esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Seismic Assessments with Capacity Spectrum Method
### Seismic Spectral Demand Curve  
Seismic demand can be expressed in elastic pseudo-acceleration, \(SA_{d}\), versus the natural period \(T\). The pseudo-acceleration response spectra, \(SA_{d}\) versus \(T\), can be obtained as per AASHTO LRFD Bridge Design Specifications [41]. However, design codes typically provide a calculation method for pseudo-acceleration response spectra that is applicable to a damping ratio of 5%. For other damping ratios, \(SA_{d}\) should be modified with a damping discount factor, \(\beta_{\xi}\)[42; 43], as follows:  
\[SA_{d}(T,\xi)=\beta_{\xi}SA_{d}(T,\xi=5\%) \tag{4}\]  
with  
\[\beta_{\xi}=\left(\frac{0.07}{0.02+\xi}\right)^{\alpha_{\xi}} \tag{5}\]  
where \(\xi\) is the damping ratio, and the exponent \(\alpha_{\xi}\) depends on the ground motion characteristics and the nonlinear properties of the system, which affect its equivalent linearization. Here, take \(\alpha_{\xi}=0.5\).  
After obtaining the pseudo-acceleration response spectra \(SA_{d}(T,\xi)\), the spectral displacement \(SD_{d}(T,\xi)\) can be computed as follows:  
\[SD_{d}(T,\xi)=\left(\frac{T}{2\pi}\right)^{2}SA_{d}(T,\xi) \tag{6}\]  
For each determined natural period \(T\), the corresponding \(SA_{d}(T,\xi)\) and \(SD_{d}(T,\xi)\) can be obtained with Equations (4) to (6). The seismic spectral demand curve is the curve of \(SA_{d}(T,\xi)\) versus \(SD_{d}(T,\xi)\). Repeat the iterative calculations from Step 6 to Step 8 until \(\delta_{est}=\delta_{i}\). At this point, the intersection of the capacity curve and the seismic spectral demand curve represents the performance point.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods
### Defect Detection
#### 2.3.1 Image Dissimilarity Calculation  
Defect images, as depicted in Figure 4a, are fed into the defect reconstruction network, yielding images as shown in Figure 4b. The subtraction of these two images on a per-pixel basis results in defective images with the texture background removed, as illustrated in Figure 4c. These images encapsulate information about the location of defect regions.
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 2 Antenna Design  
The proposed antenna is designed and fabricated on an FR4 substrate which has a relative permittivity of 4.4 and a dielectric constant of 0.002. An FR4 substrate is commonly used as a substrate due to its commercial availability [29]. The dimension of a single element is 20 \(\times\) 20 mm\({}^{2}\). For MIMO configuration, the dimension of the proposed antenna is 40 \(\times\) 40 mm\({}^{2}\). Figure 1 shows the proposed antenna viewed from the front and the back. The dimensions of the proposed antenna are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline a & 20 & b & 20 \\ c & 18 & d & 2.1 \\ e & 1.5 & f & 8.5 \\ g & 3 & h & 8 \\ i & 14 & j & 20 \\ k & 20 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimized parameters of the final proposed design.
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
## 2 RM Decoder Based on AD
### Auto-Decoder  
The AD, which plays a central role in decoding of the RM code, is modified from a conventional AE. Figure 1 shows the basic structures of the conventional AE and the proposed AD, highlighting the difference between the number of nodes in two different hidden layers. The number of nodes in the hidden layer of the AE in Figure 0(a) is less than that of the input layer. In comparison to the AE, the number of nodes in the hidden layer of the AD in Figure 0(b) is larger than that of the input layer. Figure 2 shows the typical structure of an AD that is composed of three hidden layers for the decoder; the number of nodes in each layer is presented in Table 2. The number of nodes in the input layer is the same as the length of the codeword \(n\). The number of nodes in the first hidden layer is \(2n\), in the second hidden layer is \(4n\), in the last hidden layer is again \(2n\), and finally the output layer becomes \(n\) again. In general, a neural network with a multilayer perceptron shows much better performance than a single-layer perceptron. From this perspective, we can speculate that the hidden layer structure of the AD is more suitable for decoding short length codes, such as RM code, than the AE, in which the number of nodes of the hidden layer becomes smaller as the depth of the hidden layer increases, which may result in a smaller number of nodes, especially in the middle of the hidden layer, resulting in poor decoding performance.
## 1 Introduction
In addition to yield criteria, several researchers including [PERSON] [50], [PERSON] [51], [PERSON] [52], [PERSON] [53], and [PERSON] [54] have focused on the flow rule and work hardening behavior for nominally isotropic materials, with limited studies [[PERSON] and [PERSON] [55], [PERSON] et al. [56]] regarding the effect of anisotropy on strain hardening in aluminum alloys. The material extrusion process is important in different industries. The extrusion-based layered deposition is among the most commonly used additive manufacturing technologies due to its manufacturing flexibility, capability, low cost, and relative simplicity. More on the material extrusion process and behavior of materials produced by extrusion-based additive manufacturing can be found [PERSON] et al. [57], [PERSON] et.al [58], [PERSON] et al. [59], [PERSON] et al. [60], and [PERSON] et al. [61]. As many machine parts and other devices are subjected to torsion, it is very important to learn the mechanical behavior of metal under torsion loading. There have been limited studies investigating theoretical elastic-plastic strain considering anisotropic and isotropic yielding behavior of extruded Al6061-T6 under torsion loading. In this study, the authors employ incremental plasticity with the conjugate work principle to develop the theoretical equations for predicting the elastic, plastic, and total strains due to the application of simple torsional shear stress for both isotropic and anisotropic yield criteria, (e.g., Von Mises [8] and Barlat Yld91 [17]), resulting in separate theoretical models (All six Barlat Yld91 anisotropic yield criteria parameters for each of the Al6061-T6 materials are determined through (1) uniaxial tension experiments on specimens extracted from different directions in each extrusion, and (2) a simple torsion experiment for a longitudinal cylindrical specimen). Section 2 presents both the theoretical foundations employed to model the anisotropic and isotropic material behavior, along with the procedures employed for model parameter identification using the models. Section 3 provides the material specifications while also describing in detail the experimental considerations to determine anisotropy yield function and hardening parameters in this study. Assuming post-yield strain hardening is isotropic in both models (An assumption that is consistent with those of most previous investigators. Post-yielding hardening parameters are determined from tensile experiments on a specimen extracted from the longitudinal direction in the extrusion). Section 4 provides additional modeling details, along with the predicted stress-strain behavior of the extruded Al-6061 T6 tubular sample. In this section, details regarding the models and comparison of total shear strain using the models and experimental measurements for both the Von Mises [8] and Barlat Yild91 [17] are presented. Section 5 provides additional discussion of results. A Summary and Conclusions for the work are given in Section 6.
## 9 Conclusions  
This comprehensive review highlights the transformative potential of wearable antennas in the realm of wireless communication, particularly for WBANs. These antennas, characterized by their flexibility, lightweight design, and adaptability, are pivotal in addressing the growing demands of healthcare, the IoT, and next-generation communication systems. By leveraging advanced materials, including textiles, polymers, and conductive composites, alongside innovative fabrication techniques such as inkjet printing and screen printing, wearable antennas achieve enhanced performance while conforming to complex human body topologies.  
Despite their promising applications, significant challenges remain, including optimizing SAR levels, ensuring mechanical durability, and maintaining performance under environmental stress and deformation. Advanced solutions like metamaterials, EBG structures, and FSSs show considerable promise in mitigating these issues, further enhancing antenna efficiency and safety. Future advancements in this field are expected to integrate 5G/6G technologies, energy harvesting capabilities, and biocompatible materials to create sustainable and efficient systems. These developments, coupled with scalable and precise manufacturing methods, will pave the way for wearable antennas to revolutionize healthcare, smart textiles, and communication systems, ensuring a seamless blend of functionality, user comfort, and technological innovation.  
**Author Contributions:** All the authors contributed equally to this research. [PERSON]. ([PERSON]): Material Preparation, Writing, Editing, and Analysis. [PERSON]. ([PERSON]) and [PERSON] ([PERSON]): Conceptualization, Supervision, Review, and Editing. [PERSON]. ([PERSON]): Conceptualization, Supervision, Review, and Editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT  
1 Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5 DT, UK; [EMAIL_ADDRESS] (O.T.v.E.); [PERSON]. [PERSON]RESS] (C.C.); [PERSON]RESS] (W.J.B.)  
2 Department of Computer Science, University of Hertfordshire, Hatfield AL10 9 AB, UK; [EMAIL_ADDRESS]  
As such, Host Intrusion Detection Systems (HIDS) are often avoided in the IoT ecosystem because of the resource-intensive activities, including file or process monitoring [7].  
Machine learning models are trained using large datasets to learn patterns within the data, allowing the model to determine whether an activity is benign or not [1]. In 1999 the KDD-CUP-1999 dataset [8] was released and became one of the most popular datasets in research related to machine learning Network Intrusion Detection Systems (NIDS). The KDD-CUP-99 was later updated to the NSL-KDD dataset [9]. Over the last two decades, both of these datasets have been losing relevance in modern IDS solutions because of the absence of modern attack methods and new protocols [10]. However, these datasets have been defined as biased and unrealistic by multiple studies [11; 12; 13]. In 2018, ref. [14] published the Bot-IoT dataset, which was the first dataset to focus on machine learning IoT security solutions [15].  
However, machine learning-based IDS solutions are vulnerable when the model is targeted and exploited by adversarial cybercriminals. Adversarial machine learning methods intend to cause incorrect data classification forcing the implemented model to fail [16; 17; 18; 19]. Within an adversarial environment, it is critical to anticipate the actions an adversary may take towards an implemented model [20]. To create the best model, previous research showed the importance of using adversarial examples within a dataset when training a machine learning model [17; 21].  
The Bot-IoT dataset has not knowingly been created using adversarial examples towards the machine learning model. For this reason, our work aims to evaluate the Bot-IoT dataset and model robustness against adversarial examples. Our contributions can be summarised as follows:  
* We mount label noise adversarial attacks against an SVM model that detects malicious network traffic against IoT devices using the Bot-IoT dataset [14].
* We generate adversarial examples using the Fast Gradient Sign Method (FGSM) against binary and multi-class Artificial Neural Networks (ANNs) using the Bot-IoT dataset [14].
* Finally, we analyse and critically evaluate the experimental results along with the model robustness against adversarial examples.  
The rest of the paper is organised into five sections, as follows. The literature review and background knowledge are presented in Section 2. Section 3 briefly explains our methodology for the experimental activities. The implementation and the experimental results are presented in Section 4, and are followed with our evaluation and discussion in Section 5. Finally, Section 6 draws the conclusion, giving some pointers for future work.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
###### Abstract  
Aiming at the cooperative guidance problem of multi-hypersonic glide vehicles, a cooperative guidance method based on a parametric design and an analytical solution of time-to-go is proposed. First, the hypersonic reentry trajectory optimization problem was transformed into a parameter optimization problem. The parameters were optimized to determine the angle of attack profile and the time to enter the altitude velocity reentry corridor. Then, using the quasi-equilibrium glide condition, the estimation form of the remaining flight time was analytically derived to satisfy accurately the cooperative time constraint. Using the remaining time-to-go and range-to-go, combined with the heading angle deviation corridor, the bank angle command was further calculated. Finally, the swarm intelligence optimization algorithm was used to optimize the design parameters to obtain the cooperative guidance trajectory satisfying the time constraint. Simulations showed that the analytical time reentry cooperative guidance algorithm proposed in this paper can accurately meet the time constraints and cooperative flight accuracy. Monte Carlo simulation experiments verified that the proposed algorithm demonstrates a robust performance.  
multi-hypersonic glide vehicle; reentry cooperative guidance; analytic guidance; parametric design; Monte Carlo simulation 2023134987. 10.3390/app13084987 Article
## 4 Performance Evaluation and Discussion
### Model Establishment  
The parameters of our proposed model were computed according to a four-step procedure as follows:  
1. A specific publicly available database was employed for establishing and evaluating the proposed model.
2. An LSTM-QoE model [15] was trained to predict the instantaneous QoE values.
3. The memory effects' parameters \(\{\alpha_{P},\alpha_{R},\alpha_{RP}\}\) were computed to form the memory weight vector.
4. The coefficients of memory weight \(\{\beta_{1},\beta_{2},\beta_{3}\}\) in Equation (6) and the parameters of the proposed model \(\{\lambda_{1},\lambda_{2}\}\) in Equation (7) were determined through the predicted instantaneous QoE values and the subjective DoI collected from the experiment in Section 3.3.  
The details of each step are described in the next sub-subsections.
## 3 Results
### Microbial Lipids Production in Two-Stage Batch Cultures
Moreover, no significant mobilization of lipids was observed during the cultivation, except for the last point (96 h) owing to the depletion of VFAs. The maximum lipids concentration was similar after the addition of one pulse (18 g\(\cdot\)L\({}^{-1}\)) or three pulses of VFAs, and no differences in the maximum lipids productivity were observed between these two-stage batch strategies. Thus, a two-stage batch culture with a single pulse of 18 g\(\cdot\)L\({}^{-1}\) VFAs is an efficient strategy to improve lipids production without significant lipids mobilization in the lipogenic phase. Figure 2: Time course of cellular growth (\(\Circuit\)), VFAs (\(\blacksquare\), acetate; \(\blacktriangle\), propionate; \(\blacklozenge\), butyrate), glucose (\(\bullet\)), and glycerol (x) concentration obtained in two-stage batch cultures of _Y. lipolytica_ W29. After 24 h of growth in glucose (**a**) or glycerol (**b**), three pulses of a mixture of 6 g\(\cdot\)L\({}^{-1}\) VFAs (2 g\(\cdot\)L\({}^{-1}\) acetate, 2 g\(\cdot\)L\({}^{-1}\) propionate, and 2 g\(\cdot\)L\({}^{-1}\) butyrate) was added to the culture medium at 24 h, 48 h, and 72 h. Lipids content of _Y. lipolytica_ cells at 48 h (black bars), 72 h (white bars), and 96 h (grey bars) of cultivation (**c**). The error bars represent the standard deviation of two independent replicates. Bars with the same letter do not present statistically significant differences (\(p\geq 0.05\)). Microbial lipids produced in two-stage batch cultures of _Y. lipolytica_ W29 (growth phase in glucose followed by the addition of 18 g\(\cdot\)L\({}^{-1}\) VFAs) were mainly composed of oleic acid (54% \(\pm\) 1%), followed by linoleic (15.1% \(\pm\) 0.2%), palmitic (11.9% \(\pm\) 0.4%), stearic (7.7% \(\pm\) 1.0%), and palmitoleic (5.5% \(\pm\) 0.2%) acids. Due to the presence of propionate in the lipogenic phase [8], odd-chain fatty acids such as heptadecenoic (3.8% \(\pm\) 0.4%) and margaric (2.2% \(\pm\) 0.3%) acid were also produced by _Y. lipolytica_. Lipids produced in these conditions have a significantly higher unsaturated fraction (>78%, \(p\) < 0.05) than saturated one.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 2 Methodology  
### Overview of the Proposed System  
The overview of the proposed method is shown in Figure 1. The vibration signal is collected by accelerometers, which are located at the non-drive end in the wind turbine. Wind turbines are usually constructed in areas where wind energy is abundant, such as coastlines, mountains, plains, and deserts.  
Figure 1a shows wind turbines constructed on grasslands. The data were trained in the model, which was implemented in Python language on PC. The data were divided into small segments; thus, the model can quickly analyze each segment without other processing methods, as shown in Figure 1b,c. The trained neural model was embedded in the Raspberry Pi, as is shown in Figure 1d. The last step was for the trained model to predict each segment's score, as is shown in Figure 1e.
## 3 Results
### Aluminium  
Aluminium in the soil solution increased with Mn supplementation to the acidic soil (Table 1). Wheat root Al concentrations were around 10-fold higher than those in shoots. In the control soil (0 mg MnCl\({}_{2}\)/kg soil), roots contained 6203 mg Al/kg plant DW (dry weight), while in shoots, Al was detected at 744 mg/kg DW (Figure 3a). With higher soil Mn levels, Al concentration decreased at the root system and increased in the shoots. Both whole-plant (BAC) and root (root BCF) Al uptake decreased, while shoot uptake (shoot BCF) showed no statistically significant variation (Figure 3b). Aluminium appeared to be rapidly excluded from wheat roots and/or translocated from wheat roots to shoots with increasing soil Mn concentrations, as indicated by the increase in Al translocation factor (TF) (Figure 3a).  
In both roots and shoots, Al was mostly kept at the cell wall fraction and in cellular organelles fraction (Figure 4a,b). In the roots, Al proportions seemed to increase in the organelle fraction in the intermediate soil Mn level, while in wheat shoots, there was an increase only at the highest supplemented Mn concentration.  
Figure 4: Subcellular distribution of Al (cell wall, white columns; organelle, grey columns; and vacuole content fractions, black columns) in roots (**a**) and shoots (**b**) of wheat grown for 3 weeks in an acidic soil supplemented with MnCl\({}_{2}\). Values next to bars are fraction element quantities in 100 mg of shoot fresh weight (\(\upmu\)g/100 mg FW). Different letters indicate statistically significant differences (\(p<0.05\)) on the basis of [PERSON]’s test.  
Figure 3: Concentration ((**a**), columns), translocation factor (TF, triangles) ((**a**), line), bioconcentration factors (BCF) ((**b**), columns), and bioaccumulation coefficient (BAC, triangles) ((**b**), line) of Al in roots (white columns) and shoots (grey columns) of wheat grown for 3 weeks in an acidic soil supplemented with MnCl\({}_{2}\). Different letters indicate statistically significant differences (\(p<0.05\)) on the basis of [PERSON]’s test.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of the Attacks on CC
#### 5.1.1. Sufficative Attacks  
Sufficative attacks are those that are bandwidth-based or involve volumetric attacks. This attack overloads the targeted system with garbage data to consume the bandwidth of the targeted network system, flooding the network and affecting system resources. Its magnitude is measured in bits per second (Bps). Adversaries can launch this attack using UDP, ICMP flooding, or flooding the target with various spoofed packets [42].
# A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
## 3 Materials and Methods
### Workflow of Plastic Dynamic Detection
#### 3.4.3 Calculation of Plastic Height  
The height of waste plastics varies, so each plastic should be measured, as inaccurate height measurement will directly lead to the failure of the robotic arm to grasp.  
As shown in Figure 15, the image mask technique is first employed to extract the region of interest, and the convex hull obtained during angle calculation is filled as the plastic region mask. All values of the region where the plastic is located in the depth map are extracted using the bitwise and operation of the image while assigning the other regions to 0.  
Figure 14: The output rule of minimum bounding rectangle.  
Figure 13: The process of calculating the angle: (**a**) the cropped image; (**b**) gray scale; (**c**) canny edge extraction; (**d**) extraction of contour points; (**e**) calculation of convex hull; (**f**) minimum bounding rectangle.  
Assume that the installation height of the camera relative to the conveyor belt is \(d\) meters. The plastic height calculation process is as follows:  
1. To obtain the height information of the non-black region in Figure 15c, only non-zero values are selected. Tile them as a one-dimensional list of length \(n\).
2. The topK algorithm is adopted to obtain the first \(n/\alpha\) small values.
3. To reduce the interference of the plastic surface bulge or the camera measurement error, the topK algorithm is again used to remove the first \(n/\beta\) values in the obtained values of (2). The distance set U can be obtained.
4. The height \(h\) of the object is \(d\) minus the mean of all data in the set U. The equation is as follows:  
\[h=d-\frac{\sum\limits_{i=1}^{k}x_{i}}{k}(x\in\text{U}), \tag{15}\]  
where \(k=n(\beta-\alpha)/\alpha\beta\), \(\alpha\) is 5, and \(\beta\) is 40. Theoretically, the height is obtained from the 7/40 top area of the object. For plastic with an uneven surface, the integrated vacuum sucker can successfully grasp it by sucking only a small part of the top area.