esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Bioprocess for Xanthophylls Production by Microalgae
### Factors Determining Xanthophylls Production
#### 7.2.3 Salinity  
The salinity effect on microalgae growth and xanthophyll formation is complex. Many microalgae species are able to tolerate high salinity levels because of their osmoregulation capacity, which involves constant glycerol synthesis [160]. Indeed, salt stress has a positive effect on the production of secondary xanthophylls such as ASX. For example, an ASX content of 5 mg/g was obtained with _Haematococcus pluvialis_ when treated with 0.2 g/L of NaCl, which was 42% higher than that of the control [161]. A general increase in ASX production was also observed with _Chlorella zofingiensis_ grown at NaCl concentrations of up to 0.2 M [148]. Furthermore, salinity stress alone did not change the content of lutein in _Dunaliella salina_[162], contrary to the combination of nitrate concentration and salinity [163].
# Prediction of Grain Porosity Based on WOA-BPNN and Grain Compression Experiment
## 4 Modeling Prediction of Grain Porosity  
### Back Propagation Neural Network  
The back propagation neural network (BPNN) was proposed in 1986 by a team of scientists led by [PERSON] and [PERSON] as a feedforward neural network with a backpropagation of errors. The BPNN consists of three parts: the input layer, output layer, and hidden layer. The number of layers and neurons in the hidden layer can be determined by the dimensions of the input vectors and output vectors. In general, BP  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grain Types** & **Moisture Content/\%** & **Vertical Pressure/kPa** & **Porosity** \\ \hline Wheat & 10.70 & 0 & 0.414 \\ \hline Wheat & 10.70 & 21 & 0.405 \\ \hline Wheat & 10.70 & 42 & 0.398 \\ \hline Wheat & 10.70 & 83 & 0.385 \\ \hline Wheat & 10.70 & 125 & 0.377 \\ \hline Corn & 11.58 & 0 & 0.399 \\ \hline Corn & 11.58 & 21 & 0.391 \\ \hline Corn & 11.58 & 42 & 0.384 \\ \hline Corn & 11.58 & 83 & 0.374 \\ \hline Corn & 11.58 & 125 & 0.367 \\ \hline Soybean & 8.58 & 0 & 0.373 \\ \hline Soybean & 8.58 & 21 & 0.363 \\ \hline Soybean & 8.58 & 42 & 0.353 \\ \hline Soybean & 8.58 & 83 & 0.330 \\ \hline Soybean & 8.58 & 125 & 0.306 \\ \hline Paddy & 12.93 & 0 & 0.487 \\ \hline Paddy & 12.93 & 21 & 0.480 \\ \hline Paddy & 12.93 & 42 & 0.474 \\ \hline Paddy & 12.93 & 83 & 0.464 \\ \hline Paddy & 12.93 & 125 & 0.456 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some results of the compression experiments conducted on grain specimens under different vertical pressures.  
neural networks are typically set up with a single hidden layer [39]. BP neural networks can learn and memorize a large number of input-output mapping models without the need for prior mathematical formulas describing such mappings. During the process of network learning, the signal involves both forward propagation and backward propagation. If the actual output of the output layer differs from the expected value, the error is propagated backward. The learning rule employs the steepest descent method, continuously updating the weights and biases of the neural network through iterative operations to minimize the network error. A typical three-layer BPNN topology is illustrated in Figure 4.  
The output of the hidden layer of the BPNN is given as:  
\[r_{j}=f_{1}(\sum_{i=1}^{m}\omega_{ij}x_{i}+b_{1}) \tag{6}\]  
where \(f_{1}\) is the activation function of the hidden layer, \(x_{i}\) is the value of the neuron in the input layer, \(\omega_{ij}\) is the connection weights, and \(b_{1}\) is the bias.  
The output of the output layer of the BPNN is provided as:  
\[y_{k}=f_{2}(\sum_{j=1}^{l}\omega_{jk}r_{j}+b_{2}) \tag{7}\]  
where \(f_{2}\) is the activation function of the output layer, \(r_{j}\) is the value of the hidden layer neurons, \(\omega_{jk}\) is the connection weights, and \(b_{2}\) is the bias.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 4 Joint Resource Allocation in Cognitive LEO SatComm System
### Resource Allocation  
To protect the legacy satellite service, we have to guarantee its SINR no less than the target value, \(\beta^{*}\), i.e., \(\gamma_{p}\geq\beta^{*}\), and guarantee the IoT user receive power no more than the limit value, \(\bar{P}\), i.e., \(p\leq\bar{P}\). Based on the above analysis, our first resource allocation problem in the CR system tries to maximize the sum rate for the IoT system, which can be formulated as **P1**,  
\[\max_{p} \frac{K}{N}\log(1+\gamma_{s})\] s.t. \[\gamma_{p}\geq\beta^{*}\] \[p\leq\bar{P}.\]  
From (19), it is difficult to get the closed-form expression for \(\gamma_{s}\). Similarly, the closed-form expression for \(\gamma_{p}\) is also difficult to obtain. To overcome this problem, we provide the following lemma.  
**Lemma 1**: _For any \(\alpha_{2}\) and \(q\), if we have \(p_{1}>p_{2}\), then, we have \(\gamma_{1,s}>\gamma_{2,s}\)._  
Please see Appendix A.  
From Lemma 1, we can find that the objective function increases with the growth of \(p\). However, the increase in \(p\) will decrease the asymptotic SINR of the legacy satellite users. Similar to the Lemma 1, if \(q_{1}>q_{2}\), then, \(\gamma_{1,p}>\gamma_{2,p}\). Thus, when \(q\) is small, the SINR requirement constraint, i.e., \(\gamma_{p}\geq\beta^{*}\) is dominant while the power limit is not effective. So ignoring the power constraint, we can get **P2**:  
\[\max_{p} \frac{K}{N}\log(1+\gamma_{s})\] s.t. \[\gamma_{p}\geq\beta^{*}.\]  
For the optimization problem **P2**, when \(p\) is at the maximum value, the sum rate of the IoT users will be at the maximum value, while \(\gamma_{p}\) will decrease. Thus, the objective function is maximized when the equality constraint holds, i.e., \(\gamma_{p}=\beta^{*}\). Accordingly, we can derive the optimal \(p\) of the IoT receive power as follows  
\[p=\frac{\frac{q}{\beta^{*}}-\sigma^{2}-\frac{\alpha_{1}q}{1+\beta^{*}}}{\alpha _{2}+\frac{\alpha^{2}\beta^{*}}{q}+\frac{a_{1}\beta^{*}}{1+\beta^{*}}-1}. \tag{21}\]  
When \(q\) is large enough, the value of \(p\) has achieved the power constraint while \(\gamma_{s}\) does not reach \(\beta^{*}\). So ignoring the first constraint, we can get **P3**:  
\[\max_{p} \frac{K}{N}\log(1+\gamma_{s})\] s.t. \[p\leq\bar{P}.\]  
Due to the fact that \(\gamma_{s}\) will increase with \(p\), the optimal receive power for the IoT users is  
\[p=\bar{P}. \tag{23}\]
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 2 Design Theory
### The Feeding of the Antenna, Miniaturization and Dual-Band Technology  
The typical structure of printed monopole antenna is shown in Figure 2. It has advantages of small size, light weight, easy to integrate and mass production [20; 21; 22]. The feed means of the antenna  
Figure 1: Structure of a microstrip antenna.  
is microstrip line feeding. The manufacturing process of microstrip line is simple, easy to integrate with other active and passive circuit components, which is conducive to realizing the miniaturization of the circuit system and improving the degree of integration.  
When using coaxial feed, the probe used for feeding is stretched into the resonant cavity to motivate the patch antenna. The advantages of the coaxial feed is that the feeding points can be selected at any desired position of the patch, and the coaxial feeder located in below the ground plane and the patch antenna located in the ground floor avoid the effect of the feeder on the antenna radiation.  
The bandwidth and gain of the antenna are closely related to the structure and size of the antenna. Reducing the size of the antenna will reduce the efficiency of the antenna and narrow the bandwidth. In many cases, especially in aerospace satellite communication and mobile communication, due to the limitation of physical space, small antennas are urgently needed [23; 24]. Therefore, it is necessary to reduce the size of the antenna while improving the performance of the antenna.  
With the development of WLAN technology and other communication technologies, the demand for dual-frequency and multi-frequency antennas is increasing. In light of this situation, the multi-frequency operation method of the microstrip antenna has been studied extensively. There are two kinds of basic methods: monolithic chip method and multi chip method. Monolithic chip method can be divided into monolithic multi mode method and monolithic loading method. Monolithic multi mode method works in different modes of patch simultaneously, while monolithic loading method adopts loading to form different resonance frequencies. For monolithic microstrip patch antenna, in addition to the use of multi mode method, the load method can also be employed to achieve multi-frequency work. In addition, low-frequency ratio of the two working frequencies can be obtained by using the loading method [25]. Commonly used forms of loading slots include rectangular slots, U-shaped grooves and L-shaped grooves. Through the adjustment of the length and width of the gap, the antenna can achieve excellent matching characteristics and multi-frequency characteristics [26; 27].
# Preliminary Failure Analyses of Loaded Hot Water Bottles
## 2 Methodology
### Experimental Approach
#### 2.2.1 Tensile Testing  
Tensile testing helps designers to optimise the formulation and processing of rubber materials for specific applications and investigate the causes of failure in rubber products. By examining the stress-strain behaviour and fracture surfaces of failed samples, designers can identify material defects, design flaws, or improper processing that may have contributed to the failure. Tensile test data are also used to develop predictive models for simulating the behaviour of rubber materials under different loading conditions. These models help to analyse the performance of rubber components in complex mechanical systems and predict their response to various stress scenarios. In the current study, elongation and tensile load were initially assessed according to the International Organisation for Standardisation (ISO) 37:2024 [11] specifications in a preliminary investigation to set a foundation for developing the lab test protocol. When ISO recommended tensile test sample shapes were tested, they failed next to the fixing jaw at an angle of around 10\({}^{\circ}\), indicating a shear failure rather than the anticipated tensile failure, as shown in Figure 2. As a result, it has been decided to use rectangular strips for the tensile testing in the hot water bottles' tensile testing for practical reasons.  
Figure 1: Coefficient of friction experimental estimation. (**a**) CAD representation of friction experiment; (**b**) Experimental setup.  
Tensile tests were conducted at room temperature (approximately 20 \({}^{\circ}\)C) in the Active Learning Laboratory at the School of Engineering, University of Liverpool (Liverpool, UK). In the current study, 27 strips of 150 mm \(\times\) 20 mm \(\times\) 2.25 mm specimens were excised from two separate water bottle samples, with thickness measurements taken prior to testing. Specimens were mounted within screw-tightened jaws to expose 50 mm of material between the two jaw camps. Specimens were loaded to failure at a rate of 10% min\({}^{-1}\) following ASTM D412-16 [12]. Load and elongation data were collected transiently. A tabletop Instron 3345 tensile test machine (Instron Corporation, Norwood, MA, USA) was used while fully controlled by Bluehill 5 software. The machine has a load capacity of 5 kN, total crosshead travel of 885 mm, and a loading speed range of 0.05 mm/min to 1000 mm/min, as shown in Figure 3.  
Uniaxial tensile stress \(\sigma_{t}\) and strain \(\epsilon_{t}\) were then calculated according to Equations (1) and (2), respectively.  
\[\sigma_{t}=\frac{\text{F}_{t}}{\text{A}_{t}} \tag{1}\]  
where F\({}_{t}\) is the tensile force measured by the machine and A\({}_{t}=\) 20 mm \(\times\) 2.25 mm \(=\) 45 mm\({}^{2}\), and  
\[\epsilon_{t}=\frac{\Delta\text{L}_{t}}{\text{L}_{\text{o}t}} \tag{2}\]  
where \(\Delta\text{L}_{t}\) is the extension in length and L\({}_{\text{o}t}\) is the original strip length.
## Results
### Comparison of Experiments  
Our method exhibited outstanding performance across all evaluation metrics, surpassing other license plate recognition methods. Particularly, when presented with input images containing license plate deformation and motion blur acquired from real-world traffic environments, EAND-LPRM demonstrated the most robust performance in license plate recognition, outperforming all other methods.  
To further validate the rationality of the EAND-LPRM model, we compared it with CRNN, ResNet18, and ResNet50 using our self-developed CMPD dataset, which covers diverse license plate types and is properly partitioned. As shown in Table 2, the recognition accuracy of the EAND-LPRM model on our custom dataset consistently outperforms other models. This robust performance once again confirms the ability of the EAND-LPRM model to achieve high recognition accuracy in handling challenging and complex traffic conditions. Under regular traffic conditions, the EAND-LPRM model can effectively replace other models, demonstrating its potential as a powerful choice for license plate recognition applications.  
Considering the emphasis on recognition efficiency, we conducted a comprehensive evaluation of the recognition time of various models on different datasets. Through model evaluation metrics such as test accuracy, best accuracy, average accuracy, and training time, we assessed the effectiveness and reliability of the experimental results. As shown in Figure 4, our proposed EAND-LPRM model achieved the highest recognition accuracy on both public dataset and the CPMD dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Best** & **Avg** & **Test** & \multirow{2}{*}{**Loss**} \\  & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & \\ \hline CRNN & 89 & 87 & 88 & 0.190 \\ ResNet18 & 95 & 93 & 90 & 0.064 \\ ResNet50 & 96 & 93 & 91 & 0.066 \\ EAND-LPRM & 98 & 97 & 94 & 0.039 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results on the public dataset CCPD.  
The \(x\)-axis represents the time in seconds, and the \(y\)-axis represents the recognition accuracy. Through rigorous experimental validation, our proposed EAND-LPRM model has been empirically substantiated as a feasible and effective solution. By integrating the encoder-decoder structure with self-attention mechanisms, our approach adeptly captures vital features within license plate images, facilitating accurate recognition of character sequences even amidst multiple intricate conditions. Our model possesses superior recognition capabilities and remarkable robustness in license plate character recognition tasks. Consequently, employing our model for license plate recognition in real-world traffic situations can yield outstanding results. This attests to the practical viability and efficacy of our proposed EAND-LPRM model in addressing the challenges posed by varied and challenging license plate recognition scenarios.
# Build Orientation Optimization Based on Weighted Analysis of Local Surface Region Curvature
## 3 Methodology
### Surface Region Analysis
#### 3.1.2 Curvature Estimation  
The curvature shift method proposed mainly depends on the curvature of points. The curvature of one point on the model is defined as follows: the maximum curvature of the central point on the surface, which is reconstructed by the point with its 1-ring points. The maximum curvature reflects the changes in the polar region around the point. If the maximum curvature is close to zero, it shows that the polar region is relatively flat.  
Surface reconstruction is used to estimate the curvature as shown in Figure 3. NURBS surface is adopted to the appropriate local sample. A NURBS surface is obtained as the tensor product of two NURBS curves, thus using two independent parameters \(u\) and \(v\) (with indices \(i\) and \(j\), respectively) [21]:  
\[S(u,v)=\sum_{i=1}^{k}\sum_{j=1}^{l}R_{i,j}(u,v)P_{i,j} \tag{1}\]  
with  
\[R_{i,j}(u,v)=\frac{N_{i,n}(u)N_{j,m}(v)w_{i,j}}{\sum_{p=1}^{k}\sum_{q=1}^{l}N_ {p,n}(u)N_{q,m}(v)w_{p,q}} \tag{2}\]  
as rational basis functions, let \((d)=du:dv\) be the main direction of point p on the surface \(S\), \(\vec{r}=\vec{r}(u,v)\) is the main curvature along the main direction. The curvature information is obtained following the expressions:  
\[\left|\begin{array}{cc}L-k_{n}E&M-k_{n}F\\ M-k_{n}F&N-k_{n}G\end{array}\right|=0 \tag{3}\]  
Figure 3: NURBS surface reconstruction based on sample points.  
Figure 2: Cluster strategy on test model.  
Based on the Vieta theorem, the Gaussian curvature and mean curvature is  
\[K=\frac{LN-M^{2}}{EG-F^{2}} \tag{4}\]  
\[H=\frac{EN-2 FM+GL}{2(EG-F^{2})} \tag{5}\]  
The maximum curvature is as follows:  
\[c_{max}=H+\sqrt{H^{2}-K} \tag{6}\]
## 2 Materials and Methods
### Bias Correction  
Simulated data may have systematic errors relative to observations resulting from many factors such as poor vertical and horizontal resolution, sub-grid processes that are often semi-empirically parametrized, numerical methods used to solve the equations, and misrepresentation of a lower boundary, amongst others. Therefore, the use of such data in impact, adaptation, and mitigation studies may misrepresent climate change. One way to minimize these biases is to perform bias correction (BC).  
Various authors have applied BC in similar studies [38; 39; 40; 41; 42; 43]. These studies clearly demonstrate that the application of BC methods improve the performance of the models in climate simulation, particularly of extreme values and indices.  
In this study, we applied the quantile mapping method of BC [44; 45; 46; 47]. This method corrects the Probability Distribution Function (PDF) of the data for each quantile separately considering the observations (i.e., EOBS) as reference. The procedure used here is described in detail by [PERSON] et al. [46] and is fully explained by [PERSON] et al. [23]. This method considers that future PDFs may be different from that of the present climate. BC is applied to all simulated daily data using the three months centered on the month to be corrected. This sub-sampling ensures that some seasonal signature is retained in the process.  
Firstly, HIST data are corrected by projecting raw uncorrected data (NC) onto the EOBS PDF. Corrected data for HIST are validated against EOBS data for HIST. Hereafter, NC and BC are used to refer to uncorrected and bias-corrected data, respectively. Secondly, the data for MED and LONG are corrected. The full methodology of the applied bias correction is presented in [23; 46].  
The EURO-CORDEX platform ([[http://www.cordex.org/data-access/bias-adjusted-rcm-data/](http://www.cordex.org/data-access/bias-adjusted-rcm-data/)]([http://www.cordex.org/data-access/bias-adjusted-rcm-data/](http://www.cordex.org/data-access/bias-adjusted-rcm-data/))) has BC data for many models, but the BC method is different for each model. This may pose comparison problems, particularly when considering the ensemble of the models. In this study, the BC method quantile mapping is applied to daily minimum and maximum temperature simulated by WRF and to the EURO-CORDEX models (CORDEX hereafter).  
BC is usually cross-validated by obtaining the transfer functions for a period and validated for a different independent period. Here, a bootstrapping procedure is performed. Ten years of model temperatures are randomly selected from the HIST period. The transfer functions are obtained for this period and applied to the remaining 10 years of the period. The procedure is repeated 50 times. The distributions of the 50 10-year BC temperatures are compared to that for the 20-year HIST period. The objective is to conclude that BC is not dependent on the period selected.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 7 Summary and Conclusions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & \begin{tabular}{c} **Parameter** \\ **Estimates** \\ \end{tabular} & **z-Stat** & \begin{tabular}{c} **No** \\ **Inquiry** \\ \end{tabular} & \
\begin{tabular}{c} **Minor** \\ **Injury** \\ \end{tabular} &
\begin{tabular}{c} **Severe** \\ **Injury** \\ \end{tabular} \\ \hline Constant [MI] & \(-\)1.25 & \(-\)9.68 & & & \\ Constant [SI] & \(-\)3.21 & \(-\)7.14 & & & \\ Random parameter (normally distributed) & & & & & \\ Cyclist indicator & \(-\)2.11 & \(-\)2.92 & \(-\)0.0079 & 0.0071 & 0.0008 \\ (I) if cyclists involved in the crash, 0 otherwise) [NI] & (2.87) & (3.09) & & & \\ Curve left indicator & \(-\)0.38 & \(-\)1.74 & \(-\)0.0215 & 0.0186 & 0.0029 \\ (I) if road roadway alignment was curve left, 0 otherwise) [NI] & (2.55) & (1.82) & & & \\ Temporal characteristics & & & & & \\ Weekend indicator & 1.21 & 2.48 & \(-\)0.0152 & \(-\)0.0058 & 0.0210 \\ (II) if crash occurred during the weekend, 0 otherwise) [SI] & & & & & \\ Spatial characteristics & & & & & \\ Town indicator & & & & & \\ (II) if crash occurred in town, 0 otherwise) [SI] & \(-\)0.96 & \(-\)1.97 & 0.0113 & 0.0046 & \(-\)0.0159 \\ Vehicle characteristics & & & & & \\ Commercial vehicle indicator & & & & & \\ (I) if commercial vehicles involved in the crash, 0 otherwise) [MI] & \(-\)1.72 & \(-\)2.29 & 0.2132 & \(-\)0.2377 & 0.0245 \\ Crash characteristics & & & & & \\ Aggressive driving indicator & & & & & \\ (II) if aggressive driving involved in the crash, 0 otherwise) [SI] & 1.54 & 3.43 & \(-\)0.0144 & 0.0174 & \(-\)0.0030 \\ (II) otherwise driving involved in the crash, 0 otherwise) [SI] & 1.77 & 2.07 & \(-\)0.0023 & \(-\)0.0034 & 0.0057 \\ Number of observations & & & & & \\ Number of estimated parameters & & & & & \\ Log-likelihood at zero (\(LL(\theta)\)) & & & & & \\ Log-likelihood at convergence (\(LL(\theta)\)) & & & & & \\ \(\sigma^{2}=1-LL(\beta)/LL(0)\) & & & & & \\ AIC & & & & & \\ BIC & & & & & \\ \hline \hline \end{tabular} Note: NI = no tertiary; MI = minor injury; SI = severe injury.  
\end{table} 
Table 10: Model results of mixed logit with heterogeneity in means for non-angle crashes in Florida 2018-2019.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 3 Improved YOLOX Detection Algorithm  
### YOLOX Model  
YOLOX is a new generation of the YOLO series algorithm following YOLOv5 [62], which improves on YOLOv3 and YOLOv5 and further improves the detection performance. YOLOX is mainly divided into three parts: A backbone network (Backbone), a feature pyramid (Neck), and a prediction module (Head). Among them, the backbone network adopts Cross Stage Partial (CSP) Darknet, which is the backbone feature extraction network of YOLOX, and three effective feature layers are obtained by input images, primarily including Focus, Cross Stage Partial Network (CSPnet) [63], and Spatial Pyramid Pooling (SPP) components. The feature pyramid is constructed by CSP-PAFPN (PAFPN, Path Aggregation Feature Pyramid Networks), which makes full use of the three effective feature layers obtained by the backbone network to improve the model performance by bottom-up and top-down feature fusion. The prediction module improves the detection head to Decoupled Head, which improves the training convergence speed and detection accuracy of the model and uses the Anchor frame approach based on Anchor free, which removes the preset a priori frame and predicts the edges of the target directly to reduce the computational redundancy.  
Although the original YOLOX model achieves better performance than other methods for the detection task in this paper, there are still large missed and false detections for small targets, occlusions, and low-illumination environments at night. Therefore, the model structure and loss function of the algorithm are improved in this paper to improve the detection accuracy of nighttime vehicle/pedestrian targets. One of the schematic diagrams of the improved model structure is shown in Figure 1.