esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Molecular Characterization of Coat Color Affecting Genes in Rabbits
### Mutations in the Tyrosine (TYR) Gene and the Albino Locus  
Tyrosinase (TYR) is the rate-limiting enzyme needed for the synthesis of melanin in the melanosomes. Mutations in this gene that alter or impair the function of the encoded enzyme determine a few forms of albinism in many species [64; 65]. Six alleles have been reported at the rabbit _Albino_ locus by classical genetic studies [30; 31; 66; 67; 68; 69]. These classical works indicated that a wild type allele (allele C) is needed for the production of both eumelanin and pheomelanin. Subsequent analyses of the rabbit \(TYR\) gene indicated the presence of more than one wild type allelic form that might encode isoforms with biochemically similar activities [70]. All other mutated alleles that are recessive to \(C\) lead to a progressive decline in quantity of pigments produced. Three alleles that can give three variants of chinchilla coat color have been reported by classical genetic studies [30; 31]: the dark chinchilla allele (\(c^{chd}\)), dominant over the other alleles below it, causes a reduction of phaeomelanin, whereas eumelanin remains unaffected; the medium chinchilla allele (\(c^{dim}\)), leads to a slight reduction of the production of eumelanin and a complete reduction of the production of pheomelanin; the light chinchilla allele (\(c^{chd}\)) has an effect similar to the medium chinchilla allele but with a more pronounced reduction in eumelanin production. However, molecular characterization of the rabbit \(TYR\) gene has identified only one mutated haplotype associated to the chinchilla phenotype and determined by two missense mutations (p.E294G and p.T358I [63; 70]). The Himalayan allele (\(c^{h}\)), which has a temperature-dependent action and can lead to the production of eumelanin at the extremities of the body where the temperature is lower than in the rest of the body [30; 31], is determined by only the p.E294G missense mutation [63; 70]. Californian rabbits are homozygous for this mutation. The last allele of this series (the albino allele or \(c\)), which is recessive to all other alleles when it is in homozygous condition, determines the complete absence of all eumelanin and pheomelanin pigments. This allele is caused by a missense mutation that determines the p.T373K amino acid substitution in a conserved position of the CuB binding site of the TYR enzyme [63; 70]. A list of breeds carrying the described alleles is reported in Table 2.
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 5 Experimental Results
### Key Bit-Dependent Power/Electromagnetic Attack on Software Implementation
When we exploited the power-consumption trace, subtraces \(p_{i}\) could be divided into two groups through SPA-VI with a 100% success rate, see Figure 5b,d. **(5)** Figure 5e shows the Pols chosen from electromagnetic subtraces when we targeted _Case Study 1_. They could not be clearly divided into two groups via SPA-VI; thus, the k-means clustering algorithm was needed. Secret scalar bits recovery rate was 94.17%, as shown in Table 2. This was slightly higher (0.17%) than the success rate when we divided \(p_{i}\) into two groups based on the differences from an average trace. **(6)** Unlike the result of **(2)**, Pols could not be perfectly split into two groups by SPA-VI, as shown in Figure 5f. Thus, we applied the k-means clustering algorithm and we could find approximately 95.96% of the secret scalar bits. This was slightly better than the 93.72% success rate when we divided \(p_{i}\) into two groups based on differences from an average trace. Here, we demonstrated that single-trace KBPA and KBEA can also defeat binary scalar multiplication algorithms that are resistant against SPA and DPA in software implementations. Figure 12: Software implementation (mbedTLS): one of the subtraces (**top**) and the SOST value between two subgroups (**bottom**).
## 6 Performance Evaluation on Simulator
### Performance of Path Planning
#### 6.3.2 Real-Time Performance Measure  
MGPF-Hybrid A* should satisfy real-time performance in order to operate under driving conditions. The real-time frequency should faster than 100 ms as was proven in [35]. The factors which affected the real-time performance of MGPF-Hybrid A* were parameters \(k_{1}\), \(k_{2}\), and the numbers of obstacles projected on the occupancy grid map, 'nObstacles'. Unlike the performance measure of path accuracy, real-time performance was evaluated on a MORAI simulator with an autonomous driving system. \(k_{1}\) and \(k_{2}\) were simulated by varying each value to find the optimal values and, the uncontrollable parameter, 'nObstacles', was also simulated by varying the number of points to prove real-time performance. In our environment, the occupancy grid map had a size of 200 \(\times\) 400 cm, which consists of 10 \(\times\) 10 cm pieces. On this grid map, the obstacle points were mapped and simulated. In the general scenario, the number of points was set to 200, and in the complex environment scenario, the number of points was set to 1400. The value of 1400 was the result of the arbitrary installation of many obstacles, surrounding the micro-EV, in the simulator. The experimental data are shown in Tables 2-5.  
Table 2 shows the results simulated on straight roads without obstacles, straight roads with obstacles, and curved roads with obstacles. Experiments mentioned in Tables 3-5 were on curved roads, which is the result of the lowest performance measure of Table 2. Good performance measure was obtained when the \(k_{1}\) and \(k_{2}\) set as 0.5 and 0.3, respectively. In addition, when the non-controllable parameter 'nObstacles' was set to the worst-case scenario value of 1400, it had an operating speed of 51.84 ms. The operating time, 51.84 ms, was lower than the required operating time of 100 ms, which means that the proposed algorithm could prove real-time performance using mathematical inductive methods. This performance results were obtained using the following hardware specifications: CPU i7-7700 HQ 2.80 GHz, RAM 16 GB, and GPU NVIDIA GTX 1060 6 GB.
# Online Monitoring of Torpedo Car Shells Based on Equal Angle Scanning
## 3 Experiments  
As is shown in Figure 5, the main equipment at the monitoring site includes two scanners (in the red dashed line ellipse in Figure 5); four RFID antennas (in the yellow dashed line circle in Figure 3); and RFID tags installed on the torpedo car (each torpedo car is equipped with four RFID tags, two tags both on the front and back sides). The system collects, calculates, and transmits temperature data on the shell surface, and transmits it to the central control room through an optical fiber network. The software adopts the B/S(Browser/Server) architecture. The main equipment in the central control room is the temperature data server and the large-screen display platform in the console. The large-screen display platform is used for real-time display, and the temperature data server can be used for offline data analysis.  
Figure 4: Curve fitting of temperature voltage response model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\text{t}^{G}\text{C}\) & **U/V** & **dt/dU** & **Resolution/\({}^{G}\text{C}\)** \\ \hline
100 & 0.06 & 646.02 & 0.8 \\
200 & 0.42 & 152.38 & 0.2 \\
300 & 1.55 & 61.25 & 0.07 \\
400 & 3.8 & 34.37 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature resolution in different temperature ranges.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
### The Ras-Gene-Encoded p21 Protein  
Most of the mutations involve single base changes in the first exon of the ras gene most commonly at position 12 resulting in a single amino acid substitution for the normally occurring glycine at this position [2]. Otherwise, the normal ras gene and the ras oncogene are the same. Transfection of the ras oncogene into untransformed fibroblasts, NIH 3T3 cells, results in malignant cell transformation while transfection of the normal gene into these cells has no effect on these cells [2]. Importantly, micrinjection of the cloned purified RAS-p21 protein with a Val for Gly 12(G12V) substitution into NIH 3T3 cells induces cell transformation, while injection of the wild-type (Gly 12-containing) protein has no effect on these cells [3]. Thus, the ras oncogene-encoded p21 protein is the cause of cell transformation, and a single amino acid substitution of Val for Gly 12 causes the RAS-p21 protein to become oncogenic. Further studies revealed that any RAS-p21 protein that contains the substitution of any non-cyclic L-amino acid for Gly 12 is oncogenic; only the substitution of Pro for Gly 12 results in a non-transforming protein [2]. Similar studies [2] further demonstrate that, with the exception of Glu and Pro, arbitrary amino substitutions for Gln 61 (Q61) result in an oncogenic protein. Other more limited numbers of substitutions such as at Gly 13 and several other residues in RAS-p21 also result in oncogenic proteins.  
Importantly, it was shown that the microinjection of oncogenic (G12V)-RAS-p21 into frog (_Xenopus laevis_) oocytes, that are metaphase-arrested cells in the second meiotic division, induces completion of the second division, resulting in oocyte maturation [4]. An identical effect was demonstrated by incubating these oocytes with insulin [4]. It was shown that the micrinjection of the neutralizing high affinity anti-RAS-p21 antibody, Y13 259, into oocytes that were then incubated with insulin, blocked maturation, thus demonstrating that insulin induced oocyte maturation by the activation of wild-type RAS-p21 [5]. As a result, oocytes proved to be an excellent screening system for agents that block oncogenic RAS-p21 but not its activated wild-type counterpart protein.
## 4 Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) Methodology
### Datasets Generation for the LSTM-RNN Model  
Natural frequencies and the corresponding mode shapes of structures must be obtained to minimize the oscillation in the model design. Theoretical modal analysis is concise for discrete systems but difficult for continuous or multidimensional structure systems. Many engineering problems may be analyzed effectively using finite element programs (FEM). For this purpose, the FEM ANSYS has been widely used to resolve complex engineering problems more efficiently and with less time consumption. Therefore, in this study, datasets are initially generated using ANSYS, which is further used in the LSTM-RNN model to approximate the transcendental equation roots.  
Figure 3: LSTM-RNN network block diagram.  
For this aim, a total of \(n\) = 20,000, \(f_{n}\) (Hz) datasets of the prismatic beam, having properties as mentioned in Table 1 with CF boundary conditions, are initially generated. Afterward, using Equation (19), roots (\(\beta_{n}l\)) of the transcendental equation are numerically computed based on the obtained \(f_{n}\) datasets. Finally, these (\(f_{n}\), \(\beta_{n}l\)) datasets, including beam parameters (\(A\), \(I\), \(E\), \(\rho\), \(n\)) as described in the analysis mentioned above, are used as input and output target values for the LSTM-RNN model.  
Table 2 illustrates the head of input and output datasets used in the proposed LSTM-RNN model to train and calculate the roots (\(\beta_{n}l\)) approximations for \(n\) = 1-10 as an example. The latter section shows the associated mode forms of the beam transverse vibration for the first ten natural frequencies.
## 2 Experimental Setup
### Power Supply  
The core of the power supply used in the experiments is a Behlke HTS111-06-GSM fast high-voltage transistor switch, composed of two identical MOSFET switching paths forming a so-called push-pull circuit. The basic circuit of the power supply is given in Figure 3. A 5 V DC power supply and a function generator are needed to drive the switch and control the output, the latter serving as a trigger signal. A DC voltage supply provides a constant voltage of up to 10 kV, which charges the entry capacitor with a capacitance of 7000 pF. Once the switch opens the DBD actuator sub-circuit, the voltage is primarily provided by the capacitor. Therefore, the DC power supply can operate with a low output current. The current required to charge the capacitor depends on the repetition rate of the output voltage pulse.  
The actuator was operated by means of nanosecond pulses (NS-P) generated by a [PERSON] function generator. The operating regime used was periodic (NS-P), with a constant frequency of pulses fed into the discharge gap at peak voltages between 3 and 10 KV. For the present study,  
Figure 2: Scheme of the dielectric barrier discharge (DBD) actuator.  
peak voltages of either 4 or 7 kV were selected. Typical waveforms for voltage and current used during the experiments are shown in Figure 4.  
A recurrent question relates the power consumed to create such plasmas in such nanosecond-pulsed DBD actuators and whether the energy balance is favourable to their developments: the averaged power consumption at a pulsing frequency of 1 kHz for the signals represented in Figure 4 can be estimated to be only 4.6 W due to the low duty cycle of ns-pulse (NS-P) power supplies. Using a precise estimation as reported by Thesis of Geuns [25; 26], and recently published in a very complete analysis of typical DBD  
Figure 4: Sample current and voltage waveforms for the ns-pulse (NS-P) power supply.  
Figure 3: The ns-pulse (NS-P) power supply setup, developed in-house at EPFL by the Swiss Plasma Centre.  
power consumptions by [PERSON] et al. [27], this estimation is confirmed for a peak voltage pulse of 4 kV; in addition, for a peak voltage of 7 kV, we obtain a global maximal power consumption of 6-10 watts over the duty cycle.  
The installation of the DBD actuator within the Wind tunnel model is isolated from the wind tunnel and all the acquisition systems by appropriate grounding and capacitance shielding. Indeed, the levels of electromagnetic interference effects that could occur during the fast rise of the ns-pulse must have no interaction with any electronic equipment, as this would lead to their damage/destruction.
## 2 Literature Review
### English Continuous Learning Intention  
The Intention construct provides researchers a psychological antecedent factor of human behavior, as defined as a user's willingness to carry out a particular behavior [51]. This construct appears in sorts of technology acceptance related theories, such as the theory of reasoned action (TRA), theory of planned behavior (TPB), technology acceptance model (TAM), and other extended models. In addition, the intention is a popular dependent variable for predicting or explaining human behavior. Lots of learning research, especially using technology, adopt intention as a construct to understand learners' behaviors. For example, [PERSON] and [PERSON] extended the unified theory of acceptance and use of technology (UTAUT) to test learners' continuance intentions in web-based learning [54]. Moreover, [PERSON], [PERSON], [PERSON], and [PERSON] combined IS success model and TAM model for MOOCs learners' continuance intentions. Their findings include system quality, curriculum quality, and service quality were important prerequisites for learners' continuous intentions [53]. In addition, [PERSON], [PERSON] and [PERSON] studied language learners using digital learning tools to see if the automatic scoring mechanisms of the application would impact learners' continuous learning intention [48]. In this present study, EFL English learners' continuous learning intention is the dependent variable on Internet-based resources in the English learning environment. According to the mentioned literature, learners' Internet QoE will impact their English continuous learning intention. Hypothesis H4 is proposed as follows:  
**Hypothesis 4 (H4).**_EFL students' internet quality of experience has a significant and positive influence on their English continuous learning intention_.  
In addition, this study would examine the mediation effect of internet QoE between CQ, English self-efficacy and hedonic motivation. Therefore, there are three more hypotheses in the following list.  
**Hypothesis 5 (H5).**_Internet quality of experience has a mediation effect between cultural intelligence and English continuous learning intention_.  
**Hypothesis 6 (H6).**_Internet quality of experience has a mediation effect between English self-efficacy and English continuous learning intention_.  
**Hypothesis 7 (H7).**_Internet quality of experience has a mediation effect between hedonic motivation and English continuous learning intention._  
The following figure, Figure 1, shows the proposed research model of this study.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Pressure Sensor
#### 3.1.3 Number of Postures  
Different sitting postures can vary widely, particularly in the context of ergonomics, health, and activity. Some common types of sitting postures are depicted in Figure 4.  
The number of postures recognized using sensor approaches varies from 4 to 15: 4 [56,64,67,71], 5 [68,70], 6 [58,62,80,87,89,90,91], 7 [65,75], 8 [66,72], 9 [88], 10 [87], 11 [83], 12 [61], and 15 [92] as depicted in Figure 5, which reveals that there is no straightforward relationship where an increment in the number of sensors consistently results in the recognition of a greater number of postures. Some studies with fewer sensors have successfully recognized many postures, while others with more sensors have identified fewer postures. The effectiveness of posture recognition often depends more on factors such as the tactical positioning of sensors, the quality of data collected, and the sophistication of the data processing and machine learning algorithms used. For example, a well-placed set of a few high-quality sensors can sometimes outperform a larger array of less optimally placed sensors.  
In the case of array configurations, as shown in Figure 6, higher resolution in pressure sensor arrays significantly enhances the ability to recognize a greater number of postures. Higher-resolution arrays consist of more sensors per unit area, providing finer granularity in the pressure data. This increased detail allows for more precise detection and differentiation of subtle changes in pressure distribution, which is crucial for accurately identifying various postures.  
Figure 4: Different sitting postures [61].  
Figure 5: Number of sensors used, and postures recognized.
## 5 Experiments  
### Data Collection  
The data in this study were provided by Shenyang Municipal Bureau of Big Data. The software used in this study mainly includes IBM SPSS Statistics 26 and MATLAB R2016a. Experiments were conducted using three bus routes in Shenyang, namely 239, 134, and New Area Line 1. Each bus in the above three routes is equipped with a GPS positioning device, which collects bus position data every 5 s. The onboard serial number of each bus is unique and corresponds to a fixed bus driver. This study takes the segment of the 239 bus route from the 4 th station (Jianshe Road Weigong Street) to the 13 th station (Peace Square) as the main predicted segment. The traffic flow and the number of passengers in different directions on the same road section are not the same, which will lead to a difference in travel time. Therefore, this study uses the driving data in the same direction (from west to east). Some parts of the driving segments of the three bus routes are shared. According to the shared parts, the predicted segment is divided into three sub-segments, as shown in Figure 2. Among them, sub-segment 1 spans four bus stations from Jianshe Road Weigong Street Station to Tiexi Square Station. There are 239 bus routes and the new district line 1 overlapping with sub-segment 1. Sub-segment 2 spans four bus stations from Tiexi Square Station to Shengli South Street Station on Nanwu Road. In sub-segment 2, there are three bus routes with overlapping driving intervals: 239, 134, and New Area Line 1. Sub-segment 3 runs from Shengli South Street Station on Nanwu Road to Peace Square Station and also spans four bus stops. There are overlapping driving segments of 239 and 134 in sub-segment 3.  
Among them, the travel time data set of 9 drivers was selected for each route as experimental data, and 27 drivers for a total of 3 routes is shown in Table 2. The experimental data are from 4 to 22 January 2016 (Monday to Friday). The data set is divided into three sub-data sets: D1 (4-15 January 2016), D2 (18-20 January 2016) and D3 (21-22 January 2016). Data sets D1 and D2 are used for the training data set, and data set D3 is the test sample.  
Figure 2: (**a**) Schematic diagram of multiple routes’ shared road segment division, (**b**) route map of bus No. 239, (**c**) route map of new area line 1, (**d**) route map of bus No. 134.
## 2 Design of an MR Transmission Device  
The working principle of the disc-type MR transmission device is shown in Figure 1. The support shell is fixed with the frame by screws to ensure that the coil does not rotate relatively, and the thickness of the air gap between the support shell and the output disc needs to be taken into account. Copper gaskets and sealing packing in the transmission device are magnetic field-blocking parts that are used to improve the magnetic flux distribution. The MR transmission device work process is as follows:  
1. In the initial state, the drive shaft starts to rotate under the action of the input torque from the power source. At this time, the excitation coil is not energized, and the torque transmitted by the zero-field viscosity of the MRF alone is not enough to drive the output disk to rotate.
2. After the excitation coil is energized, the magnetic flux generated by the coil passes through the gap of the MRF perpendicularly, the magnetic particles in the MRF are arranged into a chain structure along the magnetic flux direction, and the shear yield stress gradually increases, driving the output disk to start rotating. When the current increases, until the MRF reaches magnetic saturation, the device transmission torque reaches the extreme value and the driven-disc rotation remains stable.
3. In the event that the system needs to stop the power input, the coil is disconnected, and the shear yield stress of MRF decreases rapidly. Finally, the input disk and the output disk are disconnected, and the output disk stops rotating.
## 5 Results and Analysis
### Test Results
#### 5.2.3 Comparison Experiments of the Four Improved Models  
The above analysis shows that YOLOv4N2, YOLOv4H1, and YOLOv4H2 outperformed the original network in all aspects, so these three networks can be combined in different forms to obtain the following four new models and trained iteratively again.  
* YOLOv4-1 = YOLOv4N2 + YOLOv4H1;
* YOLOv4-2 = YOLOv4N2 + YOLOv4H2;
* YOLOv4-3 = YOLOv4H1 + YOLOv4H2;
* YOLOv4-4 = YOLOv4N2 + YOLOv4H1 + YOLOv4H2.  
The performance of these four networks was compared, and the results are shown in Table 3. Compared with the six models described in the previous section, the performance of YOLOv4-1, YOLOv4-2, and YOLOv4-4 showed little improvement or even a slight decrease, but the detection accuracy of YOLOv4-3 improved significantly and the mAP improved by 2.03% compared to the original YOLOv4 (K-means++) model. This indicates that the attention module works relatively best when embedded in the Head region.