esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Samples and Illuminants  
The spectral reflectance factors of 3 ceramic restorative materials of different classifications (Table 1) were measured using a non-contact measuring system. The device was composed of a spectroradiometer (PR 670-Photo Research, Chatsworth, CA, USA) and a double-fiber optic light cable (Oriol Research, Newport Corporation, CA, USA) coupled to a Xe-Arc light source (Oriol Research, Newport Corporation, CA, USA). Measurements (380-780 nm in steps of 2 nm) were performed using the CIE illuminating/measuring geometry 45\({}^{\circ}\):0\({}^{\circ}\) and a spectrally calibrated reflectance standard (SRS-3, PhotoResearch, CA, USA) in a dark room, where the specimens were placed 40 cm away from the spectroradiometer over a black 50 \(\times\) 50 mm ceramic tile background (\(L^{*}\) = 3.1, \(a^{*}\) = 0.7 and \(b^{*}\) = 2.4) (Ceram, Stafford, UK) (Figure 1). For each specimen, 3 samples (2.0 mm-thick) were prepared and 3 repeated measurements without replacement were performed for each sample, and the results were averaged into a single value. Figure 2 shows the mean measured spectral reflectance factors of the 18 ceramic specimens evaluated in this study. An experienced investigator performed all spectral reflectance factor measurements of dental ceramics.  
the same visual appearance as the initial ones. This was undertaken in order to make meaningful comparisons, since a common viewing condition was achieved. Then, from these corresponding colors, a conventional color difference formula could be used to obtain the 'color inconstancy' index [3].  
The CAT known as CIECAT16 [23; 24; 25] was used to calculate the corresponding colors for the CIE 1931 Standard Colorimetric Observer as illuminated with D65 standard ill-minant. This was achieved using the CIE tristimulus values (X\({}_{i}\), Y\({}_{i}\), Z\({}_{i}\)) of the 18 dental  
Figure 3: Normalized (Y = 100) spectral power distributions of the 9 white CIE LED illuminants and CIE D65 illuminant [2].  
Figure 2: Average spectral reflectance factors on black background of the 18 ceramic materials studied in this study.  
certains calculated using D65 standard illuminant and the 9 CIE LEDs illuminants (in all cases using the CIE 1931 2\({}^{\circ}\) Standard Colorimetric Observer), assuming an adaptation luminance \(L_{\rm A}=64\) cd/m\({}^{2}\) (equal to a photopic illuminance of 1000 lx), a degree of adaptation \(D=1\), and an average surround (\(F=1\)). Since the corresponding colors were computed under the aforementioned conditions, an adequate comparison of the CIE \(L^{*}\), \(a^{*}\), \(b^{*}\) chromaticity coordinates of each sample computed under different illuminants was possible, since a common reference viewing condition was achieved while maintaining the different visual color appearances of all dental ceramics studied under each illuminant.
## 4 Self-Organizing Maps Applied to Driving-Behavior Classification  
The SOM is a particular type of ANN suitable for clustering and visualization of complex multi-dimensional data [39, 40, 55]. It defines a mapping, or projection, from a set of high-dimensional input data onto a regular low-dimensional discrete grid. This grid, which is known as a feature map, preserves the principal features of the input data. Unlike conventional feed-forward ANNs, which are generally trained using the supervised back-propagation learning algorithm, SOMs are trained through an unsupervised strategy; that is to say, in an SOM, there are no known target outputs that are associated with each input sample. On the contrary, during the training phase, SOMs process a collection of data, only input data, in order to discover unknown clusters hidden in the data.  
The architecture of an SOM consists of a single layer neural network with neurons set along a regular grid: the output layer. Each input to the SOM is fully connected to every neuron in the output layer. Figure 6 depicts two typical two-dimensional output layers with \(M=25\) neurons set along a rectangular grid (a) and a hexagonal grid (b). Although most of the SOMs are based on a two-dimensional grid, many applications also use three or more dimensional spaces.  
Each neuron in the output layer has a double representation: an \(N\)-dimensional vector \(\mathbf{m}_{i}\), known as the weight vector, and its position in the grid. The number of components of the vector is equal to the number of input features \(N\). Figure 7 shows the structure of the SOM that was used in this work, and it is based on a two-dimensional hexagonal grid.  
\[\mathbf{m}_{i}=(m_{i1},m_{i2},\cdots,m_{iN}),1\leq i\leq M. \tag{1}\]  
Clustering a dataset by means of an SOM paradigm is carried out using a two-level approach: first, the SOM is trained; afterwards, the prototype vectors of the SOM are clustered. The advantage of using this approach, instead of clustering the data directly, is that the computational load decreases considerably, which makes it suitable for analyzing different pre-processing and initialization strategies  
Figure 5: Comparison of measured data vs. simulation results. (**a**) The inferred gear considering the computed rpm/speed ratios. (**b**) The measured revolutions per minute (RPM) of the vehicle vs. the RPM simulated by the model. (**c**) The measured speed of the vehicle vs. the speed simulated by the model.  
in a short time. In addition, a two-level approach is less sensitive to noise than a single-level strategy. Obviously, this solution is only valid if the clusters found using the SOM are representative of the original data [56].
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 6 Simulation Results and Discussion
### Performance with State Disturbances  
Due to the measurement error and inaccurate vehicle parameters, the actual state may deviate from the nominal state. In other words, the tube MPC system may exhibit state disturbances. In this section, the effectiveness of the tube MPC system when under large state disturbance conditions is demonstrated. In this paper, it was assumed that the random state disturbances had a uniform distribution over the bound [0.05; 0.2; 0.005; 0.02], as shown in Figure 5. In addition, they were added in the simulation model at each sampling time. The results were compared to a traditional MPC without robustness guarantees. A total of 20 trials were run for each controller when subjected to random disturbances. Figure 6 shows the trajectory performances of the traditional MPC when the autonomous vehicle with the traditional MPC avoided one obstacle on the road and returned to the road center. In Figures 6 and 7, the blue and red lines represented the trajectories that successfully avoided the obstacle and failed to avoid the obstacle, respectively. Table 3 lists the number of trajectories where the obstacle was avoided successfully when using the proposed tube MPC and traditional MPC methods. The trajectories of the traditional MPC were scattered over a large area, and only 14 trials avoided the obstacle successfully. The tube MPC system was carried out under the same state disturbances and the trajectories performances of the tube MPC method, as shown in Figure 7. Compared with Figures 6 and 7, it can be observed that the range of the trajectories of the tube MPC system
## 3 Feasibility Analysis of IoT Architecture Utilizing LoRa Communications on Mars
### Analysis of the Transmission Distance between IoT Node and Satellite in Martian Orbit  
This analysis aims to assess whether it is possible to establish communication between IoT nodes on the surface of Mars and satellites orbiting the planet. Furthermore, the analysis intends to determine the signal strength emitted by the IoT nodes utilizing LoRa technology. The assessment will include testing the transmission range to determine if the signal power is strong enough to be received by a satellite in a specific orbit.  
Figure 4: Constellation models: (**a**) Walker Star and (**b**) Walker Delta.  
An important factor for conducting the study is the maximum distance at which IoT nodes can reliably communicate with orbiting satellites. This is calculated using mathematical formulas and models. One of the most widely used methods is the radio propagation model for transmission distance estimation, also known as the Friis equation [45] (see Equation (1)), which establishes a fundamental relationship between free space losses, antenna gains, wavelength, reception, and transmission powers [45].  
\[P_{Rx}=P_{Tx}\;G_{Tx}(\frac{\lambda}{4\pi R})^{2}\;G_{Rx} \tag{1}\]  
where \(P_{Rx}\) is the received power; \(P_{Tx}\) is the transmission power; \(G_{Tx}\) is the transmitter antenna gain; \(G_{Rx}\) is the receiver antenna gain; \(R\) is the maximum distance, with a direct line of sight, between the transmitter and the receiver; and \(\lambda\) is the wavelength of the transmission frequency. According to the equation, communication between IoT nodes on Mars's surface and orbiting satellites is possible if the received power is greater than the satellite antenna's sensitivity.  
In the model developed, the effect of atmospheric attenuation on Mars has been neglected because the density of the Martian atmosphere is much lower than that of Earth. The atmospheric pressure on Mars is about one hundred times lower [46] than Earth's, and the attenuation caused by water vapor is 3086 times lower. Consequently, it is estimated that said attenuation is negligible for radio communications [30]. Furthermore, it should be noted that a significant portion of the communication between the IoT node and the satellite occurs through space, devoid of gases, obstacles, or intervening elements that could affect signal transmission.
# The Design of a High-Intensity Deuteron Radio Frequency Quadrupole Accelerator
## 6 Beam-Loading Effect and Power Coupler Design
### Coupling Ring Area  
A coaxial power coupler was designed for the transmission of high-frequency power into the RFQ. Magnetic coupling through the coupling ring is most effective since there is a  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol and Formula** & **Value** \\ \hline Frequency (MHz) & f & 162.5 \\ Beam current (A) & I & 0.01 \\ Cavity power (kW) & \(\mathrm{Pc}\) & 60.97 \\ Beam power (kW) & \(\mathrm{Pb}\) & 20.78 \\ Generator power (kW) & \(\mathrm{P_{g}=Pc+Pb}\) & 81.75 \\ Optimum coupling factor & \(\beta_{m}=1+Pb/Pc\) & 1.34 \\ Unloaded quality factor & Q0 & 14,124 \\ Loaded quality factor & QL = Q0/(1+\(\beta\)m) & 6036 \\ Accelerating voltage (MV) & \(V_{0}=\int_{0}^{L}Ez\ dz=\sum_{i}^{n}A_{i}V\) & 3.122 \\ Transit-time factor & T & \(\pi/4\) \\ Cavity voltage (MV) & \(\mathrm{Vc=V_{0}T}\) & 2.452 \\ Beam injection phase & \(\mathrm{cos}\varphi=\mathrm{Pb/I\ Vc}\) & 32.06\({}^{\circ}\) \\ Detuning angle & \(\mathrm{tamp}=-\frac{\beta_{m}-1}{\beta_{m}+1}tamp\) & \(-5.20^{\circ}\) \\ Effective shunt impedance (M\(\Omega\)) & \(\mathrm{Rs=Vc^{2}/Pc}\) & 98.61 \\ Beam-induced voltage (MV) & \(\mathrm{Vb=1.\mathit{Rs}\cdot\mathrm{cos}\varphi}\) & 0.420 \\ Generator-induced voltage (MV) & \(\mathrm{Vg=\frac{2\sqrt{\mathrm{P_{g}}\cdot\mathrm{P_{g}}\cdot\mathrm{Rs}}}{1+ \beta}\mathrm{cos}\psi}\) & 3.418 \\ Detuning factor & \(\gamma=-\mathrm{tamp}\) & 0.091 \\ Detuning frequency (kHz) & \(\Delta\mathrm{f}\approx\mathrm{f}\gamma/2\mathrm{QL}\) & 1.225 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Best matching parameters for a single coupler.  
large magnetic field in this cavity. Our power coupler design involves three components: a coupling ring, transition section, and radio frequency matching section. The coaxial impedance of each port is fixed at 50 \(\Omega\). Only the coupling ring and transition section are described in this paper.  
An eigenmode solver was used to simulate the external Q value (\(Q_{ex}\)) of the coupling ring. The coupling coefficient for different coupling ring areas is obtained from:  
\[\beta=\frac{Q_{0}}{Q_{ex}} \tag{15}\]  
The left side in Figure 27 illustrates the shape and position of the coupling ring, while the right side in Figure 27 shows the coupling coefficient varying with coupling ring area. When \(\beta=1\), \(\mathrm{S_{ring}}\) is 926 mm\({}^{2}\). During construction, the two coupling rings are symmetrically inserted into the cavity to maintain uniformity of the quadrupole field and ensure that the two working phases remain consistent.
## 3 Markov Modeling Analysis of IEEE 802.11 Operations
For the case of \(\sigma=0\) during the ICS procedure: after the node successfully transmits the previous data frame, it senses that the channel is idle for a DIFS and continues the ICS procedure: \[P(-1,D|-1,0)=(1-p)(1-p_{b})\] (6) If the transmitted HOL frame encounters a collision, it enters the 0-backoff stage, or if the channel is sensed to be busy after successfully transmitting the frame: \[P(0,k|-1,0)=\frac{p+(1-p)p_{b}}{W_{0}},\ 0\leq k\leq W_{0}-1\] (7)
4. In order to represent the backoff counter freezing, for \(0\leq k\leq W_{0}-1\):  
Figure 2: The 2-D Markov chain of IEEE 802.11 operations. the backoff counter freezes when the node senses that the channel is busy:  
\[P(i,k|i,k)=p_{b},\ 0\leq k\leq W_{i}-1 \tag{8}\]  
If the channel is sensed to be idle, the counter is decremented by one:  
\[P(0,k|i,0)=\frac{(1-p)\times p_{b}}{W_{0}},0\leq k\leq W_{0}-1.\]0  
Then, set the probability of the collision during the data frame transmission as \(p\), and denote the steady-state probability distribution of the system \(\left\{b_{i,\ k}\right\}\) as follows:  
\[\left\{ \begin{array}{c}b_{i,\ k}\triangleq\underset{t\rightarrow\infty}{ \lim}P\{X(t)=(i,k)\},\ \ \ 0\leq i\leq m,0\leq k\leq W_{i}-1\\ b_{-1,\ \sigma}\triangleq\underset{t\rightarrow\infty}{\lim}P\{X(t)=(-1,\ \sigma)\},\ \ \ 0\leq\sigma\leq D\end{array} \right.\]  
Under the steady-state condition of the system, we have the following steady-state equations:  
\[b_{i,0}=p^{i}b_{0,0,0}\leq i\leq m. \tag{10}\]  
\[b_{i,k}=\frac{W_{i}-k}{W_{i}}\frac{1}{1-p_{b}}b_{i,0},\ 0\leq i\leq m, \tag{11}\]  
\[b_{i,k}=\frac{p_{b}+p(1-p_{b})}{\left(1-p_{b}\right)^{D}}b_{-1,0} \tag{12}\]  
\[b_{-1,\sigma}=\frac{\sigma}{\left(1-p_{b}\right)^{\sigma}}b_{-1,0},\ 0\leq\sigma\leq D. \tag{13}\]  
From the probability normalization, \(\sum_{\sigma=0}^{D_{\sigma}}b_{-1,\ \sigma}+\sum_{i=0}^{m}\sum_{k=0}^{W_{i}-1}b_{i,\ k }=1\), and Equations (10)-(13), we can obtain:  
\[b_{-1,0}=\left[\frac{1-(1-p_{b})^{D+1}}{p_{b}+(1-p_{b})^{D}}+\frac{p_{b}+p(1- p_{b})}{2(1-p_{b})^{D+1}}\left(\frac{1-(2p)^{m+1}}{1-2p}W_{0}+\frac{1-p^{m+1}}{1- p}\right)\right]^{-1} \tag{14}\]  
According to Equations (10)-(14), we obtain the steady-state probability distribution. Define \(\tau\) as the probability that a node transmits in a time slot, i.e., the steady-state probability when a node is in the backoff stage, \(i\) (\(0\leq i\leq m\)), and the backoff counter is 0, or the ICS procedure ends. Therefore, it holds that:  
\[\tau=b_{-1,0}+\sum_{i=0}^{m}b_{i,0}.\]  
Substituting Equations (10) and (14) into the above equation, we obtain:  
\[ \begin{array}{c}\tau=\left[\frac{1-\left(1-p_{b}\right)^{D+1}}{p_{b}+(1-p_{ b})^{D}}+\frac{p_{b}+p(1-p_{b})}{2(1-p_{b})^{D+1}}\left(\frac{1-(2p)^{m+1}}{1-2p}W_{0} +\frac{1-p^{m+1}}{1-p}\right)\right]^{-1}\\ \times\left[1+\frac{p_{b}+p(1-p_{b})}{(1-p_{b})^{D}}\right].\end{array}  \tag{15}\]
## 3 Simulation Studies
### Case 1: Seven-Level PUC MLI  
The seven-level Packed U-Cell is obtained by setting the capacitor voltage to one third of the voltage source voltage. To equalize the voltage of the capacitor to one third of the input voltage of the MLI, the dual-mode PI-PI control method is employed, which is utilized here for the first time in PUC MLI topologies. The dual-mode control structure is formed by the sequential use of two PI controllers. In the control approach, the current effect is eliminated and only the voltage reference is taken into account. The control schematic of the PUC7 MLI is shown in Figure 14.  
Figure 12: PI-controlled DC–DC boost converter.  
Figure 13: The input and output voltages of the DC–DC boost converter.  
The input voltage and the auxiliary capacitor voltage of the PUC MLI are given in Figure 15a. One third of the input voltage of the PUC MLI and the auxiliary capacitor voltage are shown in Figure 15b. One of the main components of the system is the load. The voltage falling on this load produces different levels according to the switching situation. Figure 16 demonstrates the load voltage of the system designed by combining all the blocks.  
Figure 16: Load voltage of PUC7 MLI.  
Figure 14: The control schematic of the PUC7 MLI.  
Figure 15: Input voltage and capacitor voltage of PUC7 MLI: (**a**) actual input voltage, (**b**) one third of input voltage.  
The voltage and current THD for this simulated load value are 10.52% and 3.74%, respectively. The active power absorbed by the load is approximately 425 W. Figure 17 represents the load current obtained in the simulation study.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
###### Abstract  
Infrastructure supporting vehicular network (V2X) capability is the key factor to the success of smart city because it enables many smart transportation services. In order to reduce the traffic congestion and improve the public transport efficiency, many intelligent transportation systems (ITS) need to be developed. In this paper, a smart traffic signal control (STSC) system is designed and implemented, it supports several smart city transportation applications including emergency vehicle signal preemption (EVSP), public transport signal priority (TSP), adaptive traffic signal control (ATSC), eco-driving supporting, and message broadcasting. The roadside unit (RSU) controller is the core of the proposed STSC system, where the system architecture, middleware, control algorithms, and peripheral modules are detailed discussed in this paper. It is compatible with existed traffic signal controller so that it can be fast and cost-effectively deployed. A new traffic signal scheme is specially designed for the EVSP scenario, it can inform all the drivers near the intersection regarding which direction the emergency vehicle (EV) is approaching, smoothing the traffic flow, and enhancing the safety. EVSP scenario and the related control algorithms are implemented in this work; integration test and field test are performed to demonstrate the STSC system.  
emergency vehicle signal preemption; vehicular network; adaptive traffic signal control; transport signal priority; smart city infrastructure +
Footnote †: journal: Sensors
## 4 Validation Analysis Using a Real Engineering Case
### Mechanical Model Validation  
(1) Layout of the measuring stations  
A total of 162 ZZ10000/21/45D hydraulic supports, produced by the Anhui Mining Electromechanical Equipment Co., Ltd., Huaibei, Anhui, China, are deployed in the III601 UMC. Along the advancing direction of the coalface, the hydraulic supports are higher on the two sides and lower in the middle. From the ventilating roadway to the conveyor roadway, the hydraulic supports in the coalface are divided into three groups: the left, the middle, and the right. On the left are hydraulic supports #1 to #54; in the middle are hydraulic supports #55 to #108; and on the right are hydraulic supports #109 to #162. From the left, middle, and right, three supports were randomly selected as the measuring stations: hydraulic supports #13, #26, and #39 on the left; supports #67, #80, and #93 in the middle; and supports #121, #134, and #147 on the right.  
The layout of the measuring stations in the III601 UMC is shown in Figure 10.  
Figure 10: Schematic diagram of the layout of the measuring station in the III601 UMC.  
Figure 9: Distribution characteristics of the bending moment of the THR in the III601 UMC at different stages of extraction.  
The working resistance of the hydraulic supports was determined at different extraction stages in the III601 UMC. A histogram of the support load upon periodic weighting in the coalface was plotted, as shown in Figure 11. The working resistance of the hydraulic supports decreased abruptly upon periodic roof weighting in the III601 UMC but later increased until another periodic weighting. This process kept repeating itself, displaying an obvious periodicity. Depending on the local geological conditions, the three stages of extraction were significantly different from each other. The average field-measured periodic weighting lengths were 13.33 m, 19.33 m, and 22.67 m at the three stages. These values agree with those estimated from the proposed equations (that is, the limiting spans). These results prove the accuracy of the proposed mechanical model of the THR and the analysis process.  
Figure 11: Histogram of loads acting on the hydraulic supports in the III601 UMC.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 2 QKD Networks with QKP
### QKD Over Optical Network Architecture  
The photonic network is an emerging infrastructure of optical communication similar to the IP-over-optical networks. According to the QKD key technology, QKD-over-optical network architecture is depicted in Figure 3. According to the supply and demand relationship of the key,  
Figure 1: Point-to-point quantum-key-distribution (QKD) mechanism based on the four-state protocol originally proposed by [PERSON] and [PERSON] in 1984 (BB84).  
Figure 2: Trusted relays are used to extend QKD distance and ultimately generate end-to-end quantum keys.  
the whole architecture is logically divided into a key-supply part (blue area) and a key-consumption part (yellow area).  
The key-supply part is divided into QKD plane and key management plane from bottom to up. The QKD plane consists of optical links and quantum nodes that integrates one or more sets of quantum transceivers, and the channel type is QCh. The point-to-point QKD protocols such as BB84, GG02, MDI runs on the plane to provide point-to-point quantum keys for the upper layer. In key management plane, the quantum key pool (QKP) acts as a node, and its physical implementation can be thought as a server that stores and manages keys deployed on the nodes. Nodes are fully connected through the logical channels which are based on classic data channels. The quantum key is stored in the QKP in the form of binary bits. The key-management plane provides end-to-end quantum keys for the upper layer by means of hop-by-hop relay routing. The PCh required by the QKD plane and key management plane in this architecture is provided by the data channel (DCh) of the key-consumption part.  
The key-consumption part is divided into Optical network and IP network from bottom to up. The optical network is a WDM network and the nodes are facilitated with optical cross-connects (OXCs) with wavelength switching capabilities. In the IP network, the router is connected to the OXC directly, so the services can be dealt with all-optical processing. Therefore, if the quantum node is deployed on the OXC, the QKD network will provide the secure keys for both the IP network and the optical network [21; 22].