esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 2 Efficient Fuzzy MPC Algorithms with the Advanced Construction of the Dynamic Matrix
### Generation of the Dynamic Matrix
#### 2.2.2 Standard Dynamic Matrix  
In order to calculate the output values of the model (23), the normalized firing strengths of the fuzzy rules should be calculated using fuzzy reasoning; see, e.g., [44; 45]. These values are calculated using the previous values of the output and control signals. In the \(k\)th sampling instant, the normalized firing strengths \(\widetilde{w}_{k}^{f}\) are obtained, then the output values of the model are as follows:  
\[\widetilde{y}_{k}^{j}=\sum_{m=1}^{n_{u}}\sum_{n=1}^{p_{d}-1}\widehat{a}_{n}^{ j,m}\cdot\Delta u_{k-n}^{m}+\widehat{a}_{p_{d}}^{j,m}\cdot u_{k-p_{d}}^{m}\text{,} \tag{24}\]  
where:  
\[\widehat{a}_{n}^{j,m}=\sum_{f=1}^{l}\widetilde{w}_{k}^{f}\cdot a_{n}^{j,m,f}. \tag{25}\]  
In the standard approach to the dynamic matrix generation, exploited, e.g., in [46; 12; 47], the parameters \(\widehat{a}_{n}^{j,m}\) are used in the construction of the dynamic matrix in the same way as in the LMPC algorithm, i.e., they are used at each sampling instant from the prediction horizon despite being calculated using the firing strengths \(\widetilde{w}_{k}^{f}\) obtained in the current (\(k\)th) sampling instant. Thus, they are obtained for the current operating point, which in general, changes on the prediction horizon. Therefore, now the improved version of the dynamic matrix, adopting the nonlinearity of the process on the prediction horizon, will be proposed. It can be relatively easily obtained using the elements of the free response and of the trajectory of future control signals. The method is detailed below.
## Abbreviations  
AD  
applicability domain  
ADMET  
ANNs  
Artificial Neural Networks  
AI  
Artificial Intelligence  
Als  
aromatase inhibitors  
BC  
breast cancer  
BFA  
Brefeldin A  
Caco-2  
Cancer coli, \"colon cancer\"  
CADD  
computer-aided drug design  
CoMFA  
Comparative Molecular Field Analysis  
CoMSIA  
Comparative Molecular Similarity Indices Analysis  
CoMBINE  
Comparative Binding Energy Analysis  
CoMMA  
Comparative Molecular Moment Analysis  
CoRIA  
Comparative Residue Interaction Analysis  
CoSA  
Comparative Spectral Analysis  
DHFR  
Dihydrofolate reductase  
DL  
Deep Learning  
DNA  
Deoxyribonucleic acid  
DNMTs  
DNA methyltransferases  
DTs  
Decision Trees  
ER
estrogen receptor  
EGFR
epidermal growth factor receptor also known as ErbB  
EGFR-TKI  
EGFR tyrosine kinase inhibitors  
FDA
Food and Drug Administration  
HDACs  
histone deacetylases  
HDAC6  
histone deacetylase 6  
HER2  
human epidermal growth factor receptor 2  
HIFA  
Hint Interaction Field Analysis  
HMTs  
histone methyltransferases  
Holo-QSAR  
Hologram Quantitative Structure-Activity Relationship  
HQSAR
HOMO  
Highest Occupied Molecular Orbital  
HTS  
high-throughput screening  
HTVS  
high-throughput virtual screening  
JAK2  
Janus kinase 2  
k-NN  
k-nearest neighbor  
LAP  
Leucine aminopeptidase  
LBDD  
ligand-based drug design  
LUMO  
Lowest Unoccupied Molecular Orbital  
LOO  
leave-one-out  
LGO  
leave-group-out  
LSD1/LSD2  
histone lysine-specific demethylase  
MAO  
Monoamine oxidases  
ML
machine learning  
MLR  
Multiple Linear Regression  
MTAs  
microtubule-targeting agents  
NCEs  
new chemical entities  
NCI  
National Cancer Institute  
NSCLC
Non-small cell lung cancer  
OECD  
the Organization for Economic Co-operation and Development  
PAMPA  
Parallel Artificial Membrane Permeability Assay  
PAMPA-BBB  
Parallel Artificial Membrane Permeability Assay across the Blood-Brain Barrier
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 4 The Hybrid Context Reasoning Mechanism
### Principles of the Different Reasoning Methods  
This reasoning engine, depicted in Figure 4, is flexible to provide different advanced reasoning capabilities so that the most suitable method can be used to accommodate to different reasoning needs. This hybrid approach is built based on the ontological, rule-based, and MEBN reasoning techniques. Ontological and rule-based reasoning are well understood and widely used in many context-aware frameworks. MEBN, as an emerging probabilistic reasoning method, is given more emphasis in the following.  
To correctly select reasoning techniques, two key factors are necessarily considered. The performance of a specific reasoning technique is regarded as the primary criterion. In addition, given that different modeling methods are available, the reasoning techniques selected should be compatible and suit the specific modeling technique used for knowledge representation. The reasons for integrating the ontological, rule-based, and MEBN reasoning methods in the proposed approach will be given in the following sections. In addition, the basic principles of each method will be introduced.
## 3 System Implementation
### Recognition  
The ability of deep Convolutional Neural Networks (CNNs) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound recognition [28]. This study refers to the classic convolutional neural network structure to design a deep convolutional neural network, as shown in Figure 4. It is comprised of two convolutional layers interleaved with two pooling layers for feature extraction, followed by one fully connected (dense) layer for deep feature integration and complex function modeling.  
Given the input \(X_{\text{mfcc}}\), the network is trained to learn the parameters \(\Theta\) of a composite nonlinear function \(\mathcal{F}(\cdot|\Theta)\), which maps X to the output (prediction) Z:  
\[Z=\mathcal{F}(X_{\text{mfcc}}|\Theta)=f_{L}\big{(}\cdots f_{2}(f_{1}(X|\theta_{ 1})\big{|}\theta_{2})\big{|}\theta_{L}\big{)} \tag{10}\]  
where each operation \(f_{\ell}(\cdot|\theta_{\ell})\) is referred to as a layer of the network, with L = 6 layers in our proposed architecture. The convolutional \(Z_{\ell}\), \(f\in\{1,2\}\), are expressed as:  
\[Z_{\ell}=f_{\ell}(X_{\ell}|\theta_{\ell})=h(W*X_{\ell}+b),\ \theta_{l}=[W,b] \tag{11}\]  
where \(h(\cdot)\) is a point-wise activation function, W is a collection of 3D kernels, \(X_{\ell}\) is a three-dimensional input tensor, \(*\) represents a valid convolution, and b is a vector bias term. The max-pooling layers are applied after each convolutional layer \(\ell\in\{2,4\}\), which reduces the dimensions of the output feature maps and consequently speeds up training. The final layer, \(\ell\in\{5\}\), is fully connected (dense) and consists of a matrix product rather than a convolution:  
\[Z_{\ell}=f_{\ell}(X_{\ell}|\theta_{\ell})=h(WX_{\ell}+b),\ \theta_{\ell}=[W,b] \tag{12}\]  
where \(X_{\ell}\) is flattened to a column vector of length \(N\), \(W\) has a shape (256,800), b is a vector of length \(M\), and \(h(\cdot)\) is a point-wise activation function.  
Finally, recognition is completed in the fully connected layer.
## 3 Outage Rate Analysis
### Non-Cooperative Transmission  
As the baseline of outage rate performance, we derive the outage probability of a single-input-single-output (SISO) link without cooperation. For such non-cooperative networks, the outage is given by  
\[\begin{split} P_{out}^{nt}&=\Pr[\gamma_{i}<\gamma_{ th}]=\int_{0}^{\gamma_{th}}f_{\gamma_{i}}(x)dx\\ &=\int_{0}^{\gamma_{th}}\int_{0}^{\infty}\frac{1}{\Omega_{i}}\exp \left(-\frac{x}{\Omega_{i}}\right)\frac{\xi}{\sqrt{2\pi}\sigma\Omega_{i}}e^{- \frac{(10\log_{10}\Omega_{i}-\mu_{i})^{2}}{2\sigma^{2}}}d\Omega_{i}dx.\end{split} \tag{14}\]
## 3 Materials and Methods
### Evaporative Cooling (EC) Systems
#### 3.1.3 Maisotsenko-Cycle EC (MEC) System  
Maisotsenko-cycle evaporative cooling (MEC) is the system in which ambient air undergoes a thermodynamic process in which psychrometric renewable energy available from the air is utilized to reduce the temperature of ambient air [1; 38; 39]. It is also called an advanced IEC system or regenerative evaporative cooling system [40]. In the MEC system, the cooling effect is produced by two thermodynamic processes: evaporative cooling and heat transfer [1; 41] in which temperature of ambient air nearly reaches the dewpoint temperature instead of wet-bulb temperature [16; 39; 42]. It consists of three channels: one wet channel and two dry channels. The wet channel is sandwiched between two dry channels. When ambient air passes through the dry channel, it becomes cool due to convective heat transfer between wet and dry channels. In the MEC system, humidity (g/kg of dry air DA) of inlet air and outlet air remains the same. However, enthalpy (kJ/kg of dry air DA) of outlet air decreases [33; 36]. Schematic and psychometric representation of the MEC system is shown in Figure 4c. Further insights into the MEC system are governed by Equations (8)-(11) given in the literature [3].  
\[\left(DPT\right)_{inlet}\leq\left(DBT\right)_{outlet}\leq\left(WBT\right)_{ inlet} \tag{8}\]  
\[\left(RH\right)_{outlet} >\left(RH\right)_{inlet} \tag{9}\]  
\[w_{outlet} =w_{inlet} \tag{10}\]  
\[h_{outlet} <h_{inlet} \tag{11}\]  
where DPT, DBT, and WBT represent dewpoint, dry-bulb, and wet-bulb temperatures (\({}^{\circ}\)C), respectively, RH represents relative humidity (%), w and h represent the humidity ratio of air (kg/kg) and enthalpy of air (kJ/kg), respectively, and subscripts outlet and inlet represent outlet and inlet air conditions, respectively.  
Performance of the EC systems can be assessed in terms of their effectiveness, i.e., wet-bulb effectiveness (WBE), given by Equation (12).  
\[E_{WB}=\frac{DBT_{inlet}-DBT_{outlet}}{DBT_{inlet}-WBT_{inlet}} \tag{12}\]  
where \(\epsilon_{WB}\), DBT, and WBT represent wet-bulb effectiveness (-), dry-bulb and wet-bulb temperatures of air (\({}^{\circ}\)C), respectively, and the subscripts inlet and outlet represent inlet and outlet air conditions, respectively.
## 3 Results and Discussion
### Correlation Analysis and Significance Test  
Before establishing the forecasting model, the Pearson correlation analysis was carried out between the constructed NTL indices and the TOVAFAF for each provincial-level administrative region from 2013 to 2022. The significance test was conducted on the correlation using \(t\)-test. Table 2 lists the correlation coefficients for the correlation analysis and \(p\)-values for the significance test.  
As can be seen from Table 2, the constructed TNLI, LPQI, LPRI, and NLSDSI indices have a high correlation with the TOVAFAF, and the \(p\)-values are all less than 0.01, indicating that the correlation has reached the extremely significant level. The correlation between the other four NTL indices (ANLI, ALPLI, NLSDI, and NLVI) and the TOVAFAF is low, and the \(p\)-values are higher than 0.05, indicating that the correlation has not reached the significant level. Therefore, the NTL indices (TNLI, LPQI, LPRI, and NLSDSI) that are significantly correlated with the TOVAFAF are selected as feature variables to establish forecasting models.
# A Novel Dual-Rotor Ultrasonic Motor for Underwater Propulsion
## 2 Structure Design  
### Configuration of the Motor  
Figure 1 displays the three-dimensional model of an underwater robot actuated by ultrasonic motors, which can output efficient propulsion through one pair of propellers assembled to the parallel shaft ends. Figure 2 shows detailed structures of the dual-rotor ultrasonic motor employed for underwater propulsion. This motor was mainly composed of a pre-pressure modulating unit, a stator, a gear unit, two rotors, and a bracket.  
Figure 1: Three-dimensional model of the underwater robot.  
The pre-pressure modulating unit included a torsion spring and a supporting slider. The torsion spring fixed to the stator's side served as the clamping for the stator, while also adjusting the preload on the contact faces between the stator and rotors by its torsional deformation. The stator unit comprised of a spindle-shaped metal plate and a round circular piezoelectric ceramic wafer. The piezoelectric wafer was bonded to the top face of the metal plate with epoxy resin, with its polarization direction perpendicular to the metal plate. Two ends of the stator were used as driving feet, which had an oblique angle at the upper edge in order to increase the contact area between the stator and rotor [25]. The gear unit consisting of a pair of gears engaged with each other was fixed to the rotary shafts, respectively, ensuring the same rotation speed and opposite steering of shafts rotation at both ends. The gear unit guaranteed the same thrust for propellers at both ends and thus prevented the micro-underwater robot from unexpected turning. The rotor unit comprised of two driving disks assembled at two parallel rotary shafts and two pairs of bearings were adopted to support the shaft rotation. In particular, thin-type deep groove ball bearings (inner diameter: 4 mm; outer diameter: 7 mm; thickness: 2.5 mm) were selected and integrated into the walls of the bracket. Based on this dual-rotor design, one pair of propellers with opposite turning directions could be installed to each shaft end and output propulsion for the underwater robot.
# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 3 DCT-Coefficient-Based RRU-Net
### DRRU-Net Algorithm  
The down-sampling block and up-sampling block of DRRU-Net are the same as RRU-Net. When the outputs of JALM are connected to the first and second up-sampling blocks, they are combined with the previous inputs. In this process, it is added to the input by adjusting the resolution in the same way as the up-sampling of the last down-sampling output. The down-sampling block connected to JALM is implemented with the equations (Equations (1)-(3)) of RRU-Net shown in Section 2. However, this method is researched for searching visual contents and image acquisition artifacts, so it requires the verification of whether it is appropriate in the DCT domain. In this study, an activation function was designed to find a stable learning method using JALM as a ringed residual block. The function s in Equation (3) is sigmoid, the function \(F\) in Equation (2) is \(W_{2}\sigma(W_{1}\times x)\), and \(\sigma\) is ReLU. In the ringed residual architecture, the sigmoid function is used to amplify the difference in image intrinsic properties, but it is necessary to verify whether it is as an appropriate activation function in the DCT domain. Therefore, they were compared by using leaky ReLU. Leaky ReLU has a slower computational speed than ReLU, but it reflects negative output and is considered suitable for learning the expressions inherent in DCT streams. In order to maintain negative output results, the functions s and \(\sigma\) of the ringed residual architecture of the down-sampling block connected to JALM are redefined as Function \(l\), which means leaky ReLU. The modified equations are as follows.  
\[l=\left\{\begin{array}{ll}x,&\text{if }x\geq 0\\ x\times 0.01,&\text{otherwise}\end{array}\right. \tag{4}\]  
\[F=W_{2}l(W_{1}\times x) \tag{5}\]  
\[y_{f}=F(x,\;\;W_{i})+W_{s}\times x \tag{6}\]  
\[y_{b}=(l(G(y_{f})+1)\times x \tag{7}\]  
The JALM down-sampling blocks of DRRU-Net implemented with the above equations construct a ringed residual architecture in the same order as RRU-Net.
## 2 Principle
### Time Skew in Differential Driven Mode  
Finally, we modeled the influence of time skew in differential-driven mode. The modulated optical field \(E_{4}(t)\) is  
\[E_{4}(t)=e^{i(t)+j\varphi}+e^{-j(t-\tau)-j\varphi}\approx e^{i\varphi}[1+js(t)]+e ^{-j\varphi}[1-js(t-\tau)]=C+je^{i\varphi}s(t)-je^{-i\varphi}s(t-\tau) \tag{12}\]  
were \(\tau\) denotes the time skew between the differential outputs. Based on Equation (13), the optical signal is actually VSB-shaped in the frequency domain, making it more tolerant to power fading.  
\[E_{4}(f)=C\delta(f)-2 je^{-j\pi f\tau}\sin(\varphi+\pi f\tau)S(f) \tag{13}\]  
Here, the relationship \(FFT\{s(t-\tau)\}=S(f)e^{-j\pi f\tau}\) is utilized. Following the same process, the receiver-side overall transfer function can be further deduced. After fiber transmission, the received photocurrent \(I_{4}(t)\) can be obtained as  
\[\begin{split} I_{3}(t)&=|E_{3}(t)\otimes h(t)|^{2} \\ &=|C+\left\{je^{i\varphi}s(t)-je^{-i\varphi}s(t-\tau)\right\} \otimes h(t)|^{2}\\ &\approx C^{2}+C\left\{je^{i\varphi}s(t)-je^{-i\varphi}s(t-\tau) \right\}\otimes h(t)+C\left[-je^{-i\varphi}s^{*}(t)+je^{i\varphi}s^{*}(t- \tau)\right]\otimes h^{*}(t)\end{split} \tag{14}\]  
Since \(s(t)\) is a real-valued signal, the overall frequency response \(H_{4}(f)\) can be acquired as  
\[\begin{split} H_{4}(f)&=(je^{i\varphi}-je^{-i \varphi}e^{-j\pi f\tau})h(f)+(-je^{-i\varphi}+je^{i\varphi}e^{-j\pi/\tau})h^{* }(f)\\ &=2\cos(\pi\lambda^{2}DLf^{2}/c+\varphi+\pi/2)+2e^{-i2\pi f}\cos (\pi\lambda^{2}DLf^{2}/c-\varphi-\pi/2)\end{split} \tag{15}\]  
Different from Equation (11), the two terms are combined with coeffient dependent on \(\tau\). Therefore, power fading for most frequency components can be succesfully suppressed with the appropriate value of time skew.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Direction of Blockchain-Enabled Federated Learning
#### 4.2.5 Global Mechanisms  
Global mechanisms refer to mechanisms that replace FL central servers with distributed blockchain nodes or defend against malicious attacks against central servers. To address the single point of failure, FL's central server can be replaced with distributed blockchain nodes [110], model updates for local devices can be exchanged through miner nodes, and with the support of defense mechanisms, malicious attacks can be resisted. [PERSON] et al. [111] proposed a privacy-preserving oriented no trusted third party federated learning system based on blockchain (NttpFL). Blockchain makes the whole process transparent and traceable, avoiding the single point of failure problem. In [112], the author analyzes the system delay and designs an algorithm to adjust the local iteration to solve the single point of failure problem and achieve better model performance for federated learning. In addition, [PERSON] et al. [59] proposed a decentralized blockchain federated learning (BFL) framework with a simpler consensus mechanism, avoiding the risk of network paralysis caused by a central device failure.  
However, there exists a possibility for malicious blockchain nodes using blockchain instead of central servers, so it is necessary to combine defense mechanisms and privacy mechanisms to make federated learning model training more secure and efficient.
## 2 Background and Theory
### Classification Algorithms
#### 2.3.3 Cart  
The classification and regression tree (CART) [55] algorithm is employed for the third supervised learning model, which harnesses decision trees for classification and regression purposes. CART has been widely adopted in EEG studies for identifying distinct mental states and detecting patterns linked to neurological conditions. The algorithm operates following these primary steps:  
Given EEG features \(X\) and a target variable \(Y\), the algorithm proceeds as follows:  
1. Initiate with the root node containing all instances.
2. Terminate if all instances have identical \(Y\); otherwise, continue.
3. Select the feature \(x_{i}\) and threshold \(\theta\) that minimize the impurity: \[x_{i},\theta=\arg\min_{\mathrm{x},\mathrm{t}}\,\mathrm{Impurity}(X,Y,x,t)\]
4. Divide the node into two child nodes: \[X_{\mathit{left}}=\{\mathrm{x}\in\mathrm{X}|\mathrm{x}_{\mathrm{i}}\leq\theta\}\]  
\[X_{\mathit{right}}=\{\mathrm{x}\in\mathrm{X}|\mathrm{x}_{\mathrm{i}}>\theta\}\]
5. Recursively repeat steps 2-4 for \(X_{\mathit{left}}\) and \(X_{\mathit{right}}\).
6. Terminate when the maximum tree depth is reached or further splits do not significantly enhance the impurity reduction.  
In this context, \(X\) represents the set of input variables, which consist of EEG features, while \(Y\) denotes the target variable, such as the type of brain activity being classified. Each individual EEG feature is denoted as \(x_{i}\), and \(\theta\) is the threshold for splitting nodes. The measure of homogeneity within the nodes after a split, referred to as impurity \((X,Y,x,\mathrm{and}\ t)\), can be assessed using Gini impurity, entropy, or another relevant metric. The dataset is divided into two subsets: \(X_{\mathit{left}}\), containing instances where \(x_{i}\) is less than or equal to \(\theta\), and \(X_{\mathit{right}}\), which includes instances where \(x_{i}\) is greater than \(\theta\). To minimize impurity, the optimal values of \(x\) and \(t\) are determined using \(\underset{x,\mathit{l}}{\mathrm{argmin}}\).
# Use of Data Augmentation Techniques in Detection of
## 4 Detection of Antisocial Behavior Using Deep Learning Methods
### Comparison with Related Literature  
The use of EDA techniques was already explored in the available literature [39], where the authors evaluated these methods on five different text classification tasks. EDA boosted the model's performance marginally, but significant improvements could be expected on the smaller datasets. Similar behavior was observed during our experiments. Especially on the fake news dataset, which consisted of approximately 6000 training samples, the improvements were most significant. To check the issue of the possible overfitting that EDA may cause, we evaluated the models' overall performance and their performance in the minor class. This kind of evaluation supported the benefits that EDA applications can bring.  
In addition, we could compare the results obtained by applying similar techniques to the same dataset. For example, in [51], authors applied EDA and back-translation techniques to the toxic comments classifiers using traditional machine learning algorithms (logistic regression and support vector machine). Their baseline model gained an F1 performance of 0.677; after the EDA application, it improved to 0.736. Back-translation itself did not achieve better results. Relatively lower levels of F1 could be attributed to the usage of standard ML models. In addition, in this case, the authors did not perform the optimization of EDA parameters or their combination. In [19], authors used a similar technique as EDA on CNN-based toxic comments classification. The authors used synonym replacement, random mask, and unique word augmentations, which improved the baseline CNN model from a 0.846 F1 to 0.885 (a combination of all techniques). Similar to our experiments, a combination of multiple techniques brought higher benefits to the model performance. The difference in the F1 score of the baseline model can be attributed to different test sets (the testing set in our experiments consisted of 20% of samples, in comparison to a 10% test set in these experiments).  
The comparison of the fake news dataset can be difficult, as there are multiple datasets, and their usage among the studies is rather inconsistent. The majority of current research [52; 53; 54] uses the COVID-19 fake news dataset. However, mentioned studies use advanced deep learning models for classification and different augmentation techniques (translation, BiGRU-CRF, and CapsuleNet). In both cases, the effects of the applied techniques are quite similar, as they boost the F1 performance of the classifiers by 0.01. It is important to note that applying more advanced techniques can be very demanding on computational resources. EDA techniques can be relatively simple to implement, but their effects on classifier performance can be comparable.