esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrashort Vortex Pulses with Controlled Spectral Gouy Rotation
## 2 Theoretical Background and Numerical Simulation
### Numerical Simulation  
For the numerical analysis of the beam dynamics, 2D-maps of local spectral intensity distributions S(x,y,\(\lambda\)) were calculated for propagation distances z. Simulations were performed on the basis of a simplified model with the wave-propagation software VirtualLab 7.5 (LightTrans, Jena, Germany). In particular, Gaussian spectral distributions with a full-width-at-half-maximum (FWHM) bandwidth of 12 nm were assumed, which approximates realistic experimental conditions. A binary spiral phase grating with a period of 8 \(\upmu\)m and a phase stroke of 2\(\pi\) (related to a wavelength of 800 nm) was illuminated by a Gaussian beam with a waist radius of w\({}_{0}\) = 100 \(\upmu\)m to emulate the diffraction at components with small apertures. To filter out essential features of the obtained spatio-spectral maps, local statistical moments \(M^{(i)}\)(_x,y,_x_)) were determined. At the same time, this kind of spectral image processing procedure also enables one to reduce the large data containing fully extended spectra for each point in space. Figure 1 shows the results of the numerical simulations in terms of centers of gravity (COG), i.e., first spectral moments \(M^{(1)}\)(_x,y_) for selected values of \(z\) between 4 and 11 mm increasing in steps of \(\Delta z\) = 0.5 mm (see also the visualization movie in the supplementary material). The color code of the graphics reaches from about 793 nm (blue-shifted parts) until about 808 nm (red-shifted parts). The limits of the spectral interval slightly change depending on the axial distance to best visualize the spectral maps. The variation of the applied scale, however, did not exceed a value of 0.9 nm. The divergent Gaussian beam causes a stretching of the spatial scale with increasing distance.  
This rough numerical simulation enables one to draw up some predictions about the spatio-spectral propagation characteristics. The doublets of blue-and red-shifted extremal areas (spectral eyes) are expected to rotate around the phase singularity, (2) the spatial extension of spectral eyes is not constant, and spectral bandwidth and center of gravity appear to be slightly varying. Thus, the simulation of propagating spectral maps describes an angular rotation together with specific, oscillating spatio-spectral distortions, which were studied more in detail in our experiments.
## 6 Result Analysis: Medical Opinion
### Reception by Medical Professionals  
Our results and the study's CT images were presented to medical professionals and their views and medical opinions were records. The impressions of the medical professionals were then validated with the results of DL and XAI frameworks. Table 6 shows the overall feedback indicated the proposed study could effectively be utilized to identify  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Plane** & **Ev.P** & **Class** & **Model** & **Result** & **P.M** & **XAI** \\ \hline \multirow{8}{*}{[29]} & \multirow{8}{*}{Coronal, Axial} & \multirow{8}{*}{80:20:00} & Normal—1350 & Inception v3 & \multirow{8}{*}{TSA} & 61.60\% & 22.32 & \\ \cline{4-7}  & & & Normal—1300 & VGG16 & & 98.20\% & 14.74 & \\ \cline{4-7}  & & & & Resnet & & 73.80\% & 23.71 & \\ \cline{4-7}  & & & & Cyst—1300 & EANet & & 77.02\% & 6 & \\ \cline{4-7}  & & & Tumor—1300 & Swin Transformers & & 99.30\% & 4.12 & \\ \cline{4-7}  & & & & CCT & & 96.54\% & 4.07 & \\ \hline \multirow{8}{*}{[30]} & \multirow{8}{*}{Axial and Sagittal} & \multirow{8}{*}{80:20:00} & Normal—1350 & \multirow{8}{*}{TSA} & \multirow{8}{*}{99.44\%} & \multirow{8}{*}{20} & \multirow{8}{*}{N/A} \\  & & & Stone—1350 & \multirow{8}{*}{TSAM} & \multirow{8}{*}{TSAM} & \multirow{8}{*}{96.26\%} & \multirow{8}{*}{N/A} \\  & & & Cyst—1350 & & & & \\ \cline{4-7}  & & & Tumor—1350 & & & & \\ \hline \multirow{8}{*}{[31]} & \multirow{8}{*}{Axial} & \multirow{8}{*}{80:10:10} & Normal—1340 & VGG16-NB & \multirow{8}{*}{TSA} & 96.26\% & 14.74 & \\ \cline{4-7}  & & & & Densenet121-KNN & & 96.64\% & 20 & \\ \cline{4-7}  & & & VGG-DN-KNN & & 100.00\% & 14.74 & \\ \hline \multirow{8}{*}{[32]} & \multirow{2}{*}{coronal} & \multirow{2}{*}{80:20} & Normal: 1009 & \multirow{2}{*}{XResNet-50} & \multirow{2}{*}{TsA} & \multirow{2}{*}{96.82\%} & \multirow{2}{*}{23.7} & \multirow{2}{*}{N/A} \\  & & & Stone: 790 & & & & \\ \hline \multirow{8}{*}{[33]} & \multirow{8}{*}{Axial} & \multirow{8}{*}{75:10:15} & Normal—288 & YOLOv7 & \multirow{8}{*}{Fns} & 88.20\% & \multirow{8}{*}{6} \\  & & & Stone—494 & & & & \\ \cline{4-7}  & & & Cyst—498 & & & & \\ \cline{4-7}  & & & & YOLOv7 Tiny & \multirow{8}{*}{Fns} & 85.40\% & \\ \cline{4-7}  & & & & & & \\ \hline \multirow{8}{*}{Ours} & \multirow{8}{*}{Coronal, Axial} & \multirow{8}{*}{80:10:10} & Cyst: 3709 & \multirow{8}{*}{Custom CNN} & TsA & 99.39\% & \multirow{8}{*}{0.18} \\  & & & Normal: 5077 & & & AP & 99.47\% & \\ \cline{4-7}  & & & Stone: 1377 & & & AR & 99.25\% & \\ \cline{4-7}  & & & Tumor: 2283 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative table for proposed model and other state-of-the-art models. Note: ‘F.M’, ‘TsA’, ‘Pre’, ‘Fs’, ‘AP’, ‘AR’ and ‘AF’ stand for evaluation protocol, parameters in millions, test accuracy, precision, F1-score, average precision, average recall, and average F1-score.  
kidney abnormalities in patients. To conclude, the proposed methodology could assist medical teams in making more accurate diagnoses and treatments.
## 5 Experimental Verification
### In Field Application  
A distribution network PDC and two \(\upmu\)MPMUs were deployed at a 35 KV substation in Pudong New Area, Shanghai. Figure 16 shows their installation and wiring. Two \(\upmu\)MPMUs monitor different feeders respectively. The data configuration is same as the data unit above, and data such as phasor, analog quantity, switch quantity, and file are transmitted with different priorities. The effect of the on-site operation is positive, and the transmission of phasor data will not be disturbed during the progress of other tasks. The project is still ongoing and will be configured with increasing number of \(\upmu\)MPMUs in the future.  
Figure 14: Remote control process message.  
Figure 15: Fourteen days of data storage for two cases.
# On Fast Converging Data-Selective Adaptive Filtering
## 3 Data-Selective Adaptive Filtering Algorithms
### Simulation 1: Equalizer  
In this subsection, the channel we want to equalize is one of the FIR channel impulse responses provided by The Signal Processing Information Base repository [20]. The complex channel taps were obtained from digital microwave radio systems measurements, and the FIR model frequency response is illustrated in black in Figure 2a. The transmitted signal \(\mathbf{s}(k)\), modeled as realizations of a Gaussian random variable with \(\sigma_{s}^{2}=1\), transverses through the channel and it is corrupted by additive Gaussian noise with \(\sigma_{n}^{2}=10^{-3}\). The adaptive filter performs the equalization and its output is an equalized version of \(\mathbf{s}(k)\). Each complex version of the data-selective algorithm is applied and their frequency responses try to invert the channel behavior as illustrated in Figure 2a, for \(P_{\text{up}}=1\) and \(P_{\text{up}}=0.45\). We used \(\theta=9\times 10^{-4}\), \(\gamma=1\) and \(\
u=0.05\) for both LMSN and LMSQN. For CG, we used \(\lambda=0.9995\) and \(\eta=0.48\). The filter order is \(N=100\). The error variance was estimated as in Equation (16) for \(b=0.9999\). Since the channel coefficients are complex values, the threshold is computed as  
\[\tau=2(1+\rho)\text{ln}\left(\frac{1}{P_{\text{up}}}\right) \tag{50}\]  
where \(\rho=0\)[1]. The estimated probability of updating obtained by each algorithm is quite close the prescribed \(P_{\text{up}}\), as depicted in Figure 2b. As can be seen in Figure 3, it is possible to obtain the transmitted signal with only 45% of the input data with almost the same accuracy obtained when 100% of the input data are used.  
Figure 3: Simulation 1: Comparison between the transmitted and the recovered signal by the DS-CG, DS-LMSQN and DS-LMSQN algorithms for **(a)**\(P_{\text{up}}=0.45\) and **(b)**\(P_{\text{up}}=1\).  
Figure 2: Simulation 1: **(a)** Frequency response of the channel and the data-selective filters **(b)** Comparison between the desired \(P_{\text{up}}\) and achieved \(P_{\text{up}}^{LMSN}\), \(P_{\text{up}}^{LMSQN}\) and \(P_{\text{up}}^{CG}\) by the data-selection algorithms.
# Methods of Atmospheric Coherence Length Measurement
###### Abstract  
Free-space optical (FSO) communication possesses the advantages of high security, large information capacity, high-speed transmission, small terminal size, low power consumption, easy and flexible deployment, etc. It is a promising communication technique, and could be an alternative to optical fiber communication and traditional radio frequency (RF) communication. One of principal challenges that FSO faces is the susceptibility of the atmospheric channel, which is turbulent in nature. After the optical signals propagate over the atmospheric channel, their wavefronts suffer from deleterious perturbation, thus resulting in degradation in the performance of the FSO. This knowledge with respect to FSO links helps to optimize the system design and reduce the adverse effects of atmospheric turbulence. The atmospheric coherence length of FSO links reflects the atmospheric turbulence effects, and it is one of the key parameters of FSO systems. Measuring the atmospheric coherence length, as a result, is of great interest as well as importance to the FSO community. In this paper, several methods associated with atmospheric coherence length measurement are reviewed.  
atmospheric coherence length; free space optical communication; atmospheric turbulence 2022
# Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
## 1 Introduction
### Related Work
They demonstrated that, although the BFT-based blockchain solution is little used in the healthcare literature, Endermint and Hyperledger Besu are efficient solutions for the proposed e-prescription system and can be adopted for multi-stakeholder systems and frameworks. [PERSON], [PERSON], [PERSON], and [PERSON] [21] focused on an Electronic Health Record (EHR) solution for e-prescriptions. They used smart contracts on an Ethereum blockchain to automate and track patient/provider transactions that associate electronic data stored in external databases. VigilRx [22] is proposed as a patient-centric prescription management system, in which storage management is provided through blockchain technology. The Ethereum blockchain is used as a public and distributed ledger and includes smart contracts that manage the storage and access to on-chain data. To store on-chain data, they rely on National Drug Codes (NDC), whose are managed by the U.S. Food and Drug Administration. One notable limitation of VigilRx, as detailed in the study, is its reliance on a public blockchain infrastructure for managing sensitive patient data within the healthcare domain. Unlike private or permissioned blockchains, which restrict access to authorized participants, the use of a public ledger raises concerns regarding data privacy and confidentiality. Based on this study, and in terms of contributions to the research, we propose a blockchain-based prescription-management system that fosters a secure and collaborative environment between pharmacists and patients. The key contributions of this paper can be summarized as follows:  
1. A blockchain-based prescription-management system, which improves the traceability and transparency of medical prescriptions, as well as privacy and energy efficiency, in order to reduce fraud drug and counterfeiting, is proposed. 2. A smart contract is developed to manage various transactions between different actors in an efficient way. 3. Two types of blockchain, the Ethereum and Quorum blockchain, are explored in terms of energy consumption and performance evaluation. A web application is designed and implemented, integrating IoT technology and using the Quorum blockchain to provide a more energy-efficient system. This paper is structured as follows: Section 2 presents an overview of blockchain technology. Section 3 describes our proposed blockchain-based prescription management DApp, while Section 4 introduces the design and implementation details. Section 5 provides a discussion and evaluation of the proposed approach, and Section 6 concludes the paper and outlines future work.
## 3 Results and Discussion
### Temperature-Dependent Electrical Conductivity of the PLC Nanocomposite  
The electrical conductivity was found to increase with an increase in the temperature, and this was confirmed by the Arrhenius plots (Figure 13a) and the increasing trend in the conductivity with the EB dose (Figure 13b). This is mainly due to the strong hopping mechanism and formation of the polaron/bipolarons at high temperatures, as well as the EB energy. The increase in the conductivity with the EB dose is attributed to the polymer chain's degradation due to the EB's effect on the interchain separation. This result is supported by the DSC, FTIR, XRD, and dielectric constant. Polymer chain degradation or segmentation occurs when the polymer undergoes a chain scission process, which lowers the molecular weight of the polymer matrix. This makes the distance between the polymer  
Figure 12: Log conductivity vs. log frequency for the PLC nanocomposite: (**a**) unirradiated; (**b**) 40 kGy, (**c**) 80 kGy, and (**d**) 120 kGy EB doses.  
chains smaller, allowing the electrons to transfer more easily, which increases the charge carriers and, hence, the conductivity [4, 47].  
The electrical conductivity of KBr/PVA composite films increases with an increase in the electron beam irradiation dose. This is initially attributed to polymer chain cross-linking (below 150 kGy) and, subsequently, to chain scission (above 150 kGy) [48, 49]. A study on a hybrid nanocomposite comprising a polymer matrix of polyaniline emeraldine-salt form (PANI-ES) reinforced with copper oxide II (CuO) particles, synthesized via in situ polymerization, revealed electrical conductivity values of 1.1 \(\times\) 10\({}^{-4}\) S/cm for PANI-ES and 2.77 \(\times\) 10\({}^{-4}\) S/cm for the PANI-CuO nanocomposite. The electrical conductivity of the nanocomposite was 60% greater than that of pure PANI-ES. This enhancement in the conductivity was attributed to the charge transfer, which improved the carrier mobility from PANI to the CuO slab. This observation indicates a significant interaction between the CuO (1 1 1) surface and PANI, causing the nanocomposite to exhibit metallic behavior. Additionally, the contribution of Cu and O orbitals augmented the density of the states of the nanocomposite, further elevating its electrical conductivity [50]. In the current study, the increased electron beam energy must be enhancing the abovementioned mechanisms. The rise in conductivity with the temperature represents thermally triggered behavior.
## 1 Introduction
### Types of Reinforcing Fibers in Concrete Composites--Analysis
For the reinforcement of geopolymers, carbon fibers aremost commonly used in the form of fabrics, long fibers, chopped fibers, dispersed particles, and also in the form of graphite, graphene, or waste left over from the manufacture of aerospace components [49; 56; 57; 58]. The main obstacle to the large-scale use of carbon fibers is their high production cost. Another inorganic fiber used to reinforce geopolymers is basalt fiber. Basalt fiber is a material made from basalt rock, which consists of minerals such as plagioclase, pyroxene, and olivine group minerals [59]. Continuous basalt filaments range in diameter from 9 to 13 \(\upmu\)m. The specific strength of continuous basalt filaments exceeds that of steel fibers many times over. The tensile modulus (Young's modulus) of continuous basalt fibers is higher than that of E-glass fibers, making them an attractive reinforcing material. Basalt fibers show high flexibility and good resistance to fatigue loading. Yarns made of continuous basalt fibers show low friction coefficient values compared to most reinforcing materials. Basalt fibers have a high resistance to acids and alkalis compared to E-glass fibers. Table 2 summarizes selected properties of basalt fibers, glass fibers (type E), and carbon fibers [59; 60; 61; 62; 63; 64]. Nowadays, many studies have been conducted on the application of basalt fibers as reinforcement for geopolymer materials. Basalt fibers can occur as reinforcement of geopolymers mainly as roving (a continuous bundle of unidirectional fibers), reinforcing grids, mats, or fabrics [65; 66; 67; 68; 69]. The motivation for this study was a literature analysis of reinforcement with different types of fibers in geopolymer materials to obtain the best possible mechanical properties and to minimize brittle fracture in favor of a more ductile one [7; 63; 64; 68; 69]. The following research work aimed to compare the effect of adding different forms of reinforcement (glass fiber, carbon fiber, basalt mesh) on the strength properties of fly ash and sand-based geopolymers and fly ash, sand, and cement-based geopolymers. In addition, basaltic grids were used as reinforcement for metakoolin-based and sand-based geopolymers. An additional variable used in the study was the molar concentration of an alkali solution (5 M and 10 M) for particular types of geopolymer samples. The main aim of the research was to understand the influence of the type of reinforcement on the strength properties of geopolymers produced at different molar concentrations of the alkaline activator. In the case of reinforcement in the form of glass fibers, a concentration equal to 5 M was used, because glass fibers are often dissolved into alkali, and the reinforcement is ineffective. The novelty is that the tests were carried out in a way that allowed comparing different types of reinforcement, different base materials (fly ash and metakaolin), as well as different molar concentrations and different percentages of reinforcement. Based on so many variable factors, the most optimal reinforcement parameters have been predetermined.
# Structural Evaluation of Cable Bolts under Static Loading
## 2 Literature Review
### Testing of Ground Support Systems
#### 2.3.3 Double Shear Test  
Another form of experimental testing is single or double shear test, which is used to replicate rock bolt shear strength in jointed rock masses. Although single shear tests are cheaper due to less operation and set-up costs, double shear tests are more reliable as they avoid utilising asymmetric loading [38]. Double shear tests can be arranged in varying ways; however, a typical set-up involves three concrete blocks reinforced with full grouted rock bolts that can be positioned in varying angles [38].  
The concrete blocks represent rock planes found in excavations, where the boundary blocks are fixed and a static load is applied to the middle block, applying a shear load to reinforcement bolts. Double shear tests can also conduct dynamic testing, where an impact load is applied to the centre block, such that the momentum of the applied load transfers the energy through the concrete block to the bolt under investigation. Like the drop test, the double shear test offers the opportunity to obtain repeatable results, and thus excels for quality control and comparative testing. Although it cannot accurately represent in situ conditions like blast testing, it can account for rock fault lines and thus is widely accepted as a rock and cable bolt shear test.
## 6 Experiments and Discussion  
### Preliminary Experiment  
#### 6.1.1 Purpose and Hypothesis  
The purpose of the subject experiment was to determine the suitability of the spectrogram image, generated by the proposed method, and the impact sound, generated using the spectrogram image, in the presentation in the VR environment.  
The hypothesis here is that humans can estimate the sound generated by striking an object from visual information about the material, that is, they can determine the relationship between the impact sound and the image of the material with a high degree of accuracy.  
However, as described in [40], human perception skills in VR change, not only with visual information, such as texture images, but also with other factors, such as sound and rendering.  
Furthermore, there are individual differences in human perception, so it is necessary to determine whether each person has sufficient perception skills to achieve the purpose of the experiment. We, therefore, test whether each subject can distinguish the impact sound, based on each material image. In addition, we conducted a preliminary experiment to verify whether there are combinations of materials that are easy or difficult to discriminate.  
In the preliminary experiment, we measured the perceptual accuracy of each subject, by assuming that the combination of impact sounds, that matched the material of the object, can be accurately determined. We also determined the combination of materials, for accurately discriminating them. In other words, with this preliminary experiment, we wanted to create an experimental environment, in which perception can be performed, as accurately as possible, when verifying the validity of the generated data.
# Optimization of the Weighted Multi-Facility Location Problem Using MS Excel
## 3 Performance Evaluation
### Hardware and Software Configuration  
**Hardware configurations.** Calculations were performed on three computers with different hardware configurations:  
* HW 1-CPU: AMD A10-9620P RADEON R5 2.50 GHz; memory 8.00 GB RAM.
* HW 2-CPU: INTEL CORE i7-7700 3.60 GHz; memory 32.00 GB RAM.
* HW 3-CPU: INTEL CORE i9-9900K 3.60 GHz; memory 32.00 GB RAM.  
**Software configuration.** A 64-bit operating system, Windows 10 Enterprise LSTC, MS Excel 2016, 64 bit, part of the Microsoft Office Suite, with the Solver add-in installed, which performs calculations. In the basic version it allows only one method of calculation using evolutionary algorithms. The Excel environment is used to enter data, calculate intermediate results, and display the result of the entire process.