esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanical Analysis and Research Problem  
### Preliminary Knowledge  
The output torque and the deformation of air springs will affect the shaft alignment attitude, especially the horizontal offset and self-rotation angle of HPDME-ASVIS. Taking HPDME-ASVIS as the research object, the global coordinate system is established as shown in Figure 1, in which the center of gravity is regarded as the origin of the coordinate and the output shaft is regarded as the positive direction of the \(y\)-axis.  
The air springs are arranged symmetrically with respect to the \(yoz\) plane and the \(xoz\) plane at the same height. The main engine and foundation are treated as rigid bodies. Each air spring is treated as a linear spring. HPDME-ASVIS dynamics equation can be expressed [2] as  
\[\mathbf{M}\ddot{\mathbf{x}}_{g}+\mathbf{K}\mathbf{x}_{g}=\mathbf{f} \tag{1}\]  
Figure 1: Schematic diagram of HPDME-ASVIS.  
where **M** is the mass matrix of HPDME-ASVIS, \(\mathbf{x}_{g}=[x_{g},y_{g},z_{g},\alpha,\beta,\chi]^{\mathrm{T}}\) is the displacement vector of HPDME-ASVIS which includes translational displacements of the center of gravity (\(x_{g}\),\(y_{g}\) and \(z_{g}\)) and angular displacements (\(\alpha\), \(\beta\) and \(\chi\)), **K** is the global stiffness matrix formed by the stiffness matrix of the air springs and the elastic coupling, and **f** is the external force vector exerted on HPDME-ASVIS..  
The alignment state vector in the coupling center point is  
\[\mathbf{x}_{c}=[x_{c},y_{c},z_{c},\alpha,\beta,\chi]^{\mathrm{T}} \tag{2}\]  
where \(x_{c}\) is the horizontal offset, \(y_{c}\) is the axial offset, \(z_{c}\) is the vertical offset, \(\alpha\) is the vertical angularity, \(\beta\) is the self-rotation angle, \(\chi\) is the horizontal angularity.  
Considering the effect of external disturbances as a quasi-static process (\(\mathbf{\ddot{x}}_{g}=0\)), from Equation (1) the displacement response of the HPDME-ASVIS is  
\[\mathbf{x}_{g}=\mathbf{K}^{-1}\mathbf{f} \tag{3}\]  
The alignment at the coupling center point is  
\[\mathbf{x}_{c}=\mathbf{G}_{c}\mathbf{x}_{g} \tag{4}\]  
where \(\mathbf{G}_{c}\) is the position transformation matrix of the coupling center point in the global coordinate system.  
In previous studies of the ASVIS for the main engine, the alignment control objectives usually include the following three parts [37]. The first is to achieve high precision alignment control at the coupling center point. The second is to control the vertical deformation of the air springs within an acceptable range, and the third is to maintain the load distribution of the air springs as evenly as possible during the control process [37]. As HPDME-ASVIS often operate under special conditions of high power and high output torque, the research problem of this paper is how to offset the output torque while assuring that the alignment components (especially horizontal offset and self-rotation angle) are within the given control precision range in the situation of coupling/conflicting alignment objectives. The mechanical analysis of the HPDME-ASVIS is carried out in Section 2.2 to explain this alignment control problem more intuitively.
## 4 Results and Analysis
### Influence of Incident Angle  
As shown in Figure 5, the incident angle of the incident wave has an important influence on the dynamic responses of the twin tunnels. When the wave incident is vertical (\(\gamma=0^{\circ}\)), the displacement amplitudes of the two tunnels are the same and the deformation distribution is symmetric. With the increase of the incident angle of the wave, the displacement amplitude of tunnel 1 on the incoming side of the wave increases gradually and is greater than that of tunnel 2 on the outgoing side with the same tunnel relative stiffness (\(\beta_{0}/\beta\)), which is mainly caused by the \"shielding effect\" of tunnel 1 on tunnel 2. Especially when the distance between two tunnels is small, this shielding effect is more obvious. When the wave incident is horizontal (\(\gamma=90^{\circ}\)), the shielding effect of tunnel 1 on tunnel 2 is the most obvious. Highly related to the tunnel relative stiffness (\(\beta_{0}/\beta\)), for small tunnel relative stiffness (\(\beta_{0}/\beta=2\)), the displacement amplitude of tunnel 1 on the incoming side of the wave can be enlarged by six times than that of the displacement amplitude of rigid tunnel [6] and the displacement amplitude of tunnel 2 also can be increased by four times.  
The deformation of tunnel 1 is mainly at the top and bottom of the tunnel and the surface that the wave directly arrives on, while that of tunnel 2 is mainly at the top and bottom of the tunnel. This phenomenon is very worthy of attention because it does not exist in the case of a single rigid tunnel [6], indicating that the interaction between two tunnels has a significant impact on the dynamic response of the tunnel when the distance between two tunnels (\(s/a\)) and the tunnel relative stiffness (\(\beta_{0}/\beta\)) is relatively small. It is worth pointing out that it is generally believed that the dynamic response of underground structures is smaller than that of aboveground structures. Therefore, when the tunnel distance is small, the seismic design of underground tunnels should consider the interaction between tunnels.
## 3 Antenna Sensor Simulation and Parameter Optimization
### The Antenna Performance after Optimization and Measurement
be concluded that optimal receiving directions varies with different resonant frequencies, because the designed antenna sensor is a narrow-band antenna and four resonance frequencies differ considerably. Beneficially, all of them are located inside the switchgear. Therefore, the antenna sensor can effectively receive the PDs in all directions. Next a built-in antenna sensor prototype is fabricated (Figure 10), and its VSWR is measured by a vector network analyzer (Anritsu 37369D, Kanagawa, Japan; frequency range from 20 MHz to 40 GHz) to verify the accuracy of the antenna sensor simulation results. The measured VSWR is shown in Figure 11, and the results indicate that the antenna sensor presents good multi-frequency performance. The four resonant points in the low-frequency band are 463, 694, 780, and 927 MHz, and their corresponding VSWRs are 1.911, 1.419, 3.754 and 1.122, respectively. This result also shows that the antenna possesses multiple resonant points from 1 GHz to 3 GHz. Therefore, it can be concluded that the measurement results basically accord with the simulation results. Although the single resonant point shows a narrow frequency band because of the limitations of the characteristics of the microstrip antenna, the PD detection ability of the antenna sensor can be indirectly strengthened because numerous resonant points exist at the same time. As far as PD detection is concerned, the antenna sensor with multi-frequency performance can detect more characteristic PD information, which is of great benefit to identify different insulation defects.
## 3 Datasets and Methods Used in SATIDS
### Database Description
#### 3.1.2 InSDN Dataset  
The InSDN dataset covers many scenarios and attack classes, including Probe, DoS application, web attacks, brute force attack, password guessing, U2R, and DDos attacks [12]. In addition, InSDN regular traffic also encompasses several common features. The dataset source of attacks originates from both an internal and an external network to imitate the actual attack scenarios. It covers about 80 statistical aspects in CSV format such as protocol, duration, byte number, and packet number. The overall number of dataset instances for normal and attack traffic is 343,939, a total of 68,424 for normal traffic, and 275,515 for attack traffic as shown in Table 3.  
The attack traffic in InSDN emulates the same normal behavior since normal and malicious traffics are transmitted to the SDN controller for decision-making. Furthermore, the centralized perspective of the SDN network and the separation of the data plane from the control plane gives the attacker a new option compared to the traditional network of numerous attacks. These intrusions are not easy to be detected since the intruder is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks** & **No. of Instances** & **\%** \\ \hline Normal & 300,000 & 65.07 \\ \hline Backdoor & 20,000 & 4.34 \\ \hline Scanning & 20,000 & 4.34 \\ \hline Injection & 20,000 & 4.34 \\ \hline Password & 20,000 & 4.34 \\ \hline XSS & 20,000 & 4.34 \\ \hline Ransomware & 20,000 & 4.34 \\ \hline DDOS & 20,000 & 4.34 \\ \hline DOS & 20,000 & 4.34 \\ \hline MITM & 1043 & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ToN-Iot database statistics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks** & **No. of Instances** & **\%** \\ \hline Normal & 68,424 & 52.32 \\ \hline DoS-Application & 31,628 & 24.19 \\ \hline Probe & 15,225 & 11.64 \\ \hline DDOS & 9943 & 7.6 \\ \hline DoS-Network & 3772 & 2.88 \\ \hline Brute force attack & 1405 & 1.07 \\ \hline Web Application & 192 & 0.147 \\ \hline Botnet & 164 & 0.125 \\ \hline U2R & 17 & 0.013 \\ \hline \hline \end{tabular}
\end{table}
Table 3: InSDN database.  
permitted to access to the victims' server. As a result, such a data collection might be a good indicator for the model assessment that reflects the real-world scenario. Furthermore, the InSDN dataset contains no redundant records to prevent the learner model from distracting itself from the most common records [12; 13; 15].
## 2 Proposed Method
### Review of the Fusion Method
#### 2.1.2 Max Rule  
The fusion rule of inverted and acoustic images is the max rule. At the pixel (i,j), it compares the intensity of the \(k^{th}\) inverted image and acoustic image, and selects the larger one as the output. It is defined as follows:  
\[I_{f}^{k}(i,j)=\max\Bigl{(}I_{m}^{k}(i,j),I_{a}(i,j)\Bigr{)}, \tag{2}\]  
where \(I_{f}^{k}\) is the \(k^{th}\) pre-fused image, \(I_{a}\) is the acoustic image. Now, we fuse the information from both morphology and acoustic image. In the background, because the inversed image has a higher average gray level, the pre-fused image has smooth and high-intensity areas. In the boundary of the cell, the gradient from the bright region to dark region indicates the cell's shape. In the cell region, acoustic details are preserved because they have higher intensity than the inversed image. In Figure 3, as the increasing value of k, the pre-fused image provides more gradient information of the inverted morphology image but loses details of the acoustic image. Thus, the next step is to choose the image that preserves both morphology and acoustic information. We name this ideal image as the best-fit intensity image.  
Figure 3: The pre-fused images with different k values: (**a**) k = 0, (**b**) k = 50, (**c**) k = 100, (**d**) k = 150, (**e**) k = 200, (**f**) k = 250.  
Figure 2: (**a**) Morphology image, (**b**) inversed morphology image, and (**c**) acoustic image.
# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 2 Review of Artificial Neural Networks
### The Architecture of Multilayer Perceptron  
The ANN can be determined as an exceedingly interconnected array of processing elements named neurons. An extensively ANN model used by highly neurons connection known as the multi-layered perceptron (MLP) for deep learning of neurons [63]. Figure 1b shows the MLP type ANN feed-forward, a layered network of neurons contains one or more input, output layers, and hidden layers. Hence, each layer in an MLP has numerous neurons, which each neuron in the hidden layers is connected to the neurons in the neighbor layers with the nonlinear activation function and various weights. The input layer signals defined by weights and flow via the hidden layers to arrive at the output layer passing through the nonlinear or linear transfer functions termed as an activation function. Most of the common activation functions for MLP include sigmoid and hyperbolic function as shown in Table A1 in Appendix A. Figure 1b shows a typical MLP design for the popular MLP configuration, in which connections are made only between the neurons of successive layers in the network. Each circle symbolizes a single neuron in this shape. The vertical structure of the neurons in the layers is defined as the hidden layer 1, the hidden layer 2, and the output layer. A neuron via hidden layers is connected to neighbor neurons with different layers between input and output. The input neuron \(x_{i}\) connects to hidden neurons \(h_{i}\) by weight \(w_{ij}\) and \(h_{i}\) connecting to output neurons \(y_{i}\) by weight \(w_{jk}\). The multilayer perceptron executes mathematically nonlinear estimation by using sigmoidal functions as hidden units and linear weights. During the network learning, the weights of neuron vectors feature are continuously adjusted to reduce the error signal corresponding to the desired output, and the estimated network output. The circle's activation function in the MLP is given by the sigmoid function and is the standard activation function in feedforward neural networks. To compute the hidden-layer neurons output signal passing the summed input signal through a sigmoid function as shown in Figure 1a,b, where the sigmoid function is defined as \(f(u)=\frac{1}{1+e^{-u/l}}\). Hence, the hidden layer is calculated as follows:  
\[h_{j}=\frac{1}{1+\exp[-(w_{bj}+\sum\limits_{i=1}^{l}w_{ij}x_{i})]};1\leq j\leq m \tag{2}\]  
To define the value of output neurons based on hidden layers output as represented in (1):  
\[y_{n}=\frac{1}{1+\exp[-(w_{\text{\em{hk}}}+\sum\limits_{j=1}^{m}w_{jk}h_{j})]}; 1\leq n\leq n \tag{3}\]
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 4 Discussion
### Inspection Time Statistics  
A total of 320 sets of chip pictures with the same type from different batches were randomly selected and divided into four groups, each with 80 sets for testing. The test time included chip position pre-estimation, rotation correction, precise location, and defect identification. The computer configuration was as follows: Win 10, E5-2683 V4 processor with a main frequency of 2.10 GHz, 32 GB memory, and codes were programmed in Visual Studio 2019(Visual C++). The result is presented in Table 2. It can be concluded that the average inspection time of each LED chip was 1.098 ms, which meets the online inspection efficiency requirements of companies. During the chip inspection, inspection time was mainly spent on segmentation and positioning. The position pre-estimation in this study reduced the segmentation time, thereby improving the overall inspection efficiency.
## 3 Results  
We described first ten patients enrolled prospectively in the MEIO21 study undergoing surgery with high public risk and expected severe intraoperative hemodynamic instability: nine LT and one nephrectomy with thrombectomy/caval resection with intraoperative venous bypass. The features of the study population are reported (Table 1). The average age was 61.4 \(\pm\) 6.8 years while the mean ICU length of stay was 5.8 \(\pm\) 3.6 days. Seven patients were TCCD MES-negative while three had \(>\)1 MES. The three MES-positive patients subsequently underwent intraoperative TEE. Two patients had PFO shunt while one patient had an extracardiac shunt (case n\({}^{\circ}\) 3, Figure 2). Both patients with PFO had intraoperative phases with shunt reversal secondary to hemodynamic changes. In nine patients, the postoperative neurological status was unchanged or better than the preoperative one. One patient with an intraoperative diagnosis of PFO shunt (case n\({}^{\circ}\) 2) experienced postoperative reversible delirium (CAM-ICU positive) and worsening of the West Haven score from I to III without lateral neurological deficits. No patients had ischemic stroke within 30 days after surgery. In nine patients, there were no postoperative respiratory complications, while one patient (with PFO Shunt--case n\({}^{\circ}\) 1) had a postoperative respiratory failure with the need for reintubation, NIV and HFNC oxygen therapy. The mean ICU length of stay was 5.8 \(\pm\) 3.6 days. Intraoperative PFO-diagnosis and shunt-direction monitoring guided the anesthetist in choosing ventilatory and hemodynamic supports to avoid the right-left shunting. We described TCCD MES-positive cases below.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Patients** & **Age** & \begin{tabular}{c} **Type of** \\ **Surgery** \\ \end{tabular} & **MES** & **PFO Shunt** & \begin{tabular}{c} **Extra-Cardiac** \\ **Shunt** \\ \end{tabular} & \begin{tabular}{c} **Neurologic** \\ **Complication** \\ \end{tabular} & \
\begin{tabular}{c} **Post-Operative** \\ **ARF** \\ \end{tabular} &
\begin{tabular}{c} **Needs for** \\ **Re-Intubation** \\ \end{tabular} & **ICU-LOS** \\ \hline
**1** & 55 & LT & N & N & N & N & N & Y & 7 \\ \hline
**2** & 62 & LT & Y & Y & N & Y & N & 5 \\ \hline
**3** & 62 & LT & N & N & N & N & N & 4 \\ \hline
**4** & 67 & LT & N & N & N & N & N & 4 \\ \hline
**5** & 64 & LT & N & N & N & N & N & 5 \\ \hline
**6** & 65 & LT & Y & N & Y & N & N & 12 \\ \hline
**7** & 70 & LT & N & N & N & N & N & 5 \\ \hline
**8** & 59 & LT & N & N & N & N & N & 2 \\ \hline
**9** & 46 & LT & N & N & N & N & N & 2 \\ \hline
**10** & 64 & Other & Y & Y & N & N & Y & 12 \\ \hline \end{tabular} MES: microembolic signals detected by transcranial Doppler. PFO: patent foramen oval. ARF: acute respiratory failure. ICU-LOS: length of stay in intensive care unit. LT: liver transplantation. Y: present; N: absent.  
\end{table}
Table 1: Results and complications from standardized intraoperative TEE-TCCD protocol.
## Nomenclature and Abbreviations  
\begin{tabular}{l l l l} \(A\) & area & \(\mu\) & dynamic viscosity \\ \(C\) & constant & \(\lambda\) & flow resistance coefficient \\ \(c\) & ratio & \(\eta\) & efficiency \\ \(D\) & rotor diameter & \(\alpha\) & heat transfer coefficient \\ \(d\) & equivalent diameter & \(\phi\) & liquid-phase friction coefficient \\ \(F\) & correction factor & Subscripts & \\ \(f\) & friction coefficient & \(e\) & evaporator \\ \(G\) & condensation volume & \(exp\) & expander \\ \(H\) & pump head & \(g\) & gas \\ \(h\) & specific enthalpy & \(hw\) & hot water \\ \(K\) & heat transfer coefficient & \(in\) & inlet \\ \(L\) & phase length & \(l\) & liquid \\ \(l\) & specific heat & \(le\) & liquid in evaporator \\ \(m\) & mass flow rate & \(log\) & logarithm \\ \(n\) & number of flow channels or rotation speed & \(m\) & mean \\ \(P\) & pressure & \(max\) & maximum \\ \(Pr\) & Prandtl number & \(min\) & minimum \\ \(Q\)/\(q\) & heat exchange amount & \(out\) & outlet \\ Re & Reynolds number & pb & pool boiling \\ \(R\) & thermal resistance & \(pump\) & pump \\ \(s\) & single-channel cross-sectional area & \(r\) & refrigerant \\ T/\(\hbar\) & temperature & \(sc\) & supercooled \\ \(u\) & flow rate & \(sh\) & superheated \\ \(v\) & specific volume & \(tp\) & two-phase \\ \(W\) & power & \(w\) & wall \\ \(X\) & Martinelli parameters & Acronyms & \\ \(x\) & dryness & BPE & brazed plate heat exchanger \\ Greek letters & & BPE & brazed plate evaporator \\ \(\rho\) & density & BFPE & brazed nickel foam plate evaporator \\ \(v\) & kinematic viscosity & PHE & plate heat exchanger \\ \(\psi\) & liquid-phase friction coefficient & ORC & organic Rankine cycle \\ \(\delta\) & plate thickness & ORC-R & ORC with regulated heat source \\ \end{tabular}
## 2 Study Area and Data Sources
### Data Sources  
This study examines spatial dynamics from 2010 to 2020, focusing on three key time points: 2010, 2015, and 2020. Land-use data were obtained from the Zengcheng District Land Use Change Database, derived from high-resolution remote sensing imagery and validated through field surveys and accuracy assessments to ensure reliability. Environmental variables were sourced from the National Earth System Science Data Center, which adheres to standardized data processing protocols. Data on river networks and transportation infrastructure were extracted from OpenStreetMap and cross-verified with official mapping sources where available. Socio-economic indicators, including GDP and population density, were acquired from the Resource and Environment Science and Data Platform, ensuring alignment with official statistical records.
###### Abstract  
In this paper, an integrated workflow aimed at optimizing aquifer monitoring and management through time-lapse Electric Resistivity Tomography (TL-ERT) combined with a suite of predictive algorithms is discussed. First, the theoretical background of this approach is described. Then, the proposed approach is applied to real geoelectric datasets recorded through experiments at different spatial and temporal scales. These include a sequence of cross-hole resistivity surveys aimed at monitoring a tracer diffusion in a real aquifer as well as in a laboratory experimental set. Multiple predictive methods were applied to both datasets, including Vector Autoregressive (VAR) and Recurrent Neural Network (RNN) algorithms, over the entire sequence of ERT monitor surveys. In both field and lab experiments, the goal was to retrieve a determined number of \"predicted\" pseudo sections of apparent resistivity values. By inverting both real and predicted datasets, it is possible to define a dynamic model of time-space evolution of the water plume contaminated by a tracer injected into the aquifer system(s). This approach allowed for describing the complex fluid displacement over time conditioned by the hydraulic properties of the aquifer itself.  
multi temporal geophysics; cross-hole geoelectrical measurements; Vector Autoregressive algorithms; Recurrent Neural Networks; aquifer monitoring Article
## 4 Implementation and Analysis
### Performance Measures  
The predictive performance of data mining classifiers is measured in terms of accuracy, precision, recall, and F1-score, the common evaluation metrics of ML. The testing accuracy and F1-score measures are used for performance evaluation. The k-fold cross-validation score, with k set to 10, was used for all the models (XGBoost has inbuilt CV). Accuracy is the ratio of the number of correct predictions to the total number of input samples; Precision is the ratio of correctly predicted positive observations to the total predicted positive observations; Recall, or sensitivity, is the ratio of correctly predicted positive observations to all the observations in the actual class; and F1 Score is the weighted average of Precision and Recall.  
\[Accuracy=\frac{Number\ of\ correct\ predictions}{Total\ number\ of\ predictions} \tag{1}\]  
\[Precision=\frac{TruePositive}{(TotalPredictedPositive)} \tag{2}\]  
\[Recall=\frac{True\ Positive}{(Total\ Actual\ Positive)} \tag{3}\]  
\[F1-score=2*(\frac{Precision*Recall)}{(Precision+Recall)} \tag{4}\]