esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Center of Gravity Height and Rollover Stability  
Figure 7a shows the range of detected CG heights for the different vehicles. The SSFs derived from this according to Equations (12) and (13) are shown in Figure 7b. With regard to COG height, there are conflicting goals in vehicle development. To achieve a small frontal area for aerodynamics and high rollover stability, the COG should be as low as possible. This usually means low vehicle height and seating position, which limits visibility of these vehicles behind obstacles or other road users. The low seating position also restricts the driver's own view.  
The observed COG heights for the velomobiles (vehicles 1-10) were in the range of 0.28 m to 0.47 m. According to the data in [30], the COG heights for passenger cars (sedans) typically range from about 0.5 m to 0.58 m. Velomobiles, therefore, showed significantly lower center of gravity heights. The COG heights of the three trikes (vehicles 11-13) were in a comparable range to those of passenger cars. Despite low center of gravity, no three-wheeled vehicle achieved a static stability factor above 0.8 due to narrow track width and three-wheeled chassis. Most driver-vehicle combinations were still well below this value, in the range between 0.4 and 0.7. The average SSF (mean value of the mean values per vehicle) was 0.59 (SD: 0.11). The values determined for the SSF corresponded approximately to values for heavy goods vehicles [31; 32]. For passenger cars, values in the range of around 1.4 are common (average value of new American models in 2003 according to [33]). Values lower than one are very uncommon according to [33]. The NCAP rollover rating, which was based solely on the SSF until 2004, provides the worst possible rating of one star for SSFs lower than 1.04 [28].  
Figure 7: (**a**) Center of gravity height (**b**) Calculated static stability factor.  
The maximum achievable lateral acceleration during driving depends on the coefficient of friction \(\mu\) between road surface and tire. As a simplification, it can be assumed that the maximum achievable lateral acceleration due to tire forces is limited by the coefficient of friction \(\mu\) in accordance with Equation (14).  
\[\frac{a_{y,max,\,skid}}{g}\leq\mu \tag{14}\]  
As long as the static stability factor is less than \(\mu\), the lateral acceleration limit for vehicle rollover is below the lateral acceleration limit for skidding. Since the observed SSF values were below the usual friction values for tire-road contact, an imminent risk of tipping of these vehicles can be assumed on dry roads. In many vehicles, the SSF is so low that, even with reduced coefficients of friction, the roll-over limit is likely to be reached earlier than the maximum traction of the tires.
## 3 Results and Analysis
### Quantitative Impact of Climate Change on Compound Flood Drivers
#### 3.1.3 Extreme Rainfall  
As global temperatures rise, the enhanced water-holding capacity of the atmosphere leads to an increase in water vapor content when the atmosphere is saturated, though the stability is weak. An analysis of 30 years of hydrological data from North America, Europe, Oceania, and Africa found that the peak intensity of extreme rainfall events increases at a rate of about 6-7%/\({}^{\circ}\)C, with a greater increase in the peak intensity of rare, high-magnitude rainfall events and a decreasing trend in rainfall duration [61]. Additionally, global climate change has accelerated hydrological cycles, manifested by increased ocean evaporation and an intensification of the land-sea-water-atmosphere cycle, leading to shifts in the spatiotemporal distribution of global rainfall. [PERSON] et al. (2020) [62] used the CMIP5 model to predict precipitation changes toward the end of the 21 st century, and the results showed that global average annual precipitation increased, especially under the RCP 8.5 scenario, with a concurrent rise in average annual evaporation.  
The commonly used models for studying the impact of climate change on rainfall floods include one-dimensional (1D) and two-dimensional (2D) models. A one-dimensional model, such as SWMM, is primarily used to simulate the hydraulic transmission process in rivers and drainage pipelines, but it struggles to accurately represent flood evolution after drainage network overflow or river flooding. The two-dimensional model effectively addresses this limitation by simulating the flood evolution process and providing information on water accumulation extent, depth, and flow velocity. However, when using high-precision grids to divide the research area, the calculation time is relatively long. To overcome the limitation of two-dimensional models not being able to simulate the dynamic process of rainwater pipelines, researchers have explored coupling 1D and 2D models to analyze the interaction between surface flow and underground pipeline flow. This includes coupling MIKE 11 and MIKE 21 [63] and coupling the SWMM model with LISFLOOD-FP [64], as shown in Table 3. Reducing the calculation time of flood simulations in high-precision grid conditions remains a challenge for future research.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Study Area** & **Output** & **Numerical Model** \\ \hline [PERSON] et al. [55] & Yucatan and Campeche State, Mexico & Storm surge time series, inundation depth & SLOSH \\ [PERSON] et al. [56] & Taiwan & Water level changes & POM \\ [PERSON] et al. [57] & Shoalhaven Estuary, Australia & Flood extents and inundation depths & Delft3D \\ [PERSON] et al. [58] & United States & Maximum storm surge levels, inundation & ADCIRC \\ [PERSON] et al. [59] & Xiamen Bay, China & Maximum water levels & FVCOM \\ \hline \end{tabular}
\end{table}
Table 2: Numerical models for storm surge research under climate change.
## 2 Methodology
### Flowchart of the Proposed Method  
The proposed method for predicting and adjusting shield movement performance mainly consists of four steps: data preparation, model construction, model evaluation, and shield attitude correction, as shown in Figure 4. During the data preparation process, the shield construction parameters can be categorized into operational parameters and standardized stratum parameters. Operational parameters are sourced from sensors installed on the shield machine equipment, and the typical shield operational parameters include the shield attitude, total thrust, and torque. Standardized stratum parameters are determined from in situ tests and laboratory experiments. Based on engineering experience and the existing literature, the parameters that have a significant influence on the total thrust and shield attitude are selected. Following this, box analysis is used to identify outliers, Daubechies wavelet transform is applied for data noise reduction, and Pearson correlation analysis is employed to explore the interrelationships between each parameter and its impact on both shield attitude and total thrust. During the model construction process, the data is split into a training set and a testing set in a 7:3 ratio for the training  
Figure 3: Bayesian optimization-based temporal attention-LSTM model.  
and testing of the BOTA-LSTM model. The BOTA-LSTM model, utilizing a temporal attention mechanism, achieves multi-ring predictions for shield attitude and total thrust, and employs Bayesian hyperparameter optimization to obtain the optimal predictive model. In the model evaluation phase, the effectiveness of the BOTA-LSTM model and the effect of Bayesian hyperparameter optimization is evaluated using the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE). Additionally, the performance of GRU and LSTM models is compared to determine which is more suitable as the benchmark model for this study. Finally, based on the multi-ring predictions of shield attitude and total thrust, an intelligent method for shield attitude correction is proposed.
# Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model
## 5 Conclusions  
This study introduces an innovative approach to enhance the traditional two-stream convolution model. The proposed \"Two-Stream Convolution Danger Recognition Model based on CNN-LSTM\" effectively integrates the strengths of both CNN and LSTM networks. Within this synergy, the CNN network, acting as the spatial stream, is finely tuned to extract spatial motion posture information from the human body. Concurrently, in the temporal stream, an improved TS-LSTM network is employed to amplify the model's temporal feature extraction capability.  
This innovative fusion yields promising outcomes. A notable 15% increase in the MAP was observed, signifying a substantial enhancement in the model's accuracy in pinpointing unsafe behaviors. This improvement holds profound practical implications. In environments such as construction sites, the enhanced model can significantly reduce risks for workers by adeptly identifying potential hazards.  
To validate the model's credibility and robustness, it underwent rigorous testing using four distinct verification methods. Across all tests, the results consistently underscored the high accuracy of the Faster R-CNN-LSTM network framework. Particularly in complex settings, the improved Faster R-CNN-LSTM excels, especially in recognizing edge features of safety helmets. Simultaneously, when compared to models with other feature extraction networks, the framework utilized in this study distinctly outperforms, achieving a MAP of 0.773. In essence, this research successfully elevates the model's recognition accuracy and robustness, offering powerful technical support for safety monitoring in intricate environments like construction sites.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & _MAP_ \\ \hline CNN-LSTM & 0.619 \\ SE-ResNET50 & 0.702 \\ VGG16 & 0.646 \\ Faster R-CNN-LSTM & 0.773 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model comparisons.  
**Author Contributions:** Methodology, X.L. and L.Z.; Software, X.L.; Validation, T.H.; Resources, F.L.; Data curation, Z.W.; Writing--original draft, X.L.; Supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data generated or analyzed during the study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## Appendix A Influence of the Normalization Time on the Validity  
The discretisation of the main averaging interval \(\Delta T\) in different normalisation intervals \(\Delta t\) changes the data density as shown in Section 2.7.1. The calculation of the Gaussian kernel based on different data densities influences the choice of a suitable validity value \(v\). Based on the entire data set from Section 3.1.1, we performed a parameter study that considered the resulting average errors and the RMS of velocity and velocity standard deviation. For this purpose, we used different combinations of \(\Delta t\), in a range from 0.5 s to 300 s used and validity values from 10% to 100% for an averaging interval \(\Delta T\) = 10 min. The resulting errors can be seen in Figure A1.
## 2 Materials and Methods
### Implementation of SMOTE  
The SMOTE algorithm was implemented using the following steps:  
1. Identify minority samples in the dataset.
2. For each minority sample, find its \(k\)-nearest neighbors (typically \(k=5\)).
3. Generate synthetic samples by interpolating between the minority sample and its randomly chosen nearest neighbors.
4. Repeat the process until the desired balance between classes is achieved.
## 4 Experiments and Results
### Experimental Parameter Settings  
The deep neural network model used in the simulations was feedforward architecture with one input layer, multiple hidden layers, and one output layer. The number of units of the input layer was 172, which were used to input 43 neuronal morphological features, with each feature containing the maximum, minimum, average, and sum values. The number of units of the output layer was determined by the 3D neuron classification problem, which was three for the C. elegans dataset and four for the zebrafish dataset. The hidden layers were divided into many building blocks with the same number of intermediate layers, and the number of neurons of each intermediate layer (or hidden layer) was 200. Table 2 lists the base building block parameter settings of the LCCDNNs and FCCDNNs for different neuron datasets. The basic mini-batch size was 64 for the LCCDNN and FCCDNN models in the training processes.  
The implementation of deep neural networks in this paper was based on the Keras framework [33]. All parameters, except the network structure parameters, used the Keras recommended parameters. The general parameters of the neural networks were: the last output layer activation function was softmax, the activation function of other hidden layers was Leaky ReLU; the initial weight distribution of each layer was random and uniform, which guarantees that good information is reserved; and the gradient was reduced by Adam [34] (learning rate 0.001, \(\beta 1\)= 0.9, \(\beta 2\)= 0.999, \(\varepsilon\) = 10E-8, decay = 0). Furthermore, each normalized layer was used after batch normalization to optimize training, and the  
Figure 8: The neuronal morphology classification process based on the LCCDNNs.  
learning epoch was 100. In the results reported here, the training data and the test data were mainly divided by the number of random seeds at a ratio of 0.8 and 0.2 in each experiment. That is, for the C. elegans neuronal dataset, the number of training samples was 153, the number of test samples was 39; and for the zebrafish neuronal dataset, the number of training samples was 169, and the number of test samples was 43. Finally, the average and standard deviation of the statistical results after 10 repeated experiments were determined as the final results of the experiment to study the classification accuracy.
## 1 Introduction
Even if the results are promising, the considered axlebox acceleration signals are not measured, but generated numerically by using the proposed model of the wheel-rail interaction. In the aforementioned work a refined model of the wheel-rail interaction is used, since the frequency range of interest for acoustic problems is in the order of some kHz. On the other hand, the maximum frequency concerning the rail corrugation phenomenon on subway lines ranges from 400-600 Hz, depending on the maximum speed reached by the vehicles. For this reason, simplified models can be considered. An axlebox accelerometer measurement system used for the identification of track sections affected by rail corrugation is the HRSCA system presented in [11], in which the Root Mean Square (RMS) of rail irregularity is estimated by using a frequency domain model of the wheel-rail interaction. This technique is attractive because measurements can be taken at service speed and accelerometers are relatively cheap. They can be mounted on commercial vehicles with the purpose of monitoring the whole network, reducing the time required to perform the same operation with diagnostic trains. The objective of this work is to propose a modification of the HRSCA system to be applied to axlebox acceleration data from a commercial vehicle running on a subway network. A more refined model of the wheel-rail interaction for the calculation of the transfer function of the axlebox accelerometer measurement system is introduced, in order to improve its effectiveness in monitoring rail corrugation. The dynamics of the wheel-rail interaction is studied by characterising the dynamic behaviour of the wheel and track as standalone systems. In particular, the wheel is modelled as a point mass in [11], while a flexible wheelset model resting on the primary suspension is used in the proposed approach. The transfer function of the measurement system is then calculated by coupling wheel and rail dynamics. The performances of the two methodologies are tested by analysing acceleration measurements from two runs of the diagnostic vehicle along a specific section of a subway network, where it is known that rail corrugation appears.
# Optimization of Coverage and Capacity Using Smart Antennae
## 1 Introduction
With the integration of heterogeneous networks, interferences are no longer confined to macro base stations, especially in the ever-changing environments where micro base stations operate. Owing to their ease of installation by ordinary users without professional on-site testing, micro base stations face much more complex interference environments than traditional macro base stations. As a result, the probability of user device disconnection and retransmission significantly increases due to environmental interference and changes in transmission channels. In this study, we explore this issue and propose potential solutions. This paper seeks to utilize intelligent antenna technology, allowing base stations to adapt their antenna radiation patterns and power to reduce the probability of user interference or disconnection when entering high-interference areas.
## 3 Methodology
### Data Standardization  
Standardization has been used to further ensure that values of our features will be on the same scale and thus avoid certain features being outweighed. By applying this technique, effects from a potential concept drift in future datasets is minimized [39]. Furthermore, standardization of individual features is considered a prerequisite for many of the classifiers to be able to perform as expected [34]. The standardization method that was applied is literally an implementation of the Z-score normalization technique, where the mean of each feature distribution is centered at 0 and the values are scaled to represent the result of the division by the feature standard deviation.
## 7 Processing Architecture
### CHP2 Application Layer  
The application layer uses the ToA estimates and timing information shared by the lower layers to drive a ToF synchronization algorithm. This algorithm estimates the distance between each of the 16 transmit-receive antenna pairs and digitally synchronizes the user clocks. The first iteration of this algorithm is described in detail in [12]: the current version is described in great detail in a companion manuscript but is beyond the scope of this discussion. The application layer also contains several Kalman filtering techniques, manual and automatic calibration processes, and position and orientation estimation techniques.  
Figure 18: Implementation diagram of 16 simultaneous correlation banks for a 4 \(\times\) 4 configuration. (**a**) Each receiving chain has four dedicated correlators. (**b**) There are four receiver correlation channels for a total of 16 correlations – corresponding to the 16 links formed by our 4-antenna units.
## 4 Discussion  
### Effect of Mixed Coal Mass Ratio on Gas Adsorption Constant  
#### 4.1.1 Effect of Mixed Coal Mass Ratio on Gas Adsorption Constant a Value  
The Langmuir constants \(a\) and \(b\) were obtained from the adsorption test, and the Langmuir model was used to fit the test results. According to the experimental results, the relationship between the adsorption constant \(a\) and the mass ratio of tectonic coal to virgin coal was fitted and plotted, as shown in Figure 7. The curve has a good fit (0.9805). For mixed samples of hard tectonic coal in this mine, the adsorption constant \(a\) meets the fitting formula [45; 46]:  
\[a=-1.5369x^{2}+12.847x+17.658 \tag{3}\]  
where \(x\) is the mass ratio of tectonic coal to virgin coal.  
With the increase of the quality of tectonic coal, the Langmuir adsorption constant \(a\) increased gradually. When the qualities of tectonic coal and the quality of virgin coal were approximately equal, the adsorption constant \(a\) value reached its maximum; then, with the continued increase of the quality of tectonic coal, the value dropped.  
Figure 6: N\({}_{2}\) adsorption-desorption of different coal samples. (**a**) Tectonic coal; (**b**) virgin coal; (**c**) virgin coal and mixed coal with the mass ratio of 2:4; (**d**) virgin coal and mixed coal with the mass ratio of 3:3.  
Figure 7: Relationship between the adsorption constant \(a\) and the mass ratio of hard to soft coal.  
From the Langmuir model, we know that the Langmuir constant \(a\) represents the maximum adsorption volume. From our laboratory test, we found that in the mixed coal, when the quality of tectonic coal was equal to that of virgin coal, the maximum adsorption capacity was reached. From the adsorption isotherm, we found that tectonic coal has a higher adsorption capacity than virgin coal. As a result, as the percentage of tectonic coal in the mixed coal sample increased, the total adsorption volume exhibited an increasing trend. For the mixed coal adsorption, the applied force onto the mixed coal sample also had impact on the adsorption capacity. As a result, when the mixed coal sample was placed into the coal sample tank, the applied force would compact the mixed coal sample. If the sample was entirely tectonic coal, the coal sample would be easily compressed and the total pore volume would decrease. Therefore, when the tectonic coal exceeded a certain percentage, the total adsorption capacity would decrease. When the qualities of tectonic coal and the overlying virgin coal were approximately equal, the adsorption volume reached a maximum. Then the value of the adsorption constant \(a\) gradually decreased as the quality of the tectonic coal increased. It could be seen that the change tendency gradually declined after exceeding the maximum value. Moreover, by calculating the slope of the tangent line at a certain experimental point before and after the maximum value, it could be seen that the change trend slowly declined after passing the maximum value.
## 4 Experiments
### Experimental Evaluation
#### 4.2.3 Pose Evaluation  
During the training process, these three tasks are learned jointly and their accuracy is inter-dependent. For evaluation of estimated camera pose accuracy, we test our model on KITTI visual odometry split which contains 11 sequences with ground truth. To compare with prior works, we train our joint network on the sequences as the same setting as [15], that sequences 00-08 are for training and 09-10 are for testing. We also compare our method with a full version and a short version of traditional SLAM approach ORB-SLAM [8]. The input to the full version takes entire sequence while the short version takes 5 frames. We also employ another experiment where all the loss weights are naive. As shown in Table 7, our method outperforms all of the competing  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Dataset** & **Noc** & **All** \\ \hline EpicFlow [33] & - & 4.45 & 9.57 \\ FlowNetS [14] & C+S & 8.12 & 14.19 \\ FlowNet2 [20] & C+T & 4.93 & 10.06 \\ \hline DSTFlow [34] & K & 6.96 & 16.79 \\ UnFlow-C [23] & K & 4.29 & 8.80 \\ GeoNet (FlowNetS) [19] & K & 6.77 & 12.21 \\ GeoNet [19] & K & 8.05 & 10.81 \\ Ours & K & 4.41 & 9.24 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The reported Average end-point error (AEE) on KITTI flow 2015 over all pixels (All) and over non-occluded pixels only (Noc). C denotes dataset FlyingChairs, S denotes Sintel and T is FlyingThings3D.  
Figure 6: Comparison of optical flow estimation between [PERSON] et al. [19] and ours on KITTI Flow 2015 dataset. The ground truth is interpolated for visualization purpose. Our method performs better in both occluded and overall regions.  
baselines. Our occlusion-aware geometric constrained model is able to capture high-level and more reliable details.