esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Similarity Measurement Model and Clustering Model
### The Basis of Time Series Clustering  
The core task of time series clustering is to group according to the similarity between time series. This process aims to identify and assign similar patterns and trends to different categories. These patterns may include the periodicity, trend, or other features. Clustering algorithms evaluate the distance between time series by applying similarity metrics (such as Euclidean distance or Dynamic Time Warping distance) and accordingly group similar sequences into one category. The challenges faced by time series clustering include dealing with high-dimensional data, where each sequence may contain hundreds to thousands of data points. At the same time, time series are often accompanied by problems of noise and missing values, which need to be properly handled before clustering. Typical clustering algorithms, such as k-means, hierarchical clustering, and DBSCAN, all adopt different strategies to adapt to the characteristics and similarity metrics of time series. In time series clustering, the selection of an appropriate algorithm and distance metric standard is the key [42; 43].  
Time series clustering is usually divided into three major steps: feature extraction, similarity measurement, and the clustering algorithm.  
The goal of feature extraction is to transform the original time series into representative and distinguishable features so that machine learning algorithms can handle them more effectively. This step is dedicated to extracting the main information in the time series.  
Similarity measurement is crucial in time series analysis. It is responsible for defining and calculating the similarity or distance between sequences and is the cornerstone of many analysis tasks. This module improves the accuracy of the clustering results by comparing different time series and evaluating their similarity.  
The goal of the clustering algorithm is to divide a set of time series into clusters so that sequences within the same cluster are highly similar, while sequences between clusters are significantly different. The quality of clustering largely depends on the similarity measurement method adopted.
## 2 Results and Discussion  
### FTIR Spectroscopy  
The FTIR spectra of the WMS-NH\({}_{2}\) and NR/WMS-NH\({}_{2}\) materials in the selected wavenumber regions are shown in Figure 1. The O-H stretching vibration of the silanol groups was observed at 3100-3700 cm\({}^{-1}\)[19]. For the pure silica WMS, the band around 2930 cm\({}^{-1}\) was attributed to the C-H stretching of remnant ethoxy groups as incompletely condensed silicate species [20], while the O-H bending vibration of physisorbed water was observed at 1630 cm\({}^{-1}\)[21; 22]. After the functionalization, the WMS-0.10N showed the bands related to the aminopropyl group at 1530 cm\({}^{-1}\), corresponding to the N-H bending vibration of the amino groups, and at 2989, 2940 and 2869 cm\({}^{-1}\) due to the C-H stretching of methylene moieties [22; 23; 24].  
The presence of rubber entrapped in the pristine NR/WMS was deduced from an intense \(-\)C-H stretching vibration at 2989, 2940, 2869, the bands =C-H stretching at 3120 cm\({}^{-1}\) and the deformation of methylene groups at 1461 cm\({}^{-1}\)[15; 16; 25]. The bands of silanol groups and physisorbed water were less pronounced for the NR/WMS nanocomposites than the WMS counterparts, which were characterized an enhanced hydrophobicity via the incorporated rubber phase. The N-H bending band at 1530 cm\({}^{-1}\) increased in its intensity with the aminopropyl content of NR/WMS-NH\({}_{2}\). The result was collaborated with the Si-O-Si and Si-O-H stretching vibrations at 1000-1300 cm\({}^{-1}\) and 940-960 cm\({}^{-1}\), respectively [20; 26; 27], as shown in the Supplementary Material Figure S1. When increasing the APS loading in the NR/WMS-NH\({}_{2}\), the Si-O-Si band was shifted towards lower wavenumbers (from 1065 to 1048 cm\({}^{-1}\)), which was associated with an increased condensation of the silicate wall [17]. On the other hand, the Si-O-H band of the non-condensed silanol groups gradually decreased. These results confirmed the successful functionalization of the NR/WMS surface with the aminosilane at different loading levels of aminopropyl groups.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 6 Medium-to-Long-Term Climate Prediction
### Model Design
In the NNCAM model, the core workflow is divided into several key steps to achieve efficient and accurate climate simulations. First, the dynamic core, which serves as the base component of the model, is responsible for solving the underlying hydrodynamic equations and calculating the current climate state, e.g., temperature, pressure, and humidity, as well as the environmental forcings, e.g., wind and solar radiation. These calculations are then transmitted to the NN-GCM coupler. Upon receiving these data, the coupler further passes them to the neural network parameterization module. This module utilizes pre-trained neural networks, specifically ResDNNs, for faster and more accurate parameterization of the climate. Upon completion of the predictions, these results are fed back to the host GCM, i.e., NNCAM. The host GCM then uses the predictions generated by these neural networks to update the climate state in the model, and based on these updates, it performs the simulation at the next time step. Overall, the host GCM, as the core of the whole simulation, is not only responsible for the basic climate simulation but also efficiently interacts with the dynamic core and neural network parameterization modules to achieve higher simulation accuracy and computational efficiency. This hierarchical architecture ensures both computational efficiency and high simulation fidelity. It allows for seamless integration and synchronization of the model states and predictions, thereby enabling continuous and efficient operation of NNCAM. The proposed framework represents a significant stride in the realm of atmospheric science, offering a harmonious integration of machine learning and physical simulations to achieve unprecedented accuracy and computational efficiency. CGF, DeepESD, and CycleGAN are very different in their uses and implementations, but there are also some levels of similarity. All three approaches focus on mapping from one data distribution to another. Furthermore, they focus more on the mechanisms of climate change than previous models for weather forecasting. CycleGAN specifically emphasizes the importance of not only mapping from distribution A to B but also the inverse mapping capability from B to A, which is to some extent what CGF and DeepESD are concerned with. NNCAM realizes the mapping from physical parameterization to machine learning parameterization. This mapping can be viewed as a functional mapping that replaces parameterized functions in the physical process with functions learned and inferred by the machine learning model.
## 4 Digital Domain Processing
### Digital Signal Processing  
When the modem is powered on, the MCU initializes peripheral registers. After initialization, the system remains idle, waiting for interrupts from the PC via UART or an analog reception board via an analog-to-digital converter (ADC). If the MCU receives data from the PC, it carries out predetermined physical layer functions on the data and transfers the outputs to a digital-to-analog convertor (DAC) to generate sinusoidal waves for transmission. After completion, it returns to idle mode. On the other hand, if the MCU receives an interrupt from the ADC which indicates that an acoustic signal has arrived at the receiving transducer, it starts to sample the signal. The sampling frequency is set to 1 MHz and the number of bits for quantization in the ADC is fixed at 12. After the MCU performs appropriate functions on the sampled data, it delivers the outputs to the PC. Figure 3 depicts the block diagram explaining the procedures in the MCU. Here the DMA is used to fetch data from the ADC and hand over data to the DAC without the help of the Cortex-M3.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 2 The Proposed Approach
### Stacked Convolutional Neural Networks and Long Short-Term Memory
#### 2.2.2 LSTM Module  
The LSTM module is a subnet of our SCNN-LSTM which allows to easily memorize the context information for long periods of time in sequence data. In general, LSTM is proposed to model the temporal dependencies. In images, this temporal dependency learning is converted to the spatial domain [41]. Therefore, we employ a LSTM unit as described in [39] to model spatial dependencies by mapping the deep feature sequences produced from CNN to hidden states. To predict a distribution over spatial step, the softmax is employed in output layer. Finally, we average the outputs of the LSTM network's softmax layer to compute the predicted distribution, as shown in Figure 3b. Given inputs \(x_{t}\), \(h_{t-1}\), and \(c_{t-1}\), the LSTM updates at the position \(t\) are  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}x_{t-1}+b_{i}) \tag{3}\] \[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}x_{t-1}+b_{f})\] (4) \[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}x_{t-1}+b_{o})\] (5) \[g_{t}=tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c})\] (6) \[c_{t}=f_{t}*c_{t-1i}*g_{t}\] (7) \[h_{t}=o_{t}*tanh(c_{t}) \tag{8}\]  
where \(\sigma\) and \(tanh\) are logistic sigmoid (sigm) and hyperbolic tangent (_tanh_), which are defined as  
\[\sigma(x)=(1+e^{-x})^{-1}, \tag{9}\]  
\[tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{10}\]  
and \(*\) is the element-wise product. In addition, \(h_{t}\), \(i_{t}\), \(f_{t}\), \(o_{t}\), \(g_{t}\), and \(c_{t}\) denote hidden unit, input gate, forget gate, output gate, input modulation gate, and memory cell, respectively, at the position \(t\).  
Output layer: The outputs from the last hidden layer are normalized with the softmax function:  
\[y_{m}=\frac{\exp(z_{m})}{\sum\limits_{n=1}^{N}\exp(z_{n})} \tag{11}\]  
where \(z_{n}\) is a linear combination of outputs in LSTM hidden states.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 4 Results  
### Feature Construction  
As shown in the Table 1, 16 features with numerical value in the dataset can be applied to equidistant grouping. The features 'account length' and 'area code' are not the customer's daily behavior and consumption features. The features 'total intl charge' and 'customer service calls' cannot be effectively grouped equidistantly because the value range is too small. However, the value range of the selected 12 features is relatively large and can be used to mine the hidden information between the data. Equation (1) is used to determine the number (K) of groups for 12 original features. The results are shown in Table 2.  
For example, the feature 'Total day calls'has numerical value, ranging from 0 to 165. The feature construction results using Equation (1) are shown in Table 3. The feature 'Totalday calls' is divided into 8 groups equidistantly through Equation (1). There are instances in the same group, where the same value is assigned in the new feature.  
The histogram of the new features also approximates the normal distribution of the new features as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Value** & **K** \\ \hline Number vmail messages & 0–51 & 6 \\ Total day minutes & 0–350.8 & 10 \\ Total day calls & 0–165 & 8 \\ Total day charge & 0–59.64 & 6 \\ Total night minutes & 0–363.7 & 10 \\ Total night calls & 0–170 & 8 \\ Total night charge & 0–30.91 & 7 \\ Total eve minutes & 0–395 & 10 \\ Total eve calls & 0–175 & 8 \\ Total eve charge & 0–17.77 & 5 \\ Total intl minutes & 0–20 & 5 \\ Total intl calls & 0–20 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Twelve features and values for grouping.  
Figure 5: Histogram for the new feature ‘total day calls group’.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instance (Index)** & **Original Feature** & **New Feature** \\ \hline
1 & 110 & 6 \\
2 & 123 & 7 \\
3 & 114 & 6 \\
4 & 71 & 4 \\
5 & 113 & 6 \\
6 & 98 & 5 \\
7 & 88 & 5 \\
8 & 79 & 4 \\ \multicolumn{2}{c}{...} & 3333 & 113 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The grouping results of the feature ‘Total day calls’.  
Feature construction is to improve accuracy by expanding feature space. If the original dataset and the new dataset are not combined, it cannot increase feature space and improve accuracy. As shown in Figure 6, 12 new features are derived from the original features through feature construction. The new dataset is generated based on 21 original features combined with 12 new features.  
We use four machine learning algorithms with two datasets, namely, the original dataset and the new dataset to compare its performance. The results in Table 4 show that the new dataset shows better accuracy for all classifiers and that the proposed feature construction method improves the stacking model performance. The new feature grouping discovers missing latent factors from the churn dataset, regarding the relationship between features, and expands the feature space by creating additional features.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 6 Discussion and Limitations
### Security Improvement and Considerations in the SDN Architecture  
The design consideration to improve the security of the SDN architecture is identified as implementing data encryption. Alternative security methods include designs to mitigate cyber-attacks and performance application-based routing. However, they do not protect the integrity of the data in the data packet. Simulation shows that encryption is required to hide the information from snooping software on the Internet effectively. IPSec is a secure method to encrypt the transactive energy information flow between SDN nodes in the transaction process.  
The negative impact of the IPSec encryption between SDN nodes starts to become prominent when encryption between multiple nodes in a mesh topology is required. Complex solutions such as a mesh VPN configuration are required to achieve the security and flexibility requirement.  
Data encryption, however, improves the security of the SDN architecture.  
The proposed design and simulation of a suitable SDN architecture for TESMS provides a significant improvement in reliability and flexibility and should be used as guidelines for the practical deployment of SDN TESMS networks.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 2 High-Speed Train Noise Composition  
While the noise of early steam locomotives was generated by steam engines, the noise of modern electric high-speed trains consists of wheel-rail rolling noise and aerodynamic noise caused by train airflow [8].
## 5 Simulation and Mathematical Analysis  
### Simulation Configuration  
In our simulation, the test ephemeris data exploit the sample ephemeris data of the STARLINK-2569 satellite. The parameter configuration of this satellite is shown in Table 2. We prepare two groups of data: one group is the referenced ephemeris data, which are accurate (downloaded from www.space-track.org), and the other group is the test ephemeris data, which are the broadcast TLE ephemeris data (downloaded from celestrak.org).
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 3 Perception Principles and Key Technologies
### Channel State Information (CSI)  
CSI pertains to the physical layer data in wireless communication protocols and is not directly accessible. However, in recent years, researchers [22] have devised methods to acquire CSI from the physical layer in high throughput mode by leveraging OFDM (orthogonal frequency division multiplexing) technology as per the IEEE 802.11n standard. In this standard, CSI information is extracted from 30 specific subcarriers out of the total 64 subcarriers defined, with the aim of minimizing interference. By extracting CSI information, we can obtain the CSI matrix depicted in Equation (1),  
\[H=\left[H(1)H(2)\ldots H(30)\right]_{N_{T}\times N_{R}} \tag{1}\]  
where \(N_{T}\) and \(N_{R}\) represent the number of antennas at the transmitter and receiver, respectively. Each antenna pair corresponds to a total of 30 carriers. Figure 1 illustrates the amplitude characteristics of 30 subcarriers generated for users by specific antennas. These magnitude features serve as our raw data and will be input into the subsequent preprocessing stages.
## 2 Literature Review
### Meta-Analytic and Comparative Studies  
[PERSON] et al. (2024) [19] conducted a comprehensive survey of state-of-the-art technologies for fake news detection, defining the task as categorizing news along a veracity continuum with a measure of certainty. The authors highlighted the challenges posed by the evolving landscape of online news publication, where traditional fact-checking methods struggle against a deluge of content. They categorized veracity assessment methods into two primary types, as follows: linguistic cue approaches, often enhanced by machine learning, and network analysis techniques. The paper advocated for a hybrid approach that merges linguistic cues with network-based behavioral data, offering a more nuanced understanding of the factors influencing news veracity. Additionally, the authors proposed operational guidelines to facilitate the development of effective fake news classifier systems.  
Building on these insights, [PERSON] et al. (2024) [20] addressed the challenges posed by the proliferation of social networks in combating fake news. Their paper revisited definitions of fake news and introduces an updated taxonomy based on four criteria: types of features used, detection perspectives, feature representation methods, and classification approaches. They conducted an extensive empirical study evaluating various feature extraction and classification techniques in terms of accuracy and computational cost. Their findings indicate that optimal feature extraction methods are dataset-dependent, with context-aware models, particularly transformer-based approaches, demonstrating superior performance. The study emphasizes the value of combining multiple feature representation methods and classification algorithms, including classical ones, for improved generalization and efficiency.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 3 Proposed Solution
### Fronthaul Challenges and Functional Division Options  
O-RAN provides the option of placing functions on the network at different locations and along the signal path; this option is called a functional RAN split and is referred to as Lower Layer Split (LLS) and Higher Layer Split (HLS), as shown in Figure 3. The project's 5G private network is allowed to improve its performance and make some compensations due to RAN disaggregation, making the decision of which node/site/unit should control certain operations of the functional division of the RAN.  
3 GPP defined Options 1-8 of interfaces for the functional division to interconnect them, in the F2 network interfaces of the gNB-RU (Fronthaul) to connect (RU->DU), in which we have the eCPRI traffic interface (class 2, functional split 7.2x) in Figure 6. eCPRI requires time synchronization (frequency and phase) to be achieved by the packet-in-band method through IEEE 1588v2 to operate reliably in the project [43]. The F1 interface (functional  
Figure 5: 5G NR/NGC SA protocol stack. Based on [38; 39].  
split 2.0) on the gNB-DU (Midhaul) (DU-\(>\)CU) was previously discussed and is shown in Figure 6 in relation to layers L1, L2 and L3 of the RU, DU and CU [44].  
The functional division options allow the Fronthaul and Midhaul network interfaces of the DU/CU to face the challenges of increased traffic and the intelligence of placing close to the RU (AAS-RU and the RRU) versus the DUs [45,46]. O-RAN can distribute the baseband processing over the logical nodes, thus defining the characteristics of these links, choosing which of the RAN's functional divisions (Split of 2-8), logical to the sublayer of the device, improves the split ratio. Fronthaul to Backhaul (Device-\(>\)RU-\(>\)DU-\(>\)CU [5G NGC]) in relation to the sublayers of the 5G NR/NGC SA is shown in Figure 7.  
In OpenCare5G, the F2 option was used with the Fronthaul 7-2x functional division between the RU-\(>\)DU for the 5G Sub-6 GHz spectrum, to obtain more bandwidth and speed by the RU performed in the physical sublayer \" PHY-Low\" and \"PHY-High\" (Split 7 / Split PHY) in Figure 7[47].
## 3 Method
### \(T_{sl}\): Transfer Learning with Self-Supervised Learning  
Medical images have a high labeling cost, so there are many cases where data are available but the ground truth is not given. Therefore, an approach that can use unlabeled images is proposed. Methods using self-supervised learning have often been proposed; of these, patch self-supervised learning for classification learns self-information in unlabeled images without image labels [7, 89]. Therefore, the proposed approach uses a transferred model with its own information in an unlabeled image to improve the performance. However, although this study has been evaluated on all three tasks, not merely the classification task, such experiments have not been conducted for medical image analysis before this study. For self-supervised learning, the image is divided into nine patches, which are shuffled to learn the original arrangement from the random patches. There are 9! ways to arrange nine patches, but because some are similar arrangements, such as [1,2,3,4,5,6,7,8,9] and [1,2,3,4,5,6,7,9,8], only 1000 cases are considered. Thus, for the self-supervised learning, the input is unlabeled images divided into nine patches and the predicted output is a case to arrange the patches. Also, a cross-entropy loss is used to calculate the error between the predicted output and the true output. The loss is as follows:  
\[L=-\sum_{i=1}^{n}t_{i}\log(p_{i}), \tag{2}\]  
where \(n\) is the amount of data and \(t\) and \(p\) are the true and the predicted output, respectively. Furthermore, fewer cases are considered for skin lesion and pressure ulcer images because parts of the skin other than the lesion look similar. There are various other approaches to self-supervised learning, such as contrastive learning, which creates and compares positive and negative samples. However, although such methods can be applied to general datasets, such as dog or cat images, they are not appropriate for medical images, which require specific domain knowledge. Therefore, this study proposes patch self-supervised learning, which learns the original image from randomly shuffled patch images, as shown in Figure 4. For the self-supervised learning, the input is unlabeled images divided into nine patches and the fine-tuning stage uses the application dataset with labels for the input. The model's weight trained with patch self-supervised learning is transferred and empirically fine-tuned to the application task. Experiments denoted as \(T_{sl}\) using a transferred model with such self-supervised learning were performed on the three applications, and the performance was measured.  
Figure 3: \(T_{sim}\) models for cervical cancer classification (upper-row model, based on ResNet50) and skin lesion classification (bottom-row model, based on DenseNet121). The pressure ulcer segmentation model for \(T_{sim}\) is shown in Figure 5. For skin lesion classification, a transition layer is added to reduce the number of channels and the size of the feature map.