esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary
### ANFIS Architecture  
In the early 1990s, [PERSON] applied fuzzy systems to adaptive network structures to obtain ANFIS. ANFIS can be divided into two parts: artificial neural network (ANN) and fuzzy inference system (FIS) [25]. Specifically, the two methods complement each other. On the one hand, FIS gives clear physical meaning to the nodes and weights of the neural network, avoiding the \"black box\" phenomenon in the traditional neural network processing. On the other hand, ANN has strong learning ability and can optimize the assumption rules of the fuzzy system in the process of data training, which solves the problem of unclear rulesdivision in FIS operation [26; 27]. This is helpful to the development of hybrid intelligent system network [28; 29].  
In general, ANFIS is usually divided into five layers. Its structure is shown in Figure 2, where x and y are inputs and f is an output.  
Layer 1: In this layer, each square node \(i\) corresponds to a node function. As shown in formula (3):  
\[\begin{split}&\mathcal{O}_{i}^{1}=v_{A_{i}}(x),\quad i=1,2\\ &\text{or}\ \mathcal{O}_{i}^{1}=v_{B_{j}}(y),\quad j=1,2\end{split} \tag{3}\]  
where \(x\) (or \(y\)) are the two initial inputs and \(A_{i},B_{j}\) represent the linguistic labels of the degree of ambiguity. The membership function of \(A\) (or \(B\)) can be arbitrary. The bell-shaped function \(v_{A_{i}}(x)\)(or \(v_{B_{j}}(y)\)) is usually chosen, such as:  
\[v_{A_{i}(x)}=\frac{1}{1+\mid\frac{x-k_{i}}{h_{i}}\mid^{2m_{i}}} \tag{4}\]  
where \(\{h_{i},k_{i},m_{i}\}\) is the set of parameters whose values control the shape of the membership function.  
Layer 2: In this layer, each circle node \(\prod\) is used to calculate the product of the signal passed into the node and then passed into the third layer.  
\[\mathcal{O}_{i}^{2}=u_{i}=v_{A_{i}}(x)\times v_{B_{i}}(y),\quad i=1,2 \tag{5}\]  
Figure 1: Principle of belt grinding process.  
Figure 2: Structure of ANFIS.  
Layer 3: In layer 3, there are circle nodes marked with \(N\), which are normalized with the trigger intensity from the previous layer.  
\[O_{i}^{3}=\overline{u_{i}}=\frac{u_{i}}{\sum_{i}^{2}u_{i}}=\frac{u_{i}}{u_{1}+u_ {2}},\quad i=1,2 \tag{6}\]  
Layer 4: This layer receives the normalized trigger intensity \(\overline{u_{i}}\) from the third layer.  
\[O_{i}^{4}=\overline{u_{i}}f_{i}=\overline{u_{i}}(d_{i}x+e_{i}y+g_{i}) \tag{7}\]  
where \(\{d_{i},e_{i},g_{i}\}\) is the parameter set.  
Layer 5: This layer has only one circle node labeled \(\sum\). Its function is to accumulate all the signals from the previous layer and output the result.  
\[O_{i}^{5}=\sum_{i}\overline{u_{i}}f_{i}=\frac{\sum_{i}u_{i}f_{i}}{\sum_{i}u_{ i}} \tag{8}\]  
In this way, a five-layer fuzzy adaptive network structure is constructed. We can also combine some of the layers to get a network structure with fewer layers. Likewise, we can perform weight normalization in the last layer of the network. Clearly, the assignment of node functions and the structure of the network are arbitrary as long as each node and each layer make sense and perform modular functions.
## 4 Experimental Results
### Comparative Study  
We compared the embedding capacity of our scheme with that of similar RDH schemes. The comparison chart is shown in Table 5. As observed, we were able to achieve an embedding capacity of 0.0625 bpp, which was better than that of several existing schemes. Apart from the embedding rate, all the compared schemes recovered the original cover image with a PSNR of \(\infty\) and \(SSIM=1\).  
It should be noted that there are other RDH schemes in encrypted images [16; 17; 18; 23; 56], that achieve a very high payload, but the proposed scheme is very different from most of the earlier schemes. The main objective of retaining the pixel distribution of the encrypted image while embedding the data was met. An acceptable payload of 0.0625 bpp was achieved without compromising the complete reversibility feature.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Scheme** & **Airplane** & **Baboon** & **Boat** & **Peppers** \\ \hline Scheme in [26] & 0.0039 & 0.0009 & 0.0039 & 0.0015 \\ \hline Scheme in [27] & 0.0039 & 0.0009 & 0.0039 & 0.0039 \\ \hline Scheme in [29] & 0.0300 & 0.0100 & 0.0300 & 0.0300 \\ \hline Scheme in [48] & 0.0020 & 0.0020 & 0.0020 & 0.0020 \\ \hline Scheme in [49] & 0.0040 & 0.0040 & 0.0040 & 0.0040 \\ \hline Scheme in [51] & 0.0080 & 0.0080 & 0.0080 & 0.0080 \\ \hline Scheme in [50] & 0.0080 & 0.0080 & 0.0080 & 0.0080 \\ \hline Scheme in [30] & 0.0039 & 0.0039 & 0.0039 & 0.0039 \\ \hline Scheme in [28] & 0.0312 & 0.0312 & 0.0312 & 0.0312 \\ \hline Scheme in [54] & 0.0400 & 0.0400 & 0.0400 & 0.0400 \\ \hline Scheme in [47] & 0.0547 & 0.0547 & 0.0547 & 0.0547 \\ \hline Scheme in [55] & 0.0240 & 0.0045 & - & - \\ \hline Scheme in [52] & 0.0047 & 0.0011 & 0.0072 & 0.0072 \\ \hline Scheme in [53] & 0.0547 & 0.0547 & 0.0547 & 0.0547 \\ \hline Proposed scheme & 0.0625 & 0.0625 & 0.0625 & 0.0625 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of payload computed on airplane, baboon, boat and peppers images with similar RDH schemes.  
Figure 7: Rate of images recovered.
## 7 Conclusions  
Aiming at the calcium leaching model in porous media, this study proposed the inverse method into the calcium leaching parameter solution process. Then, a back analysis for permeability coefficients of the curtain at Shimantan gravity dam was realized. The objective function was constructed from time-series measurements of hydraulic head and leakage. Finite element analysis was performed to construct neural network learning samples. ELM was applied to build a non-linear mapping of leaching parameters and error values. Four optimization algorithms were devoted to accelerating the computation process. The hydraulic head and leakage errors of the positive analysis for the four optimization algorithms were compared, and the trend of the curtain's permeability coefficient in 100 years was analyzed. The reliability and uniqueness of the inverse results were improved. The main conclusions of the paper are indicated as follows:  
1. Based on ELM and four optimization algorithms, the inversion values of leaching parameters by four algorithms show small differences. The results are all within the parameter range.  
Figure 8: Impervious curtain permeability coefficient evolution in the leaching process; (**a**) permeability coefficient distribution; (**b**) permeability coefficient evolution at 63 m elevation in a century; (**c**) permeability coefficient evolution of three positions.  
2. Four sets of leaching parameter results were used in the positive analysis. Among the four results, the curves fitted by PSO corresponding to the leaching parameters are in the best agreement with the measured values and show the highest prediction accuracy, which indicates that the inversion method is reliable and effective.
3. The values of permeability coefficients on the upper and upstream sides are greater than in other areas, showing that these areas are the most vulnerable parts of the grout curtain. Focusing on these vulnerable parts and strengthening safety management is necessary for the safety of water conservancy projects. The results illustrate the feasibility of the inversion analysis for obtaining the calcium leaching parameters under advection-diffusion-driven leaching processes.  
Conceptualization, Y.S. and L.X.; methodology, Z.S.; software, Y.S.; validation, K.Z. and C.Y.; formal analysis, L.X.; investigation, K.Z.; resources, Z.S.; data curation, C.Y.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; visualization, K.Z.; supervision, Z.S.; project administration, Z.S.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"National Natural Science Foundation of China, grant number 52179130\", \"Postgraduate Research & Practice Innovation Program of Jiangsu Province, grant number KYCX18\(\backslash\)0598\", and \"Fundamental Research Funds for the Central Universities, grant number 2018B630\(\backslash\)X14\".  
The authors declare no conflict of interest.
## 2 Problem Statement
### Interval Finite Element Equation  
For the traditional finite element method, the static equilibrium equation of the certain structure can be expressed as follows:  
\[\mathbf{Ku}=\mathbf{f} \tag{9}\]  
where \(\mathbf{K}\in\mathbb{R}^{d\times d}\) denotes the global stiffness matrix, which is composed of the element stiffness matrix; \(\mathbf{u}\in\mathbb{R}^{d}\) is the node displacement vector, whose element is the displacement value of each node of the structure; \(\mathbf{f}\in\mathbb{R}^{d}\) is the equivalent load vector at the nodes, including load, constraints, and other information of the structure; and \(d\) denotes the total number of degrees of freedom of the structure.  
In practical engineering structures, parameters such as geometry size or material properties are uncertain, and the uncertainty in the structure can be expressed by interval parameters \(\mathbf{a}^{I}\). Thus, both the structural stiffness matrix \(\mathbf{K}\) and load vector \(\mathbf{f}\) are functions of \(\mathbf{a}^{I}\), and the finite element equilibrium equation can be expressed as follows:  
\[\mathbf{K}\left(\mathbf{a}^{I}\right)\mathbf{u}^{I}=\mathbf{f}\left(\mathbf{a }^{I}\right) \tag{10}\]  
where \(\mathbf{a}^{I}=\left[a_{1},\cdots,a_{n}\right]^{T}\in\mathbb{R}^{d}\) is the interval vector, composed of each uncertain parameter of the structure; \(n\) is the number of uncertain parameters; \(\mathbf{K}\left(\mathbf{a}^{I}\right)\in\mathbf{K}^{I}=\left(\left[k_{ij}, \overline{k}_{ij}\right]\right)\) is the global stiffness matrix considering the structural uncertainty; \(\mathbf{f}\left(\mathbf{a}^{I}\right)\in\mathbf{f}^{l}=\left(\left[\underline {f}_{ij},\overline{f}_{ij}\right]\right)\) is the load vector considering the uncertain structure; and \(\mathbf{u}^{I}\) is the interval displacement vector to be solved, and its solution set can be described as follows:  
\[\Gamma=\left\{\mathbf{u}\in\mathbb{R}^{d}|\mathbf{Ku}=\mathbf{f},\mathbf{K} \in\mathbf{K}^{I},\mathbf{f}\in\mathbf{f}^{l}\right\} \tag{11}\]  
The set \(\Gamma\) in Equation (11) is a non-convex polyhedral complex set whose exact features are difficult to obtain.  
Finding all possible solutions is of little significance. Therefore, we just care about the upper and lower bounds of the displacement vector \(\mathbf{u}\). According to Equation (11), the bounds of the interval vector \(\mathbf{u}\) can be defined as follows:  
\[\begin{array}{l}\underline{\mathbf{u}}=\min\left\{\mathbf{u}|\mathbf{Ku}= \mathbf{f},\mathbf{K}\in\mathbf{K}^{I},\mathbf{f}\in\mathbf{f}^{I}\right\},\\ \overline{\mathbf{u}}=\max\left\{\mathbf{u}|\mathbf{Ku}=\mathbf{f},\mathbf{K} \in\mathbf{K}^{I},\mathbf{f}\in\mathbf{f}^{I}\right\}\end{array} \tag{12}\]  
where \"max\" means to take the maximum value in the set, \"min\" means to take the minimum value in the set.
## 2 System Design
### Base Station Module
#### 2.3.1 The Base Station Module Hardware  
The base station module includes the MCU, reset circuit, capacitor banks, and serial expansion chip (Figure 2). MCU is the control core of the RWMS based on the wireless sensor networks. The embedded operating system, ZKOS (Zhongke Operating System), controls and implements the functions such as acquiring data, processing data, transferring data and so on. The ZKOS will be described in details in a later section. In the reset circuit, resistance and capacitance are in series and the reset circuit connects the RESET connections of the MCU. The capacitor banks can reduce the disturbances, such as high frequency, fluctuations, unsteady state and the power surges to ensure the MCU works normally. The base station module can provide five serial ports, eight A/D of 12 bits, two CAN buses and 10 common programmed I/O interfaces in order to conveniently connect to multiple different interface sensors. The acquired data can connect the GPRS/GSM module through the serial port and can be exchanged with data on the remote server through the GPRS/GSM module. The core hardware of the GPRS/GSM module adopts the integrated package product from the New Thinking Company. The software is highly reliable and self-developed based on the embedded ZKOS operating system.  
Figure 1: System structural framework.
# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 2 Background
### Named Entity Recognition  
Named Entity Recognition (NER) is a fundamental subtask of NLP. By definition, it enables the extraction of phrases called _'entities'_ from unstructured textual data, which are detected and classified into specific pre-determined categories (e.g., _'Person'_, _'Location'_, _'Organization'_, etc.). Many NLP libraries have been developed so far to address this subtask. A few notable examples include: GATE [12], NLTK [13], spaCy ([[https://spacy.io](https://spacy.io)]([https://spacy.io](https://spacy.io))) (accessed on 27 November 2021), StanfordNLP [14], Flair [15] and Stanza [16]. In terms of employed approaches, the libraries in question vary from rule-based (GATE, NLTK), statistical neural network based (spaCy), to deep learning based ones (Stanza, Flair, spaCy-v3-trf). Rule-based approaches, rely on carefully hand-crafted grammar-based rules, and a lot of work done by human linguistic experts, whereas statistical ones require large numbers of manually annotated data [17].  
The authors in [18] compare notable NLP frameworks from each approach, excluding the deep learning ones. In their experiments, it is shown that for the subtask of NER, StanfordNLP achieves the best performance in terms of F1 score, whereas spaCy-v2 has the second best performance. After this study was published, the Stanford NLP Research group released Stanza, and the authors of spaCy released the third version of their software; both libraries came with better pretrained models to the best of our knowledge, compared to their predecessors.  
The authors in [16] evaluated deep learning approaches (Stanza, Flair) that achieve higher accuracies than existing methods; however, those suffer from severely increased execution runtime, compared to the pretrained convolutional neural network approach of spaCy-v3. These results are also confirmed by the authors of spaCy in their benchmarks ([[https://spacy.io/usage/facts-figures#benchmarks](https://spacy.io/usage/facts-figures#benchmarks)]([https://spacy.io/usage/facts-figures#benchmarks](https://spacy.io/usage/facts-figures#benchmarks)) (accessed on 27 November 2021)). Recently, the creators of spaCy introduced a new pretrained transformer English model (_en_core_web_trf_), which reaches the state-of-the-art accuracy of deep learning approaches.  
From the aforementioned two studies, we observed that there is an execution runtime versus accuracy of results trade-off, which influences the choice between different NLP libraries.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 2 Literature Review
### Temperature Correction
#### 2.2.2 Analytical Equations  
It is necessary to take into account the fact that some assumptions and simplifications might be used to estimate the PDIV. Due to these simplifications, analytical models generally are less precise than the FEM simulations. The main advantage is that a much lower computational load is required.  
[PERSON] et al. [74] measured the PDIV value on a wide range of insulating materials and thicknesses and presented an empirical equation, Equation (8), based on the relationship between the insulation thickness and its relative permittivity:\[\mathrm{PDIV}=163\bigg{(}\frac{t_{\mathrm{ins}}}{\epsilon_{\mathrm{r}}}\bigg{)}^{0.46} \tag{8}\]  
where the PDIV is provided in V\({}_{\mathrm{rms}}\), \(t_{\mathrm{ins}}\) is the total insulation thickness in \(\mu\)m, and \(\varepsilon_{\mathrm{r}}\) is the relative permittivity of the insulation. In the twisted pairs, \(t_{\mathrm{ins}}\) is twice the enamel thickness (as there is one layer in each conductor) and, in the case of using different enamel materials, the mean value of the relative permittivity should be used (\(\varepsilon_{\mathrm{r}}\)).  
Despite this equation fulfilling a wide range of insulation thicknesses and relative permittivity values, the environmental conditions of the measurements, such as temperature, are not considered.
A survey showed that similar works in the literature present systems that are mainly focused on human-machine interfaces, comfort characterization, and riding simulation over different scenarios, with the aim of improving road safety [1; 10; 11; 12; 13; 14; 15]. They are conceived to evaluate the rider's subjective feeling or to train riders to face various situations, from normal riding to emergency manoeuvres and, except for [1], they are not able to employ a real-world motorbike. The proposed system has been conceived as an engineering tool and it is aimed at the evaluation of the motorcycle vertical dynamic behaviour and its interaction with the road. It is, therefore, based on the use of a real-world motorcycle, so that subjective feedback coming from the rider can be integrated with objective data coming from the investigation of natural frequencies, wheel hub acceleration and displacement, chassis movement, suspension characterisation, and fatigue testing, and from NVH analysis in general.  
To date very few research and development facilities in the world feature a system able to perform the above-mentioned activities on a free-standing motorcycle. A search for similar test rigs gave no results in Southern Europe, hence, the University of Brescia, and the Department of Mechanical and Industrial Engineering, in particular, decided to retrofit a four-poster test rig with a custom-designed fixture to perform tests on two-wheeled vehicles as well. In this case the motorbike (bike) is resting on two of the four actuators and an aluminium structure in such a way that the dynamic response is not affected.  
The motorcycle is restrained so that it is free to move around the centre of gravity, i.e., in pitch as well as along the vertical direction in order to enable interaction between longitudinal and vertical dynamics, which is of particular interest on motorbikes. Two pneumatic actuators have been added with the specific intent to simulate longitudinal forces due to acceleration and braking and replicate suspension, as well as fork/chassis, deflections.  
The fixture has been designed with a modular approach using extruded aluminium components so that it can be easily modified to accommodate vehicles with different geometry. The bike is connected by means of a set of adjustable connecting rods (at least three per side) to a sledge providing both vertical and pitch degrees of freedom.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 4 Experimental Results and Discussion  
### Dutasets  
In this work, we used two publicly available datasets:  
* Lung Image Database Consortium image collection (LIDC-IDRI) [32] consists of 1018 CT scans performed on 1010 patients from 7 different organisations. Each CT scan has been analysed by four radiologists, who individually identified the nodule and manually segmented the region of all the nodules with a diameter larger than three millimetres. Each CT scan can include one or more nodule regions, so the total segmented masks are 5066. Looking closely at the dataset, many nodules are very small and do not satisfy the malignancy index. Therefore, we used a diameter threshold larger than 20 mm to excluded all tiny nodules from our dataset. Afterwards, we split our final dataset, which contains 2044 nodule masks in total, into train, validation and test sets of 70%, 10%, and 20% respectively;
* LUng Nodule Analysis 2016 (LUNA16) [33] is derived from the LIDC-IDRI dataset [32]. It contains 888 CT scans from the LIDC-IDRI dataset for the grand challenge with round annotation masks for all the segmented nodules. The LUNA16 challenge dataset contains 1186 nodule annotations. We obtained 2300 nodule masks from the annotation after pre-processing. We split the dataset into train, validation and test sets similar to the LIDC-IDRI dataset.
# Adapative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
## 4 Experimental Evaluation
### Base Configurations  
After the data preprocessing stage, the distinct characteristics of the datasets were analyzed to identify their concrete constraints and establish the base configurations for A2 PM. Regarding CIC-IDS2017, some numerical features had discrete values that could only have integer perturbations. Due to the correlation between the encoded categorical features, they required combined perturbations to be compatible with a valid flow. Additionally, to guarantee the coherence of a generated flow with its type of cyber-attack, the encoded  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Dataset** & **Total** & **Class** & **Class** \\  & **(Captures)** & **Samples** & **Samples** & **Label** \\ \hline \multirow{6}{*}{Enterprise Network} & & & 873,066 & Benign \\  & & & 230,124 & Hulk \\  & CIC-IDS2017 & & 10,293 & GoldenEye \\  & (Tuesday and & 1,138,612 & 7926 & FTP-Patator \\  & Wednesday) & 1,138,612 & 5897 & SSH-Patator \\  & & & 5796 & Slowloris \\  & & & 5499 & Slowhttptest \\  & & & 11 & Heartbleed \\ \hline \multirow{6}{*}{IoT Network} & \multirow{6}{*}{IoT-23 (1-1 and 34-1)} & \multirow{6}{*}{1,031,893} & 539,587 & POAHPS \\  & & & 471,198 & Benign \\ \cline{1-1}  & & & 14,394 & DDoS \\ \cline{1-1}  & & & 6714 & C\&C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main characteristics of utilized datasets.  
features representing the utilized communication protocol and endpoint, designated as port, could not be modified. Hence, the following configuration was used for the Enterprise scenario, after it was converted to the respective subset of feature indices:  
1. Interval pattern--Modify {_numerical features_}, Integer {_discrete features_};
2. Combination pattern--Modify {_categorical features_}, Lock {_port_, _protocol_}.  
Despite the different features of IoT-23, it presented similar constraints. The main difference was that, in addition to the communication protocol, a generated flow had to be coherent with the application protocol as well, which was designated as service. The base configuration utilized for the IoT scenario was:  
1. Interval pattern--Modify {_numerical features_}, Integer {_discrete features_};
2. Combination pattern--Modify {_categorical features_}, Lock {_port_, _protocol_, _service_}.  
It is pertinent to note that, for the 'Benign' class, A2 PM would only generate benign network traffic that could be misclassified as a cyber-attack. Therefore, the configurations were only applied to the malicious classes, to generate examples compatible with their malicious purposes. Furthermore, since the examples should resemble the original flows as much as possible, the 'probability to be applied' was 0.6 and 0.4 for the interval and combination patterns, respectively. These values were established to slightly prioritize the small-scale modifications of individual numerical features over the more significant modifications of combined categorical features.