esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Structure of the PS-NETM Software
While both wavelets are orthogonal, coiflets are designed to be asymmetrical, which is useful when dealing with different scales in signal [25]. Symlets, on the other hand, are near symmetrical, making them more appropriate for symmetric patterns of signal. Other alternative filtering methods are least squares estimation (LSE), bandpass filtering, and low-pass filtering. LSE is one of the most widely used methods for noise filtering in GNSS time series due to its simplicity and computational efficiency. However, it assumes that the noise is Gaussian and independent, which is untrue in the case of a GNSS time series. Bandpass filtering is a technique that allows extracting signals of interest from a time series by removing the components that are outside a certain frequency range. Its main drawback is that it requires prior knowledge or estimation of the noise frequency band. Low-pass filtering is another simple and effective way to mitigate noise in time series by removing high-frequency noise components. This, however, may result in the attenuation of high-frequency components of the signal. The last step of PS-NETM data processing is the calculation of statistical indicators required for analyzing the series using the non-classical theory. These indicators are the skewness (\(A\)), kurtosis (\(E\)), their errors (\(\sigma_{A}\) and \(\sigma_{E}\)), and confidence intervals (\(cA\) and \(cE\)). Based on these indicators, PS-NETM concludes that the residual time series modeling is adequate, namely that the O-C differences should (or should not) ideally follow distribution (2) with the values of the \(m\) index within (3). In order to obtain correct statistical indicators, a number of requirements are imposed on the input data, namely,  
* The number of measurements in time series should be at least 500 actual values, which, in the case of GNSS coordinate series, means daily station coordinates for a year and a half observation period;
* The number of missing observations should not exceed 5% of the total amount of data;
* PS-NETM is not recommended for analyzing stations in seismically active regions.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 3 Encryption for Cloud Computing in Manufacturing  
To ensure a secure cloud computing, encryption of data is key. Thus, after introducing the fundamentals of cloud computing and encryption in Section 2, Section 3 analyzes the potential and limitations of different encryption technologies for cloud computing. First, Section 3.1 presents the limitations of classic encryption for cloud computing. Second, Section 3.2 presents the potential and challenges of using homomorphic encryption during cloud computing. To further analyze and validate both potential and challenges, Section 3.3 experimentally compares classic and homomorphic encryption.
## 3 Results
### Sampling Frequency and Social Network Analysis  
As shown in Figure 12, the number of neighbours for each individual decreased when the sampling rate changed from continuous 5 s sampling to single records within 5-min time intervals (Figure 12a-c). However, when the interval of recording was changed to 1 min recording and 4 min off or even 9 min off, all neighbours were captured for the individuals with a threshold distance of 20 cm or 30 cm (Figure 12b,c). In contrast, with a sampling rate of 5 min, it was not possible to detect the neighbours for some time points even with a threshold distance of 30 cm (Figure 12c) indicating some or all social interactions were missed between individuals.  
Figure 10: Animal social groups based on (**a**) recorded video (**b**), social network analysis, and (**c**) GNSS positional data at four time points on the third day of the study. In the social network plots, letters in the vertices (nodes) refer to individual animals and edges show the distance between two animals where a thinner line corresponds to a shorter distance and vice versa. Note: For each picture, the related social network graph and GNSS locational plot are in the same row.  
Figure 11: The pattern over time of individual gazing movement leader scores (**a**) summed for the total four-hour period during days 2 and 3; (**b,c**) first and second hour on day 2, and (**d,e**) first and second hour on day 3 presented as bar charts showing the individual movement rank for each 10 min period per hour of study (**b–e**) or the total score for 4 study hours (**a**) in which the animal (s) with the highest movement score moved first within each specific time point. A corresponding social network for each bar chart is also displayed (the larger vertex corresponds with an individual’s higher rank). Letters in the vertices refer to individual animals.  
Figure 12: Change in the number of nearest neighbours for individual sheep (A–G) based on different sampling rates (single 5 s, single 5 min, 1 min on (at 1 s) and 4 min off, and 1 min on (at 1 s) and 9 min off) and threshold distances: (**a**) 10 cm, (**b**) 20 cm, and (**c**) 30 cm, during day 2 of the study.
###### Abstract  
This paper proposes a new optimized parameter model that enhances the pointing accuracy of moving-platform electro-optical telescopes (MPEOTs). The study begins by comprehensively analyzing the error sources, including the telescope and the platform navigation system. Next, a linear pointing correction model is established based on the target positioning process. To eliminate multicollinearity, stepwise regression is applied to obtain the optimized parameter model. The experimental results show that the MPEOT corrected by this model outperforms the mount model, with pointing errors of less than 50 arcsec for approximately 23 h. In the three tests conducted, the modified azimuth error(s) (RMS) were 14.07\({}^{\prime\prime}\), 12.71\({}^{\prime\prime}\), and 28.93\({}^{\prime\prime}\), and the elevation error(s) (RMS) were 12.94\({}^{\prime\prime}\), 12.73\({}^{\prime\prime}\), and 28.30\({}^{\prime\prime}\), respectively.  
moving-platform electro-optical telescope; pointing error; error correction; optimized parameter model +
Footnote †: journal: Sensors
## 1 Introduction
### Applications of Split-Plot Factorials
As all production processes generate waste though, a considerable amount of waste CIGS solar cells (e.g., defective ones) is expected to be produced in this case as well. Proper recycling of the waste can be beneficial both for environmental (lower need for mining virgin raw materials) and financial (lower need for purchase of raw materials) reasons. Nowadays, there is unfortunately no known industrial recycling process to treat CIGS containing waste. There is, however, some limited lab scale research on recycling of CIGS rich materials ([PERSON] _et al._, 2007; [PERSON] _et al._, 2007; [PERSON] _et al._, 2007), due to the interest around the critical In and Ga ([PERSON] _et al._, 2007) the CIGS compound contains. Very recently, research on recovery of the precious Ag coming from the conductive grid of CIGS solar cell has also been initiated ([PERSON] _et al._, 2007; [PERSON] _et al._, 2007). Another elementpresent in the CIGS solar cell which can be of interest to recover is Mo, due to its economic importance for our modern society [32]. In CIGS PV manufacturing, Mo is used as a back contact layer [33]. So far, there is no literature available on its recovery from PV though, meaning that investigation of the effect of multiple parameters may be necessary, before decisions on further development of treatment processes are taken. Therefore, factorial experiments can be a useful tool when researching and developing PV recycling processes. To sum up, there has already been a lot of progress made in the analysis of split-plots, and a great effort has been made by the statisticians to make them accessible to a broader public of experimenters. However, the information on how to properly analyze them is scattered in the literature and detailed explanations for non-experts are often missing. Moreover, the fact that there is no attention given to the design and analysis of unreplicated split-plots, which are very likely to be picked by a non-expert experimenter, increases the risk of incorrect planning and, later, analysis. For a non-expert practitioner, this situation can look difficult. Therefore, taking into account the great importance of split-plots for the industry, the authors of the current paper aspire to collect all the available relevant information in one place and clarify concepts and methodology on the fundamentals of split-plots. More specifically, this paper starts by presenting the theory of design and analysis (detection of significant factors, suggestion of model, and checking of the residuals and model adequacy) of an unreplicated split-plot experiment with all factors at two levels each. Then, an example for this case is given and solved step-by-step in a simple and detailed way. After reading this guide, the reader with a basic knowledge of factorial experiments is expected to easily grasp how to handle other cases of split-plots as well, e.g., replicated designs, fractional designs, split-split plot designs, etc., which are available in the literature.
## 3 Problem Formulation
### System Model Overview  
Figure 1 presents the system model of the proposed work. Each component of the system model is explained below:  
* Request Queue (_ReqQ_): The _ReqQ_ stores the entire reserved VM provisioning request.
* Physical Machine Repository (_HostQ_): The resource details related to available memory, CPU capacity, current operating frequency, power consumption, percentage of CPU utilization, number of active VMs, state (on/off) about all the PMs are stored in the _HostQ_.
* Target VM Queue (_TargetVMQ_): _TargetVMQ_ contains VM details about the server assigned, the percentage of CPU time utilized by the VM, submission time, placement time, remaining execution time, power consumption.
* Management Node: The allocation and reallocation management (ARM) algorithm is a daemon executed in the management node. It activates the scheduling algorithm for VM to PM mapping, resource deallocation algorithm for resource recovery and task distribution algorithm for VM reallocation at specific intervals. It updates _TargetVMQ_, _HostQ_ and _ReqQ_.
* Physical Machine Manager (PMM): PMM is a daemon that is executed on each physical machine. It monitors and records the machines functional parameters. It is responsible for updating the _HostQ_ repository with server details.
* Virtual Machine Manager (VMM): VMM is a daemon that is executed on each VM. It is responsible for updating the _TargetVMQ_ with VM functional parameter details.  
Figure 1: System Model.
## 3 System Model
### The Offloading Model  
To represent the offloading decision in the FiWi network, the binary variables \(x_{1i}\), \(x_{2i}\), and \(x_{3i}\) are used. \(x_{1i}\) indicates the MEC offloading upon the ONU (1-tier). \(x_{2i}\) indicates the MEC offloading upon the OLT (2-tier). \(x_{3i}\) indicates the original data-computing in the Internet (three-tier).  
\[C1:x_{1i},x_{2i},x_{3i}\in\{0,1\} \tag{7}\]  
For different offloading, the latency is different.  
(1) MEC-1 offloading: MEC-1 offloading reduces the latency, via offloading data-computing from the Internet to ONU. The transmission latency of task \(i\) via the wireless link is \(T_{i}^{W}=D_{i}/R_{i}^{W}\). The data-computing latency of ONU is \(T_{ji}^{ONU}=\frac{\eta_{i}D_{i}}{f_{ji}^{ONU}}\). \(f_{ji}^{ONU}\) is the task \(i\) data-computing speed of servers at the place of ONU \(j\). The total latency of MEC-1 offloading is denoted as \(T_{1i}\).  
\[T_{1i}=T_{i}^{W}+T_{ji}^{ONU} \tag{8}\]  
(2) MEC-2 offloading: MEC-2 offloading imgrates data-computing from Internet to OLT. The transmission path for task \(i\) includes user/human/machine-ONU and ONU-OLT.  
The transmission latency via the wireless link from user/human/machine to ONU is still \(T_{i}^{W}\). The transmission latency via the fiber link from ONU \(j\) to OLT \(r\) is \(T_{ri}^{F}=D_{i}/R_{ri}^{F}\). The data-computing latency of OLT \(r\) is \(T_{ri}^{OLT}=\frac{\eta_{i}D_{i}}{f_{i}^{OLT}}\). \(f_{ri}^{OLT}\) is the task \(i\) data-computing speed of servers at OLT \(r\). The total latency of MEC-2 offloading is \(T_{2i}\)  
\[T_{2i}=T_{i}^{W}+T_{ri}^{F}+T_{ri}^{OLT} \tag{9}\]  
(3) MEC-3 offloading: If task \(i\) is handled in the Internet, the data-computing latency can be calculated by \(T_{Ci}^{Int}=\frac{\eta_{i}D_{i}}{f_{i}^{Int}}\). \(f_{i}^{Int}\) is the computing frequency of the server in Internet for task \(i\). The transmission path for task \(i\) includes user/human/machine-ONU, ONU-OLT, and OLT-Internet. The transmission latency via the fiber link from OLT to Internet is \(T_{Ci}^{Int}=D_{i}/R_{Ci}^{Int}\). The transmission latency for task \(i\) from user/human/machine to ONU via the wireless link is \(T_{i}^{W}\). The transmission latency for task \(i\) from ONU \(j\) to OLT \(r\) via the fiber link is \(T_{i}^{F}\). The total latency of MEC-3 offloading is \(T_{3i}\).  
\[T_{3i}=T_{i}^{W}+T_{ri}^{F}+T_{Ci}^{Int}+T_{Ci}^{Int} \tag{10}\]
# Finite Element Analysis of a Novel Approach for Knee and Ankle Protection during Landing
## 2 Materials and Methods
### Boundary and Loading Conditions  
The finite element simulation was carried out in Abaqus 6.14 (Dassault Systemes, RI, USA). Two load cases were initially simulated for validation purposes. Firstly, the foot was fixed in all six degrees of freedom, and a 600 N axial compression force was applied to the proximal tibia and fibula. The von Mises stress of the inferior articular surface of the tibia was calculated and compared with a previous vitro mechanical test to validate the biomechanical property of the ankle [25]. Another load case, which was applied to validate the biomechanical property of the knee, was also simulated. The proximal femur was fixed, and 20 N, 40 N, 60 N, 80 N, and 100 N anterior tibial loads were applied to the tibia, respectively. Then, the anterior tibial translation was analyzed [26].  
During the subsequent simulation in unbraced conditions, the instant of the peak vGRF at each landing height (0.4 m, 0.8 m, and 1.2 m) measured in landing experiments was extracted and selected for analysis in a static step. Since dynamic variations in the complex internal structure of the knee are difficult to clearly reflect simply by adjusting the displacement of the femur, tibia, and fibula, we chose to establish FE models of different knee flexion angles corresponding to the three instants simulated at three heights before calculation, according to the CT images, which was considered to be more reasonable. In each simulation, the proximal end of the model was fixed in all six degrees of freedom and a vertical downward force of 300 N was applied to the upper surface of the femur to simulate half of the body weight. Moreover, the peak vGRFs were loaded vertically upward onto the heel fat pad. The magnitudes of peak vGRFs and the corresponding knee flexion angles were all obtained from [5; 8]. As for the braced conditions, the only difference in boundary and loading conditions compared to the unbraced conditions was that the vGRFs were larger when the brace mass of 2 kg was taken into account.
## 4 Hyperspectral Image Analysis Methods  
### Line-Scan Hyperspectral Image Data  
Line-scan hyperspectral images are generally saved in a 3-D data format (x, y, \(\lambda\)) (see Figure 1a) of band interleaved by line (BIL). The BIL format stores data at all the bands from the first scan line, followed by all the bands from the second scan line. Data from subsequent multiple scan lines are interleaved in a similar manner. A complete spectrum can be plotted at any pixel, and 2-D spatial images can be compiled from all the scan lines at selected wavebands. On the other hand, line-scan spatially resolved spectral data from a single scan are usually saved in a 2-D scattering image format (y, \(\lambda\)) (see Figure 1b), in which spectra at different source-to-detector distances and spatial profiles at different wavelengths can be extracted for further analysis.  
Figure 7 shows representative line-scan hyperspectral images of Golden Delicious apples. Figure 7a shows 13 reflectance images from 450 to 1650 nm at 100 nm increments collected from a Golden Delicious apple using the two hyperspectral systems (VNIR and SWIR) shown in Figure 4[13]. These single-band images illustrate the variations of the spatial features as a function of wavelength, which can be used for various inspection purposes, such as detecting apple surface defects and contaminants. The image in Figure 7b is a light scattering image using a point halogen source with a line-scan spatially resolved diffuse reflectance measurement method [11]. Original reflectance spectra at three source-to-detector distances (i.e., 0, 3, and 6 mm) and normalized spatial profiles at three wavelengths (i.e., 600, 675, and 800 nm) are plotted to demonstrate the spectral and spatial information extracted along the vertical and horizontal dimensions of the scattering image. Light absorption and scattering properties of the apple, which are quantified respectively by the absorption coefficient (\(\mu_{\mathrm{a}}\)) and the reduced scattering coefficient (\(\mu_{\mathrm{s}}\)'), can be determined using a curve fitting method for the spatial profiles at all the wavelengths based on a diffusion theory model, and they can be used to evaluate apple internal quality attributes, such as fruit firmness and soluble solids content [15].  
Figure 6: Spatial calibrations for a line-scan hyperspectral imaging system under settings of (**a**) high spatial resolution; and (**b**) large field of view.
## 6 Applications of Mathematical Models
### Mass-Balance Mixing Model  
[PERSON] et al. (2002) [80] developed an alternative mixing model based on two isotopes and three sources and incorporates concentration variation. The calculation formulas of this model are expressed as  
\[\delta^{15}\text{N}=\sum_{i=1}^{3}fi\times\delta^{15}\text{N}_{i}, \tag{3}\]  
\[\delta^{18}\text{O}=\sum_{i=1}^{3}fi\times\delta^{18}\text{O}_{i}, \tag{4}\]  
\[\sum_{i=1}^{3}fi=1, \tag{5}\]  
where \(i\) represents the pollution source of nitrate, \(\delta^{15}\)N represents the abundance of \(\delta^{15}\)N of nitrate in mixed water sample, \(\delta^{18}\)O represents the abundance of \(\delta^{18}\)O of nitrate in mixed water sample,\(\delta^{15}\)N\({}_{i}\) represents the abundance of \(\delta^{15}\)N in pollution source \(i\), \(\delta^{18}\)O\({}_{i}\) refers to the abundance of \(\delta^{18}\)O in pollution source \(i\), and _fi_ is the total contribution ratios of three nitrate pollution sources.  
Mass-balance mixing model is only suitable for calculating the contribution ratios of no more than three major nitrate pollution sources. Besides, this model fails to consider the incorporation of several substantial sources of uncertainty, such as spatial and temporal variability of nitrogen and oxygen isotopes of nitrate as well as the isotopic fractionation during nitrification [81]. [PERSON] et al. [47], based on mass-balance mixing model, calculated the contribution ratios of irrigation water, groundwater, and precipitation to the nitrate of a river in Mecklenburg in Germany. The corresponding ratio was 86%, 11%, and 4%, respectively. Similarly, [PERSON] et al. [82] quantified the contribution ratios of three pollution sources (sewage, precipitation, and soil) to nitrate in 12 tributaries of the Baltic Sea. Mass-balance model can be used in the rivers with simple sources of nitrate pollution. It can also be used in cases where the pollution sources are similar by treating them as one source. For instance, rural domestic sewage and manure are together regarded as rural pollution, and nitrification of soil nitrogen and chemical fertilizer are regarded as one agricultural non-point source pollution. Accordingly, targeted measures can be taken so as to prevent and control the pollution.
## 3 Evaluation with Expected Total Costs
### Cost Analysis
#### 3.2.4 Expected Total Cost  
The normalized expected total cost, \(EC_{tot}\), is the function of three vectors based on Equations (1), (9)-(13), and (16):  
\[\begin{array}{l}\frac{EC_{tot}}{C_{bT}}=\left(\frac{\mu_{i}}{\mu_{T}}\right) ^{k_{b}}+\left\{k_{c}+k_{f}\Phi(-\beta_{T})\right\}F_{\ln}(m_{T}{}^{a})+k_{f} \int_{\mu_{T}{}^{a}}^{\infty}f_{\ln}(\mu_{ob})\cdot\Phi(-\beta_{ob})d\mu_{ob} \\ =func(\mathbf{I},\mathbf{K},\mathbf{V})\end{array} \tag{18}\]  
where \(\mathbf{I}\) is the vector of input \(\{\mu_{i}\}\), \(\mathbf{K}\) is the vector of parameters \(\{k_{b}\), \(k_{c}\), \(k_{f}\}\), and \(\mathbf{V}\) is the vector of variability \(\{\text{COV}_{\text{Q}}\), \(\text{COV}_{\text{R}c}\), \(\text{COV}_{\text{R}p}\}\). Figure 8 shows the relationship among the parameters, which differs between the management options--that is, the proposed and conventional cases.  
The input \(\mathbf{I}\) is the parameter the designer should select to minimize the expected total cost. Although the optimal value of \(\mathbf{I}\) is when the derivative of Equation (18) with respect to \(\mathbf{I}\) is zero, because of the difficulty of differentiation, it is obtained numerically in the next section.  
Vectors \(\mathbf{K}\) and \(\mathbf{V}\) are divided into three parts in the view of who can change them: conditions of the project \(\{k_{b}\), \(k_{f}\), and \(\text{COV}_{\text{Q}}\}\); parameters of the technique \(\{k_{c}\) and \(\text{COV}_{\text{R}p}\}\); and parameters of the alternate method \(\{\text{COV}_{\text{R}c}\}\). The conditions of the project depend  
Figure 7: Relationship between normalized failure cost and target reliability index (\(\text{COV}_{\text{R}}=0.35\), \(\text{COV}_{\text{Q}}=0.10\)).  
on the type of project as well as the location, which cannot be changed by contractors and designers. As for the parameters of the technique, makers of the piling techniques may try to reduce the CM cost and increase the reliability of the estimation with the piling data. The parameters of the alternate method will be improved due to new investigation methods [63] or new formulae for estimating the capacity [64].