esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
For the functional purpose of authentication, 5G's authentication mechanism should be able to address and prevent these problems. Thus, the secure and scalable secondary authentication of 5G should give the proper answers about the following questions:  
1. Does the authentication mechanism mitigate the problem of the NAS signaling storm? 2. Can the authentication mechanism provide enough scalability and auditability on the heterogeneous devices and services against the extension of the network? The answers about the questions should be discussed in the standard of 5G, but secondary authentication is not standardized yet in the specification of 3 GPP(3 rd Generation Partnership Project)'s 5G system Phase 1 (release 15), and release 16 will address that standard in 2020 [10]. In this paper, we propose an authentication architecture that can mitigate the traffic problem of the 5G home network by relaying mutual authentication in the middle of the network. The proposed framework consists of nodes that perform mutual authentication in the serving network and nodes that manage authentication data of the connected device nodes. These nodes are authorized by the data network so that mutual authentication with the network for the user to access the data network can be performed at the middle layer of the network. Also, to efficiently manage the authenticated devices, the data network increases the efficiency of service by managing the authentication information of devices in the edge layer. The proposed network framework can improve the efficiency and security of the entire network by controlling the traffic generated during the mutual authentication of the entire network in the middle layer. This paper is organized as follows. Section 2 describes the technical backgrounds, issues, and security requirements related to this research. Section 3 explained the state of the art researches related to the security architecture of 5G. The mechanisms and detailed operational structures of the proposed framework are described in Section 4. Section 5 shows the results of simulations and evaluations of the proposed secondary authentication framework, and we conclude this research in Section 6.
## 3 Results and Discussion
### Dynamic Strain Aging (DSA) and Temperature Domain  
The mechanical response of steels, particularly under ratcheting conditions, is significantly influenced by operating temperature. Within the domain of dynamic strain aging (DSA), typically observed between 473 K and 873 K, dislocation motion is hindered due to solute-atom interactions, resulting in an abrupt increase in material strength and a pronounced hardening response [53]. To capture the influence of this phenomenon, an exponential temperature-dependent function, \(\psi(p,T)\), was incorporated into the dynamic recovery term of the Ahmadzadeh-Varvani (A-V) hardening model. For both monotonic and cyclic loading, elevated temperatures led to a pronounced reduction in the elastic modulus, while the plastic modulus exhibited a rising trend and then a noticeable drop at magnitudes within the DSA temperature range. The exponent \(\lambda\) in this function plays a central role, reaching its peak within the DSA domain, thereby minimizing \(\psi(p,T)\). At higher temperatures, such as above 900 K, \(\lambda\) decreases significantly, leading to \(\psi(p,T)=\)1.0, indicating the vanishing effect of DSA on material behavior. Figure 3 presents the variations in terms \(\psi_{\infty}\), \(E\), \(\frac{H_{\varphi}}{\Gamma}\), \(\lambda\), \(\psi\), and \(\tau\) for steel samples versus operating temperatures.  
Figure 2: Stress–strain curves for SA508 and SA333 steel samples tested at various temperatures [51; 52].
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 3 Setup of Numerical Models  
Fine-scale modelling has been used widely to investigate various problems of recovery mechanisms and flow behaviour in both conventional and fractured reservoirs (e.g., [1; 32; 33; 34]). These models help to obtain an accurate result by avoiding the common simulation issues and artefacts such as upscaling problems and numerical convergence errors that frequently occurred at the reservoir scale modelling and simulation [35; 36; 3]. However, the detailed representation in the fine-scale model cannot be applied for the full field-scale due to the insufficient computational power resources and the simulation cost. Therefore, multiple scales were utilised in the current workflow to demonstrate the impact of low salinity waterflooding at each level, besides highlighting the upscaling challenges of the fine-scale model result into a larger scale, such as sector modelling or full-field modelling.  
The impact of the low salinity waterflooding on the matrix recovery using the new proposed weighting factor formula has been evaluated using our previous fine-scale model and the Qamchuqa reservoir model. The details of both models can be found in [PERSON] et al. [2], where a linear relationship of the weighting factor has been used in their simulation. Meanwhile, we utilised the exponential weighting factor formula in the current workflow, and then we compared the results.  
Figure 4: A comparison between the weighting factors of the mixing zone using the linear relationship in Equation (3) and the derived exponential relationship, as illustrated in Equation (4). The weighting factor value of one represents the beginning if the low salinity in the mixing zone. Meanwhile, a zero-weighting factor value represents the end of the mixing zone with high salinity concentration.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 6 Open Research Challenges
### IA in Relay Based CR Networks  
Work on IA in relay based CR networks proposes that CR relays can greatly reduce the coding dimensions needed to achieve a network's DoF, and otherwise simplify the optimal transmission strategies [18; 50; 57]. The importance of practical precoding algorithms for the CR relay-aided interference channel is further amplified by the standards community's growing interest in deploying relays in future wireless systems.
## 6 SAR for Antennas on Human Body
### Different Techniques to Reduce SAR Value  
There have been several different approaches created to reduce SAR levels to be as low as possible while still maintaining a higher level of efficiency. An antenna that does not have a ground plane is usually known as a dipole and it has substantial SAR values. This is because the SAR of on-body antennas is dependent on near-field coupling to the body. There are methods for reducing rear lobe radiation and increasing gain called SAR reduction techniques, which are shown in Figure 12.  
Figure 11: Attaching a patch antenna to a flat-body phantom.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Antenna Size** & **Size (mm\({}^{2}\))** & \multicolumn{2}{c}{
\begin{tabular}{c} **Operating** \\ **Frequency (GHz)** \\ \end{tabular} } & \multicolumn{2}{c}{**SAR 1 gm (W/Kg)**} & \multicolumn{1}{c}{**SAR 10 gm (W/Kg)**} \\ \hline
[78] & Rectangular patch antenna with inset feed line & 51 \(\times\) 45 & 2.45 & 0.54 & 0.486 \\ \hline
[79] & T-shaped rectangular patch & 70 \(\times\) 70 & 2.45 & 0.0323 & 0.0153 \\ \hline
[80] & Rectangular patch antenna with PIN diodes & 113 \(\times\) 99 & 4.85 & 0.024 & 0.353 \\ \hline
[81] & Rectangular patch antenna & 90 \(\times\) 90 & 2.45 & 0.332 & 0.0234 \\ \hline
[89] & Circular patch antenna & 100 \(\times\) 100 & 2.45, 5.8 & 0.042 & 0.09 \\ \hline
[90] & E-shaped antenna & 50 \(\times\) 50 & 3.37 & 1.14 & 1.4 \\ \hline
[91] & Folded ring-shaped antenna & 60 \(\times\) 60 & 2.45/3.45 & 0.2 & 0.1 \\ \hline
[92] & L-shaped rectangular antenna & 42 \(\times\) 13 & 2.45, 5.8 & 0.028 & 0.68 \\ \hline
[93] & Planar inverted-F antenna & 60 \(\times\) 26 & 1.82–3.72 & 0.03 & 1.6018 \\ \hline
[80] & Patch antenna & 101 \(\times\) 96 & 2.4 & 0.24 & 0.003 \\ \hline
[94] & C-shaped etching slot & 18 \(\times\) 19 &
\begin{tabular}{c} 0.09–3.94 \\ 4.23–5.65 \\ \end{tabular} & 1.58 & 0.91 \\ \hline
[95] & Patch antenna & 64.1 \(\times\) 50 & 2.45 & 0.5 & 1.06 \\ \hline \hline \multicolumn{2}{c}{} & Circle-shaped patch & 40 \(\times\) 40 &
\begin{tabular}{c} 0.01–5.30 \\ 8.12–12.35 \\ \end{tabular} & 0.12 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 9: SAR values in W/Kg are compared amongst several wearable antenna types.  
Various regions of the body were analyzed to determine SAR levels. The methods for SAR computation were evaluated systematically and compared. Without mitigation techniques, it was found that the value of the SAR peaked at 46.9 W/kg. When EBG structures were incorporated into the design, the SAR values significantly decreased to a minimum of 0.016 W/kg.  
These results confirm the significant potential of the techniques used here, namely the EBG, the AMC, and metamaterial-based techniques, to reduce SAR values. The techniques thus form potential solutions to minimize the electromagnetic energy absorbed by human tissues for the better safety and performance of wearable and implantable antenna systems [96].
## 3 Methodology
### Root Mean Squared Percentage Error  
Mean Squared Error (RMSE) or Mean Squared Deviation (RMSD) is a regularly employed measure of the difference between values predicted by a model or estimator (sample or population values) and observed values. The RMSD is the square root of the second moment of the differences between the anticipated and observed values, or the quadratic mean of these differences. When computed on the data sample used for the prediction, these deviations are known as residuals, and when calculated out of the sample, they are known as errors (or prediction errors). RMSD aggregates the magnitudes of mistakes in predictions for different data points into a single measure of predictive ability. RMSD is a scale-dependent measure of accuracy for comparing the prediction errors of different models inside a particular dataset, not between datasets [41].  
\[\text{RMSE}=\frac{1}{n}\sqrt{\sum_{\text{i=1}}^{n}{(Z_{\text{p}}-Z_{\text{a}} )^{2}}},\text{i}=1,2,\ldots,\text{n} \tag{14}\]Here, \(Z_{\rm p}\) is the predicted value, \(Z_{\rm a}\) is the actual value. Since RMSE is error data, its ideal value should be 0 (if \(Z_{\rm p}=Z_{\rm a}\)). Therefore, we can say that the larger the squared mean error is as a result of the calculation, the greater the error.  
The performance of the \(Z_{\rm p}\) estimators was measured with percent root mean square error (RMSPE). For each estimator, RMSPE is calculated as follows:  
\[\rm{RMSPE}\ =\ \frac{RMSE}{Z_{\rm a}}*100\% \tag{15}\]
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 2 Insulator Identification and Location Based on Mask R-CNN
### Network Structure
Since the regions of interest output by the RPN layer are of different sizes, that is, the corresponding feature map regions are of different sizes, and the final classifier can only process the feature map regions of the same size, the ROI Align layer is added to solve this problem. The ROI Pooling layer has the same function in the previously proposed Faster R-CNN algorithm, which unifies the size of the feature map to \(a\times a\) by pooling. When the size of the RPN output box cannot be divisible by \(a\), the quantitative rounding operation is carried out so that the input and output pixels cannot be guaranteed to correspond one-to-one, which will further cause the misalignment problem of regions of interest. However, the ROI Align layer in the Mask R-CNN algorithm adopted in this paper uses bilinear interpolation to replace quantitative rounding, and turns the discrete pooling into continuous [25], which solves this problem and enables the target detection to be accurate to the pixel level. Finally, in the target detection and instance segmentation step, the target coordinates are determined by bounding box regression, and the category is determined by softmax. In addition, a mask branch is added to improve the resolution and reduce the number of channels through deconvolution and finally output a larger size mask, so as to complete the accurate segmentation of the instance. Figure 2: Structure of feature pyramid network (FPN).
# Global Buckling Resistance of Cold-Formed Steel Beams with Omega-Shaped Sections
## 2 Normative Procedures for Assessing Buckling Safety  
### EN 1993-1-3:2006 (EC3)  
The European standard EN 1993-1-3:2006 [2] recommends the Effective Width Method (EWM) for the assessment of buckling in cold-formed profiles for global, local, and distortional modes. This method is often considered highly conceptual and less practical, particularly for profiles characterized by numerous bends.  
The general procedure for buckling verification according to EC3 [2] involves the following steps: (1) calculating the critical elastic buckling stress and identifying the susceptible buckling modes for various half-wavelengths up to the real length of the bar; (2) determining the effective width of the curved zones of the cross-section based on the minimum local buckling stress; (3) calculating the reduced thickness of end stiffeners, intermediate stiffeners, or other parts of the cross-section subjected to distortional buckling based on the minimum distortional buckling stress; and (4) calculating the global buckling resistance for the real length of the bar based on the effective cross-sectional area.  
\[M_{b,Rd}=\chi_{LT}W_{eff,y}\frac{f_{y}}{\gamma_{M1}} \tag{1}\]  
where \(W_{eff,y}\) is the effective elastic section modulus, calculated according to the EWM for local and distortional buckling, and \(\gamma_{M1}\) is the reduction coefficient for the steel profile strength, following EN 1993-1-1:2009 [25].  
The buckling resistance coefficient, \(\chi_{LT}\), is a function of the normalized slenderness, \(\underline{\lambda}_{FLT}\):  
\[\underline{\lambda}_{FLT}=\sqrt{\frac{W_{y}f_{y}}{M_{cr}}} \tag{2}\]  
The normalized slenderness, in turn, depends on the value of the critical elastic lateral-torsional buckling moment. In the context of hot-rolled profiles with I or H sections, it is common practice to adopt the equation for calculating the critical lateral-torsional moment presented in the European pre-standard ENV 1993-1-1:1992 [19].  
\[M_{cr,LT}=C_{1}\frac{\pi^{2}EI_{z}}{L^{2}}\left\{\left[\left(\frac{k}{k_{w}} \right)\frac{I_{w}}{I_{z}}+\frac{(kL)^{2}GI_{z}}{\pi^{2}EI_{z}}+\left(C_{2}z_{g }-C_{3}z_{j}\right)^{2}\right]^{0.5}-\left(C_{2}z_{g}-C_{3}z_{j}\right)\right\} \tag{3}\]  
where \(C_{1}\), \(C_{2}\), and \(C_{3}\) are factors depending on the loading and boundary conditions and \(I_{z}\), \(I_{w}\), and \(I_{t}\), are, respectively, the moment of inertia about the z-z axis, the warping inertia of the section, and the torsional inertia. The coefficients \(k\) and \(k_{w}\) are associated with rotational restraints at supports, while \(z_{g}\) is the distance from the point of load application to the shear center, and \(z_{j}\) is the factor representing profile asymmetry. In Equation (3), boundary conditions are altered by varying the value of the coefficient \(k\). To account for section warping, \(k_{w}\) is considered to be 1.0, and if the rotation is desired to be fixed, \(k_{w}\) is adopted as 0.5.
## 4 Discussion
### Influence of Vertical Resistance in Passive Segment
In order to further demonstrate the ability to delineate the influences evoked by the vertical friction of the passive segment over the lateral responses of the pile shaft, it will be of interest to examine several parameters, including the relative ratio of fixed base of the maximum horizontal deformation \(\eta_{\text{ymax},k_{pp}}\), that of the maximum bending moment \(\eta_{M\text{max},k_{pp}}\), the sequential variation ratio of maximum horizontal deformation \(\Delta y_{\text{max},k_{pp}}\) and that of the maximum bending moment \(\Delta M_{\text{max},k_{pp}}\) during the changing in the vertical subgrade coefficients \(k_{pp}\) in which the corresponding parameters are, respectively, defined as:  
\[\eta_{\text{ymax},k_{pp}}=\frac{y_{\text{ymax}}}{y_{\text{max},k_{pp}-0}} \tag{42}\]  
\[\eta_{M\text{max},k_{pp}}=\frac{M_{\text{max}}}{M_{\text{max},k_{pp}-0}} \tag{43}\]  
As shown in Figure 12, the variation tendencies of each of the above-mentioned parameters under different vertical subgrade coefficients \(k_{pp}\) are exhibited lucidly. Specifically, in Figure 12, it is shown that the relationships of \(\eta_{\text{ymax},k_{pp}}\) and \(\eta_{M\text{max},k_{pp}}\) with \(k_{pp}\) emerge as linearly and non-linearly reduction trends, respectively, which indicates that, during the growth in \(k_{pp}\), the reduction effects of vertical foundation friction in the passive segment over both the maximum horizontal deformation and the bending moment of the pile shaft are gradually decaying. Meanwhile, it can also observed that, in Figure 12, the \(\Delta y_{\text{max},k_{pp}}\) and \(\Delta M_{\text{max},k_{pp}}\) are initially diminishing dramatically and then followed increasing gently, the total amount of the drop for the \(\Delta y_{\text{max},k_{pp}}\) is larger than that of \(\Delta M_{\text{max},k_{pp}}\). Hence, it is then revealed that, while the vertical friction of the passive segment is going up, there is a critical value (denoted as \(k_{pp,cr}\)) for the vertical foundation resistance coefficient \(k_{pp}\)  
Figure 11: Influence of vertical resistance in passive segment on pile lateral behavior. (**a**) Lateral displacement/mm; (**b**) bending moment/kN \(\times\) m.  
existing where, when the value of \(k_{pp}\) is larger than the \(k_{vp,cr}\), the influence resulting from this coefficient over the lateral response of the pile shaft can be ignored.
# The Democratization of Artificial Intelligence: Theoretical Framework
## 4 Ethical Considerations: Challenges and Opportunities  
The literature proposes the discussion of ethical challenges when developing and using AI technology. [PERSON] and [PERSON] [46] proposed a unified framework of principles for ethical AI, advocating transparency, fairness, and accountability in AI development and deployment. This framework [46; 47] identifies five overarching ethical principles: benefcience, nonmaleficence, autonomy, justice, and explicability. These principles provide ethical guidelines essential for ensuring AI technologies benefit society while minimizing potential harm. As for the main risks of AI usage, [PERSON] [48] mentions ethical principles of shopping, ethics bluewashing, ethics lobbying, dumping, and shirking. In the context of AI democratization, the ethical considerations that can be of most relevance are avoiding bias, ensuring fairness, complying with privacy and security, and accountability and transparency of AI technology.  
One of the major challenges in AI democratization is ensuring that AI systems are fair and unbiased towards the datasets used to train AI models, which will definitely impact the final results. There is a risk that AI algorithms, if not properly designed and tested, can perpetuate existing biases and inequalities. Developing methodologies for detecting and mitigating bias in AI systems is crucial. As AI systems often rely on large amounts of personal data, ensuring privacy and security is a significant concern. Robust data protection measures and diverse and transparent data usage policies are essential to maintain public trust in AI technologies and to guarantee an unbiased outcome [48]. AI systems should be transparent and accountable, with clear explanations of how decisions are made [49]. This is particularly important in high-stakes areas such as healthcare, finance, and criminal justice, where AI decisions can profoundly impact individuals' lives.  
Ethical considerations are not the only dimension to consider. Several challenges and opportunities address technical barriers, economic impacts, and global disparities. Despite advancements, there are still technical barriers to AI adoption, including the complexity of AI models and the need for significant computational resources. Simplifying AI tools and improving hardware efficiency are key areas of ongoing research. AI democratization can potentially drive economic growth and innovation, particularly for small businesses and startups. However, it also poses challenges such as job displacement and the need for workforce reskilling. Policies to support workers in transitioning to new roles are essential. While AI democratization can promote global innovation, there is a risk of intensifying disparities between developed and developing countries. International cooperation and support are necessary to ensure all regions benefit from AI advancements.
## 2 Materials and Methods
### Equipment  
Electromyographic (EMG) activity was recorded from seven muscles in each leg using a Noraxon TeleMoyTM 2400T G2 Direct Transmission System (Noraxon Inc., Scottsdale, AZ, USA) at a sampling rate of 1500 Hz. The measured muscles included the rectus femoris (RF), semitendinosus (ST), biceps femoris (BF), tibialis anterior (TA), peroneus longus (PL), gastrocnemius medialis (GM), and soleus (SO). Following SENIAM standards [37], disposable, pre-gelled bipolar 22 mm AgCl surface adhesive electrodes (Ambu Neuroline 720 01-K/12; Ambu, Ballerup, Denmark) were applied with 20 mm center-to-center spacing. Electrodes were placed over the muscle bellies, as per SENIAM guidelines [37]. To minimize movement artifacts, the EMG wires were secured with tape, and the reference electrode was positioned on the tibial tuberosity of the right leg. The electrodes and skin were prepared to achieve a low-impedance resistance (<6 k\(\Omega\)) through cleaning, scrubbing, and shaving.  
Center-of-pressure (COP) displacement was recorded using an OPTIMATMAMTI force plate (AMTI, Watertown, NY, USA) with a sampling rate of 1250 Hz. To induce further posture adjustments, a multiaxially unstable balancing board (MFT Challenge Disc, Trend Sport Trading GmbH, Vienna, Austria) was used. This board features four rubber cylinders spaced eight centimeters apart on the upper 44 cm diameter round plate and base circle plate. To standardize the positioning of the balance board (Figure 1) [38; 39], the center of the balancing board was placed over the center of a reticle crossline marked on the floor. The anteroposterior and mediolateral diameters, marked with tape, were aligned to these  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total (n = 20)** & **Male (n = 10)** & **Female (n = 10)** \\ \hline Age (yrs.) & 25.2 \(\pm\) 4.0 & 25.4 \(\pm\) 2.2 & 25.0 \(\pm\) 5.3 \\ Weight (kg) & 68.7 \(\pm\) 11.0 & 75.3 \(\pm\) 11.4 & 62.1 \(\pm\) 5.2 * \\ Height (cm) & 174.7 \(\pm\) 8.9 & 181.3 \(\pm\) 7.7 & 168.2 \(\pm\) 4.0 * \\ Body mass index (kg/m\({}^{2}\)) & 22.4 \(\pm\) 2.1 & 22.8 \(\pm\) 1.8 & 22.0 \(\pm\) 2.4 \\ Physical activity participation (hours/week) & 9.1 \(\pm\) 5.3 & 9.2 \(\pm\) 6.0 & 9.1 \(\pm\) 4.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of participants (mean \(\pm\) SD); * \(p\) < 0.001.  
crossed lines. This method ensured that the same fulcrum position was set for all trials and participants. The force plate was controlled using Nexus 2.2.3 software, integrated with the EMG system (Vicon Motion Systems Ltd., Oxford, UK).
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 3 Results
### Mean Heart Rate Variability (HRV)  
RMANOVA test for mean values of HRV (Figure 7) did not reveal any statistically significant differences among the data collected during the three phases for E4 (F(1.166, 22.148) = 0.308, \(p\) = 0.619) or Faros (F(1.331, 27.941) = 2.330, \(p\) = 0.131). The same test did not reveal any statistically significant differences also in median HRV among the observed phases for E4 (F(1.219, 23.154) = 0.366, \(p\) = 0.593) or Faros (F(1.323, 27.774) = 2.332, \(p\) = 0.131).