esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Data Integration Approach for Agricultural Open Data Platforms
### Semantic Interoperability Layer (SIL)  
SIL plays an important role in the data integration process of the proposed approach. All considerable procedures for data integration such as executing SPARQL queries, applying mapping rules, making data interchange and storing mapped data within varied file formats are carried out within this layer. Crop-specific trait ontologies have also a very critical role in the subcomponent named data interchange engine of SIL. It uses crop-specific trait ontologies to map sensor measurement values to relevant traits of agricultural products for publishing more annotated data through open-data platforms for usage of any domain stakeholders. Domain experts may create these mapping rules by selecting relevant ontology classes and mapping them to corresponding sensor measurement values. Mapping rules are also used to link data obtained from IoT devices with agricultural ontology classes. They can be stored in RDF format and implemented by retrieving and manipulating with SPARQL to perform interchange operations when needed. Semantic web technologies are an important way of revealing the common knowledge of a particular domain by performing reasoning procedures [48].  
Figure 2: Decomposition raw sensor data stream.  
Discovering information in RDF datasets is performed using simple protocol and RDF query language (SPARQL); and the result of the relevant SPARQL query is formed as RDF triples [49]. The inference rules can be defined by domain stakeholders via the relevant module of the platform, which is represented on Figure A3 to help managing knowledge in the context of agriculture. These rules assist with domain stakeholders' understanding of the true meaning of sensor measurement values to perform more precise agricultural activities.
# Design and Kinematic Analysis of Deployable Antenna for Bionic Bird Tail Feather
## 3 Bionic Bird Tail Feather Antenna Configuration Design
### Design for the First Unfolding Stage  
The process of erecting the tail helix is analogous to the folding motion of the antenna rib itself. The rib and hinge are strategically positioned in a parabolic distribution at the apex to ensure optimal antenna function. The final design of the two-segment antenna rib is illustrated in Figure 9.  
Figure 8: Schematic diagram of the tail heel bone. (**a**) Dorsal view of the tail of a bird. (**b**) Lateral view of the tail helix. (**c**) Top view of the tail helix.  
Figure 7: Mechanical diagram of tail feather development of birds. (**a**)Tail feathers in the retracted state. (**b**) Tail partially spread by the Bulbi rectrictium. (**c**) Tail fully spread by the Lateralis caudate and Bulbi rectriciums.  
The horizontal alignment of the bird's tail feathers corresponds to the extended state of the antenna, whereas the vertical alignment of the bird's tail feathers corresponds to the intermediate unfolded state of the antenna. Adhering to the antenna design concept, an inverse bionic principle is employed for the bionic design. To economize on the antenna space, the second rib of the antenna is further folded from the 90\({}^{\circ}\) position to a proximity near the first rib, effectively closing the antenna.  
The mechanism parameters of the antenna are given: The first rib of the antenna has a projected length of 0.55 m to the ground, and the second rib of the antenna has a projected length of 1.2 m to the ground. The overall length of the two ribs in the unfolded state is 1.75 m.  
In the microgravity environment, only the frictional force and moment of inertia between the components are considered for the drive method. Opting for a rope drive, as opposed to the conventional methods, results in reduced mass and space utilization. A self-locking hinge was engineered to secure the final stage of locking, as depicted in Figure 10. Connector 1 is affixed to the inner rib, while connector 2 is secured to the outer rib.
# Exploring the Potential of Sensing for Breast Cancer Detection
## 5 Biosensors for Breast Cancer Detection
### Light-Based Breast Cancer Techniques  
Light can interact with the matter of different refractive indices, and the reflection, absorption, polarization, and scattering of the light can be monitored to explore the characteristics of the tissue, cells, or biological materials [150]. We have already discussed different optical biosensors, viz., SPR, SERS, fluorescence, and flow cytometry, in which physical properties of light have been considered to detect breast cancer. Several authors considered the frequency-domain photon migration (FDPM) [151; 152; 153] method for detecting breast cancer. FDPM performs content-based analysis by deriving scattering and absorption coefficients from amplitude and phase differences in backscattered, broadband-modulated (10 MHz to 1 GHz) near-infrared light [154]. Terahertz (THz) imaging is another platform for detecting breast cancer at early stages. A THz wave (0.1-10 THz) has high sensitivity to water molecules [155], is non-ionizing [156], produces low power levels [156], and has good penetration capabilities and resolution. In [155], the author demonstrated THz reflection imaging and distinguished 10 \(\upmu\)m heterogeneous tissue sections from cancer and noncancer breast tissue. In [156], TPS Spectra 3000 pulsed THz imaging and spectroscopy were considered, and it was found that tumor detection is accurate to depths of over 1 mm.
## 2 Materials and Methods
### Microalgal Growth Monitoring  
The microalgal growth was monitored daily along the 8 d of cultivation through the methodology: measurement of the culture's optical density at 750 nm (OD\({}_{750}\)) using an SP 2000 UV spectrophotometer ([PERSON], Sao Paulo, Brazil). A calibration curve was constructed between the optical density of the culture at 750 nm (OD\({}_{750}\)) and the biomass concentration (X, g\({}_{\text{DW}}\) L\({}^{-1}\)), as represented in Equation (1). The biomass concentration was estimated through centrifugation of the culture, followed by filtration of the supernatant in microfiber filters with a pore size of 1.5 \(\upmu\)m, and finally oven-drying at 60 \({}^{\circ}\)C overnight to eliminate the remaining moisture [23].  
\[\text{X}=(3.11\pm 0.09)\times\text{ OD}_{750}+(0.08\pm 0.02)\text{ \ \ \ \ \ \ \ \ \ }\text{R}^{2}=0.9976 \tag{1}\]  
The specific growth rate, \(\upmu\) (d\({}^{-1}\)), at the exponential growth phase was determined as Equation (2), where X\({}_{1}\) and X\({}_{2}\) correspond to the biomass concentration at the beginning (t\({}_{1}\)) and at the end (t\({}_{2}\)) of this growth phase [24]. The average biomass productivity (P, g\({}_{\text{DW}}\) L\({}^{-1}\) d\({}^{-1}\)) was calculated according to Equation (3), in which X\({}_{0}\) and X\({}_{t}\) represent the biomass concentration at the beginning of the experiment (t\({}_{0}\)) and at the end of the stationary phase (t\({}_{t}\)) [25].  
\[\upmu = \frac{(\ln\text{X}_{2}-\ln\text{X}_{1})}{\text{t}_{2}-\text{t}_{1}} \tag{2}\] \[\text{P}=\frac{\text{X}_{t}-\text{X}_{0}}{\text{t}_{t}-\text{t}_{ 0}} \tag{3}\]
## 2 Related Work
### Reliability in WSNs  
While reliability of homogeneous WSNs has been investigated in prior studies [21-24], reliability analysis of backbone approach in WSN is largely unexplored. In [22], authors formulate a WSN reliability measure that considers the aggregate flow of sensor data into a sink node. This measure is based on a given estimation of the data generation rate and the failure probability of each sensor. Common cause failures (CCF) have been discussed and identified as the cause of unreliability in WSN in [23,24]. Authors in [23] consider the problem of modeling and evaluating the coverage-oriented reliability of WSN subject to CCF whereas in [24] the main emphasis is on addressing the problem of modeling and evaluating the infrastructure communication reliability of WSN. In [21], the authors compute a measure for the expected and maximum message delay between data sources and data sinks in an operational distributed sensor network (DSN). Authors in [25] present a reliable routing protocol that forms the reliable routing path by utilizing network topology and routing information of the network. However, their protocol and analysis is application specific. Moreover, they have not provided any comparison with existing reliability schemes. In [5] we introduced the concept of hotlines to improve the reliability for WSNs. We proved analytically that concept of hotlines enhances the reliability of WSNs manifold. However, we did not provide any simulation experimentations to prove the result. Moreover, as explained previously in Section 1, few scenarios in hotline approach might give more end-to-end latency especially if source and the sink are just few hops away. All these scenarios are not accounted for in [5].  
Our work is quite different from the prior work described above. While we have utilized few concepts like clustering and multiple gateways from [4-13], small world graphs from [16], backbone approach from [17-19], and gateway association mechanism from [6], our emphasis is on addressing the reliability issues in WSN using the concept of hotlines between gateways. Most of the previous work, especially in backbone approach; focuses on energy efficiency in WSNs whereas we have combined versatile concepts-like backbone, clustering, hotlines-to simultaneously enhance both reliability and energy efficiency in a WSN. We also propose protocol modifications to achieve the hotline assisted topology and provide a reliability theoretic analysis of the proposed topology. Moreover, our paper suggests a brand new concept of SBR for WSNs to support end-to-end delay requirement of different applications.
## 2 Development of Proposed Controller
### Approximation Technique  
Let us consider the differentiator having complex-order \(\alpha+j\beta\) where \(\alpha\in(0,1)\) and \(\beta\in\mathbb{R}\)[5; 39].  
\[T(s)=s^{\alpha+j\beta}. \tag{6}\]  
Substitute \(s\) with \(j\omega\) in the above equation to obtain a new equation.  
\[T(j\omega)=j^{\alpha}\omega^{\alpha}e^{-\beta\frac{\pi}{2}}(\cos(\beta\ln\omega )+j\sin(\beta\ln\omega)). \tag{7}\]  
The MATLAB function provided below facilitates the derivation of the theoretical response of Equation (7) for the frequency range of \(\omega\in(\omega_{min},\omega_{max})\).  
function [M, P] = fdc(alpha,beta,wmin,wmax) w = logspace(log10(wmin),log10(wmax)); R = exp(-beta*pi/2)*(1i^alpha).*(w.^alpha)....*(cos(beta*log(w))+1i*sin(beta*log(w))); M = 20*log10(abs(R)); P = angle(R)*180/pi; end  
The proposed approach aims to achieve a rational integer-order approximation of complex fractional-order dynamical systems mentioned in Equation (6) by approximating their frequency-domain behaviour. The technique utilizes a log-Chebyshev magnitude design, which can be implemented using the built-in command fitmagfrd in MATLAB. To obtain an approximation, the frd command is used to fetch frequency-response magnitude data within the range of \(\omega_{min}\) and \(\omega_{max}\) for an order of \(N\). The steps to acquire an approximation are illustrated in the following procedure to provide a clearer understanding of the technique.  
* Choose the range of \((\omega_{min},\omega_{max})\) and the order \(N\).
* Using MATLAB built-in command frd, compute the frequency response magnitude data of \(T(i\omega)\) for the chosen range as stated in Equation (7).
* Perform a fit of the frequency response magnitude data for the selected order using the MATLAB built-in command fitmagfrd obtained in the previous step.
* Convert the state-space model obtained in the previous step to a transfer function model.  
The following MATLAB function will help in obtaining the proposed approximation of Equation (6) for \(\omega\in(\omega_{min},\omega_{max})\) and the order \(N\).  
function T = fdctf(alpha,beta,wmin,wmax,N) w = logspace(log10(wmin),log10(wmax)); R = exp(-beta*pi/2)*(1i^alpha).*(w.^alpha)....*(cos(beta*log(w))+1i*sin(beta*log(w))); D=frd(R,w); Gfit=fitmagfrd(D,N); T=tf(Gfit); end  
The function mentioned above can be used to obtain a stable and minimum-phase rational approximation for a fractional-order dynamical system with complex orders while maintaining its integer-order properties. This is particularly useful in complex engineering systems where precise modelling is necessary.
## 5 Recommendations Related to Access and Connectivity
### Design and Implementation of Policies and Programs  
Policymakers will need to address several issues to promote the deployment of broadband networks in rural areas: the development of broadband demands studies in these places, defining how the digital divide can be avoided within the same rural area, and the cost assessment and technical capacity of wireless networks in rural areas [59].  
Established and competitive service providers already offer solutions for urban and suburban areas, but there is little or no commitment to connecting zones that include smaller cities and rural villages [60]. The study of the role of niche technology infrastructure providers in the UK carried out by [PERSON] et al. [61] suggests that alternative suppliers may take advantage of unmet demand for ultra-fast broadband. These companies tend to develop unique business models, based on key partnerships and a modular approach, which compensates for problems associated with their small scale. Policymakers should consider this factor when planning public interventions to close the digital divide.  
Policymakers in Europe are increasingly aware of the contribution of specialist providers to the dissemination of state-of-the-art broadband. However, additional measures must be considered to ensure efficient allocation of public resources and minimize competitive distortion in this market. Both regulation and public subsidies should be reviewed to consider the emergence of new actors. This is likely to require a different approach to public intervention in broadband markets, which still focuses on a simplistic juxtaposition between competition in urban areas and market failure in rural ones. Indeed, public intervention has proven incapable of recognizing and harnessing potential incentives for private investment in areas where the market is supposed to fail. The positive experience of the United Kingdom analyzed by [PERSON] et al. [61] shows the potential contribution of niche suppliers to broadband development, under the European regulatory framework.  
The implementation of new solutions should begin with pilot projects, and the projects implemented should be measured according to their effectiveness, their efficiency, and the degree of achievement of their objectives [55]. The permanent monitoring of the digital dissemination is a necessary condition for the evaluation of the management and the choice of preventive and stimulating measures. Without this scientific approach it is impossible to identify the barriers to progressive changes in the agarian sector of economy [62]. The efficiency of the measures adopted can be evaluatedwith the help of indicators such as percentage of households with a computer, percentage of households with internet, fixed (wired) broadband subscriptions per 100 inhabitants, mobile (wireless) broadband subscriptions per 100 inhabitants, speed (bit/s) per internet user, number of secure servers per million inhabitants and percentage of individuals who use the internet regularly [63].
# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
## 5 Results and Discussion
Using 50 runs of simulations, both \(NSL\) and \(NSC\) are captured. Figures 10-13 show the \(NSL\) and \(NSC\) of POTC-generated schedules with respect to ELSH, MOPT, PEFT, and BDHEFT. The deadline factors \(K_{d}\ and\ K_{b}\) are set as 0.3, 0.5, and 0.7. The budget is relaxed by fixing the deadline \(K_{d}\) and varying the budget for different values of \(K_{b}\) = 0.3, 0.5, and 0.7. To assign the workflow tasks, the scheduler chooses the fastest or the expensive resources. Thus, for the created schedule under the same deadline, \(NSC\) tends to increase with the decrease in \(NSL\)[34]. Figures 10 and 12 show the average execution times of the Montage and Epigenomics workflows, and Figures 11 and 13 show the average execution costs of the Montage and Epigenomics workflows. As the value of \(Avg\). \(NSC\) increases, the execution cost decreases and \(NSL\) increases. When budget (\(K_{b}\)) is fixed, the deadline is relaxed by varying the deadline from 0.3 to 0.7. Subsequently, the inexpensive or the slowest resources are chosen by the scheduler. Thus, under the same budget, \(NSC\) is minimized and the \(NSL\) of the workflow decreases as shown in Figures 9-12. Furthermore, by varying the budget ratio and the fixed deadline, for each of these values, the budget is relaxed. Now according to the user preference, the scheduler is able to choose the fastest resource or the inexpensive resource. From the above results, it has been found that the average cost of the proposed method is minimized for montage by 7.1% and about 3.2% more for makespan with respect to BDHEFT. For the Epigenomics workflow, the average cost is minimized by 8.3%, with about 4.1% more for makespan. The overall results show that the proposed method (POTC) outperforms ELSH, MOPT, PEFT, and BDHEFT under the same budget and deadline by minimizing the average cost while improving the makespan, similar to the one given by  
Figure 3.4 Average execution time of the montage workflow for different deadline factors.
## 2 Background of the Proposed Wireless VR System
### Channel Condition of Wireless VR Environments  
To define the required processing of the proposed baseband architecture, we first analyze the channel condition of near-field wireless communications based on our previous 60 GHz RFIC in [14]. Applying the beamforming scheme [15; 16], as shown in Figure 2a, we can realize the error-free and zero-latency wireless communication when the beam is successfully aligned. In the case of misaligned beamforming, which normally occurs when the user rotates his or her head, some parts of the received packets directly disappear, resulting in a massive number of burst errors. As a result, the channel condition using the 60 GHz RF transceiver sometimes significantly deteriorates. Even with an average bit-error-rate (BER) of around \(10^{-5}\), as depicted in Figure 2b, the length of burst errors is extremely long due to the high-speed transmission rate, requiring a dedicated digital baseband processing associated with the strong ECC hardware. Based on this observation, we develop a compact baseband system supporting the transmission speed of 12.8 Gbps, recovering up to 1080 consecutive bit errors in the data frame of 106,920 bits.  
Figure 1: Processing flow of the proposed wireless VR system.  
Figure 2: Error pattern observed by the previous 60 GHz RF circuit. (**a**) Error count = 0, Error rate = 0; (**b**) Error count = 116,656, Error rate = 4.32 \(\times\) 10\({}^{-5}\).
## 4 Selection of Affective Relative AUs and Feature Extraction
### AU Feature Extracting Solution  
In present research, a wide variety of feature extraction algorithms are used in facial analysis: Gabor wavelet, PCA (Principal Component Analysis), AAM (Active Appearance Model), HOG (Histogram of Oriented Gradient), SIFT (Scale-invariant feature transform), LBP (Local Binary Pattern), WLD (Weber Local Descriptor), etc. They reveal the discriminating property of objects for a certain classification target with difference principles. As each AU has a different texture structure, we prefer to choose several diverse features and fuse their complementary contribution in representing AUs. This paper employs Gabor, AAM, LBP, and WLD feature descriptors, which have different feature extraction mechanisms. The quantitative fusion performance between these selected features is examined in the next part. The details of the employed features are briefly illustrated as follows:  
1. **Gabor feature:** Selecting a two-dimension Gabor wavelet with the scale and direction \(v=2\) and \(u=4\pi/8\) respectively. Together with PCA, dimension reduction Gabor is extracted to represent each AU.
2. **Geometry feature:** Utilizing the AAM shape model to calibrate feature points and generate corresponding geometry features for AU regions, and making the coordinate of feature points the geometry feature vector.
3. **LBP feature:** Choosing a flexible circle operator to extract the local texture of each AU, and using a histogram to record the LBP feature vectors of AU regions.
4. **WLD feature:** Using an emerging method that synthesizes multiple orientation and excitation operators of sub-image sets, and forming a histogram to record WLD feature vectors of each AU region.  
Some results of Gabor/geometry/LBP/WLD feature extraction for AUs region are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Gabor** & **Geometry** & **LBP** & **WLD** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Four kinds of feature extraction results for AU blocks.
## 2 Network Model  
Figure 1 illustrates the common architecture of a BSN. The physiological information collected by sensor nodes is first delivered to a coordinator on the body, which then forwards the information to the concerned agents via a WLAN access point [18, 19, 20]. In this paper, we only consider the communication between the coordinator and the AP for handover analysis.  
We assume that BSNs are equipped with inertial sensors such as accelerator, gyroscope, and magnetometer for healthcare applications. The raw data from the accelerator is first filtered with a low-pass filter at 5 Hz. Then we decouple the linear acceleration in the geocentric coordinate system using the results from the gyroscope and magnetometer, as the measured acceleration is the combination of gravity and linear acceleration. For details please refer to [16]. Based on linear acceleration, kinematic tracking scheme adopts kinematic relationships to estimate the position of a BSN.  
In addition, we assume that multiple APs (at least three) are placed with overlapping coverage within the area of interest. A BSN measures its RSSI from these APs, which are being used as reference points, and calculate the distances by the Log distance path loss model. A typical example of WLAN based tracking includes the Horus localization system [21].  
In this paper, we compensate the differences of the kinematic tracking scheme and the WLAN RSSI-based scheme, and fuse them to get a more accurate position prediction.  
Figure 1: The common BSN architecture.