esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a PSO-H-SVM-Based Intrusion Detection Method for Industrial Robotic Arms
## 5 Discussion and Conclusions  
In this paper, the current security situation of industrial robotic arms is analyzed and the possible physical process logic attacks on robotic arms during closure operations are discussed. For physical process logic attacks, we have established an intrusion detection method based on the PSO-H-SVM state classification model and response mechanism. The PSO-H-SVM algorithm is an improvement on the traditional SVM algorithm, and the specific construction process of the algorithm model and its performance after improvement are described in detail. The workflow of the intrusion detection method and response mechanism are also specifically described in the paper. Finally, we built a physical experimental platform in the laboratory to verify the effectiveness of the intrusion detection method proposed in the paper. The experimental results show that the PSO-H-SVM algorithm achieves an accuracy of 96.02% for the classification of five classes of operating states of the robotic arm; for the physical process logic attack, the detection accuracy of the intrusion detection method reached 90%, which has a good intrusion-detection effect. In addition, our proposed response mechanism is also verified in the experiment: when there is an intrusion, the alarm, protection measures and log recording functions can operate normally, which can effectively protect the robotic arm system from serious harm. Therefore, the proposed response mechanism is effective.  
Similarly, there are still some shortcomings in this paper: (1) The intrusion detection method in this study is for specific application scenarios, and will be optimized for different application scenarios at a later stage; (2) All the methods and tests in this paper are based on experimental environments, and it is necessary to test them in real industrial operating environments; (3) The operation of the robotic arm is a dynamic process, and the analysis of the dynamics will be added in a later study to improve the accuracy of intrusion detection.  
Nowadays, many scholars have extended their research on industrial control system security to many aspects, among which the identification of internal faults and network security attacks is one of the most important. In this paper, we mainly study the content of intrusion detection, and at a later stage, we will also include research content on the distinction between the faults and attacks of specific robotic arm systems in our work plan.  
Conceptualization, Y.Z., H.P. and L.X.; methodology, H.P.; software, Y.Z.; validation, Y.Z. and L.X.; formal analysis, L.X.; investigation, Y.Z.; data curation, H.P.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., H.P. and L.X.; supervision, L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFC2001700, and the Beijing Natural Science Foundation, grant number L192005.  
Not applicable.  
The authors declare no conflict of interest.
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 3 Proposed Method
### VGG-Ear Model
#### 3.1.2 Ear Data Augmentation  
Since the process of deep learning often requires a large number of labeled training samples, the existing ear datasets have limited data, which can easily lead to over-fitting problems. Therefore, data augmentation is often used in image research to increase the number of training samples. By artificially introducing appearance variations, multiple variants of the original image can be generated without additional labeling costs. The data augmentation step is the preprocessing of the original dataset before being fed into the model.  
Due to the limited number of images in the AWE dataset, we applied data augmentation to increase the amount of data and take into account the appearance changes caused by image changes. This paper used the Imgaug tool to enhance the original data set with translation slight rotation. Below is a list of enhancement programs that we used to increase the amount of available training data:  
* Add Gaussian noise to the image.
* Rotate the image by \(-40\) to \(+\) 40 degrees.
* Gaussian blurring the image (\(\sigma\) varies from 1 to 4).
* Adjust brightness of the image (\(\gamma\) varies from 0.5 to 2).
* Crop and occlude the image by 10% to 40%.  
Figure 4 shows some example images of data augmentation. We augment each training image to 30 images by randomly performing the above image augmentation techniques.
## 4 Results
### Application of the Monte Carlo Method
#### 4.1.2 Dependent Parameters  
The variance-based sensitivity analysis considers that all the inputs are independently and uniformly distributed. However, as mentioned in Section 3.3, some relationships exist between the input parameters, and not all of them are independent. For that reason, the method might not be correct when dependent parameters are included in the analysis.  
In order to check that statement, the same calculation was performed considering only the independent parameters as the inputs (which corresponds to the results shown in Table 5) and considering both the independent and dependent parameters as the inputs. Table 7 shows the sensitivity indices obtained for the latter case.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \(\mathbf{D_{\text{H}}}\) & \(\mathbf{h_{\text{sy}}}\) & \(\mathbf{h_{\text{m}}}\) & \(g\) & \(\mathbf{L_{\text{s}}}\) & **Total** \\ \hline Vibration & \(S_{i}\) & 0.737 & 0.129 & 0.008 & 0.115 & 0.006 & 0.995 \\ \(S_{i}^{\text{tot}}\) & 0.741 & 0.134 & 0.008 & 0.115 & 0.007 & 1.006 \\ \hline Torque & \(S_{i}\) & 0.474 & 0.000 & 0.017 & 0.017 & 0.491 & 0.998 \\ \(S_{i}^{\text{tot}}\) & 0.475 & 0.000 & 0.017 & 0.017 & 0.492 & 1.002 \\ \hline \end{tabular}
\end{table}
Table 6: Sensitivity indices—10,000 samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \(\mathbf{D_{\text{H}}}\) & \(\mathbf{h_{\text{sy}}}\) & \(\mathbf{h_{\text{m}}}\) & \(g\) & \(\mathbf{L_{\text{s}}}\) & **Total** \\ \hline Vibration & \(S_{i}\) & 0.733 & 0.124 & 0.007 & 0.113 & 0.007 & 0.984 \\ \(S_{i}^{\text{tot}}\) & 0.739 & 0.135 & 0.008 & 0.114 & 0.007 & 1.003 \\ \hline Torque & \(S_{i}\) & 0.471 & 0.000 & 0.016 & 0.014 & 0.502 & 1.002 \\ \(S_{i}^{\text{tot}}\) & 0.474 & 0.000 & 0.017 & 0.017 & 0.494 & 1.002 \\ \hline \end{tabular}
\end{table}
Table 4: Sensitivity indices—1000 samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \(\mathbf{D_{\text{H}}}\) & \(\mathbf{h_{\text{sy}}}\) & \(\mathbf{h_{\text{m}}}\) & \(g\) & \(\mathbf{L_{\text{s}}}\) & **Total** \\ \hline Vibration & \(S_{i}\) & 0.735 & 0.130 & 0.008 & 0.115 & 0.007 & 0.995 \\ \(S_{i}^{\text{tot}}\) & 0.740 & 0.135 & 0.008 & 0.115 & 0.007 & 1.005 \\ \hline Torque & \(S_{i}\) & 0.472 & 0.000 & 0.017 & 0.017 & 0.492 & 0.997 \\ \(S_{i}^{\It is quite clearly observed that illogical results are obtained when dependencies exist between the input parameters of the sensitivity analysis. For the case of vibrations, the total-effect indices of the parameters defined as dependent are much lower than the first-order indices. Moreover, the indices for \(D_{\text{ri}}\), which is defined as an independent input, are also wrong, proving that unreliable results might be obtained for any input parameter when dependencies exist between them.
## 4 Results and Discussion
### Load Forecasting Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MAPE (\%)** & **MAE (MW)** & **RMSE (MW)** & **MSE (MW)** & **NRMSE** & **R\({}^{2}\)** \\ \hline \multicolumn{7}{c}{**Monday**} \\ \hline
**DNN** & 6.0479 & 906.6782 & 1020.2530 & 1,040,916.2167 & 0.0666 & 0.7344 \\
**LSSVM** & 4.0025 & 586.6865 & 710.5824 & 504,927.3971 & 0.0464 & 0.9593 \\
**LSSVM-BFOA** & 1.9592 & 303.1394 & 374.5812 & 140,311.0720 & 0.0244 & 0.9824 \\
**LSSVM-IBFOA** & 1.4324 & 220.2668 & 280.5340 & 78,699.33177 & 0.0183 & 0.9880 \\ \hline \multicolumn{7}{c}{**Tuesday–Thursday**} \\ \hline
**DNN** & 6.5912 & 964.7379 & 1231.8604 & 1,517,480.1165 & 0.0816 & 0.3582 \\
**LSSVM** & 8.2086 & 1270.5557 & 1536.8228 & 2,361,824.3854 & 0.1013 & 0.5614 \\
**LSSVM-BFOA** & 5.1020 & 809.2579 & 897.7997 & 806,044.3621 & 0.0591 & 0.8357 \\
**LSSVM-IBFOA** & 4.8542 & 735.9945 & 861.9280 & 742,919.9222 & 0.0568 & 0.9451 \\ \hline \multicolumn{7}{c}{**Friday**} \\ \hline
**DNN** & 7.8070 & 1144.208 & 1482.2793 & 2,197,151.8130 & 0.0996 & 0.1839 \\
**LSSVM** & 8.3786 & 1303.5522 & 1503.8283 & 2,261,499.7912 & 0.1010 & 0.8292 \\
**LSSVM-BFOA** & 7.5746 & 1167.3021 & 1338.5298 & 1,791,662.0492 & 0.0899 & 0.8834 \\
**LSSVM-IBFOA** & 7.2436 & 1116.3861 & 1292.7109 & 1671,101.5480 & 0.0868 & 0.8901 \\ \hline \multicolumn{7}{c}{**Saturday**} \\ \hline
**DNN** & 4.3742 & 611.1440 & 772.4642 & 596,700.8900 & 0.0540 & 0.5979 \\
**LSSVM** & 3.7320 & 543.9430 & 661.4871 & 437,565.1258 & 0.0462 & 0.8876 \\
**LSSVM-BFOA** & 3.2253 & 464.7921 & 563.3847 & 317,402.3313 & 0.0393 & 0.9295 \\
**LSSVM-IBFOA** & 3.1348 & 449.5594 & 562.3752 & 316,265.9746 & 0.0393 & 0.9606 \\ \hline \multicolumn{7}{c}{**Sunday**} \\ \hline
**DNN** & 4.2268 & 573.4755 & 659.2291 & 434,583.0080 & 0.0482 & 0.6144 \\ LSSVM & 4.4664 & 618.7235 & 789.2292 & 622,882.7987 & 0.0577 & 0.4719 \\
**LSSVM-BFOA** & 4.1507 & 577.6956 & 725.2978 & 526,056.9152 & 0.0530 & 0.7405 \\
**LSSVM-IBFOA** & 4.1427 & 566.0997 & 652.6199 & 425,912.8607 & 0.0477 & 0.9479 \\ \hline \multicolumn{7}{c}{**Average**} \\ \hline
**DNN** & 5.8094 & 840.0488 & 3669.8426 & 43,732,764.2466 & 0.2472 & 0.4978 \\
**LSSVM** & 5.7576 & 864.6922 & 1040.3900 & 1,237,739.8996 & 0.0705 & 0.7419 \\
**LSSVM-BFOA** & 4.4024 & 664.4374 & 779.9860 & 716,295.3460 & 0.0532 & 0.8743 \\
**LSSVM-IBFOA** & 4.1615 & 617.6613 & 730.0336 & 646,979.9275 & 0.0498 & 0.9464 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Forecasting accuracy of hybrid LSSVM-IBFOA and compared models. ducing higher accuracy of load profile forecasting. The performance accuracy of models for other day types could be enhanced by incorporating specific public holiday data as additional features in the analysis. From the above analysis, the electricity load forecasting result of using IBFOA to optimize the LSSVM model demonstrated better results than standard BFOA, stand-alone LSSVM, and DNN. Thus, the effectiveness of the proposed hybrid model is proved.
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
## 2 Design of an Ultra-Wideband Antenna Element  
Figure 1 shows the geometry and parameters of the proposed UWB monopole microstrip antenna element. Table 1 shows the values for the proposed antenna design. The proposed UWB microstrip antenna was designed on FR-4 dielectric substrate material with relative permittivity of \(\varepsilon_{r}=4.5\), a dielectric loss tangent of 0.025, and a substrate height of \(h=1.5\) mm. The proposed UWB antenna element consists of three sections placed on the top layer of the dielectric substrate. The first one is a 50 \(\Omega\) microstrip feeding line with a width of 2.8 mm. This section represents the input port section and it should be electrically consistent with the input RF power source devices. The second section is a stepped isosceles trapezoidal section. It represents the interface between the 50 \(\Omega\) microstrip feeding line and the patch radiator. It is used to match the input impedance of the main feeding line and the input impedance of the UWB patch radiator. The third section is the UWB microstrip radiator. A partially metallic ground plane layer with an etched trapezoidal slit is located at the bottom side of the dielectric material. The partial ground plane is selected rather than the full ground plane for obtaining ultra-wide impedance bandwidth. The trapezoidal slit that was etched on the top edge of the partial ground plane was selected to match the high input impedance of the monopole patch antenna and 50 \(\Omega\) microstrip feeding line. The overall dimensions of the proposed antenna are 43 \(\times\) 30 \(\times\) 1.5 mm\({}^{3}\).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline \(L_{SUB}\) & 43 & \(L_{P7}\) & 1 & \(L_{\text{C2}}\) & 6.05 & \(L_{E2}\) & 3.24 \\ \(W_{SUB}\) & 30 & \(L_{P8}\) & 3.6 & \(L_{\text{C3}}\) & 2.35 & \(L_{E3}\
## 4 Test Results and Analyses  
### Failure Modes  
The failure modes of the pull-out tests are listed in Table 4. The typical failure specimens are displayed in Figure 9. Three failure modes of fiber pull-out, mortar cracking, and fiber breakage were observed in this experiment. Fibers would pull out from mortar when the relationships of fiber strength, mortar strength, bond properties, and embedded length are compatible. Fibers would break when the specimen had a relatively long embedded length and strong bond strength or a relatively weak fiber strength. In contrast, mortar cracking occurs when the transversal tensile stress of mortar was induced by the wedged effect of crimped and indentation fiber.  
The hooked-end steel fiber HFb, HF2, HF3, and HF6 were pulled out of the mortar. HF4 was broken due to the weak fiber strength of 600 MPa. Due to the mismatch between the strength of 1150 MPa and the strong mechanical anchorage of double hooked-end, HF5 was also broken in the test.  
The failure of mortar cracking took place on specimens with crimped steel fibers CF and CF2 and indentation fibers IF2 and IF3. With the pull-out loading increasing, the mortar would crack once the transverse tension component reached the tensile strength of mortar. IF4 broke due to the mismatch between strong mechanical anchorage of indentation and long embedded length. IF1, MF, and LF broke due to their low fiber strengths of 550 MPa, 850 MPa, and 350 MPa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fiber Type** & **Failure Mode** & **Fiber Type** & **Failure Mode** & **Fiber Type** & **Failure Mode** \\ \hline HFb, HF2, HF3 & Fiber pull-out & HF4, HF5 & Fiber breakage & HF6 & Fiber pull-out \\ CF, CF2 & Mortar cracking & IF1 & Fiber breakage & IF2, IF3 & Mortar cracking \\ IF4 & Fiber breakage & MF & Fiber breakage & LF & Fiber breakage \\ \hline \hline \end{tabular}
\end{table}
Table 4: Failure modes of different deformed steel fiber.
###### Abstract  
Weighting materials such as barite and limenite are crucial for controlling fluid density during deep or ultra-deep drilling operations. However, sagging poses significant challenges, especially in highly deviated high-pressure and high-temperature (HP/HT) wells. This leads to inadequate well control, wellbore instability, and variations in hydrostatic pressure in extended-reach wells. Given the challenges of experimental research, reliable prediction models are imperative for evaluating the interaction between the ratio of anti-sagging additives, temperature, and wellbore inclination on sag factor (SF). This research presents statistical-based empirical models for predicting the SF at various wellbore inclinations (0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 70\({}^{\circ}\), 80\({}^{\circ}\), and 90\({}^{\circ}\)) and assessing the influence of fly ash on the SF. The regression equations, developed using the Response Surface Methodology in Minitab 18 software, show high reliability, with R\({}^{2}\) values approaching unity. Contour and surface response plots provide a clear understanding of the variable interactions. The analysis reveals that sagging is most severe at 60\({}^{\circ}\) to 65\({}^{\circ}\) inclination. At 400 \({}^{\circ}\)F and 60\({}^{\circ}\) inclination, adding 4 lb/bbl of fly ash reduces sagging in barite and limenite-densified fluid by 63.9% and 63.1%, respectively. Model validation shows high accuracy, with percentage errors below 3%. This study offers valuable insights for optimizing drilling fluid formulations in HP/HT well environments.  
sag factor; barite-densified fluid; limenite-densified fluid; inclination; fly ash; drilling; response surface method; anti-sagging; additive +
Footnote †: journal: Applied  
0000-0001-8891-2278][PERSON]  
0000-0002-1891-2278][PERSON]
# Is Everyone an Artist? A Study on User Experience of AI-Based Painting System
## 2 Theoretical Background and Hypothesis Development
### Research Hypotheses
#### 2.3.3 Hedonic Motivation (HM)  
Hedonic Motivation refers to the pleasure or expectation of pleasure that an individual obtains through the use of AI devices [51]. Furthermore, previous studies have used hedonism as a major predictor of user behavior regarding technological systems [62]. With the continuous development of AI technologies, Hedonic Motivation (HM) has been widely used in terms of users' acceptance of AI [63; 64], involving applications such as smart banking [48] and smart voice assistants [65], and some scholars have shown that Hedonic Motivation (HM) also significantly influences the social presence of AI chat systems, and thus, the intention to use AI chat services [66]. For users, when using AI devices for hedonic motives, these devices can provide benefits by satisfying personal interests and entertainment needs [67]; in other words, hedonic motives are the pleasure or joy derived from using the technology or system and are important determinants of users' acceptance and continued use of the technology [68]. In addition, several related studies have extended the TAM model to include Hedonic Motivation (HM) factors, and one such study proposed the Hedonic Motivation System Adoption Model (HMSAM) [69]. Accordingly, the following hypotheses are proposed:  
**Hypothesis 3 (H3a).**: _The user's Hedonic Motivation for AIBPS will positively influence their Perceived Usefulness of AIBPS._  
**Hypothesis 3 (H3b).**: _The user's Hedonic Motivation for AIBPS will positively influence their Perceived Ease of Use of AIBPS._
# IoT IP Overlay Network Security Performance Analysis with Open Source Infrastructure Deployment
## 3 Components of Overlay Architecture
### IPsec  
IPsec, or Internet Protocol Security, is used for managing encrypted tunnels at the OSI Layer 3 level. It is part of a suite of protocols standardized by the Internet Engineering Task Force (IETF), the organization responsible for advancing Internet technologies. Originally developed for IPv6, IPsec has been adapted for IPv4. It offers three primary functional implementations: Databases: Security Policy Database (SPD) and Security Association Database (SAD). Encryption Process Management: Internet Key Exchange (IKE) and Internet Security Association and Key Management Protocol (ISAKMP). Transfer Protocols:  
Figure 1: Generic VPN/overlay stacking technology.  
Encapsulating Security Payload (ESP) and Authentication Header (AH). IPsec uses the AH and ESP protocols to ensure the integrity and authenticity of transmitted data. The AH protocol authenticates the data source and protects against packet modification during transmission, including a sequence number in the header to prevent replay attacks. The ESP protocol verifies data integrity and identity while also encrypting the payload data. However, ESP authentication excludes the outermost IP header, requiring additional encapsulation for correct delivery, especially across networks using Network Address Translation (NAT), such as private xDSL networks. IPsec operates in two modes: tunnel and transport. In transport mode, the IP packet header remains unchanged, with the transport protocol inserted between the header and the data area, providing security between the source and destination addresses. This mode is suitable for host-to-host or host-to-router connections. In tunnel mode, a new complete IP header is added to the data packets, concealing the original addresses and data. This encapsulated packet is forwarded between encrypted endpoints defined by the new outermost IP header. Tunnel mode is commonly used for site-to-site, host-to-site, and host-to-host data-transmission scenarios.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 5 Response Analysis of Rigid Body Dynamics of Clearance Containing Mechanism  
### Simulation Parameters for an Eight-Bar Mechanism with Clearance  
This section defines the simulation parameters of an eight-bar mechanism, which are crucial for modeling its dynamic behavior under clearance effects. The parameters are  
clearance, and comparatively, the revolute joint clearance has the smallest impact. Specifically, when the mechanism only contains revolute joint clearance, only contains prismatic joint clearance, and contains mixed clearances, the maximum acceleration deviations reach 51.4 m/s\({}^{2}\), 448.3 m/s\({}^{2}\), and 531.2 m/s\({}^{2}\), respectively.
# A Low-Cost Terahertz Camera
## 1 Introduction  
Terahertz (THz) imaging methods have considerably evolved over the past decade [1,2] driven by a wide range of highly anticipated applications [3, 4, 5, 6, 7, 8, 9, 10]. Some of the promising THz imaging prospects are for non-destructive testing of materials [3, 4, 5], medical and pharmaceutical applications [6,7], art conservation [8], security screening [9], and food inspection [10]. All these applications still face the same challenges, reducing acquisition time, simplifying the routing optics, compactness, ease-of-use, reliability and low-cost in fabrication and operation. On top of these issues, a second challenge is the technological accreditation over established technologies, such as X-ray and ultrasound imaging. These two burdens are like \"the chicken or the egg\" causality dilemma; solving the first problem will help addressing the second one, or vice-versa.  
For scientific applications of THz waves, a great wall has been eliminated when researchers were finally able to visualize the THz light emitted from a nonlinear optical generation process [11]. Especially using a THz camera based on an uncooled microbolometer array [12,13], [PERSON] et al. monitored the lithium niobate output crystal using the titled-pulse-front pumping (TPFP) scheme, and demonstrated the merit of seeing the spatial mode of the emitted THz beam [14]. This important insight on the spatial THz beam profile greatly helped in producing intense THz pulses at the focusposition, which have opened several doors for nonlinear light-matter interactions in this frequency range [15]. However, the scientific market of THz radiation applications being limited, their related products, such as imaging devices, are generally unaffordable for a straightforward commercialization procedure. Basically, other walls need to fall before a THz industrial revolution.  
Here we show a simple THz imaging demonstration using a widely available infrared camera for thermal imaging. As a light source, a broadband, single-cycle pulsed THz emitter based on the tilt-pulse front pumping (TPFP) scheme in lithium niobate crystal was employed, similar to the system used for THz pulse imaging demonstration using uncooled microbolometer array [14]. Our results indicate comparable sensitivities between a thermal imaging camera (Seek Thermal Company) and a scientific THz imaging camera (Institut National d'optique (INO) de Quebec). The main drawback of the low-cost thermal imaging camera when combined with pulsed THz measurements is the lack of dynamic background subtraction and the low-resolution red, green, blue (RGB) saving format, all fairly extrinsic features that can easily be mitigated.
## 5 Experiments and Results
### Performance Evaluation  
This section mainly evaluates the security mechanism proposed in this paper from three aspects: security capability, hardware implementation overhead, and performance overhead. These tests are all carried out on the OR1200 processor-based embedded system experimental platform.