esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Simultaneous illumination and communication using solid-state lighting devices like white light-emitting diode (LED) light sources is gaining popularity. The white light LED comprises a single-colored yellow phosphor excited by the blue LED chip. Therefore, color-quality determining parameters like color-rendering index (CRI), correlated color temperature (CCT), and CIE 1931 chromaticity coordinates of generic white LED sources are poor. This article presents the development of multi-color phosphors excited by a blue LED to improve light quality and bandwidth. A multi-layer stacking of phosphor layers excited by a blue LED led to the quenching of photoluminescence (PL) and showed limited bandwidth. To solve this problem, a lens-free, electrically powered, broadband white light source is designed by mounting multi-color phosphor LEDs in a co-planar ring-topology. The CRI, CCT, and CIE 1931 chromaticity coordinates of the designed lamp (DL) were found to be 90, 5114 K, and (0.33, 0.33), respectively, which is a good quality lamp for indoor lighting. CRI of DL was found to be 16% better than that of white LED (WL). Assessment of visible light communications (VLC) feasibility using the DL includes time interval error (TIE) of data pattern or jitter analysis, eye diagram, signal-to-noise ratio (SNR), fast Fourier transform (FFT), and power spectral density (PSD). DL transmits binary data stream faster than WL due to a reduction in rise time and total jitter by 31% and 39%, respectively. The autocorrelation function displayed a narrow temporal pulse for DL. The DL is beneficial for providing high-quality illumination indoors while minimizing PL quenching. Additionally, it is suitable for indoor VLC applications.  
LED-based solid-state lighting; visible light communication; multi-color LED; color-rendering index; jitter; correlated color temperature
## 3 Results
### Reflection Coefficient
It can be clearly observed from the figure that the patch edge of the case one antenna is more uniform than that of the case four antenna, and the unevenness of the case four antenna patch may be caused by the fluidity of CSP during fabrication, which results in a larger patch size than expected and, thus, affects the antenna's performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Frequency (GHz)** & **S11 (dB)** & **Bandwidth (GHz)** \\ \hline Simulation & 8.00 & \(-20.46\) & 0.10 \\ Case 1 & 8.01 & \(-15.71\) & 0.18 \\ Case 2 & 9.20 & \(-10.01\) & - \\ Case 3 & 11.10 & \(-18.72\) & 1.99 \\ Case 4 & 7.15 & \(-12.03\) & 0.11 \\  & 9.43 & \(-1.86\) & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured and simulated resonant frequencies, the corresponding reflection coefficients and \(-10\) dB bandwidths of the antennas. Figure 6: The measured and simulated reflection coefficients for the proposed four antennas. (**a**) Case 1, (**b**) case 2, (**c**) case 3 and (**d**) case 4.
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 4 Fuzzy Transportation Problem  
The fuzzy transportation problem (FTP) is an extension of the classical TP which introduces fuzzy sets and fuzzy numbers for representing transportation cost, supply and demand [25]. Solving the FTP involves developing mathematical models and algorithms which can affectively deal with the fuzzy nature of the input data.  
In our study, we treat the supply and demand values as crisp numbers, whereas the transportation costs are represented as fuzzy sets. The solution of the objective function \(\overset{\sim}{z}\) is also depicted as a fuzzy number.  
Let \(\overset{\sim}{c}_{ij}\) be the fuzzy cost of transporting one unit of product from warehouse \(i\) to consumer \(j\).  
Under changing circumstances, the mathematical model of the FTP could be structured as follows.  
\[min\overset{\sim}{Z}=\sum_{i=1}^{m}\sum_{j=1}^{n}\overset{\sim}{c}_{ij}\bigotimes x _{ij} \tag{5}\]  
\[\sum_{i=1}^{m}x_{ij}\geq d_{j}\;\;for\;j=1,\;2,\;\ldots,\;n \tag{6}\]  
\[\sum_{j=1}^{n}x_{ij}\leq s_{i}\;\;for\;i=1,\;2,\;\ldots,\;m \tag{7}\]  
\[x_{ij}\geq 0\;\;for\;i=1,\;\ldots,m\;,\;j=1,\ldots,n \tag{8}\]
# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 3 Results
### Sample Matrix  
The nature of the sample matrix can influence the analyte extraction efficiency and selectivity. The efficacy of the SPME fiber to extract the analyte of interest can be influenced by factors such as pH, ionic strength, and the presence of other volatile organic molecules. Three different matrices were tested herein, namely, an aqueous sample, prepared as mentioned in Section 2.1 (Matrix A), a PBS sample (Matrix B), prepared as mentioned in  
Figure 5: Effect of extraction temperature on the total peak area of analyzed ethanol. Ethanol concentration 10 mg/L; NaCl addition: 30%; extraction time: 20 min; SPME fiber: CAR/PDMS. Results are shown as the mean \(\pm\) SD (\(n=3\)). * \(p<0.05\) for 30 \({}^{\circ}\)C when compared with 60 \({}^{\circ}\)C, using the ANOVA test, followed by [PERSON]’s multiple comparisons tests.  
Section 2.2, and an artificial sweat sample, prepared as mentioned in Section 2.3 (Matrix C). The pH of Matrix C was 6.3, which corresponds to the general pH of human sweat.  
From Figure 6, it is possible to conclude that the aqueous sample (Matrix A) is more favorable to ethanol detection through the SPME-FID method when compared with PBS (Matrix B) and sweat (Matrix C), with a statistical significance of * \(p<0.05\) when compared to PBS. Several variables can contribute to the greater detectability of ethanol in watery samples. One probable explanation is that the aqueous sample's composition is more compatible with the SPME-FID method's extraction and detection processes. Inferefents in the samples might reduce the extraction effectiveness of the SPME-FID technique. In the case of sweat and PBS, these matrices may include a variety of chemicals, such as salts, proteins, and other organic molecules, which might interfere with ethanol extraction by competing for adsorption sites on the SPME fiber. Artificial sweat matrices are mixtures of compounds that simulate the chemical composition of human sweat, including electrolytes, organic acids, and other components. By testing with an artificial sweat matrix, it was possible to study the behavior of ethanol in a controlled and reproducible manner, without the variability and complexity associated with real human sweat. This information can be used to refine and improve the analysis method and to better understand the behavior of ethanol in sweat.
## 2 Internet of Things for Healthcare
### Smartphones  
Currently, smartphones provide considerable processing capabilities and a great diversity of sensors that can be used to develop mobile healthcare systems. A survey of healthcare software for smartphones was presented by [60] and concludes that the use of smartphones in healthcare systems is increasing and are useful applications for patient training, sickness self-administration, and remote supervising. Mobile applications support symptom evaluation, psychoeducation, source position, tracking of treatment development, and mental tele-health. Consequently, it is necessary to define and delineate the difference between mobile applications that support healthcare decisions and those with the goal of intervening in clinical decisions [61]. Commercial smartphone applications for healthcare allow patient participation in effective disease prevention and management, which leads to significant cost savings in personalized healthcare [62]. Smartphones provide activity recognition through the detection of physical activities, such as walking and running, climbing stairs, travelling, and sedentary behavior, without the need for extra devices that can be used for patients' activity monitoring for personalized healthcare [63; 64]. A distributed particle filter simultaneous localization and mapping (DPSLAM) process that offers restrictions towards a simple recent mounted inertial measurement unit combined into the mobile phone and specifies the core information on the movement of the user was presented by [65], which can be used to monitor a patient's activity at home after medical intervention. An intelligent communication approach for AAL that uses information collected by sensors, data traffic patterns, and the behavior of a person for providing decisions, and sends notifications via smartphone applications was proposed by [66] and demonstrates the importance of smartphones in personalized healthcare. The accelerometer, gyroscope, or light sensor incorporated in a smartphone can be used for activity recognition and monitoring. The camera and microphone can be used as multimedia sensors for personalized healthcare systems [67]. Smartphones are equipped with short-range communication technologies, such as Bluetooth and Wi-Fi, but also long-range technologies, such as GPRS, UMTS, and 3G/4G, that can be used for monitoring co-morbid patients remotely using short-range communication inside hospitals and long-range communication inside patients' home [68]. The NFC and RFID identification technologies can accelerate healthcare and medicine care procedures, and develop enhanced identification protocols that can lead to a reduction of errors in medical diagnostics [69; 70]. The preceding paragraphs are intended to provide facts that support the importance of smartphones in healthcare systems. Smartphones incorporate sensors, communication technologies, and processing power that can be used to complement healthcare systems, enabling better data accessibility and notifications to the patient but also to collect data from the environment.
## 2 Experimental Setup
### Test Pulse Characterization  
An XFROG trace was obtained using the reference pulse characterized by the above procedure. For this, one of the two replicas of the reference pulse reflected on BS3 (Figure 1) was blocked, and the transmitted beam through the beam splitter was used to gate the test pulse to be characterized. The test pulse consisting of the two-color components was non-collinearly focused into the 5 \(\upmu\)m BBO crystal together with one of the replicas of the reference pulse using the concave mirror (CM1 in Figure 1). The angle of the crossing beams was adjusted to be less than ca. 2 degree to avoid geometric smearing. The generated sum-frequency signal between the replica and test pulses was focused onto the multichannel spectrometer to obtain the XFROG trace. The energy ratio between two optical pulses is measured using a power meter (PM10X, Coherent Inc.). This multichannel spectrometer is calibrated to compensate for the change in the spectral response. Phase matching of the SHG and SFG signals using the crystal needs to be considered [23]. Figure 2 shows the phase matching curve calculated by a homemade LabVIEW software (LabVIEW8.6, NATIONAL INSTRUMENTS, 2008, Austin, TX, USA) when using the 800 nm optical pulse as a reference pulse and a 5 \(\upmu\)m BBO crystal. A flat phase matching curve is obtained from 800 nm to 1180 nm and from 340 nm to 1500 nm after compensation for a spectral response, supporting a spectral bandwidth allowing a measurement of 1.2 fs optical pulse.
# Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring
## 4 Results and Discussion
### Comparison of Different Degrees of Automation  
To verify the effects of vehicles with different degrees of automation, vehicles of automation Level 0 (no automation), Level 2 (partial automation), and Level 5 (fully autonomous) are simulated in the traffic scenario of Duisburg inner ring. In this simulation, the number of vehicles, vehicle routes, departure time, and departure location on the network are the same, and only the degree of automation is varied. The simulation is for 24 h of a day, and the traffic demand is generated from the close-to-reality results of road detectors. To observe more details in the change of traffic volume, the data collection time cycle of verification detectors is changed to 600 s. The simulation results can be seen in Figures 7 and 8; the average speed of vehicles in the simulation are shown separately (Figure 7) and in comparison (Figure 8). For the same traffic volume input, the vehicles with a higher automation level, represented by the red and blue lines in Figure 8, show a higher average speed than the vehicles without automation, represented by the black lines. In this simulation, the traffic status represents the real traffic situation of the Duisburg inner ring for a certain day. For the current traffic volume, vehicles in all three simulations can finish the driving task with average speeds close to the speed limits of the roads.  
Figure 8: Comparison of average speeds of vehicles with different automation levels in real traffic volume (100% traffic volume).  
Figure 7: Average speeds of vehicles and running vehicle numbers of simulations with vehicles of different automation levels.  
To create a heavy traffic demand situation, an extra 50% traffic volume is added to the scenario. If traffic conditions continue to deteriorate, there will be more vehicles on the road in the future, whether automated vehicles can improve the congestion problem or not. Figure 9 shows the average speed of vehicles in three different scenarios with Level 0, Level 2, and Level 5 vehicles. For the same heavy traffic volume input, the average speed of vehicles in the simulation with Level 0 vehicles is greatly affected (black line) and, in the other two simulations with vehicles of higher automation levels, the average speed is only slightly lower as in real traffic volume input. The simulation results show that, whether in the actual traffic volume or the possibly more congested future traffic situation, automated vehicles have a positive impact on the traffic flow. Vehicles with higher automation levels can take on more congested traffic conditions.
## 2 Materials and Methods
### Fabrication of CaO Stabilized Zirconia Pellet  
To prepare the zirconia specimen pellet, 9.2 g of zirconium oxide (92%) was mixed with 0.8 g of calcium oxide (8%), either CaO(CS) or CaO(CC) (8%). The mixed powder was placed in a plastic bottle containing ceramic balls, which was then placed on an electric mixer for 30 min to allow for proper mixing. Then, the inner surface of a stainless steel mold with the dimension of 32 mm in diameter and 6 mm in height was isolated with silicone wax-based separating medium. The mixed powder was placed in the stainless steel mold, and then pressed using a hydraulic press (AtlasTM Manual 15 T and 25 T Hydraulic Press, Specac, Kent, Orpington, UK) under 5 tons (60 Mpa) pressure. A total of 176 pressed zirconia specimen pellets were then divided into four groups according to the different sintering temperatures. Each group was sintered in a furnace (Linn High Therm Furnace VMK-1800) for two hours at four different temperatures (1200, 1300, 1400, and 1500 \({}^{\circ}\)C) to produce pellet blanks of calcia-stabilized zirconia (Ca-SZ) with either CaO derived from cockle shells (CS) or commercial CaO (CC), which from here on will be called Ca-SZ(CS) and Ca-SZ(CC), respectively. An example of the sintered zirconia pellet blank is shown in Figure 1.  
Figure 1: Sintered calcium oxide (CaO)-stabilized zirconia pellet blank.
## 1 Introduction
Compared to the traditional cluster head selection approach which can only be applied to small-scale networks,  
Figure 1: Cluster based directional sensor network. the proposed approach can effectively achieve energy consumption balance for networks of all scales. The cluster head selection is also an optimization problem which has been proven to be NP-hard [5]. Based on the above analysis, the sensing direction rotation and cluster head selection are all NP-hard problems. For this type of problems, a meta-heuristic algorithm such as simulated anneal, genetic algorithm and particle swarm optimization are suitable for solving it [6]. The designed algorithm needs to consider time, resource consumption and accuracy. Particle swarm optimization (PSO) is a better choice for the NP-hard problems due to its easy implementation and high precision. In order to guarantee the adaptability of the algorithm, we make an improvement for the inertia weight. In this paper, we mainly concentrate on area coverage optimization and energy consumption optimization problems. For the area coverage problem, we set up a multi-objective optimization model due to the uncertainty between coverage ratio and redundancy ratio. We reduce coverage blind areas and coverage overlapping areas by sensing direction rotation. For energy consumption optimization problem, we set up cluster head selection optimization model in order to achieve communication energy consumption balance. We make the energy consumption evenly distribute on each sensor node by considering the total residual energy ratio and energy consumption balance degree of the cluster head candidates nodes in each round. We also propose a cluster formation algorithm in which the member nodes join their cluster heads by energy efficiency weight function. We next propose an improved adaptive particle swarm optimization (IAPSO) to solve the two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Compared to traditional PSO, we make an improvement for inertial weight, and the IAPSO has higher convergence ratio and operator precision. The rest of the paper is organized as follows. Section 2 reviews the previous related work. The preliminaries of network model, directional sensor model and energy consumption model are provided in Section 3. In Section 4, the multi-objective area coverage optimization problem is described. Section 5 analyzes the cluster-based energy consumption optimization problem. Section 6 presents our proposed approach. Section 7 conducts simulation experiment to evaluate the efficiency of the proposed approach. The conclusion is in Section 8.
## 2 Materials and Methods  
### Wireless Acoustic Emission Sensor Network  
Generally, the proposed WASN is an extension of the WSN equipped with AE sensors. This specified wireless AE node is capable of managing up to two independent AE channels, and each channel is provided with a low-noise high-speed amplifier OPA627 [27]. In addition, the conditioned AE channels are connected to a 12-bit low-power analog-to-digital converter AD7356 [28] with a fixed sampling frequency of 5 MHz. Then, an ARM Cortex-M4 micro-controller STM32F405 RG [29] under 168 MHz would take charge of the whole data flow and finally, a LoRa module SX1278 [30] would be adopted for the wireless communication assignment. The composition structure of the AE node and its application architecture are shown in Figure 2.  
Figure 2: Structure and installation of system: (**a**) architecture of the wireless AE node; (**b**) diagram of system installation.  
The AE signal is generated by one mini vibration motor based on Arduino that has been mounted closely on the trunk [31]. The stress wave generated during the test would propagate through the tree trunk surface and be collected by the AE sensors attached to the bark. In order to enhance the anti-interference ability of the AE system, two R15\(\alpha\) type probes are vertically placed around the diameter of the breast height (DBH) of trees (1.3 m above the ground) with an interval of 10 cm, according to [PERSON]'s research [32]. The AE signal source is placed vertically on the upper side of the two probes 10 cm away from the near-end probe, all within a three-point line. This means that the differential term of AE signals between the far-/near-end probes can be calculated to reduce the system measuring error as much as possible. The WASN has been deployed at the sample woods of an experimental forestry farm in Nanjing Forestry University, China. A remotely deployed sink that aggregates all the nodes' feature data would take charge of the next dataset's analyzing and processing procedure.
# A Secure and Efficient Group Key Management Scheme for Clusters of String Inverters
## 4 The Group Key Management Scheme
In the initialization phase, KMS initializes inverters that form the initial membership. Such initialization takes place via offline methods. KMS assigns each group inverter \(i\) the unique identifier UID\({}_{i}\), the public-private key pair (privK\({}_{i}\), pubK\({}_{i}\)), along with the corresponding certificates \(\text{Cert}_{i}\) and \(\text{Cert}_{\text{CA}}\), and an empty \(\text{CRL}_{i}\) (i.e., with no revoked certificates). Finally, KMS generates the initial group key K\({}_{\text{G}}\) and initializes each inverter with that key. Inverters store the group key and their respective private key in the hardware security module HSM. The following description of the operations concerns the steady-state, i.e., the state in which all involved group members are up and running. For the sake of brevity, in the following subsections we will say that \"a principal \(A\) securely unicast a message \(m\) to a principal \(B\)\" meaning that a secure channel (i.e., a TLS connection) between principals \(A\) and \(B\) has been successfully established and message \(m\) has been sent by \(A\) to \(B\) through it.