esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Affected by the Taiwan Strait Warm Current, the evaporation intensity around the Taiwan Strait is significantly higher than in other southern and eastern coastal areas of China. To explore a new and effective method for maritime communication across the Taiwan Strait, we present a preliminary investigation of wireless microwave transmission in an evaporation duct. From 9 to 14 o'clock, the percentage of the evaporation duct height exceeding 10 m reached over 90% in 2020. With the favorable channel in this period, a \"straight-through propagation channel\" emerged, with a serviceable ratio of 99.00% at a 300 km transmission range. The high-quality transmission with BPSK modulation occurred 98.90% of the year from 12 to 13 o'clock, with 89.89% of the signal-to-noise ratio (SNR) exceeding 90 dB. Based on the effective use of the evaporation duct, the proposed method can provide favorable support for maritime applications across the Taiwan Strait.  
evaporation duct; Taiwan Strait warm current; maritime communication +
Footnote †: journal: Marine Science and Engineering  
In the literature [16], a regional spatiotemporal statistical database of evaporation ducts over the SCS was built, and an evaporation duct channel was found to appear at the \"Golden edge\", in the northern coastal area during May-July. As an extensive piece of research literature [16], the paper explored the meteorological causes of this phenomenon and highlighted its impact on communication. As one of the major sources of the Tsushima Warm Current, the Taiwan Strait Warm Current (TSWC) is characterized by year-round high temperature and high salinity [17; 18]. Therefore, stable evaporation intensity can be maintained with warm water in the TSWC. Our paper presents a communication method based on the evaporation duct, which can provide favorable support for maritime applications across the Taiwan Strait. Considering the spatiotemporal variation of the evaporation duct, meteorological datasets from the National Center for Atmospheric Research (NCAR) are introduced and verified. The rest of the article is divided as follows. First, the modeling method of the evaporation duct characteristics and the communication effect in the ducting layer are advanced, respectively. Then, we present a temporal distribution of the evaporation duct characteristics across the Taiwan Strait. At the same time, the communication effects, including the signal-to-noise ratio (SNR) and the serviceable ratio at a typical location during 2020, were analyzed. Finally, a summary of communication characteristics under evaporation ducts concludes this work.
## 2 System Model
### Battery  
The battery is a complex nonlinear system that is affected by temperature, voltage, and SOC. It is difficult to build a model that accurately represents the battery's dynamic characteristics in an EMS because the formula is complicated and takes a long time to compute. Therefore, the internal resistance model, which is one of the control-oriented models, was applied to the EMS. This model calculates the battery SOC by defining the equivalent circuit expressed by internal resistance (\(R_{int}\)), open-circuit voltage (\(V_{oc}\)), and  
Figure 3: Electric motor power map.  
EM power (\(P_{mot}\)). The formula is shown in Equation (6). In this formula, \(R_{int}\) and \(V_{oc}\) are functions of SOC:  
\[I_{mot}=\frac{V_{oc}-\sqrt{V_{oc}^{2}-4\cdot R_{int}\cdot P_{mot}}}{2\cdot R_{ int}}. \tag{6}\]  
The SOC of the battery can be calculated integrating the current used by the EM, as shown in Equation (7). This formula is calculated by the initial SOC (\(SOC_{init}\)) and battery capacity (\(Q_{bat}\)):  
\[SOC=SOC_{init}-\frac{1}{Q_{bat}}\cdot\int I_{mot}(t)dt. \tag{7}\]
# Plane Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network
## 2 Proposed Method
### Inception-V3 CNN Model  
When training through deep learning, it is common to obtain high precision when using it with a deep layer and a wide node. However, in this case, the number of parameters increased and the computational amount increased considerably, and an over-fitting problem or a gradient vanishing problem occurred. Therefore, we made the connections between nodes sparse and the matrix operations dense. Reflecting this, the Inception structure makes the overall network deep, but not difficult to operate [27; 28; 29].  
The left side of Figure 5 shows the structure of the Inception A, Inception B, and Inception C modules, including a \(1\times 1\) convolution filter. The \(1\times 1\) convolution filter has no change in height or width, and even if convolution is performed on a plane, there is no spatial feature loss.  
Therefore, the role of this filter is to increase the number of channels due to the performance of convolution in several layers, which functions to control the number of channels. This can reduce the number of parameters on \(3\times 3\) or \(5\times 5\) filters followed by \(1\times 1\) filters. Thus, the Inception-V3 model has the advantage of a deeper layer than other CNN models, but not having a relatively large parameter. Table 1 shows the configuration of CNN layers configured using the Inception modules. The input image size of \(299\times 299\) was set, and the first five general convolutional layers are called stems. The layers are more effective than Inception module. Furthermore, the nine Inception modules had a size of \(1\times 2048\) through a fully connected layer. For conventional convolutional neural networks, pooling was used between modules or layers to reduce the size of the parameters, but to solve the problem of representational bottleneck with increasing feature loss, a dimensional reduction method was used, as shown in Figure 6. If the stride was set to 1 and convolution was performed, the operation cost was equal to \(2d^{2}k^{2}\). In order to reduce the amount of cost that needed to be calculated, if the stride was set to 2 and convolution was performed  
Figure 5: The three main modules used in Inception-V3: (**a**) Inception A module; (**b**) Inception B module; and (**c**) Inception C module.  
with pooling, the operation cost was equal to \(2(d/2)^{2}k^{2}\). However, when the stride was set to 2, a representational bottleneck occurred. Therefore, the two forms were properly mixed to compensate for the above shortcomings. Finally, because the activation function in the final layer was a classification problem for both flame and non-flame, sigmoid was used.
###### Abstract  
As a fundamental component of data for life cycle assessment models, elementary flows have been demonstrated to be a key requirement of life cycle assessment data interoperability. However, existing elementary flow lists have been found to lack sufficient structure to enable improved interoperability between life cycle data sources. The Federal Life Cycle Assessment Commons Elementary Flow List provides a novel framework and structure for elementary flows, but the actual improvement this list provides to the interoperability of life cycle data has not been tested. The interoperability of ten elementary flow lists, two life cycle assessment databases, three life cycle impact assessment methods, and five life cycle assessment software sources is assessed with and without use of the Federal Life Cycle Assessment Commons Elementary Flow List as an intermediary in flow mapping. This analysis showed that only 25% of comparisons between these sources resulted in greater than 50% of flows being capable of automatic name-to-name matching between lists. This indicates that there is a low level of interoperability when using sources with their original elementary flow nomenclature, and elementary flow mapping is required to use these sources in combination. The mapping capabilities of the Federal Life Cycle Assessment Commons Elementary Flow List to sources were reviewed and revealed a notable increase in name-to-name matches. Overall, this novel framework is found to increase life cycle data source interoperability.  
nomenclature; life cycle assessment; elementary flow; interoperability +
Footnote †: journal:  
the Global Life Cycle Data Access (GLAD) initiative [12]. This initiative supported a global effort to analyze interoperability issues across LCA software, LCI databases and LCIA methods, and published the findings of these analyses in a critical review published in 2018 that produced 11 recommendations [10]. The evaluation of EF interoperability has been further improved with the methodology to develop pairwise mapping between major elementary flow lists developed through a project of the GLAD nomenclature working group [13].  
On a national level, U.S. federal agencies have worked collaboratively through the Technical Working Group (TWG) on Federal LCA Data Interoperability initiated in 2014 to harmonize data across U.S. national agencies to produce a common data portal called the Federal LCA Commons [14; 15; 16]. As a part of this effort, the Federal LCA Commons decided to create a harmonized nomenclature of EFs called the Federal Elementary Flow List (FEDEFL). The objective of this paper is to analyze the ability of the adoption of the novel FEDEFL, built from the critical review recommendations [10], to effectively improve interoperability for LCA software, databases and LCIA method providers. The authors provide key elements of the FEDEFL framework in Section 2 to highlight the key components of the FEDEFL that were part of the analysis.
## 2 Large Linear Electromagnetic Actuator (LLEA)
The mover comprises four sub-parts mechanically coupled. These are passive and are attracted to the stator teeth, according to the applied magnetising current. This ratio of 4:6 is akin to those used in synchronous switched-reluctance motors [1]. As shown in Figure 2a, the four sub-parts of the mover constitute one \"module\" in the same sense as the six-teeth-long stator is one module. Each coil can be energised individually, and therefore a proportionally larger actuating force could be achieved by combining several mover modules together. However, all the investigation as presented herein was limited to just one module. The design based on the switched-reluctance actuating principle is known to be very robust and capable of providing high-force density [3; 8; 9; 10; 11; 12]. Extremely large \"actuators\" have been built for the levitating trains (MagLev technology), but some of these may employ permanent magnets and/or superconducting coils [9; 12], making such devices expensive. Switched-reluctance design is also known to generate high acoustic noise [13], which was also the case for the prototype presented in this paper. However, the discussion  
Figure 1: The prototype LLEA with an initial length of 10.6 m installed in the testing building in the Port of Valencia, Spain. of the performance parameters is beyond the scope of this paper, which focuses only on the manufacturing technology and the difficulties associated with it. The FEM simulation shown in Figure 2c is included here just as an example of the possible static forces that are achievable in such a configuration. The exact excitation conditions were not finalised yet by the research partner, as there were several other optimisation constraints that were still being investigated. However, the simulation in Figure 2c is of the magnetostatic kind, with the excitation applied as 14.6 kA turns per each  
Figure 2: Illustration of the prototyped LLEA: (**a**) 3D overview (dimensions in mm); (**b**) head-on view; (**c**) typical results of 2D FEM model at the position of the maximum longitudinal force of 8.2 kN with 7.5 mm main air gap (on each side), with phase 2 coils energised (see also Figure 6). single coil. The coils in the final prototype (Figure 1) were wound from flat aluminium conductors and had the overall shape as illustrated in Figure 2a. At the beginning of the design process, the force performance of the construction was still being optimised, as is expected for such experimental prototypes. The main functional parameter was the longitudinal force, which could be tested with relative ease on a single module. This was prototyped and tested as illustrated below in Figure 4 for prototypes A and B. The static force testing revealed several shortcomings, as also described below (magnetic saturation, unsatisfactory force level unsatisfactory lamination mounting), which were rectified in the final design of prototype C, which is shown in Figure 1.
# A Lightweight Visual Odometry Based on LK Optical Flow Tracking
## 2 System Description  
### Algorithm Framework  
As shown in Figure 1, the algorithm in this paper takes an RGB-D frame input. After the preprocess input and key point detection are performed, a robust key point matching relationship between adjacent images is established by combining a uniform motion model with a pyramid-based sparse optical flow tracking algorithm, and then a combined strategy of removing mismatched points is used for the fast and precise rejection of false match points, local map tracking, and keyframe detection, followed by threads.  
Figure 1: The framework of algorithm.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Comparison Against Related Research
#### 6.8.4 Architecture of the Implemented System  
When evaluating the related systems, it can be seen that most of them do not describe their process of data acquisition and final implementation on the mobile device, but rather focus on the model training and evaluation part. What we know is that all researched papers were using devices running on the Android operating system, but the type of mechanism that was used for acquiring the necessary touch input data was not revealed. Our work implemented an Android application, which made it possible to use the generated HMM to make predictions on how likely inputs came from a user. However, the lock-out mechanism and a scientific study on the usability of our implementation were not carried out but could be subject of future research.
# Optimization Study on a Novel High-Speed Oil-Free Centrifugal Water Pump with Hydrodynamic Bearings
## 4 Results and Discussion
### Effect of Blade Number of Impeller  
The head and efficiency of the high-speed pump under different blade number of the impeller are plotted in Figure 8. From Figure 8a, it can be observed that an increase in the blade number of the impeller resulted in an increase of the head with a gradually reduced growth rate. The theoretical head was proportional to the slip factor. According to Equation (7), when increasing the blade number, the increased slip factor led to an increase of the head. Because the derivative of the head with respect to the blade number showed a decreasing trend, the increase of the head gradually reduces.  
Pfleiderer slip factor:  
\[\sigma_{p}=\frac{1}{1+P} \tag{7}\]  
where \(P\) is a correction factor and \(P=2(\psi/Z)\Big{(}D_{2}^{2}\big{/}\Big{(}D_{2}^{2}-D_{1}^{2}\big{)}\Big{)}\). \(D_{1}\) and \(D_{2}\) is the inlet radius of the outlet radius of the stay vane. \(Z\) is the number of blades. \(\psi\) is the excretion coefficient and \(\psi=a(1+\beta_{2}/60)\). \(\beta_{2}\) is the outlet angle of the blade. \(a\) is an empirical coefficient related to the pump structure.  
Figure 7: (**a**) Pressure distribution at the thrust surface and (**b**) bearing capacity of thrust hydrodynamic bearings under different film thickness at 7200 rpm.  
Darcy-weisbach Equation [25]:  
\[h_{f}=f\frac{L}{D}\frac{v^{2}}{2g} \tag{8}\]  
where \(h_{f}\) is the head loss. \(L\) and \(D\) are the length and the inner diameter of the pipe. \(v\) is the fluid velocity. \(g\) is the gravity acceleration. \(f\) is the friction factor, which is related to the fluid viscosity, Reynolds and the relative roughness of pipe wall.  
The efficiency of the high-speed pump increased to a maximum value first and then kept reducing under the increased blade number of the impeller, as shown in Figure 8b. The increased blade number of the stay vane decreased the flow area and increased the flow velocity under a given flowrate. The head loss was proportional to the square of flow velocity according to the Darcy-weisbach equation, as shown in Equation (8). The increment of the head was a result of the increased blade number and could not counteract the increased head loss. Thus, the efficiency showed a decreasing trend. In addition, the increased blade number caused more hydrodynamic loss and reduced efficiency. It can also be observed from the figure that the point with the highest efficiency kept moving toward the bottom left as the blade number increased.
## 3 Results and Discussion
### Response Surface Modelling and Optimization
#### 3.6.4 Validation of Optimized Conditions  
The selected optimal conditions were validated and confirmed experimentally (Table 10) and were in good agreement with the predicted values, as the difference between the predicted and actual values was minimal (Figure 8a,b). This suggests the model's predictability was consistent (\(p<0.05\)) at 95% confidence levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Response** & **Predicted** & **Actual** & **Difference** \\ \hline Turbidity (\%) & 96.63 & 95.17 & 1.46 \\ COD (\%) & 33.08 & 32.64 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Modified RSM-CCD optimum conditions experimental validation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Goal** & \begin{tabular}{c} **Lower** \\ **Limit** \\ \end{tabular} & \begin{tabular}{c} **Upper** \\ **Limit** \\ \end{tabular} & \
\begin{tabular}{c} **Lower** \\ **Weight** \\ \end{tabular} &
\begin{tabular}{c} **Upper** \\ **Weight** \\ \end{tabular} & **Importance** \\ \hline A: Catalyst load & maximize & 0.3 & 0.6 & 1 & 1 & 3 \\ B: Reaction time & maximize & 20 & 40 & 1 & 1 & 3 \\ Turbidity & maximize & 79.5 & 99.24 & 1 & 1 & 3 \\ COD & maximize & 1.84 & 52.64 & 1 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Constraints.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Catalyst Load** & **Reaction Time** & **Turbidity** & COD & **Desirability** &
\begin{tabular}{c} **Desirability** \\ **(w/o Intervals)** \\ \end{tabular} \\ \hline
1 & 0.600 & 40.000 & 96.628 & 33.082 & 0.747 & 0.855 & Selected \\
2 & 0.600 & 39.580 & 96.712 & 33.017 & 0.746 & 0.851 & \\
3 & 0.598 & 40.000 & 96.380 & 33.334 & 0.746 & 0.852 & \\
4 & 0.600 & 39.387 & 96.750 & 32.987 & 0.745 & 0.849 & \\
5 & 0.600 & 33.327 & 96.942 & 32.822 & 0.741 & 0.838 & \\
6 & 0.586 & 40.000 & 94.622 & 35.060 & 0.734 & 0.831 & \\
7 & 0.600 & 36.449 & 97.224 & 32.531 & 0.727 & 0.817 & \\
8 & 0.600 & 34.317 & 97.457 & 32.200 & 0.706 & 0.790 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Solutions generated.  
Figure 7: Selected numerical optimized condition ramp plots.  
It was also determined, according to the South African National Standards (SANS) 241 and the South African wastewater and industrial effluent law (2013), that the COD output parameter (498 mg/L) met the standards, whilst the turbidity output parameter (15.3 NTU) did not. This might be due to the syringe filter not being able to completely remove all TiO\({}_{2}\) nanoparticles. We observed a low value for COD removal, and these results are consistent with what was observed by [PERSON] et al. [14], who observed a 34.32% COD removal. The potential reasons for a low COD removal are discussed in Section 3.5, and this could be due to scavengers and the high ionic strength of the wastewater.
## 2 Modeling the Electric Drive Transmission System
### Dynamic Model of Helical Planetary Gears  
The dynamic model of the helical planetary gear system is illustrated in Figure 4. Three coordinate systems are defined in this model. The absolute coordinate system \(o_{c}x_{c}y_{c}z_{c}\) originates at the rotational center of the planet carrier and remains stationary. The coordinate system \(o_{i}x_{i}y_{i}z_{i}\) (\(i=s\), \(r\)) rotates with the planet carrier and originates at its rotational center. The coordinate systems \(o_{i}^{x}i_{i}\eta_{i}\tau_{i}\) (\(i=1,\ldots,N\)) are dynamic, with origins at the centers of the planet gears. Each component includes four degrees of freedom (\(x,y,z\), and rotational \(\theta\) around the \(z\)-axis). Subscripts \(s\), \(r\), \(c\), and \(pi\) represent the sun, ring, carrier, and planet gears.  
Following the dynamic planetary gear model in Ref. [22], the dynamic equations are summarized as follows  
\[\left\{\begin{array}{l}M_{n}\bar{r}_{n}(i,j,k)=F_{r}(i,j,k)+F_{t}(i,j,k)\\ I_{n}\bar{\theta}_{n}(g)=F_{r}(g)l(g)+\tau_{n}\end{array}\right. \tag{10}\]  
where \(F_{r}\) (\(i,j,k,g\)) and \(F_{t}\) (\(i,j,k\)) represent mesh force and supporting force, incorporating damping resistances. The symbol \(M\) denotes mass, and \(I\) is the moment of inertia. The moment arm acting on the mesh force is denoted by the symbol \(l(g)\), and \(\theta\) is the gear rotation angle [12]. These forces can be resolved along the \(i\), \(j\), \(k\) and \(g\) axes of the _oxyz_ coordinate system. Introducing the defined force term, \(\tau_{n}\) allows deriving the sun gear, planet gear, carrier and ring gear dynamical equations using the previously described method. Torque or motion is transmitted between the motor and gear system via a direct rotor-gear shaft coupling. This connection allows calculation of the force and motion exerted on each component, as illustrated in Equation (11)  
\[\left\{\begin{array}{l}\dot{\theta}_{hl}=T_{hl}-T_{hr}\\ T_{hr}=k_{h}(\theta_{hl}-\theta_{hr})+c_{h}\Big{(}\dot{\theta}_{hl}-\dot{ \theta}_{hr}\Big{)}\end{array}\right. \tag{11}\]  
where the symbol \(\theta\) represents the shaft angle, and \(T\) represents the transmitted torque. \(J\) is total inertia of the rotor and shaft combined. The parameters \(k_{h}\) and \(c_{h}\) represent the torsional stiffness and damping of the shaft. Subscripts \(hl\) and \(hr\) denote the left and right shaft ends, respectively. The motor rotor angle and the electric torque are defined at the left end of the shaft, while the sun gear angle and torque are applied at the right end.  
Figure 4: Dynamic model of helical planetary gears.
## 3 Discussion  
### Examination of Prediction Accuracy  
To analyze the reliability of DAGCP, the accuracy of models has to be tested. The data tested comes from the China's hydropower production from 2000 to 2015, and prediction results are given by [PERSON] et al. [25] by seasonal autoregressive integrated moving average (SARIMA) method and GM (1, 1) are provided for a direct comparison (see Figure 7). It can be seen that the present method is in close agreement with the actual values and SARIMA. Thus, compared with conventional methods, DAGCP shows a fine prediction precision and reliability. The reason for excellent prediction performance is that various dimensions of time subsequences constructed based on different starting points weakens the effects of the initial value, and the selected subsequence with continuously updated values captures time-varying performance of the system. Next, this method will be used for the forecast of tremor occurrence time.