esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Major Areas of Discussion in UR Publications
#### 5.1.6 Community and Disaster Resilience  
Many communities are vulnerable to natural disasters, resulting in economic, social, and environmental damages [112]. Due to the loss of lives and livelihoods caused by flood dangers, the government began to think about the need for research aimed at reducing flood impacts and raising awareness to build more adaptable and resilient communities [123].  
There are various tools such as the Baseline Resilience Indicators for Community (BRIC), which examines the baseline resilience to natural hazards [124]. A study finding also highlighted the value of having a robust and developed economy, excellent education, and training programs to increase public awareness of disaster prevention and mitigation, adequate funding for crucial infrastructure, particularly in the areas of transportation and communication, sound environmental policies to safeguard ecosystems and water resources, and extra care and budgets for disaster risk for vulnerable groups [120].
# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 3 Materials and Methods  
### Datasets  
This study was evaluated on two publicly available datasets, the ISBI 2017 and the PH2. The ISBI 2017 dataset was created for a challenge called Skin Lesion Analysis Towards Melanoma Detection. This data set is a small piece of the International Skin Imaging Collaboration (ISIC) archive which is consisting of 23906 dermoscopic images [50] and is available publicly for researchers [60]. The dataset consists of 8-bit RGB dermoscopic images with resolutions varying between \(540\times 722\) and \(4499\times 6748\). The images in the dataset have been labelled as seborrheic keratosis, melanoma and benign by specialist. The dataset includes 2000 images for training, 150 images for validation and 600 images for testing. The other dataset the PH2 was provided by a research group of the University of Porto, collected from dermatology service of Hospital Pedro Hispano, Portugal [52]. The PH2 dataset consists of 80 atypical nevi, 80 common nevi, and 40 melanoma cases. The total size of the dataset is 200 lesion images. Unlike the ISBI 2017 dataset, the PH2 dataset was acquired under the same conditions. All images are 8-bit RGB images with \(768\times 560\) pixels resolution and were taken by using a lens with a magnification of \(20\times\). Also, both datasets contain lesion images with their segmentation boundaries annotated by an expert dermatologist. Figure 2 shows some samples from the ISBI 2017 and the PH2 datasets as lesion images and their ground truths. Also, Table 1 summarizes the class and data distribution of images in both datasets.
## 2 Results
### Immunohistochemistry-maximum
Analysis of MMP-2 immunolocalization in the dermis showed a significant increase in its content from 7.78% to 10.86%. The comparison between groups, after 3 months of treatment, indicated that the area of Ki-67 with immunosuppressive protein in the epidermis was significantly larger in group B than in group A; in the dermis, significant differences were noted between groups A and B, and groups A and C. Regarding PCNA, in the epidermis there were significant differences between groups A and C and groups B and C, and the largest area with immunosuppressive protein was noted in group C. Regarding the dermis, the smallest area with PCNA was observed in group B, and the differences were confirmed between groups A and B and groups B and C (Table 3). Additionally, taking into account MMP-2 immunolocalization, there were no significant differences between the examined groups. The results of immunohistochemical examinations are presented in Figures 6 and 7. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Parameter** & & **Group A** & **Group B** & **Group C** \\
**(Area in \%)** & & **(n = 30)** & **(n = 30)** & **(n = 30)** \\ \hline \multirow{3}{*}{Ki67—epidermis} & Mean \(\pm\) SD & 4.25 \(\pm\) 3.09 & 6.84 \(\pm\) 4.68 & 5.31 \(\pm\) 5.11 \\  & M & 3.43 & 4.8 * & 4.05 \\  & Min–Max & 0.42–13.71 & 1.05–17.61 & 0.45–18.74 \\ \hline \multirow{3}{*}{Ki67—dermis} & Mean \(\pm\) SD & 5.54 \(\pm\) 6.08 & 2.2 \(\pm\) 2.2 & 1.83 \(\pm\) 0.67 \\  & M & 2.64 & 1.29 * & 1.85 * \\  & Min–Max & 2.64–7.56 & 0.03–7.02 & 0.87–3.11 \\ \hline \multirow{3}{*}{PCNA—epidermis} & Mean \(\pm\) SD & 4.49 \(\pm\) 2.17 & 6.03 \(\pm\) 2.2 & 10.37 \(\pm\) 5.11 \\  & M & 4.31 & 6.0 & 9.92 * \\  & Min–Max & 1.5–9.06 & 3.21–9.24 & 1.81–17.63 \\ \hline \multirow{3}{*}{PCNA—dermis} & Mean \(\pm\) SD & 2.45 \(\pm\) 0.77 & 1.87 \(\pm\) 0.46 & 2.8 \(\pm\) 1.3 \\  & M & 2.43 & 2.13 * & 2.87 * \\  & Min–Max & 0.75–3.56 & 1.16–2.3 & 1.01–5.79 \\ \hline \multirow{3}{*}{MMP2—dermis} & Mean \(\pm\) SD & 12.97 \(\pm\) 5.62 & 12.36 \(\pm\) 8.1 & 10.87 \(\pm\) 4.52 \\  & M & 14.37 & 8.08 & 10.86 \\ \cline{1-1}  & Min–Max & 4.87–23.11 & 4.64–30.03 & 4.33–25.07 \\ \hline \hline \end{tabular}  
* M: median; Min–Max: minimum—maximum; SD: standard deviation, n: number of analyzed areas in slides. Group A: micro-needle mesotherapy + snail mucus; Group B: micro-needle mesotherapy + NaCl; Group C: needle-free mesotherapy + snail mucus.
* \(p<\) 0.05 vs. Group A; \(p<\) 0.05 vs. Group B (non-parametric Kruskal–Wallis test followed by [PERSON]’s post hoc test).  
\end{table} 
Table 3: Semi-quantitative determination of Ki67, PCNA, and MMP2 after 3 months of treatments. Figure 6: Immunoexpression of Ki67 in skin of patients before and after 3 months of treatment. **(A,B)** Micro-needle mesotherapy + snail mucus 98.2%; **(C,D)** micro-needle mesotherapy + NaCl; **(E,F)** needle-free mesotherapy + snail mucus 98.2%. Obj. magn. 40\(\times\). Scale bar: 100 \(\upmu\)m.
# Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM
## 3 The Proposed Model Architecture
### Cyber Threat Event Detection  
We implemented the cyber threat event detection using the LDA and BiLSTM. Moreover, the architecture of cyber threat event detection is described below.  
Figure 1: The architecture of multitask Learning in our method.  
_The layer of LDA:_ LDA can predict the distribution of topics in documents. It can give each document the topic in the document set in the form of the probability distribution. In this layer, we use LDA to extract the subject keywords of tweets, which can summarize the tweet's content to a certain extent and facilitate the detection of cyber threat events.  
_The layer of word Embedding:_ This layer functionality just uses the trained language model (GLOVE [36] or word2 vec [37]) that maps the words to the value vector, as the Equation (1) shows. The word vector model replaces each word with its corresponding vector, which can reflect the semantic distance and the relationship between words, then gets a news article matrix \(\mathbf{X}\in\mathbb{R}^{n\times d}\). Where the \(n\) is the number of the word in the tweets, and the \(d\) is the dimension of word embedding,  
\[\mathbf{X}=(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{i}) \tag{1}\]  
where \(\mathbf{x}_{i}\in\mathbb{R}^{d}\) is the \(d\)-dimensional word vector corresponding to the \(i\)-th word in the tweets.  
_The BiLSTM Model:_ The content of a tweet is limited, and the sentences will be shorter after removing the useless information. The BiLSTM network takes into the sequential order of words in the sentence and can better capture bidirectional semantic dependencies of words. Moreover, the architecture of BiLSTM as shown in the Figure 2.  
_The layer of Output:_ We use the _Dropout_ to select the feature in the network's output layer to prevent promote generalization and overfitting. Feature nodes are used by a fully connected _softmax_ layer, which outputs the probability of tweets containing the cyber threat event information.
# A Collocation Method for the Numerical Solution of Nonlinear Fractional Dynamical Systems
## 2 Materials and Methods
### B-Spline Bases on the Semi-Infinite Interval  
The system of the integer translates \(\mathcal{B}_{n}=\{B_{n}(t-k),k\in\mathbb{Z}\}\) forms a function basis for the space of piecewise polynomials of degree \(n\) on the real line and enjoys many useful properties that make it especially suitable in approximation problems. In particular, \(\mathcal{B}_{n}\) is a partition of unity, reproduces polynomials up to degree \(n\), satisfies the Strang-Fix theory and has approximation order \(n+1\) (cf. [19]).  
To construct a function basis in the semi-infinite interval \([0,\infty)\) we restrict \(\mathcal{B}_{n}\) to this interval. The interior functions, i.e., the functions \(B_{n}(t-k)\) with \(k\geq 0\), which have support \([k,k+n+1]\) all contained in \([0,\infty)\), remain unchanged. The left-edge boundary functions, i.e., the functions \(B_{n}(t-k)\) with \(-n\leq k\leq-1\), whose support is not all contained in \([0,\infty)\), are modified introducing a knot of multiplicity \(n+1\) at the left-edge of the interval. Thus, the B-spline basis on the semi-infinite interval \([0,\infty)\) is  
\[\mathcal{N}_{n}=\{N_{kn}(t),0\leq k\leq n-1\}\cup\{B_{n}(t-k),k\geq 0\}\,, \qquad t\in[0,\infty)\,,\]  
where \(N_{kn}(t)\), \(0\leq k\leq n-1\), are the left-edge boundary functions. In [20] their analytical expressions is given in terms of suitable collocation matrices:  
\[N_{kn}(t)=(k+1)\,\frac{|T_{nk}|}{|P_{kn}|}\,,\qquad 0\leq k\leq n-1\,, \tag{5}\]  
where \(T_{kn}\) is the \((k+1)\) order collocation matrix  
\[T_{kn}=M\left(\begin{array}{cccc}x^{n-k+1},&x^{n-k+2},&\cdots&x^{n},&(x-t)_ {+}^{n}\\ 1,&2,&\cdots&k,&k+1\end{array}\right)\,,\]  
and \(P_{kn}\) is the \((k+1)\) order collocation matrix  
\[P_{kn}=M\left(\begin{array}{cccc}x^{n-k+1},&x^{n-k+2},&\cdots&x^{n},&x^{n+1} \\ 1,&2,&\cdots&k,&k+1\end{array}\right).\]  
The B-spline bases can be generalized to any sequence of equidistant knots on the interval \([0,\infty)\) by mapping \(t\to t/h\), where \(h>0\) is the refinement step:  
\[\mathcal{N}_{nh}=\{N_{knh}(t),k\geq 0\}\,,\qquad t\in[0,\infty)\,,\]  
where \(N_{knh}(t)=N_{kn}\left(\frac{t}{h}\right)\), \(0\leq k\leq n-1\), are the left-edge functions and \(N_{knh}(t)=B_{n}\left(\frac{t}{h}-(k-n)\right)\), \(k\geq n\), are the interior functions. We observe that at the left-edge of the interval the basis functions satisfy the initial conditions  
\[N_{knh}(0)=\delta_{k0}\,, \tag{6}\]  
where \(\delta_{kj}\) denotes the Kronecker delta. Consequently, just the first basis function \(N_{0 nh}\) does not vanish in the origin with \(N_{0 nh}(0)=1\).
# Obtaining Human Experience for Intelligent Dredger Control: A Reinforcement Learning Approach
## 2 Cutting Process Analysis
### Problem Formulation  
We will formulate the cutting process problem as a sequential decision-making task that can be modelled by Markov Decision Process (MDP). For the sake of consistency, in this work some notations follow [4; 17]. An MDP task is defined as a tuple \(\langle S,A,P^{a}_{ss^{\prime}},R^{a}_{ss^{\prime}},\gamma\rangle\), where  
1. \(S\) denotes the finite set of state spaces,
2. \(A\) denotes the finite set of action space,
3. \(P^{a}_{ss^{\prime}}\) represents the conditional transition probability that defines the probability of arriving at the next state \(s^{\prime}\) after performing action \(a\in A\) in state \(s\in S\),
4. \(R^{a}_{ss^{\prime}}\) specifies the expected immediate reward after executing action \(a\in A\) in state \(s\in S\) at the next state \(s^{\prime}\in S\), and
5. \(\gamma\in[0,1)\) is the discount factor that determines the importance of future rewards.  
The goal of the sequential decision-making task is to find the optimal control policy for the cutting process. To this end, a learning agent must be deployed into the environment to explore the entire state space and action space, as shown in Figure 2.  
In this work, we assume discrete time steps, so at each time-step \(t\) the agent has to choose an _action_\(a_{t}\in A\) to perform, based on the perception of the current state _state_\(s_{t}\in S\). Performing the action will result in a state transition to the next state \(s_{t+1}\). At the next state, the agent will receive the environmental feedback, called _reward_\(r_{t+1}\in R\), to examine the effect of the action. We use _policy_\(\pi:S\mapsto A\) to describe the action selection strategies in each state, i.e., what action should be selected in each state.  
However, as mentioned above, in our work we cannot allow the learning agent to make trial-and-error interactions with a physical environment, due to the safety and cost concerns. Instead, the learning agent must interact with a virtual environment that can reflect the characteristics of a physical dredging environment. Thus, the proposed learning approach consists of two parts. The first part is responsible for constructing a concise virtual environment based on the historical dredging data collected during experienced human operators manipulating the CSD. The second part takes charge of learning the optimal control policy by exacting the experience of human operators.
## 3 Results and Discussion
### Probabilistic Neural Network (PNN) Pile defect Identification  
A feed-forward neural network constructed from radial basis function networks, the Probabilistic Neural Network classifier (PNN classifier), was used in an experiment comparison to demonstrate the superiority of the CNN model further. It is a supervised network classifier that uses Bayes classification rules and is based on the idea of probabilistic statistics. This study used the Parzen window function density estimation approach to estimate the conditional probability when performing classification pattern recognition. The Parzen method yields the probability density function estimation equation shown below.  
\[f_{A}(X)=\frac{1}{{{(2\pi)}^{{p/2}}\delta{p}}}\frac{1}{m} \tag{17}\]  
\[\sum\exp\left[-\frac{{{(X-X_{ai})}^{T}(X-X_{ai})}}{{2\delta^{2}}}\right] \tag{18}\]  
In the formula, \(X_{ai}\) is the ith training vector of the defect pattern, \(m\) is the training sample quantity of the defective pattern, and the \(\delta\) smoothing parameter.  
The construction process of the PNN recognition model is as follows.  
Step 1: We created a training set consisting of 9 \(\times\) 120 data samples and a test set consisting of 9 \(\times\) 140 data samples obtained from numerical simulations.  
Step 2: Defective category is the intended result, and existing defective feature data is used as the input to the training samples. After training, a PNN model for defect classification and recognition is obtained from the network.  
Step 3: Testing of the network's performance was performed. Regression simulations were run on the training samples after the connection weights between the neurons in each layer were replaced in the network. The network was successfully trained and could be used to predict the class of unknown samples when the expected output of the training samples matched the simulated output of the PNN network.  
Step 4: Classification prediction of unknown defective sample data using the constructed PNN model.  
The category names used in this experiment adhere to the plan described in Section 3.1. Figure 16 displays the PNN classification findings, while Table 11 displays the precise classification accuracies. In Figure 16, the symbols \(\Circle\) and * stand for the actual and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CNN**} & \multirow{2}{*}{**Time Domain**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\  & & **Frequency** & **Time–Frequency** & **Multi-Domain** \\  & & **Domain** & **Domain** & **Multi-Domain** \\ \hline Train & 89.00 & 81.89 & 88.56 & 98.67 \\ Validation & 87.50 & 80.28 & 86.67 & 98.33 \\ Test & 88.89 & 77.22 & 86.11 & 97.80 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of recognition results for different domain features.  
expected labels, respectively. When they overlap, the predicted labels match the actual ones. This model's recognition accuracy was only 28.6%.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Hybrid Instrument Simulator
#### 3.3.3 Satellite Rotation Compensation
that the measurement is performed on the proof-mass reference frame, which is in rotation with respect to the satellite frame. The electrostatic forces exerted by the electrodes on the along-track axis are perpendicular to the proof-mass, as they follow the electric field lines. As a consequence, the direction of the electrostatic forces will depend on the rotation of the proof-mass around the cross-track axis. Figure 25 shows the noise on each axis with rotation of the proof-mass. Peaks at the cycle period appear. The peaks at multiples of the frequency of PM rotation are due to the non-linearities of the electrostatic forces, which are proportional for each electrode to the inverse of the square of the distance between the electrode and the proof-mass. Along radial axis \(Z\), the peaks are more visible as there is also the effect of the variable potential applied on the electrodes to rotate the proof-mass. It is necessary to take into account this orientation in order to deduce the acceleration on the along-track axis and on the radial axis. This could be done by using the angular position of the proof-mass given by the capacitive detector. The error in the estimation of the acceleration on the along-track axis due to the rotation of the proof-mass depends on the projection of the bias and noise of radial axis, as well as the error in the rotation reconstruction (due to the detector error). The rotation noise is directly related to the detector noise level, which is about 10 nrad.Hz\({}^{-1/2}\). The detector bias is about 10 \(\upmu\)rad.
# Urban Free-Space Optical Network Optimization
## 4 Results
### Genetic Algorithm Experiments
#### 4.2.2 Run Time and Solution Quality  
To evaluate the algorithm's performance, we generated red-blue visibility graphs in several variations with different graph sizes and densities. Additionally, we conducted experiments to optimize the algorithm's parameters:  
* Number of generations.
* Number of crossovers in each generation.
* Mutation probability.  
Our experiments show that the optimal formulas for deciding the initial GA parameters (number of crossovers) is  
\[Crossovers=10+\frac{n\cdot density(G)}{100} \tag{3}\]  
where \(n\) is the number of vertices and \(density(G)\) is computed according to Equation (2).  
The _mutation_ function is designed to avoid a local minimum state. Although the function can add variance to the solution pool, this function is not efficient in terms of run time because it searches for the shortest path in the full graph. In the following set of experiments, we activated the mutation function at a probability of 0.01. After fixing the parameters, we tested large graphs for run time and convergence. Figures 22-24 show the expected run time of the GA method on graphs with 4000 to 16,000 vertices.  
Figure 21: Cost difference comparison on graphs with different sizes and 0.15 density. This graph presents the average cost difference between the solutions of the GA and those of the 2-factor approximation method, which are marked by the fixed black line. The blue region represents graph sizes in which the 2-factor approximation method finds better solutions.  
Figure 22: Run time of the GA on graphs with 4000 vertices at different densities.  
The experiments show that the run time of the GA is strongly connected to the graph density. As the density grows, the run time increases. This is because the main method in our algorithm is the shortest path method. This method (using [PERSON]'s algorithm) searches over all the edges in the graph, and the number of times it is activated is proportional to the graph density.  
Next, we tested the algorithm's ability to improve over generations. Figure 25 demonstrates the algorithm's cost reduction over 60 generations on an example graph with 2000 vertices. Figure 26 presents the case of another example graph, in which a significant cost reduction occurs after 580 generations, following a long period of stable generations. The latter example suggests that in some situations it is worthwhile to run the GA for many generations. However, we observed empirically that, in most cases, the GA converges in less than 100 generations.  
Figure 24: Run time of the GA on graphs with 16,000 vertices at different densities.  
Figure 25: The cost reduction of the GA over 60 generations on an example problem. The fixed red line represents the result of the 2-factor approximation method.  
Table 1 shows the cost reduction of the GA on large graphs with different densities. The cost reduction relates to the difference (in percents) between the GA's final solution and the initial (random) solution.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design
### Bidirectional DC-DC Converter Design
#### 2.4.2 Discharging Mode  
Similarly, in discharging mode, BSBD converter is interfacing the DC-link and the battery. In this mode, the battery acts as an input source and DC-link as the output. The DC-link voltage level is higher than the battery terminal voltage. Therefore, the BSBD converter operates in boost mode with the help of the value of the corresponding components. The components, such as the inductor (\(L_{boost}\)) and capacitor (\(C_{boost}\)) values in boost mode, are calculated as follows:  
\[L_{boost}=\frac{V_{bat}D_{boost}}{\Delta I_{L}f} \tag{8}\]  
\[C_{boost}=\frac{V_{DC}D_{boost}}{R_{o}\Delta V_{DC}} \tag{9}\]  
where \(\Delta V_{DC}\) and \(R_{o}\) are the ripple voltage and output impedance for the boost mode, respectively. However, the bidirectional converter operates in both modes. The values of inductor \(L_{bat}\) and capacitor \(C_{bat}\) are considered as follows:  
\[L_{bat}=max(L_{buck},L_{boost}) \tag{10}\]  
\[C_{bat}=max(C_{buck},C_{boost}) \tag{11}\]  
This section includes all design parameters for GSBD and BSBD converters. The designed parameters are further utilized for PFC control of GSBD and buck control of BSBD converter to charge the EV batteries in G2V mode. Similarly, the designed parameters are utilized for boost control of BSBD and inverter control of GSBD converter to discharge the EV batteries in V2G mode. Control strategies for G2V and V2G modes are discussed in the next section.
# Secure Control of Linear Controllers Using Fully Homomorphic Encryption
## 3 Preliminaries
### Fully Homomorphic Encryption  
Fully homomorphic encryption plays an important role in this paper, and it is defined as follows.  
**Definition 3**.: _A fully homomorphic encryption scheme \(\mathbf{FHE}=(\mathbf{Gem},\mathbf{Enc},\mathbf{Dec},\mathbf{Eval})\) is described as follows:_  
* \(\mathbf{Gen}(1^{\lambda})\rightarrow(pk,sk,evk)\)_: input security parameter_ \(\lambda\)_, output_ \((pk,sk,evk)\) _where_ \(pk\) _is a public key,_ \(sk\) _is a secret key, and evk is an evaluation key._
* \(\mathbf{Enc}(m,pk)\to c\)_: input message_ \(m\) _and public key pk, output ciphertext_ \(c\)_._
* \(\mathbf{Dec}(c,sk)\to m^{\prime}\)_: input ciphertext_ \(c\) _and secret key sk, compute and output the plaintext_ \(m^{\prime}\)_._
* \(\mathbf{Evaluation}(f,c_{1},c_{2},\cdots,c_{N},evk)\to c^{*}\)_: input a set of ciphertexts (_\(c_{1},c_{2},\cdots,c_{N}\)_), function_ \(f\)_, and evaluation key evk. Compute and output the evaluation ciphertext_ \(c^{*}\)_._  
Fully homomorphic encryption can be performed on data in the form of ciphertexts of arbitrary complexity, which we describe more intuitively in Figure 2 below. A set of data \((m_{1},m_{2},\cdots,m_{N})\) is encrypted with the algorithm \(\mathbf{Enc}\) to obtain a set of ciphertexts (\(c_{1},c_{2},\cdots,c_{N}\)), and an arbitrary computation \(f\) is performed on the ciphertext set to obtain \(c^{*}\). The value of \(c^{*}\) after decryption should be the same as the value of the calculation done directly on the plaintext.