esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Technologies of 6G for Smart Grid Applications
### Non-Orthogonal Multiple Access (NOMA)  
NOMA provides enhanced spectral efficiency along with massive connectivity [1]. NOMA represents a multiple-access approach that is utilized in wireless communication systems. An individual user is assigned a specific orthogonal resource (e.g., time slots/frequency channels) to transmit their data in traditional orthogonal multiple access (OMA) schemes. However, NOMA takes a different approach by enabling multiple users to concurrently share identical resources. In NOMA, several users are allocated the same resources (e.g., time slots or frequencies), and the respective signals are decoded at the respective user end based on power levels, coding, or spreading sequences. This allows different users to access identical resources concurrently. Therefore, NOMA can provide  
Figure 3: The novel technologies of 6G wireless communication and its respective KPIs.  
enhanced spectral efficiency, and it can also serve a diverse number of users simultaneously. Moreover, it also provides enhanced data rates, as well as capacity, than is provided in conventional OMA techniques [82].  
However, interference cancellation and signal decoding at the user ends are crucial issues for NOMA. The successive interference cancellation (SIC) technique is capable of solving the decoding and interference cancellation issues. Furthermore, orbital angular momentum (OAM) and MIMO are also incorporated with NOMA for the significant enhancement of spectral efficiency and capacity [83; 84]. Nevertheless, full-duplex relaying (FDR) is combined with NOMA and OAM-MIMO techniques to enhance the coverage area, improve spectral efficiency, and mitigate the interference issues of FDR [85].
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Indicator Selection
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Target Layer**} & **Criteria Layer** & **Indicator Layer** & **Nature of Indicator** \\ \cline{2-4}  & **Indicator** & **Indicator** & **Indicator** \\ \hline \multirow{7}{*}{\begin{tabular}{l} \end{tabular} } & \multirow{7}{*}{\begin{tabular}{l} Severity of disaster-causing factors \\ \end{tabular} } & Maximum 24 h rainfall (mm) & Positive \\  & & Maximum 3-day rainfall (mm) & Positive \\  & & Average annual precipitation in \\  & & moderate rainfall (mm) & Positive \\  & & Average annual precipitation of \\  & & heavy rainfall (mm) & Positive \\  & & Average annual precipitation of \\  & & storms (mm) & Positive \\  & & Precipitation variability \\  & & Flood frequency & Positive \\ \hline \multirow{7}{*}{\begin{tabular}{l} Urban flood disaster risk \\ \end{tabular} } & \multirow{7}{*}{\begin{tabular}{l} Exposure to disaster-prone \\ environment \\ \end{tabular} } & River network density (km/km\({}^{2}\)) & Positive \\  & & Average elevation (m) & Negative \\ \cline{1-1}  & & Average slope (\%) & Negative \\ \cline{1-1}  & & Vegetation cover (\%) & Negative \\ \hline \multirow{7}{*}{\begin{tabular}{l} Vulnerability of \\ disaster-bearing body \\ \end{tabular} } & \multirow{7}{*}{\begin{tabular}{l} Population density (person/km\({}^{2}\)) \\ Road density (km/km\({}^{2}\)) \\ Economic density (billion CNY/km\({}^{2}\)) \\ Primary industry GDP (billion \\ CNY/km\({}^{2}\)) \\ \end{tabular} } & Negative \\ \cline{1-1}  & & \
\begin{tabular}{l} Vulnerability of \\ disaster-bearing body \\ \end{tabular} & Secondary industry GDP (billion \\ \cline{1-1}  & &
\begin{tabular}{l} CNY/km\({}^{2}\)) \\ Tertiary industry GDP (billion \\ CNY/km\({}^{2}\)) \\ Rural population (10,000 people) \\ Density of medical institutions \\ (institutions/km\({}^{2}\)) \\ \end{tabular} & Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indicator system for flood risk assessment in Liaocheng City. of primary, secondary, and tertiary industries, and the density of medical institutions were inversely related to the vulnerability of disaster-bearing bodies.
## 3 Eigenvalue Extraction for Non-Autonomous Dynamic Systems
### The QR-Householder Method  
The flow of iterations begins by factorization using the Householder algorithm by starting as input vector \(\{x\}\) the first column of matrix \([D]\) and receiving as output matrix \([H_{1}]\) with the \(n-1\) elements of the first column of the matrix product \([H_{1}][D]\) equal to zero. In our case, for the \(4\times 4\) matrix \([D]\) we have  
\[[H_{1}][D]=\begin{bmatrix}*&*&*&*\\ 0&*&*&*\\ 0&*&*&*\\ 0&*&*&*\end{bmatrix}=[D_{1}] \tag{24}\]  
The next step produces matrix \([H_{2}]\) with zero \(n-2\) elements of the second column of \([H_{2}][D_{1}]\), followed by a third step that results in an upper triangular matrix:  
\[[H_{2}][D_{1}]=\begin{bmatrix}*&*&*&*\\ 0&*&*&*\\ 0&0&*&*\\ 0&0&*&*\end{bmatrix}=[D_{2}],\ [H_{3}][D_{2}]=\begin{bmatrix}*&*&*&*\\ 0&*&*&*\\ 0&0&*&*\\ \end{bmatrix}=[D_{3}]=[R]. \tag{25}\]  
Arranging terms gives \([H_{3}][H_{2}][H_{1}][D]=[R]\), so that \([D]=([H_{1}][H_{2}][H_{3}])[R]=[Q][R]\), keeping in mind that all \([H]\) matrices are orthogonal and symmetric. The programmed structure of this method is given in Table 7 below.  
In order to determine the convergence rate of the solution of Equation (1) with the Dirac delta distribution formulation, both Laguerre and Householder-QR algorithms are tested as the size of the system matrix increases. Specifically, the ratio \(\hat{f}_{i}^{N}/f_{i}\), is examined, where \(\hat{f}_{i}^{N}\) is the \(i-th\) eigenfrequency recovered by either algorithm for a system matrix of size \(N\times N\) with \(f_{i}\) is the exact value. Thus, the left column in Figure 3 plots the values of the first four eigenfrequencies recovered by the aforementioned algorithms (in red color), concurrently with the analytical solution (in black color) for a nearly zero mass ratio of \(R=0.02\). At the same time, the left column in Figure 3 plots the rate of convergence as the number \(N\) increases. Similarly, Figure 4 plots the same information, but for a substantial mass ratio of \(R=0.20\). In both cases, the rate of convergence is satisfactory and after \(N=5\) terms, the error is negligible. Note that both algorithms give the same ratio for size \(N=2\), past which the Laguerre is no longer accurate and only the Householder-QR algorithm is used.
## Abbreviations  
The following abbreviations are used in this manuscript:  
LEO Low Earth Orbit  
LoS Line of Sight  
SatCom Satellite Communications  
DRA Direct Radiating Array  
EQA Evanescent Quadridge Antenna  
ARC Active Reflection Coefficient  
XPD Cross-Polar Discrimination  
BFN Beamforming network  
TE Transverse Electric  
TM Transverse Magnetic  
RH Right Hand  
LH Left Hand  
PML Perfect Matched Layer  
AEP Active Element Pattern  
EEP Embedded Element Pattern
## 3 Wind Tunnel Tests
### Wind Tunnel Test Results of Original Cross-Section
#### 3.2.2 Vibration Test Results  
Wind tunnel tests were conducted to measure the VIV responses of the sectional model in the construction and completion states at five wind angles of attack, \(0^{\circ}\), \(\pm 3^{\circ}\), and \(\pm 5^{\circ}\), and the results are presented in Figures 9 and 10. No VIV was observed in the tests for the construction stage model, while significant VIV occurred in the completion stage model at \(+3^{\circ}\) and \(+5^{\circ}\). The addition of various accessory elements such as railings, maintenance vehicle tracks, and sidewalk corbels significantly degraded the truss girder's aerodynamic performance, resulting in significant VIV. The completion stage sectional model tests revealed two forms of VIV: vertical VIV appeared first, followed by torsional VIV. Unlike vertical VIV, which has only one locking range, torsional VIV has two locking ranges with significant differences in amplitude, named major locking range and minor locking range, respectively. With an increase in wind angle of attack from \(+3^{\circ}\) to \(+5^{\circ}\), the maximum amplitude of vertical VIV and major torsional VIV increased significantly, while minor torsional VIV increased slightly. Furthermore, the length of the vertical VIV locking range and the starting wind speed did not change significantly, while the starting wind speed of the torsional VIV was significantly delayed. No obvious VIV phenomenon was observed under the three angles of attack of \(0^{\circ}\), \(-3^{\circ}\), and \(-5^{\circ}\).  
be locked during the VIV [46]. The characteristic frequencies of 2.23 Hz and 4.29 Hz for vertical and torsional VIV, respectively, were observed when the VIV occurred, and these frequencies were found to be close to the natural frequencies of the sectional model. The phenomenon indicates that different vortices with different shedding frequencies might drive the vertical and torsional VIV of the bridge.  
Figure 8: Aerodynamic coefficient of the truss girder. (**a**) construction stage. (**b**) completion stage.  
Figure 7: Positive directions of aerodynamic forces.  
Figure 9: VIV responses of the construction stage (converted to actual bridge values). (**a**) vertical responses. (**b**) torsional responses.  
Figure 11: VIV response time-history curves of completion stage.  
Figure 10: VIV responses of the completion stage (converted to actual bridge values). (**a**) vertical responses. (**b**) torsional responses.
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 2 Materials and Methods
### Traffic Measurement  
An additional mobile phone application, developed by the group, was used to register the traffic conditions on the basis of the Google Maps Traffic tool. The quality of traffic in this application is represented by four colors: Green--fast-flowing traffic, Yellow--slower traffic with more vehicles, Red--more congested traffic, and Dark red--the most congested or completely stopped traffic. These data were collected by registering traffic representative colors in each segment of the road of a covered path (5-s time step). Apart from that, in a separate experiment on 19 June, 2019, traffic speed and category were registered by traveling the main city avenues with a car. Subsequently, the obtained data were used to produce PM\({}_{2.5}\) pollution maps in QGIS software, using the Inverse Distance Weighting (IDW) function. IDW estimates unknown concentrations of pollutants through an interpolation method, which assumes that closer values are more related than further values.  
To get a simplified and high-resolution urban pollution model based on traffic conditions, we started by performing a thorough analysis of the real traffic and the available traffic applications. First, a comparative analysis between the vehicle velocities, measured while driving around the city of Quito, and Google Traffic category was performed. Then, traffic speeds reported by a mobile application Waze were compared with Google Traffic categories. Unfortunately, in the Ecuadorian capital, the use of Waze is limited and mostly available on the major avenues, possibly due to a small number of users. However, over a few months, prior and during the study (April-June, 2019), of random sampling in different parts of the city, we were able to collect enough Waze-based data in parallel to Google Traffic to compare the road travel velocity to the traffic categories of Google Traffic. Finally, the correlation analysis between PM\({}_{2.5}\) concentrations and Google Traffic velocity categories was performed. This data was used to create a hierarchical cluster analysis. It produces a dendrogram, which is a treelike diagram that summarizes the process of clustering, where similar variables are joined by lines whose vertical length reflects the Euclidean distance between these variables. We used the function hclust in R to get the best cutoff distance on the y-axis that separates the four levels of traffic into a set of two clusters.
# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 3 Methods
### Model Evaluation  
The performances of individual models (MobileNetV2, ResNet-50, and Inception V3) and their ensemble model for grip posture image classification were evaluated using a confusion matrix and metrics such as precision, recall, and F1-score. Initially, the number of samples for each model was counted for the four categories in the confusion matrix: false positives (FP), true positives (TP), false negatives (FN), and true negatives (TN). TP indicates samples correctly classified as positive, while FP represents samples incorrectly classified as positive when they actually belong to the negative class. Similarly, FN denotes samples incorrectly classified as negative when they actually belong to the positive class, whereas TN indicates samples correctly classified as negative. Using the number of samples in each of these four categories, precision, recall, and F1-score were computed to evaluate the model's performance. Precision is the ratio of true positives (TP) to the total number of positive predictions made by the model, reflecting how accurately the model predicts true samples. Recall is the ratio of true positives (TP) to all actual positive cases, indicatingthe model's effectiveness in identifying all true cases of the positive class accurately. The F1-score, which combines both precision and recall, is calculated as their harmonic mean. A high F1-score indicates that the model has a good balance between precision and recall, with a perfect model achieving an F1-score of 1 [61; 62].  
In this study, we evaluated and validated the performance of the model through k-fold cross-validation and assessed the model's ability and applicability using a separate validation set. To evaluate the generalization performance of our model, we employed k-fold cross-validation with k = 5. The dataset was divided into five subsets, and for each iteration, four subsets were used for training, while the remaining subset was used for validation. The process was repeated five times, and each performance was averaged to report the overall accuracy.
## 2 Designed NISC-Based Architectures
### Demapper Architecture  
The designed NlSC-based architecture dedicated to the universal demapper, as any NlSC-based architecture, is basically composed of the module, which performs the main functionality, which is referred to as \(DemaNlSC\), and a simple control unit. Figure 4 shows the hierarchical structure of the proposed architecture and its connections with input and output blocks. The inputs to the demapper architecture are the log-likelihood ratios (LLRs)from the decoder, variance \(\sigma^{2}\), control words, constellation information, received symbols, fading factors and the inverse values \(\frac{1}{2x}\). Figure 4 shows several memory blocks. \(AprMem\) stores the a priori information (LLRs), which is provided by the channel decoder through the feedback path. The control words (\(CWs\)) generated by the NlSC tool set compiler are saved in the \(CMem\) memory block. Constellation information is arranged in the \(Constellation\)\(LUT\). \(YMem\) and \(\rhoMem\) include, respectively, the received symbols and fading factorscollected from the channel or delivered by the equalizer module in case turbo equalization is adopted. The look-up table \(\frac{1}{2x}\) LUT contains the pre-computed inverse values required in inversion operations.
# Evaluation of Waste Express Bag as a Novel Bitumen Modifier
## Discussion  
### Three Empirical Indicators  
The results of the penetration (25 \({}^{\circ}\)C), ductility (10 \({}^{\circ}\)C), and softening point tests are shown in Figure 2. Like most types of polymer modified bitumen, the softening point of the WEB-modified bitumen was increased while the penetration and ductility were decreased.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **Tests** & **Aging Level** & **Specification** \\ \hline \multirow{3}{*}{Physical properties} & Penetration & unaged & ASTM D5 \\  & Softening point & unaged & ASTM D36 \\  & Ductility & unaged & ASTM D113 \\ \hline Workability & RV & unaged & AASHTO T316 \\ \hline \multirow{2}{*}{High-temperature performance} & Temperature sweep & Unaged, RTFOT-aged & AASHTO M320 \\  & MSCR & RTFOT-aged & AASHTO MP19 \\ \hline \multirow{2}{*}{Low-temperature performance} & \multirow{2}{*}{BBR} & RTFOT-aged, & \multirow{2}{*}{AOSHTO T313} \\  & & PAV-aged & \\ \hline \multirow{2}{*}{Modify mechanism and Morphology} & FTIR & unaged & N/A \\ \cline{1-1}  & FM & unaged & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental plan of the WEB-modified binder.  
Compared with the virgin bitumen, the softening point of WEB-modified bitumen increased by about 3 \({}^{\circ}\)C after mixing the 2% WEB modifier in the initial stage. However, when the dosage of WEB modifier was 8%, the softening point was increased significantly and reached 90 \({}^{\circ}\)C. The result of the 10% WEB-modified bitumen was also over 90 \({}^{\circ}\)C. There are two possible reasons of this rapid increase: (A) When the modifier content reaches a certain level (>8%), the polyethylene (PE) components of the WEB cross-link within the matrix bitumen to form a spatial network structure, which effectively improves the high temperature performance of the bitumen; and (B) The content of calcium bicarbonate (CaCO\({}_{3}\)) in waste express bags reaches a certain level where the modified bitumen becomes a modified bitumen mastic, thereby increasing its high temperature performance.  
The penetration of the WEB-modified bitumen was less than 50, which was much smaller when compared to the pen 60/70. This phenomenon corresponded to the increase of the softening point. From a macro perspective, the bitumen became harder, which means it can resist more shear damage.  
The ductility of the WEB-modified bitumen was also much lower when compared to the virgin bitumen, which indicates that the WEB-modified bitumen has poor tension resistance in a low temperature environment. Since previous research has shown that PE modified bitumen was simple physical blending, this phenomenon may relate to the WEB modifier. Qin had demonstrated that the tensile yield strength of PE will decrease with the increase of temperature in a low temperature environment (\(-\)30 \({}^{\circ}\)C, \(-\)10 \({}^{\circ}\)C) [20]. Therefore, with an increase in the WEB modifier, the tension resistance of WEB-modified bitumen will decrease.
## 2 The Rise of the Federated Learning Approach
### Delving into Decentralized Federated Learning (DFL)
#### 2.3.1 Communication Reduction
Nodes, after completing local training, send information to all the nodes they are connected to and hence compute the local average of the received data. This process is based on the C-SGD algorithm, which adopts _compute-then-communicate_ (or else computes the local average and then broadcasts it to the whole network) rather than _communicate-and-compute_ that the D-SGD algorithm employs. Transmitted information in cross-device communication via the C-DFL algorithm is compressed either by taking advantage of sparsification (using random k values out of the total ones or k values with the higher-magnitude values) methods or randomized gossip protocols (like the one reported by [PERSON] et al. [56] for addressing non-IID data heterogeneity in a random gossip communication protocol), achieving a linear convergence rate, indicating strong theoretical proofs without taking convexity for granted. As described in the Hybrid FL section, the hierarchical network topology is a step forward in the transition of the decentralization of the learning process in FL. However, in the aforementioned section (see the Hybrid FL section), there is a central coordinator. Here, there are also works that take advantage of non-flat networks in a fully decentralized spectrum. [PERSON] et al. [57] introduced a two-tier hierarchical fully distributed learning scheme, with the aim of reducing communication rounds among the entities, using similarity-based criteria. The max-min d clustering algorithm is used in order to create clusters and carry out betweenness centrality measures so that they place the clusterhead of every cluster at the center, and hence, it is easily accessible by all cluster members. In calculating small summaries (using Fourier transformation), the process identifies similarities in data from nodes in the same cluster. Extracting the Fourier coefficients requires a computational complexity of \(O(mlog_{2}m)\), where m denotes the size of the data. Since the Fourier coefficientssent to clusterheads are much smaller than the exchanged weights and can be packed in _beacon_ messages, we do not consider it as communication overhead. Based on the presence of \"diverse data\", the clusterhead chooses which cluster members to incorporate into the federation. In comparison to the conventional all-to-all method, the proposed heuristic reduces communication overhead by up to 42%, with a preserved accuracy of up to 96%. The proposed method significantly reduces the computational complexity of the algorithm, making it feasible for implementation in decentralized environments.
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 4 Carrier-To-Noise Density Ratio
### Statistical Characteristics of C/N\({}_{0}\) of GEO Satellites  
In China and parts of the Asia-Pacific region, GEO satellites are visible almost all day, and they can be selected as reference satellites in the relative positioning mode. The BDS-2 system contains five GEO satellites, and the BDS-3 system contains three satellites GEO satellites. The elevation of the GEO satellite is relatively fixed, and it is limited to a small range. Five BDS-2 GEO satellites and two BDS-3 GEO satellites were statistically analyzed, as shown in Table 2. We can find that the average elevation of all 5 GEO satellites of BDS-2 is 35.04 degrees, which is very close to the average elevation of 35.31 degree of the two GEO satellites of BDS-3. However, the C/N\({}_{0}\) values of BDS-2 GEO satellites on the B1I and B3I frequencies are 37.59 dB/Hz and 42.62 dB/Hz, respectively, which are significantly lower than the average value of the BDS-3 GEO satellites (the C/N\({}_{0}\) values on the B1I and B3I frequencies are 42.82 dB/Hz and 44.57 dB/Hz, respectively). Therefore, we can infer that the C/N\({}_{0}\) of the GEO satellite of BDS-3 at the frequency points B1I and B3I was significantly improved compared with that of BDS-2.