esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Conclusions
### Graphene/GO (rGO)-Based RF Humidity and Gas Sensors  
In the recent research of RF humidity and gas sensors based on graphene and GO (rGO), two categories of passive RF devices and RF systems for wireless sensing platforms, e.g., mobile health RFID systems, such as wireless biosensing platforms, have been approached.  
First, the proposed GO-based humidity sensor (Figure 10) showed the linear relationship between the transmission coefficient (\(S_{21}\)) and relative humidity (RH) concentration. With increasing humidity concentration, this sensor with and without GO was tested in the RH range from 11% to RH 98%. This resonator-based sensing scheme can be readily recognized with RF signal, but this sensing scheme based on GO may be challenging to determine the exact RH level if the reproducibility of GO is not secured on the resonator. The graphene or GO-based humidity sensing scheme has merits in easy test and fabrication as a sensing platform compared to the bio and gas sensing platform. Next, the developed Pt/rGO-immobilized RFID sensor (Figure 11) is a good case for wireless gas sensing platform. The bending effect of the sensor was tested. They demonstrated that the distortion did not have a critical effect on the figure of merit of the RFID antenna. The independent performance of the bending effect can be a tremendous advantage for versatile purposes and uses in severe environments.  
In conclusion, based on the recent research trends of the RF bio and gas sensing platforms, many researchers continuously focus on developing robust wireless sensing platforms for real-time and continuous health care monitoring. Moreover, to realize the robust RF sensing platforms, researchers should validate prerequisites of sensors such as sensitivity, selectivity, and reproducibility under enough test duration and severe environment and secure the signal-to-noise (SNR) ratio to determine the limit of detection as the RF bio and gas sensors.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available in a publicly accessible repository. Conflicts of Interest: The authors declare no conflict of interest.
## 6 Experimental Evaluation
### Scenarios
#### 6.3.4 Scenario 4: Federated Resource Profiling on Tree Depth  
1. **Faster convergence of ML algorithms.** When compared with the conventional approaches, the execution time of each of the five different machine learning algorithms evaluated with the proposed FWP was found to be significantly reduced, as shown in Figure 11. According to the results, Decision Tree and Random Forest performed better than other ML algorithms and converged more quickly. Even though the worst-performing model with the longest execution time was GBDT, our proposed model, FWP, cut the execution time to almost half.  
Figure 10: Cumulative network I/O Stats before and after FWP.  
2. **Memory utilization**  
Algorithms for machine learning, such as Random Forest, have memory requirements that vary with the number and size of the trees in the algorithm. When dealing with big or complex datasets, the tree might be quite deep and include thousands of nodes. In such circumstances, the memory usage will increase exponentially. A decrease in the depth of the tree will lead to a reduction in the amount of memory that is used.  
In Figure 12, the graph provides us with a visual representation of how the Random Forest Tree Depth affected execution time when FWP is utilized. It is evident that this was the reason for the decreased utilization of resources following FDP implementation.
## 3 Results and Discussion  
### Baseline Performance Characteristics  
The baseline gasification experiments were performed in the 30 kWth bubbling fluidized-bed gasification system as shown in previous work [21], to investigate the effect of various operating parameters, including air equivalence ratio (ER), temperature, feed rate, gasification medium, etc. The equivalent ratio (ER) was defined as the actual air-fuel ratio to the stoichiometric counterpart.
## 4 ML-Based Resource Allocation
### Computational Complexity  
For any algorithm, complexity is defined as the amount of time taken to run it as a function of the size of the input. From Algorithm 2, we can see that the DNN parameters of the DQN algorithm converge into a stable state after E iterations and T time slots. The time complexity of our DNN model is determined by the number of input state actions, the number of layers in the neural network, and the number of neurons used in each layer. Let us consider that the deep neural network is fully connected, and there are L layers in our DNN, including input and out layers. In our analysis, we have considered one eNB and multiple SCeNB as well as D2D pairs. Let us assume the number of neurons for \(s^{th}\) layer for small cell communication is \(m_{s}\). Therefore, the computational complexity for S layers in small cells is \(\mathcal{O}\left(\sum_{s=0}^{S-1}(m_{s-1}m_{s}+m_{s}m_{s+1})\right)\). Similarly, for D2D communication in \(l_{th}\) layer, let us assume the number of neurons is \(n_{d}\). The computational complexity for D2D layers is \(\mathcal{O}\left(\sum_{d=0}^{L-2}(n_{d-1}n_{d}+n_{d}n_{d+1})\right)\). So, the computation complexity for the overall training process considering all 3-tiers simultaneously is  
\[\mathcal{O}\left(ET\cdot(\sum_{s=0}^{S-1}(m_{s-1}m_{s}+m_{s}m_{s+1})\cdot\sum_{d =0}^{L-2}(n_{d-1}n_{d}+n_{d}n_{d+1})\right)\]  
Therefore, the computational time complexity of our proposed algorithm increases in a multiplicative manner with the increasing number of D2D pairs as well as small cells, which makes it suitable for a large and dynamic D2D-enabled cellular HetNet.
## 3 Multi-Layer Framework for Missing Values Prediction  
### Problem Description  
This study addresses the problem about the missing value prediction in FR for product customized design. We take customized elevators as an example to describe this problem. Elevators, as a classic industrial product, are customized in each building. We present FR of a real-word elevator product in Figure 2. Managers need complete FR to be transformed into DP for the design activities afterwards.  
Figure 2: Functional requirements of a customized elevator.  
For the personalized CN, many elevator manufacturing enterprises have developed the requirements management system online. Otis Elevator Company in Yonkers, NY, USA, has developed Architect's Assistant(tm) ([[http://aa.otis.com/aa/cda/cdalogin.aspx](http://aa.otis.com/aa/cda/cdalogin.aspx)]([http://aa.otis.com/aa/cda/cdalogin.aspx](http://aa.otis.com/aa/cda/cdalogin.aspx))) (Accessed on 2 March 2021) and CabCreat(tm) ([[http://cabcreate.otis.com/](http://cabcreate.otis.com/)]([http://cabcreate.otis.com/](http://cabcreate.otis.com/))) (Accessed on 2 March 2021) for requirements elicitation online. Hitachi Company in Tokyo, Japan, developed the smartDecorator software for customized elevator decoration. However, FR transformed from the obtained CN is mostly incomplete in these systems. For example, FR in the hoisting system of elevator (i.e., \(r_{1}\)-\(r_{4}\) and \(r_{11}\) in Figure 2) cannot be directly obtained or transformed from CN in Architect's Assistant(tm), because these requirements are too professional for customers to put forward. In addition, incorrect FR could impact the final design schemes. For example, with the larger predicted value of hoisting motor power (\(r_{1}\)) compared with the ground truth in FR, the following negative situations may happen: (1) the increase in the price of elevator may lower customers' satisfaction; (2) there exists more energy cost and it is not conducive to clean production; (3) long-time operation in low power may shorten the service life and increase maintenance costs.  
FR can be expressed as \(\mathbf{x}\) with \(p\) values, where the number of categorical and continuous variables are \(p_{0}\) (\(j\in\{1,2,\ldots,p_{0}\}\)) and \(p_{1}\) (\(j\in\{p_{0}+1,p_{0}+2,\ldots,p_{0}+p_{1}\}\)), respectively, \(p=p_{0}+p_{1}\). Then, FR matrix \(\mathbf{X}\) is composed of \(N\) pieces of \(\mathbf{x}\), \(\mathbf{X}=\{\mathbf{x}_{i}\}_{i=1}^{N}\). Incomplete FR matrix with missing values is denoted as \(\mathbf{X}^{\text{miss}}\). The coordinates of missing values are  
\[M_{0}=\big{\{}(i,j):1\leq i\leq N,1\leq j\leq p_{0},x_{ij}\text{ismissing} \big{\}}, \tag{1}\]  
\[M_{1}=\big{\{}(i,j):1\leq i\leq N,p_{0}+1\leq j\leq p,x_{ij}\text{ismissing} \big{\}}. \tag{2}\]  
We aim to predict the missing values with only \(\mathbf{X}^{\text{miss}}\) by mining the interconnections among different \(\mathbf{x}\) and provide the valuable predicted results to managers.
## 2 Data  
In this study, we employ temperature observation of 14 weather stations provided by the Hong Kong Observatory (Figure 1 and Table 1). The Hong Kong Observatory operates a total of 89 automatic weather stations across Hong Kong Island, the Kowloon Peninsula, the New Territories, and surrounding islands. These stations collect data of hourly prevailing wind direction, mean wind speed, maximum gust, relative humidity, rainfall, mean sea level pressure, and temperature, as well as daily data of all variables mentioned above. Of the 89 stations, 61 measure air pressure, wind speed, and temperature, while 12 measure wind only, and 16 stations measure rainfall. Among these stations, the station located at the Hong Kong Observatory (hereafter HKO station), in operation since 1884, provides the longest historical record. Note that the abbreviation HKO only refers to the HKO station in the following context.  
In this study, we utilized data from 14 stations that provide all the variables mentioned above. Table 1 lists the information about these 14 stations. Most of the data are continuous, except for some missing data due to reasons such as instrument malfunction, maintenance, and the outbreak of war. In the following analyses, the observed daily mean, maximum, and maximum, are the most frequently occurring in the data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 1: Distribution of the 14 weather stations whose data were used in this study, including the Hong Kong Observatory (HKO), Hong Kong International Airport (HKA), Cheung Chau (CCH), King’s Park (KP), Lau Fau Shan (LFS), Peng Chau (PEN), Shek Kong (SEK), Sha Tin (SHA), Sha Lo Wan (SLW), Tate’s Cairn, Ta Kwu Ling (TKL), Tai Mo Shan (TMS), Waglan Island (WGL), and Wetland Park (WLP) stations.  
and minimum temperatures of these 14 stations were used to define the historical QBIE temperature events in Hong Kong. In the following analyses, the estimation of climatological features of station data (Section 3) was carried based on data from 2006 to 2022, the common period with available data from all 14 stations (Table 1). The analyses about the long-term variability of QBIE events (Sections 4 and 5.1) were based on the HKO station data from 1885 to 2022. Lastly, the ARIMA analyses (Section 5.2) were carried out based on the HKO station data from 1950 to 2022 to reduce impacts from the missing values from 1940 to 1946. Except for the data gap from 1940 to 1946 of the HKO station, all missing data were filled by simple linear interpolation in the following analyses.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods
### Feature Extraction
#### 3.4.2 Computation of Feature Vector Using EEG Spectrum  
The PSD non-parametric methods were employed to extract PSDs of EEG signals. The power levels of the EEG signals were obtained from the power matrix using the frequency estimations of PSD methods. For each signal, PSDs were computed by setting the sampling frequency (fs) to 128 Hz. The signals contain helpful information about their type, namely delta, theta, alpha, and beta. These features were described based on their frequency ranges. The frequency ranges of these features are given in Table 3.
## 2 Test Sites and Data Preparation  
### Test Sites  
The study area is located at the Genhe forest bureau in the Inner Mongolia Autonomous Region of China (50\({}^{\circ}\)56\({}^{\prime}\)N, 121\({}^{\circ}\)29\({}^{\prime}\)E) on the northwest slope of Daxing'an Mountains. This area is on the southern border of boreal forest and belongs to the frigid-temperate zone of the coniferous forest vegetation. The elevation of this area ranges between 650 m-1269 m. The annual mean temperature is \(-\)5.4 \({}^{\circ}\)C, while the minimum temperature is \(-\)52.6 \({}^{\circ}\)C. This area is referred to as the cold polar of China. The annual mean precipitation is 450-550 mm, 60% of which occurs during July and August. The soil type is brown coniferous forest soil with a thickness of 30 cm\(\sim\)40 cm. 83.7% of the area is covered by forest. The dominant tree species is larch (_Larix gmelini_). The main broadleaf deciduous species include white birch (_Betula platyphlla_) and aspen (_Populous davidiana_). In the study area, the maximum height of the Dhurian larch is about 35 m and the maximum diameter at breast height (DBH) is about 40 cm.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 3 Problem Modeling  
To study the TSN routing problem within a complex network topology, the network was abstracted as a directed graph \(G(V,E)\). The nodes in this network included two main types: switch (SW) nodes and end system (ES) nodes, as shown in Figure 1. All of the nodes formed a node set \(V\) and each SW had multiple incoming and outgoing ports, which were responsible for forwarding the data frames received from the incoming ports to the corresponding outgoing ports, according to the routing list. The ESs could be hard real-time control units, such as servo drivers, or soft real-time or non-real-time units, such as sensors, cameras, mobile operating terminals, etc. \(E\subseteq V\times V\) was the set of edges, where each element represents a unidirectional link from one node to another. The TSN connections supported full-duplex, so the physical links between node \(v_{i}\) and \(v_{j}\) corresponded to two directed edges \([v_{i},v_{j}]\) and \([v_{j},v_{i}]\) in the model. Each link \([v_{i},v_{j}]\) was defined by a triplet \(<b_{ij},c_{ij},q_{ij}>\), which denoted the bandwidth capacity, propagation delay, and queuing delay of that link, respectively.  
Based on the common TSN traffic classification method [7; 46; 47], we classified the industrial data transmitted by TSN industrial communication systems in smart factories into three main types. For high-precision servo motors in key manufacturing equipment, such as computer numerical control (CNC) machine tools and six-axis robotic arms, the master unit needs to periodically send control data, along with time-synchronized data, as de  
Figure 1: The architecture of a TSN in a smart factory and the composition of the TSN nodes.  
fined in [9; 48], which are collectively called time-triggered (TT) data. Meanwhile, some upper-level industrial applications that rely on computer vision, such as object recognition inspection systems for workpiece shapes, require access to audio and video surveillance streams throughout the manufacturing process. These data, collectively called audio/video bridging (AVB) data, are less time-sensitive than TT data and, therefore, need to be scheduled for transmission after the highest priority TT data. In addition, TSNs also provide non-real-time transmission services for upper industrial integrated management systems, such as ERP and MES, in smart factories. These communication systems are not considered within the QoS of real-time industrial networks, collectively called best-effort (BE) communication. A summary of the above three types of data is depicted in Table 1.  
AVB and BE data cannot be transmitted in hard real-time, as with TT data, so we defined them as the same type of data, i.e., non-TT data, in our study. In the following sections, we define the two different routing problems, TT and non-TT, and propose two different optimization algorithms to solve these routing problems.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## Appendix A
### Experimental Details for UQ-TSE and System Identification Using Loop Detectors  
Experimental Configurations  
For the PI-GAN model, the generator structure is: layers = [3, 20, 40, 60, 80, 60, 40, 20, 2], meaning that the generator inputs \((x,t,z)\) to estimate both the traffic density \(\rho\) and traffic velocity \(u\). The discriminator structure is: layers = [4, 20, 20, 40, 60, 80, 60, 40, 20, 20, 1], meaning that the discriminator inputs \((x,t,\rho,u)\) and outputs a one-dimensional real number indicating whether the input \(\rho\) and \(u\) are from the real-world data or not. Note that the discriminator has a more complex structure than the generator to stabilize the training. The rectified linear unit (ReLU) is used as the activation function for each hidden layer.  
For the PI-Flow model, both the scale neural network and the transition neural network share the same structure, i.e., 2 hidden layers with 64 neurons in each layer. The number of transformations is six, that is, there are six scale neural networks and six transition neural networks in total. Leaky Relu is used as the activation function for each hidden layer.  
For the TrafficFlowGAN, its PUNN is a normalizing flow model that shares the same architecture as that in the PI-Flow model introduced above. The discriminator consists of a stack of three convolutional layers. The kernel size for each layer is three, and the number of channels of each layer is 4, 8, and 16.  
We use the Adam optimizer to train the neural networks for 5000 iterations. Different from training [PERSON] for deterministic TSE, the L-BFGS optimizer is not used, because the GAN model requires training the generator and discriminator in turn, for which the L-BFGS optimizer is not suitable. We fix the hyperparameter \(\beta=1-\alpha\) and tune \(\alpha\) from [0.1, 0.3, 0.4, 0.6, 0.7].  
The training details are presented in Algorithm 1.  
Appendix A.2. Additional Experimental Results Using Numerical Data Validation for Greenshields-Based ARZ  
The results under different numbers of loop detectors \(m\) are shown in Table 14.
## 4 Verification of Spatial Geometric Characteristic
### Track and Rotor Coplanar
#### 4.1.1 Ideal Installation  
In addition to the track and the rotor coplanar, if the track center and the rotor centroid coincide with each other, ABB can be regarded as an ideal installation. First, to discuss under ideal installation conditions, the research into ABB with single track [3; 4; 5; 14; 15; 16; 17], multiple tracks [18], multiple balls [20; 21], or radial springs [19]. After coordinate conversion and careful comparison of the equilibrium equations for ball positioning, we can confirm that each paper can reach the same conclusion and that the results shown can ensure that Equation (34) is satisfied. The geometric properties are described as follows:  
1. When ABB can completely suppress the vibration of the rotor system, as shown in Figure 6a, where \(x_{c_{i}}=y_{c_{i}}=0\), Equation (33) is necessarily satisfied. At this time, the central axis of the track completely coincides with the spin axis of the system, and the balls can be balanced to park at any position within the track. Therefore, regardless of the number of balls, track radius, and the number of tracks, the balls can balance at various corresponding positions in response to different rotor eccentricities.
2. Regardless of whether the system is operating below or above the critical speed, if ABB cannot completely suppress the vibration of the rotor system, as shown in Figure 6b, the track center axis is parallel to but not coincident with the spin axis of the system. Equation (34) can be obtained by describing \((x_{c_{i}},y_{c_{i}})\) in Equation (33) in polar coordinate form \((r_{c_{i}},\theta_{c_{i}})\). Only if either \(\alpha_{i}=\theta_{c_{i}}\) or \(\alpha_{i}=\theta_{c_{i}}+\pi\) can the solution satisfy Equation (34).  
\[R_{i}\ r_{c_{i}}\text{sin}(\alpha_{i}-\theta_{c_{i}})=0,i=1\,\sim n. \tag{36}\]  
Discussed from the view of plane geometry, if one wants to make the perpendicular from the ball balance stationing point to the spin axis to intersect with the track center  
Figure 6: The track is coplanar with the rotor: (**a**) Complete Vibration Suppression; (**b**) Incomplete Vibration Suppression; (**c**) Multiple Tracks; (**d**) Ball-Radial Spring.  
axis, as shown in Figure 6b, then the straight line passing through points O and C, which intersect the track at two points, can be both the possible and exclusive stationing positions for the balls. The phenomenon also applies to the ball positioning of ABB with multiple balls or multiple tracks or radial springs, as shown in Figure 6c,d.
# True Equalization of Polarization-Dependent Loss in Presence of Fast Rotation of SOP +
## 3 The True Equalization of PDL in the Presence of Fast RSOP  
### Obtaining Decoupling Model When PDL and RSOP Coexist  
When PDL and RSOP coexist in the form of the generally adopted model of RSOP1 + PDL + RSOP2, as shown in Figure 3a, a decoupling model is needed for simplicity. We rewrite Equation (3) and finally obtain Equation (4).  
\[\begin{array}{ll}\mathbf{H}&=R_{2}R_{eig}\mathbf{\Lambda}\mathbf{R}\mathbf{\Lambda}_{eig}^{-1 }\mathbf{R}_{1}=R_{2}R_{eig}\mathbf{\Lambda}\mathbf{R}_{eig}^{-1}\cdot\left(\mathbf{R}_{2}^{- 1}\mathbf{R}_{2}\right)\cdot\mathbf{R}_{1}=\left(\mathbf{R}_{2}\mathbf{R}_{eig}\right)\mathbf{ \Lambda}\left(\mathbf{R}_{2}\mathbf{R}_{eig}\right)^{-1}\cdot\left(\mathbf{R}_{2}\mathbf{R}_{ 1}\right)\\ &=R_{new-eig}\mathbf{\Lambda}\mathbf{R}_{new-eig}^{-1}\cdot R_{new-RSOP}=\mathbf{U}_{new- PDL}\cdot R_{new-RSOP}\end{array} \tag{4}\]  
According to Equation (4), \(R_{2}\mathbf{R}_{1}\) is equivalent to a new RSOP (denoted as \(\mathbf{R}_{new-RSOP}\)), and \(R_{2}\mathbf{R}_{eig}\) is equivalent to a new eigenmode matrix of PDL (denoted as \(\mathbf{R}_{new-eig}\)); thus, \(\mathbf{R}_{new-eig}\mathbf{\Lambda}\mathbf{R}_{new-eig}^{-1}\) constitutes a new PDL matrix (denoted as \(\mathbf{U}_{new-PDL}\)), which has the same attenuation parameter \(\rho\) and new eigenmodes \(\mathbf{R}_{new-eig}\). Therefore, the polarization model can be decoupled as a new-RSOP and a new-PDL, which are shown in Figure 3b. In this way, RSOP and PDL are decoupled. Consequently, the compensation problem is designed as the first-stage compensation for PDL (balance power inequality) and the second-stage equalization of RSOP.