esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Criteria for a Comparative Evaluation of the Guidelines
### Survey Evaluation
#### 3.3.2 Importance Ratings for Subcategories
Within this category, _Routing Protocols_ and _DNS for IPv6 Specification_ became second, followed by _DNSSEC_. _PIM_ and _Multihoming_ are of minor importance within the category as well as overall. All three subcategories of _Transition Methods_ got rated above six, and _Dual Stacking_ was even rated above seven and thus received not only the first rank within the category but also overall. _Dual stacking_ will be necessary for a long time as IPv4 and IPv6 have to be used in parallel due to compatibility. _Tunneling methods_ got in second within the category and eighth overall. These, together with _Translation methods_ that became eleventh overall, are important mechanics for connecting isles of IPv6 (or in future of IPv4) with the rest of the net when dual stacking is not possible. The last category is _IDS/IPS/Firewalling_. This category was not included in the comparisons because it is not a primary IPv6 topic, and relevant content is spread over many RFCs and is, thus, covered within the other categories. IPv6 poses only minor changes to this category and is only a small part, as this theme extends over all layers of the network, from physical layer to application layer. As the results show, there are three very important topics, which all got into the the top ten of the overall ranking. These are _ICMPv6 Handling_ at the sixth rank, _EH Support_ at the seventh and _Ingress/Egress Filtering_ at the ninth position. _Deep Packet Inspection_ is more related to the application layer and thus got rated lower, but still received six points. _EH Support_ means the ability of the security devices to handle extension headers of IPv6 and detect any corresponding malicious use. _ICMPv6 Handling_ is the ability to handle ICMPv6 messages as described in RFC 4890 [61], while _Ingress-/Egress Filtering_ includes the rules for outgoing and incoming traffic using IPv6 addresses. All of these ratings and weights are utilized in the comparative evaluation of the secure deployment guides.
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 5 Full-System Load Test and Validation of the Hub Drive System
### Output Characteristics Analysis of the Hub Transmission Reduction System
#### 5.3.1 Angular Velocity Output Characteristics of Planetary Carriers
From a frequency perspective, this indicates that the frequency of the angular velocity fluctuations gradually decreased and eventually converged to the rotational frequency of the secondary sun gear. This suggests that the secondary sun gear's rotational frequency plays a crucial role in the fluctuations of the secondary planetary carrier's angular velocity. Additionally, the smooth nature of the secondary planetary carrier's angular velocity curve indicates that the contribution of meshing frequency to these fluctuations is relatively small. Comparing the time-domain angular velocity curve of the primary planetary carrier during the 19-20 s interval in Figure 12 with the frequency-domain curve in Figure 13, it can be observed that the primary planetary carrier's angular velocity fluctuated within the range of 770-780 degrees per second, with a dominant frequency component of 14.56 Hz, which aligned with the primary sun gear's rotational frequency. Moreover, aside from the low-frequency oscillations, high-frequency components are also clearly visible. From the corresponding frequency-domain curve in Figure 12, it is evident that the high-frequency components lie within the 50 Hz to 500 Hz range, with a particularly prominent peak at 275.75 Hz, which corresponds to the secondary meshing frequency. This indicates that the secondary meshing frequency contributed to the angular velocity fluctuations of the primary planetary carrier. The more complex high-frequency components primarily resulted from the impacts caused by gear meshing, including tooth clearance and elastic deformation effects.
## 4 CLAP Correlation and Information Sharing Services
### Correlation and Cross-Correlation Process
#### 4.1.3 Definition of Directives  
Directives contain the pattern used in the evaluation of the rule. They fire an alert for every matching pattern. Directives use the statements previously defined to identify  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **EPL Statement** \\ \hline Failed\_SSH\_log & insert into Failed\_SSH\_Login select * from schema\_default where (plugin\_id in 1001, 2001) and (plugin\_sid = 1) \\ \hline jammer\_detector & insert into jammer\_detector select * from schema\_default where (plugin\_id = 100131) \\ \hline abnormal\_obs & insert into abnormal\_obs select * from schema\_default where (plugin\_id = 100001) and (plugin\_sid = 3) \\ \hline unauthorized\_write\_PLC & insert into unauthorized\_write\_PLC select * from schema\_default where (plugin\_id = 1001) and (plugin\_sid = 1111007) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of EPL Statements.  
the events that are affected by every rule. A directive is composed of a numeric identifier (ID) that will be used in the correlation bolt definition, a name and an EPL pattern (as depicted in Table 3). Additional fields defined in the Directive panel are the category and subcategory of the alert that will be generated with this directive and the reliability and priority of such directive. These values, together with the ones for the events and the importance of the assets are used to define the severity level of the generated alarm (as detailed in Section 4.2).  
The EPL directive shows the correlation rule used to fire an alarm if there is a match in the pattern defined within the brackets. The first example indicates that every sequence considering distinctively the IP source and 60 s of timeout will generate an alert, after which the fields are cleared and if an event is received with an IP source that had been previously received, it will be considered to be another alert. For this example, we have defined a failed SSH login as the event to check, if the defined condition matches (i.e., the IP source and destination are the same) and the same event occurs more than 6 times, then a correlated alert will be generated.
## Appendix A  
Deflection solution for diaphragm wall with additional thrust \(P\).  
For arbitrary loading \(q\), [PERSON] [35] gives the value of \(A_{mn}\) in Equation (2),  
\[A_{mn}=\frac{4\int_{0}^{L}\int_{0}^{H}q\sin\frac{m\pi y}{L}\sin\frac{n\pi z}{H }dydz}{\pi^{4}LHD\left(\frac{m^{2}}{L^{2}}+\frac{\hbar^{2}}{H^{2}}\right)^{2}} \tag{1}\]  
where \(D\) is bending stiffness of thin-plate, which is calculated as  
\[D=\frac{Eb^{3}}{12(1-\mu^{2})} \tag{2}\]  
in which \(E\) is the elasticity modulus of the plate and \(\mu\) is Poisson's ratio of the plate. In our condition, two circle zones (tunnels) in thin-plate are sufficient for additional thrust \(P\). In local conditions, the positions of two circle are \((x^{\prime}1,z^{\prime}1)\) and \((x^{\prime}2,z^{\prime}2)\), and have same radius of \(R\). Figure 11 shows the schematic diagram of the diaphragm wall with additional thrust. Hence, the \(A_{mn}\) can be written as  
\[A_{mn}=\frac{4}{\pi^{4}LHD\left(\frac{m^{2}}{L^{2}}+\frac{\hbar^{2}}{H^{2}} \right)^{2}}\int\limits_{\Omega_{1}+\Omega_{2}+\Omega_{3}}q\sin\frac{m\pi y}{ L}\sin\frac{n\pi z}{H} \tag{3}\]  
Due to no any force acting on zone \(\Omega_{3}\), the Equation (3) can be simplified as  
\[A_{mn}=\frac{4}{\pi^{4}LHD\left(\frac{m^{2}}{L^{2}}+\frac{\hbar^{2}}{H^{2}} \right)^{2}}\int\limits_{\Omega_{1}+\Omega_{2}}^{R}q\sin\frac{m\pi y}{L}\sin \frac{n\pi z}{H} \tag{4}\]  
Adopting polar coordinate transforms (\(r\), \(\theta\)) to solve double integral, \(y^{\prime}=r\cos\theta\), \(z^{\prime}=r\sin\theta\), and \(\mathrm{d}A=r\mathrm{d}r\mathrm{d}\theta\) is substituted into Equation (4). Equation (4) becomes  
\[A_{mn}=\frac{4}{\pi^{4}LHD\left(\frac{m^{2}}{L^{2}}+\frac{\hbar^{2}}{H^{2}} \right)^{2}}\int_{0}^{2\pi}\int_{0}^{R}Pr\left(\sin\frac{m\pi(y_{1}+r\cos \theta)}{L}\sin\frac{n\pi(z_{1}+r\sin\theta)}{H}+\sin\frac{m\pi(y_{2}+r\cos \theta)}{L}\sin\frac{n\pi(z_{2}+r\sin\theta)}{H}\right)\mathrm{d}r\mathrm{d}\theta \tag{5}\]Equation (A4) does not have a primary solution. MATLAB 2015a commercial software provides a double numerical integration function. The value of \(A_{mn}\) can be calculated using MATLAB code, which are listed as  
\[\begin{array}{l}\text{close all; clear all; clc}\\ \text{f = @(r, theta) P*r*(sin(m*pi*(y1+r*cos(theta))/L)*sin(n*pi*(z1+r*sin(theta))/H)+}\\ \text{sin(m*pi*(y2+r*cos(theta))/L)*sin(n*pi*(z2+r*sin(theta))/H));}\\ \text{Amn = 4/(pi*4*L*H*D*(m^2/L^2+n^2/H^2))*dblquad(f,0,2*pi,0,R,1.0\times 10^{-3}, \text{`quadl}^{\prime});}\\ \end{array}\]  
Simply supported
# How to Prevent Aseptic Loosening in Cementless Arthroplasty: A Review
## 3 Materials and Methods  
A systematic review of the MEDLINE database was performed for articles published between 2011 and 2021 containing the terms \"loosening hip\". We included meta-analysis, randomized controlled trials, reviews, and systematic reviews. The authors reviewed the abstract of each article and only English-written articles were considered. All studies involving preoperative, intraoperative and postoperative factors that affect aseptic loosening in cementless total hip arthroplasty were included. More specific, implant properties (design, material, bearing surfaces), surgical factors, patient-related factors (comorbidities, demographics, genetics, habits), postoperative protocol (activities, weight-bearing), and use of perioperative drugs (antiosteoporotic drugs, opioids, statins, beta-blockers) were all included in our review. Studies on the aseptic loosening of both the acetabular component and the femoral stem were taken into account. Regarding the exclusion criteria, studies where only physiopathology, diagnosis or treatment of aseptic loosening were addressed were not included in our review. Moreover, only studies performed on cementless primary total hip replacements were included, while loosening of mega-prosthesis, hip revisions, hemiarthroplasties or hip resurfacing were not considered. The PRISMA flowchart is available in Figure 1.
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 5 Role of PGPB in Alleviating Salinity Stress to Plants
### Phytohormone Signals  
Phytohormones are very important for plants as they use them to regulate their own processes [57; 58]. They are involved in plant interactions with their environment. The synthesis of phytohormones is precisely regulated by the plant, but bacteria are also able to produce phytohormones and to release them out of the cell, either in the rhizosphere (rhizobacteria) or in plant tissues (endophytes). The excretion of these molecules by bacteria affects plant behavior under salinity stress, as, in some situations, plants do not generate sufficient quantities for optimal development [59; 60].
## 4 System Analysis for a Four User System and Machine Learning
### Optimization of Reflective IBFD with ML
#### 4.1.2 Sum Secrecy Capacity Optimization Problems  
There are several techniques for system optimization; however, the computation efficiency of using unlimited data leads towards the use of MLGA. The optimization of the sum secrecy capacity requires the secrecy capacity of the individual users affected by the Eve. In this system, we have considered \([PERSON]_{3}\) and \([PERSON]_{4}\) as the affected users.  
To make the secrecy capacity better, we optimize it by using MLGA. We deal here with the optimization of the data rate and power coefficients of the system. For optimization of sum secrecy capacity \(S_{T}\) of the system, it is considered as the fitness function of the algorithm as shown in Figure 3.  
The individual secrecy capacities of each affected vehicle can be calculated as: \(S_{n}=R_{n}-R_{nte}\), where \(n=\{3,4\}\). The problem formulation for the secrecy capacity maximization is given as:  
\[\text{P1: max:}S_{T}=\big{(}R_{[PERSON]_{2}}-R_{[PERSON]}\big{)}+\big{(}R_{ [PERSON]_{4}}-R_{[PERSON]}\big{)}. \tag{22}\]  
The constraints on the power coefficient are:  
\[\text{C1: }A(\alpha_{1},\cdots,\alpha_{4})=\sum_{n=1}^{4}\alpha_{n}=1,\] \[\text{C2: }\alpha_{n}>0,n=\{1,\cdots,4\}\]  
and the SINR \(\zeta_{n}\)  
\[\text{C3: }\zeta_{n}>\zeta_{T},n=\{1,\cdots,4\}\]  
where \(\zeta_{T}\) is the threshold for the received SINR. In the left hand side of the above equation, there are two round brackets that shows the secrecy capacity of \(Bob_{3}\) with the difference between the capacity of \(Bob_{2}\) and Eve and \(Bob_{4}\).  
In the secrecy capacity expression, each term shows the received SINR of the respective \(Bob_{n}\) and Eve.  
To make the secrecy capacity highly positive we have used the modulation orthogonality and the AN. Since [PERSON] does not have the information of the modulation component,  
Figure 3: Flow chart description of genetic algorithm based ML.  
therefore it will face more interference and can not decode the information properly. Additionally, the _AN_ increases interference of the Eve. A legitimate user is not affected by _AN_ because it is the null space of the offered channel. The sum secrecy capacity of the system is given as:  
\[S_{T}=R_{2}-R_{Eve}+R_{4}-R_{Eve}. \tag{23}\]  
The above equation clearly shows the dependence on the capacity of [PERSON] and the affected Bobs. Hence, it is highly subjected to the individual's received SINR, channel, power, power coefficient and interference. Eve has lower received SINR and hence low \(R_{Eve}\) due to high interference as shown in (21). \(R_{2}\) and \(R_{4}\) are higher due to less interference for R-IBFD. Therefore, The secrecy capacity will be highly positive.
## 4 Multiple Non-Linear Regression Analysis of Strength
### Analysis of Tailings Cemented Backfill Strength Date  
The regression equation for the compressive strength of tailing sand on different days under natural grading was solved in MATLAB software and the results are shown in Equations (2)-(4):  
\[S_{3}=-61.228690+4.5879748e^{0.010817x_{1}}+1352.385013x_{2}{}^{-18813.828534}- 0.00678x_{1}x_{2} \tag{2}\]  
\[S_{7}=19.482506+4.275669e^{0.037325x_{1}}+1352.682409x_{2}{}^{-18813.828534}- 0.014656x_{1}x_{2} \tag{3}\]  
\[S_{28}=57.62098+4.799642e^{0.044648x_{1}}+1352.703635x_{2}{}^{-18822.989533}- 0.03159x_{1}x_{2} \tag{4}\]  
The strength values of the filler at 3, 7, and 28 days were predicted according to the above model, and nine sets of data were taken out of the predicted values based on the single variable principle (e.g., see Table 11). According to the results of the data analysis in Table 11, a three-dimensional surface visualization model was constructed with the level of the respective variable factors as the \(X\)-axis coordinates, the 3 d, 7 d, and 28 d time sequence as the \(Y\)-axis coordinates, and the strength values as the \(Z\)-axis coordinates, and the results are displayed in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Slurry Concentration (\%)**} & \multirow{2}{*}{**Cement Addition (kg\(\cdot\)m\({}^{-3}\))**} & \multicolumn{3}{c}{**Strength Value (MPa)**} \\ \cline{3-5}  & & **3 d** & **7 d** & **28 d** \\ \hline
68 & 300 & 1.3403 & 1.8888 & 3.0099 \\
70 & 300 & 1.5457 & 2.0001 & 3.5452 \\
72 & 300 & 1.7447 & 2.3237 & 4.7096 \\
68 & 270 & 1.2299 & 1.65 & 2.4952 \\
70 & 270 & 1.4415 & 1.7741 & 3.0595 \\
72 & 270 & 1.6453 & 2.1087 & 4.2462 \\
68 & 240 & 1.0937 & 1.3556 & 1.8605 \\
70 & 240 & 1.3562 & 1.5904 & 2.6621 \\
72 & 240 & 1.5191 & 1.8358 & 3.658 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Validation of the regularity of the regression model for different models with natural grading.  
Figure 8: Compressive strength of the filler at different ages for different factors. (**a**) Slurry concentration. (**b**) Cement content.  
Table 11 and Figure 8 suggest that the strength of the filler increases with increasing slurry concentration, and the greater the cement addition, the greater the strength of the filler under the same conditions. Hence, the predicted strength of the filler satisfies the variation law of the strength of a single influencing factor. At the same time, through the establishment of the 3D visualization model, the fitting coefficients (R\({}^{2}\)) of the strength model are 0.94 and 0.93, respectively. This is a high fitting accuracy, and therefore, the strength model is valid.
## 3 Benchmark Results
### Digital Signature Algorithms
#### 3.5.2 Energy Costs
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{**Sign**} & \multicolumn{4}{c}{**Verify**} \\ \cline{2-9}  & \(\Delta\) & MB/J (files 1 K) & MB/J (files 1 M) & \(\Delta\) & MB/J (files 1 K) & MB/J (files 1 M) \\ \hline
**RSA-512** & \(1.05\cdot 10^{-2}\) & \(1.27\cdot 10^{-1}\) & \(10.87\) & \(1.09\cdot 10^{-2}\) & \(4.52\cdot 10^{-1}\) & \(11.56\) \\ \hline
**RSA-1024** & \(8.82\cdot 10^{-3}\) & \(3.76\cdot 10^{-2}\) & \(9.06\) & \(1.09\cdot 10^{-2}\) & \(2.95\cdot 10^{-1}\) & \(11.40\) \\ \hline
**RSA-2048** & \(5.56\cdot 10^{-3}\) & \(1.07\cdot 10^{-2}\) & \(5.70\) & \(1.08\cdot 10^{-2}\) & \(2.18\cdot 10^{-1}\) & \(11.25\) \\ \hline
**DSA-512** & \(1.07\cdot 10^{-2}\) & \(1.63\cdot 10^{-1}\) & \(11.07\) & \(1.07\cdot 10^{-2}\) & \(1.67\cdot 10^{-1}\) & \(11.09\) \\ \hline
**DSA-1024** & \(9.93\cdot 10^{-3}\) & \(7.30\cdot 10^{-2}\) & \(10.23\) & \(9.51\cdot 10^{-3}\) & \(5.46\cdot 10^{-2}\) & \(9.78\) \\ \hline
**DSA-2048** & \(7.73\cdot 10^{-3}\) & \(2.34\cdot 10^{-2}\) & \(7.93\) & \(7.41\cdot 10^{-3}\) & \(2.07\cdot 10^{-2}\) & \(7.61\) \\ \hline
**ECDSA-112** & \(8.65\cdot 10^{-3}\) & \(3.47\cdot 10^{-2}\) & \(8.89\) & \(8.66\cdot 10^{-3}\) & \(3.48\cdot 10^{-2}\) & \(8.89\) \\ \hline
**ECDSA-160** & \(8.32\cdot 10^{-3}\) & \(2.98\cdot 10^{-2}\) & \(8.54\) & \(7.90\cdot 10^{-3}\) & \(2.51\cdot 10^{-2}\) & \(8.11\) \\ \hline
**ECDSA-224** & \(8.14\cdot 10^{-3}\) & \(2.76\cdot 10^{-2}\) & \(8.35\) & \(7.69\cdot 10^{-3}\) & \(2.31\cdot 10^{-2}\) & \(7.89\) \\ \hline
**BLS** & \(3.81\cdot 10^{-3}\) & \(4.41\cdot 10^{-3}\) & \(3.90\) & \(3.07\cdot 10^{-3}\) & \(3.44\cdot 10^{-3}\) & \(3.14\) \\ \hline
**BB** & \(1.73\cdot 10^{-2}\) & \(1.73\cdot 10^{-2}\) & \(17.71\) & \(5.13\cdot 10^{-3}\) & \(5.13\cdot 10^{-3}\) & \(5.25\) \\ \hline \end{tabular}
\end{table} 
Table 7: Energy costs (MB/J) of digital signature in Hx2790. Figure 6: Energy costs of digital signature algorithms.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 4 Numerical Simulation of Simply Supported Beam and Space Truss
### Space Truss Model  
#### 4.2.1 Model and Damage Scenario  
The size of this space truss model is \(2.4\times 0.56\times 0.45\) m, and the bars are articulated between each other. This model consists of 51 bars. Each bar is a hollow pipe, in which the outer diameter is 1 cm and thickness is 3 mm. The position and number of bars are shown in Figure 9. The elastic modulus, density, and position ratio of structural material were 206 GPa, \(7.85\times 10^{3}\) kg/m\({}^{3}\), and 0.3, respectively. The damage scenario is assumed that the 3 rd, 15 th, 29 th, 30 th, and 48 th bars respectively contain 50%, 70%, 60%, 77%, and 82% residual stiffness. And the position of damaged bars have been marked with read circle in Figure 9.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: I-V Characteristics and Figures of Merit (FOM) for MIM Diode
#### 2.2.7 Responsivity (S)  
In Equation (9), the responsivity, also called sensitivity (S), is expressed as the ratio of the second derivative to the first derivative of the I-V curve at a specific bias voltage [7; 23; 27; 28; 29].  
\[S=\frac{I^{\prime\prime}}{I^{\prime}},\text{ with S }>\ 7V^{-1} \tag{9}\]  
where \(I^{\prime\prime}=\frac{dI^{2}}{d^{2}V}\) and \(I^{\prime}=\frac{dI}{dV}\).  
This parameter represents the rectified signal measurement vs. the input power. The greater the numeric value of responsivity, the greater the rectification ability of the diode. To achieve great responsivity, a large curvature in the I-V graph is required. It is useful to introduce another parameter, referred to as zero-bias responsivity, which represents the measure of the rectified signal as a function of the input power at zero-bias voltage. If this requirement is not met, the diode requires an external signal to generate a response that prevents the rectenna from operating as an energy harvester. In general, a trade-off is observed. In fact, as the barrier height increases, the responsivity increases too, and as a consequence, higher resistance is also observed.
# Study on Mechanical Properties of Simply-Supported Composite Beams Considering Creep and Slip
###### Abstract  
In the current industry, steel-concrete composite beams are used in large-span bridges and super-high-rise building structures due to their excellent overall performance. Concrete's creep and slip effects in the combined structure can adversely affect the structure, thus affecting the safe use of bridges and buildings. It is necessary to study the mechanical properties of the combined structure considering creep and slip. In order to further study the mechanical properties of steel-concrete composite beams under creep-sliding coupling, in this study, based on the energy variational method principle, the energy equation of a composite beam considering creep and slip coupling is established. The second-order differential equation of the axial force of steel-concrete composite beams is derived by introducing basic assumptions. The calculation formulas for the axial force, deflection, and slip of simply-supported composite beams under different loads are obtained using different boundary conditions. Then, the creep effect of composite beams is simulated using the creep criterion in the ANSYS finite element software when the concrete material parameters change with time. The results show that a simply-supported composite beam considering both slip and creep will have a significant effect on the structure; the more strongly the studs constrain the concrete slab, the greater the adverse effect of concrete creep on the combined beam. The formula derived in this paper is consistent with the numerical simulation solution and is suitable for different creep and slip conditions. The research results can provide a theoretical basis for the calculation of the axial force, deflection, and slip of combined beams under uniform and concentrated loads in practical engineering considering slip and creep.  
steel-concrete composite beam; axial force; deflection; interfacial slip; creep; variational methods