esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Classification Algorithms  
For classification, we considered two of the most widely used supervised algorithms in time series classification: LSTM-based classifiers and SVM [16; 30; 31; 36; 37; 38; 39; 49]. In the case of LSTM-based classifiers, we have employed models based on simple LSTM, Bidirectional Long Short-Term Memory (BiLSTM) and ConvLSTM with typical parameters (refer to Figure 3, and Table 1). BiLSTM provides additional training by analyzing the data from  
Figure 2: Proposed workflow. left to right and right to left [50]. On the other hand, ConvLSTM is a type of LSTM that includes a convolution operation inside the cell [51]. Thus, such models are useful to analyze data sequences with spatio-temporal information [37; 52; 53]. The decision function of the LSTM-based classifiers relies on the value of the softmax function of the classification layer according to:  
\[\delta(x_{i})=\operatorname*{argmax}_{j\in[1,\ldots,|L|]}\frac{e^{z_{j}}}{ \sum_{k=1}^{l}e^{z_{k}}}, \tag{1}\]  
where \(z=\{z_{1},\ldots,z_{l}\}\) is the intermediate output of the softmax layer for the test data point \(x_{i}\). With regard to SVM, the decision function depends on \(n_{s}\) vectors that help to form the class separation hyperplane (support vectors) with their respective weights \(\alpha_{j}\) and classes \(y_{j}\) and a kernel function that makes it possible to transform the input data \(K(\cdot)\) to another dimension, where \(b\) is a scalar value and \(\operatorname{sign}(\cdot)\) is the sign function [54; 55; 56]:  
\[\delta(x_{i})=\operatorname{sign}(\sum_{j=1}^{n_{s}}\alpha_{j}y_{j}K(\vec{x}_ {i},\vec{x}_{j})+b), \tag{2}\]  
In SVM classifiers, we apply kernel rbf to capture the nonlinear patterns generally present in temporal data such as COVID-19. The selected kernel is defined as \(K(\cdot)=\exp(-\gamma||\vec{x}_{i}-\vec{x}_{j}||)\) with \(\gamma\) a hyperparameter that controls the tradeoff between error due to bias and variance in the model, keeping the other parameters as default (refer to Table 1) [33; 57]. Finally, in both classifiers we considered \(s=4\) time steps for each of the \(i\)-th sample features [58; 59]. In the case of SVM, an average of the four time steps was considered to form an \(f\)-dimensional feature vector at each time instant.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 2: Remain the Structure of the IEEE 802.11p
#### 3.2.1 Wiener Filter (WF) Based Scheme  
The LS estimation is not sufficient for the rapidly varying channel conditions. Therefore, a work in [14] proposes a Wiener filter based estimation. The Wiener filter is deployed between the channel estimation and equalization. It searches the optimal coefficients to minimize the mean square error (MSE) of the channel response. The design of the Wiener filter is very important in order to gain channel estimation quality. The Wiener filter is based on three parameters such as, (1) the estimated SNR as described in [34]; (2) maximum excess delay \(\tau_{max}\); and (3) the power delay profile (PDP), which is represented as \(P(\tau)\). In the vehicular time-varying channels, the derivation of these parameters is complex and quite a challenging task, especially the \(\tau_{max}\) and \(P(\tau)\). One simple approximation is to consider the value of \(\tau_{max}\) equal to 1.6 \(\upmu\)s, which is the time taken by the GI. The shape of PDP can be assumed as rectangular or exponentially decaying. It is observed through the simulation results that the Wiener filter application can achieve better MSE, which eventually improve the packet error rate (PER). However, the assumptions related to the value of \(\tau_{max}\) and the shape of PDP can lead to suboptimal performance due to the uncertainties due to these assumptions.
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 5 Data Integrity Algorithms
### SHA 256 Hash Function  
Hash functions are one-way functions that take a variable length input and result in a fixed length output called a hash value or a digest value. Strong hash functions should have the properties of collision resistance, preimage resistance, and second preimage resistance [38]. Collision resistance means that it is computationally infeasible to have two different hash inputs that output the same hash value. Preimage resistance means that knowing a specific hash value, it is computationally infeasible to find an input hash that outputs that specific hash value. Preimage resistance gives hash functions their one-way characteristic. Second preimage resistance means that knowing a specified hash input, it is computationally infeasible to find a second hash input that outputs the same hash value.  
In a secure boot process, hash functions are applied to the software to be validated to output the validation value; therefore, only approved strong hash functions should be used. This is because collision resistance guarantees that any change in the software will result in a different hash value, which will fail the validation process. Also, second preimage resistance guarantees that an attacker will not be able to find malicious software that generates a similar hash value to the authentic software, even if the attacker has knowledge of the authentic software. And lastly, preimage resistance guarantees that even if the reference validation value is not confidential, the attacker will not be able to produce malicious software that will bypass the validation.  
SHA 256 [39] is a hash function from the SHA-2 family of hash algorithms, which are approved by the National Institute of Standards and Technology (NIST) [38]. SHA 256 outputs a 256-bit hash value and has 128-bit collision resistance strength, and 256-bit preimage resistance strength and second preimage resistance according to Equation (1):  
\[\text{second preimage resistance}=256-\text{L}(\text{M}) \tag{1}\]  
where L(M) is defined as  
\[\text{L}(\text{M})=\text{Log}_{2}\bigg{(}\frac{\text{Len}(\text{M})}{512} \bigg{)} \tag{2}\]  
and Len(M) is the length of the software in bits [38].  
Since SHA 256 does not require a cryptographic key, it is recommended to use a unique ECU identifier in the hash computation. This makes the reference validation value ECU-specific and prevents attacks on a single ECU from scaling to similar ECUs that execute the same software [29].  
Aside from being used for integrity checks, hash functions are the building blocks of multiple cryptographic algorithms, such as HMAC, ECDSA, and RSASA-PSS. Hash functions are also used in pseudo-random number generators (PRNGs), in the construction of block ciphers, in key derivation algorithms, and in digital time-stamping algorithms [40].
## 3 Satellite Precipitation Uncertainty Distribution Approximation
### Comparison with Rain-Gauges Network  
The ability to approximate the observed satellite-gauge uncertainty (relative bias) distributions using a single versus a mixture of two Gaussians is presented in Figure 9. As mentioned in Section 2.2.3, the main feature of the observed uncertainty distributions is the systematic underestimations found throughout the entire precipitation range (seen in Figure 3). Overall, the GMM model is clearly a more suitable choice compared to the single Gaussian model.  
The single Gaussian model showed several weaknesses, especially in representing the observed underestimation peaks--the highest densities of which were found at \(-30\%\) RE (Figure 3). Although there was a tendency for the concentration of residues to decrease as precipitation increased, the pattern of uncertainty was persistent throughout the range of precipitation accumulation. In fact, the Gaussian model tended to increase the weaknesses (less high-skewed) distributions, over- (under-)estimating the positive (negatives) uncertainties and varying as a function of the precipitation accumulations. In addition, an upward shift of the RE medians and density peaks distributions of about +60% and +80%, respectively, was observed at precipitation accumulations of up to 10 mm day\({}^{-1}\).  
In contrast, the GMM model properly captured the general uncertainty distribution in all precipitation bins (from 1 to 80 mm day\({}^{-1}\)), especially the large positive weakness shapes of low-to-moderate precipitation amounts (e.g., 1-10 mm day\({}^{-1}\)). At high precipitation amounts, GMM had an opposite residual performance with a more positively skewed  
Figure 9: Density scatterplot of the **(a)** Gaussian and the **(b)** GMM (with M = 2) models and its respective residuals **(c,d)** in representing the relative error distributions (\(y\)-axis, in %) as a function of precipitation (\(x\)-axis, in mm day\({}^{-1}\)) for the relative bias from the GSMaP NRT V6 precipitation product compared with the ground-based over Brazil (land-only) during the period of DJFM of 2014/5/6. Black crosses indicate residual values above/below the \(\pm\)0.2 density values. Solid and dashed black lines in c-d correspond to the differences between the model and the observed median (Q\({}_{2}\)) and the density peaks (D\({}_{P}\)), respectively.  
distribution than the Gaussian model (less right-skewed). The density peak as well as the median were correctly located at the negative RE distribution portion. Nonetheless, although presenting an overall lower residual feature with nearly constant and unbiased median differences, the GMM model slightly shifted the RE density peaks upward (overestimated) in roughly +7% of precipitation of up to 20 mm day\({}^{-1}\). According to [PERSON] et al. [17], such error modeling weaknesses at large precipitation amounts can be attributed to the model's difficulty in capturing the nonlinear behavior due to satellite data clustering saturation.
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
## 1 Introduction
Other selection factors for input layers are the availability of data, the computation resources (e.g., processing time and capacity), and data storage [15; 19; 21]. In this context, the results of this study shall indicate whether the usage of fine resolution layers is non-neglectable for NBS suitability mapping for the NBS Toolkit. Based on the existing literature in other research fields, we hypothesise that for urban areas, fine resolution input datasets are required, while for rural areas, coarser resolutions may be acceptable. To test this hypothesis, firstly, we introduce the test study area and NBS and its suitability criteria (Section 2). Secondly, we identify open-source European datasets to represent the suitability criteria (Section 3.1), and describe the methods used for the suitability mapping (Section 3.2), as well as analysis and validation of the results (Section 3.3). Section 4 will present the results, which will be discussed in Section 5, and concluded in Section 6.
## 3 Results
### Satellite Inversion 10 m Wind Field
#### 3.3.1 HY-2C, CFOSAT Inversion 10 m Wind Field Distribution
The figure shows that under common wind speed conditions, the HY-2C retrieval results tended to be lower in most areas. At the same time, CFOSAT exhibited lower values in the northern part and higher values in the southern region. Hence, the CFOSAT satellite retrieval product better agree with the WRF simulation results under common wind speed conditions. The selected transit data for moderate wind speeds conditions included the HY-2C satellite retrieval data for 10 October at 10:28:44 and 18 October at 23:13:03, as well as the CFOSAT satellite retrieval data for 10 October at 10:19:50 and 18 October at 23:07:33. Moderate wind speeds were defined as wind speeds ranging from 6 m/s to 12 m/s. The figure shows that under moderate wind speed conditions, both satellite retrieval products tended to exhibit higher values in the northern part and lower values in the southern region. The transit data for high wind speed conditions included the HY-2C satellite retrieval data for 16 October at 08:45:15, as well as the CFOSAT satellite retrieval data for 16 October at 09:56:42. High wind speeds were defined as wind speeds greater than 12 m/s. The figure shows that both satellite retrieval products exhibited minor deviations in most sea areas. Overall, the HY-2C satellite retrieval product generally showed higher values, while the CFOSAT satellite retrieval product fell within the \(-1\) to 1 m/s range.
## 4 Results  
### Experimental Settings  
We evaluated rice parameters using Sentinel-1 data with standard machine learning approaches. The aim of these approaches is to use the ground measurements to train models (based on DaS with single polarization (VV or VH) and dual-polarization (VH and VV)) to predict rice height and biomass values. We note that using both VV and VH polarizations we are able to estimate DaS with the RMSE less than 3 days. There is any ways to combine VV and VH polarizations (e.g., VV+VH, VV-VH, VV/VH, etc). However, to avoid many model considerations and also to verify the helpfulness of combination VV and VH, we do a Principal Component Analysis (PCA) S  
that the parameters were quite similar in both single and dual polarizations. For the \(SVR\) model, we use the RBF kernel with the complexity parameter of 50 and the gamma of 0.9 for rice biomass, whereas, for rice height, they equal to 0.7 and 60, respectively. For the \(RF\) model, we set the number of parameters to be used at each node split at 7 and the number of trees at 80 for both rice biomass and height estimations. For all three methods, we use the _Matlab_ implementation provided by the Regression Learner.
## 3 Sample Preparation for Carotenoids Determination
### Saponification and Chemical Modifications  
After extraction, alkaline saponification is commonly used to hydrolyze carotenoid esters into their free forms while removing interfering lipids and chlorophylls, improving chromatographic resolution [87]. This step typically involves potassium hydroxide or sodium hydroxide, with concentrations adjusted based on the sample matrix and carotenoid composition [87; 88]. However, alkaline-sensitive carotenoids such as lutein, violaxanthin, and astaxanthin can degrade, leading to byproducts like astacin [87; 89]. For these compounds, enzymatic hydrolysis is recommended as a milder alternative.  
Studies have examined the impact of temperature and reaction time on carotenoid stability during saponification. While earlier research suggested that prolonged exposure to high temperatures promotes degradation and isomerization, [PERSON] et al. (2023) found no significant loss of carotenoids across various temperatures (25-65 \({}^{\circ}\)C) and reaction times (10-70 min) when using a 15% KOH solution [90]. These findings indicate that controlled saponification conditions can effectively process carotenoid-rich samples without excessive degradation.  
In some cases, chemical modifications are applied to improve the stability, detection, and ionization efficiency of carotenoids in HPLC and MS-based analyses. These modifications enhance chromatographic behavior and refine identification and quantification. Protonation strategies, such as the use of 1% formic acid in MeOH, have been shown to stabilize diapocarotonoids and improve ionization efficiency during LC-MS analysis, facilitating the detection of low-abundance carotenoid metabolites [20]. Additionally, ammonium acetate and ammonium formate buffers are commonly used to enhance ionization stability in high-resolution mass spectrometry [18]. These approaches significantly contribute to the accurate profiling of carotenoid-derived compounds, supporting further studies on their biological roles and analytical characterization.
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 1 Introduction
The enriched BEV features enable the RPN to predict high-quality 3D proposals, thereby improving network detection performance, especially for detection tasks with smaller targets such as cyclists and pedestrians. Our contributions are summarized as follows:  
1. We propose a new multi-view joint learning and BEV feature-fusion network, which can overcome the information loss with a single view and enrich BEV features by adaptively fusing BEV feature maps. 2. We propose a new module in the voxel-based feature extraction branch that supplements the information lost in the BEV feature map due to the compression of the Z-axis resolution by projecting 3D features in the RV (range view) and SV (side view) and by incorporating them into the BEV feature map. 3. We compare the KITTI dataset with popular 3D object-detection methods. The results show that our network improves the detection performance of cyclist categories. The remainder of this paper is structured as follows. We discuss the related work in Section 2, introduce our 3D object-detection network in Section 3, discuss our experimental results in Section 4, and summarize this paper in Section 5.
## 3 Product Service System Case Study
### Optimization Results and Discussion
The confidence level of availability that the PSS can achieve with the optimized field repair kit is plotted in Figure 5. For each scenario, 10,000 simulation runs were performed with the spare parts failure rate distribution and the periodic PSS usage distribution. The horizontal axis represents the confidence level, while the vertical axis represents the PSS availability. Figure 4: Field repair kit cost optimization results for scenarios analyzed. Figure 5: Availability confidence level of the PSS with optimized field kit in place. In the figure, the topmost curve shows the confidence level of the PSS with the field repair kit optimized for 95% availability. The specific data point singled out shows that this particular PSS with the optimized field repair kit can meet the 95% availability requirement with a 75% confidence level. The second curve shows the confidence level of the PSS with field repair kit optimized for 90% availability. It shows a 70% confidence level for meeting the 90% availability target. Finally, the two optimized field repair kits, in contrast with the original field repair kit in the base scenario, performed particularly well in terms of expected confidence level to meet the PSS availability requirements. Results obtained and shown in Figures 4 and 5 and Table 5 can be a valuable guideline for assessing the PSS availability with a specific field repair kit. Cost savings realized by the adjustment of the field repair kit inventory should be balanced against the expected PSS confidence level for meeting availability requirement promised to customers. The company can also create a mitigation plan based on the confidence level to minimize the risk of not meeting the availability requirement.
# Research on an Improved Method for Galloping Stability Analysis Considering Large Angles of Attack
## 4 Numerical Simulation
### Results of the Numerical Simulations  
#### 4.5.1 Characteristics of the Evolution of Vorticity around the H-Shaped Section  
The evolution of vorticity around the section over a period under the critical wind speed of galloping was obtained in the CFD numerical simulation, as shown in Figure 13. In this section, the Q criterion was used to identify vorticity. When Q > 0, the rotation rate is greater than the strain rate, and this area is represented as a vortex (red part in the figure). When Q < 0, the rotation rate is less than the strain rate, and the motion of the fluid unit is non-rotating (blue area in the figure).  
In Figure 13, it can be seen that there was a pair of obvious and large vortex structures in the wake region of the H-shaped cross-section at a wind attack angle of 70\({}^{\circ}\). The vortex located on the upper side of the section was named U, and the vortex located on the lower side of the section was named D. In the initial stage, when the airflow passed through the upper edge of the H-shaped section, it first caused airflow separation and generated a vortex structure (U1). At the same time, a small vortex structure (D) developed at the lower edge of the H-shaped section. During the T/4 period, the vortex (U1) developed in the main vortex and gradually began to detach from the H-shaped cross section. At this time, the vortex structure (D) below the cross section gradually developed and became larger. In the T/2 period, the vortex U1 generated on the upper surface had already separated from the cross section, and the lower vortex (D) had become the main vortex structure of the  
Figure 12: Analysis of the vibration results: (**a**) vertical displacement; (**b**) amplitude spectrum.  
Figure 13: Diagram of the instantaneous evolution of vorticity in the H-shaped section (the wind attack angle was 70\({}^{\circ}\)).  
H-shaped cross section, occupying the dominant position. At the same time, a new vortex structure (U2) was generated at the upper edge of the H-shaped section, and the vortex gradually developed, separated, and detached. It can be seen that during a cycle, when the airflow passed through the H-shaped section, vortex separation and shedding phenomena continuously occurred in the wake area of the section, which caused periodic forces on it, leading to the aerodynamic instability of the H-shaped section.
# A Narrative Review of Metabolomic Insights into Olive Oil's Nutritional Value
## 3 Techniques Used for Metabolite Profiling and Evolution
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique for the** **Analysis of Olive Oil** & **Strengths** & **Drawbacks** \\ \hline \multirow{8}{*}{LC-MS} & Does not require derivation prior to analysis. & Ionization methods can lead to the formation of dimers or adducts, which within a single compound amplify the spectrum of molecular characteristics. \\  & Ionization methods (such as ESI) can be applied to allow simultaneous of metabolites. & Matrix effects and ion suppression arising from co-eluting compounds are common challenges associated with this methodology. \\  & Sample preparation is relatively simple. & & Systems using TOF have decreased resolution for lower masses. \\  & The possibility of using different analytical platforms, such as time of flight (TOF) analyzers or orbital ion traps (Orbitraps), guarantees high resolution. & Systems with Orbitraps exhibit variations in the MS/MS spectra, depending on concentration. \\ \hline \multirow{8}{*}{GC-MS} & Chemical derivation is able to increase the thermal stability, sensitivity, volatility, and detector response. & Requires compounds in volatile form, and thus chemical derivation can be performed before the analysis. \\  & The application of ionization methods can occur. & The fragmentation caused by ionization can be too extensive. \\  & Capable of overcoming the challenges associated with LC-MS, such as matrix effects and ion suppression arising from co-eluting compounds. & Primarily applicable for the separation and analysis of low-molecular-weight compounds. \\  & Provides superior chromatographic resolution. & To ensure sufficient numbers of points per peak, especially in the case of GCxGC separation, fast acquisition speeds may be required. \\  & It is possible to acquire high-resolution MS/MS spectra (TOF). & Salt and lipids can affect reproducibility and the possibility of comparing data, and hence sample cleanness is required. \\ \hline \multirow{8}{*}{CE-MS} & Small samples and decreased reagent volumes can be used. & Salt and lipids can affect reproducibility and the possibility of comparing data, and hence sample cleanness is required. \\  & Minimal or no organic solvent consumption. & Capillary coating is needed to prevent protein adhesion and, consequently, the alteration of retention times for metabolites. \\ \cline{1-1}  & Incline{1-1}  & The usage of MALDI is not only time-consuming but also requires fractioning on a target plate. It can be associated with loss of resolution. \\ \cline{1-1} \end{tabular}
\end{table} 
Table 1: Commonly employed methods for analysis of the metabolites present in olive oil, along with their respective advantages and disadvantages.