esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Instrument Design
#### 4.1.3 Face Validity  
[PERSON] (2003) highlighted that the measurement instrument for data collection needs to be validated to ensure high quality data. Ref. [55] proposed several types of validity test for determining the integrity of measures: face and content validity. Ref. [52] referred to face validity as a method of testing whether the instrument appears to measure what it is designed for. The survey was reviewed and validated by seven experts, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Field of Specialization** & & **Results/Outcomes** \\ \hline A1 & PhD in IT & - & Modified the main covering letter to be more precise and explain the purpose of the questionnaire. \\ A1 & PhD in IT & - & Modified all the items for the different variables in all the cloud computing areas (in the English language) to facilitate understanding of the Likert scale of measurement. \\ B1 & PhD in IT & - & Recommended collecting the questionnaires face to face to answer any queries and to increase the response rate. \\ B1 & PhD in IT & - & Modified some items in the field of culture factors. \\ B1 & PhD in IT & - & Suggested modification to the compatibility questions (Section 3), to make them more understandable. \\ C1 & MSc in IS & - & Modified the wording (in Arabic) of some items in different fields \\ D1 & PhD in Project & - & Suggested modification of the formulation of the Independent Variable (Regulatory policy) in part3 to facilitate understanding and response. \\ E1 & MSc in Project & - & Modified an item in the field of security factor (in English), which needed more explanation \\ A2 & PhD in English literature & - & Everything was clear and understandable; minor translating corrections the Arabic version. \\ B2 & MSc in Information Management & - & Everything was clear and understandable \\ C2 & PhD in IT & - & Everything was clear and understandable \\ B2 & BSc in Computer Networking. & - & Everything was clear and understandable \\ E2 & PhD in Business IT & - & Everything was clear and understandable no correction was required. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Questionnaire Pre-test Modification.  
The seven experts represent specialists in IT (cloud computing) as well as statistics, as recommended by [43]. The draft questionnaire was delivered by hand and by email at different times, and many useful and important modifications were made on the specialists' recommendation. The length of the survey was reduced as a result of the feedback, and various questions were reworded for improved clarity.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 6 Conclusions  
Natural calamities such as floods, earthquakes, and landslides are inevitable, and can cause severe damage to human life. With the advent of new technologies, sophisticated mechanisms are available for rescue operations in such situations. One of the major threats that rescue teams face is damage to communication infrastructure, which hinders the progress of rescue due to the difficulties faced in coordination. The easiest method of communication in the present era is by cell phone. However, cell phones are unusable if the base stations providing network coverage are damaged in the calamity. An easy way to establish network coverage for cell phone communications is by hosting base stations in drones. However, drones have limited battery life and may require a substantial amount of energy to fly to their destinations. They have to frequently return to the nearest base  
Figure 21: View of the network coverage obtained in simulation for a coverage angle \(\alpha=30^{\circ}\): (a) network coverage front view and (b) network coverage top view.  
station to be recharged, then fly back to their destination. This may introduce voids in network coverage, making rescue operations inefficient. In this paper, we introduce a novel algorithm (the region-filling algorithm) for deploying drones in disaster-affected areas in a way that ensures that there are no voids in coverage and that maximum average leftover energy is available for the network. In addition, we ensure that the algorithm finds a deployment pattern that uses the minimum possible number of drones. We selected _Pettimudi_, India, a disaster affected location, for simulation experiments to find the drone deployment pattern for the region based on our algorithm. Our experiments show that the algorithm is able to find a drone deployment pattern that provides coverage to the entire area. Furthermore, our approach ensures that the maximum average leftover energy is available for the drones via deployment at different heights while taking the local terrain into consideration. We prove our claim that this approach uses the minimum number of drones, and present a simulation of our algorithm using Matlab and _OMNet++_.
# Framework for the Ensemble of Feature Selection Methods
## 3 Results
### Main Concepts  
The different types of FS algorithms are described below: filters, wrappers, embedded, and the methods to aggregate experts' opinions.  
* Filters: techniques that are easy to implement and can be scaled to use datasets with high dimensionality. Nonetheless, this type ignores the interaction with a classifier. For example, an \(R(f)\) function evaluates the relevance of each feature, and the output of the filter algorithm corresponds to a ranking that orders the features according to \(R(f)\)[30].
* Wrappers: methods, which evaluate the relevance of the subsets of features by using a classifier. Thereby, the best subset of features is selected by the learning algorithm. However, the computational cost of these techniques is high because when choosing the best subset, many subsets must be evaluated [5].
* Embedded: type of mechanism, which combines the advantages of the filters and wrappers. The main objective is to get the best performance in the learning process from a learning algorithm using a subset of features [2].
* Consensus: In ensemble learning, it is also called consensus theory of aggregation. Widely used in social sciences and administration, its main objective is to find a way to combine expert opinions through consensus rules [31].
## 2 Eddy Current Magneto-Optical Imaging System Settings  
### The Principle of Magneto-Optical Imaging Based on the Faraday Effect  
The Faraday effect [30; 31; 32] is when a beam of linearly polarized light passes through the MO medium and the polarization direction of the linearly polarized light is rotated due to the influence of the magnetic field in the propagation direction. The rotation angle is shown as  
\[\beta=\mathrm{VBL} \tag{1}\]  
where V is the Verdet constant reflecting the rotation performance of the MO medium, B is the corresponding magnetic induction intensity, and L is the distance of light propagation in the MO medium. MO detection and imaging are realized by using a medium with a strong Faraday effect as the sensor and combining the optical path as shown in Figure 1.  
The collimated light from the light source becomes linearly polarized after passing through the polarizer. The polarization direction of this light beam is rotated by the Faraday effect through the MO medium, which changes the light intensity reaching the photodetector through the analyzer. The specific relationship is shown in Malus' law:  
\[I=I_{0}\cos^{2}(\alpha-\beta) \tag{2}\]  
where \(\alpha\) is the angle between the light transmission direction of the polarizer and the analyzer, \(I_{0}\) is the incident light intensity to the analyzer, \(\beta\) is the Faraday rotation angle, and \(I\) is the light intensity to the detector. With a linear MO sensor [21], \(\beta\) is linear with the change of magnetic induction magnitude B in the detection range. When \(\alpha\) is 45 degrees and \(\beta\) is around 0 degrees, the maximum light intensity occurs, and Formula (2) can be transformed into \(I=\frac{I_{0}}{2}(1+\sin 2\beta)\). When \(\beta\) is small, based on the Taylor expansion of the \(\sin\) function, the above formula can be approximated as \(I\approx\frac{I_{0}}{2}(1+2\mathrm{VBL})\). This rate of change is influenced by the \(\cos\) square term, hypothesis \(\alpha-\beta=\mathrm{x}\). Taking the derivative of \(\cos^{2}\)x, it is found that the absolute value of \(-\sin\)2x is 1 when x = 45 degrees, and it is the maximum value at this time. When x changes slightly, its derivative changes very little. For example, when x changes by 1 degree, its absolute derivative value changes from 1 to 0.9994. At the same time, the magnetic field measured in this experiment is relatively weak, \(\beta\). The variation of the magnetic field is usually within 1 degree, so it can be approximated that the magnetic field changes linearly with light intensity.
## 2 Experimental Investigation  
### Material Properties  
Table 1 and Figure 1 show, respectively, the chemical composition and particle size distribution of the cement type I/II, SCMs (class F fly ash, class C fly ash, GGBFS), and filler (limestone powder) used in the experimental investigation. Two different types of coarse aggregate, crushed limestone and gravel, were used in this investigation. The two types were combined with fine aggregate (natural sand) using three different fine-to-coarse aggregate ratios of 0.45, 0.47, and 0.50. All physical properties and particle size distribution of fine and coarse aggregates are shown in Table 2 and Figure 2, respectively, and the combined aggregate gradations used in SCC mixtures are listed in Table 3. Chemical admixtures included polycarbonate type high range water reducing admixture (HRWRA) that meets the requirements of ASTM C494 type F admixture; viscosity-modifying admixture (VMA) that meets the requirement of ASTM C494 type S admixture; and air-entraining admixture (AEA) that meets the requirements of ASTM C 260. All materials used in this investigation were obtained from suppliers in the Midwest states of Nebraska, Iowa, and Minnesota.
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 2 Materials and Methods
### RNA Isolation, Library Construction, and Deep Sequencing  
Total RNA was extracted from the insect tissues using the TRIzol reagent, as previously described [19; 20]. The quality and quantity of total RNA were determined by multiple instruments, including a Nanodrop 2000 (Thermo Scientific, MA, USA), Qubit 4 Fluorometer (Invitrogen, CA, USA), and Agilent 2100 Bioanalyzer. Then, the total RNA (1 ug) of each sample was used to build the cDNA library using the TruSeq RNA Library Preparation Kit v2 protocol (Illumina, CA, USA), as described [20]. After the cDNA libraries were quality controlled by the Agilent 2100 Bioanalyzer and qRT-PCR, they were sequenced on the Illumina HiSeqXTEN platform with the paired-end 150 strategy. Raw sequencing reads of these samples can be accessed from the NCBI SRA platform under the accession numbers SRR10766480-SRR10766488.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Command and Data Management System
#### 3.5.2 Software  
The SKITH hardware is designed in a modular way, having different modules for different tasks. For the software, the same modularity principle is used. Each SKITH module runs software with common tasks (e.g., housekeeping) and module-specific tasks. To address modularity in software, we follow an app-centric approach for the software design.
## 7 Real Data Analysis  
In reference to [23], we utilize the aforementioned methods on a real dataset comprising 50 observations, see Table 5, as detailed below:  
This dataset presents the cluster maxima of daily ozone concentrations exceeding 0.11 ppm during the summer months at the Pedregal station from 2002 to 2007. This research is highly relevant to efforts aimed at protecting public health in urban areas.  
To conduct the analysis, we first calculate the maximum likelihood estimates for two parameters, \(\alpha\) and \(\lambda\). Following this, we perform a goodness-of-fit assessment for the generalized Pareto distribution using several practical criteria, including the Bayesian Information Criterion (BIC), the Akaike Information Criterion (AIC), and Kolmogorov-Smirnov statistics (K-S). Additionally, for comparative purposes, we evaluate alternative distributions such as the Shifted Exponential Distribution (SED) and the Inverse Weibull Distribution (IWD).  
The PDF of SED is as follows:  
\[f_{SED}(x|\alpha,\lambda)=\frac{1}{\lambda}e^{\frac{-(x-\alpha)}{\lambda}}, \alpha,\lambda>0,x\geq\alpha. \tag{66}\]  
The PDF of IWD is as follows:  
\[f_{IWD}(x|\alpha,\lambda)=\lambda\alpha x^{-(\alpha+1)}e^{-\lambda x^{-\alpha}},\alpha,\lambda>0,x>0. \tag{67}\]  
The statistical results and the maximum likelihood estimates are presented in Table 6. The findings indicate that the generalized Pareto distribution is the most appropriate model.  
To illustrate the aforementioned methods, we present two schemes of censored samples obtained from the dataset under progressive Type-II censoring.  
Scheme 1: \((0^{*15},3,0,1,1,0^{*17},3,0^{*2},2)\)  
Scheme 2: \((0^{*30},1,1,1,1,0^{*10})\)  
The maximum likelihood estimates obtained using the expectation-maximization algorithm, the Bayesian  
We have computed maximum likelihood estimates employing the expectation-maximization algorithm. Bayesian estimates were derived utilizing the TK method and the Metropolis-Hastings method. In the absence of prior information, all parameter settings were initialized to values close to zero.  
The results for both MLEs and Bayesian estimations are presented in Tables 7 through Table 8. Furthermore, Table 9 provides Bayesian point predictions along with 95% Bayesian interval predictions for variables \(y_{2}\) and \(y_{6}\) in future samples, with K set to 10.
# Evolutionary Strategy for Practical Design of Passive Optical Networks
## 5 Simulation Setup  
In our simulation scenarios, we considered two Brazilian regions and we called them Region I and Region II. For both regions, we suppose an ISP willing to deploy a new GPON network. The GPON technology operation is outlined in the ITU-T G.984.x standards series and its main characteristics are described in Table 5 (together with values adopted in all simulations carried on in this work).  
The first region is the central part of Pituba neighborhood (Region I). This region is located in the south of Salvador city, Bahia, Brazil. This region has approximately 1.85 km\({}^{2}\) of area and, after the importation process, is represented by a graph of 314 nodes. This choice is due to the medium-large size of the neighborhood. The second region is significantly larger and has a larger number of nodes. It is the central part of Camacari, another city of Bahia state in Brazil (Region II). This second region has approximately 5.7 km\({}^{2}\) of area and, after the importation process, is represented by a graph of 714 nodes. This choice is mainly due to the large size of the resultant graph and also because this region represents a large-sized non-capital city in Brazil.  
Both regions (Region I and Region II) were tested in two different scenarios, called non-dense and dense scenario (NDS and DS, respectively). The first one (NDS) consists of a scenario in which subscribers are distributed in a dispersed manner in the region of interest. The second scenario (DS) has a considerably higher number of subscribers distributed in a very close way to each other. Figure 5 shows the network initial state for non-dense and dense simulation scenarios, considering a network with 314 nodes (Region I) and 714 nodes (Region II). It is worth mentioning that the subscribers' distribution in the graph was carried out in a random manner. For the non-dense scenario, there is a predominance of nodes with two subscribers (green nodes). For the dense scenario, there is a higher incidence of nodes with seven subscribers (red nodes).  
All the simulations in this paper were done under Intel(r) Core i5 1.8 GHz processor, with 8 GB of RAM and Windows 8(r). The PONs planning optimization was based on a GA  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **ITU-T G.984** & **ADOPTED** \\  & **(GPON) [12]** & **GPON SYSTEM** \\ \hline Downstream Rate & 2.5 Gbps & 2.5 Gbps \\ Upstream Rate & 1.25 or 2.5 Gbps & 1.25 Gbps \\ Downstream Wavelength & 1490 nm & 1490 nm \\ Upstream Wavelength & 1310 nm & 1310 nm \\ Broadcast RF Video Wavelength & 1510 nm & - \\ Physical Reach & 10 or 20 km & 20 km \\ Split ratio & 1:32, 1:64, (1:128 planned) & 1:64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GPON characteristics and adopted values.  
technique (as described in detail in Section 4.3) and developed in Matlab(r) software. Table 6 describes the network initial characteristics for Region I and Region II (with non-dense and dense scenarios).
## 3 Experimental Study  
### Experimental Setup  
A two-layer hybrid isolation device is constructed by combining PID active control with passive control. The performance of this hybrid isolation device under seismic waves is evaluated. A six-degree-of-freedom simulation shaking table is used for the earthquake simulation experiments, as shown in the Figure 4. The shaking table measures 2.5 m by 1.5 m and consists of a motion platform, control cabinet, electric cylinders, and connecting components. It operates with a motor servo control system, supporting the input of sinusoidal waves and the writing of random wave spectrum data files. The system can meet the motion requirements for both linear velocity and angular velocity vibrations. The specific parameters of the simulation shaking table are presented in Table 1.  
The data acquisition system uses the DH8302 high-performance dynamic signal testing and analysis system from Donghua Testing. This system has a sampling frequency of up to 1 MHz and features 18 acquisition channels. Six ICP accelerometers are employed to measure the acceleration responses of the platform and the storage cabinet. The specific performance parameters of the accelerometers are shown in Table 2.  
This paper conducts experiments using three harmonic waves. The harmonic waves include a sinusoidal wave with an amplitude \(|\,\mathrm{A}\,|=1\) and a frequency of \(1\,\mathrm{Hz}\), a sinusoidal wave with an amplitude \(|\,\mathrm{A}\,|=1\) and a frequency of \(5\,\mathrm{Hz}\), and a sinusoidal wave with an amplitude \(|\,\mathrm{A}\,|=0.5\) and a frequency of \(10\,\mathrm{Hz}\).
# AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications
## 3 Materials and Methods
### Gesture Recognition
#### 3.5.3 Handling Similar Gestures  
In a system with arbitrary one-handed gestures, the algorithm to recognize static gestures described in Algorithm 2 is not sufficient since it can only recognize the closest matching gesture. In case a similar hand shape is required for multiple gestures, this algorithm will search for the best match, and therefore, only one gesture will be recognized. In a system with arbitrary hand gestures, however, there could be many similar gestures that should be interpreted differently. The Swipe gesture for example can be performed by swiping left or right with the same hand shape. However, swiping left and right is performed for different desired actions. In the proposed framework, each swipe with a different motion direction would be considered as a separate dynamic gesture, e.g., \"Swipe Left\" and \"Swipe Right\" would be recorded separately.  
To achieve this, the algorithm described in Section 3.5.1 is adjusted to allow multiple gestures with the same hand shape to be activated at the same time:  
* Instead of choosing the gesture with the lowest joint distance (closest match to the stored gestures), all gestures are marked as found that fit within the chosen thresholds.  
* If there is more than one gesture found, check if there are gestures marked \"Allow Similar Gestures\". If yes, then allow all gestures marked as such to be performed. If visualization is enabled, the user receives visual cues for the different motion directions the hand joints can follow.
* If there is more than one gesture found, but no gesture is marked as \"Allow Similar Gestures\", then only allow the closest match to be performed.
## 3 Materials and Methods  
This study utilizes a 3D seismic dataset provided by Sinopec Northwest Oilfield Company, covering an area of approximately 360 km\({}^{2}\). The raw data underwent pre-stack  
Figure 1: Integrated geological characteristics of the Tahe Oilfield in the Tarim Basin, China. (**a**) Geographical location of the study area. The red rectangle marks the study region, situated in the Tarim Basin of northwestern China. (**b**) Geological structural map of the Tarim Basin. The yellow area represents the Tahe region, located in the southern part of the Akel Uplift and positioned within the marginal zone of the lacustrine basin. (**c**) Distribution of key well locations in the study area of the Tahe Oilfield. Orange and blue lines denote the well tie lines analyzed in this study. (**d**) Composite stratigraphic column and sedimentary facies distribution from the western part of the study area. The lower section comprises mudstone-dominated lacustrine facies deposits, while the upper section consists of deltaic deposits characterized by interbedded sandstone and mudstone with variable thicknesses.  
time migration (PSTM) processing to enhance the signal-to-noise ratio, with an effective frequency band of 15-120 Hz (dominant frequency: 80 Hz) and a sampling interval of 2 ms. Based on the average seismic wave velocity of 4000 m/s in the fourth member, the vertical resolution was calculated to be 12 m. Additionally, logging data from over 130 exploration wells in the study area, including spontaneous potential (SP), gamma ray (GR), and deep induction resistivity (RILD) curves, were integrated for lithology calibration and sedimentary facies constraints.  
Grain size data were analyzed using a combination of sieve analysis and sedimentation methods. The dried and disaggregated samples were passed through a 62.5 um sieve to separate the coarse and fine fractions. The coarse fraction was subjected to sieve analysis using a series of standard sieves (e.g., 1 mm, 0.5 mm, 0.25 mm, and 0.125 mm) for sequential sieving. The residual mass retained on each sieve was weighed. For the fine fraction, sedimentation analysis was performed: the dried particles were mixed with a dispersant (sodium hexametaphosphate) solution, ultrasonically treated to ensure dispersion, and then transferred to a 1000 mL graduated cylinder. At specific time intervals, 10 mL of suspension was extracted from a depth of 10 cm using a pipette, dried, and weighed to calculate particle concentration at each time point. The settling velocity \(v=\frac{h}{t}\), where \(t\) is the settling time and \(h\) is the extraction depth, was substituted into Stokes' law to derive the particle diameter: \(d=\sqrt{\frac{18\eta h}{(\rho_{s}-\rho_{f})\,g^{t}}}\); \(\eta\): fluid viscosity (Pa\(\cdot\)s); \(\rho_{s}\), \(\rho_{f}\): particle and fluid densities (kg/m\({}^{3}\)); \(g\): gravitational acceleration (m/s\({}^{2}\)); \(h\): settling height (m); \(t\): settling time (s).