esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To summarize, we have numerically shown the role of intra-cavity propagation delay in message encoding and decoding using chaotic semiconductor lasers. Based on the chaos shift keying scheme, a digital message signal of 1.2 Gbit/s bit rate with a modulation depth of 0.2 is encoded at the transmitter laser and is recovered at the receiver by comparing its output with the transmitter laser. We found that despite the magnitude of intra-cavity propagation delay \(\tau_{PD}\) is less, it has an impact on the quality of message recovery. The influence of \(\tau_{PD}\) and the associated bit error rate of the recovered message signal is carried out for different values of injection rates. The results convey that, \(\tau_{PD}\) correction in the recovery process does improve the quality of message recovery. Next, the influence of frequency detuning on \(\tau_{PD}\) and thus on the message recovery is investigated. The quality of message recovery shows an improvement after the \(\tau_{PD}\) correction. Furthermore, we have presented the effect of \(\tau_{PD}\) on the quality of message recovery for the range of modulation depths and message bit rates. We note that recovered message quality found to increase (BER is less) after the \(\tau_{PD}\) correction for each value of modulation depths and bit rates. Although, the \(\tau_{PD}\) magnitude (order of pico-seconds) is much less, it does have an effect in the signal recovery, where we find the effect is also less. Nevertheless, we emphasize that, in long-haul communication, where more than one receiver laser is considered in the communication system, the \(\tau_{PD}\) would have noticeable effect, since it is a dynamically arising time delay in the system and has an accumulative nature as TL output propagates through multiple receiver lasers in the cascading configuration [48]. However, the study of \(\tau_{PD}\) influence in the long-haul  
Figure 13: Estimation of BER values of the recovered messages for different bit rates at modulation depth \(b=0.2\). Red and blue traces correspond to BER value prior to and after the \(\tau_{PD}\) correction, respectively, in the receiver laser.  
communication system is beyond the scope of our present study. In conclusion, we can emphasize that the intra-cavity propagation delay correction needs to be incorporated to the receiver laser in order to achieve a better quality of message recovery in a system of synchronized chaotic semiconductor diode lasers.  
[PERSON] was responsible for the numerical simulation, analyzing the results and writing of the paper. [PERSON] was responsible for the conceptualization. [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON] were responsible for writing and revising the manuscript. [PERSON]-[PERSON] and [PERSON]-[PERSON] were responsible for the discussion of the results and reviewing/editing/proof-reading of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61575163, Grant 61775184, and Grant 61875167.  
The authors declare no conflict of interest.
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
## 1 Introduction
In terms of theory, the bandwidth of the microstrip antenna can be broadened by increasing its profile and reducing the dielectric constant (\(\epsilon_{r}\)) of the substrate [18]. Meanwhile, the reliability and stability of the antenna are important indicators of its application prospects. Hence, the simple design of the antenna structure can effectively improve its stability and reliability [1]. In this paper, an efficient and simplified method to realize CLAs with broadband has been presented. The proposed antenna array consists of a Wilkinson power divider circuit and four microstrip antenna elements which are composed of three layers of the flexible substrate and two layers of the honeycomb core. Stacked between three flexible substrates, the honeycomb layer and antenna are integrated into the structure, which broadens the bandwidth of the antenna in terms of structure. Then, the principle of its wideband operating is analyzed through the characteristic mode analysis (CMA) theory. Meanwhile, the sidelobe level (SLL) is reduced by reflection metal printed on the back of  
Figure 1: The conventional structure of conformal load-bearing antenna array. the lower substrate. Additionally, it is known that the EM parameters of the honeycomb core layer have a great influence on the performance of the conformal antenna [19; 20; 21; 22]. For this purpose, an equivalent medium model of the honeycomb core layer is proposed for the simulation of the conformal load-bearing structure. Furthermore, the mechanical properties of the proposed structure have been verified through the three-point bending test and the quasi-static compression test. In Section 2, the antenna element and array design are described. Section 3 introduces the equivalent medium model of the honeycomb core layer and the principle for wideband operation. Section 4 illustrates the simulated and measured results of the analysis of fabricated antenna, including the radiation characteristics and mechanical performance, followed by the comparison. The work has been concluded in Section 5.
## 2 Materials and Methods
### Data Preprocessing
#### 2.1.2 Photovoltaic Potential Calculation  
When assessing the electricity production of photovoltaic (PV) panels, the primary step is to estimate the solar potential available to them. Solar potential estimation comprises two components, namely direct and diffuse irradiation [34]. Both components are highly dependent on the meteorological measurements on a horizontal surface, which are obtained from the closest meteorological station to the given location (in our case, each TR). Direct irradiance (\(I_{p_{\text{p}}}\)) for the given moment is calculated by considering the angle of incidence  
Figure 2: (a) A characteristic day of power load and PV production profiles for a given TR and month. (b) Prospects of power load and PV power profiles for a TR over the next 10 years.  
(\(\theta_{p}\)), zenith angle (\(\theta_{p_{2}}\)), inclination (\(\beta\)), orientation (\(\gamma\)), solar declination (\(\delta\)), hourly angle (\(\omega\)), and direct irradiation (\(I_{b}\)) measurements as follows [35]:  
\[\begin{split}& R_{P_{b}}=\frac{\cos(\theta_{p})}{\cos(\theta_{p_{2}})} \\ &\cos(\theta_{p_{z}})=\cos(\delta)\cos(\varphi_{p})\cos(\omega)+ \sin(\delta)\sin(\varphi_{p}),\\ &\cos(\theta_{p})=\sin(\delta)\sin(\varphi_{p})\cos(\beta_{p})- \sin(\delta)\cos(\varphi_{p})\sin(\beta_{p})\\ &\times\cos(\gamma_{p})+\cos(\delta)\cos(\varphi_{p})\cos(\beta_ {p})\cos(\omega)+\cos(\delta)\\ &\times\sin(\varphi_{p})\sin(\beta_{p})\cos(\gamma_{p})\cos( \omega)+\cos(\delta)\sin(\varphi_{p})\\ &\times\sin(\gamma_{p})\sin(\omega),\,\text{where}\\ &\delta=23.45^{\circ}\sin\left(360^{\circ}\frac{(284+n)}{365} \right)\text{, and}\\ &\omega=\left(((h+1.0)-12.0)*15.0\right)*0.0174532925\\ &\qquad\qquad\qquad I_{p_{b}}=I_{b}R_{p_{b}}\Big{[}\,\,\text{kW/ m}^{2}\Big{]},\end{split} \tag{1}\]  
Diffuse irradiation (\(I_{p_{d}}\)) represents the part of solar irradiation after it scatters in the atmosphere and is calculated based on the well-known Liu-Jordan model [36] by considering the measurements of diffuse irradiation, denoted as \(I_{d}\), and the inclination of the given surface as follows [35]:  
\[\begin{split}& R_{p_{d}}=\cos^{2}\frac{\beta_{p}}{2}\\ & I_{p_{d}}=I_{d}R_{p_{d}}\Big{[}\,\,\text{kW/m}^{2}\Big{]}\end{split} \tag{2}\]  
The solar potential for a given time is estimated based on the assumption that there is no shadowing from surroundings (\(S_{p}=0\)) as follows:  
\[I_{p}=I_{p_{b}}\big{(}1-S_{p}\big{)}+I_{p_{d}}\Big{[}\,\,\text{kW/m}^{2}\Big{]} \tag{3}\]  
Instantaneous PV potential (\(I_{pv}\)) is then calculated based on the efficiency of a PV system, which is denoted as \(C\).  
\[I_{pv}=I_{p}*C\Big{[}\,\,\text{kW/m}^{2}\Big{]} \tag{4}\]  
The final yearly PV potential is then obtained by a summation of PV potential for each hour of the year.
## 2 Materials and Methods
### Predict Age Information of the Unlabeled Dataset
#### 2.3.1 Perform Experiments of Age Predictions with Labeled Datasets  
This study's multi-label target, \(\mathbf{agescore}_{\mathbf{news}}(n)\), was a numerical vector, so it considered machine learning techniques for regression that had been commonly used in prior studies. The six regression models are: Multiple Linear Regression (MLR), Neural Network Regression (NNR) (in this study, NNR is a prediction technique for regression using MLP), Decision Tree Regression (DTR), Support Vector Regression (SVR), \(k\)-Nearest Neighbors Regression (\(k\)-NNR), and Random Forest Regression (RFR).  
Moreover, this study required a regression model that could deal with a multi-label target (i.e., multi-output regression). Out of these six regression models, as only NNR and \(k\)-NNR could be used as prediction techniques for multi-label targets, this study adopted a strategy that fits one regressor per target. Therefore, for this study's multi-label target task, it applied the multi-output regression module in the machine learning package, scikit-learn, to the six prediction techniques. ([[https://scikit-learn.org/stable/modules/multiclass.html#multioutput-regression](https://scikit-learn.org/stable/modules/multiclass.html#multioutput-regression)]([https://scikit-learn.org/stable/modules/multiclass.html#multioutput-regression](https://scikit-learn.org/stable/modules/multiclass.html#multioutput-regression)) (accessed on 14 November 2023)) Therefore, for predicting the \(\mathbf{agescore}_{\mathbf{news}}(n)\), this study considered eight prediction techniques in total, which can be grouped into two categories: (1) single output predictors, denoted as NNRSingle and single \(k\)-NNRSingle, and (2) multiple output predictors, denoted as MLRmulti, NNRmulti, DTRmulti, SVRmulti, \(k\)-NNRmulti, and RFRmulti.  
These eight prediction techniques were used to perform age prediction experiments with the labeled datasets after their hyperparameters were optimized via the grid search method, which used 10-fold cross-validation. As an experiment, it performed 10-fold cross-validation for each regressor, set by its optimized hyperparameters, and the same experiment was repeated 50 times. Here, a different random seed was used for experimental repetition, but the random seed was kept identical for the same iteration of different prediction techniques [2; 32; 33; 34].
# A Feasibility Study of the Use of PZT Actuators for Active Control to Enrich Engine Sound
## 4 Experiment Results and Discussions  
### Properties of the Secondary Path  
The secondary path of the PZT-plate actuator was measured under various conditions, such as temperature variation, within given bounds, and the frequency of the input signal, a sinusoid, was linearly swept from 30 Hz to 400 Hz for 10 s when the amplitude of the input voltage to the PZT actuator was 200 V. One of the measured magnitude responses of the secondary path is plotted in Figure 7. This magnitude response shows not only the acoustic characteristics of the cabin but also the mechanical properties of the PZT-plate actuator.  
Figure 6: Set-up of the PZT-plate actuator.  
From the magnitude response, it was found that the PZT-plate actuator was able to effectively produce structure-borne sound near 50, 130, 170, 280, 325, and 375 Hz corresponding to the resonance frequencies. However, it was relatively poor at generating sound around the anti-resonance such as 90, 190, 210, and 230 Hz. Besides, the magnitude response at other frequencies showed a fluctuating aspect. These features represent a characteristic of vibro-acoustic sound from the PZT-plate actuator, and the magnitude response contrasts obviously with that of a door loudspeaker, which was relatively flat, as reported in reference [25].  
The secondary path model \(\hat{S}(z)\) was designed from the nominal path as an FIR filter with 3000 coefficients at the sampling frequency of 10,000 Hz. The normalized impulse response of \(\hat{S}(z)\) was plotted in Figure 8. It was found that the impulse response lasted even at 2600 samples due to the low damping caused by the mechanical properties of the PZT-plate actuator. Such a long impulse response can increase the required modelling delay and filter length in the design of the inverse control filter of the secondary path [26].
## 3 Results
### Review the Handover  
As mentioned before, one of the issues addressed in the long call scenario is the handover issue. Our model investigated this issue separately. By reducing the quality of the signal received by the user, which is a function of the bit fault rate, the network is obliged to transfer the user's call to a better cell. Sometimes, outsourcing may happen later than the appropriate time, and this phenomenon leads to common dissatisfaction with the network.  
To solve this problem, the proposed solution is to use a time window to detect the quality and level of the signal received by the user in a long call. The time window length and the threshold value were considered on the quality and level of the signal according to the expert's opinion. If the subscriber received signal quality worse than, or equal to, 5 and the signal level was less than \(-75\) within six times of receiving information from the central transmitter station, our system requested retransmission in the network because otherwise the user's call would be disconnected, which would lead to user dissatisfaction. Figure 16 shows the signal quality and level at different network points.  
This section introduces the drive test, one of the scientific and engineering methods for identifying the location of the fault and the cause of its occurrence. Different scenarios performed in a drive test by a team of experts were investigated separately in this research. For the scenario of idle mode, three separate signal strength categories were considered for the subscriber, which divided the network mode into three categories: good, average, and bad. In the short call scenario, where the network mode was examined in terms of the call establishment parameters, after identifying the exact location of the blocked calls and the state of the received signal, the characteristics of the indicators of the performance monitoring system were used to analyze the cause of the problem. Finally, the long call  
Figure 16: Silhouette coefficient value of different clustering methods–long call scenario.  
contact of four categories was obtained for the most critical scenario. Their interpretation is available in detail in Section 3.7 To evaluate the clustering method, in addition to the Silhouette coefficient, data classification using the cluster number label was used, the accuracy results of which are given in the training and test data at the end of Section 3.7. Relocation, one of the most important goals of long call scenarios, was also examined separately in this section. The results of the handover evaluation are given.
# VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application
## 3 Background
### Source Code Representation  
The source code is a list of instructions the programmer writes when developing software. Source code representation helps the computer to understand the code instruction so that the computer can deal with the source code correctly for machine learning or deep learning. Before the program is executed, the compiler or script interpreter must convert the source code into an operation code that the machine can recognize. Similarly, it is also necessary to convert the source code into an intermediate representation object when using machine learning or deep learning to detect vulnerabilities. This object should contain as much vulnerability feature information of the source code as possible, including syntax and semantic features, such as specific character sequences, control dependency, data dependency during program execution, etc., but also be able to convert into vectors easily and directly input into machine learning models. At present, the mainstream source code representation technologies include:  
* Token Sequence.
* Abstract Syntax Tree (AST).
* Control Flow Graph (CFG).
* Data Flow Graph (DFG).
* Program Dependence Graph (PDG).
* Code Attribute Graph (CPG).  
The appropriate intermediate representation retains more knowledge of the source code and has a richer ability to express vulnerability features [39]. In this article, we use a sub-graph of PDG to represent source code, which is discussed below.  
A Program Dependence Graph (PDG) includes control and data dependence during program execution, which is crucial in accurately analyzing vulnerability characteristics. It can improve the effectiveness of the vulnerability detection system. PDG is a directed graph where each node represents an instruction (program statement), and each edge represents the data or control dependence between two program instructions. A control dependence edge \(N_{i}\to N_{j}\) indicates that the execution of \(N_{j}\) is determined by \(N_{i}\), reflecting the control flow or execution order between two instructions. The data-dependence edge \(N_{i}^{\prime}\to N_{j}^{\prime}\) indicates that the data defined at \(N_{i}^{\prime}\) acts on \(N_{j}^{\prime}\), reflecting the \"definition-use\" relationship of variables between two statements.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 1 Introduction
Specific objectives were to (1) develop a ground-based hyperspectral imaging system to measure diurnal SIF changes using the FLD method; (2) calculate effective quantum yield and ETR to derive light curves for characterizing plant photosynthesis; and (3) validate the efficacy of the characterization method by comparing the hyperspectral imaging derived measurements with PAM-derived measurements and destructive crop growth analysis.
## 2 Methodologies
### Numerical Validation
#### 2.2.1 Model Building  
Three CFST models were designed as shown in Table 3. Model 1, as a comparison, did not contain a void area, Model 2 was created with a 2.5 cm cavity, and Model 3 was created with a cavity height of 5.0 cm. The outer diameter of the steel tubes was 273 mm, and the wall thickness was 8 mm. The steel tubes were filled with C50 concrete. The outer surface of the steel pipe's upper semicircle was the temperature load inlet, and the load form was set to the boundary of the constant heat flux. The heat flux was 800 W/m\({}^{2}\). Based on the literature regarding the temperature field analysis of concrete-filled steel tube and the temperature detection data of some bridges, it was found that the arch rib's temperature could reach above 50 \({}^{\circ}\)C under sunshine. This value could make the structure close to the actual temperature that the authors expected to reach after heating. The outer surface of the lower semicircle was set to the outlet. It could exchange heat with the outside and the heat transfer coefficient was 8.7 W/(m\({}^{2}\cdot\)K). The two ends of the cylinder were defined as the adiabatic boundary without taking into account the influence of this part, because it was only a small section of the whole concrete-filled steel tube structure. There were three possible boundary contacts in the interior: the contact between steel tube and concrete, the contact between steel tube and air, and the contact between air and concrete. All boundary conditions were set to coupled wall. Convection heat transfer was carried out between them. Considering the influence of the heating position in the actual environment, the heating surface was rotated counterclockwise 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) for the simulations. The specific location of the heating surface is shown in Figure 1.  
The initial temperature was assumed to be 20 \({}^{\circ}\)C. The parameters of the thermo-physical properties of the different materials are shown in Table 1. The form of heat transfer was set to unsteady heat transfer and the k-epsilon equation was used to calculate. The computation time was 1800 s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Temperature (\({}^{\circ}\)C)** & **20** & **30** & **40** & **50** & **60** & **70** \\ \hline Density (kg/m\({}^{3}\)) & 1.205 & 1.165 & 1.128 & 1.093 & 1.060 & 1.029 \\ Thermal conductivity (W/m\(\cdot\)K) & 0.0259 & 0.0267 & 0.0276 & 0.0283 & 0.0290 & 0.0296 \\ Specific heat (J/kg\(\cdot\)K) & 1005 & 1005 & 1005 & 1005 & 1005 & 1009 \\ \hline \end{tabular}
\end{table}
Table 2: Thermo-physical properties of air under different temperatures.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number** & **Diameter(mm)** & **Wall Thickness (mm)** & **Length (mm)** & **Cavity Height (mm)** & **Concrete Strength** \\ \hline
1 & 273 & 8 & 300 & 0 & C50 \\
2 & 273 & 8 & 300 & 25 & C50 \\
3 & 273 & 8 & 300 & 50 & C50 \\ \hline \end{tabular}
\end{table}
Table 3: Size of specimens.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 2 Properties and Mechanisms of MNs
### NBs: Characteristics, Applications, and Radical Generation
[9] reported that, owing to their numerous uses in industries such as water treatment, biomedical engineering, and nanomaterials, MB and NB technologies have attracted considerable attention in recent years. The physics, generation processes, and capacities of MBs and NBs to generate free radicals were examined in this study. Their applications in cleaning or defouling solid surfaces, including membranes, disinfecting water, and breaking down harmful substances were the primary focus. Given their capacity to produce free radicals, MBs and NBs are anticipated to have a bright future ahead, with much more to be discovered. By highlighting their distinct physical and chemical qualities--such as their small size (less than 1000 nm), high internal pressure, vast surface area, and capacity to form reactive oxygen species (ROS)--this section addresses the properties, mechanisms, and uses of NBs. Because of these characteristics, NBs are quite versatile and can be used in a variety of industries, including environmental remediation, surface cleaning, and water  
Figure 5: Temporal evolution of the bubble size distribution in NB suspensions of different initial bubble number densities: (**a**) \(2.09\times 10^{8}\); (**b**) \(2.75\times 10^{8}\); (**c**) \(5.82\times 10^{8}\); and (**d**) \(8.89\times 10^{8}\) bubbles-mL\({}^{-1}\). Reproduced with permission from [42], CC-BY license, Copyright © 2018 American Chemical Society. treatment. The section discusses the creation of NBs using membrane, ultrasonication, and decompression techniques, as well as how they improve the processes of cavitation and emulsion formation. The stability and attachment mechanisms of NBs, their interactions with surfaces, and their capacity to produce radical species--all of which are crucial for applications ranging from agriculture to wastewater treatment--are also covered in detail. The significance of comprehending how NBs behave under different circumstances and the developments in their uses for specific industrial applications are both emphasized in this section.