esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 5 Results
### Impact of Assignment Function  
The proposed workload assignment policy, which we denote as _min-cost\({}_{H}\) policy_, aims at assigning VMs to DCs in which the energy cost is currently the lowest among the various DCs. In order to perform the workload assignment taking into account also the future energy price variations, which are observed during the period corresponding to the average VM lifetime, we defined a slightly different workload assignment policy, denoted as _min-cost\({}_{LT}\) policy_. We assumed to know in advance the hourly energy prices and that the hourly energy demand was constant during the VM lifetime. Considering that no predictions about actual RE production were made and that the average VM  
Figure 11: Cost-saving achieved by installing the PV panels in one DC only.  
Figure 12: Cost-saving achieved by installing the same total PV panel capacity (80 kWh) according to different configuration scenarios: (**A**) 20 kWp per DC in four DCs; (**B**) 40 kWp per DC in two DCs; (**C**) 80 kWp in a single DC.  
lifetime was 5 h, we assumed that the hourly RE generation was the same during the entire VM lifetime. The new utility function is the following:  
\[f^{i}_{assign}=\sum_{t=h}^{h^{\prime}}\left(C_{i}-E_{i}\right)P_{t,t} \tag{5}\]  
where \(h\) represents the current time and \(h^{\prime}\) corresponds to \(h+lt_{A}-1\), with \(lt_{A}\) being the average VM lifetime.  
Based on our preliminary results, the performance of both policies in terms of cost-saving looked quite similar under any combination of PV panel and storage size. Only in a few cases, especially when some storage was present, the application of the \(min\)-\(cost_{H}\) policy may provide some benefit, but still negligible with respect to the \(min\)-\(cost_{LT}\) policy. Higher values of the average VM lifetime may affect the performance of the \(min\)-\(cost_{LT}\) policy, although we do not expect significant variations.  
The fact that the \(min\)-\(cost_{H}\) did not significantly improve the cost-saving might be due, on the one hand, to the variability of the actual VM lifetime duration, whereas our strategy considered the average VM lifetime. On the other hand, it could be due to the slow variations in energy prices. Furthermore, the knowledge of the actual future RE production might impact on the predicted energy cost, hence affecting the assignment process. Future work is required to better study the performance of the latter proposed strategy under varying configuration settings and to investigate whether an accurate prediction of RE generation could play a relevant role in the VM assignment process, allowing further reduction of the energy cost.
# Enhancing Border Gateway Protocol Security Using Public Blockchain
###### Abstract  
Communication on the Internet consisting of a massive number of Autonomous Systems (AS) depends on routing based on Border Gateway Protocol (BGP). Routers generally trust the veracity of information in BGP updates from their neighbors, as with many other routing protocols. However, this trust leaves the whole system vulnerable to multiple attacks, such as BGP hijacking. Several solutions have been proposed to increase the security of BGP routing protocol, most based on centralized Public Key Infrastructure, but their adoption has been relatively slow. Additionally, these solutions are open to attack on this centralized system. Decentralized alternatives utilizing blockchain to validate BGP updates have recently been proposed. The distributed nature of blockchain and its trustless environment increase the overall system security and conform to the distributed character of the BGP. All of the techniques based on blockchain concentrate on inspecting incoming BGP updates only. In this paper, we improve on these by modifying an existing architecture for the management of network devices. The original architecture adopted a private blockchain implementation of HyperLedger. On the other hand, we use the public blockchain Ethereum, more specifically the Ropsten testing environment. Our solution provides a module design for the management of AS border routers. It enables verification of the prefixes even before any router sends BGP updates announcing them. Thus, we eliminate fraudulent BGP origin announcements from the AS deploying our solution. Furthermore, blockchain provides storage options for configurations of edge routers and keeps the irrefutable history of all changes. We can analyze router settings history to detect whether the router advertised incorrect information, when and for how long.  
BGP hijacking; BGP security; blockchain; management; network +
Footnote †: journal: Sensors
## Appendix A Examinations of Smaller-Sized Cylinders and Multiple Cylinders
### Effect of the Object Size  
The effect of the object size is considered by examining the results of a single GC with the two different diameters plotted in Figure 12. While the plots in black dots indicate the result of 2.0 mm, the blue dots shows that of 0.5 mm. Comparing these, the result at 0.5 mm exhibits systematic deviation in the positive velocity direction, meaning that a higher velocity is obtained with the smaller diameter than with the larger one. This was attributed to the systematic deviation of the detection angle \(\theta\), which had a larger contribution for the smaller diameter case. In the positive direction, when the object moves close to the UST, a consistently faster velocity is obtained. On the other hand, in the negative direction, when the object moves away from the UST, a consistently slower velocity is obtained. The sensitivity of the detection angle increases for the smaller diameter. Nevertheless, the result of the smaller diameter at 0.5 mm exhibits the same tendency as the larger one at 2.0 mm.
## 5 Results and Discussion
### Engineering Application III: Shield Yawing Angle Prediction  
The third engineering application is the prediction of the yawing angle. The \(\mathbf{\Delta s_{v}}\) is calculated in the first stage. The theoretical value of the pitch angle at each moment is shown in Figure 20. The known \(\mathbf{\Delta s_{v}}\) and \(\beta\) are brought into the calculation process of the third stage to obtain the RCYH (Figure 21). The \(M_{A}^{\alpha}\) actively applied by the EPB can be calculated by the thrust difference between Group B and Group D of the advance system. The change curve of the \(M_{A}^{\alpha}\) is shown in Figure 22. Then, the theoretical value of the yawing angle can be obtained. The comparison between the theoretical value and the measured value is shown in Figure 23. The trends of theoretical and measured values are both similar to the trend of the \(M_{A}^{\alpha}\). The \(M_{A}^{\alpha}\) is obtained by measurement. It indicates that the \(M_{A}^{\alpha}\) applied by the shield can act more fully on the shield than \(M_{A}^{\beta}\).  
Comparing the measured values with the theoretical values in Figure 23, it can be found that the trend of the theoretical value is basically similar to the measured value, but the theoretical value is generally larger than the measured value. In the calculation of the theoretical value, the deflection moment value is the initial data, and the moment used for the shield correction is reduced due to mechanical loss, etc., so that the final yawing angle formed by the shield is small.  
Figure 20: Contrast between the measured and the theoretical value of the shield pitch angle.  
Figure 23: Contrast between the measured and the theoretical value of the shield yawing angle.  
Figure 21: The relationship between the yawing angle and \(M_{A}^{a}\).  
Figure 22: The change graph of \(M_{A}^{a}\).
## 4 Discussion
### Relationship Between Halophyte Biomass and Ecological Factors  
Plant biomass is the main indicator of overall plant growth and damage. In the natural environment, plant growth is not only affected by its own physiological effects and photosynthesis, but ecological factors such as light, water, and temperature also play an important role in the development and reproduction of plants [48]. The results of this study, through redundancy analysis, showed that meteorological factors (atmospheric temperature and relative humidity), growth morphological factors (plant height and stem diameter), physiological factors (chlorophyll a, chlorophyll b, and malondialdehydeheyde), photosynthetic factors (net photosynthetic rate, stomatal conductance, and intercellular carbon dioxide concentration), and salt concentration all had a significant effect on the biomass of halophytes. In order to further investigate the complex correlations between various ecological factors and biomass and to analyze the main factors affecting the biomass of halophytes, a structural equation model was developed in this study. Structural equation modeling can not only clearly show the relationships and interactions between the variables but can also explain the direct and indirect effects between the variables. For example, [PERSON] et al. used structural equations to accurately analyze the relationship between biomass and the climate and soil [49]. [PERSON] et al. also used structural equations to analyze the association characteristics between productivity and stand factors in _Cunningham lancolata_[50].  
The present study, using structural equation modeling, revealed significant differences in the mechanism of influence of ecological factors on the biomass of different halophytes. Among them, salt concentration and growth morphological factors were central drivers of biomass in all three halophytes, and the direct and indirect effects were more pronounced. This may be due to ion imbalance and metabolic dysfunction in the plant when subjected to salt stress, and the plant mitigates the effects of salt damage by regulating its morphological growth. The effect of salt concentration and growth morphology factors on biomass was more pronounced as the plant morphology became progressively smaller with increasing salt concentration and eventually showed significant biomass suppression [51]. This is the same as the findings of [PERSON] et al., who found that the plant height and biomass of _Platyodon grandiflora_ gradually decreased with increasing salt concentration [52]. Meteorological factors, physiological factors, and photosynthetic factors vary according to the variability and complexity of halophytes. This is because the three plants belong to three different types of halophytes: Ha being euhalophytes, Tc being retretohalophytes, and Pa being pseudohalophytes. In response to salt stress, different types of halophytes also show different responses, such as ions and osmosis in their bodies [53]. Thus, meteorological factors, physiological factors, and photosynthetic factors have different effects on different halophytes.
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 6 Experimental Validation and Remarks  
Now that the analytical model has been numerically validated, its applicability in real cells should be tested. This was performed in [10], where 18 multicrystalline silicon solar cells with different bulk resistivities and cell architectures were measured at multiple temperatures. For the studied cells, Equations (18) and (19) predicted the experimental \(P_{\text{mpp}}\) and \(V_{\text{mpp}}\) with relative discrepancies below 0.2% and 0.7%, respectively. It is worth mentioning that low relative discrepancies were obtained at all the measured temperatures.  
Besides the numerical limitations that the model derived in the present work may have (e.g., Equation (26)), practical limitations of the model should be addressed. These may include factors that real cells will eventually experience, for instance, degradation due to aging or shunt resistance effects. Although the derived model cannot account for, e.g., cell degradation, it is worth noting that neither can the diode equation in Equation (1), nor the modified diode equation in Equation (6). The analytical expressions derived in the present work were subjected to the same practical limitations that the modified diode equation was. The advantage that the derived model presents with respect to Equation (6)is that it allows seeing how the series resistance affects the MPP analytically. On the other hand, shunt resistance effects do not usually have a relevant impact in laboratory cells, as these typically appear due to defects in manufacturing. In fact, in [10], the shunt resistance in the measured cells had only a negligible effect on the comparison between the model and the experiments. Although it might be possible to obtain an expression analogous to Equation (19) that also accounts for the effect of shunt resistance, this goes beyond the scope of the present work.
## 2 Theory and Methods  
### Theory of FRAM  
The FRAM used in this study originated from psychological and cybernetics research conducted in the 1950s and the 1960s, and the principles of structured software development from the late 1960s. It was developed for resilience engineering [29]. Furthermore, FRAM is a systematic approach for determining how parts of a task and a series of actions usually occur. The model can be used to explain a selected event or performance based on the functions required to perform the activity, potential couplings between functions, and the general variability of the functions [7]. This study provides the basic data necessary to understand and predict the causality of human error and accidents by modeling the nonlinear interaction of system functions using FRAM to analyze the 2020 MV Milano Bridge crane contact accident at Busan New Port. Moreover, to effectively determine the causes of recent marine accidents in the complex systems of ships and ports, it is necessary to understand the connection variability between individuals and organizations, individuals and systems, and organizations and systems. As FRAM is a functional modeling technique that can systematically analyze the causes of complex marine accidents, it can be used in the future to develop algorithms for autonomous maritime surface ships, smart ship management, and investigation and judgment of marine accidents [25].
## 1 Introduction
These methods can be divided into two main categories: (1) methods based on pixel radiometry and (2) methods based on local statistics [23]. Each of these two categories has its own advantages and disadvantages. The approaches based on pixel radiometry are widely used and easy to design in multi-temporal SAR images, but they are easily influenced by speckle noise. The approaches based on local statistics combine the homogeneous pixels together, which effectively reduces the influence of speckle [24; 25; 26], but they do require many prior parameters. As a result, none of these methods can be regarded as optimal, and there is also no common standard at present [11]. In this paper, we focus on change detection based on pixel radiometry. The methods based on pixel radiometry are widely used with multi-temporal SAR images. Such methods include the ratio or log-ratio operator of SAR amplitudes or intensities [13; 15; 16], change vector analysis (CVA) [27], principal component analysis (PCA) [28; 29], the hidden Markov chain model [30; 31], and the Kullback-Leibler divergence method [32]. These methods are usually applied in multi-temporal single-channel SAR-based change detection. When a test statistic [5] was first used in change detection, it was assumed that the covariance (or coherency) matrices follow a complex Wishart distribution, but only the azimuthally symmetric case and the diagonal-element-only case were used to detect the changes. The method proposed in [4] uses all the elements of the covariance (or coherency) matrices to obtain the change detection map. In the third step of change detection analysis, the change detection map can be obtained by the decision threshold of the CI. Several algorithms have been widely used to automatically determine the threshold, such as the constant false alarm rate (CFAR) algorithm [33; 34], [PERSON]'s thresholding method [35], [PERSON]'s entropy algorithm [36], and the Kittler and Illingworth (K&I) algorithm [37]. In this paper, we assume a complex Wishart distribution for the coherency matrix and apply a test statistic based on maximum likelihood estimation (MLE) and an improved K&I algorithm based on a generalized Gaussian model (GGM). The test statistic using more scattering information from the multi-temporal PolSAR images can obtain a high-precision CI. Moreover, the probability density function (PDF) of the CI can be simulated by the GGM. Because the traditional K&I algorithm using the Gaussian model to select the threshold automatically is not fit for CI from PolSAR data at different dates, we apply the GGM to the K&I algorithm to choose the threshold. After finishing the procedure, we can obtain the binary change detection map (changed and unchanged). The remainder of this paper is organized as follows. Section 2 presents a brief introduction to the fundamental theory and details of the proposed change detection framework. Section 3 describes the experimental results. In Section 4, we draw our conclusion.
# Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer
## 3 Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer (DHSMEO)  
The EO serves as a novel swarm intelligence method, which shows some merits with high solution accuracy and a strong global search ability in basic performance testing. However, the EO still faces issues with vulnerability towards a local optimum, population diversity attenuation, and inadequate search efficiency. To handle these problems, by incorporating three improvement strategies to establish a heterogeneous population hybrid search mode, a dynamic heterogeneous search-mutation structure-based equilibrium optimizer (DHSMEO) is proposed to strengthen the optimization performance. Firstly, through constructing a dynamic dual-subpopulation adaptive grouping strategy, a dynamic dual-subpopulation structure is formed to improve population diversity, and it provides an effective information-exchange structure for the heterogeneous hybrid search strategy. Secondly, by introducing a heterogeneous hybrid search-based concentration-updating strategy, a differentiated population search strategy is formed to enhance search efficiency. Finally, by integrating a dynamic Levy mutation-based optimal equilibrium candidate-refining strategy, the optimal equilibrium candidate is refined with the unique randomness of dynamic Levy mutation to strengthen the performance in order to escape local optima. The following subsections elaborate on the three enhancement strategies from DHSMEO.
# Influence of Surface Tilt Angle on a Chromatic Confocal Probe with a Femtosecond Laser
## 4 Experimental Investigation Results for the Tracking Local Minimum Method
### Alignment Method of the Optical System
Figure 11: Experimental results regarding the effect of the change in the object tilt angle \(\theta_{X}\) on the TL method based on our developed dual-fiber-detector chromatic confocal probe (**a**), the axial response curve of the reference light \(I_{\text{R}}\) (**b**), the relationship between \(\theta_{X}\) and \(z\)-position in \(I_{\text{R}}\) (**c**), the relationship between \(\theta_{X}\) and \(z\)-position about \(I_{\text{TLM}}\) (**d**) the relationship between centroid wavelength \(\lambda_{\text{focused}}\) and \(z\)-position in different \(\theta_{X}\). Discussion about the Influence of the Tilt Angle of the Measurement Object on the Tracking Local Minimum Method  
Based on the above results, the influence of the object tilt angle \(\theta_{X}\) on the TL method was discussed by using the following equation shown in Step 3 of Figure 1:  
\[z=\frac{dz}{d\lambda}\times\lambda_{\text{focused}}+z_{0} \tag{16}\]  
The \(z\)-\(\lambda\) sensitivity (\(dz\)/\(d\lambda\)) and \(z\)-intercept (\(z_{0}\)) obtained in the experiments were shown in Figure 12a. The calculation was performed by a linear approximation using the least-squares method. The coefficient of determination for the linear approximation \(R^{2}\) is shown in Figure 12b. As can be seen in the figures, \(dz\)/\(d\lambda\) and \(z_{0}\) were found to vary in proportion to \(\left|\theta_{X}\right|\), except for \(\theta_{X}=-2\) degree, where \(R^{2}<0.99\). This suggests that the confocal optics should be configured according to the tilt angle \(\theta_{X}\) of the object to be measured. In addition, numerical results showed that \(dz\)/\(d\lambda\) is constant regardless of \(\theta_{X}\), but experimental results show that it varies. This phenomenon can be expressed by the following equation:  
Figure 6:0  
This means that the \(z\)-\(\lambda\) sensitivity (\(dz\)/\(d\lambda\)) and \(z\)-intercept \(z_{0}\) change with the object tilt angle \(\theta_{X}\). This factor is thought to be due to the effect of aberrations \(\Phi\) existing in the confocal optics. In addition to the improvement for a new confocal optics that was not affected by the aberrations \(\Phi\), it was also realized that a new \(z\)-position detection method can simultaneously measure the angle \(\theta_{X}\) information of the measurement point is required.
## 4 Simulation Experiment and Discussion
### Scenarios  
To thoroughly assess the performance of the algorithm, six variable search scenarios were employed to evaluate the optimal search performance of CC-MPSO. Scenarios 1 to 4 are drawn from the works of [PERSON] and [PERSON] and [PERSON] (2013) and [PERSON] et al. [PERSON] et al. (2017). Scenarios 5 and 6 are developed based on scenarios 1 to 4. In addition, scenarios 5 and 6 encompass more intricate initial probability distributions of targets, coupled with an increased count of UAVs. According to Markov motion model, this will influence the probability values of the other regions, thus improving the search difficulty of the algorithm.  
Figure 6 depicts the tested scenarios whereby the target probability map is color-coded such that warmer color cells represent higher target probabilities. A red dot indicates the initial position of UAVs. A white arrow depicts the dynamics of the moving targets; the values on the arrows correspond to the probability of the target moving in different directions.  
The scenarios represent different searching situations and are described as follows:  
Scenario 1 has one small dense region moving rapidly. The motion state transfer probability of this target indicates that its approximate direction of motion is northeast. Two UAVs are searching at the same time in this scenario; it thus tests the algorithm on its exploration and adaptability capability.  
Scenario 2 has two separated high-probability regions with two UAVs on either side of them; their approximate direction of motion is both northeast. The scenario necessitates the algorithm's autonomous selection of a more optimal region for target detection.  
Scenario 3 consists of two adjacent regions with a high probability of occurrence. Their position and values differ slightly, which can make it challenging to identify a more suitable place to search for the target.  
Scenario 4 includes two separated high-probability regions located opposite to each other over the two UAV locations. The algorithm has to quickly identify the better region to search and track as the target is moving southwest.  
Scenario 5 has three neighboring probability regions, all of which show the direction of target movement to be southeast according to the motion state transfer probability. Three UAVs are searching simultaneously on the map. This setting evaluates the algorithm's ability to efficiently search for the correct target region along a diagonal trajectory.  
Figure 6: Settings for search scenarios.  
Scenario 6 has two sets of targets, each containing two adjacent high-probability areas. Four UAVs are searching simultaneously; the increased complexity of the initial probability distribution of the targets and the elevated number of UAVs make the solution space of the algorithm much higher and put high requirements on the efficiency and robustness of the algorithm.