esa-sceva/satcom-chunk-collection huggingface dataset:
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 3 Results and Discussion
### Influence of Nozzle Inner Diameter on Mixing Uniformity  
In view of investigating the effect of nozzle inner diameter on the gas-liquid mixing uniformity in the jet-stirred tank, five different working conditions were analyzed, i.e., the ratios of the nozzle radius to the radius of the stirred tank were 1:20, 1:30, 1:40, 1:50, and 1:60, respectively. These ratios corresponded to a nozzle radius of 12.5 mm, 8.4 mm, 6.3 mm, 5.0 mm, and 4.2 mm, respectively. The models of these five nozzles were orientated in the circumferential direction, and the nozzle height was 80 mm from the bottom surface. Table 10 shows the nozzle inner-diameter condition setting. Figure 18 is the contour of gas volume fraction distribution for different nozzle diameters.  
From the analysis of Figure 19, it is evident that gas volume fraction distribution trends are generally consistent among the cases with nozzle radii (R) of 6.3 mm, 5.0 mm, and 4.2 mm. Notably, between heights Z = 0.18 m and Z = 0.30 m, the gas volume fraction in the R = 4.2 mm case is 7.3% higher compared to the R = 5.0 mm and R = 6.3 mm cases. Below Z = 0.22 m, the R = 5.0 mm case predominates in the gas volume fraction, while, above this height, the R = 6.3 mm case marginally surpasses the gas volume fraction levels. Overall, the gas volume fraction distribution curve for the R = 5.0 mm case exhibits the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Ratio of Nozzle Radius to Stirred Tank Radius** & **Nozzle Radius** \\ \hline \hline
1:20 & 12.5 mm \\
1:30 & 8.4 mm \\
1:40 & 6.3 mm \\
1:50 & 5.0 mm \\
1:60 & 4.2 mm \\ \hline \hline \end{tabular}
\end{table}
Table 10: Nozzle inner-diameter condition setting.  
Figure 18: The contour of gas volume fraction distribution for different nozzle diameters.  
shallowest slope, indicating a more uniform gas volume fraction distribution along the axial height.  
Table 11 presents the mean, variance, and dimensionless variance of the gas volume fraction for different nozzle inner diameters. Analysis of the gas volume fraction distribution curves and data identifies a nozzle inner diameter of R = 5.0 mm as the optimal condition for subsequent experiments. This selection highlights the impact of nozzle diameter on gas volume fraction distribution and provides a basis for optimizing reactor performance.
## 3 Calibration
In particular, if the training set contains \(N\) samples for which both \(\mathbf{x}\) and \(y\) are known, for each one of these samples an equation analogous to Equation (6) can be written:  
\[y_{n}=\hat{y}_{n}+e_{n}=b_{1}x_{n1}+b_{2}x_{n2}+b_{3}x_{n3}+\cdots+b_{J}x_{nJ}+ e_{n} \tag{7}\]  
where the subscript \(n\) indicates that the equality holds for the \(n\)-th training object--here, it should be noticed that, since the same functional relation is valid for all the samples, the coefficients \(b_{1},b_{2},b_{3},\cdots,b_{J}\) are identical for all the individuals. By gathering all the training set recordings in the spectral matrix \(\mathbf{X}\) (whose rows contain the profiles of all the individuals) and the corresponding true values of the property of interest in the column vector \(\mathbf{y}\), Equation (7) can be globally expressed for all the training samples as:  
\[\mathbf{y}=\hat{\mathbf{y}}+\mathbf{e}=\mathbf{X}\mathbf{b}+\mathbf{e} \tag{8}\]  
where the column vector \(\mathbf{b}\) carries the regression coefficients (\(\mathbf{b}=\left[ \begin{array}{cccc}b_{1}&b_{2}&,&b_{3}&\cdots&b_{J}\end{array} \right]\)), the approximated values of the response are the elements of \(\hat{\mathbf{y}}\) (\(=\mathbf{X}\mathbf{b}\)), and the residuals constitute the vector \(\mathbf{e}\). As anticipated, the estimation of the optimal value of the coefficients \(\mathbf{b}\) is usually carried out by the so-called least squares approach:  
\[\underset{\mathbf{b}}{\text{argmin}}\sum\
olimits_{n=1}^{N}e_{n}^{2}=\underset {\mathbf{b}}{\text{argmin}}\ \mathbf{e}^{\text{T}}\mathbf{e}=\underset{\mathbf{b}}{\text{ argmin}}(\mathbf{y}-\mathbf{X}\mathbf{b})^{\text{T}}(\mathbf{y}-\mathbf{X} \mathbf{b}) \tag{9}\]  
where the operator argmin indicates that the optimal set of values is the one minimizing the overall approximation error across all the training objects, i.e., \(\sum_{n=1}^{N}e_{n}^{2}=\mathbf{e}^{\text{T}}\mathbf{e}=(\mathbf{y}-\mathbf{ X}\mathbf{b})^{\text{T}}(\mathbf{y}-\mathbf{X}\mathbf{b})\). Resolving Equation (9) makes it possible to estimate the regression coefficients as:  
\[\mathbf{b}_{\text{MLR}}=\big{(}\mathbf{X}^{\text{T}}\mathbf{X}\big{)}^{-1} \mathbf{X}^{\text{T}}\mathbf{y} \tag{10}\]  
Since the calibration approach, which postulates a linear regression model and uses the least squares criterion for the calculation of these regression coefficients, is also generally referred to as Multiple Linear Regression (MLR [48; 49]), the subscript MLR was explicitly added to Equation (10). Once the values of the coefficients have been estimated using the training samples, it is possible to predict the value of the response for any new (unknown) specimen, \(\hat{y}_{\text{new}}\), based on its spectral profile, \(\mathbf{x}_{\text{new}}\), as:  
\[\hat{y}_{\text{new}}=\mathbf{x}_{\text{new}}\mathbf{b}_{\text{MLR}} \tag{11}\]  
Sometimes, the same spectroscopic profile can be used to estimate the values of multiple properties; for instance, one might want to determine, for the same individual, the protein, lipid, starch and moisture content, based on its measured NIR spectrum.
## 4 Absolute Dose Measurement
### Passive Detectors  
One of the most commonly used passive detectors for clinical beams is radiochromic films. These are self-developing radiation dosimeters. The film contains an active layer with a dye that changes color when exposed to ionizing radiation. The intensity of the colorization is proportional to the dose. In particular, Gafchromic(r) EBT3 films (Ashland ISP Advanced Materials, Kearny, NJ, USA) are used for dosimetry with the clinical photon, electron, and proton beams [50]. In several studies, it has been shown that the response of Gafchromic films should be independent of the dose rate [11]. EBT3 films have already been employed in FLASH studies with protons, e.g., Reference [51].  
Alanine is an amino acid that forms a very stable free radical when exposed to ionizing radiation. Irradiated dosimeters are read out by means of Electron Spin Resonance (ESR), and the signal produced by the free radical is proportional to the dose. This kind of dosimeter has been used for dose verification in the first FLASH treatment of a human with electrons [12]. Although alanine detectors have been shown to be dose-rate dependent, it was not significant for delivered doses below 5000 Gy [52].  
Thermoluminescent dosimeter (TLD) is another variety of passive dosimeters. It contains a thermoluminescent crystalline material, which absorbs and traps some of the energy when exposed to ionizing radiation. Some of the excited electrons which reach the conduction band do not recombine to the valence state but are trapped instead in intermediate energy levels by existing material defects or added impurities. For reading out, the detector has to be heated so that the trapped electrons reach the conduction band and recombine to the ground state emitting photons of visible light. The intensity of the light is related to the dose to which the crystal was exposed. TLDs have been shown to keep the dose-rate independence also for ultra-high dose rates while exposed to electrons and photons [53; 54; 55]. The use of TLDs in FLASH experiments with electron beams has been reported [55; 56].  
Optically-stimulated luminescence dosimeters (OSLDs) are very similar to TLDs. The major difference is that OSL dosimeters only require optical stimulation, instead of heat, to bring electrons to the conduction band and make them recombine with the holes in the valence band. Therefore, a different readout technique is used. These detectors can be cut to the desired size and are, thus, easy to accommodate in any experiment. OSL detectors of various sizes have been tested with protons at ultra-high dose rates up to 9000 Gy/s, and no dose-rate dependency was found [57].
## 2 Material and Methods
### Mathematical Formulation Part
#### 2.2.1 Heat Pump Modeling  
The cooling production (\(Q_{cool}\)) can be written as:  
\[Q_{cool}=m_{a}\cdot(h_{1}-h_{15}) \tag{1}\]  
The heat input from the ambient (\(Q_{in}\)) can be written as:  
\[Q_{in}=m_{d}\cdot(h_{3}-h_{14}) \tag{2}\]  
The medium-temperature heat production (\(Q_{heat,m}\)) can be written as:  
\[Q_{heat,m}=m_{b}\cdot(h_{5}-h_{6})+m_{e}\cdot(h_{6}-h_{12}) \tag{3}\]  
The high-temperature heat product (\(Q_{heat,h}\)) can be expressed as:  
\[Q_{heat,h}=m_{c}\cdot(h_{8}-h_{9}) \tag{4}\]  
The electricity consumption of the compressor (a) can be written as:  
\[P_{el,a}=\frac{m_{a}\cdot(h_{2}-h_{1})}{\eta_{m}} \tag{5}\]  
The isentropic efficiency of the compressor (a) can be written as:  
\[\eta_{is,a}=\frac{h_{2 is}-h_{1}}{h_{2}-h_{1}} \tag{6}\]The electricity consumption of the compressor (b) can be written as:  
\[P_{el,b}=\frac{m_{b}\cdot(h_{5}-h_{4})}{\eta_{m}} \tag{7}\]  
The isentropic efficiency of the compressor (b) can be written as:  
\[\eta_{is,b}=\frac{h_{5,is}-h_{4}}{h_{5}-h_{4}} \tag{8}\]  
The electricity consumption of the compressor (c) can be written as:  
\[P_{el,c}=\frac{m_{c}\cdot(h_{8}-h_{7})}{\eta_{m}} \tag{9}\]  
The isentropic efficiency of the compressor (c) can be written as:  
\[\eta_{is,c}=\frac{h_{8,is}-h_{7}}{h_{8}-h_{7}} \tag{10}\]  
The total electricity input (\(P_{el,in}\)) in the trigeneration scenario is:  
\[P_{el,in}=P_{el,a}+P_{el,b}+P_{el,c} \tag{11}\]  
For the cogeneration scenario, the term (\(P_{el,a}\)) is set to zero.  
The mixing at the state-point (4) can be modeled with the mass flow rate balance plus the respective energy rate balance as:  
\[m_{a}+m_{d}=m_{b} \tag{12}\]  
\[m_{a}\cdot h_{2}+m_{d}\cdot h_{3}=m_{b}\cdot h_{4} \tag{13}\]  
The energy balance in the internal heat exchanger can be written as:  
\[(h_{7}-h_{6})=(h_{9}-h_{10}) \tag{14}\]  
The heat transfer in the internal heat exchanger is modeled with the minimum approach temperature difference:  
\[T_{7}=T_{9}-\Delta T_{HEX} \tag{15}\]  
The mixing at the state-point (13) can be modeled with the mass flow rate balance and the respective energy balance as:  
\[m_{c}+m_{e}=m_{f} \tag{16}\]  
\[m_{c}\cdot h_{11}+m_{e}\cdot h_{12}=m_{f}\cdot h_{13} \tag{17}\]  
Additionally, the mass flow rate balance in the state-point (14), in combination with the mass flow rate balance in the state-point (4), can express the (\(m_{f}\)) as:  
\[m_{f}=m_{b} \tag{18}\]  
The throttling valves in the system reduce the pressure to the proper levels without thermal losses to the ambient. Thus, the inlet enthalpy is the same as the outlet enthalpy. Therefore, it can be written for the respective processes:  
\[h_{11}=h_{10} \tag{19}\]  
\[h_{14}=h_{13} \tag{20}\]  
\[h_{15}=h_{14} \tag{21}\]Lastly, the state points (1), (3) and (6) are saturated vapor, while the state points (9) and (12) are saturated liquid.
# Three-Dimensional-Printed Polymeric Cores for Methane Hydrate Enhanced Growth
## 1 Introduction
Unfortunately, the cited work does not provide a complete analysis of the shavings' surface and the presence of possible contaminants in the shavings (e.g., contaminant chain lubricants and mineral oils). Anyway, it does show that the presence of certain surfaces can stimulate intense upward hydrate growth. Various materials of natural origin (e.g., sand and clay) can be considered the most studied porous ones for accelerating hydrate formation. Other promising porous materials for the production of hydrates are organic and metal-organic frameworks [32; 33], carbon materials [34; 35], cellulose [19; 36; 37], and powdered cryogels [17]. It should be noted that the kinetics of hydrate growth in such systems is often improved due to the increased water-gas interface and some influence of the surface structure on nucleation (although such studies are few). Also of note is the potential of porous media design for hydrate obtaining [35]. Three-dimensional printing of various materials is actively developing and allows for optimizing production processes [38; 39; 40; 41; 42; 43; 44; 45]. In the case of gas hydrates, such an approach makes it possible to vary the medium's parameters for obtaining hydrate in a wide range, allowing optimizing artificial materials for developing a particular hydrate-based technology. The current work employs porous materials in an unusual role, namely to control the direction of hydrate growth (while maintaining a high growth rate). It is proposed to use 3D-printed plastic cores as the porous media.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
###### Abstract  
The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.  
Keywords:air traffic forecasting; hybrid modeling; empirical mode decomposition; seasonal autoregressive integrated moving average +
Footnote †: journal:
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results  
This section demonstrates the effectiveness of our newly proposed method using three well-known HSI data sets. The proposed method is compared with five state-of-the-art methods, including LCMR [37], SCMK [25], CNN-enhanced GCN (CEGCN) [32], random patch network (RPNet) [42], and LCEM [39]. This paper uses three commonly used indicators, including overall accuracy (OA), average accuracy (AA), and \(k\) coefficient [43]. In this paper, the average OAs, AAs, and k coefficients of 10 random runs are reported. In addition, full classification maps of different methods are presented for visual analysis. On the Pavia University dataset, the training samples are set to 0.1% of each class; On KSC and Botswana datasets, the training samples are set to 1% per class, while the others were used for testing.  
In the experiment, algorithms LCMR, SCMK,RPNet, LCEM, and SPCM are implemented by MATLAB R2020b, and are conducted using the Intel Core E5-2620 2.10 GHz CPU with 64 GB memory. However, algorithm CEGCN is conducted on a device with a 2.6 GHz CPU, 128 GB of RAM, and an A6000 GPU, and is performed on PyCharm 2021.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 1 Introduction
presented a novel hybrid GPS/INS/Doppler velocity log (DVL) positioning method and a new robust adaptive federated strong tracking Kalman filter (RAFSTKF) algorithm for data fusion [17]. An improved robust adaptive Kalman filtering algorithm was proposed by [PERSON] et al., which includes a classification robust equivalent weight function model based on t-test statistic [18]. Most of them focus only on reducing the error of one or several sources, but not reducing the error of multiple sources. Recently, deep learning has become one of the most active technologies in many research areas. Deep learning usually refers to stacking multiple layers of neural network to perform machine learning tasks [19], which can provide a different level of abstraction to improve the learning ability and task performance [20]. Recurrent neural network (RNN) with Long Short-Term Memory (LSTM), which was originally introduced by [PERSON] et al. [21], has emerged as an effective and scalable model for several learning problems related to sequential data [22; 23; 24; 25]. Since the SPP solution process is also a sequential data problem, the LSTM network can be used to reduce the error caused by multiple sources, including satellite clock error, ephemeris error, ionosphere and tropospheric delays, multi-path effect and the receiver error. In this paper, a novel standard point positioning approach to integrate BDS/GPS (all GNSS systems would be applicable), which uses the learning method to predict the multi-source error as a whole, is proposed to reduce the multiple sources errors. The contributions of this paper can be summarized as follows:* The SPP calculation model of Integrated BDS/GPS is implemented based on the BDS/GPS original ephemeris file and observation file data, which are collected through Sinan M300 GNSS receiver. * The LSTM-based error correction method is proposed and implemented combined with the traditional filtering method to reduce the multiple sources errors, in which the LSTM recurrent neural network is used to predict the positioning error of the next epoch, so as to reduce the positioning error at the receiving end. * Experiments in static and dynamic scenarios were conducted on the data collected by Sinan M300 GNSS receiver and the result of proposed approach was compared with the traditional positioning methods, which proved that the proposed approach can improve the standard point positioning performance of integrated BDS/GPS significantly. The rest of the paper is organized as follows. In Section 2, the method of standard point positioning of integrated BDS/GPS is described. The LSTM-based error correction method is detailed in Section 3. Section 4 presents the experimental results, including the WLS method, Kalman filter, WLS-LSTM error correction method and Kalman-LSTM error correction in a static scene and the correction results in a dynamic scene. Finally, conclusions are given in Section 5.
# Research on Part Tolerances for RV-40E Reducer Based on Virtual Prototype Technology
## 2 Materials and Methods
### Structural Composition of the RV-40E Reducer  
The RV-40E reducer serves as the primary transmission part in industrial robots. Its working principle is as follows: the motor shaft is connected to the input shaft, and power is transmitted to the output shaft through a series of parts, including an input shaft gear (1), two planetary gears (2), two crankshafts (3), four tapered roller bearings (10), four cylindrical roller bearings, two cycloid wheels (4), a pin wheel (8), pin-wheel housing (5), two angular contact ball bearings (11), the rigidly integrated input-end planet carrier (6), and the output-end planet carrier (7), as illustrated in Figure 1a. The RV-40E reducer employs a two-stage reduction system, consisting of a planetary gear reduction system at the input shaft end and a cycloid wheel reduction system within the pin-wheel housing. In this system, the crankshaft support (10) and the planet carrier support (11) on both sides are fixed. The output shaft is integrally connected to the planetary carrier, and the centerlines of the four tapered roller bearings rotate around the axes of the input and output shafts, while the centerlines of the four cylindrical roller bearings rotate around the centerlines of the tapered roller bearings. The physical structure of the RV-40E reducer is depicted in Figure 1b.  
Figure 1: Structural composition of the RV-40E reducer. (**a**) RV reducer working principle. (**b**) Structural composition of the RV reducer. 1. Input shaft; 2. planetary gear; 3. crankshaft; 4. cycloid wheel; 5. pin-wheel housing; 6. input-end planet carrier; 7. output-end planet carrier; 8. pin wheel; 9. cylindrical roller bearings; 10. tapered roller bearings; 11. angular contact ball bearing.
## 2 Related Works
### Dual-Images RDH Techniques Using LSB Matching Method  
In 2015, [PERSON] et al. [14] introduced a sophisticated RDH method leveraging the LSB matching technique with two identical cover images. These images are represented as two sets, \(X_{1}=\{x_{1}^{1},\ x_{2}^{1},\ \ldots,\ x_{n}^{1}\}\) and \(X_{2}=\{x_{1}^{2},\ x_{2}^{2},\ \ldots,\ x_{n}^{2}\}\). By applying the LSB matching to the corresponding pixel values--\(x_{i}^{1},\ x_{i+1}^{1}\) in \(X_{1}\) and \(x_{i}^{2},\ x_{i+1}^{2}\) in \(X_{2}\)--two new sets, \(X_{1}^{\prime}\) and \(X_{2}^{\prime}\), are produced, each containing the modified pixels.  
More precisely, the pixel pair \((x_{i}^{1},\ x_{i+1}^{1})\) is used to hide the messages \(m_{1}\) and \(m_{2}\), and the corresponding pixel pair \((x_{i}^{2},\ x_{i+1}^{2})\) is used to hide the messages \(m_{3}\) and \(m_{4}\). This method can simultaneously hide two sets of data that first detect the location of a pixel change.  
To restore the original pixel values, they calculate the average of the two marked pixels. Specifically, the calculated pixel values, \(p_{i}\) and \(p_{i+1}\), are obtained by applying the floor function to the average of each corresponding pixel pair from the two sets. This process is illustrated in Equation (2), where the floor function truncates a real number to the nearest  
Figure 1: Schematic diagram of LSB matching revisited method [10; 14].  
smaller integer. This method ensures the restoration of the original pixel values to their nearest possible approximation.  
\[\left\{\begin{aligned} p_{i}&=\lfloor(x_{i}^{1}+x_{i}^{2})/2 \rfloor,\\ p_{i+1}&=\lfloor(x_{i+1}^{1}+x_{i+1}^{2})/2\rfloor \end{aligned}\right. \tag{2}\]  
When using the LSB matching technique, there are cases where the original pixel values cannot be recovered using the Equation (2). When the conditions \(x_{i}\
eq p_{i}\) or \(x_{i+1}\
eq p_{i+1}\) are satisfied, the original pixels-- namely, \(x_{i}^{1}\), \(x_{i+1}^{1}\), \(x_{i}^{2}\), and \(x_{i+1}^{2}\)--are modified to produce obfuscated pixels. These changes are made according to guidelines given in a predefined rules table. A summary of this table can be found in Table 1.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 3 Smart Grid Communication
### QoS Requirements for Smart Grids  
SG applications result in increased data, these applications have different QoS requirements. Secure bi-directional communication that satisfies the different SG applications' QoS requirements is essential [34]. Control, management, and automation applications such as demand response (DR) and substation automation require low latency and high reliability to ensure grid operation. Other applications such as meter readings can tolerate a higher latency, but still require high reliability [34; 50]. Table 4 lists QoS requirements for different SG applications.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 2 Background
### Domain Analysis
#### 2.2.2 Storage and Retrieval  
In a typical warehouse, the storage of the goods merely refers to placing the cartons on shelves or racks. There is no particular pattern or order designed for that purpose, which can not only decrease the efficiency but also make it difficult to track the cartons if they are not tagged. On the contrary, in a smart warehouse, several algorithms are designed to store the goods. From receiving the goods to putting them on the shelves, every action is performed based on a pre-defined set of instructions, to maintain the flow of the system [25]. The goods are transferred to shelves or pallets either manually or, in the case of a smart warehouse, using Automated Guided Vehicles (AGVs), conveyer belts, or line-follower robots [26]. Whenever any product needs to be exported, it is retrieved using the same functionality.