esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Overturning and Yawing during Underwater Penetration of Projectile at an Attack Angle  
Figure 8 shows the photos of the three groups of tests taken by the high-speed camera. After the projectile enters the water with an attack angle, the cavitation region generated by its high-velocity motion is asymmetric and curved. This is because the penetration attitude and trajectory of the projectile with an attack angle are not stable in water, and the penetration direction of the projectile at the moment it enters the water is related to the initial attack angle of the projectile. In the case of positive initial attack angles, the projectile tends to yaw upward such as in Tests 2 and 3; in the case of negative initial attack angles, the projectile tends to yaw downward, such as in Test 1 (the anticlockwise rotation of the attack angle about the axis is defined as positive attack angle and the clockwise rotation as negative one). The attitude angle of the projectile in water is an important factor affecting the yaw of the projectile. Taking Test 3 as an example, it can be clearly observed from the photos that the attitude of the projectile keeps changing, overturning anticlockwise first and then clockwise. The attitude angle of the projectile on the XY plane, its Y-axis velocity and displacement are read from numerical calculations (Figures 9-11). When the projectile enters the water with its nose up, the nose and lower side of the projectile are the main positions contacting with water. Because the lower side of the projectile is subjected to the dynamic pressure of the water, which is perpendicular to the contact surface, a lift force is exerted on the projectile, generating a Y-direction velocity, leading to the upward yawing of the projectile. At this time, the force exerted by water on projectile nose is the largest. Due to the existence of the attitude angle of the projectile, the force does not pass through the center of mass of the projectile, and the resultant force is on the upper side of the center of mass. This gives an anticlockwise overturning moment to the projectile, leading to the anticlockwise overturning of the projectile. At t = 130 us, the projectile overturns to the maximum incident flow area (around 90\({}^{\circ}\)), and the resultant force on the projectile basically passes through its center of mass. However, as the projectile continues
## 3 Results
### Colour Changes  
Time significantly affected the colour of meat stored in MAP, while the proportion of oxygen in the atmosphere did not affect the colour parameters (Table 2). The colour parameter \(L^{*}\) (lightness) increased during storage. The first significant change was visible on the second day of storage and persisted until the seventh day. The \(L^{*}\) value continuously increased during storage, which led to significant differences between days 7 and 15 of storage. Significant changes in the \(a^{*}\) parameter (increases in value) were found between days 0, 5, 7, 1, 14, and 15. However, the proportion of red colour remained at a similar level from the second day of storage until the end of the experiment. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multicolumn{8}{c}{**Time (T)**} & \multicolumn{8}{c}{_p-Value_} \\ \cline{2-11}  & **2** & **5** & **7** & **9** & **12** & **14** & **15** & **T** \\ \hline LO & & & & & & & & & \\ O\({}_{2}\) (\%) & 53.6 \(\pm\) 0.7 & 54.7 \(\pm\) 0.9 & 54.8 \(\pm\) 0.2 & 53.9 \(\pm\) 0.5 & 54.8 \(\pm\) 0.3 & 54.3 \(\pm\) 0.2 & 54.9 \(\pm\) 0.4 & NS \\ CO\({}_{2}\) (\%) & 39.9 \(\pm\) 0.1 & 38.9 \(\pm\) 0.5 & 39.5 \(\pm\) 0.8 & 39.3 \(\pm\) 0.3 & 40.3 \(\pm\) 0.5 & 38.5 \(\pm\) 0.2 & 39.6 \(\pm\) 0.4 & NS \\ N\({}_{2}\) (\%) & 6.2 \(\pm\) 0.9 & 6.5 \(\pm\) 0.5 & 5.6 \(\pm\) 0.6 & 6.7 \(\pm\) 0.4 & 5.0 \(\pm\) 0.5 & 7.2 \(\pm\) 0.2 & 5.5 \(\pm\) 0.7 & NS \\ \hline HO & & & & & & & & & \\ O\({}_{2}\) (\%) & 75.2 \(\pm\) 0.3 & 76.4 \(\pm\) 0.6 & 76.5 \(\pm\) 0.4 & 76.7 \(\pm\) 0.4 & 75.9 \(\pm\) 0.3 & 76.1 \(\pm\) 0.4 & 76.0 \(\pm\) 0.3 & NS \\ CO\({}_{2}\) (\%) & 19.8 \(\pm\) 0.4 & 19.5 \(\pm\) 0.3 & 19.0 \(\pm\) 0.2 & 19.0 \(\pm\) 0.3 & 19.2 \(\pm\) 0.3 & 19.0 \(\pm\) 0.1 & 19.1 \(\pm\) 0.1 & NS \\ N\({}_{2}\) (\%) & 5.0 \(\pm\) 0.4 & 4.6 \(\pm\) 0.9 & 4.5 \(\pm\) 0.5 & 4.3 \(\pm\) 0.3 & 5.6 \(\pm\) 0.6 & 4.9 \(\pm\) 0.5 & 4.9 \(\pm\) 0.3 & NS \\ \hline \hline \end{tabular} LO—lower oxygen atmosphere (55% \(\mathrm{O}_{2}\)/40% CO\({}_{2}\)/5% N\({}_{2}\)); HO—higher oxygen atmosphere (75% O\({}_{2}\)/20% CO\({}_{2}\)/5% N\({}_{2}\)); T—storage time; NS—nonsignificant differences (\(p\geq 0.05\)); SEM—standard error of the mean.  
\end{table} 
Table 1: Modified atmosphere composition in packages containing pork loins during cold storage (4 °C) (mean values \(\pm\) SEM).
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 2 Numerical Analysis
### Finite Element Modelling
The commonly used amplitude of initial bow imperfection L/1000 [2,28] (Figure 4) was used in these numerical simulations. The shape of the initial geometric imperfection was extracted from preliminary linear buckling analysis. Figure 4: Initial geometrical imperfection. Figure 3: Material overlap using Shell elements. The structural imperfections were included in the numerical analysis in the form of residual stresses. The commonly used distribution of residual stresses for hot rolled I and H-profiles can be seen in Figure 5. Although a parabolic distribution according to measurements seems to be more appropriate, simplified linearized stress distributions provided more conservative but similar results. Therefore, the linear stress distribution of residual stresses was used in the numerical analysis. The measurements of residual stresses performed by Alpsten or ECCS also proved [2] that the magnitudes of the residual stresses are not dependent on steel grade, so the magnitude of the residual stresses for higher strength steels should be also considered with relevance to steel grade S235. More detailed background research about this topic can be found in [2]. The boundary conditions on both member ends were modelled in one \"Master node\", which were located in the centre of gravity of end cross-sections, and these nodes were shifted 1 mm away from them. Therefore, the lengths of the members were shorter by 2 mm. These master nodes were modelled by one node element, MASS 21, which has six degrees of freedom. The master nodes were coupled to all nodes of end cross-sections by \"constraint equations\" in a such way that they behaved like a rigid plate, and the displacements and rotations of end cross-sections were dependent on the motion of the master node. Therefore, it was possible to apply the boundary conditions and loads directly to one node, simplifying the modelling process. This also compensates for modelling material zones in the ends of the member with the higher value of Young's modulus of elasticity [29], which prevents the development of stress concentrations under concentrated loads. As there is also analysis of IPE members with free warping of the end cross-sections in this paper, a different modelling technique of such boundary conditions is explained later in the paper.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 4 Experiments and Results
### Dataset  
Our research aims to gather comprehensive and practical data to improve the accuracy of our ensemble model in identifying phishing attempts. In order to achieve this, we obtained data from a range of publicly available databases. The collection comprised authentic and fraudulent emails obtained from Apache Spam Assassin's datasets. Our research aimed to enhance the durability of our phishing detection model by specifically augmenting the data. The dataset used comprises 5000 legal emails and 1000 fraudulent emails. In order to increase the dataset's diversity and improve the model's ability to generalize, we utilized data augmentation techniques with TensorFlow. To be more precise, we utilized a method called random insertion, which entails incorporating arbitrary words into the original text. This strategy exposes the model to several forms of input data, hence improving its capacity to identify a wide range of text patterns.  
In order to comply with the specified requirements, an uneven allocation of classes was put into effect. More precisely, a ratio of 1:5 was used to categorize phishing and benign emails, indicating that phishing emails account for around 5% of the benign samples. To conduct a thorough study, the dataset was partitioned into two subsets: one for training and validation, and the other for testing the trained models. The training and validation subset consists of 70% of the samples, while the remaining 30% is reserved for testing. The training set has 10,500 samples, whereas the testing set contains 4500 samples. The sets are divided into 7350 benign samples and 3150 phishing samples for training/validation, and 3150 benign samples and 1350 phishing samples for testing.  
Table 2 provides a comprehensive overview of the number of email samples present in the training and testing sets, as well as the cumulative total.
## 4 Model Formulation
### Upper-Layer model
#### 4.1.1. Forwards Recursion  
When the signal duration of a cycle is determined, as seen in Figure 6, the total vehicle delay time can be estimated. Figure 8 illustrates the recursive reasoning.  
The best choice is represented by \(u_{j-1}^{*}\big{(}s_{j}\big{)}\), and the middle-level model's calculation of \(f_{j}\big{(}s_{j},u_{j}\big{)}\) makes use of the phase duration, variable virtual waiting zone switching time, and CAV trajectory optimization.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 4 The Megalos Framework
### Containers and Their Interfaces  
Kubernetes does not allow to execute raw containers but needs to wrap them into Pods. A Kubernetes _Pod_ is a logical host, which can enclose one or more containers that can share volumes and a network. To place VDs into Pods, we had to solve two main problems, one related to the network interfaces and the other one related to the Pod life-cycle.  
Regarding interfaces, we have that Kubernetes implements the so called \"IP-per-pod\" [23] model which means that a Pod has only one network interface and one IP address. Of course, since our containers represent network devices, having at disposal just one interface is not satisfying. Also, for the same reason, having more than one container into a single Pod would require to share the same interface for more than one network device. This exacerbates the above problem. Hence, we deploy just one container per Pod and exploit a specific technology to provide each Pod with more than one interface. Namely, the single Pod interface provided by Kubernetes is configured by a plug-in implementing the Container Network Interface (CNI) [24], a general model for managing container networks. In order to assign multiple interfaces we exploited the Multus CNI [25] plug-infor Kubernetes, which acts as a \"meta plug-in\", a CNI plug-in that can call multiple other CNI plug-ins.  
Regarding the Pod life-cycle problem, as suggested in [26], Pods that are directly scheduled (the so called \"naked\" Pods) are not recommended because they do not reschedule on failure. To ensure the availability of the containers we used _Kubernetes Deployment objects_. These objects represent a set of identical Pods managed by the Kubernetes Deployment controller, which organizes multiple replicas of the same service and automatically reschedules them whenever they fail or whenever the number of running replicas does not match the desired amount.
# Joint Power Charging and Routing in Wireless
## 3 Optimization Algorithms
### Joint Optimization of Routing and Charging
#### 3.2.2 Fitness Functions and Natural Selection  
In the GA, the selection operation is applied to choose individuals to participate in reproduction, which has a significant impact on driving the search towards a promising trend and finding optimal solutions in a short time. We adopt the famous roulette wheel selection method to select the individual on the basis of its selection probability, which is proportional to its fitness function. The selection probability of the \(r\)th individual is defined as  
\[q_{r}=\frac{f\left(r\right)}{\sum_{r\in\mathcal{R}}f\left(r\right)} \tag{15}\]  
where \(f(r)\) is the fitness function of individual \(r\). In this paper, the fitness value is defined as the charging efficiency obtained from Algorithm 1 with the given routing tree.
## 2 SDoF Model for Low-Velocity Heavy-Mass Impact  
Consider a clamped-clamped beam of span \(l\) with a solid rectangular cross-section of width \(B\) and thickness \(H\), transversely struck by a rigid wedge with a heavy mass \(m_{0}\) and a low velocity \(V_{0}\) at a point \(l_{1}\) from the left-hand support, as shown in Figure 1. Without loss of generality, the dimension \(l_{1}\) is taken as smaller than \(l_{2}=l-l_{1}\). The contact width between the wedge and the beam is assumed to be negligibly small in comparison to the beam length. The beam is assumed to be made of elastic-perfectly plastic material, with the mass density \(\rho\), Young's modulus \(E\), the yield stress \(\sigma_{0}\) and Poisson's ratio \(\
u\).  
Figure 1: A heavy mass m\({}_{0}\) with a low velocity \(V_{0}\) striking a clamped beam at a point \(l_{1}\) from the left-hand support.  
It is common to simulate the impact process using a mass-spring system, which has been demonstrated to provide reliable assessment of structural responses under impact [21]. The impact problem can be simplified as an equivalent SDoF model, of which the dynamic equilibrium equation is written as Equation (1), the detailed derivation of which can be found in [19].  
\[(m_{0}+m_{b}^{e})\ddot{W}+F(W)=0 \tag{1}\]  
where \(W\), \(\dot{W}\) and \(\ddot{W}\) are the transverse deflection, velocity and acceleration of the beam at the impact point; \(F(W)\) is the quasi-static resistance of the beam; and \(m_{b}^{e}\) is the equivalent mass of the beam, which can be calculated by an integration of the kinetic energy over the length of the beam [15]:  
\[\frac{1}{2}m_{b}^{e}\dot{W}^{2}=\int_{l}\mu\dot{w}^{2}(x)dx \tag{2}\]  
where \(\mu=\rho BH\) is the mass per unit length of beam; \(x\) is the global coordinate along the length direction of the beam, and the origin \(x=0\) is located at the impact point; and \(\dot{w}(x)\) is the transverse velocity field of the beam.  
The beam's resistance is assumed to be governed by the bending moment \(M\) and axial force \(N\), while the transverse shear effect can be neglected [22]. This assumption seems reasonable since the effect of axial force dominates the response for the transverse deflections larger than the beam thickness even when the impact location is close to the support of the beam. Thus, based on the moment equilibrium of the fully clamped beam shown in Figure 2, the beam's resistance in any regime can be expressed as:  
\[F=F_{1}+F_{2}=\bigg{[}\frac{M_{1}+M_{0}}{l_{1}}+\frac{M_{2}+M_{0}}{l_{2}} \bigg{]}+N\bigg{(}\frac{1}{l_{1}}+\frac{1}{l_{2}}\bigg{)}W \tag{3}\]  
In what follows, a complete impact process is composed of two components, loading and unloading, for which the analytical predictions of the beam's behavior are given below.
## 3 Results
The tunable sensing response of the D-shaped PCF with the metamaterial is illustrated in Figure 6, in which the sensitivity and resolution were estimated from the linear fitted curve. In both cases, the determination coefficient was greater than 0.98, indicating that the modeled sensing structure can be accurately regarded as linear. For refractive indexes ranging from 1.30 to 1.39, the average sensitivity increased more than five times and resolution reached 10\({}^{-5}\) RIU in order of the magnitude as the fractional volume of silver in the metamaterial changed from 0.3 to 0.7. Figure 4: Distribution of the Y-polarized electric field intensity at the SPR wavelength \(\lambda_{p}\) for three different silver volume fractions: (**a**) \(f=0.3\) at \(\lambda_{p}\) = 485 nm, (**b**) \(f=0.5\) at \(\lambda_{p}\) = 500 nm, and (**c**) \(f=0.7\) at \(\lambda_{p}\) = 514 nm. Figure 5: Confinement loss spectra for different filing factors and a constant refractive index of the analyte, \(R_{analyte}=1.33\). Figure 6: Variations of the SPR wavelength with the refractive index of the analyte obtained for volume fractions of silver in the metamaterial: (**a**) \(f=0.3\) and (**b**) \(f=0.7\). In contrast, the shift of the SPR spectrum with the variations in the filling ratio of silver in the metamaterial enabled the designed sensor to estimate metamaterial constitutive parameters, which is particularly attractive to characterize effective properties of metamaterial samples whose constituents are not uniformly distributed [10]. Figure 7 shows that the spectral shifts of the SPR wavelength and the corresponding variation of the sensitivity with the fractional volume of silver in the metamaterial layer were approximately linear. In other words, the refractive index SPR sensor can depict the amount of metamaterial constituents from a proportional relation between sensitivity and ratio of metallic inclusions, which is of major importance to estimate bulk optical properties of the nanowire metamaterial.
## 3 Methodology
### Participants and Recruitment  
Purposive sampling was used to identify the initial participants. Purposive sampling helps gain greater insights and provides flexibility to grasp more information from the interviewee [51]. After identifying the first participant, snowball sampling is used. The snowball sampling method is defined as accessing informants through the contact information provided by other informants, usually via social networks and personal contacts [52]. It is best to use when the purpose of the study is exploratory and descriptive. Cybersecurity experts and top managers employed in the HERS were chosen as participants for the current study. Thirty-one interviews with security experts and top-level managers were conducted. All these interviewees were part of the organization during the recent major crisis. All the participants were assigned a pseudo-identifier while reporting the findings of this study to ensure their identities remained anonymous. Table 3 shows the details of the participants' profiles and the case codes. Cybersecurity experts were selected based on having at least 5 years of experience in cybersecurity and managerial positions, as well as direct involvement in handling or managing cybersecurity crises. Likewise, top managers were chosen if they held senior leadership roles with decision-making authority over cybersecurity investments and strategies and had experience managing cybersecurity incidents or challenges within their organizations. Also, selecting participants from both technical and managerial positions helped to capture a range of views, reducing bias and enhancing the validity and reliability of the findings.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Case Code** &
\begin{tabular}{c} **Pseudo** \\ **Code** \\ \end{tabular} & **Role** & **HEIs Code** & **Code** & **Role** \\ \hline C1 & R001 & Chief Security Officer (CSO) & C3 & R017 & Senior IT Officer \\ \hline C1 & R002 & Information Security Manager (ISM) & C3 & R018 & Senior Security Officer \\ \hline C1 & R003 & Senior IT Manager & C3 & R019 & Head of Change Management \\ \hline C1 & R004 & Cybersecurity Analyst & C4 & R020 & Information Security Assistant \\ \hline C1 & R005 & Security Testing Manager & C4 & R021 & Development Manager \\ \hline C1 & R006 & Strategic Manager & C4 & R022 & ISO \\ \hline C2 & R007 & Cybersecurity Lead & C4 & R023 & Senior Manager \\ \hline C2 & R008 & Data security analyst & C4 & R024 & Senior Changes Management Personnel \\ \hline C2 & R009 & CSO & C5 & R025 & ISO \\ \hline C2 & R010 & Information Security Officer (ISO) & C5 & R026 & Senior business analyst \\ \hline C2 & R011 & CSO & C5 & R027 & Security Engineer \\ \hline C3 & R012 & ISM & C5 & R028 & Security Consultant \\ \hline C3 & R013 & Senior IT Manager & C5 & R029 & Technology Audit Manager \\ \hline C3 & R014 & Security Engineer & C5 & R030 & ISO \\ \hline C3 & R015 & ISO & C5 & R031 & Senior Changes Management Personnel \\ \hline C3 & R016 & Senior Executive Officer & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Participant Profiles.
## 3 Proposed Tracking Algorithm
### Strong Tracking Kalman Filter
 \tag{24}\]  
where \(\lambda_{0(k)}=\frac{\mathcal{I}(N_{k})}{\mathcal{I}(M_{k})}\), \(V_{0,k}= \begin{cases}r_{0}\mathcal{T}_{0}^{T}&k=0\\ \frac{\rho_{0,k-1}+r_{k}\mathcal{T}_{k}^{T}}{1+\rho}&k\geq 1\end{cases} \), \(N_{k}=V_{0,k}-H_{k}Q_{k-1}H_{k}^{T}-l_{k}R_{k}\), and \(\rho\) is the forgetting factor. It can be seen that when the system state mutates, the increase in \(V_{0,k}\) will increase with the \(r_{0}r_{0}^{T}\), so the time-varying fading factor will also increase, and the tracking ability of the filter will be enhanced.
## 5 Results and Discussion
### Calculation of Information Quantity of Evaluation Factors  
Using the information quantity model as a guide, we calculate each evaluation factor's information quantity values. Next, we determine each evaluation factor's weight using the entropy approach. Lastly, we use Equation (4) to determine the weighted information quantity values for each secondary evaluation factor (Table 1). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Primary Impact Factor** & **Secondary Impact Factor** & **Disaster Spot** & **Area (km2)** & **Disaster Spot** (km2) & **Weight** & **Weighted Information Quantity** \\ \hline \multirow{5}{*}{Eevation} & 869–1606 & 85 & 1735.1870 & 0.0490 & \multirow{5}{*}{0.3558} & 0.26862 \\ \cline{2-6}  & 1606–2191 & 108 & 1973.0442 & 0.0547 & & 0.30812 \\ \cline{2-6}  & 2191–2799 & 4 & 1745.8658 & 0.0023 & & \(-\)0.82102 \\ \cline{2-6}  & 2799–3392 & 2 & 1733.8125 & 0.0012 & & \(-\)1.06517 \\ \cline{2-6}  & 3392–4891 & 0 & 1454.9939 & 0.0000 & & 0.00000 \\ \hline \multirow{5}{*}{Slope} & 0–15 & 113 & 3077.3652 & 0.0367 & & 0.02693 \\ \cline{2-6}  & 15–30 & 67 & 2855.9755 & 0.0235 & & 0.00108 \\ \cline{2-6}  & 30–45 & 16 & 2132.4848 & 0.0075 & & \(-\)0.06470 \\ \cline{2-6}  & 45–60 & 2 & 503.5000 & 0.0040 & & \(-\)0.10139 \\ \cline{2-6}  & \(>\)60 & 1 & 73.5780 & 0.0136 & & \(-\)0.03042 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weight and weighted information of each influencing in a landslide. Figure 12: Multi-year average rainfall map for Tekes County.
# Performance Analysis of Interferometer Algorithm under Phase Measurement Error
###### Abstract  
Direction finding has been extensively studied over the past decades and a number of algorithms have been developed. In direction finding, theoretic performance prediction is a fundamental problem. This paper addresses the performance analysis issue of interferometer-based 2D angle of arrival estimation using uniform circular array (UCA). We propose an analytic method for performance analysis of interferometer in the presence of Gaussian or uniform error in phase measurement of incident signal on each sensor. The analytic mean square error (MSE), which is approximately equal to the MSE of actual interferometer-based DOA estimation, is derived via Taylor expansion and approximation. The derived analytic MSE is useful for predicting how the MSE of the interferometer-based DOA estimation algorithm is dependent on the phase measurement error.  
analytic MSE; interferometer; performance analysis; angle-of-arrival (AOA) estimation Article