esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Enabled Smart Cities: Evolution and Outlook
## 4 Open Challenges for IoT-Enabled Smart Cities
### Develop Resiliency to Support Mission-Critical Applications  
In addition to the evolution of mobile broadband networks that the future generation of communication networks and services, so-called 5G, will be, it will also provide additional network and service capabilities of utmost importance for smart cities and, in particular, for IoT-enabled smart cities [99]. 5G is expected to be a fundamental enabler for the IoT by supporting the connection of a massive number of sensors, supporting devices and actuators with strict computational and power constraints. However, the modelling of these scenarios (mainly at the access network level), and which combination of technologies will be part of these future communication systems is still not yet fully developed.  
Moreover, IoT-enabled smart cities will have to support real-time services that require very high reliability on the communication services [101]. Thus, there can be unquestionably zero down-time for mission critical services. In order to create robust situational awareness, it is of utmost importance that both the physical and communication infrastructure at the sensing layer and the fog/cloud platforms at the analysis and service layer have the security, availability and self-healing capabilities to support 99.99% Grade of Service under any circumstance.  
There are no existing examples of mission/time critical applications for smart cities deployed at the moment since it is still necessary to develop solutions that increase the robustness and resiliency of the ecosystem for this kind of application to be supported with the required Grade of Service.
## 4 Federated System  
The training and evaluation stage of the centralized DRL system, including an individual instance of this model, was concluded with positive results in terms of the functionality of the implemented models in [7]. However, this centralized approach has a clear disadvantage: the agents only learn based on their own experience. The agents learn exclusively from their own experiences, which can result in these models responding inaccurately to scenarios where the network behaves in an atypical or unexpected manner.  
In order to solve this problem and provide the proposed system with a greater capacity for effective management in different traffic environments, a FDRL system was implemented, extending the principles of Horizontal Federated Learning (HFL) to RL [18, 19]. In this way, multiple agents (located in different devices or environments) collaborate to train a shared RL model without centralizing their interaction data.  
This approach provides the system with important advantages:  
* **Scalability**: Multiple agents can learn in parallel in different environments.
* **Data privacy**: Since agents do not share raw data (environment states or actions), the system maintains privacy.
* **Exploration diversity**: Since agents operate in different environments, where data have the same format characteristics but different arrival and life cycle distributions, the aggregated model benefits from a diverse set of experiences, which can lead to better generalization.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Novel Features
#### 3.1.4 Mu Mimo  
Wi-Fi 6 proposes both downlink and uplink MU MIMO. Moreover, eight parallel streams are supported. In contrast to Wi-Fi 5, MU MIMO can be applied to only parts of the spectrum in Wi-Fi 6 so that it can be combined with OFDMA. The details are discussed in Section 7.
# Deep Instance Segmentation of Laboratory Animals in Thermal Images
## 1 Introduction
### Single-Stage Methods  
Though single-stage methods simplify the procedure by removing the re-pooling step, usually they cannot produce masks that are as accurate as the two-stage methods. Some of those methods works in a per-pixel prediction way, similar to the semantic segmentation.  
The algorithm Fully Convolutional One-Stage detection (FCOS) [33] has fully eliminated pre-defined anchor boxes, thus avoids complicated computation such as calculating boxes overlapping during training.  
EmbedMask proposed in [32] unifies both segmentation- and proposal-based methods and takes the advantages of both. It is built on top of the detection models and thus has strong detection capabilities, just like the proposal-based methods. It applies extra embedding modules to generate embeddings for pixels and proposals, what enables EmbedMask to generate high-resolution masks without missing details from repooling, like segmentation-based methods.  
TensorMask is an example of a dense sliding-window instance segmentation method [34]. The idea is to use structured 4D tensors to represent masks over a spatial domain. It performs multiclass classification in parallel to mask prediction. Authors show that this algorithm yields similar results to Mask R-CNN.  
In this paper, we are mainly interested in the detection of each rodent in reference to each other and in reference to the background objects. Therefore, we focus on instance segmentation approach and more specifically deep instance segmentation architectures represented by two different approaches: one- and two-stage methods. In particular, we addressed the problem of animals instance segmentation in close physical contact on thermal images. The aim of this study was also to verify whether thermal data bit depth reduction or re-scaling has an effect on the results.  
The paper is organized as follows: Section 2 presents the general description of methods and materials. Results are introduced in Section 3 and discussed in Section 4. The last section concludes the work.
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
## 1 Introduction
In Section 4, the results of a single beam formed using the new digital/geometrical MIMO system are presented. These results ensure pinpoint accuracy, fast steering, low computational burden, and low memory, enabling the system to be able to cater to fast-moving mobile users in low-latency future wireless networks.
## 3 Comparison and Analysis of Acceleration Response Results
### Comparison of Slope with Adjusted Elastic Modulus
#### 3.3.1 Acceleration-Time History  
Figure 10 shows the Acceleration-time history graph of the 1 \(g\) shaking table test and ABAQUS analysis by sine 8 Hz.  
Figure 10a-e are the acceleration-time history at the center of the slope model for sine 8 Hz. As the depth decreases, the acceleration peaks between the experiment and ABAQUS gradually increase, and the difference gradually increases, but the trend is still very close. Figure 10f-h are the slope acceleration time history of the slope model for sine 8 Hz. As the depth decreases, the acceleration peaks of the test and ABAQUS gradually increase, and the acceleration time history is very close.  
Figure 11 shows the acceleration-time history graph of the 1 \(g\) shaking table test and ABAQUS analysis for sine 10 Hz.  
Figure 11a-e show the acceleration-time history at the center of the slope model for sine 10 Hz. As the depth decreases, ABAQUS and the experimental acceleration continue to increase and are very close. Figure 11f-h are the acceleration-time history analysis of the slope model for sine 10 Hz. As the depth increases, ABAQUS and the experimental acceleration continue to increase, and the results are getting closer and closer.  
Based on the analysis of acceleration-time history for both flat ground and slope, it turns out that the numerical analyses with ABAQUS can reasonably capture the acceleration-time history obtained from the 1 \(g\) shaking table tests.  
Figure 10: Part of acceleration-time history graph for experiment and analysis in slope ground: (**a–e**) Sine 8 Hz at depth 0.5 m, 0.3 m, 0.2 m, 0.1 m, 0.05 m; (**f–h**) Sine 8 Hz at slope 0.2 m, 0.1 m, 0.05 m.  
Figure 11: Part of acceleration-time history graph for experiment and analysis in slope ground: (**a–e**) Sine 10 Hz at depth 0.5 m, 0.3 m, 0.2 m, 0.1 m, 0.05 m; (**f–h**) Sine 10 Hz at slope 0.2 m, 0.1 m, 0.05 m.
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 4 Performance Evaluation
### Performance Metrics  
1. Spectrum utilization: The higher the spectrum usage, the better the design of the wireless cognitive sensing mechanism, and the higher the overall network performance.
2. Average scan time: The shorter the scan time, the shorter the interruption time of transmission and the increase of the time for transmitting data.
3. Total number of handovers: The handover situation may be due to the switching of the BS, the sudden use of the spectrum or the transmission collision of the SU. If the total number of spectrum handovers is too many, it means that the stability of the spectrum scanning mechanism is not good.
## 3 Results & Discussion
### Elucidation of the Phytochemical Composition of LRE
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline
**Peak** & **RT (min)** & **Molecular Formula** & **_m/z Experimental [M-H]_** & **_m/z Calculated [M-H]_** & **Score** & **Proposed Compound** & **Content (mg g\({}^{-1}\))** & **Class** & **Reference** \\ \hline
1 & 6.07 & C27H30O15 & 593.1497 & 593.1506 & 99.92 &
\begin{tabular}{} \end{tabular} & 1.2 \(\pm\) 0.0 & Flavone & [41] \\ \hline
2 & 7.44 & C26H30O13 & 549.1610 & 549.1608 & 549.1608 & 99.95 &
\begin{tabular}{} \end{tabular} & 43.0 \(\pm\) 0.1 & Flavone & [41] \\ \hline
3 & 8.04 & C26H30O13 & 549.1597 & 549.1608 & 98.83 &
\begin{tabular}{} \end{tabular} & 39.8 \(\pm\) 0.2 & Flavone & [41] \\ \hline
4 & 8.72 & C26H30O14 & 565.1564 & 565.1557 & 96.6 &
\begin{tabular}{} \end{tabular} & 0.2 \(\pm\) 0.0 & Flavone & [51] \\ \hline
5 & 9.17 & C33H38O18 & 721.1982 & 721.1980 & 99.52 &
\begin{tabular}{} \end{tabular} & 0.5 \(\pm\) 0.0 & Flavone & [41] \\ \hline
6 & 9.34 & C26H30O14 & 565.1522 & 565.1557 & 99.55 &
\begin{tabular}{} \end{tabular} & 0.8 \(\pm\) 0.2 & Flavone & [51] \\ \hline
7 & 9.76 & C27H30O13 & 561.1596 & 561.1608 & 91.36 &
\begin{tabular}{} \end{tabular} & 6.0 \(\pm\) 0.2 & Isoflavone & [41] \\ \hline
8 & 10.68 & C15H12O4 & 255.0634 & 255.0657 & - &
\begin{tabular}{} \end{tabular} & 0.2 \(\pm\) 0.0 & Flavone & [41] \\ \hline
9 & 11.11 & C26H30O13 & 549.16 & 549.1608 & 96.6 &
\begin{tabular}{} \end{tabular} & 50.3 \(\pm\) 3.2 & Flavone & [41] \\ \hline
10 & 11.2 & C21H22O9 & 417.1161 & 417.1186 & 95.14 &
\begin{tabular}{} \end{tabular} & 1.1 \(\pm\) 0.1 & Flavone & [41] \\ \hline
11 & 11.27 & C16H14O5 & 285.0735 & 285.0763 & 99.44 & LicoChalcone B & 0.6 \(\pm\) 0.0 & Chalcone & [43] \\ \hline
12 & 11.631 & C35H36O15 & 695.1982 & 695.1976 & 91.82 & licorice glycoside D1/D2 & nq\({}^{*}\) & Flavanoid & [43] \\ \hline
13 & 12.02 & C36H38O16 & 725.2093 & 725.2082 & 92.81 & licorice glycoside C1/C2 & 7.3 \(\pm\) 0.3 & Flavanoid & [43] \\ \hline \end{tabular}
\end{table} 
Table 2: Identification and quantification of phytoconstituents in licorice root extract using LC-DAD-qTOF-MS.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Peak** & \begin{tabular}{l} **RT** \\ **(min)** \\ \end{tabular}  & \begin{tabular}{l} **Molecular** \\ **Formula** \\ \end{tabular}  & \begin{tabular}{l} **\(m/z** \\ **mental** \\ **[M-H]** \\ \end{tabular}  & \ \begin{tabular}{l} **\(m/z** \\ **Calculated** \\ **[M-H]** \\ \end{tabular}  & **Score** & **Proposed Compound** & \begin{tabular}{l} **Content** \\ **(mg g\({}^{-1}\))** \\ \end{tabular}  & **Class** & **Reference** \\ \hline
14 & 12.12 & C35H36O15 & 695.1989 & 695.1976 & 94 & licorice glycoside D1/D2 & 4.6 \(\pm\) 0.3 & Flavonoid & [41] \\ \hline
15 & 13.51 & C36H38O16 & 725.2103 & 725.2082 & 98.69 & licorice glycoside A & 3.9 \(\pm\) 0.3 & Flavonoid & [41] \\ \hline
16 & 13.66 & C35H36O15 & 695.1994 & 695.1976 & 99.75 & licorice glycoside B & 3.9 \(\pm\) 0.2 & Flavonoid & [41] \\ \hline
17 & 13.89 & C16H12O4 & 267.0641 & 2
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.4 Calculation of the Overlap Angles  
The overlap angle of the rectifier \(\mu_{\text{R}}\) can be calculated according to:  
\[\cos(\alpha+\mu_{\text{R}})=\cos\alpha-2d_{\text{xRN}}\frac{I_{\text{d}}}{I_{ \text{dN}}}\frac{U_{\text{dioNR}}}{U_{\text{dioR}}} \tag{37}\]  
The overlap angle of the inverter \(\mu_{\text{I}}\) can be derived from the steady-state mathematical model of the CCC in Section 3.1.
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 2 Controller Design
### The Model of the Driverless Bus  
The kinematic model of the driverless bus is shown in Figure 2. It collapses the two front wheels into a single wheel at the center of the front axles. Meanwhile, the two rear wheels are also collapsed into a single wheel at the center of rear axles. Furthermore, there is no lateral slip in the wheels, and only the front wheel is steerable. Then, the bicycle model is mapped into a two-dimensional plane. \(\delta\) is the steering angle. \((x_{f},y_{f})\) is the center of the front axle. Equally, \((x_{r},y_{r})\) is the center of rear axle. L is the wheelbase. The heading of the bus is represented as \(\theta\).  
Restricting the model to motion in a plane, the kinematic model [16] of the bus is defined in Equation (1):  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{array}\right]=\left[\begin{array}{c}\cos(\theta)\\ \sin(\theta)\\ \tan(\delta)/L\end{array}\right]v \tag{1}\]  
where \(v\) is the velocity of the bus, and \((x,y)\) is the position of the bus.  
According to the kinematic model of driverless bus, the relationship between \(R\) and \(\delta\) is represented as Equation (2):  
\[\tan(\delta)=\rm{L}/R \tag{2}\]  
where \(R\) is the radius of the circle.  
It is obvious that, if the steering angle does not change, the center of the rear axle moves along an arc and the center of the arc is \(O\). Equally, the center of the front center also moves along an arc, and the center of the arc is \(O\).  
Figure 1: The structure of the path-tracking controller.  
Figure 2: Kinematic bicycle model.  
The extended kinematic bicycle model is presented in Figure 3. The steering angle is determined as Equation (3):  
\[\tan(\delta)=\L/R^{\prime} \tag{3}\]  
In Figure 3, arc 1 and arc 2 are arcs of the rear axle center and front axle center, but \(R<R^{\prime}\). The relationship of \(R\) and \(R^{\prime}\) is shown in Equation (4):  
\[{R^{\prime}}^{2}=L^{2}+R^{2} \tag{4}\]  
It is difficult to obtain an accurate model of a driverless bus. It is assumed that the extent of the kinematic model is feasible for the driverless bus under low-velocity conditions. Figure 4 shows the overview of the driverless bus and sensors. The bus's parameters are provided at Table 1 and it is an actuated-by-wire electrical bus (AWEB). Statuses of driverless bus, such as velocity, heading angle, and positions, are obtained from GPS/IMU (provided by StarNeto, Beijing, China).
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 1 Introduction
* Finally, extensive simulation results demonstrate that our approach is highly adaptable. Basing on statistical data, the F1 score of the link evaluation method reaches more than 90%. And our implementation makes a real-world product stably run for more than a week while some of the the packet loss failure is injected in some of the nodes. The rest of the paper is structured as follows. In Section 2, we introduce the related work in failure detection and troubleshooting on decentralized clusters. In Section 3, we present the formal model of our problem. In Section 4, we introduce our PingBased algorithm for enhancing the availability of decentralized cluster. In Section 5, we extensively evaluate the performance of our proposed algorithms compared with existing algorithms.
## 2 Related Work
### Coupling and Coordination Studies
The range for \(C\) ranges between \(-1\) and \(1\) instead of between \(0\) and \(1\). Furthermore, the model assumes that the comprehensive index for each subsystem would significantly change over the period of time of interest. In [6], the model is applied to four regions around the Yangtze river using the 2005-2016 data. For the technological innovation subsystem, three categories of eight predictors are used. For the financial development subsystem, also three categories of eight predictors are used. In [27], the coupling and coordination between technological innovation and industrial structure optimization, as well as the coupling and coordination financial innovation and industrial structure optimization, are studied. The formulas used are not explicitly provided but are presumably similar to those of [3,4,28]. For the technological innovation subsystem, three categories of eight predictors are used. For the financial development subsystem, two categories of nine predictors are used. This study examines the China 2013-2017 data. In [8], the coupling and coordination of three subsystems instead of two are studied, including culture, technological innovation, and financial development. For the technological innovation subsystem, five predictors of three categories are used. For the financial development subsystem, also five predictors of three categories are used. This study uses the China 2005-2016 data. In [9], the coupling and coordination between technological innovation, green finance, and industrial policies are studied. Seven predictors of two categories are used to model the technological innovation subsystem. This study uses the China 2008-2015 data. In [25], 10 predictors for three categories of technological innovation, and 12 predictors for three categories of financial development are used to study the coupling and coordination between different geographical regions instead of those of the two subsystems using China 2006-2016 data. In [29], the relationship between finance policy and the innovation performance from small-to-medium high-tech businesses is studied. The focus of this study is quite different from (i.e., much narrower scope than) other studies that aim to investigate the correlation between financial development and technological innovation in general. This study uses five predictors for finance policy, and four predictors for the innovation performance. The model used to compute the coupling and coordination degree is slightly different from that adopted in [3,4].
## 16 Durability of High Volume Glass Powder Self-Compacting Concrete  
The transport characteristics of waste glass powder incorporated self-compacting concrete (SCC) for a number of different durability indicators are reported in this paper [15]. SCC mixes were cast at a water to binder ratio of 0.4 using glass powders with a mean particle size of 10, 20, and 40 \(\upmu\)m, and at cement replacement levels of 20, 30, and 40%. The oxygen permeability, electrical resistivity, porosity, and chloride diffusivity were measured at different ages from 3 to 545 days of curing. The amount and particle size of the incorporated waste glass powder was found to influence the durability properties of SCC. The glass incorporated SCC mixes showed similar or better durability characteristics compared to general purpose (GP) and fly ash mixes at similar cement replacement level. A significant improvement in the transport properties of the glass SCC mixes was observed beyond 90 days.
## 4 Simulation Results Using FEA Method
### Influence of Shaft Diameter on Mode Frequency  
During the cutting process, the diameter of the shaft decreases constantly. Therefore, the behaviour of the transverse vibration frequency of the shaft with a decrease in its diameter was analysed for a fixed bearing-support stiffness and axial pressure at the tailstock.  
According to Figure 7, when the tailstock support stiffness was \(Kt=0\) N/m, and the axial pressure was \(P=10^{5}\) N, plotting transverse-vibration natural frequency of the shaft against diameter resulted in a curve that was concave downward with positive slope. When the tailstock support stiffness was \(Kt=10^{5}\) N/m and the axial pressure was \(P=10^{5}\) N, the curve was concave upward with a clear minimum between 10 and 12 mm. When the tailstock support stiffness was raised to \(Kt=6\times 10^{5}\) N/m and the axial pressure was kept the same, the transverse vibration natural frequency of shaft first decreased until the diameter fell to a certain value, then increased rapidly with the diameter decrease; the rate of increase was much higher than the rate of decrease. When the tailstock support stiffness was \(Kt=14\times 10^{5}\) N/m and the axial pressure was \(P=10^{5}\) N, the natural frequency increased with decreasing shaft diameter.  
Figure 5: Influence of tailstock radial bearing stiffness \(Kt\) on the shaft’s natural frequency for axial force (**a**) \(P=0\) N and (**b**) \(P=1\times 10^{5}\) N. \(\varnothing\) = diameter (mm).  
Figure 6: Influence of axial force \(P\) on the shaft’s natural frequency for tailstock radial-bearing stiffness (**a**) \(Kt=0\) N/m and (**b**) \(Kt=6\times 10^{5}\) N/m. \(\varnothing\) = diameter (mm).