esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling
### PV Array Output Power Model  
The output power of PV module \(P_{PV,ij}\) is represented as  
\[P_{PV,ij}=\eta_{ij}\cdot Q_{sun,ij} \tag{23}\]  
where \(\eta_{ij}\) is the PV conversion efficiency of the PV module, which can be expressed as a function of temperature.  
\[\eta_{ij}=1+\delta(T_{ij}-T_{ref}) \tag{24}\]  
where \(\delta\) is the power temperature coefficient of the PV module, typically a negative value, indicating that as the PV module temperature increases, the efficiency decreases. \(T_{ij}\) and \(T_{ref}\) are operating temperature and reference temperature, respectively. Thus, establishing a thermal model to calculate the temperature of a PV module is necessary. The thermal equilibrium equation of a PV module is defined as [38]  
\[c_{p}\cdot\rho_{p}\cdot A_{ij}\cdot\dot{T}_{ij}=Q_{sun,ij}-Q_{con,ij}-Q_{ire, ij}-P_{PV,ij} \tag{25}\]  
where \(c_{p}\) is the specific heat capacity of the PV module, \(\rho_{p}\) is the areal density, \(Q_{con,ij}\) and \(Q_{ire,ij}\) are the convective heat transfer energy and infrared radiation energy, respectively. The convective heat transfer \(Q_{con,ij}\) between the PV module and the atmosphere is given by [6]  
\[Q_{con,ij}=A_{ij}\cdot\left(T_{ij}-T_{air}\right)\cdot\left(h_{free}^{3}+h_{ forced}^{e}\right)^{\frac{1}{3}} \tag{26}\]  
where \(T_{air}\) is the atmospheric temperature, \(h_{free}\) and \(h_{ forced}\) are the natural convective heat transfer coefficient and forced convective heat transfer coefficient, which can be expressed as  
\[h_{free}=Nu_{air}K_{air}/L \tag{27}\]  
\[h_{forced}=K_{air}\left(2+0.41\cdot\text{Re}_{air}^{0.55}\right) \tag{28}\]  
where \(Nu_{air}\) is the free convection Nusselt number, \(K_{air}\) is the atmosphere thermal conductivity, \(\text{Re}_{air}\) is the Reynolds number, and \(L\) is the feature size.  
The infrared radiation between the PV module and the atmosphere is calculated as  
\[Q_{ire}=\varepsilon_{e}\cdot\sigma\cdot\left(T_{ij}^{4}-T_{air}^{4}\right) \cdot A_{ij} \tag{29}\]  
where \(\varepsilon_{e}\) is the external emissivity of infrared radiation, \(\sigma\) is the Stefan-Boltzmann constant (\(1.38\times 10^{-23}\) J/K).  
Figure 2: Schematic of \(\theta_{DIP}\).
Indeed, these characteristics are essential in order to develop reliable TD-NIRS systems, that started to be developed in the nineties. During these early days of development, no commercial pulsed laser diodes were available, and one had to rely on homemade SC generation or Ti-Sapphire lasers in order to be able to produce a coherence pulsed light with a spectral power density sufficient to be detected by the photon counting electronic. Therefore, SCL sources have been a true driver of TD-NIRS development in the early days, and have more recently unlocked new horizons like spectroscopy, which have broadened the fields of applications. Finally, besides traditional sparse measurement of tissue properties with diffuse optics, Diffuse Optical Tomography (DOT) has also been rapidly expending in the past decades. This imaging technique is based on the acquisition of a large number of channels, coupled to a more refined data analysis, based on anatomically realistic models of tissue. This allows to reconstruct 3D spatial distribution of optical/physiological parameters of the tissues, rather than providing tissue information on a limited number of points. An extension of this technique is called fluorescence DOT, that uses extrinsic near-infrared fluorescent dyes which targets specific tissues to harvest extra information and enable molecular imaging. The interested reader can refer to reviews [12; 13] for more information about these techniques. We will see that SCL sources have also a role to play in order to improve the performances of the DOT instruments. In this work, we will review the use of SCL in the field of biomedical diffuse optics. Thus, we will begin by a short overview of the history and basic principle of supercontinuum generation. Then, we will review works that have been using SCL in biomedical diffuse optics, from the instrument development to the application. Finally, we will summarize and highlight the strength and weakness of SCL for these applications and show what areas can benefits from its use.
# A Rapid and Inexpensive Method for Finding the Basic Parameters of Involute Helical Gears
## 2 Materials and Methods
### Helical Gears
#### 2.3.1 The Procedure for Finding the Characteristic Parameters of a Helical Gear with Involute Teeth  
In a recent work, the authors proposed a quick and cost-effective technique, along with the associated device, to accurately determine the three geometric characteristics of an involute toothed spur gear: the number of teeth \(z\), the module \(m\), and the profile shift coefficient \(x\). The method is based on the manner of precision control of the spur gear with involute teeth using the measurement over pins. Two main disadvantages are obvious for the method of over pins measurement:  
* The mechanical system formed by the measuring instrument, the two pins, and the measured wheel is not steady, and the distance to be measured is accepted as the maximum value of the distance between the two pins;
* The application of the method varies depending on whether the number of teeth is odd or even.  
The method proposed in [25] eliminates these drawbacks by employing a number of three or four pins, placed symmetrically with respect to the measuring direction, Figure 9a (even teeth number) and Figure 9b (odd teeth number).  
As a fundamental premise, the method involves experimentally determining the dimension over pins by utilizing a specifically designed device and expressing this dimension in terms of the gear's three primary geometric features. Finally, an equation of two unknowns, the module \(m\) and the profile shift coefficient \(x\), is obtained:  
\[inv\Big{\{}acos\Big{[}\frac{mzcos\alpha_{0}}{2(D-d_{R})}(cos\psi_{up}+cos\psi _{down})\Big{]}\Big{\}}=inv\alpha_{0}-\frac{\pi}{2z}+\frac{2x}{z}tana_{0}+ \frac{d_{R}}{mzz\alpha_{0}} \tag{43}\]  
Therefore, two equations are necessary to find the two parameters \(m\) and \(x\). To overcome this, it is essential to note that the dimensions of the wheel do not undergo significant change for the typical values of the profile shift coefficient:  
\[-0.5<x<0.5 \tag{44}\]So, by replacing \(x=0\) in Equation (43), the following equation of the unknown module \(m\) is obtained:  
\[inv\bigg{\{}acos\bigg{[}\frac{mzzcos\alpha_{0}}{2(D-d_{R})}(cos\psi_{up}+cos\psi _{down})\bigg{]}\bigg{\}}=inv\alpha_{0}-\frac{\pi}{2z}+\frac{d_{R}}{mza_{0}} \tag{45}\]  
This equation can be solved simply. Next, knowing that the module \(m\) takes standard values, the actual module of the gear is chosen as the standard value \(m_{std}\), closest to the solution of Equation (45). Now, the adopted standard value of the module \(m_{std}\) is replaced in Equation (43) and an equation from which the profile shift coefficient \(x\) is found is obtained:  
\[inv\bigg{\{}acos\bigg{[}\frac{m_{std}zcos\alpha_{0}}{2(D-d_{R})}(cos\psi_{up}+ cos\psi_{down})\bigg{]}\bigg{\}}=inv\alpha_{0}-\frac{\pi}{2z}+\frac{2x}{z}tan\alpha_{0}+ \frac{d_{R}}{m_{std}z\alpha_{0}} \tag{46}\]
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Training Configurations  
All utterances are resampled to 16 kHz. During STDCT and ISTDCT, a Hamming window of length 32 ms is employed, with a hop size of 8 ms between consecutive frames. The DCT point is set as 512. In the training phase, we utilize RMSprop optimizer and the dynamic learning rate strategy. The learning rate is initially set as \(2\times 10^{-4}\), and it will be halved if the model performance does not improve for 8 consecutive epochs. The batch size and total training epochs are 16 and 100, respectively.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 3 Extraction and Purification Methods
### Reversed micellar extraction  
Reversed micellar extraction (RME) is a feasible, relatively low-cost and energy-efficient process that is simple to scale-up and is characterized by protein solubilisation towards micellar inner polar cores. This technique can selectively separate enzymes and proteins such as those contained in the raw bromelin extract. This method involves two steps named forward and backward extraction; the first is a selective solubilization of proteins into reverse micelles and the second is the release of proteins from the micelles to an aqueous phase. [PERSON] et al. have reported a recovery of 85% (four-fold purification) of bromelin using this approach [47]. [PERSON] et al. have obtained significant bromelin recovery using small-scale (10 mL) RME purification methodologies. Similar results have been obtained at a larger scale, although a small loss in bromelin recovery was observed [48].
## 4 Experiment Results and Analysis
### The Influence between the Number of Training Samples and the Classification  
During the experiment, we changed the number of training samples to study the effects on the classification performance for various methods. Here, we set the parameters as same as used in Section 4.4. In each experiment, 50, 100, 150, and 200 samples are chosen randomly in each class as training sets, and the rest were set to be testing sets. The overall accuracy plots under different conditions are shown in Figure 10. As shown in Figure 10, in most cases, when the percentage of training samples increases, the overall accuracy also increases. Furthermore, the proposed method achieves about 93% classification accuracy in both datasets by using only 50 of samples in each class, which is higher than the other two methods when using 200 samples. Therefore, it can be said that the spectral-spatial method based on PCA uses less samples to obtain higher classification accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Spectral** & **Spectral + PCA** & **Spectral-Spatial + PCA** \\ \hline
14 & **98.97** & 96.92 & **98.97** \\
15 & 70.50 & 57,31 & **88.80** \\
16 & 99.11 & 99.28 & **99.78** \\ \hline OA & 88.84 & 90.48 & **96.41** \\ \hline AA & 93.73 & 91.15 & **98.09** \\ \hline Kappa & 87.61 & 89.38 & **96.01** \\ \hline Time (s) & 2.3799 & **1.3771** & 5.0755 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Spectral** & **Spectral + PCA** & **Spectral-Spatial + PCA** \\ \hline
1 & 83.74 & 81.81 & **97.45** \\
2 & 85.81 & 83.67 & **98.47** \\
3 & 80.32 & 77.23 & **97.33** \\
4 & 95.43 & 93.37 & **98.43** \\
5 & 99.78 & 99.48 & **100.00** \\
6 & 84.67 & 87.55 & **98.91** \\
7 & 94.43 & 90.90 & **99.47** \\
8 & 82.16 & 85.17 & **92.42** \\
9 & **100.00** & 99.89 & 99.89 \\ \hline OA & 86.48 & 85.43 & **97.89** \\ \hline AA & 84.19 & 83.58 & **95.57** \\ \hline Kappa & 82.46 & 81.18 & **97.22** \\ \hline Time (s) & 1.3992 & **1.0734** & 4.2585 \\ \hline \end{tabular}
\end{table}
Table 5: Classification results of the Pavia University dataset, including classification accuracies for every class, AA, OA, Kappa, and Time obtained by methods based on spectral, spectral + PCA and spectral–spatial + PCA. The best results are highlighted in bold.
## 4 Experiment
### Location Estimation by Existing Methods
#### 4.2.2 Location Estimation by k-NN  
The proposed method first determines the initial location of location estimation based on k-NN (Section 2.1.2), then uses a particle filter in order to improve the estimation accuracy of the k-NN-based method. The k-NN was first experimented separately, Figure 9 shows the value of \(k\) influences the accuracy. This study sets \(k=4\), which offers the best estimation accuracy.
###### Abstract  
Nowadays low Earth orbit (LEO) Earth observation (EO) satellites commonly use constant coding modulation (CCM) or variable coding modulation (VCM) schemes for data transmission to ground stations (G/S). Compared with CCM and VCM, the adaptive coding modulation (ACM) could further improve the data throughput of the link by making full use of link resource and the time-varying characteristics of atmospheric attenuation. In order to comprehensively study the data transmission performance, one new index which could be utilized as a quantitative index for the satellite-to-ground data transmission scheme selection, the transmission efficiency factor (TEF) of LEO satellites is proposed and defined as \"the product of the link availability and the average useful data rate\". Then, the transmission efficiency of CCM, VCM and ACM at typical G/S with different weather characteristics at Ka-band is compared and analyzed. The results show that ACM is more suitable for the G/S with moderate and abundant rainfall. Compared with the CCM of MCS 28, Beijing G/S and Sanya G/S, ACM not only improves the transmission efficiency with the TEF increased by 3.62% and 24.51%, respectively, but also improves the link availability with the outage period reduced by 82.47% and 75.18%, respectively.  
adaptive coding modulation; transmission efficiency; Ka-band data transmission; EO satellites 2022, 22, 5423. 18 June 2022  
18 July 2022  
20 July 2022  
###### Abstract  
Nowadays low Earth orbit (LEO) Earth observation (EO) satellites commonly use constant coding modulation (CCM) or variable coding modulation (VCM) schemes for data transmission to ground stations (G/S). Compared with CCM and VCM, the adaptive coding modulation (ACM) could further improve the data throughput of the link by making full use of link resource and the time-varying characteristics of atmospheric attenuation. In order to comprehensively study the data transmission performance, one new index which could be utilized as a quantitative index for the satellite-to-ground data transmission scheme selection, the transmission efficiency factor (TEF) of LEO satellites is proposed and defined as \"the product of the link availability and the average useful data rate\". Then, the transmission efficiency of CCM, VCM and ACM at typical G/S with different weather characteristics at Ka-band is compared and analyzed. The results show that ACM is more suitable for the G/S with moderate and abundant rainfall. Compared with the CCM of MCS 28, Beijing G/S and Sanya G/S, ACM not only improves the transmission efficiency with the TEF increased by 3.62% and 24.51%, respectively, but also improves the link availability with the outage period reduced by 82.47% and 75.18%, respectively.
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 2 Model and Analysis
### Construction of Gold Wire Bonding Model  
Figure 2 shows a schematic diagram of the structure model of a gold wire bonding interconnection line. The gold wire bonding model is established by HFSS simulation software, which is connected between the chip and T-type microstrip line by a single gold wire interconnection line, the arch height of the bonding alloy wire is h, the span is l, the microstrip line width is w. The substrate material at the bottom of the model consists of a double-sided copper-clad plate (Rogers 4350) with a relative dielectric constant of 3.66 and a loss angle tangent value of 0.004. The small square that fits tightly the chip material is nickel-plated and gold-plated (Mo80 Cu20), the T-shaped microstrip material is copper, the bottoms of the first two materials are coated with copper, the thickness is 0.035 mm, the middle interconnected gold wire material is gold. For the accuracy of the post-test, the encapsulation environment uses nitrogen (N2) environment. The six geometric parameters we designed are the arch height h, span l, and microstrip line width w of the bonding alloy wire, which are numbered as w1, w2, w3, and w4 in the order from left to right. Figure 2 shows two ports, port 1 and port 2. Port 1 is connected to the microstrip line 4, the bonding alloy wire is connected to the chip at port 2, and both port excitations are 50 ohms.
## 1 Introduction
#### 2.2.2 Self-Navigation  
A further limitation of the d1D NAV is the added scan complexity it requires, including scan planning and defining separate imaging parameters for the navigator acquisition, on top of the already complicated CMRA examination. These limitations have motivated a recent trend in respiratory motion compensation, so called self-navigation [52, 53, 54, 55]. In this approach respiratory motion can be estimated directly from the CMRA data, provided that a few conditions are met. The most important requirement is that the central line of k-space is repeatedly measured, at least once per k-space segment, and ideally the readout should be aligned with the FH direction to maximize the sensitivity for respiratory motion. Self-navigation has been implemented to allow for respiratory motion estimation along the FH direction, however 3D self-navigation has been proposed as well [56]. An advantage of self-navigation is that respiratory-induced motion of the heart can be measured directly, which obviates the need for a diaphragmatic motion model. However, as self-navigation images are typically acquired as 1D projections of the FOV, static tissue such as the chest wall is also included in the navigator image and may reduce the motion estimation performance of the navigator. Although methods have been proposed to suppress signal from static tissue for 1D self-navigation [54,56], an extension of this method overcomes this problem by separating moving from static tissue in the navigator using spatial encoding, so called image-based navigation.
## 1 Introduction
The SEI layer is critical for preventing unwanted side reactions between the electrolyte and electrode, thereby ensuring long-term battery stability and performance [49; 50]. However, the formation mechanism of the SEI layer remains unclear, and consequently, reverse engineering based on its principles is still considered an unattainable goal [51; 52; 53]. Therefore, before attempting to understand and reverse engineer the complex SEI layer, we aimed to generate novel candidate single molecules based on the HOMO (the highest occupied molecular orbital) and LUMO (lowest unoccupied molecular orbital) characteristics, which are referenced in the design of new electrolyte additives. In the current electrolyte additive development process, new molecules are proposed, synthesized, and tested based on previously used electrolyte additives, relying on the expertise and experience of skilled professionals. Since the synthesis of actual molecules and cell fabrication experiments require significant time and cost, this study aims to establish a virtual screening stage before the synthesis process by utilizing a Variational Autoencoder (VAE) model and molecular property predictive models. We utilized the Natural Product Variational Autoencoder (NPVAE) model, which employs a previously proposed VAE-based model to generate complex natural products while simultaneously predicting their Natural Product Score (NP score) characteristics [54]. We fine-tuned NPVAE using a dataset tailored to our objective of electrolyte additive development. This electrolyte additive dataset includes various molecules actually used in lithium-ion battery electrolytes and their associated information, such as 3D structural data, SMILES, molecular weight, energy values, and HOMO (highest occupied molecular orbital)/LUMO (lowest unoccupied molecular orbital) values. The details of the dataset are described in Section 2.1. To evaluate whether the trained model accurately predicts the HOMO and LUMO values of newly generated molecules, we performed Density Functional Theory (DFT) calculations and compared the results with the model's predictions. Through this process, we confirmed that the trained model not only accurately predicts the HOMO and LUMO values of the molecules in the training dataset but also demonstrates strong predictive performance for the HOMO and LUMO values of newly generated molecules. This approach enables the construction of a virtual screening pre-process using deep learning models within the traditional electrolyte additive design workflow.
## 4 Evaluation and Experiments  
### Evaluation  
To check if the time required by the solution in this system is acceptable, the following software platform is used to measure performance: Protege 3.4.4, JVM 1.6.02-b06, and Windows 8.1. The hardware platform is Core2 Duo 2G HZ CPU and 2G DDR2 RAM. The size of the WSN ontology is 270,336 bytes and the ontology contains 30 rules. The effect of the number of rules on performance has been analyzed. Measurements have been taken and the trend is presented in Figure 15, which shows the real time used and the linear trend line. We can observe that the time taken has a linear relationship with the rules needed to be processed. The maximum value is 3.56 s. The results show that the total execution time is not very high and enough for the water-quality monitoring.
## 6 Individuation of Competing Factors
### Technical Information Emerged by Patents  
At the end of an advanced search on Esp@cenet, where more than 300 patents were screened by means of their titles and mosaic pictures, 70 patents (covering a period spanning from 1950 up to 2019; see the histogram in Appendix A) were considered relevant to this study. The complete list is presented in Appendix A. Details about the search and selection process to determine relevant patents are documented through the PRISMA diagram shown in Figure 4.  
Each patent was classified according to the eight categories described in Section 5. In addition, when a patent did not refer to a machine as a whole but to a sub-system (and hence it could fit several categories), this was classified as \"other devices\" (ODs). It is interesting to notice that the majority of the inventions are related to mechanical solutions (53 patents) rather than vacuum solutions (12 patents). In addition, it is worth remarking that, in the last 20 years, about half of the inventions refer to mechanical/portable solutions (12 out of 25 patents). Moreover, considering the whole selected patent dataset, the most explored solutions are the portable and the self-propelled machines (in total 22 patents each), followed by trailed systems (12 patents) and mounted machines (9 patents).  
This attention to portable machines is mainly due to two factors:  
1. They are less complex than other categories and, therefore, easier to prototype and test even by inventors that do not operate in large companies; and
2. They are aimed at owners of small orchards that are becoming increasingly more mechanized in recent years.  
The investigation of patents' content has given rise to many connections between problems and related solutions. In particular, many patents propose solutions for limiting the collection of foreign material (during gathering) or improving the capability of the machines to separate the shell fruits from the foreign materials (after gathering). Both of these kinds of solutions try to obtain the same useful result, i.e., to obtain a clean product (limited amount of foreign materials among the stored fruits) without compromising the productivity of the machine. In addition, the capability of preventing the suction of foreign material by vacuum solutions is considered as the main solution to ensure their proper functioning.  
Other types of explored solutions are those aimed at improving the productivity of the machines by increasing the gathering area without worsening the other performances. This is often achieved through mechanisms (integrated in the machines) that prearrange the shell fruits to be therefore harvested efficiently. Further inventions propose solutions for improving the adaptability of the mounted architectures to different soil conditions. Others target the improvement of the vacuum machines' ergonomics by limiting the production of dust or noise.