esa-sceva/satcom-chunk-collection huggingface dataset:
# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 3 TAKEN-Traffic Knowledge-Based Navigation for CAVs
### Knowledge Sharing  
Figure 7 shows the knowledge sharing module of the TAKEN system. This module shares knowledge with the newly introduced vehicles in a network to facilitate appropriate decision making for those vehicles even without processing information from their environments. This module mainly runs in the cloud with two major tasks--one, to store the knowledge acquired by different self-driving agents and two, to extract knowledge from different scenarios encountered by self-driving cars (SDCs). The SDCs provide continuous data to perceive the environment through various calibrated cameras and sensors and consistently upload the processed information to the cloud. To achieve knowledge sharing, the raw information acquired by sensors are classified into various possible events that an SDC would face while driving.  
Figure 6: Transition diagrams for handling different traffic situations. The transition diagram for: handling pedestrians is shown in (**A**), vehicles approaching a traffic light is shown in (**B**) (yellow and red lights are treated as the same colour), and normal road condition and overtaking is shown in (**C**).  
As a result of the training process, the function approximator is associated with parameters tuned to recognise different events. Every SDC in the network bears a database which would store the tuned parameters, the low dimensional representation of scenarios along with the decision that it took and the corresponding outcome. Although the cloud stores a combined copy of such databases across the network, autonomous vehicles in the network can access the processed information from other vehicles. This avoids requesting and packet jamming at the cloud node. To account for reliability, the architecture enables the client (SDC)--cloud interaction for the decision-making process, only if the network latency is beyond a certain threshold. Therefore, the task of decision making reduces to interpreting the scenario as a low dimensional vector using parameters stored in its database and deducing decisions by communicating in the network. The cloud periodically updates the status of the database across the network and fine-tunes the model so as to keep it informed. The advantage of this architecture is that the computational power and the time required for processing data and the amount of data to be stored in an SDC are reduced. This is depicted in Figure 8. The unused part of computational resources can be used as subordinates to the cloud which would help in better performance.
## 3 Results
It is clear that the metalens array focused the three wavelengths of light, although it was designed at the wavelength of 4.6 \(\upmu\)m. The focal length for the three wavelengths were 63.2 \(\upmu\)m, 58.2 \(\upmu\)m, and 53.6 \(\upmu\)m separately, and it is reasonable that the focal length decreases with increasing wavelength, according to Equation (1). The light fields in the focal plane and the normalized intensity along the \(x\) axis for the three wavelengths are shown in Figure 8b,d,f. The FWHM of the spots in the focal plane were 4.50 \(\upmu\)m, 4.80 \(\upmu\)m, and 4.90 \(\upmu\)m, respectively, indicating that all of them break through the resolution limit for a conventional lens. Similar effects can be observed across the whole range while the focusing efficiency stayed above 50%, which proves the excellent broadband focusing performance of the designed metalens array. However, the proposed actively tunable metalens array not only acts as the focusing metalens array or transparent dielectric, but also contributes in the dynamic display area. The switching speed for GST is very fast (nanosecond or less) when the electrical or optical tuning method is adopted [29]. By controlling the phase state of GST instantaneously, any spot distributions can be obtained. Figure 9a-d show the focused graphics of letters and symbols \"H\", \"L\", \"+\" and \"\(-\)\" with the same metalens array. When the metalens array is large enough with N \(\times\) N unit cells, more complicated graphics can be formed in the focal plane. Benefitting from the ultrafast switching speed of GST and high resolution of the metalens, the proposed actively tunable metalens array may have great application potential in the dynamic display field. Figure 6: (**a**) The normalized intensity of the light field along the \(z\) axis when \(x\) = \(y\) = 0 for amorphous GST. (**b**) The normalized intensity of the light field along the \(x\) axis at the focal plane when \(y\) = 0. The FWHM is 4.60 \(\upmu\)m.  
Figure 8: (**a,b**) The broadband performance of the designed metalens array. The light field in the \(xz\) plane and \(xy\) plane when the incident light was 4.5 \(\upmu\)m. (**c,d**) The light field in the \(xz\) plane and \(xy\) plane when the incident light was 4.85 \(\upmu\)m. (**e,f**) The light field in the \(xz\) plane and \(xy\) plane when the incident light was 5.2 \(\upmu\)m.  
Figure 7: (**a,b**) The abrupt phase variances with the wavelength and rotating angle when GST is in the amorphous state or crystalline state. (**c,d**) The conversion efficiency versus wavelength and rotating angle when GST is in the amorphous state or crystalline state.
## 5 Results and Discussion  
In this section, results acquired from the simulation of MRLAM, MP-OLSR, and MP-OLSRv2 routing schemes under different node speed scenarios are presented. The results of mentioned routing schemes are compared on the following metric, i.e., throughput, average end-to-end delay, packet loss ratio, energy consumption, energy cost, and convergence time. Furthermore, critical analysis of acquired results is also conducted in this section.
## 2 Materials and Methods
### Image Acquisition System  
In order to obtain the surface mildew images of maize seeds, a maize seed detection platform was designed according to the existing conditions of the laboratory (Figure 2).  
The system primarily consisted of a digital camera (DSC-H10, SONY, Tokyo, Japan) with an 8-megapixel sensor with 8.1 million effective pixels and a maximum of 8.3 million pixels. The camera supports a maximum resolution of 3264 \(\times\) 2448 and USB 2.0 connectivity. It was mounted on a platform 500 mm above the detection platform to ensure stable image capture. The inspection platform was modeled using SolidWorks 2021 (Dassault Systemes, Waltham, MA, USA) and a 3D printer (Aurora A6 Industrial, Aurora Innovation, Shenzhen, China). The platform measures 200 \(\times\) 200 \(\times\) 7 mm (Length \(\times\) Width \(\times\) Height) and contains 100 grooves arranged in a 10 \(\times\) 10 grid. Based on the initial measurements of the maize seeds, the grooves were designed with dimensions of 15 \(\times\) 15 \(\times\) 6 mm (Length \(\times\) Width \(\times\) Height), with a 5 mm spacing between adjacent grooves.  
The quality of an image is of great consequence to the training of the model; the quality of the light source is similarly a significant factor affecting the quality of the data set and subsequent experiments. The light source was used to provide an environment conducive to achieving the desired imaging effect of the target, increasing the brightness of the environment and the target itself. Two LED strips with a size of 365 \(\times\) 45 mm (Length \(\times\) Width) and a power of 37.2 W were used. The LED strips were protected by a three-dimensional aluminum housing, and the angle of the light source could be adjusted by rotating slots on both sides of the strips. The adjustment results are shown in Figure 2b. A LED dimming studio with a size of 400 \(\times\) 400 \(\times\) 400 mm (Length \(\times\) Width \(\times\) Height) was used as a light source obscura (Figure 2a). This can guarantee the closure of the collection environment, prevent the interference of natural light and other stray light, and ensure the uniformity of lighting. A total of 100 maize seeds were imaged at a time, with all seeds positioned on the detection platform. The camera was situated in a fixed position to obtain images of the maize seeds through a designated aperture at the summit of the light source box. Subsequently, the captured images were transferred to a personal computer (PC) via a USB cable for further analysis and processing.
## 4 TinyOS
### Additional Features  
In this section, we discuss some additional features provided by TinyOS.  
* **File System** TinyOS provides a single level file system. The rationale behind providing a single level file system is the assumption that only a single application runs on the node at any given point in time. As node memory is scarce, having a single level file system is therefore sufficient.
* **Database Support** The purpose of sensor nodes is to sense, perform computations, store and transmit data, therefore TinyOS provides database support in the form of TinyDB. Further details on TinyDB can be found in [13].
* **Security Support** Communication security in wireless broadcast medium is always required. TinyOS provides its communication security solution in the form of TinySec [14].
* **Simulation Support** TinyOS provides simulation support in the form of TOSSIM [15]. The simulation code is written in NesC and consequently can also be deployed to actual motes.
* **Language Support** TinyOS supports application development in the NesC programming language. NesC is a dialect of the C language.
* **Supported Platforms** TinyOS supports the following sensing platforms: Mica [16], Mica2 [16], Micaz [16], Telos [17], Tmote [17] and a few others.
* **Documentation Support** TinyOS is a well documented OS and extensive documentation can be found on the TinyOS home page at [[http://www.tinyos.net](http://www.tinyos.net)]([http://www.tinyos.net](http://www.tinyos.net)).
## 2 Materials and Methods
### Fractional Extraction of Dandelion Roots  
The procedure of inulin isolation from dandelion roots is illustrated in Figure 1. Firstly, the ground dandelion roots (100 g) were successively extracted in a Soxlet apparatus with n-hexane, CHCl\({}_{3}\), and ethyl acetate in a solid-to-solvent ratio of 1:10 (\(w/v\)). The solvents were removed after each extraction step through vacuum concentration. The defatted residues from dandelion roots were air-dried. A preliminary study of the inulin content in the initial plant material was performed before its further extraction. The obtained defatted roots (5 g) were extracted with water as previously described and the fruct, inulin, and sugar content in water extracts were determined as described previously [5; 17].  
Inulin was extracted from the defatted dandelion roots using distilled water as a solvent (1:10 \(w/v\)) in three different techniques: conventional, ultrasound-assisted, and microwave-assisted extractions.
## 3 Proposed Method
### Sinusoidal Transform Coding  
In speech processing, a linear-predictive coefficient (LPC) is commonly used to model a spectral envelope, also known as a formant. However, it is not so trivial to find the spectral envelopes of musical signals due to their polyphonic nature. Because the spectral characteristics of high frequency content can be precisely represented using sinusoidal transform coding (STC) [4; 5; 6], a method for extracting features representing the spectral shapes and envelopes of a musical signal using STC is introduced in this work.  
Since STC can precisely represent spectral features, including preservation of high frequency components, STC is often utilized to model the spectral characteristics of audio signals. STC models the input audio signal as the sum of \(K\) sinusoids. In discrete short-time, the sinusoidal signals at \(m^{th}\) analysis frame are then represented as follows:\[s(n;m)=\sum_{k=1}^{K}A_{k}(m)\cos[(n-n_{(\phi k)}(m))2\pi f_{k}(m)+\psi(m)], \tag{1}\]  
where \(A_{k}(m)\) and \(f_{k}(m)\) are the k-th amplitude and harmonic frequency at m-th frame, and \(n_{(\phi k)}(m)\) is the onset time of its corresponding k-th components \(A_{k}(m)\) and \(f_{k}(m)\)[6].  
Many different methods exist for estimating \(A_{k}(m)\) and \(f_{k}(m)\). Adaptive quasi-harmonic model (aQHM) [13] and Analysis-by-Synthesis/Overlap-and-Add (ABS/OLA) [14] were proposed to estimate the sinusoidal components using iterative methods to reduce a reconstruction error rate. [PERSON] and [PERSON] introduced a method for modeling musical signals by modelling time-varying spectra as a collection of sinusoids and a time-varying filtered noise component as white noise through a time-varying filter [5].  
However, these methods are computationally expensive and may not be suitable for real-time implementation. A more simple and straightforward method for estimating the spectral components is a peak-picking routine operation in a spectral envelope estimation vocoder (SEEVOC) framework [6; 15].  
The SEEVOC method first searches for the largest peak, \(A_{1}\) at \(f_{1}\) in the interval \([\frac{f_{2}}{2},\frac{3f_{6}}{2}]\), then searches for the largest peak in the next interval \([f_{1}+\frac{f_{2}}{2},f_{1}+\frac{3f_{6}}{2}]\). The process is continued until the edge of the audio bandwidth is reached [15]. Without confusing spurious sidelobes, the procedure will locate the peaks of a spectrum [15]. A spectral line connecting the peaks is a spectral envelope estimate as shown in Figure 1a.
# Mechanical Faults Analysis in Switched Reluctance Motor
## 1 Introduction
### Paper Novelty and Contents  
Considering the SRM diagnostics research, a distinguishable deficit exists between those carried out on a physical with suitable universal application purposes and those based only on simulation and mathematical modelling. Experimental motor diagnostics with mechanical faults are rare and, in the case of SRMs, almost nonexistent, probably due to the necessary interference with the motor's construction, required by precise technical knowledge and competence with setting a given damage (disassembling the original bearing, etc.). Also, after such experiments, the motor can be irretrievably broken.  
The authors overcame these adversities by constructing a test stand for SRM diagnostics. It included particular elements for setting the mechanical faults, i.e., a sleeve with a bearing support for the dynamic eccentricity and a disc for the imbalance. The instrumentation was comprised of dynamic load control software written in LabView 2023.  
The appropriate design of the measurement station allows for a wide range of diagnostic tests in the electric motor. It is possible to freely change the load and motor speed ranges for different types of eccentricity and imbalance.  
The author's approach allowed for illustrating a comparison between two motors' states (faulty and proper operation) utilizing the real-time, physical signal (acceleration and phase current) of the SRM. The analysis employed fast Fourier transform (FFT) on the measured signal spectrum at a constant motor speed for different torque loads and the rotational speed of the SRM. The results obtained during an experiment on a real object can demonstrate the universal effects of the given faults wherever the SRM is applied.  
The rest of the paper is organized in the following manner:  
The succeeding chapter describes the construction of the reluctance motor along with its principle of operation and properties. The test stand--with mechanical devices for inflicting damage and electronics for acquiring measurements--is also presented in detail. The following section presents and compares the selected results obtained from it. In the end, the results obtained were interpreted, and the hypothesis posed at the beginning of this paper was replied to. Additionally, proposals for further research on this issue are indicated.
## 2 Materials and Methods
### Dimensional Analysis  
According to Figure 1 and by considering the geometric and hydraulic parameters, the effective parameters can be written as Equation (1):  
\[f_{1}(q,\rho,\mu,g,B,H,\varepsilon,y_{c},y_{0},D)=0 \tag{1}\]  
Here, \(q\) is flow rate per unit width, \(\rho\) is the density of water, \(\mu\) is dynamic viscosity, \(g\) is gravitational acceleration, \(B\) is the channel width, \(H\) is drop height, \(\varepsilon\) is screen porosity ratio, \(y_{c}\) is critical depth, \(y_{0}\) is the upstream depth of the drop and \(D\) is the pore diameter of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Percentage of** **Screens Porosity**} & \multirow{2}{*}{**D/H**} & \multicolumn{5}{c}{**Parameters**} \\ \cline{3-6}  & & \(Q\) **(l/s)** & \(y_{0}\) **(cm)** & \(y_{1}\) **(cm)** & \(y_{c}\) **(cm)** & \(y_{p}\) **(cm)** \\ \hline \multirow{3}{*}{50\%} & 0.067 & & & 2.6–7.1 & & 3.7–10.3 \\  & 0.133 & 2.5–13.5 & 4.45–6.5 & 2.8–7.0 & 1.92–5.86 & 3.5–9.8 \\  & 0.2 & & & 2.8–7.1 & & 3.2–9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variation range of parameters in the present study.  
the screens. Using the \(\pi\)-Buckingham dimensional analysis method, the relative energy dissipation and relative pool depth were obtained as Equation (2):  
\[\frac{\Delta E}{E_{0}},\frac{y_{p}}{H}=f_{2}(Fr_{0},\varepsilon,\text{Re}_{0}, \frac{y_{c}}{H},\frac{D}{H}) \tag{2}\]  
In the above relations, \(Re_{0}\) is the Reynolds number upstream of the drop and \(Fr_{0}\) is the Froude number upstream of the drop. Given that the upstream Reynolds number is in the range 8333-44,444, the flow is Completely turbulent and viscous effects can be ignored. Additionally, the porosity of the screens is constant and equal to 50%, so the effect of porosity variation can be ignored. Furthermore, upstream of the drop, there is subcritical flow and the relative pool depth is independent of the upstream Froude number, so the upstream Froude number can be eliminated [24; 25; 26; 27]. Consequently, Equation 2 can be rewritten as Equations (3) and (4):  
\[\frac{\Delta E}{E_{0}}=f_{3}(Fr_{0},\frac{y_{c}}{H},\frac{D}{H}) \tag{3}\]  
\[\frac{y_{p}}{H}=f_{4}(\frac{y_{c}}{H},\frac{D}{H}) \tag{4}\]  
Figure 1: Schematic of laboratory flume equipped with horizontal screen.
## 3 Self-Sovereign Identity: An Overview  
An SSI model is a model for managing digital identities made possible thanks to distributed ledger technologies, including the blockchain. It operates in a similar way to real identities and thus it contrasts with traditional account-based models. In this model, a direct relationship is created between the entities that want to communicate or exchange data through so-called VCs. VCs are the digital representation of a person's attributes or other information, such as a driver's license, a school diploma, or a birth certificate.  
VCs are digitally represented through the use of credential schemas, which are json-ld schemas. These schemas represent the data template for a credential. They allow the issuer to structure the data within a credential in a very precise way so that the holder and verifier can correctly interpret them. Typically, a credential includes the following fields: _contexts_, _identifiers_, _types_, _the credential subject_, _the issuer_, _the issuer_, _the issuer_, _the issuer_, and _proofs_ (signatures) that define the terminology to be used to communicate, identify the credential, describe the type of credential, identify the holder of the credential, identify the issuer of the credential, identify the date of issuance, and present the cryptographic proof to verify the validity of the credential, respectively.  
In the SSI model, there is a trilateral relationship in which a holder, who is the one who possesses a given credential, an issuer, who is the one who issued the credential, and a verifier, who is the one to whom the credential is presented and who has to verify whether the credential is valid, operate. In an SSI system, the data are not stored in a centralized system but are archived in digital wallets owned by the users, where the user also stores his/her keys.  
In such a system, two entities communicate with each other by creating a connection and operating thanks to the DIDs and DDO which allow the parties to identify themselves and to prove their identity to the other party. A protocol for communication between DIDs widely used in the community is DIDComm, [29], which creates a secure communication channel in which the control of the private key by a DID proves the authenticity of the DID entity.  
The _DID method_ specifies the precise operations by which DIDs and the DDO are created, resolved, updated, and deactivated (refs. [30; 31]). _DID:ethr_: is a DID method based on the Ethereum blockchain. In the model proposal, which uses the local blockchain, Ganache, a DID is, for example, _did:ethr:1337:0x0278 facee82103 dd0b079235 ed93f29187117a10e9e8e__a6 dfc4 df517d06666_, where 1337 identifies the local network and the hexadecimal string is the public key that identifies a specific entity.
## 3 Results
### Optimization  
The desirability function is a statistical method used to optimize multiple response variables at once by combining them into a single composite desirability score. Each answer variable is translated into a scale of 0 (di = 0 least desirable or totally undesired) to 1 (di = 1 most desirable or entirely acceptable) using preset criteria. The total desirability is computed by taking the geometric mean of these individual desirability values [47; 48].  
The aim was to maximize the specific volume, the spread ratio, the expansion factor, and the overall acceptability. The response parameter is optimal when the desirability indices are around 1. With a desirability of D = 0.7399 (which presents the mean of individual desirability), the optimal amounts of starch for the cookie formulations are 18.5084 g corn starch, 6 g potato starch, and 5.4916 g tapioca starch (Figure S1). These starch amounts represent the optimal compromise among various responses selected by the desirability function, resulting in a premix with the best technological properties (Table 3).  
To validate the model, gluten-free cookies with the optimal mixture were developed. A T-test (paired test) comparing predicted and observed parameters showed a high degree of agreement between them (the T-test showed no significant difference between predicted and observed parameters), supporting the validity of the model and the optimization process (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Predicted** & **Observed** & **Individual** \\  & **Parameters** & **Parameters** & **Desirability (D)** \\ \hline
**Specific volume (cm3/g)** & 1.961 ** & 1.989  & 0.8347 \\ \hline
**Spread factor** & 7.325 ** & 7.803  & 0.6954 \\ \hline
**Expansion ratio** & 1.033 ** & 1.038  & 0.7873 \\ \hline
**Overall acceptability** & 6.466 ** & 6.45  & 0.6424 \\ \hline \hline \end{tabular} a—letter indicates a non-statistical difference of means in the same column (\(p<0.05\)).  
\end{table}
Table 3: Observed and predicted parameters for the optimized gluten-free cookies.