esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The authors, motivated by the aforementioned observations, studied beamforming optimization for an IRS-aided multi-antenna communication system by incorporating signal distortions caused by hardware impairments [39]. The authors optimized both source's transmit active and passive beamforming to maximize the SNR received at the destination. Elsewhere, the application of an active refracting RIS-enabled transmitter for a secure internet of things network was investigated to enhance secure communication in the considered network and develop an alternating optimization algorithm to optimize the sum secrecy rate by jointly designing the power allocation, transmit beamforming, and the phase shifts of an RIS in [40]. Due to the high beam training costs and the effect of user location errors in vehicular MIMO communication systems, we propose an RIS-assisted location-based hybrid beamforming algorithm as well as a double-step iterative algorithm for minimizing the effect of user vehicle location error, which has not yet been investigated in the literature. The contributions of this study are listed as follows:  
* Due to blockage awareness, we first develop an RIS-assisted V2V MIMO channel model and demonstrate the geometrical relation between the RIS controller and user vehicle position. By using the distance between the transmitting and receiving vehicle in relation to the distance of an RIS controller, we estimate the path amplitude and phase. * We then design an RIS-assisted low-dimensional equivalent effective channel. In response to a sub-design problem of an original beamformer, we propose a conjugate gradient and location-based hybrid beamforming algorithm. We apply a Karush-Kuhn-Tucker condition and a Lagrangian method to solve an original problem into a sub-optimal problem for developing the conventional hybrid beamforming algorithm, which reduces the user sub-rate maximization problem. The proposed algorithm attains significant spectral efficiency performance. * We next consider a covariance splitting method to reform the error covariance matrix. Since the channel is geometrically contained in location information, we propose a double-step iterative algorithm that minimizes the effect of location error in user vehicles. Finally, the simulation results demonstrate the superiority of the proposed algorithms over their counterparts. The rest of the paper is organized as follows. The signal and channel model and the geometrical relation between the RIS controller and connected autonomous vehicle position are discussed in Section 2. The proposed hybrid beamforming and user vehicle localization is presented in Section 3. Simulation results are provided in Section 4. The conclusions are offered in Section 5.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
## 3 Results
### Evaluation of Synchronization Algorithms
#### 3.2.1 Case Study 1: Amplitude Changes
In particular, the results show (i) how the harmonic content affects the estimated frequency when using SRF-PLL (Figure 16a), (ii) a lower error of the estimated voltage in SS of time \(t_{5}\) when using DSOGI-PLL with LPF (see the RMSE values in Table 10), and (iii) an \(t_{ex}\) of the DSOGI-PLL algorithm being 91% higher than that of the SRF-PLL algorithm. The results confirm the behavior of the SRF-PLL algorithm under distorted grid conditions reported in [8].
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 3 Location-Based Deferred Broadcast Scheme
### Application Name Design  
The first step for ad-hoc NDN to support CAV applications is to design a naming convention for the application data. Take a vehicular named data network as an example, the traffic information needs to be categorized and represented (such as emergencies, accidents or signals). Given these data are tightly coupled with the locations relating to the information, we consider the traffic information is critically categorized in terms of geographical locations and time. Thus, the naming structure can contain the location, being a point of interest or a region of interest. An example of using a point of interest would be in this format _/traffic/latitude, longitude)/sequence-in-time._ If data are generated during movement, new data pieces with new locations and times may be generated per the preference of the application. Additional attributes of the data can be further appended to the structure. This name structure will be used in all the network packets.  
GPS devices have been popularly equipped in smart phones, vehicles, and so forth. Data tagged with location information is also abundant. When these data are expressed including their location attribute in the names, the locations of the data sources will be included in the interest packets and data packets when consumers retrieve the data by names. Such location information is ready to be used in our deferred broadcast scheme.
###### Abstract  
A proton-frequency-transparent (PFT) birdcage RF coil that contains carbon-proton switching circuits (CPSCs) is presented to acquire \({}^{13}\)C MR signals, which, in turn, enable \({}^{1}\)H imaging with existing \({}^{1}\)H RF coils without being affected by a transparent \({}^{13}\)C birdcage RF coil. CPSCs were installed in the PFT \({}^{13}\)C birdcage RF coil to cut the RF coil circuits during \({}^{1}\)H MR imaging. Finite-difference time-domain (FDTD) electromagnetic (EM) simulations were performed to verify the performance of the proposed CPSCs. The performance of the PFT \({}^{13}\)C birdcage RF coil with CPSCs was verified via phantom and in vivo MR studies. In the phantom MR studies, \({}^{1}\)H MR images and \({}^{13}\)C MR spectra were acquired and compared with each other using the \({}^{13}\)C birdcage RF coil with and without the CPSCs. For the in vivo MR studies, hyperpolarized \({}^{13}\)C cardiac MRS and MRSI of swine were performed. The proposed PFT \({}^{13}\)C birdcage RF coil with CPSCs led to a percent image uniformity (PIU) reduction of 1.53% in the proton MR images when compared with the case without it. FDTD EM simulations revealed PIV reduction of 0.06% under the same conditions as the phantom MR studies. Furthermore, an SNR reduction of 5.5% was observed at \({}^{13}\)C MR spectra of corn-oil phantom using the PFT \({}^{13}\)C birdcage RF coil with CPSCs compared with that of the \({}^{13}\)C birdcage RF coil without CPSCs. Utilizing the PFT \({}^{13}\)C birdcage RF coil, \({}^{13}\)C-enriched compounds were successfully acquired via in vivo hyperpolarized \({}^{13}\)C MRS/MRSI experiments. In conclusion, the applicability and utility of the proposed 16-leg low-pass FFT \({}^{13}\)C birdcage RF coil with CPSCs were verified via \({}^{1}\)H MR imaging and hyperpolarized \({}^{13}\)C MRS/MRSI studies using a 3.0 T MRI system.  
hyperpolarized magnetic resonance imaging; birdcage RF coil; Carbon-13; magnetic resonance spectroscopic imaging; slab dynamic MRS; free induction decay chemical shift imaging
## 2 Materials and Methods
### Feature Extraction  
Voice is a continuous-time signal; however, for computation, it is represented as a discrete-time signal. We measured the amplitude at equal distances in a set number of points per second, along with the continuous signal. We set the sampling rate at 48 kHz in this study. Sampling refers to the recording of the speech signals at a regular interval (Figure 1).  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Class** & **Patient Symptoms** \\ \hline I & No limitation of physical activity. Ordinary physical activity does not cause undue fatigue, palpitation, dyspnea (shortness of breath) \\ \hline II & Slight limitation of physical activity. Comfortable at rest. Ordinary physical activity results in fatigue, palpitation, dyspnea (shortness of breath). \\ \hline III & Marked limitation of physical activity. Comfortable at rest. Less than ordinary activity causes fatigue, palpitations or dyspnea. \\ \hline IV & Unable to carry on any physical activity without discomfort. Symptoms of heart failure at rest. If any physical activity is undertaken, discomfort increases. \\ \hline \hline \end{tabular}
\end{table}
Table 1: New York Heart Association Functional Classification.  
Figure 1: Original signal representing the samples amplitudes over time. In this audio, a female pronounced “33” once.  
The raw data were processed to compute the input for the proposed machine-learning algorithms. Calculating the Mel-Frequency Cepstral Coefficients (MFCCs) is an essential step to extract the relevant voice features and reduce each file's dimension. Figure 2 presents the steps for MFCCs extraction, where Discrete Fourier Transform (DFT) is applied on the time signal generating the frequency spectrum. The logarithm function is used, and the Inverse DFT is computed. The final step is to add the Mel Cepstrum or Discrete Cosine Transform (DCT) [37] (Figure 2).  
In the described method, the output is represented by up to 40 feature vectors; 20 feature vectors were used, graphically represented in Figure 3. As the dataset comprises different length audio files, the mean MFCC is computed for each feature. To minimize the noise impact, the values are normalized (Figure 3).  
Figure 3: Visual representation of the 20 feature vectors—the Mel Frequency Cepstral Coefficients.  
Figure 2: Steps involved in MFCC Features′ extraction.
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 3 Preparation Methods of Synthesizing Cobalt-based Active Materials
### High-Energy Mechanical Milling  
As a facile preparation technology, high-energy mechanical milling (denoted as HEMM) has also been commonly applied to the fabrication of cobalt-based materials owing to its large-scale production [34; 42; 45; 80; 81; 82; 83]. For HEMM, the products are synthesized under high temperatures and pressures generated by highly energetic collisions of tiny balls.  
Carbon-coated CoP\({}_{3}\) (CoP\({}_{3}\)@C) nanocomposites were synthesized by HEMM methods with a certain proportion of phosphorus, cobalt powders and carbon black [80]. The synthesis of the schematic illustration is displayed in Figure 7a. HRTEM images shown in Figure 7b reveal the presence of CoP\({}_{3}\) nanoparticles and a carbon matrix. [PERSON]'s group successfully transformed a certain proportion of Sn, Co and graphite into a nanosized SnCoC-2 alloy through HEMM [34]. Figure 7c shows the SEM image of the obtained SnCoC-2 alloy. [PERSON] et al. prepared Sn\({}_{30}\)Co\({}_{30}\)C\({}_{40}\) electrode materials by mechanical attrition with CoSn\({}_{2}\), Co and graphite as the precursors [81]. SEM image of the obtained Sn\({}_{30}\)Co\({}_{30}\)C\({}_{40}\) (Figure 7d) reveals that the average size of Sn\({}_{30}\)Co\({}_{30}\)C\({}_{40}\) particles is about 1 \(\upmu\)m.  
Figure 6: Preparation methods and morphology characterizations of cobalt-based composites. (**a**) Schematic illustration of the preparation of CoSe@porous carbon polyhedral (PCP). (**b**) TEM image of CoSe@PCP [68]. (**c**) TEM image of 3D spongy CoS\({}_{2}\)/C [12]. (**d**) TEM and (**e**) HRTEM images of triple-shelled Co\({}_{3}\)O\({}_{4}\) hollow microspheres [69]. (**f**) SEM image of the reduced graphene oxide nanosheet (RGO@CoP@FeP) composite [70]. (**g**) SEM images of the CoSn\({}_{2}\)/Sn alloy [22]. (**h**) SEM image of the Co\({}_{3}\)S\({}_{8}\)/N-C hollow spheres [28]. (**i**) TEM image of the Co/(NiCo)Se\({}_{2}\) nanocubes with a box-in-box structure [71].
## 4 Applications
### Performance and Security Challenges Faced by Incorporating Blockchain in Industry  
Now, we highlight the performance and security challenges faced by deploying blockchain in industrial sectors. First, the current application scales in industry are usually huge, which requires blockchain platforms to process transactions on a very high throughput rate. Most of the state-of-the-art blockchain platforms cannot handle these high transaction rates and the performance will be severely degraded. Especially for the financial services, the poor scalability and high transaction processing delays of blockchain become the major performance bottleneck. For IoT applications, the bottleneck is different. The work in [72] evaluates the performance of a cloud and edge hosted blockchain where blockchain is proposed as a service for IoT. The work shows that the network latency is the dominant performance factor when blockchain is adopted in IoT systems. Scalability has become a pressing issue with the rapid growth in the application size and transaction volumes. Secondly, applications in industry are deployed over the Internet, and hence are vulnerable for various cyber-attacks. Endpoint security is the major security concern in financial services. There are more challenges in linking the cryptographic identities to the real world identities, which can help to identify the suspects of money laundering. Many industrial applications such as healthcare applications involve sensitive or private information. Thus, it is important for blockchain technologies to provide different and acceptable levels of security and privacy measures that meet different needs of different industrial sectors.
## 3 Results
### Parameters for Performance Evaluation  
To assess the efficiency of the proposed TDOA based localization method using DE with the previous FA, evaluation parameters used in this research are mean square error (MSE) and cumulative distribution function (CDF).  
The proposed algorithm has a higher precision, which means it is more effective than the previous one. Additionally, the probabilities of false detection and correct detection are calculated to find how accurate the proposed algorithm is at detecting the attacker.
# Versatile Video Coding-Post Processing Feature Fusion:
## 2 Related Works  
Extensive research has been undertaken in the area of video compression artifact removal, encompassing both traditional methodologies and deep learning-based solutions, to address the degradation of video quality caused by compression. This section provides an overview of various research studies that have employed both conventional and deep learning-based approaches to tackle video compression artifact removal, highlighting their respective strengths and limitations.
## 3 Control System Design
### Pre-Emptive Braking Controller
#### 3.3.2 Dual PID Longitudinal Motion Control  
This study adopts a dual PID controller for the vehicle longitudinal motion control, as shown in Figure 5. Where \(S_{r}\), \(V_{r}\), \(a_{r}\) denote the desired position, desired speed, and desired acceleration, respectively; \(S\), \(V\), \(a\) denote the actual position, actual velocity, and actual acceleration of the vehicle, respectively; \(V_{d}\), \(a_{d}\) denote the adjusted speed and acceleration, respectively. The first PID controller adjusts the speed by the position error \(e_{1}\). The second PID controller adjusts the acceleration by controlling the velocity error \(e_{2}\). The torque is then transmitted to the motor through the torque distribution. The differential equations of the PID controller are expressed as follows in Equations (9) and (10).  
\[V_{d}=K_{p1}\left[{\left({S_{r}-S}\right)+\frac{1}{{{T_{11}}}}{{\int_{0}^{t} }\left({{S_{r}-S}}\right)}dt+{{T_{d1}}}\frac{{{d({S_{r}-S})}}}{{{dt}}}}\right] \tag{9}\]  
\[a_{d}=K_{p2}\left[{\left({V_{d}+V_{r}-V}\right)+\frac{1}{{{T_{2}}}}{{\int_{0}^ {t}}\left({{V_{d}+V_{r}-V}}\right)}dt+{{T_{d2}}}\frac{{{d({V_{d}+V_{r}-V})}}}{{{ dt}}}}\right] \tag{10}\]  
where \(K_{p1}\), \(T_{i1}\), and \(T_{d1}\) represent the proportional coefficient, integral time constant, and derivative time constant of the speed compensation PID, with values of 3, 10, and 1, respectively. \(K_{p2}\), \(T_{i2}\), and \(T_{d2}\) denote the proportional coefficient, integral time constant, and derivative time constant of the acceleration compensation PID, with values of 9.5, 0.13, and 1, respectively. The PID tuning process can be referred to in [38].  
Figure 5: Dual PID controller.
## 3 Challenges
### Channel Selection  
Because there is no dedicated channel to send data, sensors need to negotiate with the neighbors and select a channel for data communication in CR-WSNs. This is a very challenging issue, because there is no cooperation between the PUs and SUs. PUs may arrive on the channel any time. If the PU claims the channel, the SUs have to leave the channel immediately. Therefore, data channels should be selected intelligently considering the PU's behavior on the channel and using some AI algorithms.
## 3 Proposed Approach
### Feature Tracking in Orth-Rectified GPI  
The proposed visual odometry method in this study is using the ground motion in ortho-rectified GPI. Because the ortho-rectified GPI is the top-view image from the virtual camera, the ground motion in GPI can be measured in real metric dimensions using the focal length and the height of the camera. To obtain the ground motion in GPI, we use a simple and robust KLT optical flow algorithm [24]. Moreover, to obtain a reliable motion vector that represents the motion of the robot, we obtain only the median vector between previous and current frames, as shown in Figure 10.  
Usually, the height of a pallet robot is very low, and the camera mounted close to the ground captures the details of the ground or floor patterns. The ground pattern in the ortho-rectified GPI is random enough to extract and track the features in consecutive image frames. Therefore, many features are usually obtained in a single image frame, and they are tracked using KLT. In an ideal case, all motion vectors via KLT are the same because the ortho-rectified GPI is a perfectly orthogonal view of the ground. However, there can be tracking errors in KLT, thus using the median of the motion vectors is the best calculation for motion estimation.
## 3 Bridge and Monitoring System Descriptions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Speed (km/h)**} & \multicolumn{2}{c}{**Semi-static (mm)**} & \multicolumn{2}{c}{**Acceleration (g)**} & \multicolumn{2}{c}{**Frequency (Hz)**} & \multicolumn{2}{c}{**Damping Ratio**} \\ \cline{2-9}  & **Disp.** & **Strain** & **Acc.** & **Strain** & **Acc** & **Strain** & **Acc** & **Strain** \\ \hline
20 & 7.67 & 7.60 & 0.097 & 0.095 & 2.88 & 2.88 & 0.37 & 0.32 \\
30 & 7.54 & 7.59 & 0.050 & 0.046 & 2.86 & 2.86 & 0.74 & 0.61 \\
40 & 7.58 & 7.61 & 0.084 & 0.088 & 2.86 & 2.86 & 0.59 & 0.79 \\
50 & 7.42 & 7.40 & 0.073 & 0.093 & 2.88 & 2.88 & 0.77 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time and frequency domain evaluation for the bridge response. Figure 12: (**a**) Frequency and (**b**) damping ratio of the bridge with a 40 km/h truck speed. As presented in Table 4, the accuracy of the model decreased with the position of the measured girder relative to girder G6. The R\({}^{2}\), RMSE and AME were 0.99, 0.16 mm and 0.08 mm, respectively for the predicted displacement of girder G5; while R\({}^{2}\), RMSE and AME were \(-\)1.41, 0.25 mm and 0.16 mm, respectively, for girder G1 with 20 km/h speed. The R\({}^{2}\), RMSE and AME were 0.98, 0.14 mm and 0.07 mm, respectively for the predicted displacement of girder G5; while R\({}^{2}\), RMSE and AME were \(-\)1.84, 0.10 mm and 0.06 mm, respectively, for girder G1 with 50 km/h speed. Furthermore, the model error increased with the low speeds, the range of error was 1.26 mm and 1.08 mm under 20 km/h and 50 km/h, respectively. The difference between the observed and predicted displacements reached its maximum values with the far girder from G6. Therefore, for accurate performance estimation, a separate model should be designed for each girder. Furthermore, it can be seen that the estimated displacement values were greater than the actual one; thus, the designed model could be used as an early safety alarm.
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
## 3 Results
### Most Common Types of Repair Techniques
#### 3.2.1 Repair Techniques in Roof Claddings
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Building Element/** & \begin{tabular}{c} **Defects Associated** \\ **with the Same Repair** \\ **Technique** \\ \end{tabular} & \begin{tabular}{c} **Repair Techniques** \\ **Considered in ECPR** \\ \end{tabular} & \begin{tabular}{c} **Repair Techniques** \\ **Considered in FR** \\ \end{tabular} & \
\begin{tabular}{c} **Repair Techniques** \\ **Considered for Both** \\ \end{tabular} \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Roof claddings– \\ external claddings of \\ pitched roofs (CFR); \\ flat roofs (FR) \\ \end{tabular} } & A-A3 and A-B1 & R-A1 & R-A1 & R-A1 \\ \cline{2-5}  & A-B1 and A-B2/ & R-A1, R-A2, R-A7 & R-A1 & R-A1 \\ \cline{2-5}  & A-C1 and A-C2/ & R-A2, R-A3, R-A4, & & \\ \cline{2-5}  & A-C2 and A-C1 & R-A6, R-A7, R-A8, & R-B6 & - \\ \cline{2-5}  & A-C1 and A-C2/ & R-A9, R-A11 & & \\ \cline{2-5}  & A-C2 and A-C3/ & R-A2, R-A3, R-A4, & & \\ \cline{2-5}  & A-C3 and A-C2 & R-A6, R-A7, R-A8, & R-B6 & - \\ \cline{2-5}  & A-C2 and A-C3/ & R-A6, R-A7, R-A8, & R-B6 & - \\ \cline{2-5}  & A-C3 and A-C2 & R-A9, R-A11 & & \\ \cline{2-5}  & A-D7 and A-D9 & R-A5, R-C2 & R-B6 & - \\ \cline{2-5}  & A-D9 and A-A1 & R-A5, R-A11, R-B13, & R-B6 & - \\ \hline \hline \end{tabular} Note: refer to Table 3 and Table 2 for the designation of each defect and repair technique, respectively.  
\end{table} 
Table 14: Analysis of the results for claddings of roofs based on the new inter-defects correlation matrix considering repair techniques. Also, in Table 14, the results referring to the repair techniques associated with the most relevant correlations are presented. It is found that, for the repair of roof claddings, the most likely technique to be used is \"R-A1 Cleaning\". It is actually common knowledge that, together with the biannual inspections of roofs, cleaning procedures should be carried out (checking the drainage system for clogging and cleaning any debris). Furthermore, given the future assessment of the life cycle of repair techniques, a superficial analysis of the resources associated with each repair technique can be performed. This is a seminal approach to the topic. In the case of Table 15, \"R-A1 Cleaning\" refers to (i) in external claddings of pitched roofs, cleaning gutters manually (removal of any accumulated dirt, including debris, nests and leaves), and cleaning ceramic roof tiles manually and applying an adequate cleaning agent for the removal of mould, fungus and grease stains; (ii) in flat roofs, using a blower to remove debris from the current surface and gutters, manually removing any vegetation, using a water jet to wet the surface, brushing the roof surface and rinsing the surface thoroughly with running water.