esa-sceva/satcom-chunk-collection huggingface dataset:
# A Resilience Engineering Approach for the Risk Assessment of IT Services
## 3 Case Study
Based on this, to reduce the above risks, the migration process was modified as follows:  
* the request for transfer was postponed, it occurs when the migration is completed;
* the use of a temporary domain (alias domain) was foreseen so that if the migration fails or the services on the new platform are unstable, it is possible to go back from the alias domain to the current domain, deleting the redirect to the temporary solution. In this way, once the migration is completed and both the functionality and consistency of the services are verified, it is possible to operate the request for the domain transfer. Such a new procedure modifies the list of functions as follows:  
1. Transfer request;
2. Web service analysis;
3. Web hosting analysis;
4. Domain name analysis;
5. DNS provider analysis;
6. Backup of the source system;
7. Migration test;
8. Functionality test;
9. New server configuration;
10. Platform installation;
11. Platform migration;
12. Redirecting the source domain to a temporary domain;
13. Consistency test and services verification;
14. Request for transfer of the domain name;
15. Configuration of all the services related to the domain name;
16. Completing the migration. In Figure 7, the FRAM model of the new procedure is reported, while the difference between the current model and the new one is illustrated in Figure 8, where the two migration procedures are compared. More in detail, in the new procedure, the functions \"Configuration of all the services related to the domain name\" and \"Request for transfer on the domain name\" have a new position, while a novel function was introduced (function 12n, Redirecting the source domain to a temporary domain). This function adds a sound control phase aimed at augmenting the resilience of the procedure. Actually, the use of an alias domain allows one to go back to the \"old\" platform, deleting the redirecting process and thus stopping the migration process if the controls made in the new function 13n (consistency test and service verification) fail. Thanks to this modification, customers do not notice any dysfunctionality since the platform is always available. Moreover, in the current migration procedure, a delay (\"too late\") or anticipation (too early) in the completion of function 11c (Request for transfer on the domain name), which depends on two different suppliers (the old and the new maintainers), can cause errors in the migration process. This can entail the platform malfunctioning or being unavailable. In addition, when inaccuracy occurs, as in the case of a mistake by the provider regarding the company or a mistake by the company regarding the new supplier in communicating the Domain Authorization Code of the Extensible Provisioning Protocol (EPP), the migration process will be blocked. Figure 7: Application of the FRAM model to the new transfer procedure. Conversely, in the new configuration setting, the inclusion of function 12n allows technicians in both of the above cases to maintain the availability of the platform while mistakes and problems are solved.
# Multi-Objective Configuration Optimization of
## 2 Multi-Objective Optimization Model for Allocating HESS  
### HESS Model  
As illustrated in Figure 2, in order to make \(P_{WDCom}\) approximate \(P_{WDref}\) as closely as possible, the power fluctuation is constantly compensated by the real time power output of HESS, \(P_{SC}+P_{B}\). The compensation effect is determined by the remaining energy in the HESS, the rated charging/discharging power, and the real time available maximum charging/discharging power. When the ESS \(i\) (\(i=SC\), \(B\)) is charging or discharging, the remaining energy, \(E^{t}\), at time \(t\), can be expressed as Equations (1) and (2), respectively. Both exhausted and saturated states have bad effects on the ESS lifetime. The state of charge (SOC) of an ESS that can be expressed as a percentage of its rated capacity, \(E_{rated}\), reflects its available capacity, and it can be formulated as Equation (3) in which \(SOC_{\max}\) and \(SOC_{\min}\) represent the upper and lower limits, respectively.  
\[E^{t}_{\ i}=(1-r_{SDCi})E^{t-1}_{\ i}+P^{t}_{\ i}\Delta t\eta_{Ci} \tag{1}\]  
\[E^{t}_{\ i}=(1-r_{SDCi})E^{t-1}_{\ i}+P^{t}_{\ i}\Delta t/\eta_{Di} \tag{2}\]  
\[SOC^{t}_{\ i}=E^{t}_{\ i}/E_{rated}. \tag{3}\]  
For ESS \(i\), \(r_{SDC}\) indicates the self-discharge rate, and \(\eta_{C}\) and \(\eta_{D}\) denote the charge efficiency and discharge efficiency, respectively. \(P_{t}\) represents the ESS power at time interval \(t\), which can be mathematically described as Equation (4) when it is charging and Equation (5) when it is discharging,  
\[P^{t}_{\ i}=\min(P_{Cratedi},P^{t}_{\ refi},P^{t}_{\ maxi}) \tag{4}\]  
\[P^{t}_{\ i}=-\min(P_{Dratedi},\left|P^{t}_{\ refi}\right|,P^{t}_{\ maxi}) \tag{5}\]  
where \(P_{Crated}\) and \(P_{Drated}\) denote the rated charging and discharging power, and \(P_{\ ref}^{t}\) indicates the reference output power whose calculation will be analyzed below. \(P_{max}\) represents the allowed maximum charging/discharging power. It is related to ESS charging/discharging characteristics and the maximum left energy at \(t\). Its formulation can be expressed as Equation (6) if ESS is charging or Equation (7) if ESS is discharging.  
\[P^{t}_{\maxi}=SOC_{maxi}E_{ratedi}-(1-r_{SDCi})E^{t-1}/\eta_{CI}\Delta t \tag{6}\]  
\[P^{t}_{\maxi}=(1-r_{SDC})E^{t-1}_{\ i}-SOC_{mini}E_{ratedi}\eta_{Di}/\Delta t. \tag{7}\]
## 3 Control Structures
### Implementation of DVC in RSCAD
#### 3.1.4 Voltage Limitation  
The AC voltage (\(V_{AC}\)) of the GSC is generally limited by the DC (\(V_{DC}\)) voltage value. The factor that brings the relationship between the AC and DC voltages is the modulation index. The modulation index (\(m\)) is computed, as shown in Equation (16). The maximum value of the modulation index is set around to 1, and GSC voltage must be limited at that point to avoid non-saturation of the GSC.  
\[m=\frac{2\sqrt{2}V_{AC}}{\sqrt{3}V_{DC}}. \tag{16}\]  
Figure 5: Current limitation algorithm in Grid Side Converter (GSC).
## 3 Results
### Definitive--Screening Design (DSD)
#### 3.2.1 Data Analysis and Modelling  
Data modelling for each response was performed using the following quadratic equation, Equation (3).  
\[\text{Y}=m_{0}+m_{1}X_{1}+m_{2}X_{2}+\ldots\ +m_{n}X_{n}+\sum m_{ik}X_{i}X_{k}+ \sum m_{ii}\ X_{i}^{2} \tag{3}\]  
where Y denotes the measured responses, \(m_{0}\), \(m_{1}\), etc. are the regression coefficients with \(m_{0}\) being a constant, \(X_{i}\) is an independent variable (A, B, C, or D), and \(X_{i}X_{k}\) and \(X_{i}^{2}\) are the interaction and quadratic terms, respectively.  
The regression equation, (Equation (4)), was obtained following response transformation employing Box-Cox response transformation together with stepwise selection of terms.  
\[\begin{split}\ln(q_{e})=2.6945+0.07433\ \text{pH}+0.007701\ \text{DC}-0.004120\ \text{AD}+0.003658\ \text{ST}-0.000132\\ \text{pH}\times\text{DC}-0.000385\ \text{pH}\times\text{ST}+0.000004 \ \text{DC}\times\text{AD}-0.000003\ \text{AD}\times\text{ST}\end{split} \tag{4}\]  
\[[\text{R}^{2}=99.43\%,\text{R}^{2}-\text{Adj.}=99.04\%,\text{R}^{2}-\text{ Pred}=98.31\%]\]  
Values of R\({}^{2}\), R\({}^{2}-\text{Adj}\) and R\({}^{2}-\text{Pred}\), associated with each regression model are shown following Equation (4). As shown, value of the coefficient of determination (R\({}^{2}\)) was high implying the goodness-of-fit. Moreover, R\({}^{2}-\text{Adj}\) value was close to that of R\({}^{2}-\text{Pred}\), signifying the capability of the suggested model to expect the response for any new observation. Value of R\({}^{2}-\text{Pred}\) was not markedly less than that of R\({}^{2}\), speculating that the model was not over-fit. The model predicted values for the measured response are shown in Table 3. The percentage of prediction error (%Er) was employed to amount the precision of the prediction of the model. Values of %Er were between 0.20-1.83% [38; 39; 40; 41]. The percentage of prediction errors as shown is relatively small (less than 5%), inferring that the proposed model has reasonable predictive capabilities.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Clustering
#### (2) LEACH-AEC  
[PERSON] and [PERSON] in [18] proposed the LEACH Adaptive Energy Consumption (LEACH-AEC) protocol that uses more comprehensive information in the selection of CH, i.e., the energy residual, the distance to the base station and the distance between CH. It is expected that this selection of CH can be more optimum for the nodes and network condition. In addition, the protocol already supports a multi-hop communication from CH to the base station to anticipate a large network. This protocol is validated through simulation. As the result, it can be proved that LEACH-AEC is more effective in reducing energy consumption and increasing the lifetime of the network.
# Enhancing the Accuracy of Advanced High Temperature Mechanical Testing through Thermography
## 3 Results
### Infra-Red Considerations
#### 3.1.3 Size and Location of Control Area  
A powerful advantage of thermography over thermocouples and pyrometers is that the size, location and mode of temperature control are flexible. Every surface in view can be measured, however, depending on the specimen geometry and material a number of options can be taken. During this investigation it was found that when using flat HE23 coated surfaces a single measurement point (SP) or a user defined area control, gave accurate temperature readings in comparison to spot welded thermocouples as seen in Figure 11a,b. This was the case when using both the averaging and maximum temperature command outputs.  
Throughout the investigation it was noted that when using uneven surfaces, due to either oxide growth or material structure, it was difficult to ensure accuracy. In such circumstances with uneven structures or non-uniform surface emissions, controlling temperature by averaging over the entire control zone proved most accurate, Figure 11c. However, the emissivity of an object is known to vary with the angle at which you are measuring [30]. Thus, when controlling temperature of surfaces that  
Figure 11: Size and location of possible measurement points and areas employed during the thermography control method. (**a**) Single point control; (**b,c**) Average area control through a furnace window; (**d**) Single point control; (**d,e**) Average line control possible through induction coil designs.  
Figure 10: Thermography view of a dog bone rectangular specimen through the furnace window of the 2 nd generation infra-red lamp furnace. (**a**) Pre-oxidised polished metallic surface; (**b**) HE23 thermal paints (TP) coated metallic surface; (**c**) Non-metallic un-coated ceramic surface.  
are not flat such as cylindrical surfaces, the most accurate results were obtained using either a SP or averaging Line control down the centre of the specimen's \(y\)-axis, Figure 11d. In some instances where helical induction coil configurations are used, areas of the specimen are shadowed and could not be measured, Figure 11e.
## 4 Results and Discussion
The presented results provide information about conditions that can be transferred to other fields of underground mining in the study area, in order to map zones of potential land deformation (sinkhole) occurrence, such as in test field three (Figure 11). The location of this sinkhole is shown in Figures 5 and 10. The feature was spotted in Autumn 2019 by  
Figure 10: Interpreted subsidence risk areas in zones of ground disturbed by mining activity. Figure 9: Diagram of interpretation of the hydrogeological conditions along profile (shown in Figure 5) in test-field three. local forest services, and has been under monitoring with a terrestrial laser scanner since the Spring of 2020. The results of the most recently processed scans (3.2021) indicate an average sinkhole depth of 4 m below surface level. This is the first study using geophysical methods in this post-mining area, and probably the first one conducted in a structurally complex glaciotectonic environment. The only known description of mining-related deformations in a comparable area was prepared by [59], and concerns simple inventory and description of different deformation types, e.g., subsidence basins and sinkholes in former brown coal mines located to the north of the study area. Different types of deformations were also briefly mentioned in [50]. A study of deformations caused by ceased shallow underground brown coal mining, conducted with the application of geophysical and geological surveys in different geological and tectonic settings, was described in [60]. There are numerous studies using different geophysical methods to investigate and map voids resulting from old mining activity, and aimed at identifying potential deformation risk areas. Notable examples are listed in the Introduction section. None of them, however, were conducted in a glaciotectonic area and concerned shallow underground mining of brown coal deposits in complicated conditions of mineral extraction.
## 4 Media Access Control
### Local Sensing Configuration
#### 4.1.3 Tradeoffs of Local Sensing Operation Configurations  
Table 2 briefly summarizes the advantages and disadvantages associated with choices of spectrum-sensing mode and duration discussed above.  
Spectrum sensing in the reactive mode is triggered only when nodes need to access the medium. Such an on-demand manner allows for higher throughput compared with the proactive approach. However, the reduced sensing operation potentially limits the volume of spectral knowledge, which could affect the accuracy of spectrum decisions. Similarly, the length of sensing periods poses a tradeoff between throughput and accuracy, as well. The longer devices spend in spectrum sensing, the more information can be collected; yet the less time there is left for data transmission. However, the optimization mechanisms discussed earlier aim at the detection of certain spectral events, whilst in many cases of _IoT_ applications the purpose of spectrum sensing is to discover the channel desirability via observing the level of interference. As a result, this still remains an open challenge that needs to be addressed for individual scenarios. For instance, applications requiring high data rates cannot endure long sensing periods and are more inclined to adopt the reactive mode.
## 5 Discussion  
The applicability range of the approach presented above may span various aspects of computational electromagnetics. MAS has already been used in the literature to simulate practical problems, such as scattering by a raindrop [24], although that particular geometry is smooth, without wedges. Wedge treatment, as discussed in this paper, enhances MAS accuracy for applications such as jet engine inlet modeling [25; 26; 27], where interior blades contain sharp edges, or even further military aircraft scattering simulation, such a stealth design. Moreover, additional possible applications may include automotive modeling, for example, the functionality of a telecommunications antenna in the presence of the vehicle surface. Furthermore, the algorithm may be used in the design of absorbers and cloaking materials [28; 29; 30], or generally wave manipulation [31] when the geometry layout contains wedges.  
Although the numerical results presented were very good, they are limited to wedges formed by intersecting circular arcs only. It is anticipated that analogous techniques would be equally efficient in more general cases (i.e., arbitrarily shaped curved wedges); however, this is yet to be proven in future research. Likewise, other issues to be investigated include TE (Transverse Electric) incidence, the three-dimensional counterpart of this problem, unconventional materials, as well as a robust methodology to choose the optimal deformation parameters.  
Furthermore, by studying various geometries, proposing schemes for wedge treatment and obtaining solutions by choosing alternative configurations for the incident wave, material properties, etc., an extensive data set can be acquired. The latter, a combination of input variables and their respective exported results may be used as training data for building a mathematical model via Machine Learning algorithms. Machine Learning, the usage of computational methods in order to produce/\"learn\" information directly from data without relying on explicit instructions, improves its accuracy and performance proportionally to the amount of available training data. A larger data set is, therefore, required to find hidden patterns or intrinsic properties that can be utilized in the procedure of determining the optimal settings for the scattering problem and, hopefully, automating it.
## 2 Results and Discussion
### The Laveder EO Composition Is Affected by Harvest Time  
The chemical composition of _Lavandula angustifolia_ cv Etherio EO was reported to be affected by diurnal harvest time [23]. We repeated the same analysis in this study to ensure that EO pattern in the current study follows a similar pattern. Thirty-six compounds were identified by GC-MS analysis (Table 1). Specifically, more than 70% of the EO compounds consisted of linalool and its acetate, followed by smaller amounts of borneol and camphor (Table 1). At 9:00 in the morning, more alcohols, hydrocarbons, ketones and ethers were observed (Table 1). In midday, afternoon and evening, higher esters were observed while alcohols were reduced. The amount of linalool acetate was higher in the 18:00 sample. Furthermore, the total amount of linalool and linalool acetate was also higher in the afternoon compared to the morning EO, 75.85 vs. 73.94. This effect is more pronounced by the accumulation of linalool acetate in the afternoon (Figure 3a). Several minor EO components, such as lavandulyl acetate and \(\beta\)-catryophyllene, are also accumulated in the afternoon (Figure 3b). In contrast, the amount of camphor and 1,8 cineole is decreased during the day (Figure 3b). These results suggest that specific lavender volatile compounds emission is increased in the afternoon.
## 1 Introduction
### Volume Reconstruction for Freehand 3D Ultrasound Imaging  
Volume reconstruction methods interpolate the input data to a regular 3D array (voxel array) to obtain the 3D volume model of the VOI. Please refer to [13; 14] for a review of freehand 3D US volume reconstruction algorithms. The different volume reconstruction algorithms have been classified into three groups in [13]: Voxel-Based Methods (VBM), Pixel-Based Methods (PBM), and Function-Based Methods (FBM). More papers about volume reconstruction methods can be found in references [15; 16; 17; 18].  
Because the volume reconstruction is performed without segmentation of the 2D US images in a prerequisite step, it is not able to display the VOI distinct from the rest of the reconstructed volume. This can only be achieved by surface reconstruction of the VOI. In order to achieve this goal, a 3D segmentation step followed by a triangulation algorithm (e.g., the Marching Cubes algorithm proposed by [PERSON] and [PERSON] [19; 20]) should be performed. Because the 3D volume model is in a regular 3D voxel array, it is somewhat similar to the voxel lattice in CT or MRI imaging. If the reconstructed 3D volume is dense enough (_i.e._, no holes or gaps in between), many segmentation methods could be used to segment the VOI from the rest of the volume in the same way as they are used in CT or MRI imaging, such as level sets [21], 3D live-wire [22], image-adaptive radial basis functions [23], Random Walker [24], and Graph Cuts [25]. However, due to the wave interference phenomenon inherent in any coherent imaging process, US will suffer from speckle noise that makes the segmentation of US difficult, whether it is carried out by humans or using automatic segmentation methods. Moreover, if the input data are sparse, all the volume reconstruction methods cannot reconstruct a volume efficiently without introducing geometrical artifacts or distorting the images, which may lead to incorrect segmentation of the VOI by automatic segmentation methods. In clinical applications, manual segmentation is still the only universally reliable method for ultrasound data [9].
## 5 Conclusions  
This study presents a methodology for RFM optimization and demonstrates its potential for precise image registration and 3D reconstruction using tri-stereo and multi-date CubeSat images. Our method relies on a first order polynomial bias compensation in the image-space, and, as implemented in the open source COSI-Corr RFM module, can be applied to any image with a geometry model provided in the RFM standard. So it can also be applied to push-broom images delivered in the RFM format. Note however that, when applicable, a Rigorous Sensor Model should yield even better geometric accuracy than any RFM refinement method, including ours. Using data from SkySat, we demonstrated that, if each acquisition is decomposed in sub-images corresponding each to a single detector array, we could achieve a sub-pixel relative orientation. Without using GCPs, we obtain reprojection errors of 0.4 pix (at the 68% confidence level) with SkySat tri-stereo images and is 0.6 pix with PlanetScope Classic Dove images. The refinement of the RFMs allows for effective 3-D extraction, when combined with our sub-images pairing strategy. Our DEM of the Morecni Mine area is comparable to a DEM obtained from a LiDAR airborne survey in terms of spatial resolution and elevation. The comparison of the two DEMs, which were acquired 4 years apart, demonstrate that the DEMs calculated from Cubesat images should be accurate enough to detect and measure topographic changes due to mining operations, constructions or vegetation changes. We show that even better measurements can be achieved with DEM calculated in the image space compared with using standard photogrammetric techniques. This approach is however more demanding than the object-space approach. The application to the Shispher glacier shows the potential of Cubesat repeated imagery to monitor ice surges and the evolution of mountain glaciers due to climate change.  
This study demonstrates the huge potential of Cubesat optical imaging systems for environmental monitoring. With the existing constellations, it is already possible to use such data to detect and measure changes of the topography of various origin, due for example to mining, landslides, ice flow or coastal erosion. SkySat and PlanetScope platforms appear to be quite complementary in that regard. Thanks to its daily global coverage, PlanetScope can be used to produce DEMs over any area of interest with 10 to 15 m accuracy. If a better post event topography is needed, SkySat can be tasked to cover the area of interest with an optimal accuracy.  
Avenues for further improvement that are left for future research include the determination of the optimum number and distribution of GCPs to improve the orientation of push-frame images, and the application of a rigorous sensor model refinement methods on level L1A images.