esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanical Characterization
#### 3.2.1 Tensile Testing
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Source**} & \multicolumn{1}{c}{**Sum of Squares**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Mean Square**} & \multicolumn{1}{c}{**F Value**} & \multicolumn{1}{c}{**\(p\)-Value**} \\ \hline
**Model** & 0.1109 & 3 & 0.0370 & 25.6417 & \textless{}0.0001 \\ \hline
**A-Material** & 0.0575 & 1 & 0.0575 & 39.8659 & \textless{}0.0001 \\ \hline
**B–Geometry** & 0.0056 & 1 & 0.0056 & 3.8861 & 0.0674 \\ \hline
**AB** & 0.0575 & 1 & 0.0575 & 39.8659 & \textless{}0.0001 \\ \hline
**Pure Error** & 0.0216 & 15 & 0.0014 & & \\ \hline
**Cor. Total** & 0.1325 & 18 & & & \\ \hline
**Std. Dev.** & 0.038 & **R-squared** & 0.8368 & & \\ \hline
**Mean** & 2.19 & **Adj. R-squared** & 0.8042 & & \\ \hline
**C.V. \%** & 1.73 & **Pred. R-squared** & 0.7442 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA table for tensile test (response is Young’s modulus). Figure 6: Normal probability plot for: (**a**) Young’s modulus; (**b**) ultimate tensile strength; (**c**) flexural strength; and (**d**) interlaminar shear strength. Figure 8: Effects diagram for tensile test. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F Value** & \(p\)**-Value** \\ \hline
**Model** & 12.4925 & 3 & 4.1642 & 0.4413 & 0.7266 \\ \hline
**A–Material** & 1.7387 & 1 & 1.7387 & 0.1843 & 0.6735 \\ \hline
**B–Geometry** & 2.0563 & 1 & 2.0563 & 0.2179 & 0.6469 \\ \hline
**AB** & 8.6975 & 1 & 8.6975 & 0.9218 & 0.3513 \\ \hline
**Pure Error** & 150.9726 & 16 & 9.4358 & & \\ \hline
**Cor. Total** & 163.4652 & 19 & & & \\ \hline
**Std. Dev.** & 3.0718 & **R-squared** & 0.0764 & & \\ \hline
**Mean** & 59.1469 & **Adj. R-squared** & \(-0.0967\) & & \\ \hline
**C.V. \%** & 5.1935 & **Pred. R-squared** & \(-0.4430\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA table for tensile test (response: UTS). Figure 7: Normal probability plot for tensile test (Young’s modulus).
## 2 Related Work
### Few-Shot Learning  
Few-shot learning (FSL) [7] aims to recognize novel classes where a few samples are available. FSL does not learn a fixed task during training but learn on a set of similar tasks from a task distribution. Embedding-based methods are popular among few-shot learning approaches which typically project input to embedding space so that similar and dissimilar samples can be easily discriminated. Generally, embedding function is learned in a task-invariant manner so that task-specific information can be quickly assimilated by feeding samples without retraining. [PERSON] [13], [PERSON] et al. [8] and [PERSON] et al. [36] made pair-wise comparisons which is equivalent to do kNN in embedding space. To reduce the influence of noisy samples, many models extract class prototypes from a few samples to replace the kNN with the nearest neighbor classification between the queried sample and class prototypes, for instance Prototypical Network proposed by [PERSON] et al. [9] which computes mean as class prototypes and classifies according to square Euclidean distance. [PERSON] et al. [14] used dynamic routing algorithm to obtain class prototypes and [PERSON] et al. proposed Hybrid Attention-Based Prototypical Network [15] referring to attention mechanism to compute prototypes so as to suppressnoise. Some other work such as [PERSON] et al. [10] and [PERSON] et al. [11] can be viewed as extensions of above embedding-based work.  
Many researches tried to solve few-shot learning in other perspectives. MAML proposed by [PERSON] et al. [37] is a general training strategy trying to offer good initial value to fine-tune and fast adaption to target tasks. Simple neural attentive learner (SNAIL) [38] is also a general few-shot learning framework using causal convolution and attention mechanism. Like MAML, Meta-SGD [39] offers good initial value and it can adaptively adjust the step direction and step length using a parameterized term controlling the gradient. Long Short-Term Memory (LSTM) meta learner proposed by [PERSON] et al. [12] trains a LSTM-based meta learner to output optimization steps at each iteration. Meta Network proposed by [PERSON] et al. [40] allows fast generalization through shift of inductive bias using meta learner. Besides, lots of researchers try to generate auxiliary samples using a few samples in a class to alleviate overfitting [41; 42; 43] where Generative Adversarial Network (GAN) is commonly used. Generating process called hallucination is often guided by attribute, semantics and regularization to reduce deviation between results and original samples. However, hallucination-based methods are difficult to implement where quality of hallucinated data is hard to guarantee.
## 3 The Proposed Architecture
### Data Center  
Data Center maintains a table, called the performance table, which records network status and node information collected by Information Manager, and several score tables that list the features of a node and their scores. Here, a request node is a node from which users submit their user requests, whereas a resource node is a node that serves users with its own resources. Request nodes and resource nodes together are called user nodes, and a request node may also be a resource node.
## 2 Data and Theory Description
### The CPRAP Usage Data Description
#### 2.1.2 ERA5 PWV Data  
ECMWF was formally established in 1975 and ERA5 is the fifth-generation reanalysis dataset of ECMWF. Compared with the fourth-generation reanalysis dataset, ERA5 has a higher temporal resolution with a value of 1 h. Two- and three-dimensional specific humidity, T, P and other 16 types of hierarchical meteorological data information are provided by ERA5. The advanced data assimilation and model systems were used to integrate large amounts of historical observation data into global estimates [27]. In this study, the corresponding meteorological data provided by ERA5 was selected over the period of 2012 to 2020.
## 3 Multiple RFI Sources Geolocation Method
### RFI DOA Estimation
In the case of a low ISR, the pair-matching relationship can be obtained by constructing the cost function of the elevation angle and the azimuth angle and using an optimization algorithm to solve it [26]. However, this method will cause a large computational burden, and for RFI, the energy of the interference in the contaminated SAR signal is much greater than the energy of the echo and noise, which means the method of using optimization algorithms is unnecessary when dealing with the RFI DOA estimation problem in the situation described in this paper. Therefore, we adopt an effective method with lower computational complexity. Firstly, we use the eigenvectors to construct the following equation, and then realize the angle pairing according to the order of the maximum value [25]:  
\[\mathbf{Q}=\mathbf{T}_{2}^{H}\mathbf{T}_{1}. \tag{40}\]  
The specific approach is that after obtaining \(\mathbf{T}_{1}\) and \(\mathbf{T}_{2}\), assume that the order of \(\mathbf{C}_{Y}\) is correct, then calculate \(\mathbf{Q}\) based on Equation (40), and adjust the order of \(\mathbf{E}_{Y}\) according to the coordinates of the largest element in each row of \(\mathbf{Q}\), the pair-matched \(\mathbf{C}_{Z}\) and \(\mathbf{E}_{Y}\) are obtained in this way. The steps of the RFI DOA estimation method used in this section are as follows. * Step 1: In accordance with Equation (21) to get \(\mathbf{S}_{Y1}\), \(\mathbf{S}_{Y2}\), \(\mathbf{S}_{Z1}\), and \(\mathbf{S}_{Z2}\). * Step 2: Construct \(\mathbf{W}\) with Equation (27), and perform singular value decomposition on it to get the signal subspace \(\mathbf{D}\). * Step 3: Calculate \(\mathbf{\Theta}\) and \(\mathbf{\Psi}\) with Equations (32) and (36). * Step 4: Perform singular value decomposition on \(\mathbf{\Theta}\) and \(\mathbf{\Psi}\) to get \(\mathbf{C}_{Z}\) and \(\mathbf{T}_{1}\), \(\mathbf{E}_{Y}\) and \(\mathbf{T}_{2}\). * Step 5: Perform pair-matching procedure with Equation (40). * Step 6: Obtain \(\phi_{k}\) and \(\hat{\theta}_{k}\) with Equations (34) and (38).
## 4 Distribution Problems
### Allocation Problem  
For the transportation problem described in Section 4, it was possible to provide the required quantity by composing partial quantities from different suppliers (from different warehouses) when fulfilling the requirements.  
However, in the _Allocation Problem_, it is required that the required quantity is provided from a single location so the mathematical model of the transportation problem has to be modified by [10] to account for this condition. With the same notation used for the symbols, the meaning of the decision variables \(x_{ij}\) is now different. They only assume binary values and \(x_{ij}=1\) if the quantity \(b_{j}\) required by the \(j\)th customer is sourced from the \(i\)th supplier, if not, \(x_{ij}=0\).  
If more than one customer receives the required quantity from the same supplier, the sum of their requirements must not exceed the capacity of that supplier (stock).  
The model of the allocation problem with these conditions then takes the following form:  
\[z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\rightarrow\min \tag{39}\]  
subject to  
\[\sum_{i=1}^{m}x_{ij}=1,\;j=1,\,\ldots,\,n \tag{40}\]  
\[\sum_{j=1}^{n}b_{j}x_{ij}\leq a_{i},\;i=1,\,\ldots,\,m \tag{41}\]  
\[x_{ij}\in\{0,1\},\;i=1,\,\ldots,\,m,\;j=1,\,\ldots,\,n \tag{42}\]
## 2 LiCSAR System Architecture
### LiCSAR Processing Chain
#### 2.1.3 Formation of Differential Interferograms  
After ensuring the RSLC mosaics exist for a selected interferometric combination, a simulated DEM-based interferogram is formed containing a prediction of the topographic phase. This topographic phase is removed during the formation of a standard \(4\times 20\) multilooked interferogram (GAMMA  
Figure 3: Detailed overview of the coregistration process for a new SLC image (generation of RSLC and complementary files).  
commands phase_sim_orb and SLC_diff_intf). By default, interferograms are formed between each epoch and the four preceding and/or following epochs (EPT 15 min/interferogram).
## 2 The Continuous Damage Mechanics Model
### Elastic-Plastic Damage Model  
Adopting the simplified model proposed by [PERSON] [12], this model was derived from the simplified version of the Chaboche [13] elastic damage model. It is used for computing the loss in the effective cross-sectional area. The simplified model is expressed as follows:  
\[dD_{e}=D^{\alpha}\cdot\left[\frac{\sigma_{a}}{2M_{0}}\right]^{\beta}dN \tag{3}\]  
where \(D_{e}\) represents elastic fatigue damage; \(\sigma_{a}\) represents stress amplitude; \(N\) represents cycle numbers; \(\alpha\), \(\beta\) and \(M_{0}\) are coefficients of the fatigue damage law [13].  
By solving Equation (3), the relationship between elastic fatigue damage \(D_{e}\) and the number of cycles \(N\) can be obtained:  
\[D_{\text{e}}(N)=\left[(1-\alpha)\left[\frac{\sigma_{a}}{2M_{0}}\right]^{\beta }N\right]^{\frac{1}{1-\alpha}} \tag{4}\]  
Based on the experimental data [14], the material parameters can be determined as follows: \(M_{0}\) = 5740 MPa, \(\alpha\) = 0.75, \(\beta\) = 2.97.  
[PERSON] [13] proposed a ductile damage evolution model based on plastic strain. This model can be extended to calculate incremental fatigue damage caused by plastic strain. The cumulative damage calculation for each cycle is as follows:  
\[dD_{p}=\left[\frac{(\sigma_{\text{max}}^{*})^{2}}{2 ES(1-D)^{2}}\right]^{m} \Delta\epsilon\cdot dN \tag{5}\]  
where \(D_{p}\) represents plastic fatigue damage; \(\sigma_{\text{max}}^{*}\) is the maximum value of the damage equivalent stress; \(E\) represents [PERSON]'s modulus; \(S\) (MPa) and \(m\) are the two characteristic creep damage coefficients for the material; \(\Delta\epsilon\) is the accumulated plastic strain.  
Due to the lack of reliable experimental data, the plastic fatigue damage evolution equation proposed by [PERSON] et al. [14] is used:  
\[dD_{p}=(1+\frac{\Delta\epsilon}{\epsilon})\cdot D^{\alpha}\cdot\left[\frac{ \sigma_{a}}{2M_{0}}\right]^{\beta}dN \tag{6}\]  
where \(\epsilon\) is the maximum elastic strain.
# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 3 Proposed Methodology
### Assessment of Security  
As safety computations are implemented, the delay in V2X transmission systems can also be raised, as demonstrated in Section 2. This method record all safety computations depending on the degree of confidence specified in this part. After that, a GPU-based architecture will be shown, with its efficiency compared to conventional systems.  
Public-key encryption is used in existing V2X data transmission to verify over internet transmissions. Despite various variations between the US stack (IEEE 1609) and the EU stack (ETSI ITS G5) [5], the techniques used regarding safety are almost comparable, as per [38]. This paper categorize safety computations based on the needs. There are three primary types of security features in V2X data transmission for the most common use cases: information source authenticity, information content consistency, and information encoding.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 2 Meteorological Stations and Data  
### The Meteorological Stations  
The two first studies were realized using meteorological data measured in the meteorological station belonging to the Renewable Energies Development Centre (CDER) located in Bouzareah near Algiers (latitude: 36.8\({}^{\circ}\) N; longitude: 3.17\({}^{\circ}\) E) at an altitude of 347 m. The site is characterized by a Mediterranean climate with dry and hot summers and damp and cool winters. The data were measured each second and stored each 5 min from April 2011 to April 2013 (24 months of 5-min data). The measured data and the calculated astronomical data (horizontal extraterrestrial irradiation (EHI), solar declination, \(\delta\), and zenith angle, \(\theta_{z}\)[22]), are presented in Table 1.  
The tilted solar data were measured for a 36.8\({}^{\circ}\) tilt angle equal to the latitude of Bouzareah (optimal angle for a maximum annual irradiation). The data basis contains 12 5-min parameters, 9 measured and 3 calculated. Each data was previously verified in order to extract outliers.  
The forecasting work was realized from GHI and DNI data provided by the PROMES laboratory (CNRS UPR 8521) located in the south of France in Odeillo (Pyrenees Orientales, France, 42\({}^{\circ}\)29 N, 2\({}^{\circ}\)01 E, 1550 m asl), the station is located in the mountains, at about 100 km from the Mediterranean sea and presents an often high nebulosity. The solar data are measured and stored with a 1 min time granularity. This meteorological station is in altitude, the climate is very perturbed, the rainfall continues to be present during the driest months, the variability of solar radiation is high, and thus its forecasting is more difficult to realize. Two years of hourly data were available i.e., 17 520 data, for both GHI and DNI.
# Deep Learning for Computational Mode Decomposition in Optical Fibers
## 3 Results
### Performance of the DNN Using Experimental Data
Using a DNN, which was trained with random mode combinations, achieves worse results. The mean correlation coefficient is 92.24% with \(\sigma=3.17\%\). The respective correlation coefficients of both DH and the DNN trained with the SMC dataset are shown in Figure 5b. It can be seen that the achieved correlation coefficients in the experiment are reduced with respect to the simulation results. This can be explained with imperfections of the optical system, including the SLM, optical components, and the camera. A thorough description of the individual error sources is given in the discussion. \begin{table}
\begin{tabular}{c c c c c} \hline  & \(\overline{\Delta\mathbf{\phi_{s}}}\) & \(\overline{\Delta\mathbf{\rho_{s}}}\) & \(\overline{\Gamma}\) & \(\sigma\) \\ \hline DH & 4.20\% & 3.32\% & 96.97\% & 2.26\% \\ DNNSMC & 9.45\% & 6.02\% & 95.10\% & 1.90\% \\ DNNrandom & 11.03\% & 9.56\% & 92.24\% & 3.17\% \\ \hline \end{tabular}
\end{table} 
Table 2: Mean relative errors of the mode weight determination using Digital Holography (DH) or a DNN. Figure 4: Optical setup, which is used to generate random mode field distributions using an SLM. The generated light fields can be measured either purely intensity-based or holographically.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
###### Abstract  
Image filtering systems have become essential in large-scale content platforms to prevent the dissemination of unauthorized data. While extensive research has focused on identifying images based on categories or visual similarity, the filtering problem addressed in this study presents distinct challenges. Specifically, it involves a predefined set of filtering images and requires real-time detection of whether a distributed image is derived from an unauthorized source. Although three major approaches--bitmap-based, image processing-based, and deep learning-based techniques--have been explored, no comprehensive comparison has been conducted. To bridge this gap, we formalize the concept of image equivalence and introduce performance metrics tailored for fair evaluation. Through extensive experiments, we derive the following key findings. First, bitmap-based methods are practically viable in real-world scenarios, offering reasonable detection rates and fast search speeds even under resource constraints. Second, despite their success in tasks such as image classification, deep learning-based methods underperform in our problem domain, highlighting the need for customized models and architectures. Third, image processing-based techniques demonstrate superior performance across all key metrics, including execution time and detection rates. These findings provide valuable insights into designing efficient image filtering systems for diverse content platforms, particularly for detecting unauthorized images and their transformations effectively.  
Keywords:image DNA; fingerprinting; unauthorized content; image filtering system; image equivalence
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 6 Experimental Evaluation
### Data  
The dataset used in this study [29] consists of the trajectories of all the vehicles crossing the eastbound I-80 Freeway in Emeryville, California (USA). Measurements date back to 13 April 2005 and were taken on a tract of approximately 1640 ft, comprising six lanes and an on-ramp. The full dataset is partitioned into three 15 min sequences: 4:00 p.m. to 4:15 p.m., 5:00 p.m. to 5:15 p.m., and 5:15 pm to 5:30 p.m. Intuitively, they correspond to different traffic patterns, from the build-up of congestion to the peak period.  
The dataset contains a total of 5678 unique vehicles tracked over 19,679 frames. For each vehicle and each frame, it contains the position of the vehicle (lateral and longitudinal offsets with respect to a reference position), its velocity, its size (width and length), and a lane identifier. All these attributes have been extracted by the creators of the dataset by means of image processing and computer vision techniques: we refer the reader to [29] for more details. Figure 3 provides a graphical representation of the information contained in a frame of the dataset.  
Figure 3: Sample frame reproducing the traffic of the dataset [29]. Each colored box represents a car. Dotted lines are lane separators, while solid lines are guardrails. The two segments projecting out from the first level of the road are the boundaries of the on-ramp. The second level of road is the continuation of the top one, while the red shaded rectangle is the range for the trajectory endpoints.
# Seismic Response of a Large LNG Storage Tank Based on a Shaking Table Test
## 2 Shaking Table Test of Storage Tank Models  
### The Design of Storage Tank Models  
This paper mainly studies the seismic response law and isolation bearing performance of the LNG storage tank structure. Considering that the structure of the actual LNG storage tank is very complex, it is difficult to consider the structural detail of the actual storage tank when designing the experimental model. Therefore, the detailed structure of the LNG storage tank is ignored in the design, and the experimental model is simplified to some extent. Referring to the structure of a large-scale full-contained LNG storage tank, the test model retains the pile foundation, outer tank and dome. The inner diameter of the outer tank of the test model is 2.5 m, the wall thickness is 0.2 m, the tank bottom thickness is 0.3 m and the height is 2 m. The diameter of the pile foundation is 0.4 m and the height is 0.3 m. The material parameters of the outer tank are shown in Table 1.  
The test models are divided into non-isolated storage tanks and seismically isolated storage tanks, as shown in Figure 1. The isolation storage tank is supported on 5 isolation bearings, and the non-isolation storage tank is supported on 5 pile foundations. The vibration-isolating bearing is a lead-core rubber bearing whose model is LRB300, as shown in Figure 2. The mechanical performance parameters of the bearing are shown in Table 2.  
The test shaking table is a three-direction horizontal excitation hydraulic drive equipment. The size of the table is 6 m \(\times\) 6 m, the maximum bearing capacity is 60 t, the maximum anti-overturning moment is 1800 kN\(\cdot\)m and the limit displacement of the table is \(\pm\)250 mm. The measurement point layout of the storage tank model is shown in Figures 1 and 2. According to the structural characteristics of the storage tank, the acceleration sensor is arranged at the pile foundation, the bearing platform, the height of the center of mass and the dome. An accelerometer sensor capable of measuring three-direction acceleration is arranged at each measuring point. The X, Y and Z directions of the acceleration sensor test are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**-** & **Elastic Modulus** & **Poisson’s Ratio** & **Density** \\ \hline C50 concrete & 34.5 GPa & 0.167 & 2500 kg/m\({}^{3}\) \\ HRB400 steel bar & 210 GPa & 0.3 & 7800 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the storage tanks.  
Figure 1: Dimensions of the test tank model: (**a**) Non-isolated storage tank; (**b**) Isolation storage tank.