esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Battery Pack Protection Strategy Development
### Solution 2: Integration of Bionic Thin-Walled Energy-Absorbing Structures  
Preliminary side pole collision simulations reveal that the side impact beam and door sill beam primarily absorb energy through bending deformation. This finding necessitated the design of a structure relying mainly on bending deformation for energy dissipation.  
Figure 11: Observation points of the battery pack.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Status** & **L\({}_{\text{J}}\)/mm** & **L\({}_{\text{2}}\)/mm** & **E/J** & **\(M\)/kg** \\ \hline Before optimization & 397.5 & 343.5 & 8045.0 & 33.6 \\ After optimization & 313.0 & 257.0 & 11,126.6 & 33.2 \\ Percentage change/\% & \(-\)21.3 & \(-\)25.2 & \(+\)38.3 & \(-\)1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multi-objective optimization results.  
Studies on thin-walled energy-absorbing units [36; 37; 38; 39] indicate that multi-porous structures effectively absorb energy through bending deformation. Inspired by natural structures such as cattail fluff and lotus root cross-sections [38; 40], an innovative bionic energy-absorbing structure is proposed (Figure 12). This structure mimics the multi-porous nature of these biological models, aiming to convert kinetic energy into internal energy through structural deformation and cavity wall buckling upon impact. The proposed structure is designed for integration with the battery pack's right longitudinal beam, with the goal of enhancing the overall safety performance of electric vehicle battery assemblies in side impact scenarios.  
The geometric parameters of the thin-walled energy-absorbing structure are derived from the dimensions of the side longitudinal beam in an electric mini commercial vehicle. The structure has a length of 200 mm and a thickness of \(T\) = 1 mm. The outer tube diameter is \(D_{1}\) = 72 mm, the inner tube diameters are \(D_{2}\) = 40 mm and \(D_{3}\) = 20 mm, and the reinforcement tube diameter is \(D_{4}\) = 8 mm. The structure includes five ribs, each with a thickness of 1 mm. The angle between adjacent ribs is \(\theta\) = 72\({}^{\circ}\).  
The material selected for this structure is B340/590 DP steel, which has a density of 7900 kg/m\({}^{3}\), a [PERSON]'s modulus of 213 GPa, and a Poisson's ratio of 0.3. Since the front energy-absorbing box and side longitudinal beams of the vehicle under study are made of B340/590 DP steel, which is known for its good energy-absorption performance, this steel was chosen as the raw material for the energy-absorbing structure. This selection not only ensures efficient energy absorption but also facilitates assembly and welding in actual production, as it is the same material as the side longitudinal beams.  
As shown in Figure 13, the bionic lotus root-like thin-walled energy-absorbing structure is embedded in the middle section of the longitudinal beam adjacent to the battery pack.
## Data and Methods
### Multiple Linear Regression  
Multiple linear regression (MLR) is a widely used method for analyzing the correlations of climatic factors with variations in hydrological and ecosystem variables [9; 31; 33; 52; 53]. It is primarily utilized for examining the linear correlations between several independent variables and a single dependent variable. In addition, MLR depends on the least squares method, which is used to minimize the sum of squares of differences between actual and predicted values of the dependent variable. In this study, an MLR was performed to quantify the relative contribution of the climate extremes to interannual variations in the ecosystem variables. The processing steps are mainly modified from [PERSON] et al. [29]. Prior to applying the model, all independent variables and dependent variables were first normalized as follows:  
\[X_{i}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where \(X_{i}\) represents normalized variable; \(x_{i}\) represents the value of the detrended sequential data; and \(x_{min}\) and \(x_{max}\) represents the minimum and maximum values of the detrended sequential data, respectively. This processing eliminates the effects of dimension and enhances the signal of interannual variations in the original sequential data. Multiple regression was then performed, where individual normalized ecosystem variables were dependent variables and normalized climate indices were independent variables. The general formula of multiple regression is shown as follows:  
\[EI^{\prime}=\sum_{i=1}^{n}\beta_{i}X_{i}+\varepsilon \tag{2}\]  
where \(EI^{\prime}\) represents individual normalized ecosystem variables; \(X_{i}\) represents normalized predictors of climate indices; \(\beta_{i}\) represents standardized regression coefficients which directly indicate the relative sensitivity of individual climate indices; \(\varepsilon\) represents the regression constant; and \(n\) represents the number of independent variable. In a stepwise multiple regression model, the \(p\)-value for an F-test of the change in the sum of squares for error (SSE), determines the removal and addition of the term in the model. Candidate variables from the 21 climate indices were selected for the model based on the 0.1 significance level. However, for an MLR, all of the selected variables were chosen to model the
# Ultra-Thin Metamaterial Beam Splitters
## 4 Partially Polarizing Beam Splitter  
In this section, to further illustrate the versatility of metamaterials in designing on-demand beam splitters, we present an example PPBS useful in constructing a common linear optical quantum CNOT gate [17]. The desired splitting ratios for such an ideally lossless PPBS are the followings: \(R_{TE}=1\), \(R_{TM}=1/3\), and \(T_{TM}=2/3\). We begin with the geometry of PBS and then optimize the structure to satisfy the requirement. The geometric parameters to obtain the required PPBS are \(w_{x}=260\) nm, \(w_{y}=140\) nm, \(\Lambda_{x}=600\) nm, \(\Lambda_{y}=560\) nm, \(t_{Ag}=15\) nm, and \(t_{MgF_{2}}=30\) nm. The results calculated through simulations are \(R_{TE}=0.75\), \(T_{TE}=0.02\), and \(R_{TM}=0.25\), \(T_{TM}=0.5\). Since there is always absorbance inside the structure, we cannot obtain the lossless transmittance and reflectance. However, we need to point out that in order to achieve the same probability output under different polarization, the transmittance and reflectance for TE and TM polarization should scale down with the same scale factor from the ideal case, respectively. For example, the specific PPBS design here ideally requires approximately 67% transmittance for the TM-polarized light. If this transmittance is not achieved due to absorbance, then all the reflectance and transmittance values must be scaled by the same factor. This inevitably introduces some inefficiency in the design. Once the reflectances and transmittances are scaled proportionally considering those losses, it can be seen that the largest disagreement with the ideal PPBS is only 2%, which corresponds to \(T_{TE}\).
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 5 Validation of the New Objective Function
### CFD Simulation and Validation of the New Objective Function  
From the CFD simulations, the shear stress and streamline curvature were obtained in the cross sections for each of the 12 positions. It was assumed that the flow component in the tangential direction to the intersection had little contribution to the deburring. Thus, the values of the new objective function were calculated in terms of the cross-sectional values. As in the case of preliminary test simulations, the observation point definition of 0.1 mm offset from the vertex along the angular bisector was adopted.  
The new objective function values at the 12 positions for the forward and backward flows are listed in Table 10. Figure 29 is a graphical representation of Table 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Position**} & **Forward** & **Backward** & **Average** \\  & **(Nm/s2)** & **(Nm/s2)** & **(Nm/s2)** \\ \hline
1 & 10,910.4 & 1662.9 & 6286.7 \\
2 & 1124.7 & 458.4 & 791.6 \\
3 & 3204 & 2197.6 & 2700.8 \\
4 & 4595.4 & 3491.0 & 4043.2 \\
5 & 11,027.9 & 5955.4 & 8491.7 \\
6 & 16,173.9 & 15,396.9 & 15,785.4 \\
7 & 6958.5 & 3962.9 & 5460.7 \\
8 & 276 & 192.2 & 234.1 \\
9 & 629.5 & 458.8 & 544.2 \\
10 & 346.6 & 311.2 & 328.9 \\
11 & 627.4 & 394.9 & 511.2 \\
12 & 4736.2 & 2884.6 & 3810.4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: New objective function values at 12 positions (Figure 24) for the forward and backward flows.  
Figure 28: ANSYS Fluent CFD model for deburring at the intersection between the offset holes: (**a**) tetrahedral mesh for the specimen; (**b**) physical specimen.  
As shown in Table 11, the ratio of the average values for position 1 to position 4 is 1.55 for the new objective function while it is 0.41 for the previous objective function suggested by [PERSON] et al. (2018). The ratio of the deburred areas from position 1 to the position for flow lengths of 56 km and 112 km are 1.50 and 1.58, respectively. The new objective function based on CFD simulation seems to represent the deburring performance with reasonable accuracy.
## 2 Experiments  
We prepared two groups of samples. Group 1 was for measuring the resonant frequency of the LSPs, i.e., a 4-m-thick undoped GaN (u-GaN) layer sample grown by metal-organic chemical vapor deposition (MOCVD) on a \(c\)-sapphire substrate. Group 2 was for measuring the EFs in the SP-LEDs, i.e., green InGaN/GaN LEDs grown via MOCVD in which the epi-structure of the sample consisted of a 30-nm-thick low temperature GaN buffer layer, a 4-m-thick u-GaN template layer, a Si-doped n-Al\({}_{0.025}\)Ga\({}_{0.975}\)N layer, 10 pairs of GaN/In\({}_{0.05}\)Ga\({}_{0.95}\)N superlattices (SLs), and 5 pairs of MQWs composed of a 3-nm-thick QW sandwiched between 15-nm-thick GaN barriers. On top of the MQWs, a 100-nm-thick Mg-doped p-GaN layer was grown. Then, the Ag thin film and Au thin film multilayers were sequentially deposited on top of the u-GaN layer for Group 1 and on p-GaN for Group 2 via electron beam evaporation in an identical manner. To form the self-assembled double-metallic NPs, all the samples were thermally annealed using a rapid thermal annealing system in N\({}_{2}\) atmosphere for 10 min at 250 \({}^{\circ}\)C. Figure 1a,b shows a schematic illustration of the process of sample preparation. Here, we changed the thickness of the Ag and Au thin film layers to control the ratio of Ag to Au in the NPs. We fixed the total thickness of the Ag/Au multilayer to 4 nm to maintain similarly sized double-metallic NPs for each sample. Table 1 summarizes detailed information of the deposited Ag and Au layer thicknesses required to form the self-assembled double-metallic NPs.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 4 Results
### Color-Biased MNIST Dataset  
The color introduced in the dataset misled the model while performing the digit classification task. The model learns the color features instead of learning digit features to categorize the digits. We use the DcCNN model to remove color bias from features learned by the network. The performance of the DcCNN model is compared with existing methods such as Adversarial Training [6] and the Blind Eye method [19]. Adversarial Training without Pre-trained model (Advers Training-no Pretrain) is the same as the Adversarial Training model but it is trained from scratch without using any pre-trained parameters. The baseline model is trained with no decorrelation function, i.e., \(\lambda=0\) which means bias mitigation is not performed. The results are included in the Table 4 and the variance values (Var) control the amount of color bias in the dataset.  
The results show that our model DcCNN achieves almost the same accuracies as adversarial training and is better than all other methods for all values of variances. However, in order to achieve the same results using the Adversarial Training method, we need to use pre-trained parameters. If we don't utilize a pre-trained model and train the model from scratch, then there is a lot of fluctuation in the accuracies. In fact, as shown in Table 4, accuracies dropped by a significant amount for low variance values for Adversarial Training without using the pre-trained model. This indicates that the Adversarial training algorithm is very unstable, and it also requires a lot of fine-tuning. Thus, the DcCNN model is simple and requires less fine-tuning since it has only one hyperparameter (\(\lambda\)) for fine-tuning to successfully mitigate the color bias while achieving high performance.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Presentation of Prediction Results and Analysis
#### 3.5.2 Single-Step Prediction
The predicted power curves from the different models are shown in Figure 15. The scatter of prediction results for the original model and several extracted optimization models are shown in Figure 16. The scattering of prediction results shows that the LSTM network model is able to overcome noise to achieve accurate and effective predictions by learning data pre-processed with the appropriate SG filter.
## 3 Numerical Simulation  
### UDEC Trigon Model  
In order to clearly reproduce the spalling of the inby fender and the subsidence of the roof in numerical modelling, the distinct element code UDEC is selected in this study.  
Figure 5: Break structure of the fine-grained sandstone roof after the longwall panel is completed. (**a**) the detached block is located in the gob, (**b**) the detached block is located above the supports, (**c**) the detached block is located above the recovery room.  
A rock mass in the UDEC Trigon model can be represented as a collection of triangular blocks joined at their contacts. These contacts will break if the force exerted on them exceeds their tensile or shear resistance [15]. The mechanical performance of triangular blocks is controlled by the Coulomb friction law, as indicated in Table 1. The UDEC Trigon model can offer an accurate representation of fracture initiation, propagation, as well as the behavior of the rock masses after failure. The effectiveness of the UDEC-Trigon method in studying fragile fracturing has been acknowledged and verified on scales ranging from the laboratory to the mine [16].
# Evaluating the Degradation of WE43 for Implant Applications: Optical and Mechanical Insights
## 2 State of the Art
### Degradation Experiments on Magnesium Alloys  
[PERSON] et al. developed an experimental test chamber where a dynamic flow state of the body replacement fluid can be set for the abstraction of a blood vessel. The samples are placed within a single tubular section, with a pump installed ahead of it. In addition, the change in the pH value was recorded over the duration of the experiments. The degradation rate is established by assessing the Mg\({}^{2+}\) concentration using an atomic absorption spectrophotometer [24]. Other approaches for determining the degradation rate are electrochemical measurement using electrochemical impedance spectroscopy or determination of the mass loss by weighting. The same tendencies can be seen in the results, whereby the determination of the weighting cannot be carried out continuously during the decomposition process, but only when the process is interrupted or terminated [25].  
The determination of the degradation rate using the mass loss approach (weighing) was compared with the volume loss approach using \(\mu\)CT measurements in the work by [PERSON] et al. Both approaches provide similar results, but the volume loss approach is particularly suitable for very small samples and short degradation times. In addition, the degradation products do not have to be removed with this approach. However, the measurements and data analyses are time-consuming. The mass loss approach (weighing), on the other hand, is faster and can always be performed [6].  
A simplified experimental setup for measuring the influence of degradation on the mechanical properties and the degradation form is described in the work of [PERSON] et al. [26]. Here, the degradation rate was determined on the basis of H\({}_{2}\) evolution. For this purpose, a burette was fixed over the sample and the rising gas was collected. To ensure a uniform pH value in the body replacement fluid, a magnetic stirrer was placed at a  
Figure 2: Idealised target degradation rate in relation to bone formation (modified after [10]).  
certain distance below the sample. Parafilm was wrapped around the areas to be protected so that the sample was not exposed to the fluid in all areas [26]. The same approach for establishing fluid circulation was also used by [PERSON] et al. [27].
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 7 Experimental Analysis  
### Static Experiment  
A Huawei Honor V9 (HV9) and XiaoMi8 (XM8) smartphone were used to collect static data. The collection time was about 30 min, and the sampling frequency was 50 Hz. Three methods were used to calculate the heading using these data. The first was the heading calculated by the outputs of the magnetometer (the method discussed in Section 3.1), the second was the heading obtained by a gyroscope (the method introduced in Section 3.2), and the last was the proposed method by this paper.  
Figure 2 shows the heading error of different methods. The solid line is the error of heading calculated by magnetometer (Mag), and we can see that the margin of error is about 5 degrees eventhough the data are static in Figure 2a, and the jump range of error is 1 degree for XM8 in Figure 2b. The dashed line is the error of heading obtained by gyroscope (Gyr). The error is low and smooth in the short term, but it gradually accumulates with time, and the maximum error reaches 50 degrees for HV9. The chain line expresses the error of heading estimated by the proposed method that the AKF based on AR is used to estimate heading (ProA). We can see from Figure 2 that the drift error of heading is effectively contained by the proposed algorithm compared with the method of Gyr. Compare with the method of Mag, the error is smooth and accurate for XM8, however, the result of Pro is worse than that of Mag for HV9 with time. We can see from Equation (32) that the accuracy of a heading calculated by Gyr will affect the result of ProA, so the accuracy of ProA is affected by the cumulative error of Gyr in Figure 2a. Comparing Figures 2a and 2b, we found that the heading calculated by XM8 is better than that estimated by HV9. That's because the sensors accuracy of XM8 are better than that of HV9.  
In summary, the heading calculated by a single sensor embedded in a smartphone still has disadvantages even under great observation.
# Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
## 3 Short-Term Emotion Recognition with Single-Pulse PPG Signal
### Feature Extractor Using Single-Pulse PPG Signal
#### 3.2.3 1D-Convolutional Neural Network  
Deep learning frameworks, particularly CNNs, are widely used for learning features from labeled data. CNNs have been shown to be effective feature extractors for various fields. Basic CNNs are composed of successive convolutional and pooling layers with a nonlinear function before pooling and fully connected classification layers. The final layer within the classification layers is commonly a softmax layer to score the class probability. The class is determined by a value with the greatest probability.  
Figure 7 shows the proposed 1D CNN architecture to extract PPG signal features to detect the valence class. Network input was a preprocessed single pulse PPG signal which was a \(140\times 1\) vector. Since the physiological signal was a 1D vector, the convolutional operation was also a 1D operation. Feature extraction included two pairs of convolutional and pooling layers with activation functions, C1, C2, S1, and S2; where convolutional filters C1 and C2 were \(3\times 1\). The C1 layer contained 10 convolutional filters with stride 1 to allow padding, hence the C1 layer output 10 feature maps. Between the C1 and C2 layers, C1 outputs were subsampled in pooling layer S1 to reduce the features to only meaningful features. We used max pooling to return the maximum values in the \(2\times 1\) filters. The second convolutional layer, C2, contained 20 convolutional filters and the same process was repeated as in C1. To avoid overfitting, we introduced a batch normalization layer after each convolutional layer, which also resolved gradient vanishing and improved learning speed. Rectified linear unit (ReLU) was used as an activation function.  
A total of 700 features were output from the second max pooling layer, F1. The hidden layer, F2, was formed by 600 nodes with a fully connected layer and ReLU layer was also used as a activation function. The final (classification) layer used the softmax to classify the level of valence (high or low). It calculates the probability of high valence and low valence. A higher probability value represents the level of valence (high or low). Likewise, the CNN model of the same structure with Figure 7 is applied to the arousal case that classifies the level of arousal (high arousal or low arousal).  
Figure 7: Proposed one-dimensional (1D) convolutional neural network (CNN) architecture for valence.
## 1 Introduction
### Accompanying Development Model and Software Life-Cycle with Proper Software Structure
#### 1.7.2 Microservices Architecture  
What is the microservices architecture? It is a development concept in which the entire software to be built is broken down into several small, independent, and loosely coupled services that communicate with each other. The communication between those microservices can be carried out using HTTP, WebSockets, AMQP, or even MQTT. The simplest way to explain the difference between microservice-based and monolithic architectures is to demonstrate the following Figure 7:  
Figure 7: Monolithic vs. microservice-based software structure.  
Obviously, connecting such a decomposition approach to the idea of a SCRUM-like decomposed process when developing smaller fractions/modules of the CubeSat software makes perfect sense. Practically, this will allow the CubeSat teams to develop different software modules simultaneously, isolate quality issues and errors in a particular module of the software, reuse someone's else modules in a much simpler manner, and use a bigger development team to shorten the time of the development. Having said that, the one big limitation still remains unsolved is the non-continuous and low-to-medium-skilled development teams. Microservices architecture is based on a strong and well-designed message and event exchange structure, where the typical undergraduate and postgraduate students are simply not skilled enough to reach the interfacing agreement.