esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Construction of Simplified Models  
The solid element is used to analyze the structural sealant and the shell element is used to analyze the supporting frame. According to the boundary section in Figure 1, the structural sealant can be treated as an elastic connecting device between the glass panel and the supporting frame; that is, the load received by the panel is transferred to the supporting frame. Because the structural sealant has a certain width, it also has a certain ability to resist bending moments. Therefore, the structural sealant can be simplified into two spring models, shown in Figure 7a,b. Figure 7b ignores the influence of rotational stiffness of structural sealant. In order to calculate the stiffness coefficient of the spring, it can be known from the beam theory that the spring stiffness coefficient of unit length structure sealant can be expressed as [22]:  
\[k_{z}=\frac{E_{s}b_{s}}{d_{s}} \tag{4}\]  
\[k_{\theta}=\frac{E_{s}b_{s}^{3}}{3d_{s}} \tag{5}\]  
where \(b_{\text{s}}\) and \(d_{\text{s}}\) are the width and thickness of the structural sealant, respectively; \(E_{\text{s}}\) is the elastic modulus of the structural sealant.  
The cross-section of the column and beam of the supporting frame is much smaller than the length of the member, so it can be simplified as a beam element. Therefore, the following simplified calculation model is proposed:  
Model 4: The supporting frame is a shell element, and the structural sealant is simplified as a vertical translational spring element.  
Model 5: The supporting frame is a shell element, and the structural sealant is simplified as a vertical translational and rotating spring element.  
Figure 6: Relative error of natural frequency calculated by finite element model.  
By comparing the calculation results of Model 4 and Model 5, the difference in natural frequency between them is less than 1%, indicating that the rotational stiffness of structural sealant has little influence on the natural frequency of the system. Therefore, to simplify the calculation, the influence of rotational stiffness can be ignored. Based on Model 4, Model 6 further simplifies the supporting frames to beam elements; the results show that the error of the natural frequencies is less than 5% except for the first natural frequency of sample A and the third natural frequency of sample B, whose errors are slightly more than 5%. Therefore, Model 6 can be used as the simplified calculation model of hidden frame curtain wall panel units. Model 6 is simplified from Model 3, which includes 3424 shell elements and 1324 entity elements. After simplification, Model 6 includes 1840 shell elements, 176 spring elements and 172 beam elements. The degrees of freedom (DOFs) before and after simplification are 17,913 and 7341, respectively. Due to the simplification, the DOFs decreased more than 59%, and consequently reduced the computational processing time.
## 3 State of the Art
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Type** & **Year** & **Technique** & **Model** & **ML Type** & **Protocols** & **Encoding** & **Real-Time** & **Epochs** & **Layers** & **Accuracy** [Y1] & **Datasets** & **Ref.** \\ \hline \multirow{4}{*}{OT} & 2021 & CNN & AM-ADCNN + LSTM & Supervised & 4 & No & No & - & - & 93.0 & Own & [9] \\ \cline{2-13}  & OT & 2021 & CNN & PEBLUD & Unsupervised & 1 & No & - & - & - & Own & [10] \\ \cline{2-13}  & \multirow{4}{*}{OT} & 2021 & Network Packet & Deterministic Finite-state & - & 9 & No & No & - & - & - & Own & [11] \\  & & 2021 & Inspection & Automation & - & 9 & No & - & - & - & - & Own & [12] \\ \cline{2-13}  & \multirow{4}{*}{Protocol} & 2021 & Fingerprinting & - & - & 4 & No & Yes & - & - & 96.0 & Own & [12] \\ \cline{2-13}  & & 2021 & CNN + RNN & CNN + RNN -2a & Supervised & 15 & No & 60-90 & 9 & 99.8 & Redshifts [13] & [14] \\ \cline{2-13}  & IT & 2020 & CNN & PrecNN & Supervised & 4 & No & Yes & - & 9 & 96.0 & DANPN [15] & [16] \\ \cline{2-13}  & \multirow{4}{*}{OT} & 2020 & CNN & - & Both & 3 & No & - & - & 75.8 & -99.8 & Own & [17] \\ \cline{2-13}  & & 2021 & Pattern matching & - & Supervised & 4 & No & No & - & - & 93.8 & -100 & DANPN [15] & [18] \\ \cline{2-13}  & & 2021 & CNN & KLSTM & Supervised & 12 & Yes & No & - & - & 97.5 & ISCX 2016 [19] & [23] \\ \cline{2-13}  & & 2023 & CNN & - & Supervised & 8 & Yes & No & - & - & 98.2 & ISCX 1778-noVFN [19] & [23] \\ \hline \multirow{4}{*}{OT} & 2019 & MT & DT, KNN, SVM, NB & Supervised & 1 & Yes & No & - & - & 95.0 & Own & [22] \\ \cline{2-13}  & \multirow{4}{*}{OT} & 2019 & Traffic & \multirow{4}{*}{CART} & - & - & No & - & - & 94.8 & SCAD Network Data Sets & [25] \\ \cline{2-13}  & & Fingerprinting & \multirow{4}{*}{CART} & - & - & - & No & - & - & 94.8 & SCAD Network Data Sets & [25] \\ \cline{2-13}  & & 2020 & ML & RNN, SVM, DT, NBC, BEN, RF, AdaBoost, CB & & & & & & & & & \\ \cline{2-13}  & \multirow{4}{*}{T Traffic classification} & 2022 & DNN & - & Supervised & 1 & Yes & No & 100 & 10 & 94.5 & Own & [27] \\ \cline{2-13}  & & 2022 & ML & DT & Supervised & 2 & No & No & - & - & 99.9 & Own & [28] \\ \cline{2-13}  & & 2022 & RNN & - & Supervised & 1 & No & No & - & 97.5 & Own & [29] \\ \cline{2-13}  & & 2029 & ML & C4S, AdaBoost, NB & Supervised & 8 & Yes & No & - & - & 98.4 & AAPAP [9], & [22] \\ \cline{2-13}  & IT & 2017 & CNN & 1D-CNN & Supervised & 12 & Yes & No & 40 & 7 & 99.5 & ISCX VFN-noVFN [19] & [33] \\ \cline{2-13}  & & 2018 & CNN & CNN+LSTM & Supervised & 9 & Yes & No & 30 & 8 & 91.0 & ISCX VFN-noVFN [19] & [34] \\ \cline{2-13}  & & 2021 & Fuzzy Inference System & Fuzzy Inference System & Fuzzy Inference System & 6 & Yes & Yes & - & - & 90.5 & ISCVFN2016 [3] & [35] \\ \hline \hline \end{tabular} A.\({}^{+}\) indicates points that were not included in the publication.  
\end{table} 
Table 1: Comparison of relevant literature in protocol recognition and traffic classification from the perspective of IT and OT infrastructures.
## 2 Background  
### Reinforcement Learning  
Consider a standard reinforcement learning problem in which an agent interacts with a stochastic environment and attempts to maximize cumulative reward [25]. The stochastic environment is formalized by a Markov Decision Process (MDP), which is denoted as the tuple \(\mathcal{M}=(\mathcal{S},\mathcal{A},\mathcal{P},\rho,R,\gamma)\), a state space \(\mathcal{S}\), an action space \(\mathcal{A}\), a transition probability distribution \(\mathcal{P}:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow[0,1]\), an initial state distribution \(\rho:\mathcal{S}\rightarrow[0,1]\), a reward function \(R:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}\), and a discount factor \(\gamma\in[0,1)\). Without prior knowledge about the transition probability distribution, the agent utilizes its policy \(\pi_{\theta}\) to collect a trajectory \(\tau=\{s_{0},a_{0},r_{0},s_{1},a_{1},r_{1},\cdots\}\) for training. The agent improves its policy \(\pi_{\theta}\) to maximize the expected discounted return for each episode  
\[J(\pi_{\theta})=\mathbb{E}_{s_{0}\sim\rho,a_{t}\sim\pi_{\theta}s_{t+1}\sim \mathcal{P}(\cdot|s_{t},a_{t})}\left[\sum_{t=0}^{H}\gamma^{t}r_{t}\right], \tag{1}\]  
where \(r_{t}=R(s_{t},a_{t})\), and \(H\) is the maximal length of one episode. The fundamental result underlying this problem is the Policy Gradient Theorem [25], which derives the policy gradient \(\
abla_{\theta}J(\pi_{\theta})\) with respect to the policy parameter \(\theta\). Executing gradient ascent \(\theta\leftarrow\theta+\
abla_{\theta}J(\pi_{\theta})\) results in policy improvement and maximizes the expected discounted return. The policy gradient estimate is  
\[g=\mathbb{E}_{\tau\sim\pi_{\theta}}\left[\sum_{t=0}^{H}\
abla_{\theta}\log\pi_ {\theta}(a_{t}|s_{t})G_{t}\right], \tag{2}\]  
where \(G_{t}=\sum_{t^{\prime}=t}^{H}\gamma^{t^{\prime}-t}r_{t^{\prime}}\) is the discounted return for a trajectory following step \(t\). The General Advantage Estimation (GAE) technique [26] is proposed to reduce the variance of the policy gradient estimate at the cost of some bias  
\[\hat{g}=\mathbb{E}_{\tau\sim\pi_{\theta}}\left[\sum_{t=0}^{H}\
abla_{\theta} \log\pi_{\theta}(a_{t}|s_{t})\hat{A}_{t}\right], \tag{3}\]  
where \(\hat{A}_{t}\) is a GAE estimator of the advantage function [26]. The policy gradient algorithm requires _on-policy_ trajectory for training (i.e., the target policy and behavior policy should be consistent).
## 2 System Model
### OFDM Receiver Model  
The receiver block comprises the learning PA model, removal of cyclic prefix, then the use of FFT for demodulating the symbols, which is followed by channel equalization. During the start of communication, learning the PA model is necessary. The learned PA model should be updated at some specific intervals. In this work, we have not studied the time frames when the PA model should relearn; it is an optimization proposal. The learning PA model depends upon the frame time, call duration and data rate. The next segment in the receiver model is a distortion-removal block, as shown in Figure 2.  
Figure 1: OFDM (orthogonal frequency divisional multiplexing) transmit blocks with clipping.  
In this portion, we used a signal processing algorithm for distortion removal, which is caused by the PAPR. After distortion removal, the parallel stream of the signal is obtained before being downsampled by factor L. The last part describes the unmapping or demodulation of the QAM symbols.
## 4 Experiments
### Experiment Materials and Setup
#### 4.1.2 Backbone Models  
For our experimental evaluation, we selected three different types of time series models as the backbone models for our PCC method to serve as the baselines for the comparison. The selected backbone models have shown the state-of-the-art performance among their categories in multivariate time series forecasting tasks. The chosen models included an RNN-based model (SegRNN), two transformer-based models (iTransformer and PatchTST), and an MLP-based model (DLinear). In addition to these advanced general models, we also selected stackLSTM as a backbone model, as it has been extensively utilized in numerous weather time series research studies to model temporal variations and has demonstrated versatile performance in this regard. The detailed settings of the backbone models are listed in Appendix D.
## Computer Aided Diagnosis System for Early Lung Cancer Detection  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi,  
AD 127788, U.A.E; E-Mails: [EMAIL_ADDRESS] (N.W.); [EMAIL_ADDRESS] (H.A.-A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.:+971-02-501-8576; Fax: +971-02-447-2442.  
Academic Editor: [PERSON]  
_Received: 2 June 2015 / Accepted: 10 November 2015 / Published: 20 November 2015_  
* **Abstract:** Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM) for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.  
**Keywords:** compute-aided diagnosis; sputum images; lung cancer detection; Bayesian theorem; mean shift segmentation; feature extraction; neural network; support vector machine
## 2 Materials and Methods
### Methods  
#### 2.2.1 PHBV Extraction  
Purified PHBV was recovered from the PHBV-rich biomass produced at the PHBV pilot facility following the extraction and purification. Frozen PHBV-rich biomass (1-2 kg) of each operational day was lyophilized. To remove lipids, the lyophilized biomass (165-260 g) was acetone washed in three steps (24 h, 5 L) stirring continuously, recovered by vacuum filtration, and dried in air. Lipid-free PHBV-rich biomass was Soxhlet extracted for 24 h with CHCl\({}_{3}\) (3.5 L) then filtered under vacuum to remove any residual biomass and the extract was concentrated to dryness (rotary evaporator) to obtain crude PHBV. Each of the crude PHBV samples were dissolved in CHCl\({}_{3}\) (100 mL) and added dropwise to cold (4 \({}^{\circ}\)C) petroleum ether (1.5 L) with constant stirring to precipitate the PHBV, recovered by filtration and dried. The recovered PHBV was further purified by dissolving in CHCl\({}_{3}\) (100 mL) and added dropwise to cold (4 \({}^{\circ}\)C) methanol (1.5 L) with constant stirring to precipitate the purified PHBV, recovered by filtration, dried under vacuum, and yield recorded. The crude and purified PHBV yields are given in Table 1. The sample code name for purified PHBV obtained at an operational day will coded PHBV-xx (e.g., PHBV-15--PHBV at 15 th operational day).
# Design and Experimental Analyses of Hybrid Piston Rods Used in Hydraulic Cylinders under Axial Load
## 2 Materials and Methods
### Numerical Analysis and Results  
Finite element analyzes of the solid steel and hollow steel rod were performed to investigate their buckling behavior under axial loading and calibrate the numeric results to determine the appropriate outside diameter of steel rod to be wrapped with CFRP ABAQUS/STANDARD (LANCZOS method) was used to obtain numerical results. The actual dimensions of the steel specimens used in experimental analysis were used to design the numerical model. A hexahedral element with 8 nodes (C3D8R) with reduced integration in ABAQUS was used to mesh the numerical model. A deformable homogeneous volume element was chosen to build the numerical model. Totally, 46,027 hexahedral elements were generated to solve each case. In such a case, Abaqus composes boundary conditions that couple the displacement and rotation of each shell node to the average displacement and rotation of the solid surface. The boundary conditions are applied such that the nodes at the bottom of the specimen are constrained to displacement and rotation in all degrees of freedom. At the top of the specimen, rotation is fixed in all directions, and translation is free only in the axial direction of the model (i.e., loading direction). The axial load of 98,066 N is applied in the vertical direction, and the numerical model is solved for 3 eigenmodes using the LANCZOS eigen solver as shown in Figures 9 and 10 [20; 21; 22; 23; 24].  
Numerical analysis was carried out for different diameters of solid and hollow steel rod, and results are tabulated in Table 4. Any bar whose outside and inside diameters could not withstand this minimum buckling load of 1 kN was neglected, and thus, the minimum outside diameter was determined to be 7.5 mm for solid steel rod and 15 mm for hybrid rods. The inner diameter for hollow steel rods was determined to be 10 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Steel Rod** & **Outer Diameter** & **Inner Diameter** & **Length** & **Young’s Modulus** & **Slederness Ratio** & \multicolumn{3}{c}{**Buckling Load (kN)**} \\ \cline{3-10}
**Type** & **(mm)** & **(mm)** & **(mm)** & **(N/mm\({}^{2}\))** & **(No Unit)** & **Theoretical** & **Numerical** & **Experimental** \\ \hline \multirow{4}{*}{Solid} & 7.50 & - & 650.00 & 206,000 & 346.67 & 2.94 & 2.92 & 3.79 \\  & 15.00 & - & 650.00 & 206,000 & 173.33 & 34.32 & 39.13 & 41.31 \\  & 22.50 & - & 650.00 & 206,000 & 115.56 & 114.74 & 121.36 & 120.72 \\  & 30.00 & - & 650.00 & 206,000 & 86.67 & 280.39 & 297.32 & 291.26 \\ \hline \multirow{3}{*}{Hollow} & 15.00 & 10.00 & 650.00 & 206,000 & 144.22 & 28.66 & 25.12 & 29.43 \\  & 22.50 & 10.00 & 650.00 & 206,000 & 105.60 & 101.99 & 106.31 & 110.09 \\ \cline{1-1}  & 30.00 & 10.00 & 650.00 & 206,000 & 82.22 & 246.09 & 231.34 & 257.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Theoretical, numerical and experimental results of steel (only) column.  
Figure 10: Numerical results—buckling mode of a steel rod under FEA.
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 3 Deep Neural Networks (DNNs)
### Training and Evaluation Methods  
In this paper, the training of the network model was divided into two parts. On the one hand, only the fully connected layers were trained, and the weights of all the other layers were frozen. On the other hand, all the network layers were unfrozen, and the parameters of all the network layers were fine-tuned. Additionally, all the models were trained for 50 epochs. Furthermore, Python 3.8 [42] with Tensorflow 2.8 [43] and Keras 2.8 [44] deep learning frameworks were used as the training environment. Adam [45] optimizer was used to improve the accuracy of the model. Binary cross-entropy was used as the loss function (Equation (1)). The cross-entropy function is widely used in deep learning classification, which is based on maximum likelihood estimation to fit a model. It minimizes the distance between two probability distributions (predicted and actual). Combined with the activation function in the output layer (such as sigmoid or softmax), it can accelerate the training of deep learning models faster via logarithmic operations.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Size (MB)** & **Depth** &
\begin{tabular}{l} **Number of** \\ **Parameters (Million)** \\ \end{tabular} \\ \hline VGG19 [34] & 549 & 26 & 144 \\ ResNet50 [35] & 98 & 168 & 25.6 \\ InceptionV3 [36] & 92 & 159 & 23.9 \\ InceptionResNetV2 [37] & 215 & 572 & 55.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter information regarding network models.  
Figure 5: Architectures of typical deep neural networks. (**a**) VGG19; (**b**) ResNet50; (**c**) InceptionV3; (**d**) InceptionResNetV2.  
Additionally, the evaluation metric \(Perf\)[46] (Equation (2)) was selected to compare the performance of different models, and both training and validation performance were considered in the method.  
\[\text{Loss}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\times\log(p(y_{i}))+(1-y_{i})\times \log(1-p(y_{i})) \tag{1}\]  
\[Perf=\mu_{train}\times\frac{acc_{train}}{loss_{train}}+\mu_{wild}\times\frac{ acc_{valid}}{loss_{valid}} \tag{2}\]  
where \(N\) is the total number of samples, \(y_{i}\) is the binary label 0 or 1, and \(p(y_{i})\) is the probability of the output \(y_{i}\) label. \(\mu_{train}\) and \(\mu_{valid}\) are the weights of the training and validation results, respectively; the values are 0.1 and 0.9, respectively. The \(acc_{train}\) and \(loss_{train}\) are the accuracy and loss of the training results, and \(acc_{valid}\) and \(loss_{valid}\) are the accuracy and loss of the validation results. Here, the mean value of the last 10 epochs is presented.
## 4 Performance Evaluation and Comparison
### Results and Comparison
#### 4.2.4 Overall Comparison
This will enable us to assess the advantages and limitations of different models and provide a more comprehensive understanding of their applicability in IoT security. Bi-LSTM Advantages:  
Sequential Data Modeling: The primary objective of our research is to effectively model and analyze sequential data, such as network traffic patterns. The Bi-LSTM architecture is well suited for this task as it can capture dependencies in both forward and backward directions, enabling a more comprehensive understanding of sequential information. Enhanced Feature Learning: Bi-LSTM has the capacity to learn and extract features from sequences with a higher level of complexity compared to its unidirectional counterpart (Vanilla LSTM). This feature extraction capability is crucial in the context of intrusion detection, where identifying subtle patterns in network traffic is essential. Robustness: Bi-LSTM's bidirectional nature enhances the robustness of our intrusion detection system. It can capture dependencies that may be missed by unidirectional models, improving the overall accuracy of intrusion detection. Previous Success: Prior studies on intrusion detection and sequential data analysis have reported promising results with the use of Bi-LSTM. By building upon this established success, we aim to leverage its strengths in our research. Rationale:  
Our rationale for choosing Bi-LSTM was driven by the need to effectively model and learn from the sequential nature of network traffic data. We believe that the bidirectional architecture's ability to capture dependencies and patterns from both directions aligns with the requirements of our research, where accurate intrusion detection relies on a holistic understanding of data flows. However, we acknowledge that different LSTM variants, such as Stacked LSTM or Vanilla LSTM, have their own merits and may be suitable for specific applications. In future research, we intend to explore and compare various LSTM architectures to provide a more comprehensive understanding of their respective strengths and limitations in the context of intrusion detection.