esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Smoke-Exposed Pinot Noir Wines by Volatile Phenols and Volatile Phenol-Glycosides
## 3 Materials and Methods
### Untargeted Data-Dependent Acquisition Analysis of VP-Glycosides by HPLC-HR-MS/MS  
Untargeted VP-glycos analysis in wine was conducted according to [PERSON] et al. (2020) [32] with some modifications. A Shimadzu (Tokyo, Japan) Nexera UHPLC system, connected to a high-resolution AB SCIEX Triplet OF 5600 (SCIEX, Framingham, MA, USA) mass spectrometer equipped with a Turbo V ionization source, was operated in positive electrospray ionization (ESI) mode. Chromatographic separation was accomplished using an Inertsil Phenyl-3 column (4.6 mm \(\times\) 150 mm, 100 A, five \(\upmu\)m; GL Sciences, Torrance, CA, USA). One milliliter of each wine was diluted five times with aqueous methanol (70% \(v/v\)) and then centrifuged at 10,000 rpm for 5 min. From the supernatant, 5 \(\upmu\)L was injected in triplicates. Gradient elution was performed using a mobile phase consisting of solvent A (water containing 0.1% \(v/v\) formic acid) and solvent B (methanol containing 0.1% \(v/v\) formic acid). The flow rate was 0.4 mL/min. The chromatographic method was 30 min, and the gradient was as follows: an initial one min at 5% B, followed by 5 to 30% B from 1 to 10 min, then 30 to 100% B from 10 to 20 min, hold at 100% B from 20 to 25 min, and then return to 5% B from 25 to 30 min.  
Data-dependent acquisitions (DDAs) were performed to obtain precursor and fragment ion information, which facilitated the annotation of compounds in the wine samples. DDA analyses were performed using positive ionization mode (ESI+). The following parameter settings were used for detecting positive ions: spray voltage of 4500 V, source temperature of 550 \({}^{\circ}\)C, and a period cycle time of 950 ms. In addition, the following settings were used: full scan with ion accumulation of 150 ms, followed by a dynamic MS/MS selection of the eight most intense ions with 100 ms accumulation; after two MS/MS acquisitions, the precursor (fragmented) ions were excluded for 30 s; collision energy 35 V with collision energy spread (CES) of 15 V ramped through each MS/MS scan using a range of \(m/z\) 100-1200. Mass calibration was automatically performed after every two hours with a calibrant delivery system.
## 2 Position and Heading Estimation Approach Based on Cross-Modal Image Matching
### Fine Alignment Based on End-to-End Trainable Matching Network
#### 2.2.2 Implementation Details and Discussion  
The network is developed based on [24] which is originally trained on visible light images. Therefore, we build a training dataset with 5000 pairs of infrared aerial image and visible light satellite image. We generate the ground true pairs by applying homography transformation to the manually aligned pairs, since there are no datasets with completely correct transformation parameters between infrared aerial image and visible light satellite image.  
The resolution of the input image is 640 \(\times\) 480. The input image will be divided into four subareas and each subarea is then resized to 240 \(\times\) 240. The feature map is extracted with ResNet-101 network [26] rather than VGG-16 network [27], since the former achieves better matching performance as reported in [28].  
The partition approach increases the resolution of the input image and the resolution of the features extraction, which helps in improving the matching precision. Of course, one can also enhance the precision by inputting the image with high resolution rather than using partition, but that may introduce an enormous increase in parameters to the network, which makes the retraining process (for cross-modal images) difficult and eventually lowers the performance of the inference.  
In the partition approach, the four subarea pairs share the backbone network and the regression network, which means the number of the parameters of the network is not increased. This facilitates the retraining process and the deployment of network, which will eventually enhance the performance of the homography estimation with a modest training dataset.  
During the inference, the global homography transformation for the fine alignment is estimated with RANSAC algorithm [29], which minimizes the re-projection errors of the four homography transformations of the subarea pairs.  
Examples of the coarse matching and the fine alignment are shown in Figure 6. We can see that the features in the results of fine alignment are aligned more accurately compared with the results of coarse matching.  
Figure 5: Image partition. Image (**a**) shows that the input image is divided into four subareas. In image (**b**), the arrow indicates the translation between the four subareas and the real-time image.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Proposed Fusion Network for Change Detection with Panchromatic Imagery
### Fusion Network for Change Detection  
For a change detection, an FDN architecture is commonly used for identifying changed pixels. Such an architecture uses low-level differential features that are relatively sensitive to noises. It is caused by direct low-level features comparison, which misalignments of geometric error and a different angle view are very influential. This FDN assigns a DI or JF to a single path network. They conduct dependent learning of both low-level features together which lead to hard learning for invariant changes and above-mentioned noisy conditions. Thus, this approach would produce a global change detection, resulting in true positives and more false positives. In addition, BDN architectures are designed to avoid low-level differential features, thereby reducing the false-positive detection rate. These architectures apply strict identification for a high-level differential, which may cause some false negatives. Therefore, an FDN is suitable in terms of the true-positive detection rate, and a BDN is extremely reliable for overcoming false positives. To obtain a proper change detection, a fusion network architecture is proposed by fusing an FD-DCN and a BD-dDCN with a dense-connectivity of the convolution layers, as shown in Figure 3. There are three branch networks, \(N^{1}\), \(N^{2}\), and \(N^{3}\), receiving two temporal images (\(I^{1}\) and \(I^{2}\)) in which \(N^{1}\) and \(N^{2}\) correspond to the back-end network, and \(N^{3}\) refers to the front-end network by concatenating these two inputs (\(I^{1}\) and \(I^{2}\)). A dense convolutional connection was employed in the proposed fusion network to enhance the feature representation [35]. This dense architecture is very effective at covering invariant change representations by reusing all preceding feature maps of the network. The proposed network was designed using dual outputs, namely, the dissimilarity distance (\(D\)) and change probability (\(O\)) at the last layer, corresponding to the back-end and front-end networks, respectively. Let us assume that the feature maps of the \(l\)-th layer at the \(r\)-th dense block and the \(i\)-th network are computed as:  
\[F^{i}_{l,d_{r}}=H^{i}_{l-1,d_{r}}([F^{i}_{0,d_{r}}\ F^{i}_{1,d_{r}},\ \dots,\ F^{i}_{l-1,d_{r}}]),\ r=0,1;\ i=1,2,3, \tag{1}\]
## 1 Introduction
[24] carried out a series of subscale experiments by additionally considering the effects of the tunnel structure such as the angle between the inclined shaft and the construction section, the inclined shaft slope, and the length of the tunnel, as well as ventilation parameter settings such as the location of the duct outlet. [PERSON] et al. [25] concluded that the relationship between ventilation time and the three variables of the upper limit of dust concentration, volume flow rate of the forced air duct, and total explosive mass satisfy logarithmic, power, and logarithmic functions, respectively. [PERSON] et al. [13] derived the ventilation time as a function of distance (\(x\)) and pollutant concentration (\(C\)) for the entire tunnel construction period through a series of numerical simulations. Later, some scholars studied the calculation of the CO distribution function during tunnel construction in high-altitude environments [26]. A large number of scholars have made outstanding contributions in the field of ventilation during the construction of tunnels. However, the majority of prior research has focused primarily on analyzing the influence of ventilation parameters on the flow field, with less attention given to the conjoined impact of ventilation parameters and tunnel structure. The former conclusions are more applicable to ordinary flat tunnels, and research  
Figure 1: Construction process of a V-shaped tunnel. on ventilation in recent years has predominantly relied on numerical simulation, with limited systematic experimental verification. Systematic experimental research on the ventilation of tunnels with special structures is needed. Therefore, a series of small-scale ventilation experiments focusing on the process of forced ventilation for the construction of a V-shaped tunnel were conducted. The changes in the return flow and pollutant distribution under the influence of ventilation parameters (distance between the duct outlet from working face \(L_{0}\), air velocity at the duct outlet \(u_{0}\)) and tunnel structure (digging length \(L_{d}\), slope of the uphill section of the V-shaped tunnel \(\theta\)) were investigated in this paper. The effects of each factor are discussed in detail. The difference between the ventilation characteristics of a V-shaped tunnel and a normal flat tunnel is clarified. Finally, the formula of CO concentration and air return velocity applicable to V-shaped tunnels are derived. This work aims to address the research gap in traditional experience during the construction of V-shaped special-structure tunnels. It also helps to ensure the environmental safety and respiratory health of personnel during the construction of special-structure tunnels.
## 3 Results
### Fpar  
Unlike the MOD_GMAO GPP algorithm, the GMAO_GLASS GPP algorithm followed the MOD17A2H GPP algorithm by substituting GLASS FPAR (derived from GLASS LAI datasets) for MODIS FPAR (derived from MOD15A2H products). Therefore, comparing these two types of algorithms will be helpful for understanding the impacts of FPAR on the GPP estimates. In the case of the eight-day GPP estimates, the effectiveness of the GMAO_GLASS GPP algorithm for estimating the Flux_Tower GPP was much better than that of the MOD_GMAO GPP algorithm (Figure 1a,c; Table 2). Additionally, the GMAO_GLASS GPP algorithm achieved substantial improvements over the MOD_GMAO GPP algorithm for the annual GPP estimates (Figure 3a,c; Table 3). The GMAO_GLASS GPP algorithm estimated the Flux_Tower GPP far more effectively than the MOD_GMAO GPP algorithm for EBF site (Tables 2 and 3).  
In our study, comparisons were made between GLASS FPAR and MODIS FPAR (Figure 5, Table 4). Overall, the correlation between the two types of FPAR was not strong (R\({}^{2}\) = 0.45). The correlations between GLASS FPAR and MODIS FPAR were also weak for every single biome type, except ENF; the correlation between them for EBF was lowest (R\({}^{2}\) = 0.02). Obviously, GLASS FPAR overestimated MODIS FPAR at most sites. This overestimation problem was most serious at GH-Ank (bias = 0.479), PA-SPs (bias = 0.375) and BR-Sa3 (bias = 0.292). Moreover, GLASS FPAR also overestimated MODIS FPAR for all six biome types.  
Figure 4: Scatter plots of the daily GMAO meteorology data against the daily site meteorology data for: (**a**) \(T_{avg}\); (**b**) \(T_{min}\); (**c**) \(VPDavg\); and (**d**) PAR. Significance levels: \({}^{\star\star}\)\(p\) < 0.01.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 2 Static Buckling Criteria
### Static Simulations  
#### 2.3.1 Model  
Several numerical simulations have been conducted to validate the precise expression of buckling formulation and the relevant parameters by regression analysis. The models have simulated identical parameters to the initial studies with thicknesses of 0.05 m and 0.10 m, respectively. In order to obtain the critical load data, including pressure and point load, the different uniform pressure and displacement have been applied 1.0 m at the apex [52; 53; 54]. The bottom of the shell is set to be fixed, as shown in Figure 2a.  
Figure 3 shows the typical results of reaction force versus the corresponding displacement. When the displacement is at 0.10 m, the reaction force reaches its maximum value. Then, it decreases with the increment of displacement, implying that the shell has been buckled. Note that the point load has to be in the inverse direction (negative value) in order to enable the large deformation. The buckling load  
Figure 2: The verification of the model and the result. (**a**) The geometry and finite element model (FEM) of structural shell for analysis; (**b**) The material constitutive model for FEM simulation; (**c**) The plot of reaction load vs. defection (0.3 m).  
can be obtained as \(p=9.8\) MN with \(q=16\) MPa. In addition, the von Mises stress at the apex versus the displacement is depicted in the same figure. The maximum stress is about 415 MPa, which is less than the yield stress, considering that the structure may be failed for either exceeding the limited strength or buckling without exceeding the limited strength, so that the material behaves as an elastic model before buckling.
## 3 Results
### Spectral Index Evaluation  
A total of eight spectral indices were statistically compared to the Gi* z-score from the optimized hotspot analysis. Four indices, namely bare soil index (BSI), green coverage index (GCI), moisture stress index (MSI) and normalized difference moisture index (NDMI), were best described by linear models based on the Akaike information criterion [90], while atmospherically resistant vegetation index (ARVI), green normalized difference vegetation index (GNDVI), modified soil adjusted vegetation index 2 (MSAVI2) and normalized difference vegetation index (NDVI) were best described using quadratic models. Strong (r\({}^{2}\) > 0.8) models were achieved for GCI, MSAVI2 and NDVI, with the latter producing the strongest (r\({}^{2}\) = 0.82) model (Table 3). Figure 4 graphically shows the best performing regression models.
## 5 Numerical and Simulation Results
On account of FC, \(N_{ps}=N_{T}N_{RF}\), and for SC, \(N_{ps}=N_{T}\). Under such analysis, the maximum transmitted power, \(P_{T}=30\) mW. For all precoder algorithms, it is taken for granted to be the same. Every RF chain uses \(P_{rf}\)=300 mW of power. Similarly, power consumption associated with the phase shifter and baseband processing are \(P_{ps}=40\) mW and \(P_{bb}=200\) mW, respectively. Figure 2.3 Spectral efficiency versus the number of iterations for the joint PA and PS optimization. Figure 2.4 Spectral efficiency against SNR [3, 17]. The EE comparison of several precoders versus the SNR variation is shown in Figure 2.5 For this performance analysis, the following parameters are considered: \(N_{T}=64\), \(N_{RF}=8\), \(K=10\), \(\mathcal{L}=10\), and \(B=4\). As seen in Figure 12, it is clear that the EE is worse in an FD system when compared to other systems. This is due to the FD system's energy consumption, which is caused by the fact that the number of RF chains and base station antennas are equal. On the other hand, in an SC system, there are far fewer RF chains. Figure 2.6 Impact of imperfect CSI. Figure 2.7 Energy efficiency against SNR. Figure 2.8 Spectral efficiency variation with the fading parameter \(m\). Because fewer PS are used in SC-HPs than in FC-HPs, they are more energy efficient. The suggested SSOR-CRZF beats the existing schemes. For example, at SNR = 15 dB, the EE for SC-SSOR-CRZF, SC-CRZF, SC-SSOR, SC-Kalman, SC-RZF, and SC-ZF have 2.077 bps/Hz/W, 1.935 bps/Hz/W, 1.870 bps/Hz/W, 0.810 bps/Hz/W, 1.711 bps/Hz/W, and 0.572 bps/Hz/W, respectively. Figure 13 presents a comparison of EE among different precoders as the number of BS antennas (\(N_{T}\)) varies. For this analysis, the following parameters are considered: \(N_{RF}=8\), \(K=10\), \(\mathcal{L}=10\), and \(B=4\). Figure 13 indicates that there is an optimal antenna size that maximizes the EE of the system, given a fixed \(N_{RF}\) chain.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 3 Solution Method
### Discretizing Wind Distribution
#### 3.1.1 Wind Distribution  
The probabilistic description of wind speed can be accurately presented by the Weibull distribution [27]. Due to the great applicability, Weibull distribution has been widely used to describe the probably distribution of the wind speed, which is defined as follows:  
\[f(x;\lambda,k)=\frac{k}{\lambda}(\frac{x}{\lambda})^{k-1}e^{-(x/\lambda)^{k}} \tag{13}\]  
where \(k\) represents the shape parameter, and \(\lambda\) is the scale parameter.  
To obtain the wind power distribution, a linear approximation equation that established the relationship between the wind speed and the wind power is presented in (14):  
\[Y=\left\{\begin{array}{ll}0&if\ x\leq V_{ci}\ or\ x>V_{co}\\ \alpha+\beta x&if\ V_{ci}\leq x\leq V_{no}\\ M&if\ V_{no}\leq x\leq V_{co}\end{array}\right. \tag{14}\]  
where \(M\) is the maximum power of wind turbine; \(\alpha\) and \(\beta\) are the linear coefficients; and \(V_{ci}\), \(V_{co}\), and \(V_{no}\) denote the cut-in wind speed, the cut-out wind speed, and the normal wind speed, respectively.
## 3 Proposed Method for Real-Time Mouse Data Protection Using Generative Adversarial Networks (GANs)
### Overall Flow of Real-Time Mouse Data Protection Method Using GANs  
This subsection outlines the methodology for protecting mouse data using GANs in an image-based authentication system. Figure 1 shows the flowchart illustrating how GANs protect real-time mouse data. The blue and red colors in the figure represent the generation and storage flows of real mouse data and fake mouse data within the system, respectively. The defense tool collects mouse sequence data from a user when they interact with an image-based authentication webpage, applying GANs to generate synthetic mouse sequence data for each authentication page. If the user revisits the authentication page, the defense tool integrates the synthetic data by using the SetCursorPos() function, making the cursor movement reflect the generated fake data.  
This process naturally mixes real and synthetic mouse sequence data. If an attacker collects data using the GetCursorPos() function, they will capture both real and synthetic data that closely resemble real mouse data. Unlike random methods of generating fake data, the proposed method uses previous mouse sequence data, trained using the Conditional Tabular Generative Adversarial Network (CTGAN) [20; 21] to generate synthetic data. There are certain constraints to consider when generating synthetic fake data using CTGAN. First, generating synthetic data with CTGAN requires training time. In our experiment, it takes approximately 1 to 2 min to generate synthetic data for a 5 to 10-s image authentication process, performed on a machine equipped with an NVIDIA GeForce RTX 4090 GPU using CUDA, which is produced in Taipei, Taiwan. More importantly, the entire mouse movement sequence data from the authentication process is necessary, as adversarial learning networks require complete data during the initial training. Consequently, real-time generation is not feasible.  
To address this limitation, this paper proposes pre-generating synthetic data by training on historical mouse sequence data for each user and authentication page, which is then blended with current mouse sequence data. This approach effectively counters real-time data theft attacks.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 4 Design
### Selection of Packet Size Threshold  
By setting \(\alpha\) to 3, as discussed in Section 4.2, MARV selects high throughput paths when the current selected link is a 1 Mbps path and the new path consists of either two 11 Mbps links or one 5.5 Mbps link and one 11 Mbps link. As a result, if the medium time taken by one 5.5 Mbps path and one 11 Mbps path should be smaller than the medium time taken by one 1 Mbps path, the RREQ will be rebroadcasted.  
We calculate the medium time to get the packet size threshold of choosing between min-hop and MTM. The medium time taken by a data payload length \(x\) bytes at \(m\) Mbps data rate are given by  
\[MT_{Rate=m}(L=x)=MT_{PHY}+MT_{MAC}+\left\lceil\frac{x\times 8\ (bits)}{m\times 10^{6}\ ( bps)}\right\rceil \tag{1}\]  
where \(MT\) denotes the medium time, and \(MT_{PHY}\) and \(MT_{MAC}\) are the medium times taken by the physical and MAC overheads, respectively.  
\[MT_{PHY}=tPLCPPreamble+tPLCPHeader \tag{2}\]  
\[\begin{array}{rl}MT_{MAC}=&aDIFSTime+AverageBackoff+RTSTime+aIFSTime\\ &\qquad+CTSTime+aIFSTime+aIFSTTime+\frac{ACKsize}{m(Mbps)}\end{array} \tag{3}\]  
To meet the constraint in the previous section, the following equation should hold.  
\[MT_{Rate=11.0}(L=x)+MT_{Rate=5.5}(L=x)<MT_{Rate=1.0}(L=x) \tag{4}\]  
Note that if the above equation holds, it means that the min-hop metric outperforms the high-throughput path using the MTM metric. Inserting the values yields \(x<240\) bytes. Therefore, if the IP packet size (equivalent to the MAC payload) is smaller than 240 bytes, the source selects the min-hop path, whereas the source selects the MTM metric path when the packet size exceeds 240 bytes.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 2 Related Work
### Visual Pose Estimation
Our pose estimation method predominantly belongs to the second category. By exploiting a shape prior for an antenna and matching its geometric features, we automatically find an initial pose to avoid potential human interaction and any overhead incurred for the building and matching of templates. Moreover, in the subsequent pose refinement step, we construct bounds on the pose search space to transform the original unconstrained optimization problem into a bounded one, which is then solved using global optimization techniques. Recently, depth cameras have begun to be used for 3D pose estimation. However, current consumer-level depth cameras are not capable of detecting objects at long distances. For example, the maximum detection distance for a Kinect v2 is 4.5 m. Therefore, such approaches have limited applicability to our problem.