esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analyses
\begin{array}{l}A_{11}^{j,m}\,P_{m}^{1}+A_{12}^{j,m}\,P_{m}^{2}+A_{13} ^{j,m}\,P_{m}^{3}+A_{14}^{j,m}\,P_{m}^{4}=-\omega^{2}\rho\cdot M_{m}^{j}p_{m}^{ 1}\\ A_{21}^{j,m}\,P_{m}^{1}+A_{22}^{j,m}\,P_{m}^{2}+A_{23}^{j,m}\,P_{m}^{3}+A_{24}^{ j,m}\,P_{m}^{4}=-\omega^{2}\rho\cdot M_{m}^{j}p_{m}^{2}\\ A_{31}^{j,m}\,P_{m}^{1}+A_{32}^{j,m}\,P_{m}^{2}+A_{33}^{j,m}\,P_{m}^{3}+A_{34}^{ j,m}\,P_{m}^{4}=-\omega^{2}\rho\cdot M_{m}^{j}p_{m}^{3}\\ A_{41}^{j,m}\,P_{m}^{1}+A_{42}^{j,m}\,P_{m}^{2}+A_{43}^{j,m}\,P_{m}^{3}+A_{44}^{ j,m}\,P_{m}^{4}=-\omega^{2}\rho^{2}C_{e}\cdot MT_{m}^{j}p_{m}^{4}\end{array} \right. \tag{13}\]  
Equation (13) can now be stated as follows:  
\[ \begin{bmatrix}A_{11}^{j,m}&A_{12}^{j,m}&A_{13}^{j,m}&A_{14}^{j,m}\\ A_{21}^{j,m}&A_{22}^{j,m}&A_{23}^{j,m}&A_{24}^{j,m}\\ A_{31}^{j,m}&A_{32}^{j,m}&A_{33}^{j,m}&A_{34}^{j,m}\\ A_{41}^{j,m}&A_{42}^{j,m}&A_{43}^{j,m}&A_{44}^{j,m}\end{bmatrix} \left\{ \begin{array}{l}p_{m}^{1}\\ p_{m}^{2}\\ p_{m}^{3}\\ p_{m}^{4}\end{array} \right\}=-\omega^{2} \begin{bmatrix}\rho M_{m}^{j}&0&0&0\\ 0&\rho M_{m}^{j}&0&0\\ 0&0&\rho M_{m}^{j}&0\\ 0&0&0&\rho^{2}C_{e}MT_{m}^{j}\end{bmatrix} \left\{ \begin{array}{l}p_{m}^{1} \\ p_{m}^{2}\\ p_{m}^{3}\\ p_{m}^{4}\end{array} \right\}\end{bmatrix} \tag{14}\]  
Equation (14) is an eigenvalue problem, where \(-\omega^{2}\) denotes the eigenvalue, the phase velocity can be obtained from \(V_{ph}=\omega/k\), and \(p_{m}^{j}\) denotes the displacement as well as the distribution of the temperature field, by solving this eigenvalue problem, the eigenvalues as well as the eigenvectors of the equation can be obtained, and the acceptable solutions are those that tend to be numerically stable as the expansion term M increases. For the longitudinal guided wave, its axisymmetric modes can be obtained when the circumferential order n = 0. At this point, Equation (7b) is independent of the other three equations, which represent the torsional modes. Equations (7a) and (7c), and the heat conduction Equation (7d) are coupled to represent the coupled thermoelastic wave. Thus, the dispersion characteristics of different modes can be obtained based on suitable fluctuation equations. (1) When the circumferential order n = 0, there are symmetric modes. Where the coupled thermoelastic wave longitudinal mode control equation is:  
\[ \begin{bmatrix}A_{11}^{j,m}&A_{13}^{j,m}&A_{14}^{j,m}\\ A_{31}^{j,m}&A_{33}^{j,m}&A_{34}^{j,m}\\ A_{41}^{j,m}&A_{43}^{j,m}&A_{44}^{j,m}\end{bmatrix} \left\{ \begin{array}{l}p_ {m}^{1}\\ p_{m}^{3}\\ p_{m}^{4}\end{array} \right\}=-\omega^{2} \begin{bmatrix}\rho M_{m}^{j}&0&0\\ 0&\rho M_{m}^{j}&0\\ 0&0&\rho^{2}C_{e}MT_{m}^{j}\end{bmatrix} \left\{ \begin{array}{l}p_{m}^{1}\\ p_{m}^{3}\\ p_{m}^{4}\end{array} \right\}\]  
Torsional modes:  
\[ \begin{bmatrix}A_{22}^{j,m}\end{bmatrix} \left\{p_{m}^{2}\right\}=-\omega^{2} \begin{bmatrix}\rho M_{m}^{j}\end{bmatrix} \left\{p_{m}^{2}\right\}\]  
(2) When the circumferential order \(n\
eq 0\), this time indicates the bending mode, and the dispersion characteristic curve of the bending mode can be obtained according to control Equation (14).
# Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC
## 2 The Reweighting Problem  
In particle physics analyses, we are typically interested in studying a particular process called _signal_. To be sensitive to seeing it, selection of specific data is usually performed, defining regions that contain events of interest called _signal regions_ (SR). Non-signal events, referred to as _background_, with the same experimental signature, can pass these selections; this is problematic and requires them to be distinguished from the signal. The estimation of _background_ events is typically performed by definition of additional regions, called _control regions_ (CRs), which are complementary to the SR where no _signal_ events are expected. By studying the data distribution in these CRs, the expected distribution of _background_ events in SR can be inferred; however, since these two are not expected to have identical kinematic characteristics, a reweighting function has to be derived.  
More technically, if \(p_{0}\) and \(p_{1}\) are the probability distribution functions (_pdf_s) of a real variable \(X\) describing two different kinematic regions (labelled 0 and 1), the reweighting function between them corresponds to their _likelihood ratio_ (or _importance_) \(w(X)\):  
\[p_{1}(X)=w(X)\cdot p_{0}(X)\to w(X)=\frac{p_{1}(X)}{p_{0}(X)}. \tag{1}\]  
In the real world, we do not know the _pdf_s and the most we can do is to calculate an approximation of the data sampled from the two different kinematic regions 0 and 1. Unfortunately, even this problem is often not simple and straightforward to solve; however, there is a means of addressing this, which consists of directly estimating the _likelihood ratio_ from data without determining the single _pdf_s. In statistics, this is referred to as _direct importance estimation_ and can represent a positive solution if only the _pdf_ ratio is needed.  
In this paper, _background_ estimation involves obtaining the _likelihood ratio_ between SR and CRs from (1). This has previously been performed using \"classical\" approaches, involving attempting to approximate the _pdf_ of each region with the most discriminant variable distributions; however, obviously, this technique cannot take into account the complex multi-dimensionality of data. The use of a deep neural network, which is known to be able to perform well in complex learning and the identification of hidden correlations in multi-dimensional datasets, appeared to offer a natural solution to this problem. Its use is described in the following Section.
## 1 Introduction  
The increase and change in the consumption pattern in the population is generating serious energy problems, which affect, among other things, food production and wastewater treatment (WWT). Simulations play an important role in the previous implementation of systems that contribute to controlling these issues, since they represent a long-term approximation of the technical economic viability, contributing to deciding the appropriate configuration for its implementation in reality. Furthermore, efficiency in the use of water and energy in agriculture is an increasingly important issue due to the growing scarcity of the former and the increasing costs of the latter [1]. Both constrain crop irrigation in many areas of the world, conditioning productivity. However, the need for WWT constitutes a challenge in any population and economic activity, especially in rural areas and developing countries, where the use of activated sludge treatment systems can produce high capital and operating costs. For that reason, nature-based technologies have been proposed in small populations [2]. One of these technologies is a High-Rate Algae Pond (HRAP), which consists of the use of microbial populations present in wastewater and inoculated microalgae in the medium to obtain a metabolic coupling that produces WWT [3]. Microalgae-based processes are much simpler, impose low CAPEX (capital expenditure), and maintenance costs are also easier than in conventional systems due to the less machinery required and less energy consumption [4]. Furthermore, the HRAP performance can be accurately described using only two variables: pH and dissolved oxygen [5]. However, microalgae are very sensitive to variations in climatic parameters, such as temperature and irradiance [6; 7], particularly photosynthetically active radiation (PAR). Therefore, to manage these biological systems, it is important to use climatic data and their long-term estimates to adopt adequate irrigation strategies in areas with water scarcity to optimize crop productivity or for WWT with HRAP systems. In this case, it is possible to estimate, with appropriate simulation programs, not only the microalgal productivity in the process and estimate the energy generation by means of biogas and its use as an energy source in the plant itself but also the efficiency of the WWT system. On the other hand, a Typical Meteorological Year (TMY) [8] developed by Sandia National Laboratories is a time sequence widely used to describe the most likely meteorological conditions (including solar radiation, temperature, humidity, and others) in an arbitrary location. It is made up of 12 months statistically selected and concatenated from a series of years to generate a complete year [9], which offers a representative climatology at a location in the long term [10]. The variability of the meteorological series generated by this methodology is greater than that of a series of variables consisting of climatic averages. However, a TMY is not necessarily a good indicator of the climatic conditions of a specific year in the future or extreme meteorological events.
## 2 Experimental Section
### Characterization  
A Perkin Elmer 1000 FTIR spectrometer (Perkin-Elmer, Ltd., Buckinghamshire, UK) equipped with Spectrum software was used to perform FTIR analysis. The spectra were obtained by preparing dried KBr powder pellets containing 2% _w/w_ of the investigated sample. The discs were scanned over the range 4000 to 400 cm\({}^{-1}\) with a total of 16 scans at a resolution of \(\pm\)4 cm\({}^{-1}\).  
XPS experiments were carried out using a Kratos Axis Ultra spectrometer (Kratos, Manchester, UK). A 225 W monochromatic aluminum source (Alk\(\alpha\)) was used. Survey scans were taken with 1.0 eV step and 160 eV analyzer pass energy, while the high resolution regional spectra were recorded with 0.1 eV step and 40 eV pass energy. The pressure was typically 10\({}^{-9}\) Torr. An area of 2 mm\({}^{2}\) at three different spots was analyzed in order to average over the sample, to avoid any bias due to an eventual heterogeneity. The position of the detector was at an angle of 90\({}^{\circ}\) to the sample surface. Deconvolution analysis was performed with casa XPS 2.3.15 (Kratos, Manchester, UK).  
Thermal stability analysis (TGA) data of the samples was taken on Perkin-Elmer (Pyris Diamond) Thermoanalyzer (Perkin-Elmer, Yokohama, Japan) in nitrogen atmosphere. Samples were heated in open alumina pans. First the sample was heated from room temperature to 105 \({}^{\circ}\)C at heating rate of 20 \({}^{\circ}\)C/min. Then samples were maintained at 105 \({}^{\circ}\)C during 15 min to remove water. After that, samples were heated from 105 \({}^{\circ}\)C to 575 \({}^{\circ}\)C at a heating rate of 5 \({}^{\circ}\)C/min. Finally, samples were heated from 575 \({}^{\circ}\)C to 950 \({}^{\circ}\)C under air at a heating rate of 10 \({}^{\circ}\)C/min.
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 3 Vector-CFNN
### Fusion Layer  
The fusion layer is mainly used to characterize features from feature maps to increase classification accuracy. In this study, five fusion methods (global average pooling (GAP) [25], global max pooling (GMP), channel average pooling (CAP), channel max pooling (CMP) [26], and network mapping) were adopted. GAP inherently reflects the spatial information of feature maps, which indicates the robustness of the interpretation of input images. By contrast, GMP can enhance the local spatial information of feature maps, which is also important in classification tasks. Furthermore, the generalization ability of a network can be significantly improved by learning the discriminative features among channels. Finally, network mapping assigns each feature element a weight and fuses all elements into a new feature. Network mapping can be expressed using the following equation:  
\[f_{j}=\sum_{i=1}^{n}w_{zi}\times x_{i} \tag{11}\]  
where \(x\) is a feature element; \(f\) is the network mapping result; \(w\) is the feature's weight; and \(n\) is the number of features.
# Banana Peels: A Genuine Waste or a Wonderful Opportunity?
## 3 Banana Composition and Waste
### Banana Peel
#### 3.2.1 Nutritional Composition of Banana Peel
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Component** & \multicolumn{2}{c}{**Dessert Banana Peel**} & \multicolumn{3}{c}{**Plantain Peel (Cooking Banana)**} \\ \cline{2-6}
**(\% of Dry Matter)** & **Cavendish [57]** & _M. sinensis_[58]** & **Yangambi KnS [59]** & **French Chair [57]** & _M. **paradisiaca_[58]** & **Big Ebanga [59]** \\ \hline Total dietary fibre & 43.2 (green) & 47.9 (green) & 49.9 (green) & 32.9 (green) & 35.9 (green) \\  & 49.7 (ripe) & 4.17 (ripe) & 46.9 (ripe) & 4.06 & 37.3 (ripe) \\  & 6.3 (green) & 2.48 & 6.9 (green) & 8.3 (green) & 2.23 & 8.1 (green) \\  & 8.1 (ripe) & 7.9 (ripe) & 9.1 (ripe) & 8.6 (ripe) \\  & 11.1 (green) & - & 14 (green) & 35.4 (green) & 39.3(green) \\  & 3.3 (ripe) & - & 2.6 (ripe) & 3.2 (ripe) & - & 0.1 (ripe) \\ Other soluble & 3.7 (green) & 67.29 (total & 1.4 (green) & 1.7 (green) & 1.7 (green) \\ sugars & 32 (ripe) & sugar) & 33.2 (ripe) & 42.2 (ripe) & 68.57 (total sugar) & 38.3 (ripe) \\  & - & - & 15.2 (green) & - & - & 6.4 (green) \\  & 3.8 (green) & 1.24 & 6.3 (green) & 4.6 (green) & 1.10 & 2.2 (green) \\  & 5.7 (ripe) & 10.9 (ripe) & 5.9 (ripe) & 1.10 & 3.6 (ripe) \\  & - & - & 7.3 (green) & - & - & 7.9 (green) \\  & & 13.3 (ripe) & - & - & 15 (ripe) \\ Ash & 9.6 (green) & & 10.4 (green) & 8.8 (green) & & 6.4 (green) \\  & 12.8 (ripe) & 3.95 & 10.7 (ripe) & 8.2 (ripe) & 2.23 & 7.4 (ripe) \\  & 4.3 (green) & & & 5.8 (green) & & \\ Amino acids & 4.6 (ripe) & - & - & 5.6 (ripe) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between the proximate composition of banana peel from dessert banana and cooked banana. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Minerals** & \begin{tabular}{l} **Banana Pulp (M. sinensis)** \\ **mg/100 g**[44]** \\ \end{tabular}  & \begin{tabular}{l} **Banana Peel (M. sinensis)** \\ **mg/100 g**[58]** \\ \end{tabular}  & \ \begin{tabular}{l} **Banana Peel (M.** \\ _sapientum_) mg/100 g**[61]** \\ \end{tabular}  & \begin{tabular}{l} **Banana Peel (M.** \\ _Musa ABB_)** \\ **mg/100 g**[44]** \\ \end{tabular}  \\ \hline P & 15.47 & 27.84 & 211.3 & 283.1 \\ Fe & 0.06 & 0.07 & 47 & 2.72 \\ Mg & 29.39 & 28.62 & 44.5 & 138.12 \\ K & 350.39 & 1708.6 & 4.39 & 3502.5 \\ Ca & 4.64 & 40.9 & 59.1 & 142.9 \\ Na & 7.73 & 9.28 & 11
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 2 Additive Manufacturing Technology for Metal Mirrors
### Metal Additive Manufacturing Technology
#### 2.1.1 Electron Beam Melting (EBM)  
EBM was invented in 1993 in Sweden at the University of Technology in Gothenburg. Arcam was founded in 1997 and sold its first commercial system in 2002. The most significant difference between EBM and the other two PBF technologies is that the heat source replaces laser beams with electron beams. The EBM system consists of an electron beam gun, vacuum chamber, forming a cylinder, and powder distribution mechanisms (Figure 8). In a vacuum environment, high-energy fast electron beams are controlled to melt the powder selectively and build up a forming part [38].  
The power of electron beams is about 3-6 kW, which is an order of magnitude higher than laser beams. At the same time, the EBM has the advantage of high energy utilization. EBM is suitable for printing high melting temperature powders such as Ti alloy. Moreover, EBM is not influenced by the optical reflectivity of the powder. However, the disadvantages of EBM are also evident. Intense X-rays can be produced during EBM, and therefore adequate protection measures must be taken. Besides, EBM has limited dimensions for building parts, with a maximum diameter of 350 mm and a height of 380 mm. Due to the unique energy conversion mechanism of the electron beam, serious liquid metal splash which results in porosity occurs in the process of printing aluminum alloy via EBM. Furthermore, the precision of printing is low. There are few reports on fabricating Al alloys by EBM to date because of its process characteristics [39]. In the research on metal mirrors, only the University of Arizona used EBM to print a Ti alloy mirror. Its surface was poor, so EBM is not an appropriate technology for mirrors.
## 4 Hydrocarbon Potential of formations in Malaysia
### Organic Geochemical Characteristics of Formations in Peninsula Malaysia (PM)
#### 4.1.4 Generic Potential (GP)  
GP refers to the quantity of hydrocarbons a rock can generate if subjected to sufficient temperature [76]. The GP represents the summation of the S1 and S2 (GP = S1 + S2). S1 denotes previously generated hydrocarbons, while S2 stands for the remaining hydrocarbon generating potential in the sediment's kerogen, so combining the two gives an idea of the formation's potential to generate hydrocarbons [75; 76]. The GP values ranged from 0.02 to 143.07 mg HC/g with an average of 38.44 mg HC/g. The Batu Arang had the highest value of 143.07 mg HC/g with the Belata having the lowest value of 0.02 mg HC/g (Table 7). According to the [PERSON] [75] classification of GP, all the formations had very poor GP, except for the Batu Arang formation and Penyu basin which had very good GP. The low GP values of the Belata, Kroh, Semantan and Semanggool formations could be attributed to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{**Formation**} & \multicolumn{1}{c}{**Av R\({}_{\text{o}}\) (\%)**} & \multicolumn{1}{c}{**Dow [88] Thermal Maturity**} & \multirow{2}{*}{**Reference(s)**} \\  & & & **Interpretation** & \\ \hline
**1** & Belata & 3.58 & Dry gas & [8] \\ \hline
**2** & Semanggol & 1.21 & Late oil & [13; 14; 52] \\ \hline
**3** & Kroh & 1.40 & Wet gas & [53] \\ \hline
**4** & Semantan & 1.26 & Late oil & [13; 52] \\ \hline
**5** & Batu Arang & 0.41 & Immature & [54; 55] \\ \hline
**6** & Penyu & 0.39 & Immature & [56] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Thermal maturity interpretation for formations in PM according to Dow [88] using R\({}_{\text{o}}\).  
Figure 7: Tmax vs. PI plot showing level of kerogen maturity for formations in PM.  
the matured nature of those formations. As the maturity of the sediment increases, the S2 reduces, resulting in lower GP.
## 3 Results and Evaluations
### Features and Properties of RF Sputtered SiC Thin Films  
Amorphous SiC films were chosen because of their comparatively low growing temperature, which ensures greater compatibility with silicon-based technologies. However, in  
\begin{table}
\begin{tabular}{c c} \hline
**Process Parameters** & **Values and Comments** \\ \hline \  
order to maximize the growing circumstances, high-quality films must be obtained [33]. We sputtered SiC thin films from a ceramic-type solid SiC sputtering target using the RF magnetron sputtering technique. The transmission spectrum of sputtered SiC film (as shown in Figure 3) confirms the semitransparent properties that can be varied depending on the film thickness.  
It is well known that the refractive index of amorphous SiC strongly depends on film composition and film thickness. According to [PERSON] et al. [30], the refractive index of SiC thin films decreased from 2.97 to 2.77 with increasing film thickness, confirming that the performance of SiC in diverse applications may be improved by tuning of optical characteristics.
## 7 Long-Range Underground Monitoring
### Technology Description
#### 7.2.3 Wi-Sun  
Wi-SUN is an open standard based on IEEE802, IETD, TIA and ETSI. The physical layer is based on IEEE802.15.4g and allows for data rates between 50 and 300 kbps. Wi-SUN is IP-based and hence interconnects with Ethernet and Wi-Fi [104]. The standard offers mesh network functionality but also a star topology [10].  
Wi-SUN technology is used for smart utility networks in smart cities, e.g., advanced metering infrastructure and smart traffic lighting or in smart agriculture application. The city of Miami uses Wi-SUN-enabled devices to connect nearly 500,000 streetlights within a mesh network [104].  
To the best knowledge of the authors, use cases in underground mining do not exist; however, the idea of using the technology underground is suggested in [105]. Offering Mesh functionality might be useful for underground application, since the network can be scaled dynamically, which might be beneficial in mining processes in dynamic environments. The technology and its applicability in underground mining is, however, up to future research.
In addition, for OPO laser system, as shown in [20], the output pulse energy and o-o conversion efficiency decrease with the increase in PRR due to the severe thermal effect. Further, the available 1064 nm light source with high pulse energy in our laboratory works at 10 Hz. Therefore, in our work, high pulse energy 6.45 \(\upmu\)m MIR laser with the PRR of 10 Hz is researched. Here, by improving the NLO BGSe crystal growth technique and optimizing crystal length and the transmittance of the output coupler (OC), we have demonstrated a stable and compact high pulse energy, high-efficiency MIR laser from BGSe-OPO system pumped directly by 1064 nm Nd:YAG oscillator. Compared to our previous work, we increased the NLO crystal length from 18 to 25 mm in order to achieve a higher MIR pulse energy and the pump beam diameter from 2 to 6 mm to prevent the risk of damage in the optical components for higher pump levels. Thus, unprecedented pulse energy of 6.76 mJ at 6.45 \(\upmu\)m was obtained with a pulse width of \(\sim\)18 ns by a double pump passing singly resonant OPO (DP-SRO) configuration, corresponding to o-o conversion efficiency up to 7.23%. So far as is known to the authors, this is both the highest pulse energy and the highest o-o conversion efficiency that have been reported so far for MIR OPOs at 6.45 \(\upmu\)m laser pumped directly by a 1 \(\upmu\)m laser. The fluctuation of the output energy and the average beam quality factors M\({}^{2}\) were evaluated to be less than \(\pm\)0.5% over 165 min and to be 9.515, respectively, at idler output pulse energy of \(\sim\)4.5 mJ. This work illustrates that the BGSe-OPO pumped by 1 \(\upmu\)m is an effective way to achieve a high pulse energy, high-efficiency and high-performance MIR laser radiation.
## 3 Results and Discussion
### Simulation of ADRC of Hot-Compression Molding Temperature
#### 3.3.2 Simulation Results under Sinusoidal Signal Noise Interference  
A sinusoidal disturbance signal was integrated into the system, and the controller parameters were optimized offline. The resulting simulation outcomes are depicted in Figure 8. Upon the introduction of the steam pressure pulsation interference simulation signal, the traditional PID control exhibited jittering during the cooling phase, preventing the temperature from stabilizing and thereby compromising the quality of the plate  
Figure 6: Simulation test results of ADRC without signal interference. (**a**) Comparison results of response without signal interference; (**b**) comparison results of error output without signal interference.  
Figure 7: State estimation of ESOs in the absence of signal interference.  
after the hot-pressing molding. Conversely, the ADRC controller, though exhibiting a minor jitter amplitude under a step response, remained unaffected in terms of its subsequent performance.  
The state estimation of the ESO is presented in Figure 9. It becomes evident that the expanded state observer, formulated with the fal function, smoothly and precisely estimated the system state. This, coupled with the compensatory role of the control law, effectively achieved the objective of interference mitigation.