esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pin Equation Calculation  
The force-deflection values for the 4 interface tests and the pin bending test were used to calculate an equation for displacement of the pin, based on the perpendicular force acting on it. Individual polynomial curve fitting was conducted for each situation to understand the form of the equation and coefficients. Each curve was assumed a polynomial and uni-variate regression was used. Aggregate RMSE values of each tightening torque in each degree of polynomial were plotted against the maximum degree of the polynomial to obtain the elbow point. A polynomial with 4 degrees was identified as optimum. Form of the equation for displacement based on force and tightening torque was postulated based on the assumption that the equations should reduce to a linear equation when torque value tends to infinity, depicting results obtained in the pin bending test: \(x=(a*e^{q*\tau})F^{4}+(b*e^{r*\tau})F^{3}+(c*e^{s*\tau})F^{2}+(0.01945+d*e^{l* \tau})F\).  
Coefficients obtained during initial uni-variate regression analysis were used as starting values for the fitting to ensure global minima was obtained when fitting. Non-linear least square method was used for fitting. Final equation RMSE value was 0.1425 and adjusted R-square 0.9992. Equation for pin bending and slip in the clamp-pin interfaces (torque in Nm and Force in N):  
\[x_{1}=(5.33*10^{(}-7)e^{-0.2376\tau})F^{4}+(0.001742e^{-0.6249 \tau})F^{3}\\ -(0.004182e^{-0.2307\tau})F^{2}+(0.01945+0.03022e^{-0.0293\tau})F \tag{5}\]  
Leading term was disregarded based on the value of the coefficient. Displacement values for each combination were calculated using Equations (1)-(3) and (5). Calculations were performed for a set of loading conditions (Figure 16).  
Figure 15: System test behavior for each configuration. Configuration 1—Magenta, Configuration 2—Red, Configuration 3—Blue, Configuration 4—Green, Configuration 5—Cyan, Configuration 6—Black.
## 3 Application for Testing a SCADA System Operator Knowledge
Initial configuration of the question database. The configuration stage enables adding an explanatory drawing to the questions (Figure 18);
2. Initial application configuration involving setting the operation mode (single- or multi-station);
3. Setting, among others, the number of stations participating in the test, user data, and IDs is possible from the master station (examiner) level in the case of multi-station operator;
4. Setting the number of test questions, activity duration, source location, and question file;
5. Configuration of client apps connecting with the examiner application through indication, among others, the appropriate communication topic. The topic is a system setting that enables defining the operator station communication method;
6. Entering trainee (i.e., tested operator) data;
7. Starting the test (permission) from the master station level or in single-station mode. Figure 17: Examiner module—settings window. Figure 18: Examiner module—settings window. The process of diagnosing a SCADA system operator-trainee is as follows:  
1. The test is launched at the trainee station;
2. The trainee takes the test through marking appropriate answers to subsequent questions, until they are exhausted or the set time elapses;
3. The test automatically ends after the set time elapses or it is possibly to end it manually at an earlier time. Due to the problem nature of certain questions, it is possible, as per previous consideration, not to set the maximum time for solving the test. This enables a tested trainee to focus on the subject-related aspects of the question, without the stress related to the time factor. Therefore, test results seem more reliable;
4. It is followed by automatic saving of the test results--depending on the selected option--to an archiving spreadsheet file (example in Figure 19) or a text file. Depending on the initial settings, this can be a separate file for each trainee or a collective, common file for a certain group.
# A Study on Improving M2M Network Security through Abnormal Traffic Control
## 3 Implementation of M2M Intelligent Security Filter  
Linux and embedded Windows, which are the foundations of M2M intelligent devices, are vulnerable to network attacks if they are not secured or properly updated on the device [33]. The proposed security filter is based on Linux's iptables. It has a behavior-based filter to match the characteristics of M2M intelligent devices. Iptables consist of four tables that supports a \"filter to control the allow/block, IP Network Address Translation (NAT) and NAT to control routing, and RAW, which controls connection tracking for sessions and manages the modification and marking of communication packets\". The role of the table and the function of the chain are the same as shown in Table 2.  
The iptables used in the proposed security filter is intended to control abnormal traffic in the M2M intelligent network environment using the following options. The first is the ability to manage and check the state of the session with the conntrack option which is a part of Linux network stack, specifically part of the firewall subsystem.  
When a new kind of packet arrives, it generates a conntrack and finally records whether the packet is allowed/blocked. If the packet is allowed, it can be considered as safe, and it is safe to continue accepting packets at least for that session. However, since the firewall for packet filters allows each rule to be applied to the entire packet, it is checked continuously even if the packet is allowed once. Since this operation is an unnecessary waste of resources, if the same connection is allowed, conntrack leaves a record of the session through conntrack and allows matching packets without checking rules. The second invalid option provides control over abnormal session creation. If you want to create a session with an unauthorized access during the network communication process, it is helpful to obtain the availability by the advance control through the option. The third option to TCP-flags is to provide control for each flag in TCP. As shown in Figure 3, TCP prepares flags according to the situation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Chain**} & \multicolumn{3}{c}{**Table**} \\ \cline{2-4}  & **Filter** & **IP NAT** & **Raw** & **Manage** \\ \hline INPUT & \(\circ\) & - & - & \(\circ\) \\ FORWARD & \(\circ\) & - & - & \(\circ\) \\ OUTPUT & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) \\ PREROUTING & - & \(\circ\) & - & \(\circ\) \\ POSTROUTING & - & \(\circ\) & \(\circ\) & \(\circ\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Iptables table and chain based on Linux.  
Figure 2: DRDoS attack using M2M intelligent network.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 3 Materials and Methods
### Low-Level Video Feature Selection  
Based on the selection of video descriptors described in Section 2, a set of low-level video features was obtained from the six video ads used in the observational crowdsourcing study. These video features include Lighting Key, Color Variance, Mean Motion Activity, Standard Deviation of Motion Activity, and Average Shot Length.  
For the Lighting Key and Color Variance features, the values for each video frame were determined first. The average of the values assigned to the frames was calculated to obtain a single scalar value for each video. Similarly, optical flow vectors were calculated for each successive pair of video frames. The measurement of motion activity with respect to the two frames was calculated using the mean and standard deviation of the absolute values of the flow vectors. The two corresponding scalar motion-related video features associated with the entire video were then calculated as the average over the corresponding video frames.  
The dynamics of the video processing were also considered. The Average Shot Length represents the average video scene length between two video edits during the video production. Scene transition estimates were obtained using the PySceneDetect [32] software package. A content-based detector is also used. This method is based on jump-cut detection and seeks the differences in consecutive video frames exceeding a certain threshold. The experimentally determined threshold was set as \(t=10\), where the threshold was an 8-bit integer. The entire process was set to operate in the HSV color space by using the average value of the three color channels. The scene transitions obtained were thoroughly verified by adjusting the values in rare cases where the automatically determined timestamps of the transitions did not match the actual shot boundaries.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Methods
#### 2.2.2 Step Detection
In particular, the variance analysis has been performed over a 4-s moving window and the threshold of 1.5 m\({}^{2}\)/s\({}^{4}\) has been empirically determined to distinguish the motion activity from the stationary case.
## 2 Overview of Multi-Link Operation in IEEE 802.11 be WLANs
### Multi-Link Transmission and Reception
#### 2.2.2 One-to-Many TID-to-Link Mapping (Packet-Level Aggregation)  
To fully exploit the throughput increase and latency decrease effects of MLO, a TID can be mapped to a set of multiple links, allowing traffic with that TID to be transmitted using any link in this set, as illustrated in Figure 2. In this way, a higher rate of traffic with that TID can be handled, and a congested queue for that TID will be flushed faster. The UMAC on the transmitter side plays a role in distributing traffic of each TID to its mapped links (LMACs) at the packet level.  
However, such a strategy is inherently accompanied by the reordering of packets; thus, the UMAC on the receiver side must buffer the received traffic and pass packets in sequence only to the LLC layer. Therefore, received packets may wait in the receiver buffer until they can be arranged in sequence. To achieve this, multiple links for same-TID packets must use a common sequence number space shared across multiple links.
## 2 Materials and Methods
### Gel Permeation Chromatography (GPC)  
GPC was used to obtain molar mass (M\({}_{\rm n}\) and M\({}_{\rm w}\)) and polydispersity index (PDI) of PLA films using Viscotek VE 2001 GPCMax (USA) equipped with three columns (Styragel HR-4, HR-3, and HR-1) in a column oven and differential refractometer (maintained at 40 \({}^{\circ}\)C). Tetrahydrofuran (HPLC grade) was used as the eluent with a flow rate of 1 mL/min. Polystyrene standards (from Polymer Laboratories Inc., 580 Da-3039 kDa) were used for calibration. For GPC sample preparation, 2-4 mg of dry PLA film sample was dissolved in 2 mL of THF (HPLC grade).
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 2 Related Work
For instance, in [24], the authors proposed a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict the depth information in a CFRP material inspected using laser infrared thermography. The proposed method notably outperformed the other two neural-network-based methods, CNN and RNN. Another study, presented in [25], employed a Gated Recurrent Unit (GRU) neural network to estimate the defects' depth in CFRP specimens. This research used a pulsed thermographic synthetic dataset, simulated by the Finite Element Modeling (FEM) software Comsol. The proposed methods demonstrated high performance, particularly noteworthy in terms of accuracy. Additionally, an artificial neural network was applied in [26] for defect depth prediction within CFRP coupons using PT. The model used in this study was trained on synthetic data and tested on synthetic datasets, as well as on experimental datasets. The network demonstrated remarkable accuracy, achieving 97% on synthetic datasets and 90% on experimental datasets.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
###### Abstract  
The satellite-terrestrial integrated network (STIN), as an integration of the satellite network and terrestrial, has become a promising architecture to support global coverage and ubiquitous connection. The architecture of software-defined networking (SDN) is utilized to intelligently coordinate the global STIN, in which the placement schemes of SDN controllers, including the locations, number, and roles, would produce various performances. However, the uneven distribution of global users leads to the unbalanced energy consumption of satellite resources, which brings a heavy burden for satellites to maintain the control flows for network management. To provide green communication for international economic trade in the countries along the Belt and Road, in this paper, we focus on the energy-efficient controller placement (EECP) problem in the software-defined STIN. The satellite gateways are located in the countries along the Belt and Road, which accounts for a large number of traffic demands and a dense population. The controllers are deployed on the LEO satellites, where each LEO satellite is a candidate controller. The energy consumption for the control paths and the user data links is modeled and then formulated as the flow processing-oriented optimization problem. A modified simulated annealing placement (MSAP) algorithm is developed to solve the EECP problem, in which we use the greedy way to obtain the initial set of controllers, and then the final optimal controller placement result is obtained by the simulated annealing algorithm. Extensive simulations are conducted on the simulated Iridium satellite network topology and statistics data. Compared with other algorithms, the results show that MSAP reduces network energy consumption by 20% and average latency by 25%.  
satellite-terrestrial integrated network; software-defined networking; controller placement; simulated annealing; energy consumption
###### Abstract  
The realization of the electromagnetically induced transparency (EIT) effect based on guided-mode resonance (GMR) has attracted a lot of attention. However, achieving the multispectral EIT effect in this way has not been studied. Here, we numerically realize a double EIT-like effect with extremely high Q factors based on a GMR system with the double-bar dielectric grating structure, and the Q factors can reach 35,104 and 24,423, respectively. Moreover, the resonance wavelengths of the two EIT peaks can be flexibly controlled by changing the corresponding structural parameters. The figure of merit (FOM) of the dual-mode refractive index sensor based on this system can reach 571.88 and 587.42, respectively. Our work provides a novel method to achieve double EIT-like effects, which can be applied to the dual mode sensor, dual channel slow light and so on.  
guided-mode resonance; electromagnetically induced transparency; high quality factor; double spectral; refractive index sensor +
Footnote †: journal: Applied Sciences
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 3 Results
### Endogenous Contrast Agents
#### 3.1.4 In Vivo Studies
They adopted a technique in which the PA image was fused with the deformed MRI, and this fused image enabled precise visualization of tumor location. A comparison of PA and MR images of a healthy breast is shown in Figure 4. As shown in the fused PA-MR image (Figure 4c), the blood-vessel structures and morphological characteristics of the PA images were more detailed than MRI. 'More detailed' here refers to finer vasculature, more detailed blood vessel branching structures, and more detailed morphological vessel characteristics. Figure 3: Comparison of PA, US and dual mode PA-US images of SNL showing the lymph node and needle. (**a**) US image, (**b**) PA image, and (**c**) co-registered PA-US image. Reproduced from [PERSON] et al. (2017), which is licensed under a Creative Commons Attribution 4.0 International License ([[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)]([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))). [PERSON] et al. [110] evaluated the feasibility of functional PAT at 733 nm, 775 nm, and 808 nm for high-resolution detection and characterization of breast cancer on six lesions. All the six breast lesions were successfully detected by this system and the results were consistent with that of MRI reports. Out of the six, five cancer cases were consistent with that of final pathology findings.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 4 Performance Analysis
### Exact BER of Proposed Transmitter and Receiver
 \tag{40}\]  
Since \(s_{\mathrm{c}}\) is a combination of \(s_{\mathrm{c,1}}\) and \(s_{\mathrm{c,2}}\) combined in series, its average error probability for user 1 can be represented as the average value of the error probabilities for the \(s_{1}\) and \(s_{2}\) of user 1 in conventional NOMA, as follows:  
\[\overline{\bar{\mathrm{P}}(Y^{1})}=\frac{1}{2}\bigg{[}\overline{\bar{\mathrm{ P}}_{1}(e)}+\overline{\bar{\mathrm{P}}\big{(}X^{1}\big{)}}\bigg{]}. \tag{41}\]  
By substituting (12) and (32), (41) is obtained as follows:\[\overline{\overline{\mathrm{P}}(Y^{1})}= \frac{1}{4}\Bigg{(}2-\sqrt{\frac{\overline{\gamma_{\mathrm{C}}}}{2+ \overline{\gamma_{\mathrm{C}}}}}\Bigg{)}+\frac{1}{16}\Bigg{(}-\sqrt{\frac{ \overline{\gamma_{\mathrm{A_{1}}}}}{2+\overline{\gamma_{\mathrm{A_{1}}}}}} \tag{42}\] \[-3\sqrt{\frac{\overline{\gamma_{\mathrm{B_{1}}}}}{2+\overline{ \gamma_{\mathrm{B_{1}}}}}}+\sqrt{\frac{\overline{\gamma_{\mathrm{D}}}}{2+ \overline{\gamma_{\mathrm{D}}}}}-\sqrt{\frac{\overline{\gamma_{\mathrm{E}}}}{2+ \overline{\gamma_{\mathrm{E}}}}}\Bigg{)}.\]  
By substituting (40) and (42) into (38), the average error probability of user 2 is given as follows:  
\[\overline{\overline{\mathrm{P}}_{1}(e)}= \frac{1}{2}\Bigg{(}1-\sqrt{\frac{\overline{\gamma_{\mathrm{F_{1}}} }}{2+\overline{\gamma_{\mathrm{F_{1}}}}}}\Bigg{)}+\sqrt{\frac{\overline{ \gamma_{\mathrm{F_{1}}}}}{2+\overline{\gamma_{\mathrm{F_{1}}}}}}\times\Bigg{\{} \frac{1}{4}\Bigg{(}2-\sqrt{\frac{\overline{\gamma_{\mathrm{C}}}}{2+\overline{ \gamma_{\mathrm{C}}}}}\Bigg{)} \tag{43}\] \[+\frac{1}{16}\Bigg{(}-\sqrt{\frac{\overline{\gamma_{\mathrm{A_{1 }}}}}{2+\overline{\gamma_{\mathrm{A_{1}}}}}}-3\sqrt{\frac{\overline{\gamma_{ \mathrm{B_{1}}}}}{2+\overline{\gamma_{\mathrm{B_{1}}}}}}+\sqrt{\frac{\overline {\gamma_{\mathrm{D}}}}{2+\overline{\gamma_{\mathrm{D}}}}}-\sqrt{\frac{ \overline{\gamma_{\mathrm{E}}}}{2+\overline{\gamma_{\mathrm{E}}}}}\Bigg{)} \Bigg{\}}.\]
## 5 Conclusions  
In this article, an innovative SFSA-DLIDS method was devised for the classification and identification of intrusions from the CPS environment. The presented SFSA-DLIDS model primarily performed a min-max data normalization approach to convert the input data to a compatible format, followed by the SFSA technique which was applied to select a subset of features. Finally, the CSO-DSAE approach was utilized for the identification and classification of intrusions. The design of the CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model was tested using a series of experiments. The experimental results established the enhanced performance of the SFSA-DLIDS method over the existing ones with maximum accuracy of 99.35% and 99.48% on the test NSLKDD 2015 and CICIDS 2017 datasets, respectively. Therefore, the presented SFSA-DLIDS model was implemented as an effectual tool to recognize intrusions in the CPS environment. In future, outlier detection approaches should be integrated for improving the overall detection efficiency of the SFSA-DLIDS technique. In addition, the proposed model can be realized on a big data environment in our future work.  
Conceptualization, M.A.D. and K.A.A.; methodology, F.S.A.; software, I.Y.; validation, S.S.A., K.A.A.; formal analysis, E.M.T.E.D.; investigation, K.A.A.; resources, A.A.A.; data curation, A.A.A.; writing--original draft preparation, S.S.A.; writing--review and editing, K.A.A.; visualization, A.M.; supervision, F.S.A.; project administration, A.M.; funding acquisition, F.S.A. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number PNURSP2022R319, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work, grant number 22 UQU4210118 DSR34.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article as no datasets were generated during the current study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline SFSA-DLIDS & 99.44 & 99.44 & 99.44 & 99.44 \\ \hline GSAE & 97.44 & 96.44 & 98.79 & 97.74 \\ \hline AE-RF & 97.55 & 97.08 & 98.15 & 97.66 \\ \hline WISARD & 96.22 & 97.27 & 96.85 & 98.75 \\ \hline Forest-PA & 96.53 & 96.99 & 96.85 & 97.88 \\ \hline LIB-SVM & 96.56 & 97.38 & 97.32 & 97.75 \\ \hline FURIA & 98.82 & 97.83 & 96.94 & 98.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of SFSA-DLIDS approach with existing algorithms.
## 2 Materials and Methods
### OCO-2 SIF Data  
The OCO-2 satellite was launched into a polar orbit on 2 July 2014. It is a National Aeronautics and Space Administration (NASA) satellite, and is specially designed to monitor CO\({}_{2}\) concentrations in the atmosphere [12]. The OCO-2 SIF product is an unexpected but critical complementary by-product of OCO-2's primary mission target [14]. The OCO-2 spectrometer measures the spectra in the O\({}_{2}\) A band, with far-red SIF being retrieved at wavelengths of 757 and 771 nm (SIF\({}_{757}\) and SIF\({}_{771}\)) based on the infilling of the Fraunhofer lines at 13:36 local time. Each second, OCO-2 acquires 24 data points with a footprint of 1.3 km \(\times\) 2.25 km. The precision of the OCO-2 SIF is better than 0.05 W m\({}^{-2}\) sr\({}^{-1}\) um\({}^{-1}\) with just a single detector footprint in the ideal case of an entirely opaque Fraunhofer line [8; 34; 35].  
The OCO-2 SIF Lite data consist of daily points that contain both 'value' and location information, as well as a description of statistical bias and cloud cover. In this study, we used SIF data derived from the OCO-2 Lite products (V9r) collected during the period 2015-2020. The data were sourced from the NASA Earth Data archive ([[https://earthdata.nasa.gov/accessed](https://earthdata.nasa.gov/accessed)]([https://earthdata.nasa.gov/accessed](https://earthdata.nasa.gov/accessed)) date: 15 July 2019). Many researchers have argued that SIF\({}_{757}\) is more accurate than SIF\({}_{771}\) because it is closer to the spectrum peak [35; 36]. Hence, we opted to use SIF\({}_{757}\) data in this research.