esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 5 Experimental Results
### Large-Scale Dataset Experiments  
We conducted experiments on four instances and compared the results with those obtained by the TACA and three improved ant colony algorithms [20]. The number of iterations of all algorithms was set to 500, and the number of ants was 40. Each algorithm was run 20 times, and the results were recorded in a table. The experimental results are shown in Table 11, where the numbers in the IACA column in bold indicate that the algorithm performs better than BKS. Furthermore, we used TACA as the benchmark to calculate the reduction rate of the optimal solution for other algorithms, as shown in Table 12.  
The IACA performs well on large-scale datasets, and both the best solution and the average solution are better than those of the other algorithms. The best solution of the IACA is reduced the most on average compared to the TACA, by 6.87%, and reduced the least on average compared to the MMGCT, by 3.64%. When compared to the BKS of these datasets, the IACA finds better solutions on the kelly03 and kelly04 datasets, reducing the path length by 2.51% and 1.41%, respectively. In addition, the solutions found by the IACA on the kelly01 and kelly02 datasets are also close to the BKS, with difference values of 0.29% and 1.03%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**BKS**} & \multicolumn{6}{c}{**Best Solution**} & \multicolumn{6}{c}{**Average Solution**} \\ \cline{3-11}  & & **TACA** & **ASGCT** & **ACGCT** & **MMGCT** & **IACA** & **TACA** & **ASGCT** & **ACGCT** & **MMGCT** & **IACA** \\ \hline kelly01 & 5646.46 & 5879.58 & 5794.95 & 5769.36 & 5754.20 & 5662.98 & 5893.62 & 5841.34 & 5837.55 & 5824.72 & 5684.91 \\ kelly02 & 8566.04 & 9195.57 & 9176.28 & 9117.09 & 9077.50 & 8654.37 & 9257.48 & 9208.78 & 9170.32 & 9152.06 & 8735.00 \\ kelly03 & 11,649.06 & 12,428.92 & 12,282.97 & 11,935.64 & 11,843.65 & **11,356.66** & 12,813.61 & 12,361.17 & 12,345.98 & 12,078.94 & 11,486.70 \\ kelly04 & 14,639.32 & 15,909.32 & 15,731.11 & 15,553.70 & 15,065.53 & **14,433.08** & 16,356.85 & 15,932.12 & 15,777.64 & 15,593.20 & 14,679.80 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of the calculation results obtained on large-scale datasets.  
Figure 5: Comparison of algorithm running times.  
A comparison of algorithm running times is shown in Table 13. In addition, we calculated the reduction rate of the running time for other algorithms based on TACA, as shown in Table 14.  
These tables show that the running time of the IACA is better than that of the TACA and the ASGCT. However, on some datasets, the running time of IACA is slightly longer than that of the ACGCT or the MMGCT. This may be because the vehicle path self-search and intervehicle exchange search in the IACA take some time. Although the proposed improved method does not outperform all algorithms in running time, the comparison of running time and solution quality shows that the improved method proposed in this paper is effective on large-scale datasets.
## 3 Results
### Hip
#### 3.1.2 Mechanical Properties  
The averaged mechanical properties from three tensile tests and the associated standard deviations are represented in Figure 8. Compared with pure Al, the addition of 10 mass-% and 30 mass-% Fe and Ni leads to an increase in tensile strength as well as yield strength, see Figure 8a,b. In comparison, the composite of Al and Co has a low tensile strength. The addition of 10 mass-% Co leads to a higher yield strength, at 30 mass-%, it has similar values as the pure Al. The plastic deformability decreases with the addition of the magnetic particles, where 10 mass-% Co and Ni show the highest values, see Figure 8c,d.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process** & **Puls** \\ \hline Protective gas & & argon \\ \hline Forward speed of torch & [cm/min] & 30.00 \\ \hline Forward speed of wire [m/min] & [m/min] & 9.50 \\ \hline Voltage & [V] & 21.50 \\ \hline Current & [A] & 152.00 \\ \hline Gas supply & [L/min] & 4.00 \\ \hline Powder supply & [g/min] & 60.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Process influencing the variables of the GMAW process.  
Figure 6: Images of the different microstructures through a reflected light microscope of (**a**) Al and Al-matrix with 10 mass-% for (**b**) Ni-, (**c**) Co-, (**d**) Ni-, and (**e**) Fe-particles, and (**f**) Al-matrix with 30 mass-% Fe.  
Figure 7: EDX analysis of the Al matrix with 10 mass-% Co: (**a**) EDX mapping showing the distribution of the elemental composition over the selected range, see (**b**). EDX mapping indicates that a Co and Al phase has formed in the transition region.  
The mechanical properties varied strongly as a function of the mass-%. The increase from 10 mass % to 30 mass-% shows no classified behaviour. With Fe, the tensile stress and the plastic deformation capacity are increased, but the yield strength is decreased. Co leads to a lowering of all mechanical properties. Increasing the Ni content decreases the tensile stress and plastic deformability, while increasing the yield strength. For illustration purposes, a selected stress-strain curve of each material is shown in Figure 9.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 4 The Estimation of Intrinsic Parameters
### Fitting Method of Focal Length  
The focal length of the camera increases as zoom increases, but the variation trend and fitting model for focal length is different for different PTZ cameras (Figure 4). For the PTZ camera in Ref. [12], the variation rate of the focal length also increases as zoom increases, the variation trend of which we used as the exponential function model. In the case of Ref. [18], where the focal length is approximately linear with the zoom value, we used the linear interpolation method or a quadratic function to establish the function expression.  
Figure 4: Variation of focal length with zoom of different cameras.  
We recorded \(z\) as the current zoom value. By observing the changing trend of the curve [12], the model of exponential function fitting of the focal length can be written as:  
\[f(z)=p_{1}e^{p_{2}z}+p_{3}e^{p_{4}z} \tag{3}\]  
For the linear model, we can easily devise a linear interpolation and quadratic function approach.  
A linear interpolation approach to fit the focal length, \(f\), into a piecewise function of \(z\), according to the focal length previously calibrated under the different zoom value, can be written as:  
\[f(z)=\frac{z-z_{i}}{z_{i}-z_{i-1}}(f_{i}-f_{i-1})+f_{i} \tag{4}\]  
In Expression (4), \(f_{i}\) is the known focal length under the zoom value, \(z_{i}\). Obviously, the estimation accuracy depends on the quantity and distribution of the previous calibration data.  
With respect to the focal length, the quadratic function of \(z\) can also be modeled as:  
\[f(z)=az^{2}+bz+c \tag{5}\]  
In practice, we can compare the two models to find out which one is better. Based on the results above, we can estimate the intrinsic parameter matrix, \(K\):  
\[K=\begin{bmatrix}f_{x}&u_{0}\\ &f_{y}&v_{0}\\ &&1\end{bmatrix} \tag{6}\]
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
###### Abstract  
Due to lack of research data on the rates of deterioration of FRP properties under a harsh environment exposure, it was pointed out in the design guidelines that the durability of FRP needs to be further developed. Therefore, in this study, 48 FRP samples were tested under wet-dry cycles exposure. The effect of wet-dry cycling times on the failure modes, tensile strength, and the probability distribution of different FRP (GFRP and CFRP) composite specimens were investigated. The experimental results showed that the wet-dry cycles have a significant adverse influence on the tensile strength, have a certain adverse effect on the elongation, and a very limited influence on the elastic modulus of FRP. According to the experimental results, a probability analysis was conducted on the degradation of tensile strength. Five widely used test methods were adopted to verify the possible distribution types of tensile strength, and a reliability index \(\beta\) was then calculated. Subsequently, the effects of the design tensile strengths of ACI-440, TR-55, GB 50608-2010, GB 50367-2013, European Fib Bulletin 14 and Italian CNR guidelines on the \(\beta\) were investigated. The investigation illustrates that only the design value of the TR-55 code can guarantee sufficient long-term safety of a CFRP composite, whereas all the six codes cannot guarantee the long-term safety of a GFRP composite and the partial safety factors in these codes are still not conservative. Therefore, a more conservative safety factor was suggested. Moreover, the design value of tensile strength needs to be further conservative when the standard deviation of the load is large.  
FRP composites; tensile strength; degradation; wet-dry cycles; reliability Article
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 2 Related Work  
### Circuit Switching  
In circuit switching, an actual dedicated, end-to-end physical path should be established between the two communicating parties before data exchange. Figure 1 shows the whole process of circuit switching, including three parts: line establishment before transmission, data transmission during transmission, and line release after transmission [7]. The transmission delay of circuit switching is small, which is real-time. In addition, the transmission reliability of circuit switching is high, and there will be no out-of-order packets. However, circuit switching has low link utilization and is not flexible.  
Figure 1: The whole process of circuit switching.  
In order to meet the different bandwidth requirements of different applications, multi-rate circuit switching has emerged, which has multiple rate types. Fast circuit switching allocates bandwidth and related resources only when information is to be transmitted to overcome the shortcoming of the fixed bandwidth of circuit switching, which improves bandwidth utilization.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 6 Applicative Example
### Situation  
#### 6.1.1 Context  
In a healthcare service, we can have a central database securely storing the health information of the citizens. Every citizen has access to his personal health data and can securely share it with the medical staff when needed. This will help to operate appropriate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(n\) & \(p\) & **500** & **1000** & **1500** \\ \hline
256 & 1.85\% & 0.93\% & 0.62\% \\ \hline
512 & 1.75\% & 0.88\% & 0.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proportion of additional data induced by ICRP depending on \((n,p)\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Size and Parameters \((n,k,p)\)** & **Sending Method** & **Direct** & **ICRP** \\ \hline
10 Mb \((250,80,500)\) & 4.668 & 6.138 \\ \hline
40 Mb \((250,320,500)\) & 20.010 & 21.936 \\ \hline
100 Mb \((250,800,500)\) & 44.384 & 54.323 \\ \hline
200 Mb \((250,1600,500)\) & 89.916 & 106.508 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall sending times in seconds for large files.  
treatments on time. For instance, when a doctor, specializing in emergency medical assistance, arrives at a car crash, he usually has to apply some urgent and precise treatments and operations to save the lives of the victims. Any mistake (e.g., potential allergies, health issues...) or delay may cause a human life loss. To help the doctor, he may have mobile equipment enabling him to remotely access (through the mobile network) the victim's health information that is securely stored in the central health servers and eventually add notes and remarks for the nursing staff who will take care of the patient once in the hospital.  
In this situation, sensitive information will regularly travel from the doctor's device to the e-health/hospital database and have to be protected in rest as well as in transit. Protecting data at rest means securing all the infrastructure storing sensitive data at both hardware and software levels while protecting data in transit means providing assurance that the data has not been compromised in any sense during its travel from one point to another. Indeed, 5G tends to be a flexible and dynamic network, namely using network virtualization. This allows the running of virtualized network functions over any hardware machine located in any area (i.e., inside the same country or in different countries) and even those not owned by the network operator. This arises new challenges, especially in sensitive contexts such as e-health use cases where stringent requirements about e-health data are imposed. E-health data are considered private and sensible and so, must be protected in rest and in transit.  
The protocol we have presented in this article can precisely serve last requirements by quickly detecting a stealthy relay attack or even an accidental route alteration.
# Development of an Energy-Efficient Smart Socket Based on STM32F103
## 3 System Development
### Flowchart and System Control Operations  
Given the methodology and hardware development stated in previous sections, this section is primarily focused on the control logic design and associated procedure scheduling. From the perspective of function requirement, a status flag was employed in the main program to indicate the status of the host-slave operator. Values of the status flag correspond to the following electricity supply status: 0 for both the host and slave jacks are powered off; 1 for host jacks are switched on, but slave jacks are switched off; while 2 for both the host and slave jacks are powered on. As shown in the flowchart Figure 5, the logical operations can be conducted with the following steps:  
1. System initialization, the value of the flag is set as 0.
2. Read current and voltage values for the host and slave jacks, with the data then transmitted to the power management unit for processing.
3. After processing and calibration, the values of voltage and current then can be loop displayed on the LCD module.
4. Overvoltage and overcurrent checking. Both the host and slave jacks would be powered off if either overvoltage or overcurrent was detected.
5. Logical decision making considering the flowing three scenarios: Scenario 1, the value of the status flag is 0. If infrared control instruction has been received, the host jacks then would be powered on with the flag set as 1, otherwise, both the host and slave jacks would be powered off with the flag set as 0. Scenario 2, the value of the status flag is read as 1. If the current in host jack continues within 2 s, the slave jacks would be powered on with the flag set as 2, otherwise, both the host and slave jacks would be switched off with the flag set as 0. Scenario 3, the value of the status flag equals to 2. If the host jacks are powered on, no response required, otherwise both the host and slave jacks would be switched off with the flag set as 0. The procedure for smarter control operations can then be described as: If (Status flag == 0) Both the host and slave jacks are-off If (Infrared signal received) The host jacks are on and the status flag is set as 1 else Both the host and slave jacks are off; the state flag is set as 0 end else if (Status flag == 1) Host jacks are on but slave jacks are off If (continuous current was detected in host jacks within 2 seconds) The slave jacks are on and the status flag is set as 2 else Both the host and slave jacks are off; the status flag is set as 0 end else if (Status flag == 2) Both the host and slave jacks are on If (current was detected in the host jacks) Both the host and slave jacks are on; the status flag is set as 2 else Both the host and slave jacks are off; the status flag is set as 0
## 5 Examples  
### Example 1: Mathematical Problem with Results and Discussion  
Consider the LSF shown in Equation (36):  
\[g(\mathbf{Z},t)=-(Z_{1}+Z_{2})t^{2}+25Z_{2}t-\frac{Z_{1}^{2}sin(t)}{2}+300 \tag{36}\]  
where both \(Z_{1}\) and \(Z_{2}\) follow normal distribution with \(Z_{1}\sim N(5,0.25)\) and \(Z_{2}\sim N(5,0.25)\). The PDF of FCTP is calculated as shown in Figure 4. The variation in cumulative failure probability is obtained by integration over time, shown in Figure 5. In the figure, MCS-FCTP denotes the Monte Carlo-based method, ASVR-FCTP denotes adaptive method-based on support vector regression model [30], and EDFK-FCTP denotes the method proposed in this paper.  
Comparing the PDFs in Figure 4, it can be seen that the FCTP probability density function obtained by EDFK-FCTP is very close to MCS-FCTP. Comparing the failure probability as shown in Figure 5, the results of both EDFK-FCTP and ASVR-FCTP are better, but the EDFK-FCTP is closer to the results of MCS-FCTP. When failure probability is greater  
Figure 4: FCTP probability density function of mathematical problem.  
Figure 5: Failure probability trend of mathematical problem.  
than \(10^{3}\), the results of three methods almost coincide, indicating that EDFK-FCTP can accurately estimate trend of time-variant failure probability over the product life cycle. To further reflect the accuracy of EDFK-FCTP, the relative error with MCS-FCTP is calculated by Equation (37):  
\[Err(\%)=\left|\frac{P_{f}^{EDFK-FCTP}-P_{f}^{MCS-FCTP}}{P_{f}^{MCS-FCTP}} \right|\times 100\% \tag{37}\]  
Six-moment points were selected, and the relative error of each point was calculated as shown in Table 1. From Table 1, the accuracy of EDFK-FCTP meets engineering requirements. It should be noted that although the fitting accuracy of EDFK-FCTP in the low-failure region is better than ASVR, it is still lacking.  
In addition, the computational efficiency of the various methods is comparatively analyzed in terms of the number of performance function calls \(N_{call}\) and the number of training samples \(N\), and the results are shown in Table 2.
## 4 Results and Discussion
### CGCW Performance Analysis  
#### 4.2.1 Influence of the Hole Radius  
The influence of the hole radius (\(r_{\rm G}\)) on the breakthrough curve of the CGCW is shown in Figure 7. The \(H_{\rm G}\) is assumed to be 3.0 m. It can be concluded that increasing \(r_{\rm G}\) can lead to a decrease in the breakthrough time. However, this effect can be insignificant when \(k_{\rm G}\) is small (e.g., \(<\)10\({}^{-10}\) m/s). For example, the breakthrough times for \(r_{\rm G}\) = 0.005 m and 0.05 m are 47.3 and 48.1 years when \(k_{\rm G}\) = 10\({}^{-10}\) m/s, respectively. Additionally, the breakthrough time for \(r_{\rm G}\) = 0.005 m is 5 years longer than that for \(r_{\rm G}\) = 0.05 m when \(k_{\rm G}\) = 10\({}^{-9}\) m/s. The main reason is that the impermeability of the CGCW is good when \(k_{\rm G}\) is small (as mentioned in Section 3.2). This will weaken the impacts of the hole geometric properties on the leakage. Moreover, it can also be observed from the empirical equations in Table 2 that leakage is directly proportional to both \(k_{\rm G}\) and \(r_{\rm G}\)[192], respectively. The influence of \(k_{\rm G}\) is more significant than that of \(r_{\rm G}\). In other words, reducing \(k_{\rm G}\) can minimize the negative influence of GMB holes on the service time of the CGCWs. Therefore, it is essential to use the cut-off wall materials with lower hydraulic conductivities in the actual projects.
## 1 Introduction
### The Antibacterial Effectiveness of Honey  
The antibacterial effectiveness of honey has been demonstrated in many studies [23; 24; 25; 26; 27]. The exact mechanism is not yet clear, although it is commonly attributed to (i) the high sugar content, which induces high osmolarity and inhibits bacterial growth, (ii) oxygen peroxide, which is produced by the activation of glucose oxidase and (iii) the low and acidic pH (usually 3.2-4.5) [6; 7; 22; 26]. However, there is more to be said on the matter. Some studies have shown that <<artificial>> honey (a solution saturated in mono- and oligosaccharidesrides) is not always able to inhibit bacterial growth effectively and that adding catalase does not remove all the antibacterial activity of honey [28; 29]. These findings strongly suggest that there must be other factors which contribute to the total antibacterial effect of honey and that these factors act either separately or synergistically [26; 27]. Such factors could be peptides such as defensin-1 or the phenolic compounds as well as the flavonoids, which are contained in honey (Scheme 1) [30; 31].  
Manuka honey is a well-studied product famous for its antibacterial properties and serves as a paradigm and a reference material for similar studies of other honeys. The high phenol content of manuka honey is impressive and accounts for a large part of its antibacterial activity. However, it is the methyllycoxal, a compound derived from dihydroacetone, that completes its antibacterial activity [32; 33; 34]. Another substance (its structure not yet identified by the researchers) described in manuka honey induces cytokine production by interaction with TLR4 on macrophages [35]. This study has demonstrated that the activity of cytic cells intimately involved in the repair of wounded tissue is modulated by honey. The mechanisms by which honey affects the release of anti-inflammatory agents and growth factors from monocytic cells are so far unclear, and this represents an area for further study, e.g., whether honey affects other cell types, particularly endothelial cells and fibroblasts. Finally, manuka honey affects the proteins forming the septal ring and thus affecting the cell division [36]. Such findings fuel the research of the antibacterial properties of different honeys as alternatives to standard antibiotics.
## 3 Results and Discussion
### Ion Exchange Runs
#### 3.2.3 Thermodynamics of Anion Exchange
Lastly, the data in the table confirm the maximum uptake values recorded by the thermodynamic tests in which the maximum value of removable As was 5.34 and 1.29 mg g\({}^{-1}\) for the PHI-Br and PHI-Cl samples, respectively, and 7.86 and 4.87 mg g\({}^{-1}\) for CLI-Br and CLI-Cl samples, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Mathematical Model**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{3-6}  & & **K\({}_{\mathbf{1}}\) (L mg\({}^{-1}\))** & **n** & **S\({}_{\mathbf{m}}\) (mg g\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline PHI-Br & Langmuir & 0.048 & & 5.34 & 0.983 \\  & Sips & 0.033 & 0.79 & 5.99 & 0.990 \\  & Toth & 0.095 & 0.57 & 6.48 & 0.991 \\ \hline PHI-Cl & Langmuir & 0.055 & & 1.29 & 0.983 \\  & Sips & 0.060 & 1.19 & 1.25 & 0.985 \\  & Toth & 0.042 & 1.41 & 1.23 & 0.986 \\ \hline CLI -Br & Langmuir & 0.172 & & 7.86 & 0.962 \\  & Sips & 0.161 & 0.79 & 8.19 & 0.972 \\  & Toth & 0.275 & 0.74 & 8.22 & 0.996 \\ \hline CLI-Cl & Langmuir & 0.058 & & 4.87 & 0.982 \\  & Sips & 0.058 & 1.02 & 4.84 & 0.982 \\  & Toth & 0.053 & 1.09 & 4.79 & 0.983 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Isotherms parameters.