esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Locality-Aware Sparse Attention  
The multi-head self-attention mechanism is a specific implementation of the attention mechanism and a commonly adopted form due to its effectiveness in capturing long-term dependencies [29]. The basic operation in multi-head attention is scaled dot-product attention, defined as:  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{D}}*M\bigg{)}V \tag{21}\]  
where \(Q=K=V,Q,K,V\), and \(D\) are queries, keys, values, and their dimensions, respectively. The mask matrix \(M\) is an optional term to avoid future information leakage.  
Metro passenger flow dynamics are also susceptible to perturbations induced by unforeseen events, such as extreme weather occurrences or holidays. Consequently, the variations in flow patterns at the time of observation are closely linked with the local contextual cues. Nevertheless, conventional self-attention mechanisms, tailored for discrete tokens, compute similarities between queries and keys solely based on their point-wise values, disregarding the local contextual nuances. This limitation challenges the effective application of such mechanisms to sequence transformations in passenger flow analysis, potentially causing query-key mismatches and optimization difficulties. [48] endeavored to mitigate this discrepancy by leveraging causal convolution [49]. However, the adequacy of extracting local context information through fixed window convolutions remains questionable. Concurrently, the computational complexity of self-attention is O(\(L^{2}\)), making it infeasible to directly model long-term series. To alleviate these two problems, we design a locality-aware sparse attention block (LSA), as illustrated in Figure 3. In the Mixture of Causal Conv, a set of causal convolution kernels of different sizes are employed to extract and fuse multiple local context information, transforming the input into queries and keys, and combining them using a set of data-dependent weights. The details can be expressed as:  
\[Q=\text{Softmax}(\text{L}(X))*F_{Q}(X) \tag{22}\]  
\[K=\text{Softmax}(\text{L}(X))*F_{K}(X) \tag{23}\]  
where \(F_{Q}\), \(F_{K}\), are two different sets of causal convolution kernel, and L is a fully connected layer. \(\text{Softmax}(\text{L}(X))\) is the weight for combing. Then, the max-mean measurement [30] method is used as a select function to select top u dominant queries. The details can be expressed as:  
\[\overline{Q}=\text{Select}(Q) \tag{24}\]  
\[\text{LSAttention}\big{(}\overline{Q},K,XW\big{)}=\oplus(\text{LShead}_{1}, \cdots,\text{LShead}_{\text{h}})W^{O} \tag{25}\]  
\[\text{LShead}_{i}=\text{Attention}\big{(}\overline{Q}_{i},K_{i},XW_{i}\big{)} \tag{26}\]  
where Select () is the operation of max-mean measurement, and Attention is the operation of scaled dot-product attention, and \(W\) is the projector.
## 5 Case Study
### Research and Development of MTCP  
According to the concept, connotation, characteristics, architecture and technical requirements of the mining technology collaboration platform in Section 4 combined with the results of the requirement survey and analysis, the mining technology collaboration platform is developed.  
The hardware and software environments for the development and implementation of the MTCP include the following--(1) a server with 512G memory, 600G*12 + 300G*8 hard disk, RAID6; and (2) the software environments of the server include Windows server 2012, PostgreSQL 9.3.14, Tomcat 6.0.36 and jdk1.6.0_38. The client browser for this R&D and implementation support is IE 8 and above, Chrome, Mozilla Firefox and the business software is DIMINE, iVent and iSchedule of Changsha Digital Mine Co., Ltd. (Changsha, China).  
The research and development of the mining technology collaboration platform include the design and development of the mining technology collaboration platform management side, the design and development of the database and the transformation of the business software; its composition and relationship are shown in Figure 6. The mining technology collaboration platform is supported by information technology, such as the Internet network, through the mining technology collaboration platform management side, all the businesses, such as geology, surveying and mining, are carried out in the form of business processes and according to the business process and the information and authority of the role, the business software connected to the mining technology collaboration platform management side through the standard interface and the Internet is started to process and manage the technical businesses, such as geology, surveying and mining and the data required by the mining  
Figure 4: The new organizational structure of the mining technology post for Fujian Makeng Iron Mine.  
Figure 5: The Orebody modeling standardization business process.  
technology collaboration platform management side and each business software comes from the database and the generated data is stored in the database, forming an organic whole composed of the mining technology collaboration platform management side, the business software and the database.
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods
#### 3.3.1 Entropy-Based Detection Method
The wavelet packet energy entropy is used in the UWPEE system to identify attacks, while UAVs are sent to collect the real traffic from IoT devices. In this method, the traffic sequence is partitioned into multiple layers of wavelet packets, and the wavelet packet coefficients of each layer are then reconstructed to reveal the sequence's influencing factors. The energy entropy is then calculated to determine if the traffic data exhibit distinct properties at various scales. A traffic signal with a higher degree of order has a lower entropy value than one with a higher degree of disorder [11]. Entropy can, therefore, reflect the distinct traits of malicious nodes when they periodically emit fake packets. The experimental results show that the UWPEE scheme can effectively identify traffic-based attacks with an accuracy rate of 84.47% and an average recognition efficiency of 4.89 for malicious nodes. Meanwhile, compared with the greedy algorithm, the flight path of the UAVs is reduced by 15.44%. In [73], a threshold-based detection scheme was proposed to detect RREQ flooding attacks in mobile ad hoc networks (MANET). In this study, the throughput, packet delivery fraction, and end-to-end delay of network traffic were compared with legitimate network traffic (without flooding attacks) and a network with one or more flooder nodes. A sender node is regarded as normal if its rate of RREQ falls below a certain threshold; otherwise, it is considered malicious. Simulation results indicate that a flooding attack could be detected, although the effectiveness depends on the threshold value chosen. Additionally, the method experiences more false positives and misdetections due to seasonal fluctuations in network traffic.
# Current Context and Research Trends in Linear DC-DC Converters
## 2 Switch Mode Power Supplies (SMPS)
### Issues with SMPS  
In theory, SMPS can achieve 100% efficiency. With the assumption of the ideal behaviour of the basic circuit elements used in SMPS, such as inductors, capacitors, and transistors, 100% theoretical efficiency can be achieved. However, in reality, those circuit components have losses due to their internal parasitic resistance and various operating conditions. Due to these losses, theoretical higher efficiency may not be achieved in reality. The drawbacks of SMPS are discussed in the following subsections.
## 3 Results
### CO2 and Climate Effect on iWUE* and LUE Trends
#### 3.3.2 A Source of Systematic Error: The Sensitivity of LUE and iWUE* Trends to Climate Forcing Data  
The sensitivity of simulated iWUE* and LUE trends to the choice of a climate forcing (CF) defined by the trend difference between S2 and S2-WFD simulations, which were obtained using WFDEI and WFD respectively, is shown in Figure 7. For the MTE product, the sensitivity of the iWUE* trend to CF is mainly related to differences in VPD trend between WFDEI and WFD (data not shown). The sensitivity of LUE trend to CF can be explained by differences of solar radiation trends between WFDEI and WFD.  
The sensitivity of the iWUE* to CF is consistent between simulations and MTE-iWUE*, especially in the central African forest. The choice of CF, thus, has a significant effect on the iWUE* trend over  
Figure 6: Individual effect of rising CO2 on the 1982–2010 trends of **(A)** iWUE* and **(B)** LUE. The effect of CO2 is attributed by taking the difference between a simulation with transient CO2 increase and one with fixed CO2. Trends are expressed in % of iWUE* or LUE obtained with a transient CO2. Same color code as in Figure 3  
the central African forest region. In fact the difference between iWUE* trends from the WFDEI and WFD CF reaches up to 75% of the iWUE* trend from WFDEI for both modeled and MTE products (Figure 7). In contrast, for the central African forest, the MTE-iWUE* trend obtained with WFDEI CF is weak over the northern savannah, especially over Cameroon and South Sudan in comparison to that MTE-iWUE* calculated from the WFD CF. ORCHIDEE shows a weak sensitivity of iWUE* to CF over the Sahelian zone and Southern Africa, in contrast to the MTE product (Figure 7).  
The sensitivity of the LUE trend to the choice of CF shows different geographical patterns between ORCHIDEE simulations and the calculated MTE-LUE (Figure 7). High sensitivity of the MTE-LUE trend to CF is sparse and mainly located over Cameroon and Gabon: the MTE-LUE trend obtained with WDF climate is negative and higher than the one obtained with WFDEI (Figure 7). In contrast to the MTE-LUE, the simulated LUE trend shows higher sensitivities to CF in the central African forest. The simulated LUE trend at 95% CI is positive with WFDEI-CF and larger than the one obtained with WFD climate over Cameroon, whereas LUE trend is weaker over Angola (Figure 7).  
Figure 7: Systematic error (sensitivity) of iWUE* trends and LUE trends to the choice of climate forcing (CF). Each plot shows the trend difference between a simulation with WFDEI CF minus one with WFD CF. **(A)** Difference between MTE-iWUE* trends calculated with WFDEI and WFD VPD; **(B)** Difference between iWUE* trends of ORCHIDEE 2-layers simulations; **(C)** MTE-LUE calculated with WFDEI and WFD APAR; and **(D)** Difference between LUE trends of ORCHIDEE 2-layers simulations.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 3 Preliminaries
### Layer-Two Forwarding  
Two forwarding mechanisms are possible in 6 LoWPANs: mesh-under and route-over. Mesh-under forwarding, also named layer-two forwarding, occurs at the link layer. The adaptation layer manages the mesh-under forwarding by making forwarding decisions based on the 6 LoWPAN headers. Even though RFC 4944 defines the format of mesh frames, it does not specify a mesh routing protocol to fill in the forwarding table. An example of such a protocol is Ad hoc On-Demand Distance Vector Routing (AODV) [19]. For route-over forwarding, the forwarding decisions are made at the level of the network layer. An example of such a routing protocol, defined by the IETF, is the RPL.  
The RPL [20] organises the network in a specific tree structure called a Destination Oriented Directed Acyclic Graph (DODAG). The network construction is triggered by the root node with the help of DODAG Information Object (DIO) messages, which carry information about the characteristics of the DODAG. Nodes that receive these messages and want to be part of the network might answer with a Destination Advertisement Object (DAO) message, to which they can obtain a DAO Acknowledgement (DAO-ACK) as a response. The DIO messages are periodically sent by the devices that are part of the network for maintenance purposes. The DODAG Information Solicitation (DIS) messages are sent by nodes that want to join an already formed network.  
Figure 2: Example of how an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) DODAG Information Object (DIO) packet is compressed by IPv6 over Low-power Wireless Personal Area Network (6 LoWPAN). **(a)** The uncompressed packet. **(b)** The 6 LoWPAN compressed packet. Note that only the Internet Protocol version 6 (IPv6) header is compressed since no compression format is defined for Internet Control Message Protocol for IPv6 (ICMPv6). The source IPv6 address is fully elided, and the destination IPv6 address can be shortened since it is a multicast address. The traffic class and flow label are elided since they are not used in this case. The payload length is not needed and is removed as well. Since the hop limit is 64, it can be elided as well.  
The RPL control messages, DIO, DAO, DAO-ACK and DIS, are ICMPv6 messages. They are carried in the Content field of an ICMPv6 packet (Figure 2a).
## 2 Method
### Data Analysis  
The collected data underwent several preprocessing steps to ensure their suitability for statistical analysis. The binary weld path factors F, G, H, were encoded as \(-1\) and \(1\) to represent the two levels, the reverse and forward directions. The locating scheme, factor D, was encoded using one-hot encoding, creating separate dummy variables for each level, reference, N-I, and N-O, while excluding the reference category to avoid multicollinearity. The welding sequence factor E was encoded into three dummy variables corresponding to each welding sequence combination for Assembly I and two variables for Assembly II. After gathering all the required experiment data, the statistical analysis was conducted to assess multicollinearity among predictors. As capturing high-order interactions between the factors results in model instability due to the limited data in this experimental design, model reduction was utilized to capture the effect of the most significant factors and their interactions. For this purpose, the variance inflation factor (VIF) was calculated for each predictor to determine multicollinearity levels among the factors. Later, the analysis of variance (ANOVA) approach was taken to identify the significant main effects and interactions influencing welding displacement. The ANOVA model included main effects and significant interactions based on initial screening. To verify the assumptions of ANOVA, including normality and homoscedasticity, the residual plots were captured and evaluated. Furthermore, the normal quantile-quantile plot was established to evaluate the normality of residuals. Later, the plots of the main effects and interaction plots were utilized to further evaluate the influential factors. All data analysis was conducted using Python, utilizing the statmodels package for statistical modeling and ANOVA.
# A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection
## 6 Conclusions  
In this study, we proposed a novel ensemble BRB model to predict fraud detection. Compared to the traditional ensemble learning framework, we made several improvements as follows:  
* When generating data subsets through random sampling, we retained all fraud samples and performed sampling only on normal samples;
* The feature selection process is added the model construction;
* The model selection process is added to the model construction.  
With these improvements, the ensemble BRB model can better handle fraud detection problems. The experimental results demonstrate that the ensemble BRB achieves optimal performance across multiple evaluation metrics in Dataset #1. In Dataset #2, the ensemble BRB achieved a performance comparable to the best-performing model. Also, by using BRB as the base classifier, the ensemble model gains enhanced interpretability. The model proposed in this paper focuses on addressing the imbalanced classification and interpretability issues in credit card fraud detection tasks. By combining the ensemble learning framework with the BRB model, we enhanced the model's interpretability, making the decision-making process more transparent. For instance, when a transaction is predicted as fraudulent, the prediction process can be traced through the BRB's principle formulas, a feature that helps financial institutions and customers understand the basis for decisions, thereby increasing trust in the system. Additionally, data imbalance often leads to high false positive rates in fraud detection systems, which the ensemble learning framework effectively mitigates. This not only reduces the inconvenience caused to customers and merchants by false alarms but also strengthens financial institutions' confidence in the detection results.  
Conceptualization, F.Y.; methodology, F.Y.; validation, F.Y., G.H. and H.Z.; formal analysis, F.Y. and G.H.; writing--original draft, F.Y.; writing--review & editing, F.Y., G.H. and H.Z.; visualization, G.H.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Postdoctoral Science Foundation of China: No. 2020M683736, Teaching reform project of higher education in Heilongjiang Province: No. SJGY20210456, Natural Science Foundation of Heilongjiang Province of China: No. LH2021F038, Social Science Foundation of Heilongjiang Province of China: No. 21 GLC189 and Foreign Expert Projects in Heilongjiang: No. GZ20220131.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are available upon request from the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 5 Performance Evaluation
### Dataset Generation  
The vehicular communication signals dataset used in this study is a synthetically generated dataset obtained using MATLAB [37]. It contains feature vectors extracted from ITS-G5, LTE-V2X and NR-V2X signals along with their respective labels. For each label (signal type), the simulations are performed at 15 different Signal-to-Noise Ratio (SNR) levels ranging from \(-10\) dB to 18 dB, and each level consists of the same number of signals. As a result, the dataset covers, for the three wireless technologies, a total of 4500 signals, whose parameters are summarized in Table 1 and which stem from the possible configurations previously described in Section 3. An example of each signal type received at SNR = 10 dB is depicted in Figure 2 in both time and frequency domains.  
For the feature extraction step, we consider two feature types. The first one is the SCF of the generated signals, estimated by Equation (6) and used as an input to the SVM classifier [20]. The length of the feature vector is set here, as in the original study, to \(1\times 16\),385, leading to a dataset dimension of 4500 \(\times\) 16,386. The second feature is the singular values of IFs, given by Equation (16) and fed into the random forest classifier, as seen in Figure 1. We set \(K\), the number of IMFs, to the lowest value that can be considered leading to a feature vector of three elements and a dataset dimension of 4500 \(\times\) 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ITS-G5** & **LTE-V2X** & **NR-V2X** \\ \hline Bandwidth & 10 MHz & \{10, 20\} MHz & \{10, 20, 50\} MHz \\ Subcarrier spacing & 156.25 kHz & 15 kHz & \{15, 30\} kHz \\ FFT size & 64 & \{1024, 2048\} & \{512, 1024, 2048\} \\ CP size & 16 & [72; 144] & \{36, 72, 144\} \\ QAM order & \{4, 16, 64\} & \{4, 16\} & \{4, 16\} \\ \hline \hline \end{tabular}
\end{table}
Table 1: PHY layer parameters of generated vehicular communication signals.  
Figure 2: Time and frequency domain representation of received (a) ITS-G5 (b) LTE-V2X and (c) NR-V2X signals at SNR = 10 dB.
## 3 Proposed Method
### Distributed Features of Text; dh1, dh2, and dh3  
In this study, features are also extracted from the textual sentences with the emoticons. Herein, we obtained features from tweets containing emoticons to be able to extract semantic features that take into account the positions of the emoticons in the text. Various methods have been proposed in the literature for extracting linguistic features from sentences, such as word n-grams, a method using semantic features of words, those that use sentence syntax patterns, and those that use word semantic distribution expressions. In these methods, the sentences are decomposed into certain units and features are extracted thereof. Therefore, in many cases, the positional relationships between the elements in the sentence cannot be used.  
In recent years, methods considering the order of appearance of words in a sentence have often been used. For example, the methods using recurrent neural networks that can handle time-series information and a method that can efficiently handle word position information based on transformer and self-attention mechanisms are being used. The natural language model BERT [27] developed by Google in 2018 is a general-purpose model that trains a large-scale network model from large-scale training data by combining transformer and attention. Many methods that improve upon BERT are still being proposed. However, BERT is a very large model, so retraining for dedicated tasks can be costly. When applied to an actual task, it is often finetuned by relearning only the layer close to the output layer using a small amount of task-specific training data.  
In the present study, we extract features from tweet sentences using DistilBERT [28], which can infer at high speeds without significantly compromising accuracy by reducing the training parameters. In the proposed method, the emotion label classifier is trained by applying distributed expressions obtained from the trained DistilBERT model as the inputs and using the tweet-sentence corpus with emotions labeled by hand as the learning data. Tweet sentences are input to the constructed trained model, and features specialized for emotion estimation are extracted from the three hidden layers. The configuration of the network used is shown in Figure 4, where DistilBERT obtains features with the same number of dimensions (768) as regular BERT. Using this 768-dimensional feature vector as the input, we learn a model that predicts four emotions: \"joy\", \"anger\", \"surprise\", and \"sorrow\". Using this pretrained model, we extract the outputs (256, 128, and 64 dimensions) of the three hidden, fully connected layers, respectively. In the following description, these feature vectors output from the hidden layers are denoted as dh1, dh2, and dh3, respectively.
## 3 Effects of Incidence Angle and Circumferential Location of Splitter Vane
### Determination of Incidence Angle and Circumferential Location for SVs  
In summary, \"impact-free entry\" design and PP = 0.5 should be selected for the model, which are supposed to achieve the maximum loading and reduce the loss as much as possible. Equation (3) gives the formulation of local incidence angles for SVs to satisfy the \"impact-free entry\" design when the CR is systematically increased. The incidence angle of SVs gradually transforms into the reference minimum-loss incidence angle of equivalent conventional blades in a polynomial manner when \(C_{\text{sv}}\) is longer than \(C_{\text{sv,cr}}\) from the value of 0:  
\[i_{\text{sv}}=\left\{\begin{array}{ll}i_{\text{eq}}\Big{(}\frac{C_{\text{sv }}-C_{\text{sv,cr}}}{C_{\text{pb}}-C_{\text{sv,cr}}}\Big{)}^{2}&,&C_{\text{sv }}\geq C_{\text{sv,cr}}\\ 0&,&C_{\text{sv}}<C_{\text{sv,cr}}\end{array}\right.. \tag{3}\]