esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Testbed and Simulation Results  
### WSN Testbed Setup  
We setup our testbed in a garden in Jordan. The current network deployment has five nodes, which are 5 m apart from each other, but more generally the architecture supports many nodes [15].  
Figure 5: Watermark Soil Moisture Sensor and Weather Station having Rainfall Sensor.  
In our protocol, the sensor data are sampled for 10 s after every 3 h, when there is no rainfall. During these 3 h the sensors are in sleep mode to save energy and after the data is sampled they again go to sleep mode for another 3 h. If we have rainfall event, the data is sampled after every 1 s for the sensor node experiencing the rainfall event. That means the affected nodes are up for 10 s and down for one second. In our protocol we also increase the priority of this affected node to transfer data to the base station through the gateway. All the other sensor nodes where there is no rainfall still keep sampling after every 3 h.  
The setup of this scenario consists of 5 wireless nodes from node 0 (N0) to node 3 (N3), as shown in Figure 6 and one gateway node (GW) which is connected to the server. We will have two constant bit rates (cbr) streams, one from node 0 and the other from node 2. The destination for both streams will be gateway node (GW). We will call them cbr0 and cbr2 respectively. According to AOMDV protocol, cbr2 traffic will go from node 2 to node 0 then node GW, and cbr0 traffic will go to node GW directly. Node 2 traffic, cbr2 will go through two paths, the primary one through node 0 and the secondary through node 1. After three h during sending packets, node 0 is affected by RAIN (which is the event).  
Tables 1 and 2 show the result of routing table before and after rain is detected at node 0, the routing table for node 2 and 3 has been changed which take another route through node 1 instead of the primary one through node 0 to send data to GW (destination) because node 0 is affected by RAIN. Hence node 0 has very important data (soil moisture) and it has to send it first and should take the highest priority to send data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **N0** & & & **N1** & & **N2** & \\ \hline
**Dest No.** & **Nxt hop** & **Hop cnt** & **Dest No.** & **Nxt hop** & **Hop cnt** & **Dest No.** & **Nxt hop** & **Hop cnt** \\ \hline
3 & 3 & 1 & 3 & 3 & 1 & 3 & 3 & 1 \\ GW & GW & 1 & GW & GW & 1 & 1 & 1 & 1 \\
2 & 2 & 1 & 0 & 0 & 1 & GW & 1 & 2 \\ \hline \multicolumn{2}{c}{**N3**} & & & & & & 0 & 2 \\ \hline
**Dest No.** & **Nxt hop** & **hop cnt** & **Dest No.** & **Nxt hop** & **Hop cnt** & & \\ \hline
1 & 1 & 1 & 0 & 0 & 1 & & \\
0 & 0 & 1 & 2 & 0 & 2 & & \\ GW & 1 & 2 & & 1 & 2 & & \\  & 0 & 2 & 3 & 0 & 2 & & \\  & & & & 1 & 2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Routing table before rain being detected at node 0.  
Figure 6: Five Node wireless sensor network (WSN) Test Bed Setup.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 2 Background
### Deep-Learning-Based IDS  
Deep learning is a subfield of machine learning. Using several layers of representation helps the modeling of intricate relationships and concepts [6]. Using the output characteristics of lower levels, supervised and unsupervised learning algorithms are utilized to generate increasingly higher levels of abstraction.  
IDSs play an important part in cybersecurity as they defend the network from cyberattacks by monitoring the network. IDSs in cybersecurity have evolved using deep learning (DL) due to their findings in computer vision, image processing, and natural language processing [15]. Due to their two key properties, hierarchical feature representations and the acquisition of long-term temporal patterning, this structure of hierarchical and heuristic search is highly effective. DL is popular among researchers. Therefore, considerable thought has been given to DL approaches for enhancing the intelligence of IDSs, despite a lack of research comparing such machine learning methods with openly available datasets. DL's complex structuring architecture facilitates high-quality learning for complex data processing. Rapid progress in parallel processing technology has produced a robust system basis for DL approaches. Common prevalent issues with existing ML-based models are as follows: (1) such models do have a false positive rate (FP) with a wider variety of malicious invasions [16]; (2) such proposed models are not able to generalize, because most available detection systems skip novel attack vectors because of obsolete ID datasets; (3) better solutions are required to sustain today's rapidly expanding rising internet traffic in a heterogeneous network.  
The Canadian Institute for Cybersecurity intrusion detection system (CICIDS) 2018 dataset, which is an upgrade to CICIDS 2017, is frequently utilized. One of the primary reasons for its consideration is that it was developed to address the issues observed in its earlier CICIDS 2017 dataset [17]. It also comprises various kinds of traffic and real-world network traffic, which is one of the primary reasons for its popularity [18] Although CICIDS 2018 is a suitable dataset, there is a significant issue that must be addressed. The issue's impacts result in a high-class imbalance that directly misleads the classifier [19].
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
###### Abstract  
The phenomenon of acoustic shadowing on ultrasonography is characterized by an echo signal void behind structures that strongly absorb or reflect ultrasonic energy. In medical ultrasonography, once the ultrasound energy is shielded, acoustic shadowing makes it difficult to create an image, leading to misinterpretations and obscure diagnoses. Hence, instead of dealing with the defocused problem encountered in an ultrasound scan (US), this current research focuses on revealing the existence of an acoustically shadowed target (or a potential lesion) using a well-known restoration algorithm, i.e., the synthetic aperture focusing technique (SAFT). To demonstrate the effects of an acoustic shadow on an ultrasound scan (US), a forward model study is carried out. In laboratory manipulations, a purposely designed physical model is created and then scanned using B-mode and pitch/catch arrangements to carry out shadowed and shadow-free scans in a water tank. Thereafter, making use of a delay-and-sum (DAS) operation, the echo signals are processed by the synthetic aperture focusing technique (SAFT) to perform image restoration. The results of the restoration process show that the SAFT algorithm performs well with respect to directional shadowing. Once the target or lesion is positioned in a total anechoic zone, or even in a multi-channel scan, it will fail.  
Keywords:ultrasound; acoustic shadow; image restoration; SAFT +
Footnote †: journal: Medical Imaging
## 5 Analysis of Experimental and Simulation Results
### Thermally-Stratified Water Body Conditions
#### 5.2.1 Case 5 Working Conditions
In the irrelevance validation in Section 4.2, simulation calculations are carried out to save the temperature field data of the thermal jet at intervals of 5 s, 10 s, 15 s, 20 s, 25 s, and 30 s in the uniform water environment according to the grid setup size and calculation time step. The results of the Case 5 simulation calculations are shown in Figure 14.  
When the jet is discharged for 5 s, a temperature difference of 10 \({}^{\circ}\)C between the jet core area and the surrounding water causes minimal temperature attenuation. The horizontal diffusion distance of the temperature is approximately 10 cm, leading to an upward floating trend. As the discharge continues, the rate of temperature diffusion to the stratified interface decreases rapidly as it reaches a floating rate of 85%. Within the jet's temperature diffusion region, about 60% of the ambient water temperature difference ranges within 2 \({}^{\circ}\)C. Constrained by the stratified interface, the upward floating stops and transitions to horizontal movement to the right, with a main body temperature difference below 2 \({}^{\circ}\)C.  
The background environmental stratification conditions have a significant impact on the jet temperature diffusion. When reaching the stratification interface, influenced by  
Figure 13: Temporal variation of the thermal jet temperature field under the experimental conditions. density differences, it prevents further upward movement and diffusion towards the water surface. Instead, there is an increase in lateral diffusion near this interface. Based on trends in the jet motion and the range of temperature field diffusion data along its main axis, the velocity selected from the experimental results and simulation calculations are compared, as shown in Figure 15.  
At the initial stage of discharge, the jet velocity decays more rapidly. As the jet spreads to a height of 5 cm, the velocity decreases by 50%. Subsequently, as the jet moves towards the layered interface, its amplitude gradually reduces to 0.03 m/s, and the vertical velocity component diminishes to 0. Under the influence of the layered interface, the direction of the jet velocity initially experiences dominant vertical uplift, which then gradually transitions into horizontal transverse motion. This change occurs because, as the jet reaches the layered interface, uplift ceases and, therefore, vertical uplift only extends up to this point. Furthermore, once reaching this stratified interface, any further upward movement is halted resulting in a maximum vertical jet velocity amplitude only up to that specific height. Figure 14: Simulation calculated results of the thermal jet temperature field. Figure 15: Vertical variation of spindle linear velocity of thermal jet.
# Recovery Behavior of Fe-Based Shape Memory Alloys under Different Restraints
## 3 Experimental Program
### Activation Method
#### 3.4.2 Rigid Restraint  
The recovery stress in the Fe-SMA was investigated through an activation process using 100 kN UTM. Type of Fe-SMA (FSMA-A, FSMA-B, FSMA-C), level of pre-strain (0.02, 0.04, 0.06, 0.08) and heating temperature (120 \({}^{\circ}\)C, 160 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 240 \({}^{\circ}\)C) were considered as experimental variables. Test parameter details are given in Table 2. The first letters A, B and C represent the type of Fe-SMA, respectively. The following Arabic numbers 2, 4, 6 and 8 represent pre-strain of the Fe-SMA, respectively. Next to the symbol \"-\", T, represents the temperature. The following Arabic numbers 120, 160, 200 and 240, represent the heating temperatures 120 \({}^{\circ}\)C, 160 \({}^{\circ}\)C, 200 \({}^{\circ}\)C and 240 \({}^{\circ}\)C, respectively. The displacement of the specimen with approximately 50 MPa of pre-stress was fixed to inhibit buckling behavior from the initial heat expansion in activation process. The specimen was activated using an electronic supply device as described in free restraint test. The temperature and stress were monitored through a DAQ system every 1 s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Type of SMA** & **Pre-Strain** & \begin{tabular}{c} **Heating Temperature** \\ (°C) \\ \end{tabular} & \begin{tabular}{c} **Stress at Maximum** \\ **Temperature**, \\ **\(\alpha\)\({}_{\text{nr}}\) (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Recovery Stress**, \\ **\(\alpha\)\({}_{\text{nr}}\) (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Final Recovery** \\ **Stress \(\sigma\)\({}_{\text{f}}\) (MPa)** \\ \end{tabular} \\ \hline A2-T160 & & 0.02 & 160 & 93 & 424 & 424 \\ \hline A4-T120 & & 0.04 & 120 & 90 & 372 & 372 \\ \hline A4-T160 & FSMA-A & 0.04 & 160 & 97 & 424 & 424 \\ \hline A4-T200 & & 0.04 & 200 & 136 & 519 & 518 \\ \hline A4-T240 & & 0.04 & 240 & 139 & 559 & 555 \\ \hline A6-T160 & & 0.06 & 160 & 96 & 434 & 433 \\ \hline A8-T160 & & 0.08 & 160 & 73 & 445 & 445 \\ \hline B2-T160 & & 0.02 & 160 & 170 & 381 & 364 \\ \hline B4-T120 & & 0.04 & 120 & 132 & 327 & 327 \\ \hline B4-T160 & FSMA-B & 0.04 & 160 & 157 & 373 & 368 \\ \hline B4-T200 & & 0.04 & 200 & 180 & 418 & 391 \\ \hline B4-T240 & & 0.04 & 240 & 189 & 431 & 401 \\ \hline B6-T160 & & 0.06 & 160 & 124 & 342 & 341 \\ \hline B8-T160 & & 0.08 & 160 & 110 & 374 & 374 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of variable and summary of activation test result under rigid restraint.  
Figure 2: Test setup for activation test.
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 4 Comparative Analysis of the Results
### Generalization Ability of the Proposed Model  
To evaluate the generalization capability of the proposed model, this study also applied it to calculate the missing wind pressure data using wind tunnel tests. The wind tunnel test data were obtained at the CLP Power Wind/Wave Tunnel Facility of the Hong Kong University of Science and Technology [29; 30]. The wind tunnel dimensions were 29.2 (length) \(\times\) 3 (width) \(\times\) 2 m (height). In addition, the wind pressure data were collected by a simultaneous multi pressure sensor system (SMPSS). The dimensions of the test model were 50.8 (d) \(\times\) 50.8 (d) \(\times\) 915 mm (h), with an aspect ratio (H/D) of approximately 18:1. There were 162 pressure sensors distributed on nine levels on all four faces of the test model.  
Figure 15: Evaluation metric comparisons results for the white dataset using the EEMD-based algorithms: (**a**) 1-step forecasting of the four hybrid models; (**b**) 5-step forecasting of the four hybrid models; (**c**) 10-step forecasting of the four hybrid models; (**d**) 15-step forecasting of the four hybrid models.  
As defined in AS/NZS 1170.2:2002, terrain category II was simulated in the wind tunnel by adjusting the roughness elements and spikes upstream of the test section. The average wind speed and flow turbulence intensity was measured using a hot-wire anemometer. Without loss of generality, a wind pressure sensor located on the side of the test model was used, as shown in Figure 16.  
The sampling frequency of the wind pressure data was 500 Hz, and the selection process of the model parameters was the same as in the previous paper. The evaluation results are shown in Figure 17, and it can be seen that the models' performances in terms of wind pressure data were similar to that of the acceleration data. The accuracy of the EEMD-LSTM model was better than the other models, and the indicators of _MSE_, _MAE_, and _RMSE_ were the smallest. The results show that the method has good generalization ability and performs well on different datasets. It is worth mentioning that the \(R^{2}\) value of the method for the missing data complement of wind pressure was more significant than that of the acceleration data, which indicates that the missing complement data and the measured data fit better on the wind pressure dataset, and the method was better for the wind pressure data. A possible reason for this is that wind pressure data change more frequently and have a stronger periodicity; thus, the time correlation between the data was more significant.  
Figure 16: Distribution of the pressure sensors (units: mm) [30].  
Figure 17: Evaluation metric comparison results for the wind pressure dataset using the EEMD-based algorithms.
###### Abstract  
The usage of wireless sensor devices in many applications, such as in the Internet of Things and monitoring in dangerous geographical spaces, has increased in recent years. However, sensor nodes have limited power, and battery replacement is not viable in most cases. Thus, energy savings in Wireless Sensor Networks (WSNs) is the primary concern in the design of efficient communication protocols. Therefore, a novel energy-efficient clustering routing protocol for WSNs based on Yellow Saddle Goatfish Algorithm (YSGA) is proposed. The protocol is intended to intensify the network lifetime by reducing energy consumption. The network considers a base station and a set of cluster heads in its cluster structure. The number of cluster heads and the selection of optimal cluster heads is determined by the YSGA algorithm, while sensor nodes are assigned to its nearest cluster head. The cluster structure of the network is reconfigured by YSGA to ensure an optimal distribution of cluster heads and reduce the transmission distance. Experiments show competitive results and demonstrate that the proposed routing protocol minimizes the energy consumption, improves the lifetime, and prolongs the stability period of the network in comparison with the stated of the art clustering routing protocols.  
wireless sensor networks; yellow saddle goatfish algorithm; clustering routing protocol; metaheuristic algorithms; energy efficiency; Bio-inspired algorithms +
Footnote †: journal: International Journal of Network Science
## 3 Geophysical Techniques and Geophysical Characteristics
### Geophysical Techniques
#### 3.1.3 Wide-Area Electromagnetic Method  
The wide-area electromagnetic method is a new electromagnetic detection technology developed in the early 21 st century, which was invented by academician [PERSON] from Central South University. Based on the precise expression of the electromagnetic field, the apparent resistivity parameters of the wide-area electromagnetic method are strictly defined, and the distortion effect in the distant region is improved. Therefore, the sounding can be carried out over a wide area, not limited to the \"far zone\", and the exploration depth is larger under the condition of the same transceiver distance. This method inherits the advantages of using a human field source in the CSAMT and magnetic dipole source frequency sounding method (MELOS) with \"not far zone\" detection. The weakness of the CSAMT remote signal is improved, the range of observation is expanded, and the correction method of the MELOS method is abandoned. A formula suitable for the whole field is used to calculate the apparent resistivity, which retains the high-order term of the calculation formula, greatly expands the observation range of the artificial source electromagnetic method, and improves the observation speed, accuracy, and field efficiency [18]. In this study, the data collected by the controlled-source audio-frequency magnetotelluric method is used to invert the resistivity of the whole area only using electric field scalar and geoelectric field observation parameters without track data, and the frequency is 0.125 Hz-8192 Hz.
## 2 Simulation Model and Parameters  
### Line Tower Model and Parameters  
In order to study the impact of lightning strikes more accurately on overhead conductors regarding the overvoltage of natural gas pipelines in the vicinity, it is essential to calculate the distribution of lightning current between overhead lines and towers after a lightning strike. For this study, a 500 kV overhead line is selected and the frequency-dependent JMarti line model in ATP-EMTP is utilized to determine the line parameters [13; 14]. The relevant parameters for the overhead conductor and lightning line can be found in Table 1. The line insulators consist of 28 glass insulators of type U210 BP/170, and voltage-controlled switches are employed to model these insulators in ATP-EMTP [15; 16].  
The tower mode chosen for this study is a more realistic equidistributed, multi-wave impedance tower model, and the tower is taken to be a ZB4 wine-glass-type linear tower. The structure of the power pole tower is illustrated in Figure 1.
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 4 FE Analysis for Reproduction on Beam Loading Experiment
### FE Analytical System \"COM3\" and Its Constitutive Laws
#### 4.2.1 Model of Concrete and Rebar
This shear model can describe shear stiffness by the ratio of averaged shear strain to averaged tensile strain without crack spacing or crack width. When cracks close, cracked concrete should have high shear stiffness and that phenomenon can be considered by taking uncracked shear stiffness into account. Because the value of \(G_{c}\) is significantly higher than \(G_{st}\), the value of \(G_{st}\) becomes dominant with high tensile strain normal to crack surface. For the rebar model, COM3 applies a tri-linear stress-strain relationship considering elastic modulus, yield strength, yield plateau strain, and tensile rupture strain.
# Experimental Study on Flexural Capacity of Corroded RC Slabs Reinforced with Basalt Fiber Textile
## 4 Flexural Capacity Analysis
### Parameters Calibration  
Due to corrosion of the steel reinforcements and the fact that the cracks were investigated at the bottom of the test specimens, the mechanical performance of the tensile reinforcement was reduced. It was therefore essential to calibrate the nominal yield strength of corroded steel bars and the bonding performance of concrete (the nominal yield strength of a corroded steel bar is defined as the tensile yield load divided by the section area which is calculated by the nominal diameter of the bar). According to [PERSON] et al. [46], a certain relationship exists between the nominal yield strength of a corroded bar and the corresponding corrosion ratio, which can be expressed as follows:  
\[\frac{f_{\text{y,c}}}{f_{\text{y,n}}}=1-\alpha\eta \tag{9}\]  
where \(f_{\text{y,c}}\) denotes the nominal yield strength of the corroded steel bar, \(f_{\text{y,n}}\) denoted the yield strength of a normal steel bar, \(\eta\) denotes the corrosion ratio of a steel bar, and \(\alpha\) denotes the regression coefficient. In this study, coefficient value \(\alpha\) was adopted as 1.75 through the test described in reference [44]. However, to consider the challenge of describing the bonding performance of concrete to corroded reinforcement, the effective cross-sectional area of the steel bar was selected as a reduction factor in the following equations proposed by [PERSON] et al. [47]:  
\[A_{\text{s,se}}=k_{\text{s}}\beta_{\text{s}}A_{\text{s}} \tag{10}\]  
\[k_{\text{s}}=-0.2722\omega+1.0438 \tag{11}\]  
\[\beta_{\text{s}}=1-\eta, \tag{12}\]  
where \(A_{\text{s,se}}\) denotes the effective cross-sectional area of corroded steel bars, \(k_{\text{s}}\) denotes the coordination coefficient, \(\beta_{\text{s}}\) denotes the reduction coefficient to cross-sectional area, and \(\omega\) denotes the crack width of test specimens caused by expansion.  
Through the above improved Equations (7)-(12), the tested and calculated flexural capacity of the specimens are compared in Table 8. It can be seen that the test results were relatively close to the corresponding calculated results, the mean value of \(M_{\text{u,exp.}}\) / \(M_{\text{u,cal}}\), was equal to 0.9514, and the corresponding standard deviation was 0.0953.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen ID**} & **Corrosion Ratio** & \(M_{\text{u,exp.}}\) & \(M_{\text{u,cal.}}\) & \(M_{\text{u,exp.}}\) / \(M_{\text{u,cal.}}\) \\  & \(\eta\)**(\%)** & **(kN-m)** & **(kN-m)** & **(kN-m)** \\ \hline M-J-3 & 7.68 & 16.73 & 15.76 & 1.062 \\ M-J-5 & 9.55 & 18.83 & 19