esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Method
### Data
#### 3.1.2 Data Preprocessing  
Preprocessing is a crucial step in machine learning and deep learning projects. In the context of stock data, raw data might exhibit missing values, noise, or other inconsistencies due to various factors. Consequently, preprocessing proves to be vital for the stock price prediction task. Addressing missing values ensures data integrity and continuity, offering the model a more stable input. Furthermore, data normalization and smoothing can eliminate noise and short-term fluctuations in the data, allowing the model to more effectively capture long-term trends. Additionally, normalized data stabilize gradient descent during model training, accelerating model convergence.  
1. Missing Value Handling: Firstly, the presence of missing values in the data is examined. In the case of stock data, some trading days might be missing due to various reasons. Multiple strategies are available for addressing these missing values, such as deleting rows with missing values, filling gaps using the average value of preceding and succeeding days, or employing time series prediction models for estimation. In this study, gaps are filled using the average value of the previous and next days since this method maintains data continuity and is relatively straightforward.
2. Data Normalization: Considering the potential for a vast range of values in stock data, with trading volumes possibly reaching several millions while prices might range from tens to hundreds, it is customary to normalize data prior to model input, compressing all data between 0 and 1. This can be achieved using Min-Max normalization, expressed mathematically as: \[x_{\text{norm}}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}\] (15) where \(x\) denotes the raw data and \(x_{\text{min}}\) and \(x_{\text{max}}\) represent the minimum and maximum values of the data, respectively.
3. Data Smoothing: Stock data might exhibit short-term fluctuations. To counteract these effects and better discern long-term trends, the moving average method can be employed for data smoothing. A window size, for instance, five days, is determined. Subsequently, the average stock data value within this window is computed to represent the value for the day. This method effectively smoothes the data, minimizing short-term fluctuations that might otherwise interfere with the model.
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 2 Introduction of Relevant Basic Models
### Convolutional Neural Network  
The Convolutional Neural Network (CNN) is one of the typical neural network structures at present. CNN has good data feature extraction capability without excessive data preprocessing. Moreover, CNN is widely used in natural speech processing and image recognition due to its characteristics of local receptive field, spatial subsampling, and weight sharing [26; 27; 28; 29; 30]. In this study, for vibration signals or frequency domain signal sequences, 1 DCNN is used for feature extraction, which is briefly introduced below.  
Suppose the input data sequence is \(x=[x_{1},x_{2},\ldots,x_{n}]\), where \(N\) is the length of the sequence, the length of the convolution kernel is \(1\times l_{k}\), and the sliding step is \(s\), then the sequence intercepted when the convolution kernel slides \(i\) step on the input sequence is \(X_{i}=\big{[}x_{iss+1},x_{iss+2},\ldots,x_{iss+l_{k}}\big{]}\). Finally, the convolution operation can be defined as:  
\[z_{ij}=f\big{(}\sum_{i\in N_{k}}X_{i}\otimes K_{j}+b_{j}\big{)}, \tag{1}\]  
\[N_{s}=\frac{N-l_{k}}{s} \tag{2}\]  
where the output \(z_{i}\) is the feature learned by the convolution kernel during the sliding \(i\) step, \(\otimes\) is the convolution operation, \(N_{s}\) is the sliding number, \(f\) is the nonlinear activation function, \(K_{j}\) is the \(j\) convolution kernel, \(b_{j}\) is the offset. Then, the characteristic graph of the \(j\) convolution kernel \(z_{j}=\big{[}z_{1j},z_{2j},\ldots,z_{N,j}\big{]}\) is obtained.  
Typically, a pooling layer is added between adjacent convolutional layers to extract important local information and reduce the dimension of the output matrix. Common pooling layer operations include maximum pooling, average pooling, and L2 pooling. Finally, the convolutional neural network flattens the learned features to the full connection layer, which is used to integrate the abstract features extracted from the previous layers and realize the direct link between the output data and the CNN.  
The operation of multiple convolutional kernels enhances the ability of network data extraction, while multiple convolutional layers and pooling layers improve the learning ability of the CNN. The reasonable setting of CNN network parameters enables the CNN to better learn fault diagnosis knowledge.
## 2 Related Work and Theory
### Theoretical Analysis  
The combination of wavelet transform and attention mechanisms creates a synergistic effect in both frequency and spatial domains. Wavelet transform decomposes rain streak features into high-frequency subbands, while the attention mechanism applies adaptive weighting in the channel and spatial dimensions, allowing for rain streak separation and background preservation. This synergy enhances deraining performance by optimizing feature extraction, region-based weighting, and adaptive handling of various rain conditions, ultimately improving the model's generalization ability while retaining image details and structure.
## 2 HSI Procedure
### Preprocessing
#### 2.2.8 Minimal Noise Fraction (MNF)  
Minimal Noise Fraction is the linear transformation method that tends to improve the SNR of spectral data. It assumes no correlation between signal and noise, involving the simultaneous filtering of both low- and high-frequency signals with a view to estimating a covariance matrix. This was originally very good at extracting useful spectral information, although it has some limitations with regard to the automatic separation of signal and noise in real-time processing [39].  
While MNF has sometimes been considered a method of preprocessing, others have referred to it as a method of feature extraction because of its part played in the transformation of spectral data [16; 67]. [PERSON] et al. (2024) have clearly defined what MNF does to spectral data, hence the reason it is placed firmly here within preprocessing tool categories [39]. Also, the noise-reduction feature, transforming noisy data into components ordered by signal-to-noise ratio, was mentioned in other studies [68; 69].  
So far, in practical applications, MNF has been employed to detect milk powder adulteration using near-infrared HSI. Variability and noise were introduced into the data by the brand diversities; hence, MNF was used in the pretreatment of the data to separate noise from the data carrying information for the detection of adulterants like vanillin and melamine in milk powder. Also, treatments such as MNF yielded robust PLSDA and PLSR models that were able to identify the presence of adulterants at very low concentrations [70].  
In comparison with PCA, MNF mostly overcame the sensitivity to the noise of PCA by filtering noise from the informative signal separately through successive applications of PCA [71; 72]. Unlike standard PCA, this makes sure that the first few components are guaranteed to contain significant variations while containing minimal noise [70]. However, it was once demonstrated in a study that even with the noise-reduction capability of MNF, applying PCA resulted in a better capturing of strong signals of absorption in spectral data. [PERSON] et al. (2024) identified that models based on PCA were superior to MNF in prediction capability related to moisture content and in ginseng. It has been stated that MNF is useful in datasets with significant noise, but PCA remains a formidable alternative when the data contain critical chemical absorption signals [73]. In this situation, further research is required to understand the performance of MNF and PCA in different aspects of food quality. The effectiveness of MNF as a preprocessing tool in HSI, especially in food engineering, requires its comparison with other methods in order for the performances to be optimized over a wide area of applications.  
It is noteworthy that due to the characteristics of different food products, the performance of MNF varies. More research on the efficiency of applying MNF in different products, from fruits and vegetables to ready-to-eat foods, can better determine the benefit rate of this preprocessing method.
## 2 Materials and Methods  
### Survey Experimental Design  
For this study, a vehicle from the GeoSNav laboratory of the University of Trieste was used, which was already equipped with a POS/LV (Position and Orientation System/Land Vehicles) Mobile Mapping System from Applanix Corporation, namely Applanix POS LV-420, Version 4 (Applanix, Richmond Hill, ON, Canada). For the purposes of this experiment, the vehicle was additionally equipped with two u-blox devices, namely ZED-F9P and ZED-F9R, an Xiaomi Miss smartphone and a Sepentrtio AsteRx-U geodetic receiver (Septentrio, Leuven, Belgium) with a PolaNt-x MF antenna (Figure 1).
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
###### Abstract
This work opens doors for several other exhaustive search techniques by casting the outage probability expression as an objective function. Following the introduction in Section 1, Section 2 deals with the mathematical built-up for the system model of collocated MIMO radar for target and clutter waveform. Section 3 outlines the analysis of outage probability using the IQF approach. Section 4 deals with the waveform design vis-a-vis two distinct optimization algorithms. Results and Conclusions follows thereafter. _Notations_: Matrices, vectors, and scalar quantities are denoted by bold uppercase, bold lower case, and italicized letters, respectively. \((\cdot)^{T}\), \((\cdot)^{H}\), and \((\cdot)^{\frac{H}{2}}\) indicate the transpose, conjugate transpose, and sort representation of \(((\cdot)^{\frac{1}{2}})^{H}\), respectively. \(\ast\), \(\otimes\), \(sign(. )\), and \(u(. )\) are used to express the convolution operator, Kronecker product, signum function, and unit step function, respectively. Moreover, \(\left\|\mathbf{x}\right\|_{\mathbf{A}}^{2}\triangleq\mathbf{x}^{\mathbf{H}} \mathbf{A}\mathbf{x}\).
## 2 Design Method Development
### Design Charts for the Analysis of Maximum Strain in Reinforcement  
The maximum strain in the reinforcement can been given by design charts, which have the advantage of being more intuitive and easier to use.  
The vertical stress carried by the subsoil, \(\sigma_{\rm s}\), can be acquired through iteration with the implicit solutions listed in Equations (18)-(21). The surface settlement of the subsoil, \(y\), was then calculated by substituting \(\sigma_{\rm s}\) into Equations (13)-(16). The strain of the reinforcement, \(\varepsilon\), was thereafter obtained with Equation (10).  
\[\varepsilon=\frac{4}{3}(\frac{y}{s-b})^{2} \tag{22}\]  
Figure 4: Stress analysis of the GRPE.  
The normally consolidated soil is presented here as an example, under which condition, \(\varepsilon\) is given by  
\[\varepsilon=\frac{4}{3}\frac{h^{2}C_{C}^{2}}{(s-b)^{2}(1+\varepsilon_{0})^{2}}[ \lg(1+\frac{(4\alpha+1)\sigma_{s}}{\gamma_{0}h})]^{2}. \tag{23}\]  
The design charts are shown in Figure 5, plotting the maximum strain in the reinforcement (i.e., \(\varepsilon\)) in ordinate versus a dimensionless term correlated to the vertical stress carried by the subsoil, shown in Equation (24), in abscissa.  
\[\lg(1+\frac{(4\alpha+1)\sigma_{s}}{\gamma_{0}h}) \tag{24}\]  
In the design charts, the horizontal dotted line represents the settlement of the subsoil but normalized by the term '\(s\)-\(b\)', which is, therefore, dimension  
Figure 5: Design charts for a GRPE with a normally consolidated subsoil; (**a**) Full-scale chart; (**b**) Enlarged chart.  
another dimensionless fraction somehow representing the coupling effects of the embankment geometry and the subsoil properties. It is an inverse of the square root of the first multiplier in Equation (23) as shown in Equation (25).  
\[\frac{(s-b)(1+e_{0})}{hC_{C}} \tag{25}\]  
The maximum strain of the reinforcement can be obtained by substituting the values of Equations (24) and (25) into the charts. Similarly, Figures 6-8 show the maximum strain curves of reinforcement for under-consolidated soil and over-consolidated soil. Among them, the over-consolidated soil was divided into two cases to discuss according to Equations (15) and (16). These charts are intended to provide a guideline for the design of practical engineering; engineers no longer need to do complex mathematical calculations.  
Figure 6: Design charts for a GRPE with under-consolidated soil; (**a**) Full-scale chart; (**b**) Enlarged chart.
###### Abstract  
The probability of accidents, including conductor breakage and tower collapse, for the transmission tower-line system significantly increases under combined ice and wind loads. The existing research on the failure probability of the tower-line system under combined ice and wind loads is limited to static calculation, ignoring the fluctuating effect of wind. In addition, uncertainty in the material strength and geometric dimensions of the structure due to the production process and other pertinent factors could affect the bearing capacity of the tower. To accurately assess the failure probability of transmission lines under combined ice and wind loads, this paper first established numerical models of the transmission tower-line system considering structural uncertainty based on the Latin Hypercube Sampling method. And then, the limit performance indexes of the uncertain models were determined by Pushover analysis. Subsequently, considering the joint probability distributions of ice thickness-wind speed and wind speed-wind direction, the failure probability of the tower-line system under ice and wind loads was calculated. Finally, the influence of structural uncertainty and fluctuating wind on the failure probability was discussed. The results showed that, compared with structural uncertainty, the fluctuating effect of wind had a more significant influence on the failure probability of the tower-line system under combined ice and wind loads. After considering the fluctuating effect of wind, the smaller ice loads can potentially lead to the failure of the transmission tower-line system.  
failure probability; ice and wind loads; uncertainty analysis; dynamic analysis; transmission tower-line system +
Footnote †: journal: applied sciences  
0000-0001-6500-5820]L. [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Failure Probability Analysis of the Transmission Line Considering Uncertainty Under Combined Ice and Wind Loads. _Appl. Sci._**2024**, 14, 10752. [[https://doi.org/10.3390/appl42210752](https://doi.org/10.3390/appl42210752)]([https://doi.org/10.3390/appl42210752](https://doi.org/10.3390/appl42210752))
## 3 Estimation of the Appropriate Fairway Width
### Density Analysis  
According to the trajectories shown in the figure and our maritime traffic analysis, ships with a length of 50 m or more occupy a wide area near Gun-san Port and passing in north-south and east-west directions. It can be seen that the width of the occupied sea area is at least 6 km to 20 km or more, and the fairway is spread widely. In terms of efficiency, it is not practical to design a route limited only to the area it occupies; even if the  
Figure 7: (**a**) Comprehensive result of the vessel trajectory pattern analysis; and (**b**) A–D classification vessel trajectories for each pattern.  
sea area is occupied, the low-density sea area can be excluded from the priority range of the traffic route.  
In this study, a density analysis was carried out in two ways. The first involved analyzing the maritime traffic density (\(Density1(\rho 1)\)) by calculating the fairway frequency of vessels within the gridded sector per unit of time (1 s), as shown in Equation (6) below.  
\[Density1(\rho 1)=\frac{\mathrm{V_{GA}}}{86,400\mathrm{sec}*\alpha(\mathrm{ day})}(\mathrm{V_{Q}}/\mathrm{dt}(\mathrm{m})^{2}) \tag{6}\]  
where:  
\(\mathrm{V_{GA}}\)  
The total number of ships in the grid cell per second;  
\(\mathrm{V_{Q}}\)  
Number of ships;  
\(\alpha\)  
Analysis period.  
The second is a method for analyzing the density (\(Density2(\rho 2)\)) of marine traffic in consideration of the occupied area (\(L^{2}\)) of a standard vessel (a vessel with a total length of 82 m, which is the average length of ships entering Korean ports in Korea in 2003) compared to the occupied area of passing vessels, as shown in Equation (7) below [34; 35].  
\[Density2(\rho 2)=\frac{\sum V_{GE}*\left(L_{V}\right)^{2}}{86,400\mathrm{sec}* \alpha(\mathrm{day})*\left(L_{S}\right)^{2}} \tag{7}\]  
where:  
\(V_{GE}\)  
The number of each vessel in the gridded sector per second;  
\(L_{V}\)  
Length of each vessel;  
\(L_{S}\)  
Length of standard vessel.  
The density analysis results for the target sea area are shown in Figure 8. In (a), which calculates the frequency of fairway within the gridded sector per unit time (1 s), the high-density section was found to be about 5 to 6 km. On the other hand, in (b), where the density was calculated in consideration of the area occupied by passing vessels, it was determined that the section with a high density appeared as wide as 10 km or more.  
Figure 8: (**a**) The density used to determine the frequency of passage along fairway within the gridded sector; and (**b**) the density which was calculated in consideration of the area occupied by passing vessels.
# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies  
Due to the variety of information, the different network topologies, which are normally irregular, and the different node characteristics, different types of models for community detection have been proposed. Complex modern networks are represented by graphs. Graph clustering aims at partitioning graphs into several densely connected disjoint communities [19; 20]. The deep community detection strategies can be divided into three big families: (i) AE-Based Community Detection (Auto-encoders are used), (ii) CNN/GNN-Based Community Detection (Convolutional Neural Networks (CNNs) and their variant Graph Neural Networks (GNNs) are used), and (iii) GAN-Based Community Detection (Generative Adversarial Networks (GANs) are used). In the remaining of this section, we describe the more recent, state-of-the-art papers that fall in these categories.
###### Abstract  
Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST-SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean.  
sea surface temperature; solar-induced chlorophyll fluorescence; Indian vegetation; plant photosynthesis; ocean-vegetation interactions 2023
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment Two--MAUI ROS
#### 4.2.2 Testing and Results  
For the initial metric of interest in this experiment, we consider the MLS update frequency. We vary the MLS update frequency using a range of 2, 10, 50, and 300 updates per 1000 messages. At runtime, the user creating the group has the option to run a benchmark test, selecting the total messages to send and how many MLS key updates to perform. We selected 1000 messages to send with two key updates during the message session. Without performing key updates, MLS-protected messages could be sent between two VMs at a rate of 1000 messages per second. When performing key updates MLS protected messages could be sent between two VMs at a rate of 100 messages per second, for the reasons as follows: The trial's first test showed that key updates were not being processed in sequence, causing key schedule inconsistency in the MLS session. It was identified that this error was due to group members receiving new messages while an **add** and **commit** of the new key update was being performed (This issue derives from the absence of Delivery Service design inclusion into our application. This is further discussed in the limitations, Section 4.4). To mitigate this issue we manually reduced the message delivery rate of the default ROS talker.cpp file in our application. We found the optimal message transmission rate for all ciphersuites to be 100 messages per second.  
After completing the initial test in this phase, we ran two benchmark tests to observe MLS protocol performance under MAUI ROS: initial group setup and message handling. We first tested and measured the time it took to set up and initialize an MLS group depending on the ciphersuite chosen; initializing a group requires the creation of user credentials and the group itself. For message handling, we tested and measured how long it took to encrypt and send 1000 messages of 15 bytes each while updating the encryption key two times during the sending period.  
We cycled through all ciphersuite options with the same parameters for baseline comparison. The results of the group initialization times per ciphersuite are shown in Figure 8. These tests reveal that the average setup time for five out of the six best-performing ciphersuites was 5.1 ms; with P521_AES256 GCM_SHA512_P521 being the outlier performing 3.5 times slower than the average. Furthermore, the message handling results are shown Figure 9 indicated that following group initialization, X25519_AES128 GCM_SHA256_Ed25519 and P256_AES128 GCM_SHA256_P256 ciphersuites had the worst performance, although their initial group set up times were faster than average. These combined results indicate points for consideration when selecting a particular ciphersuite suitable for the use of MLS within UxS applications.  
Figure 8: Ciphersuite setup time comparison in milliseconds, MAUI ROS.  
Figure 9: Message handling time comparison in milliseconds per 1000 messages, MAUI ROS.