esa-sceva/satcom-chunk-collection huggingface dataset:
# GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier
## 3 Materials and Methods
### Proposed Approach
#### 3.2.1 Data Pre-Processing  
Before describing the proposed approach, we would like to briefly explain the pre-processing followed for the experiments conducted to evaluate the performance of both the proposed approach, as well as, the selected machine-learning classifiers. Often the dataset contains noise in the form of unnecessary data that does not contribute towards the classification and needs to be cleaned. Data pre-processing is the process used for removing noisy and incomplete data. Pre-processing plays a pivotal role to improve classification accuracy [21]. The dataset used in this study contains a large amount of unnecessary data that does not play any role in the prediction. Since training and testing time increases when the dataset is larger, so, removing un-necessary data can speed up the training process as well. Pre-processing involves the steps carried out to clean the data so that the learning efficiency of the models can be enhanced. For this purpose, the natural language tool kit (NLTK) of Python has been utilized. It is a suite of text processing libraries that can be used for a variety of processing tasks and we used NLTK 3.5b1 with Python 3 [45]. Figure 4 shows the pre-processing steps that are followed in the current study.  
Figure 3: Diagram for the proposed methodology. Initially pre-processing is carried out to remove noise and unnecessary data. Term Frequency-Inverse Document Frequency (TF-IDF) features are then extracted to train the classifiers. For evaluation train-test split is done as 70–30.  
As a first step, all the reviews with missing values are identified and removed as the missing data can degrade the performance of the classifiers. Next, numerical values are removed from the text as they do not contribute towards the learning of the classifiers. It decreases the complexity of the training classifiers. Occasionally, reviews contain special symbols like a hear sign, thumb sign, etc. that need to be removed to reduce feature dimension and improve performance. After that the following punctuation []() /!, ;'is removed from the reviews in view of the fact that it does not contribute to the text analysis. It cripples the model's ability to discriminate between punctuation and other characters.  
As a next step, words are converted to lowercase because the text analysis is case sensitive. If this step is not carried out, the machine learning models will count for example 'Excellent' and 'excellent' as two different words which will ultimately affect the classifier's performance [46]. In the end, stemming is performed. It is a very important pre-processing step that removes the affixes from the words. It transforms the extended words into their base forms. For example, 'loves', 'loved', and 'loving' are the modified forms of 'love'. Stemming changes these words into the original/root form and helps to increase the performance of a classifier [47].
## 2 Problem Description and Methodology
### MEO Satellite Failure and Staged Deployment
#### 2.2.2 Staged Deployment Model  
The staged constellation deployment strategy involves breaking down the overall mission requirements into multi-stage components, taking into account performance index, ground coverage, vehicle selection, launch conditions, and various other factors that influence the design outcomes at each stage. Consequently, the design process is conducted based on both current and future mission requirements within the framework of this strategy. Furthermore, it is essential for the staged constellation design to ensure that each stage's constellation builds upon its predecessor and possesses the capability to expand into the subsequent stage. Redundant design was considered in the optimized design of the LEO constellation so that the performance impact arising from the failure of several satellites could be ignored, while the cost of LEO satellites as well as the launching costs were low, and the performance degradation of the LEO compensatory constellation was not taken into account.  
Figure 3: Analysis of three-satellite failure based on Monte Carlo method.  
In accordance with the current task requirements and future performance demands of the constellation, it is divided into \(Z\) sub-constellations for staged deployment. The staged deployment strategy of the constellation is illustrated in Figure 4. The primary factor influencing constellation performance is the number of orbital planes, and ensuring satisfactory service performance in target areas constitutes a fundamental condition for constellation design. The deployment model is as follows:  
The optimization metric is the PDOP, which is achieved by adding LEO constellations to make the PDOP of the hybrid constellations meet the corresponding requirements after the failure of the MEO navigation constellation satellites. The optimization variable is the number of orbital planes deployed in the LEO constellation so that the hybrid navigation constellation performance meets the required PDOP value. The deployment of orbital planes and the number of satellites per orbital plane depends on the configuration of the LEO constellation design.  
\[\begin{array}{c}\min(P)\\ \min\sum\limits_{j=1}^{n}\sum\limits_{k}^{L}\Bigl{(}e_{jk}-f_{jk}\Bigr{)}\\ s.t.\quad e_{jk}-e_{jk,\max}<0\\ \quad f_{jk,\min}-f_{jk}<0\\ \quad\gamma=\gamma_{\min}\end{array} \tag{10}\]  
where \(e\) and \(f\) represent performance parameters of the constellation, \(n\) denotes the quantity of stage project constellations, \(L\) stands for the number of performance parameters, and \(\gamma\) indicates the minimum elevation requirement.
# Impact of Different Nose Lengths on Flow-Field Structure around a High-Speed Train
###### Abstract  
In this study, the time-averaged and instantaneous slipstream velocity, time-averaged pressure, wake flows, and aerodynamic force of a high-speed train (HST) with different nose lengths are compared and analyzed using an improved delayed detached-eddy simulation (IDDES) method. Four train models were selected, with nose lengths of 4, 7, 9, and 12 m. To verify the accuracy of the numerical simulation results, they were compared with wind tunnel test results. The comparison results show that the selection of the numerical simulation method is reasonable. The research results show that with increasing nose length, the peak values of the time-averaged slipstream velocity of the trackside position (3 m from the center of track and 0.2 m from the top of rail) and the platform position (3 m from the center of track and 0.2 m from the top of rail) decrease continuously, and show a trend of rapid reduction at first, and then a slow decrease. As the nose length increased from 4 to 12 m, the time-averaged slipstream velocity at the trackside position and platform position are decreased by 57% and 19.5%, respectively. At a height of 1.6 m from the top of the rail, \(\Delta C_{P\text{ max}}\) (maximum pressure coefficient), \(|\Delta C_{P\text{ min}}|\) (the absolute value of minimum pressure coefficient), and \(\Delta C_{P}\) (pressure change coefficient) decrease with increasing nose length, which is similar to the peak value of time-averaged slipstream velocity, decreasing rapidly at first and then slowly. As the nose length increased from 4 to 12 m, decreases of \(\Delta C_{P\text{ max}}\), \(|\Delta C_{P\text{ min}}|\), and \(\Delta C_{P}\) by 26.5%, 58.5%, and 44.8% were shown, respectively. Different nose lengths also have a significant impact on wake flow.  
high-speed train; nose length; wind tunnel test; slipstream velocity; wake flow Article
## 2 Proposed Fault Diagnosis Method
### Health Condition Classification Based on the SSDAE
#### 2.4.1 Autoencoder and its Variant Algorithms
The training objective is to make the output as close as possible to the raw input to improve robustness of the features. The new feature \(h\) and the reconstructed data are defined in Equations (13) and (14), respectively, where \(s_{i}\) is the activation function of coding, \(s_{d}\) is the decoding activation function, \(w\) is the weight matrix, \(\widetilde{w}\) is \(w^{T},b\) is the coding deviation, and \(p\) is the decoding deviation. Finally, the loss function of DAE is obtained as shown in Equation (15), where \(N\) is the training sample set, and \(\theta=\{w,b,p\}\). \[h=i(\overline{x})=s_{i}(w\overline{x}+b) \tag{13}\]  
\[\hat{x}=d(h)=s_{d}(\overline{w}h+p) \tag{14}\]  
\[J_{DAE}(\theta)=\sum_{x\in N}L(x,d(i(\overline{x}))) \tag{15}\]
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation
#### 5.2.3 Boycott Attack  
In a boycott attack, the attacker exploits compromised membership services to grant partial network access: the victim can initially connect to the blockchain but is later blocked from completing critical operations due to revoked permissions. The malicious MSP configures permissions to allow the client application to submit transactions but denies access to query or read data. To demonstrate this attack in HLF, we simulated a malicious certificate revocation to disrupt user authentication. The attack involved the following steps. We added the target user's certificate serial number to the MSP's Certificate Revocation List (CRL) by editing the local MSP configuration files. Next, a channel configuration update transaction was generated, embedding the modified CRL, and submitted to the ordering service. This propagated the revocation to the channel's configuration block. Using the revoked user's credentials, we attempted to submit transactions. As expected, peers rejected these requests during certificate validation by cross-referencing the updated CRL.  
Figure 6 illustrates a boycott attack targeting the client application. However, attackers could similarly sabotage peer nodes or the peering system. Removing peers weakened the network distribution, which lowered the barrier for a 51% attack. Networks with a high fault tolerance may remain operational despite peer removals, but the reduced node count makes consensus manipulation easier. Our results confirmed that peers consistently blocked the revoked user, which proved the attack's efficacy in disrupting the network access. This attack highlights how compromised MSP configurations can bypass legitimate identity management protocols.  
Figure 5: Comparison of transaction throughput and successfully recorded transaction rate in block-discarding attack.
# Determining the Quality of a Dataset in Clustering Terms
## 2 Materials and Methods
### Methodology
#### 2.2.10 Community Comparison  
In general, to perform proper clustering of a particular graph, partitioning is carried out using various algorithms. One of the parameters for evaluating partitioning is the aforementioned modularity.  
To compare splits among themselves, the normalized mutual information parameter NMI (normalized mutual information) [30] can be used.  
Let \(A=A_{1},\ldots,A_{R}\); \(B=B_{1},\ldots,B_{S}\) denote two divisions of \(V\) into communities. A common part of \(A\) and \(B\) can be expressed as a contingency matrix \(C\) of size \(R\times S\), where \(C_{ij}\) denotes the number of common vertices of \(A_{i}\) and \(B_{j}\) (\(C_{ij}=|A_{i}\cap B_{j}|\)).  
The normalized mutual information parameter is defined as:  
\[\text{NMI}(A,B)=\frac{-2\sum_{i=1}^{R}\sum_{j=1}^{S}C_{ij}\text{log}\Big{(} \frac{C_{ij}n}{C_{i}C_{j}}\Big{)}}{\sum_{i=1}^{R}C_{i}\text{log}\Big{(}\frac {C_{i}}{n}\Big{)}+\sum_{j=1}^{S}C_{j}\text{log}\Big{(}\frac{C_{j}}{n}\Big{)}},\]  
where \(C_{i}\). \(=\sum_{j=1}^{n}C_{ij}\) and \(C_{j}\). \(=\sum_{i=1}^{n}C_{ij}\).  
Parameter NMI has its values in the interval \([0,1]\). If communities \(A\) and \(B\) are identical, then \(\text{NMI}(A,B)=1\), in the lack of similarity \(\text{NMI}(A,B)=0\).  
So, if the partition in clusters is repeated or similar in several algorithms, it can be considered appropriate.  
The Fowlkes-Mallows Index (FMI) can also be used to compare the similarity of two clusterings [31]. It is defined as the geometric mean of precision and recall:  
\[FMI=\frac{TP}{\sqrt{(TP+FP)(TP+FN)}},\]  
where \(TP\) is the number of pairs of points that belong to the same groups in both clusterings, \(FP\) the number of pairs of points that belong to the same groups in the first clustering but not in the second, \(FN\) the number of pairs of points that belong to the same groups in the second clustering but not in the first.  
The index takes values between 0 and 1. The higher the value, the more similar the two clusterings.  
The Table 1 lists the main notations that appear in Section 2.
## 2 Materials and Methods
### Workflow
#### 2.2.8 Statistical Analysis  
All the measurement and calculation results were recorded in Microsoft Excel (2016, Microsoft, Redmond, WA, USA). The statistical analysis was performed with the SPSS (version 25.0; IBM, Armon, NY, USA). The Kolmogorov-Smirnov test was used to test the normality of the data. The data were normally distributed. The matching percentages obtained by deviation analysis were compared by an independent t-test. The entire mandible and mandibular functional units (alveolar process, mandibular body, condylar process, chin, coronoid process, mandibular ramus, and mandibular angle) were compared between the study group and the control group. \(p\) values \(<\) 0.05 were considered statistically significant. The intraclass correlation coefficient (ICC) values were used to check the intra-examiner and inter-examiner reliability after the steps were repeated.  
Figure 5: Best-fit alignment of the mandibular units. Original mandibular units models (blue) and the mirrored units models (green).  
Figure 6: Surface deviation analysis of the mandibular units. The map showed the difference between the original model and the mirrored 3D models. The colored maps showed the negative (blue) and positive (yellow and red) deviation values. The ranges of tolerance at 0.5 mm are shown in green.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 1 Introduction
### Problem Statement  
In order to overcome the issues explained, the fibre-wireless (FiWi) technology based on RoF connections can be exploited to support 5G communications by jointly transporting all radio frequency (RF) signals and using the optical fibre features as an RF transport solution to accomplish the work items of 5G bands stated in 3 GPP [21].  
Multi-input multioutput (MIMO) is a conceivable method for most new mobile/wireless networks, which are pushed by today's users' high data rates. When compared to the performance of a single-input single-output (SISO) system, the MIMO approach is expected to improve transmission distance, data rate and reliability. As a result, it is a primary objective that the MIMO approach must be addressed when designing any FiWi system to enable wire free connection for next generation (NG) broadband networks.
## 3 Checkpointing Scheme for Clustering Routing Protocols
### System Design  
We propose a checkpointing scheme for the cluster head in clustering routing protocols that will minimize recovery cost and recovery latency. During the cluster head election step, our scheme elects additional backup nodes for checkpointing the cluster head information. All collected information sent by normal nodes to the cluster head is also saved in the backup nodes. The backup nodes periodically detect the state of the cluster head, and if the cluster head has a transient problem, then one of backup nodes replaces the failed cluster head to play the role of a new cluster head.  
Figure 2 presents an overview of our scheme applying the cluster head checkpointing mechanism. When the cluster head operates properly (see clusters a, c, d in Figure 2), backup nodes save only the checkpoint information and they monitor the state of the cluster head. In the case of cluster b, the cluster head cannot carry out its tasks when it encounters an S/W or H/W problem. A backup node then operates as a cluster head based on the obtained checkpointing information. Through this checkpointing scheme, we can prevent information loss caused by failure in the cluster head, and we can reduce recovery latency related to the frequent re-election of a cluster head.  
In clustering routing protocols, the communication range of a cluster head is larger than that of its cluster. To prevent network partition and orphan node problems, cluster heads adjust their communication ranges properly. In our mechanism, backup nodes can also adjust their communication range to cover all member nodes of their cluster.
## 2 Materials and Methods
### Long Short-Term Memory (LSTM)  
The LSTM model is designed to process varying lengths of input sequences initiated by a sequence input layer. The architecture includes two LSTM layers: the first has 750 units with the output mode set to'sequence', enabling it to return sequences for the subsequent layers, and the second has 500 units with the output mode set to 'last', which helps in reducing the output to the final state only. Between and after these LSTM layers, dropout layers with a rate of 0.2 are applied to mitigate overfitting by randomly omitting a portion of the features during training. The model concludes with a fully connected layer that maps to the number of classes, followed by a softmax layer for classification probabilities and a final classification layer. The structure is summarized as follows [35]:  
layers = [...  sequenceInputLayer(numFeatures)  lstmLayer(750, 'OutputMode','sequence')  dropoutLayer(0.2)  lstmLayer(500, 'OutputMode', 'last')  dropoutLayer(0.2)  fullyConnectedLayer(numClasses)  softmaxLayer  classificationLayer]; The training utilized the Adam optimizer, with the following hyperparameters set:  
* learning rate: 0.001;
* maxEpochs: 1000;
* batchSize: 128;
* L2 Regularization: 0.0001.  
The training utilized the Adam optimizer, with settings adjusted to accommodate GPU execution for enhanced performance. The options included shuffling data every epoch, displaying training progress plots, and setting a maximum of 1000 epochs.  
The model's performance was evaluated using k-fold cross-validation, with \(k\) set to 10. This approach partitioned the data into 10 sets, using 9 for training and 1 for testing in each fold. Predictions were made on the test sets, and the results were aggregated to compute the overall performance metrics, such as accuracy and the confusion matrix.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 2 Preprocessing Chain for SoNaR Signals
### Lofar  
Low-frequency analysis and recording (Lofar) is a method for extracting the parameters of a time-frequency representation of the incoming SoNaR signal. Lofar involves creating a time-frequency representation of the input signal windowed to smooth spectral leakage. In this study, we used a Hamming window to generate the Lofar [18].  
A fast Fourier transform (FFT) is applied to windowed signal blocks to transform the signal into the frequency domain. This results in the lofargram representing the frequency magnitude along the time in the signal acquisition bearing, that is, the azimuth. A lofargram denotes a time-frequency representation of the signal, which is represented graphically with the frequency on the vertical axis and the time on the horizontal axis. If there are multiple hydrophones, then different bearings can result in different lofargrams.  
The magnitude of the Lofar of a signal may be significant for machine learning classifiers. Large peaks indicate the presence of high-signal components. Nevertheless, background noise must be whitened to facilitate the search for signals [19]. Before the Lofar can  
Figure 2: Preprocessing flow of this work.  
be used for classification, it is processed using a _two-pass split window_ (TPSW) to mitigate the effects of noise [20].  
The acoustic signature of ship machinery typically spans frequencies up to 5 kHz [21]. Working with the correct frequency band [21] ensures efficient processing and focuses on the relevant information.  
The audio signal must be represented using a fast Fourier transform (FFT) [22]. This involves representing the waveform of the signal with a discrete vector sampled at specific times with a sampling interval \(T_{s}\) and a sampling frequency \(f_{s}=1/T_{s}\) that exceeds the Nyquist frequency. The sampling rates used were 1411 and 2116 kbit/s to record the frequencies of 44 kHz and 66 kHz, respectively.  
To provide adequate resolution of the signal spectrum, 2048 points were used to represent the entire frequency band. When a 66 kHz sampling rate was used, the first 1870 points covered the band from 0 to 6 kHz, while the first 280 points covered the same range for 44 kHz, which slightly exceeded a 5 kHz range of interest. Considering the potentially higher sampling rates in the future, we worked with the first 400 frequency lines. The idea is to work with signals captured from different sources in the future, either from a hydrophone or SoNaR. Nevertheless, 400 samples still constituted many attributes, which increased the classifier's computational complexity and stabilized the model learning, but required a large dataset to train the models effectively. To address this challenge, dimensionality reduction techniques were explored to simplify the problem and reduce the computational complexity while retaining essential information. Specifically, Principal Component Analysis (PCA) and Nonlinear Principal Component Analysis (NLPCA) techniques were evaluated for preprocessing.
## 3 Preliminaries
### Federated Learning  
Federated learning (FL) [38] is a distinctive form of distributed learning that excels in scenarios where training on the device provides a distinct advantage over training on proxy data at a central data center. This is particularly applicable when dealing with large datasets compared to the model size or sensitive data that are better kept private, leading to the decision not to transmit such data to the central data center. The goal is to avoid storing massive volumes of data on a centralized server or cloud by having machines cooperatively learn a shared prediction model while retaining all the training data on the machinesthemselves [39]. In the FL paradigm, a server collects the learned parameters periodically to update the global model, which is then distributed back to the clients (agents) for local training and inference. In the server, the local models are aggregated by weighting each local model by the number of available training samples, ensures a collective improvement in the global model while preserving the decentralized nature of the learning process as depicted in Equation (1), where \(W_{t+1}\) are the central model parameters, \(K\) is the sample of all participants, \(n_{k}\) is the sample of participants \(k\), and \(w_{t+1}^{k}\) is the local model parameters of all \(k\) participants:  
\[W_{t+1}\leftarrow\sum_{k=1}^{K}\frac{n_{k}}{n}w_{t+1}^{k} \tag{1}\]