esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Application of Precipitation Nowcasting  
### Events Selection  
Normal monthly precipitation from 1943 to 2018 over Mexico (see Figure 3), as collected by the National Water Commission, was chosen as the representative precipitation events. It can be seen that the intensity of precipitation is relatively high during the months of June, July, August, September and October. Hence, based on the level of precipitation, five precipitation events were used in the STEPS model for precipitation nowcasting because a large number of precipitation events are more predictable [41]. Each precipitation event presented different characteristics in terms of date and type, as shown in Table 6.  
After processing the GPM precipitation modified data by the STEPS model, the spatial distribution of the nowcast precipitation was obtained as shown in Figure 11. Figure 11a,b are the precipitation estimated by GPM and 30-min forecast precipitation from STEPS on 22 October 2015, respectively. They can be compared with each other to observe the actual and simulated precipitation cloud movements. Eleven figures for precipitation nowcasting from 1 h to 6 h are shown in Supplementary Materials. It can be clearly seen that the nowcast accuracy decreases rapidly with lead time, which was also found by [PERSON] et al. [54]. Please check Figures in Supplementary Materials for precipitation on 22 October 2015 from 9:30 to 14:30, based on (a) GPM estimated precipitation and (b) 30-min forecast precipitation from STEPS precipitation nowcast.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Event**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Time Period**} \\ \cline{3-4}  & & **Start** & **End** \\ \hline
1 & 22 October 2015 & 01:00 & 24:00 \\ \hline
2 & 17 June 2017 & 01:00 & 24:00 \\ \hline
3 & 18 July 2017 & 01:00 & 24:00 \\ \hline
4 & 1 August 2017 & 01:00 & 24:00 \\ \hline
5 & 1 September 2017 & 01:00 & 24:00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dates and durations of selected rainfall events.  
Figure 10: Mean daily precipitation (all gauge stations) between GPM and rain gauges (2015–2017).
## 3 Related Work
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Year** & **Framework** & **Methods** & **Limitations** \\ \hline
[62] & 2014 & BMR & Detecting credit card fraud using the Bayes minimum risk approach to achieve improved results, quantified by monetary savings & Probability estimations are not consistently accurate across all classification algorithms. Additionally, utilizing the full dataset tends to yield better results compared to under-sampling, which is less effective. \\
[68] & 2015 & RMNLS & Utilizing undersampling to address a class imbalance. Incorporating investigators’ feedback to enhance the accuracy of alerts & Non-stationary data distribution. Highly unbalanced class distributions. \\
[63] & 2016 & APATE & Prewetting a transaction by employing a graph-based automated fraud detection system before approving it & Utilizing a redesigned APATE tailored to the e-commerce sector’s realities by incorporating a limited set of confirmed fraudulent transactions \\
[64] & 2017 & PD-FDS & A fraud detection system based on purchase density does not necessitate the presence of pre-existing fraudulent transactions & Balancing between exploration and exploitation. Demonstrates superior performance over offline models, particularly in scenarios involving concept drift \\
[65] & 2017 & SPD & Representing transactions in a bipartite graph enables the identification of suspicious patterns from known compromised cards. This involves defining new attributes to capture suspiciousness and employing a non-linear classifier for evaluation \\
[66] & 2018 & CMAB & A modified version of the Contextual Multi-Armed Bandit algorithm outperforms commonly employed offline models in terms of cumulative rewards & Balancing between exploration and exploitation. Demonstrates superior performance over offline models, particularly in scenarios involving concept drift \\
[67] & 2018 & RawLR & Grouping cardholders according to similar transaction behaviors Aggregating transactions, extracting behavioral patterns, training classifiers, and implementing a feedback mechanism \\  & & FlowScope surpasses state-of-the-art baselines in accurately identifying accounts engaged in money laundering, across both injected and real-world data scenarios & Current methods concentrate on detecting dense subgraphs. Decreased accuracy in detecting high-volume flows of funds. \\
[58] & 2021 & FD-META & Federated meta-learning for enhancing fraud detection. Enhanced triplet-like metric learning and classifier based on meta-learning. \\
[57] & 2023 & APPROX-SMOTE & Enhancing the performance and accuracy of traditional fraud detection models poses a challenge \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of related works and state of the art.
## 2 Materials and Methods
### Description of the Experimental Database for the Cast AlSi10 Mg  
The AlSi10 Mg Aluminium-based alloy is a versatile, lightweight alloy well known for its high strength, dynamic load-bearing capacity, and corrosion resistance. Furthermore, its ductility and conductivity properties can be enhanced via heat treatment. This alloy is commonly used in aerospace and automotive applications, as well as in lightweight design [31]. Within the framework of the BWMD project [30] and out of the 'Use Case Metals', a comprehensive database was generated for the cast AlSi10 Mg. To this end, different manufacturing routes were followed, aiming to improve the mechanical properties of the cast AlSi10 Mg. Various process parameters were applied in the permanent mold casting process, as well as variations of the amounts of Si and Mg in the melt. After the permanent mold casting process, the batches of material produced were heat treated in two steps: solution annealing and artificial aging. Small variations were also introducedduring these two steps by modifying the duration and the maximum temperature applied during the solution annealing and aging heat treatment steps. The resulting manufacturing routes led to several material batches with different mechanical properties, such as tensile strength, ductility, and hardness.  
To investigate and mechanically characterize the different material batches, the heat-treated cast parts were finished as tensile test specimens and metallographic specimens. Those were used to perform tensile tests and Brinell hardness measurements. Additionally, component-like parts were cast, and their defect or porosity distribution was investigated via computer tomography.  
The resulting experimental database requires a data architecture capable of seamlessly linking the data produced by the different processes of the manufacturing process chain to each of the manufactured or investigated objects. Therefore, a Semantic Web solution was sought to provide interoperable data and design a general process structure pattern to enable the construction and retrieval of the digital process chain of an object. The aim is to digitalize the different production routes with all the relevant contextual data from a different viewpoint, creating the possibility of storing process information about an object in terms of its chronological history.
## 3 Spectrum Sensing Quality Analysis
Based on the insight, we model the stability metric of local spectrum sensing by the relative divergence between the entropy of the detection probability distribution and the entropy of the false alarm probability distribution. Thus, the stability metric of local spectrum sensing for the \(m\)-th SSN over the entire uplink channels denoted by \(H_{m}\Big{(}\Upsilon_{d}(m)\|\Upsilon_{f}(m)\Big{)}\) can be calculated as follows:  
\[H_{m}\Big{(}\Upsilon_{d}(m)\|\Upsilon_{f}(m)\Big{)}=\Big{|}H(\Upsilon_{d}(m)) -H\Big{(}\Upsilon_{f}(m)\Big{)}\Big{|} \tag{16}\]
# Torsional Fatigue Performance of a Spot-Welded Structure: An XFEM Analysis
## 3 Results and Discussion
### Influence of Nugget Size  
Second, an analysis was conducted to determine the impact of the spot weld's diameter on the spot-welded box's fatigue life. This size was adjusted for this reason, going from 4.5 mm to 6.5 mm in increments of 1.0 mm. Table 3 presents the obtained \(N_{f}\) values, where they are 20,524, 42,375, and 211,845 cycles, respectively. When the diameter of the spot-welded region was increased, \(N_{f}\) increased. This result brings us to the point that an infinite fatigue life could be attained for the box studied when \(D\) is increased to 7.5 mm or 8 mm. On the other hand, the average crack propagation rate decreased by more than 7 times from \(2.2\times 10^{-4}\) to 3.07 \(\times\) 10\({}^{-5}\) mm/cycle when \(D\) was increased from 4.5 to 6.5 mm. Figure 6 presents the resulting fatigue failure patterns. While the cracks originated and propagated at the central four spot weld twins when \(D\) equaled to 4.5 mm, it was only the 6 th spot weld twin itself that accommodated the overall failure. Here, it was concluded that the spot weld's size had a major impact on the box's fatigue performance. This is consistent with the discovery in [4; 19], where longer fatigue lifetimes were obtained by the bigger nugget diameter due to the notch effect, which led to reduced strains and stresses evolving near the spot-welding site; hence the cracks could originate less possibly.
# Facile Synthesis of Highly Conductive Vanadium-Doped NiO Film for Transparent Conductive Oxide
## 3 Results
### Study of Material Properties
(**e**,f) HRTEM images of both **(e)** NiO and **(f)** 3 at.%V:NiO and **(g)** statistics of the particle distributions of NiO and 3 at.%V:NiO NPs. Elemental analysis was performed via X-ray photoelectron spectroscopy. The survey spectra of pristine NiO (Figure 5a) confirmed the presence of Ni and O elements, whereas Ni, O, and V elements were confirmed in the 3 at.%V:NiO sample (Figure 5d). Typically, NiO 2P\({}_{3/2}\) consists of a main peak at 854 eV, shoulder peak at 856 eV, and shake-up satellite peak at 861 eV [31]. No major changes were observed in a comparison of the Ni 2P\({}_{3/2}\) spectra of both pristine and doped samples (Figure 5b,e). However, in the case of the doped sample, we detected the presence of V\({}^{4+}\) and V\({}^{5+}\) in the binding energy range between 526 eV and 510 eV, which reveals that V atoms were successfully incorporated into the NiO crystal lattice. Interestingly, after V occupied the interstitial sites in the NiO lattice, Ni\({}^{3+}\) ions were possibly induced by V\({}^{5+}\) ions (Figure 5e). Further, we calculated the Ni\({}^{3+}\)/Ni\({}^{2+}\) ratios to be 0.96 and 1.04 for the NiO and 3 at.%V:NiO samples, respectively. The higher density of Ni\({}^{3+}\) states in the 3 at.%V:NiO crystal would contribute to the improvement of p-type conductivity [23; 24; 31].
## 4 Discussion: Crystal Structure Deformation as a Function of the Applied Pressure  
The structure of this material under the effect of different pressures applied along the direction of the minimum Poisson ratio was optimized to analyze the structural variations produced by the application of pressure. The lattice parameters and volumes obtained for 11 different applied pressures along this direction are reported in Table 3.  
Figure 1: Elastic properties of rutherforidine as a function of the orientation of the applied strain: (**A**) Compressibility; (**B**) Young modulus; (**C**) maximum shear modulus; (**D**) maximum Poisson ratio. Figure 2: Computed surfaces of the maximum (**A**) and minimum (**B**) Poisson ratio of rutherforidine. \begin{table}
\begin{tabular}{c c c c c} \hline
**P (GPa)** & _a (Å)_ & _b (Å)_ & _c (Å)_ & **Vol. (Å3)** \\ \hline
0.0 & 4.8257 & 9.3726 & 4.2740 & 193.3119 \\
0.152 & 4.8172 & 9.3656 & 4.2768 & 192.9505 \\
0.318 & 4.8062 & 9.3650 & 4.2823 & 192.7497 \\
0.515 & 4.7959 & 9.3646 & 4.2854 & 192.4651 \\
0.667 & 4.7865 & 9.3637 & 4.2899 & 192.2711 \\
0.826 & 4.7763 & 9.3639 & 4.2951 & 192.0967 \\
1.016 & 4.7670 & 9.3632 & 4.2981 & 191.8452 \\
1.329 & 4.7494 & 9.3638 & 4.3053 & 191.4686 \\
1.636 & 4.7317 & 9.3625 & 4.3133 & 191.0823 \\
1.978 & 4.7146 & 9.3634 & 4.3195 & 190.6871 \\
2.325 & 4.6975 & 9.3628 & 4.3277 & 190.3378 \\ \hline \end{tabular}
\end{table} 
Table 3: Computed lattice parameters and volumes of the rutherforidine unit cell at different applied pressures directed along \(U^{L}_{min}\). The material is compressed along \(U^{L}{}_{min}\) for positive pressures and, as a consequence, the unit cell volume decreases (as the length along the \(U^{L}{}_{min}\) direction, which in this case coincides with the [100] crystallographic direction). However, although the \(c\) lattice parameter increases, the length along the transverse direction (\(b\) axis) decreases instead of increasing. That is, the material shrinks laterally when compressed, thus exhibiting negative Poisson ratios. Table 4 reports the values of selected interatomic distances between the atoms in the structure of rutherforine. The meaning of these distances may easily be understood from Figure 3.  
As it can be readily observed in Table 4, the main variations of the distances resulting from the application of increasing pressures directed along \(U^{L}{}_{min}\) are the decrease of the U-O3 and C-C bond distances, responsible for the decrease of the \(a\) lattice parameter caused by the application of pressure, and the expected increase of the C-C bond distance yielding to the increase of the \(c\) lattice parameter. However, the distance between the rutherforine layers (or, equivalently, the \(b\) lattice parameter or the distance along the transverse direction), which are held together only by van der Waals forces, decreases. This last variation is opposite to the expected behavior. This is the reason of the negative Poisson ratio (NPR phenomenon) in rutherforine.
## 2 Literature Review
### Finite Element Method (FEM) and 3D Printing  
The Finite Element Method (FEM) is a standard method for numerically analysing structures. Since FEM uses the discretisation of the structure into small elements, it is deemed an accurate and reliable method for structural analysis [6].  
FEM can also be used to assess the accuracy of 3D-printed buildings. This helps analyse the performance of a proposed 3D-printed structure prior to its construction. In one study, it was observed that FEM could give an accurate quantitative estimation of unconfined uniaxial compression tests (UUCT) and direct shear tests (DST) of a 3D-printed building [40]. Since the numerical and experimental results were consistent, FEM could be a viable method for analysing 3D-printed structures.
## 1 Introduction
This finding is usually visible in the sound transmission loss _TL_ (dB) spectra as a peak in the spectrum. Dips in _TL_ and peaks in the sound absorption spectra are caused by symmetrical eigenmodes. An analytical vibroacoustic membrane model as a tool for the design of MAMs was developed in [20]. Another analytical model was developed to compute the sound transmission loss of a mass loaded rectangular membrane in a fast manner. It was shown that the mass of the membrane is especially affecting the first normal incidence transmission loss (_nTL_) peak and the resonance frequency, while the second resonance frequency strongly depends on the membrane properties [21]. In the publication, the effects of the membrane tension and the membrane surface density on transmission and characteristic frequencies were investigated as well. [PERSON] later focused on the low-frequency sound attenuation by MAMs carrying different masses at adjacent cells [22]. However, the most universal tool to predict MAMs behaviour is still the Finite Element Analysis (FEA) approach (with a commonly accepted uncertainty of about 20 dB peaks and dips of transmission loss spectra. Additionally, the MAMs with coaxial ring masses were experimentally and numerically analysed. The multiple coaxial arranged rings resulted in multi-peak profile (uniform mass) or broadband _TL_ peak (non-uniform mass) [19]. In later works, [PERSON] investigated the scaling of LRSMs [23] by multi-celled structure analysis. The impact of two parallel cells with mass and air cavity was analysed. It appeared that the cavity thickness difference (from 2-4 mm) had a negligible effect on its normal incidence sound transmission loss (_nTL_ (dB)) spectra in the frequency region below 1 kHz. It must be mentioned at this point, the applications based on MAMs principle can find the application not just in the membrane type constructions. By application the dynamic vibration absorbers the significant local control of low-frequency noise can be achieved [24; 25; 26; 27].
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BS & Base Station \\ dB & Decibel \\ dBi & Decibel-isotropic \\ dBm & Decibel-milliwatts \\ GPS & Global Positioning System \\ GW & Gateway \\ IoT & Internet of Things \\ IoV & Internet of Vehicles \\ LoRa & Long Range \\ LoRaWAN & LoRa Wide-Area Networks \\ LPWAN & Low Power Wide Area Network \\ LTE & Long-Term Evolution \\ MHz & Megahertz \\ NB-IoT & Narrow Band Internet of Things \\ PER & Packet Error Rate \\ RPMA & Random Phase Multi Access \\ VTU & Vehicle Tracking Unit \\ \end{tabular}
## 6 Our New Model: A Robust Trustless and Privacy-Preserving Framework for VANETs
### The P2P Network Layer of the Architecture  
The P2P network layer's interconnected nodes demonstrate a mesh network topology. This arrangement allows direct contact between any two network nodes, improving resilience and data redundancy. Node failures and network topology changes require dynamic routing and reconfiguration, which the mesh-like structure provides.  
Procedure 1 outlines the steps for setting up the P2P network layer in a VANET environment. This includes creating mesh connectivity.  
**Procedure 1:** P2P Network Layer Operation  
* **Step 1:** Initialize the VANET P2P network layer.
* **Step 2:** Connect OBUs and RSUs to the mesh.
* **Step 3:** Identify and establish connections with nearby OBUs.
* **Step 4:** Integrate network protocols.
* **Step 5:** Generate network-related data.
* **Step 6:** Ensure continuous data propagation.
* **Step 7:** End any process upon completion or condition.
## 3 Method
### Demodulate Bridge  
The process of the encoder processing the feature map can be regarded as a modulation process. The Demodulate Bridge serves to filter out any irrelevant information present in the feature map while simultaneously retaining the necessary feature information. Due to the constraints inherent in the encoder's encoding methodology, the feature map generated by the encoder will encompass redundant texture data corresponding to the encoder's encoding approach. It is thus necessary to filter the redundant texture information at this stage, a process analogous to demodulation. Figure 6 illustrates the effect of the Demodulate Bridge in filtering redundant information. Figure 6A depicts the original feature map, which exhibits numerous redundant black stripes. In contrast, Figure 6B shows that the Demodulate Bridge has significantly reduced the black numerical stripes, thereby enhancing the clarity and detail of the feature map. The reduction of redundant information facilitates the accurate restoration of the various details of the feature map by the decoder.  
Figure 4: Internal structure of a MaxViT block.  
Figure 5: Internal structure of a CSWin block.  
The output feature maps from all four stages in the encoder are provided to the Demodulate Bridge for the purpose of undergoing a demodulation operation. In the case of feature maps with a data dimension of \(\left[\left(\frac{H}{4},\frac{W}{4},C\right),\left(\frac{H}{8},\frac{W}{8},C \right),\left(\frac{H}{16},\frac{W}{16},C\right),\left(\frac{H}{32},\frac{W}{32 },C\right)\right]\), Demodulate Bridge will perform a convolution operation on the feature maps using a convolution kernel size of \(\left[k_{1},k_{2},k_{3},k_{4}\right]\) and a padding size of \(\left[p_{1},p_{2},p_{3},p_{4}\right]\). Particular parameter settings of the Demodulate Bridge will be elucidated in the experimental section. Internal structure of Demodulate Bridge is illustrated in Figure 7, and the formula representation of the Demodulate Bridge is shown in Equation (5).  
\[x^{{}^{\prime}}=x_{i}+Conv2d_{k_{i},p_{i}}(x_{i}),i\in\left[1,4\right] \tag{5}\]  
\(x_{i}\) represents the feature map output from the heterogeneous decoder at stage \(i\), \(Conv2d_{k_{i},p_{i}}\) represents the 2D convolution operation at stage \(i\) with convolution kernel size \(k_{i}\) and padding size \(p_{i}\). The organized feature map is fed into the multiscale sampling encoder for the up-sampling operation.  
Figure 6: Feature map demodulation effect diagram: (**A**) feature map output by the decoder; (**B**) feature map with redundant information filtered by Demodulate Bridge; (**C**) feature map upsampled at the same stage.  
Figure 7: Internal structure diagram of Demodulate Bridge.
## 5 Experiments
### Limitations of the Proposed Method  
It has been observed that the proposed method fails to detect RCMs under several conditions. The main reasons for this are motion blur, changes in ambient light, and the partial obstruction of the markers.  
First, regarding motion blur, according to the performance evaluation results shown in Table 4, the proposed method fails to detect RCMs when the motion blur intensity exceeds 0.0175. This is because motion blur alters the color of the color markers, making color discrimination difficult. Figure 19 shows an image in which RCM detection fails due to motion blur.  
Second, RCM detection may fail due to the discoloration of the color markers caused by changes in ambient light. Specifically, when lighting is inadequate, such as at night, the color of the markers changes due to insufficient ambient light, which leads to RCM detection failure. In Figure 20, RCM detection fails because the color marker at the predicted location (circled in blue) is too dark.  
Figure 19: Example of RCM detection failure due to motion blur.  
Figure 20: Example of RCM detection failure due to changes in ambient light.  
Finally, RCM detection may fail when a portion of the color marker is completely obscured. As shown in Figure 21, if part of the color marker is missing, the detection program determines that the error exceeds the tolerance during the matching process and concludes that RCM detection has failed.  
These causes of detection failure can be categorized according to two main factors: first, environments where the color markers become discolored, and second, environments where the color markers are not visible in the image. Discoloration can be counteracted to some extent by adjusting the color gamut applied to the markers. However, in environments where the color markers are completely invisible, there are physical limitations on the system. For example, although the placement pattern of the color markers in the proposed method offers a certain degree of flexibility, depending on the camera's field of view (FoV) and the distance from the markers, the markers may fall outside the FoV. In such cases, optimizing the camera's FoV and the placement of markers is required to prevent poor system performance.