR image of the original LDR image is the ground truth \(I^{\text{target}}\). During training and testing, the agent selects appropriate actions for intensity adjustments from the set of actions listed in Table 1. In Table 1, we define a set of actions for image denoising and restoration that the agent can execute. These actions are empirically decided and are classical image-filtering algorithms. Our approach can select the adequate action in the action set to adjust the pixel value. After adjustment, the resulting image can be obtained. We include several classic image filters and values up/down in the action set. The reward is given by  
\[r_{i}^{t}=\left(l_{i}^{target}-s_{i}^{(t)}\right)^{2}-\left(l_{i}^{target}-s_{ i}^{(t+1)}\right)^{2}, \tag{3}\]  
Figure 5: The histogram matching results. (**a**) LDR image; (**b**) resulting HDR image; (**c**) ground-truth image.  
where \(I_{i}^{target}\) is the \(i\)-th pixel intensity of the ground truth. It calculates the squared error between the ground truth and the current image being decreased by action \(a_{i}^{(t)}\). Therefore, maximizing the total reward is equivalent to minimizing the squared error between the final image \(s^{t_{max}}\) and the ground truth \(I^{target}\).  
For training hyper-parameters, we use a batch size of 32 and perform data augmentation with 70 \(\times\) 70 random cropping, horizontal image flipping, and random rotation. The ADAM optimizer and the polynomial learning rate strategy are used in the training process. The initial learning rate is set to \(10^{-3}\) and is decreased by \((episode/max\_episode)^{0.9}\) in each episode. We set the maximum episode \(max\_episode\) to 3000, the length of each episode \(t\_max\) to 15, and store weights every 20 episodes to select the best results.
## 4 Discussion  
In the present work, an exploratory experimental application of CFs as stabilizing fibers for the production of a bituminous mixture has been proposed. The final aim of this research project is to develop a new recycling solution in order to promote the recycling of waste cigarette butts. Of course, a life cycle analysis (LCA) will assess the sustainability of the proposed recycling process and final application. However, it should be highlighted that the lack of disposal control for this waste is a significant problem, and only with a specific collecting and disposal protocol would the recycling of cigarette butts be feasible.  
As for this first experimental application, based on the results:  
* The production of the experimental fibers was based on the simple shredding of brand new CFs. The entire outcome of this process was added to the bituminous mixture as a common commercial stabilizing fiber. From the drain-down test results, the shredded CFs show properties similar to the reference fibers. Thus, from an absorption point-of-view, the behavior of the experimental fibers can be compared to the normal cellulose-based stabilizing fibers.
* The addition of shredded CFs into the experimental SMA does not affect the workability and compactability properties of the mixture. The air voids content is in line with the reference mixture and with the technical specifications for this type of asphalt concrete.
* In terms of mechanical properties, the presence of the experimental fibers leads to a slight reduction in the ITS, despite the fact that the obtained values are above the requirements imposed by the reference technical specifications. This reduction could be an indication of a lower cohesion, probably due to the limited effect of the experimental fibers in stabilizing the high bitumen content, and in generating a strong mastic with the finer particles of the mixture. However, the stiffness of the experimental SMA, as well as its thermal sensitivity, was not negatively affected by the addition of CFs.  
Figure 5: RLAT average results at 40 \({}^{\circ}\)C.  
As for the water susceptibility, a minimum ITS reduction was registered for the experimental mixture. Furthermore, this reduction was lower even if compared to the reference SMA.
* The presence of shredded CFs does not alter the rutting resistance properties of the bituminous mixture, which were in line with common values for stone mastic asphalt mixtures.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## 2 Methodology
### Heat Balance Governing Equations  
In the present work, a PV/T transient model was conducted based on a heat balance established for solid and fluid domains. In all solid components (glass, PV module, Tedlar, aluminum absorbers), the heat exchange process occurs by conduction mechanism, while in the flow duct, the thermal transfer to fluid (water) is through the convection mode. For each PV/T layer, the energy balance equation is a function of the layer internal energy variation taking into account some external parameters, e.g., the solar irradiance, ambient temperature, wind speed, and mass flow rate. Thus, the energy governing equations for solid and fluid layers were obtained considering the selected thermo-physical proprieties summarized in Table 2.