esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Applying Fuzzy Theory in Simulation-Based Education
## 4 Empirical Analysis
### The Second-Stage Questionnaire Analysis
#### 4.2.1 Consistency Testing  
To ensure the validity of this questionnaire survey, after recollecting the questionnaire, the consistency testing followed. [PERSON] [39] suggested the \"Consistency Index\" (CI) and \"Consistency ratio\" (CR) for conducting consistency resting. In this study, the second-stage questionnaire content had seven items, and each relative scenario importance and the competency factors were evaluated. The scenarios were listed as follows: take-off with different tailwind but not informed; high-altitude area or high payload made horsepower insufficient; multiple external NAV failure, NAV capability deteriorated; under \"Visual Flight Rule\" (VFR), visibility deteriorated (cloud or rain); recognized \"Air Traffic Control\" (ATC) unsafe terrain clearance; and increasing tailwind on final approach (not reported). To avoid the inconsistency of pairwise comparison, these six scenarios and competency factors underwent consistency testing to obtain their CRs. When CR \(\leq\) 0.1, the evaluation matrix was highly consistent; if CR \(>\) 0.1, the evaluation matrix was deleted and not used in fuzzy weighting. The CR values are listed in Table 7. In this study, after all the experts completed the questionnaire survey and checked each factor CR \(\leq\) 0.1, the evaluation matrix was acceptable except take-off with different tailwind but not informed and increasing tailwind on final approach (not reported). These two scenarios only had two competency factors to compare and did not have any inconsistencies; thus, they were accepted into the prioritizing process.  
After the second-stage screening evaluation indexes, the fuzzy weighting values of the scenario and competency were obtained; then, we used the simple center of gravity method to defuzzify the fuzzy weight to obtain the definite value as the reference for prioritizing. The results are listed in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Relative** & **Take-off** & **High-Altitude** & **Multiple** & **Under “Visual** & **Recognized** & **Increasing** \\  & **IMPORTANCE** & **with** & **Area or High** & **External NAV** & **Flight Rule** & **“Air Traffic** & **Tailwind** \\
**No. of** & **of Scenario** & **Different** & **Payload** & **Failure. NAV** & **(VFR)**, & **Control”** & **on Final** \\
**Questionnaire** & **Factor and** & **Tailwind** & **Made** & **Capability** & **Visibility** & **(ATC) Unsafe** & **Approach** \\  & **Competency** & **but not** & **Horsepower** & **Deteriorated** & **Deteriorated** & **Terrain** & **(not** \\  & **Factor** & **Informed** & **Insufficient** & **(Cloud or Rain)** & **Clearance** & **Reported**) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 7: Consistency ratio (CR) values of the second-stage questionnaire.
## 3 Materials and Methods
### Analysis Based on Real Datasets
#### 3.4.3 Expected Number of Impressions  
The goal is computing the expected number of impressions emulating what we have done in Section 3.2.1. However, in this case, we will use the approximation we have proposed in Section 3.4.1, which allow us to get a real approximation to the number of expected impressions and the effect of \(FC\) according to them.  
We refer to \(X\) as the variable of the number of received impressions. \(P(X)\) is the probability of getting \(X\) impressions. Using the computed formula, we have that the probability of getting a new impression given that we have received \(n-1\) is  
Figure 4: Click rate in each state for each client based on the number of impressions that the user have received.  
Figure 5: Click rate in each state for each client based on the number of previous non-clicked impressions for the given state.  
\[P(X\geq n|X\geq n-1)=\frac{n}{n+1}\]  
but for computing the expected value, we need to compute \(P(X\geq n)\). The our result is  
\[P(X\geq 1) =1\] \[P(X\geq 2) =\frac{1}{2}\] \[P(X\geq 3) =\frac{1}{2}\times\frac{2}{3}=\frac{2}{6}=\frac{1}{3}\] \[P(X\geq 4) =\frac{1}{2}\times\frac{2}{3}\times\frac{3}{4}=\frac{6}{24}= \frac{1}{4}\] \[P(X\geq n) =\prod_{j=1}^{n}\frac{j}{j+1}=\frac{1}{n}\ \ \forall n\geq 1\]  
We can now use the obtained formula to compute \((E/X)\)  
\[E(X)=\sum_{i=1}^{\infty}P(X\geq i)=\sum_{i=1}^{\infty}\frac{1}{i}\]  
The sum in the formula above is infinite, which means that in an ideal case where we can Send impressions to the users forever, the expected number of impressions will also be infinite. However, we want to find a result in practical scenarios where it is not plausible to send impressions infinitely. For a practical scenario, we find  
\[E(X)=\sum_{i=1}^{n}\frac{1}{i}\]  
where \(n\) is the maximum number of impressions that the user can receive. This is the case for \(FC\), where \(n\) will be capped on the maximum number of impressions a user will receive. Using this formula, we can draw the curve that describes the relation between the number of expected impressions and \(FC\). The result is depicted in Figure 6.  
In a real scenario, we observe that the expected number of impressions that the users will receive is relatively small, even if \(FC\) is very high.  
Figure 6: Expected number of impression for each user based on different \(FC\).
## 1 Introduction
### Research Gaps and Motivation  
While recent advancements in ML and DL have significantly improved intrusion detection in IoT environments, several limitations persist across existing works [30]. Many studies, such as those utilizing LSTM, CNN, or hybrid architectures [21; 23; 25], rely on traditional DL models that, despite their high accuracy, struggle to generalize effectively in the presence of high-dimensional, imbalanced, and dynamic IoT traffic. Moreover, approaches like federated learning [20] and RL [24] introduce promising adaptability and privacy-preserving features, but often lack advanced data augmentation or fail to capture long-range temporal dependencies critical in evolving network attacks. Even models based on attention mechanisms, such as Transformers [22; 29], do not typically integrate adversarial training, leaving them vulnerable to performance degradation in low-resource or highly skewed class distributions. Furthermore, several works rely on outdated datasets [21; 23], limiting their real-world applicability, or overlook critical optimization of hyper-parameters, which can hinder convergence, stability, and overall detection accuracy.  
Motivated by these challenges, this study proposes a novel hybrid IDS framework that combines the representational power of Transformer networks with the generative capabilities of GANs to improve robustness and detection performance in complex IoT traffic scenarios. To address the problem of class imbalance and limited diversity in training data, our model leverages adversarial learning to synthesize representative samples and refine feature representations. Additionally, we incorporate an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm to jointly fine-tune network parameters and critical hyper-parameters, enhancing convergence speed, generalization, and model stability. By evaluating the model on the realistic CIC-IoT-2023 dataset, which reflects diverse attack types and device interactions, we aim to overcome the shortcomings identified in existing literature and deliver a more resilient and scalable IDS for next-generation IoT systems.
# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 3 Results  
For each experiment, the GAN variants are trained independently on a subset of the Train data for a number of epochs to generate synthetic data. Table 1 summarizes the epoch numbers for the GAN models used for two BCW and BCC datasets, determined by optimizing the pairwise correlation difference between the real and synthetic data [42; 43; 66]. The same amount of generated data are then added to the subset of the Train data, and they form an extended dataset that is eventually used for training the classifiers. The relevant question here then is how these additional synthetic data can improve the performance of the classifiers. This is especially important when only a small amount of (real) Train data is available, e.g., for rare diseases in the medical field.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **GAN** & **CGAN** & **CTGAN** & **CopulaGAN** & **WGANGP** \\ \hline BCW & 900 & 100 & 1000 & 800 & 300 \\ BCC & 300 & 300 & 300 & 900 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of epochs for training GAN variants using two different datasets, BCW and BCC.  
The results are presented separately for two different datasets: First, using the BCW and then the BCC. For each dataset, we show the results of two sampling setups, namely RS and RE, whose outputs are than evaluated by two different classifiers, SVM and MLP. Figure 3 illustrates the mean accuracy of the classifiers trained on extended datasets of different sizes. The latter contain generated data from GAN variants as well as the Train data; see Figure 2. In fact, for each training data size, expressed as a percentage of the Train data, the same amount of synthetic data is generated and added to the subsets of the Train data. As a baseline for comparison, the classifiers are trained once using only the Train data, without generated data, which is indicated as the Silver Standard in the graphs. We note that the classifiers consider a larger dataset in the case of GAN variants, where they include synthetic data. In this way, the comparison of the GAN results with the Silver Standard shows how the inclusion of synthetic data can improve the performance of the classifier on (unseen) Test data. We also note that we use the terms Train and Test data to refer to data from the original real dataset and training data to refer to subsets of the Train data of different sizes.  
Figure 3: The mean accuracy of classifiers trained on data generated by GAN variants versus the size of the training data (as a percentage of the Train data) using the BCW dataset. Rows (**top** and **bottom**) show the RS and RE sampling, while columns (**left** and **right**) indicate the SVM and MLP classifiers. The Silver Standard only considers Train data (no generated data) for training the classifier. Error bars show the standard error of the mean over 10 samples for each point.
# Performance Analysis of Direct Torque Controllers in Five-Phase Electrical Drives
###### Abstract  
The industrial application of electric machines has grown in the last decades, thanks to the development of microprocessors and power converters, which have permitted their use as variable-speed drives. Although three-phase machines are the common trend, the interest of the research community has recently focused on machines with more than three phases, known as multiphase machines. The principal reason lies in the exploitation of their advantages in terms of reliability, i.e., post-fault operating capability. Additionally, multiphase machines provide a better current distribution among phases, and lower current harmonic production in the power converter, than conventional three-phase machines. However, multiphase drive applications require the development of complex controllers to regulate the torque (or speed) and flux of the machine. In this regard, direct torque controllers have appeared as a viable alternative due to their easy formulation and high flexibility to incorporate control objectives. However, these controllers face some peculiarities and limitations in their use that require attention. This work aims to tackle direct torque control as a viable alternative for the regulation of multiphase drives. Special attention will be paid to the development of the control technique and the expected benefits and limitations in the obtained results. Case examples based on symmetrical five-phase induction machines with distributed windings in the motoring mode of operation will be used to this end.  
direct torque control; multiphase; electrical drives 2021  
11
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 5 Experiment
### Mean Throughput  
The mean node throughput was derived by dividing the average size of the replicated file by the transfer time. Figure 12 shows the mean throughput based on the number of files that were replicated from Computer 3 to Computer 1 and Computer 2 with either the IPFS + IPFS-Cluster or Rsync. In general, the results showed that the throughput by the IPFS + IPFS-cluster was greater than that of Rsync for each file size. The main observation was that the file size affected the level of throughput produced. The highest throughput generated was 2.64 MiB/s for the IPFS + IPFS-Cluster and 2.59 MiB/s for Rsync for a 1 GB (\(100\times 10\) MB) file size. Meanwhile, a slight decrease in throughput of approximately 0.25 MiB/s and 0.57 MiB/s occurred in the IPFS + IPFS-Cluster and Rsync, respectively, when replicating files with a size of 25.6 MB (\(100\times 256\) KB) and 100 MB (\(100\times 1\) MB). This was in  
Figure 11: Mean of time to replicate files with a size of {25.6 MB, 100 MB, 1 GB} as a function of latency.  
contrast to the throughput generated by Rsync and IPFS + IPFS-Cluster, which showed an increasing trend of approximately 16% and 32% for files with sizes of 100 MB (100 \(\times\) 1 MB) and 1 GB (100 \(\times\) 10 MB), respectively.  
The network bandwidth measurement was supported by the _Iperf_ for deriving the average network bandwidth between Computer 3 and Computer 1. Figure 13 shows the measured bandwidth with _Iperf_ between Computer 3 (client) and Computer 1 (server). The average bandwidth of the measurement was calculated at 28.7 Mbits/s. If we compared the highest node throughput score of the IPFS + IPFS-cluster with that of Rsync, it was seen that the IPFS + IPFS-Cluster throughput almost approached the average throughput generated by the network with a rating 2.64 MiB/s \(\times\) 8 = 21.1 Mbits/s. This indicated that the IPFS-IPFS-Cluster supported high-throughput so that it reduced the time consumed by file distribution.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 5 Evaluation
### Evaluation in a Real-Life Setup
In order to take a deeper look at the group response times, we summarize them in Figure 16 as Tukey boxes with whiskers and a central point, which are five-point summaries of datasets, corresponding to the minimum, 25 th percentile, median, 75 th percentile and maximum. Because of the well-defined back-off mechanism used by CoAP, it is possible to extract information about the amount of retransmissions, and thus, the reliability, from a Tukey boxplot of the latency. Looking at the box for unicast under low network usage, one can say that roughly in 70% of the cases, all group members replied after the first request, since the EM replied in less than 2 s and the first retransmission occurs after 2 s to 3 s. For the remaining 30%, at least one member of the group needed one retransmission. A second retransmission, taking place between 6 s and 9 s, was never needed. In the case of multicast communication, the latency is significantly higher because of the way we configured the leisure period. Here, every packet loss will directly result in a decrease of the group reliability. Under low network usage, the reliability dropped to 62%, which is more or less in line with the packet losses observed during the unicast experiments, which was executed under similar conditions. Now, looking at the box for unicast under normal network usage, we see that less than 25% of the cases required no retransmission. For the remaining 75% of the cases, one or two retransmissions were needed for at least one member of the group. A third retransmission, which would occur between 14 s and 21 s, was never needed. Figure 16: Entity response times for both unicast and multicast group communication in a real-life environment under low and normal network usage. Unicast reliability is achieved by exponential retransmissions and leads to a large increase in the response times. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Communication Type**} & \multirow{2}{*}{**Network Usage**} & \multicolumn{2}{c}{**Reliability**} & \multicolumn{2}{c}{**Response Time (s)**} \\ \cline{3-7}  & & **Member** & **Group** & **Min.** & **Average** & **Max.** \\ \hline Unicast & Low & 100\% & 100\% & 1.02 & 1.70 & 4.53 \\ Unicast & Normal & 100\% & 100\% & 2.91 & 7.21 & 13.87 \\ Multicast & Low & 92.2\% & 64\% & 2.62 & 4.92 & 8.36 \\ Multicast & Normal & 91.2\% & 58\% & 2.45 & 4.79 & 7.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of the results of the experiments on the w-iLab.t office wireless sensor testbed.
## 4 Experiments Results and Analysis
### Discussion
#### 4.3.2 Different Types of Entity Recognition Results  
The BERT-MF-BiGRU-CRF model proposed in this paper consists of an input layer, an embedding layer, a BiGRU layer, and a CRF layer. Firstly, the character-level features of shipping enterprise credit text are obtained through BERT; secondly, the unlabeled shipping enterprise credit data are separated by jieba, and word reorganization is performed using four sets of B, M, E, and S to extract word vector features, word length features, and pos features of shipping enterprise credit text; then, the obtained feature vectors are spliced and used as the input of BiGRU to obtain the contextual features of shipping enterprise credit text; finally, classification is performed through the CRF layer.  
In order to further understand the credit NER task of shipping enterprises, the recognition effects of various types of entities are analyzed based on the BERT-MF-BiGRU-CRF model proposed in this paper. Table 7 shows the recognition of shipping enterprise credit entities by BERT-MF-BiGRU-CRF in detail.  
As can be seen from Table 5, the recognition of Honor, Intel-pro, and Certificate are not satisfactory, both in terms of accuracy and recall, which are only about 70%, and the F1 values of these three types of entities are 73.34%, 74.89%, and 75.24%, which are much lower than those of Person (92.54%) and Organization (91.89%), which are the two types of entities. Analysis of the reasons may be that the format of Honor, Intel-pro, and Certificate entities in the shipping enterprise credit text is not uniform and complex in composition, with both Chinese characters and English letters and numbers, for example, the Honor entity sample \"\"\(
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 2 Materials and Methods
### Brightness and Contrast Enhancement
#### 2.2.4 Classification  
In machine learning, the support vector machine (SVM) is a statistical learning algorithm for classification and learning problems. SVM maximizes the margin between positive and negative classes by placing a hyperplane into them. A kernel function makes SVM capable of learning adaptively with the help of features. The SVM is used to classify windows between hemorrhages and non-hemorrhages. A radial basis function (RBF) is used as a kernel to classify the two categories. The windows that belong to the hemorrhages are labeled as the positive class. The rest of the windows are the negative class. The features are used to train the SVM and tested for the classification of hemorrhages.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 3 Recursive Structure of Fault Location for the Distribution Network
### Theoretical Foundation  
#### 3.1.1 Compatible Sets  
Let \(\mathbf{E}\) be the set of sections in the distribution network, \(\mathbf{X}\subseteq\mathbf{E}\) be the set of fault sections, and \(x_{i}\in\mathbf{X}\). The meaning of the compatible set of section \(x_{i}\) is the condition that the road vector \(\mathbf{P}_{k}\) of the other sections \(x_{k}\in\mathbf{X}-x_{i}\) in \(\mathbf{X}\) should be satisfied for \(\mathbf{X}\) to satisfy the inequality constraint described in Equation (20). We define the compatible set \(C_{i}\) of section \(x_{i}\) according to the inequality constraint expression (20).  
\[C_{i}=\{x_{k}\in\mathbf{E}|\mathbf{P}_{i}\
eq\mathbf{P}_{i}*\mathbf{P}_{k}\ \&\ \mathbf{P}_{k}\
eq\mathbf{P}_{i}*\mathbf{P}_{k}\} \tag{28}\]  
The properties of compatible sets are the basis of the argument for recursive structures. These properties are stated first, and their mathematical proofs are given in Appendix B.  
**Theorem 4**.: _Let the set of sections in the distribution network be \(\mathbf{E}\), sets of fault sections be \(\mathbf{X}_{a},\mathbf{X}_{b}\subseteq\mathbf{E}\), and \(\mathbf{X}_{a}\cup\mathbf{X}_{b}=\varnothing\). Then, \(\mathbf{X}_{a}\cup\mathbf{X}_{b}\) satisfies the inequality constraint equivalent to the following conclusion: \(\mathbf{X}_{a},\mathbf{X}_{b}\) satisfies the inequality constraint and \(\forall x_{i}\in\mathbf{X}_{a},\mathbf{X}_{b}\subseteq C_{i}\)._  
**Theorem 5**.: _If section \(x_{i}\) is on the road of section \(x_{k}\), then \(C_{i}\subseteq C_{k}\)._  
**Theorem 6**.: _If section \(x_{i}\) is on the road of section \(x_{k}\) and the path from \(x_{k}\) to \(x_{i}\) does not contain a T-type section, then \(C_{i}=C_{k}\)._
# A Probabilistic Hyperspectral Imagery Restoration Method
## 4 Probabilistic LRMA Model for HSI Restoration  
From above analysis, we can see that HSI restoration is to estimate low-rank matrix \(\mathbf{L}\) from \(\mathbf{X}\) inversely in Equation (1). In this section, we first introduce how traditional methods estimate \(\mathbf{L}\) from \(\mathbf{X}\), and then give the details of how we solve this problem with the probabilistic model.  
Without any prior knowledge on \(\mathbf{S}\), estimating \(\mathbf{L}\) from \(\mathbf{X}\) is ill-posed. Certain constraints or priors on \(\mathbf{S}\) are then needed to make \(\mathbf{L}\) efficiently solvable. The original LRMA model assumes \(\mathbf{L}\) and \(\mathbf{S}\) are low-rank matrix and sparse matrix, respectively, with which the estimation of \(\mathbf{L}\) is modeled as  
\[\min\
olimits_{\mathbf{L},\mathbf{S}}\boldsymbol{rank}(\mathbf{L})+\lambda \|\mathbf{S}\|_{0}\quad\text{s.t.}\qquad\mathbf{X}=\mathbf{L}+\mathbf{S}. \tag{3}\]  
The above formula is a nonlinear optimization problem, which is hard to solve. To make it tractable optimized, Formula (3) is converted into  
\[\min\
olimits_{\mathbf{L},\mathbf{S}}\|\mathbf{L}\|_{\star}+\lambda\|\mathbf{ S}\|_{1}\quad\text{s.t.}\qquad\mathbf{X}=\mathbf{L}+\mathbf{S} \tag{4}\]  
by replacing \(\mathbf{L}_{0}\) norm with \(\mathbf{L}_{1}\) norm and replacing the rank with the nuclear norm. \(\lambda\) is a weight to balance the importance of \(\mathbf{L}\) and \(\mathbf{S}\)[39; 40].  
Though the original LRMA model is suitable for incomplete data restoration, from the constraint on \(\mathbf{S}\), we can see that the original LRMA model is only suitable for noise in a sparse form.  
However, the degradation factors such as multiple kinds of noise, stripe corruption, and missing data coexist in HSI, among which only few kinds of noise are sparse. This characteristic makesEquation (4) inappropriate to represent such kind of degradation in reality. In [11], the LRMA model is remodeled as  
\[\mathbf{X}=\mathbf{L}+\mathbf{S}+\mathbf{N}, \tag{5}\]  
where the new supplemented item \(\mathbf{N}\) is a Gaussian-distributed noise. Then, \(\mathbf{L}\) can be solved by  
\[\min_{\mathbf{L},\mathbf{S}}\lVert\mathbf{L}\rVert_{*}+\lambda\lVert\mathbf{S }\rVert_{1}\quad\text{s.t.}\qquad\lVert\mathbf{X}-\mathbf{L}-\mathbf{S} \rVert_{\text{F}}\leq\delta, \tag{6}\]  
where \(\delta\) is a constant [41].  
Though [11] can deal with more kinds of noise by dividing the noise item into \(\mathbf{S}\) and \(\mathbf{N}\), from Equation (6) we can see the noise that [11] can essentially deal with is only Gaussian noise, sparse noise, and their combinations, which is still insufficient for real applications.