esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technical Overview
### Auditory Excitation Calculation  
The auditory excitation energy calculations of the model are accomplished in four stages. This procedure measures the energy of each input signal under specific limitations. The window size of real-time processing defines the principal limitation of this procedure and regulates the number of frequency bands. Moreover, a dynamic sorting algorithm has a decisive role in the \(BL\) adaptation block for the smooth operation of the ARA mix and hence for the desired audible result.  
In the first stage, the calibrated input signals are transformed into the frequency domain to obtain the energy of the predetermined real-time 1024 simulation sample window.  
Figure 4: This figure presents the construction of the binaural loudness conversion matrix.  
The FFT for spectral analysis was chosen as an effective function for real-time implementation in the frequency-domain. In the second stage, the input signals in the discrete frequency domain are divided into frequency bands that are of non-equivalent one octave band levels. The analysis according to FFT bins allows for the following 7-band separation with central frequencies of 0.25 kHz, 0.5 kHz, 1 kHz, 2 kHz, 4 kHz, 8 kHz, 16 kHz. The third stage of the energy calculation pipeline is to compute the average energy of each frequency band, which is subsequently represented as dB SPL intensity. The fourth stage consists of a dynamic sorting algorithm, which defines the final energy of each band in the time domain. This sorting algorithm allows the energy of each band to take specific values within specific thresholds, and thus helps to control the final energy values and to avoid high instant spectrum fluctuations, which could affect the function of the \(BL\) adaptation algorithm. The dynamic sorting algorithm makes a real-time check according to four defined thresholds that were tuned during the real-time testing of the model Figure 5. More specifically, two low thresholds of a three dB increase and decrease and two high thresholds of a 28 dB increase and decrease were set to control the occasion of high fluctuations of energy and validate the return of energy variables in the next lower or higher position, respectively.
## 4 Particle Filter Model for Experiments
### Initialization Process  
To process LiDAR data and calculate the relative direction between the target and the robot, a particle filter algorithm was employed. The initialization process involved creating 50 particles with random initial states. The particles' distances followed a uniform distribution between 0.5 m and 1.0 m, and the angles ranged from \(-\pi\) to \(\pi\).  
\[R_{0}^{(i)}Uniform(0.5,\ 1.0),\ \theta_{0}^{(i)}Uniform(-\pi,\ \pi) \tag{1}\]  
In Equation (1), \(R_{0}^{(i)}\) represents the distance to the object, and \(\theta_{0}^{(i)}\) represents the angle distance \(R_{0}\) as uniformly distributed between 0.5 m and 1.0 m; in addition, angle \(\theta_{0}\) was distributed between \(-\pi\) and \(\pi\), under the assumption that the target is located around the robot. Through an estimation process based on actual measurement data, the estimated position gradually converged to higher accuracy.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 5 Discussions and Perspectives
### Packaging Issue of Optical Couplers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Description** & **Coupling** & **Bandwidth** & **Polarization** & **Comments** \\ \hline Polarization-splitting & 4.2 & / & TE and TM & EBL plus shallow etch, low PDL, \\ grating coupler [114] & 4.2 & / & TE and TM & fair coupling efficiency. \\ \hline Bi-wavelength grating coupler [115] & 7.1 & 35 nm @ 3 dB & TE and TM & 248 nm DUV plus shallow etch, \\  & & & & high loss, dual wavelength bands. \\ \hline Two-tip slot edge coupler [56] & 1.8 & / & TE & Feasible with EBL, low loss, simple \\  & & & & structure, hard to align. \\ \hline SWG edge coupler [67] & 0.9 &
\begin{tabular}{c} \textgreater{}200 nm @ \\ 3 dB \\ \end{tabular} & TE & EBL, ultra-low loss, ultra-broad \\  & & & & bandwidth, high feasibility \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._single-channel packaging. Thus, it is extremely expensive and time-consuming when multiple fibers need to be connected; some research groups and companies also aim at developing multi-channel edge coupling under the benefit of its potential high coupling efficiency [119].
## 3 Physical and Mathematical Model
### Mathematical Analysis Model
#### 3.2.4 Thermal Balance in Indoor Air  
The heat loss disparity in every room results in the difference of indoor air temperatures between rooms in natural operating conditions because the rooms in multi-space solar buildings are arranged at diverse locations. The temperature difference between adjacent rooms forces heat transfer through the wall dividing adjoining spaces. In unit time, the rise of indoor air temperature in a room is related to radiation heat exchange, convection heat transfer, heat conduction, cold air infiltration, and other factors. An indoor air thermal equilibrium equation is expressed as following:  
\[\begin{array}{l}\sum\limits_{k=1}^{N_{i}}F_{k}a_{k}^{c}[t_{k}(n)-t_{r}(n)]+[q_ {1}^{c}(n)-q_{2}^{c}(n)]+L_{a}(n)(c\rho)_{a}[t_{a}(n)-t_{r}(n)]/3.6\\ -HE_{s}(n)=V(c\rho)_{r}\frac{t_{r}(n)-t_{r}(n-1)}{3.6\times 3\tau}\end{array} \tag{12}\]  
The above formulas cover the heat balance equation of opaque building envelope inner and outer surfaces, transparent building envelope, and indoor air. These equations together constitute the mathematical model of multi-space passive solar buildings. Temperature indicators in every room will be obtained by solving these equations.
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 5 Key Outcomes and Conclusions  
### Key Outcomes  
As demonstrated by the experimental results, the proposed algorithm is a simple yet efficient technique that can restore audio signals that are corrupted by speckle noise. The proposed combination of decorrelation preprocessing, average short-term energy, and kurtosis to detect the speckle noise in detected signals involves relatively little calculation, thereby increasing the computing speed. The decorrelation preprocessing and the double threshold criterion greatly improves the noise positioning accuracy. The method of replacing contaminated samples with linear coded samples is also efficient in restoring the signal and reducing the distortion. The advantages of the proposed method can be summarized as follows:  
1. Through decorrelation preprocessing, the influence of the captured speech on speckle noise detection is eliminated and the noise signal ratio is increased. This technique renders the speckle noise prominent, making it easy to detect.
2. Combining the energy and distribution in the noise detection significantly increases the noise location accuracy because the two complement each other. The results show that our method can deal with speckle noise with different durations (clicks, bursts, outliers, crackles, scrapes, etc.) and different degradation types.
3. Having a limited range of parameters and basing both the decorrelation and interpolation phases on LPC, keeps the calculation of the coefficients to a minimum, making it possible to process quickly and automatically.
4. Replacing the degraded samples with more appropriate values greatly improves the voice quality and intelligibility, and avoids the generation of distortion.
5. The method involves less calculation and less delay. Therefore, it is able to fit the practical needs of laser voice detection applications. The algorithm itself is not restricted to use in laser speech measurement, but also can be used for traditional microphone communication.
## 3 Results and Discussion
### FESEM Studies of the PLC Nanocomposite  
The FESEM images of Unirr and EB-irradiated PLC nanocomposite at 40, 80, and 120 kGy doses are shown in Figure 2. The morphology of the PLC nanocomposite changed following irradiation, and the variation was primarily determined by the irradiation dose. A SEM photograph of the EB-irradiated PLC nanocomposite shows a gradual change from an uneven, flat, and smooth structure of unirradiated turns into an interconnected nanofiber-like morphology (Figure 2d) that was more porous after the 120 kGy EB dose of irradiation. This dense nanofiber-like structure creates a conducting network or path within the polymer matrix.  
Figure 1: FTIR spectra of the PLC nanocomposite at varying dosages before and after EB irradiation.  
Figure 3 illustrates the EDX spectrum of the PLC nanocomposites irradiated with the three EB dosages, providing insights into elemental identification and quantitative compositional data. The PLC nanocomposite before and after irradiation exhibited basic elements, such as N, C, Cl, S, and O elements. In addition, changes corresponding to the weight % and atom % of the abovementioned elements were observed with increases in the EB irradiation dose to 40, 80, and 120 kGy (shown in Table 1). The observed results suggest that, the element weight % and atom % varies with the EB dose because of the change in the atomic level or disturbance in the chemical bonds as a result of chain scission and/or cross-linking due to the high level of EB energy when it enters the polymer matrix. This observation confirms that there is a change in the chemical structure of the PLC nanocomposite after irradiation, which is strongly supported by the FTIR results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**PLC Unirr**} & \multicolumn{2}{c}{**PLC 40 kGy**} & \multicolumn{2}{c}{**PLC 80 kGy**} & \multicolumn{2}{c}{**PLC 120 kGy**} \\ \cline{2-9}  & **Weight \%** & **Atom \%** & **Weight \%** & **Atom \%** & **Weight \%** & **Atom \%** & **Weight \%** & **Atom \%** \\ \hline N & 0.05 & 23.89 & 0.57 & 24.52 & 0.41 & 23.23 & 1.01 & 25.35 \\ \hline O & - & - & 1.00 & 37.55 & 0.67 & 32.98 & 1.83 & 40.26 \\ \hline S & 0.02 & 4.58 & 0.11 & 2.07 & 0.10 & 2.46 & 0.18 & 1.99 \\ \hline Cl & 0.11 & 22.00 & 0.81 & 13.68 & 0.89 & 19.88 & 0.99 & 9.81 \\ \hline C & 0.03 & 17.98 & 0.44 & 22.19 & 0.33 & 21.46 & 0.77 & 22.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EDX parameters of the PLC nanocomposite with various dosages before and after EB irradiation.  
Figure 2: FESEM images of the PLC nanocomposites: (**a**) PLC unirradiated; (**b**) PLC 40 kGy; (**c**) PLC 80 kGy; (**d**) PLC 120 kGy EB irradiation doses.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 6 Network Solutions at Data-Center Back-Ends to Meet Performance Requirements of 5G
### Identified Research Gaps  
Each of the reviewed technologies shows great development within the domain; however, we could not find research efforts that investigate the combination of physical network solutions, especially the combination of the two basic high performance networking solutions, DPDK and RDMA. GPU utilization is a hot topic nowadays, however, there is no public research activity to be found in heterogeneous GPU clusters. On the other hand, the diversity of accelerated network solutions limits the standardization of linking virtualized environments to the underlying high performance network.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Kernel socket** & **RDMA** & **DPDK** \\ \hline Event mode latency (128 / 8192 bytes) (μs) & \(\sim\)20 & \(\sim\)53 & \(\sim\)15 & \(\sim\)23 & \(\sim\)17 & \(\sim\)28 \\ Poll mode latency (128 / 8192 bytes) (μs) & \(\sim\)16 & \(\sim\)37 & \(\sim\)5 & \(\sim\)15 & \(\sim\)5 & \(\sim\)15 \\ Hardware capabilities & \multicolumn{2}{c}{Commodity} & RDMA capable & \multicolumn{2}{c}{Supported but not by all} \\ GPU virtualization compatibility & Yes & Yes & Yes & & \\ Physical network capability & Yes & Yes & Yes & Yes \\ Virtualized underlay network capability & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of network solutions.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **SmartNIC** & **FPGA** \\ \hline Stateless task offloading & Yes & Yes \\ Offloading general tasks & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 5: Capabilites of Smart NICs and FPGAs.
## 2 Literature Review
### Challenges and Issues  
Recently, several research works related to data privacy protection and the identification of cyber-attacks particularly using cloud and edge computing integrated with blockchain are proposed to tackle the above issues [43]. However, it should be noted that there are still various drawbacks in the system, which are listed in Table 2. The most important issue is that designing a privacy-preservation method to protect the sensitive data transactions against being accessed by unauthorized users is required. Moreover, ensuring a secured authentication data transfer scheme and maintaining data integrity when communicating the data over an IoT network is a challenging issue. Second, designing an adaptable security mechanism that can efficiently distinguish normal and attack instances in the IoT is also a challenging issue [44]. As such, the IoT network comprises various interconnected medical sensors, actuators, and machines located at multiple locations. Third, developing a new framework for deploying blockchain and deep learning techniques in current cloud-edge-assisted industrial systems is strenuous. As such framework often faces issues related to scalability, moreover, due to different computing powers of the participating edge nodes, it is infeasible to store the complete block in the edge networks [45].  
Figure 2: Comparative Analysis of different Privacy and Security Approaches.
# Effect of Ajwa Date and Germinated Barley on Lipid Profile in Rats Fed High-Fat Diet
## 2 Materials and Methods
### Animals and Experimental Design  
Thirty Wistar Albino rats were purchased from the Experimental Animals Unit at the College of Pharmacy, King Saud University and their weight ranged from 150 to 200 g. They were placed in clean plastic cages with a controlled temperature (25 \(\pm\) 2 \({}^{\circ}\)C), a humidity of about 50 \(\pm\) 10%, and a light and dark cycle of 12 h. Rats were allowed free access to water and fed a normal diet (ND) or a high-fat diet (HFD, 20% fats) that was prepared by the Experimental Animals Unit at the College of Pharmacy, King Saud University in Riyadh, Saudi Arabia), according to the method [25].  
After the rats were allowed to adapt to the laboratory environment for seven to ten days, the experiment was conducted according to the instructions of the College of Agriculture and Veterinary Medicine at Qassim University for experimental animals. The rats were divided into five groups (\(n\) = 6 rats in each group), as follows: (1) the first group (NC; negative control): rats were fed a standard diet throughout the experimental period. (2) The second group (PC; positive control): rats were fed a HFD throughout the twelve weeks of the experimental period. (3), (4), and (5) The third (ABM20), fourth (ABM30), and fifth groups (ABM40): rats were fed a HFD in addition to a mixture of AD and GB in a ratio of (1:1) in different proportions (20, 30, and 40 gm/100 gm of the diet weight) daily for 12 weeks, respectively. The HFD contained 60% of fat by energy, as reported by [26,27]. Ethical approval was granted by the Committee of Research Ethics (Institutional Review Board, IRB) of Qassim University, Saudi Arabia (approval no. 21-05-08).  
At the end of the experiment, the animals fasted for 12 h before sample collection (blood and tissues). The food consumption and conversion ratio for each animal and its weight were recorded during the experimental period (12 weeks). Moreover, the initial and final body weights were recorded, and body weight gain was calculated. At the end of the experiment, blood was pinched and centrifuged directly (3000 rpm, 10 min), and the collected serum was stored at \(-20\)\({}^{\circ}\)C until biochemical analysis. Hepatic tissues were discarded and weighted for each group. An electronic balance estimated all previous traits. The liver index was calculated using the following formula: liver index percentage = liver weight/body weight \(\times\) 100.
# Blockchain-Based Loyalty Management System
## 4 Discussion
### Comparison with Other Systems  
There are several advantages that can be highlighted when comparing the proposed loyalty system with the related works discussed in Section 2. The universal blockchain system [5] supports multiple loyalty programs and partnerships, which our system can incorporate. However, this system limits the loyalty mechanism to points and uses a backend server for the users' application to communicate. Additionally, it stores user data in a database. In contrast, the proposed system eliminates the need to store personal information. Moreover, it does not rely on any backend system and achieves a more decentralized approach.  
The system proposed in paper [13] has similarities with the universal blockchain system, since both employ the points mechanism using tokens. However, the proposed system incorporates additional types of loyalty mechanics and abstracts the users from the concept of tokens, enabling them to interact with points in a manner similar to non-blockchain-based platforms. Another distinguishing factor is that the author in [13] employs the ERC 20 interface for token creation, which requires that each token exists as a smart contract, whereas the proposed system uses ERC-1155 for managing multiple tokens. Additionally, the expectation that users possess knowledge of Ethereum wallets in [13] may present challenges for novice users.  
The loyalty program that utilizes the Waves blockchain and a mobile application [19] employs a token creator that generates a unique token for each retailer. However, this system also stores personal information and potentially creates a token contract for each retailer, similar to the aforementioned system [13].  
The reward system in [21] uses tokens that can be converted into points, and the platform is specifically designed for the FMCG industry, limiting its scope to this domain. However, this system resembles our transaction process, in that it employs QR codes to initiate the transfer of assets.  
Although the systems proposed in [22; 23] do not directly aim to manage loyalty programs, they improve these programs by facilitating the exchange of assets between different loyalty programs. Currently, our loyalty management system lacks features that allow for partnerships between retailers; we plan to implement these features in the future.  
Since the existing loyalty systems discussed in Section 2 do not specify their authentication process, it is assumed that they have created one and currently manage it. This results in an additional step for user onboarding, as they have to enter new information to create a new account. In contrast, the proposed system employs Web3 Auth, which uses Web 2.0 credentials to streamline the authentication process. Additionally, a significant advantage of the proposed system is the use of meta transactions, which provide a superior user experience by eliminating the need for users to pay blockchain fees.
## 3 Results
### Environmental Risk Factors  
According to Figure 4, the following environmental risk factors have been mentioned most frequently in the empirical literature as factors investigated using low-cost technology tools: light and noise (53% of publications); and temperature and humidity (47%); followed by CO\({}_{2}\) (27%) and wind velocity (20%). At the same time, precipitation, daily cloud cover, total volatile organic compounds (TVOCs), PM\({}_{2.5}\), and CO are analyzed least frequently using SPs or other low-cost technology devices (7% of the publications for each).
## 4 Frequency and SOC Restoration Control
The parameters used in Figures 2 and 3 are shown in Table 3.  
Figure 2: Proposed active power reference controller for grid-feeding distributed generation (DG). In summary, the conventional droop control, which is one of the most widely used control methods, is used for the primary control in the proposed method. The proposed method is focused on the secondary control of restoring the frequency and the SOC. The methodology for this proposed control is just adding the active power reference changer for grid-forming unit as shown in Figure 1 and grid-feeding unit as shown in Figure 2 on to the conventional DG control schemes. The rate of active power reference change by the proposed part of the controller is small since we have added LPFs on them. Note that the change rate of the active power reference in the tertiary control is usually larger than that of the secondary control. However, it does not mean that the proposed method can perfectly guarantee the stability of the system. For precise analysis, the small-signal and large-signal stability have to be analyzed and all the parameters in Tables 2 and 3 should be reconsidered according to the stability analysis. Since the scope of this paper is limited to the basic concept of the controller, the stability analysis and mathematical proof remain as future works. Consequently, the proposed part of the controller acts as a small-signal change in the conventional droop-based DGs. So, if the conventional droop-based control scheme is stable against a small signal, the proposed methodology can be regarded as stable system. The small-signal stability of microgrids with droop-based DGs has been researched in many other works in the literature and can be referred to [39; 40; 41].
# Forensic Analysis on Damage of a Cable-Stayed Bridge without Backstays Using BIM Information
## 4 Numerical Analysis
### Effect of Cable Force Reduction  
In order to analyze the effect of cable force reduction on bridge mechanical performance, the deformations and stresses of the pylon and girder with design and measured cable force are compared.  
With the design cable forces, the structural deformation under self-weight is shown in Figure 18a, and the deformation under self-weight and live load is shown in Figure 18b. The girder is arched upward by 64 mm and 46 mm, respectively. The vertical displacement (UY) at the pylon top ranges from \(-7\) mm to \(-6\) mm, the longitudinal displacement (UZ) is about 8 mm, and the pylon tips forward.  
With the measured cable force, the structural deformation under self-weight is shown in Figure 18c. The girder deflects downward by about 28 mm, UY at the pylon top is \(-10\) mm, and UZ is \(-3\) mm. The pylon tips backward due to the insufficient cable force. After loading the live load in the middle span of the girder, the cable force increases significantly, and the deformation of the pylon changes to a forward tilt, as shown in  
Figure 18: The deformation of pylon and girder with different cable forces: (**a**) design cable force self-weight; (**b**) design cable force self-weight and live load; (**c**) measured cable force self-weight; (**d**) measured cable force self-weight and live load.  
Figure 18d. The girder deflects downward by about 47 mm, UY at the pylon top is \(-9\) mm, and UZ is 2 mm.  
The stress of the pylon under self-weight with different cable forces is shown in Figure 19. With design cable force, the pylon is generally compressed, with less than 1.5 MPa tensile stress distributing at the pylon top, the transverse joints of the pylon wall, and the anchorage area of the stayed cables.  
With measured cable force, the pylon tilts backward, creating a negative bending moment at the root of the pylon, resulting in a change from compression to tension in Region 3. About 1.5 MPa tensile stress occurs in Region 3. The distribution of tensile stress at the pylon top, the transverse joints, and the anchorage area of the stayed cables are enlarged.