esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, this study investigated the reliability instability associated with the lattice-mismatched metamorphic buffer in InAlAs/InGaAs MHEMT and compared it with LM-HEMTs. Both types of devices underwent the same fabrication process to enable a direct comparison. MHEMT exhibited poor DIBL characteristics with higher off-state current and larger swing values due to high gate leakage current, but their DC and RF performances were found to be comparable to LM-HEMTs. Pulsed I-V measurements revealed the presence of the fast transient charging effect, leading to more than threefold greater degradation of the drain current in MHEMT compared to LM-HEMTs. In addition, it was shown that the drain current decrease over time but does not saturate and continues to decrease due to charge trapping. Furthermore, \(1/f\) noise measurements provided valuable insights into the dominant trap sites in the devices. The results showed that the dominant trapping location in MHEMT was situated near the bulk traps, whereas for LM-HEMTs, it was at the InGaAs channel interface. The \(N_{t}\) value obtained using the CNF model was 3.27 \(\times\) 10\({}^{16}\) eV\({}^{-1}\cdot\)cm\({}^{-3}\) for LM-HEMTs, whereas was is an order of magnitude higher, 3.56 \(\times\) 10\({}^{17}\) eV\({}^{-1}\cdot\)cm\({}^{-3}\), for MHEMT.  
The presence of a lattice-mismatched metamorphic buffer led to a dislocation density and rough surface, which contributed to reliability instability in MHEMT. Understanding the mechanisms behind this instability is crucial for future research aiming to improve the  
Figure 5: Illustrate the \(S_{\Delta t}\)/\(I_{D}\)\({}^{2}\) represented by black dots and the (\(g_{m}\)/\(I_{D}\))\({}^{2}\) shown as red curves for (**a**) LM-HEMTs and (**b**) MHEMT. These measurements were conducted at a \(V_{DS}\) of 0.05 V and \(L_{g}\) of 50 nm.  
reliability of the metamorphic buffer layer, enhance the quality of each layer in order to create a better epitaxy, and optimize device performance.  
This study experimentally determined the degradation mechanism caused by the graded metamorphic buffer layer, shedding light on the structural instability of MHEMT. The findings suggest the need for further exploration to enhance the reliability and stability of the metamorphic buffer layer and mitigate its adverse effects in future device designs.  
Supervision, H.-M.K. and T.-W.K.; resources, J.-P.S., S.-T.L., H.J. and C.-S.S.; formal analysis, J.-W.S., S.C. and W.A.; writing--original draft preparation, K.-Y.S.; review and editing, H.-M.K. and T.-W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT and Future Planning, NRF-2019M3F5A1A01076973).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## Appendix B  
In this section, we explain the process of how to derive the user data rate of our analysis results presented in Section 5. Based on our given non-uniform user distribution, users are generated randomly over the evaluating area. For each snapshot of the generated scene, the following process is conducted. First, the association (connection) between the UAV and the user is determined based on the specific UAV placement algorithms presented in Section 4.1. If frequency division (planning) is employed, then the different frequency resource division mechanisms explained in Sections 4.2 and 4.3 are introduced, depending on the extent of coverage overlap among the UAVs. Based on these simulation setups, numerical analyses were conducted using MATLAB software to compute each user's SINR from which each user's channel capacity can be derived using equations presented in Section 3.2.2. Moreover, since this paper assumes a TDMA communication method when a UAV needs to serve multiple users, the overall communication resources in the time domain need to be shared equally for each accommodated user of the cell. In other words, the computed channel capacity is divided by the total number of users associated with the target cell. This procedure is repeated until it reaches the maximum iteration of user generation; that is, 300 in this paper. All the derived data were then analyzed to compute the CDF as well as the average data rate or the outage data rate. The process for a given number of deployable UAVs is summarized in Figure A1.
## 3 Results and Discussion
### Characterization and Optical Properties of S-Phosphor
Therefore, it can be seen that the solvothermal method in this work is universal. Figure 6: Before and after reaction. (**a**) Emission spectra of SrAl2O4:Eu2+, Dy3+ phosphors (SAOED) with different particle sizes. (**b**–**d**) Size distribution of SAOED with different particle sizes. Emission spectra of (**e**) Sr4 Al14O25 and (**f**) Sr2 MgSi2O7. Size distribution of (**g**) Sr4 Al14O25 and (**h**) Sr2 MgSi2O7. As the reaction proceeded, the surface of the bulk phosphor was continuously washed by the solvent under high temperature, high humidity, and high pressure, in which the surface morphology of the bulk changed continuously during the reaction, so the powders were analyzed by field emission scanning electron microscopy (FESEM) after the solvent heat treatment at different times. It can be seen that the surface of the untreated strontium aluminate luminescent material was relatively smooth (Figure 7a). At 4 h of solvent heat treatment, the surface of the block started to become rough and uneven, and a number of small-sized (<2 um) phosphor particles were attached to the surface (Figure 7b). At 12 h of treatment, grooves different from the previously discussed tearing surface appeared, and the direction of the grooves showed regularity, while the corners of the blocks began to become less obvious (Figure 7c). When treated for 18 h, part of the needle-like morphology phosphor began to adhere to the surface of the grooves of the large-grained bulk phosphor (Figure 7d). Further increasing the treatment time to 24 h (Figure 7e), it was found that the surface of the large-particle chunks had basically been covered by the small-sized and needle-shaped phosphors. As the experiment proceeded to 48 h (Figure 7f), the needle phosphors appeared to agglomerate and formed uniformly covered cluster needle structures on the block phosphor surface. It was found that the surface of the bulk phosphor will be eroded by the solvent, and with the increase of the experimental time, in the middle and late stages of the reaction, the surface of the block will gradually generate nano-sized special features and the present experiment mainly generates needle-like special features and some flaky special features.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 6 Challenges  
The selected papers show that publish/subscribe middleware can tackle a variety of ITS problems: they can improve connectivity, enable infrastructure-less communication using P2P technologies, reduce latency and increase reliability using QoS settings, and support real-time systems using real-time publish/subscribe middleware. However, many challenges affect the deployment of publish/subscribe-middleware-based ITS. This section discusses some of them publish/subscribe-middleware-based ITS face.
## 2 Inverse Analysis Containing to LFWD Test
### Result of Inverse Analysis  
#### 2.3.1 Analysis  
The analysis has three steps, i.e., Step I (when the FE model contains only the ground layers, and an initial geostatic stress field is generated by performing a static analysis by using the ABAQUS initial condition option), Step II (the ballast and subgrade layers are added to the model, and the resulting vertical displacements at the sensors are computed), and Step III (the LFWD load is applied to the subgrade surface, and a time integration dynamic analysis is performed). The time step is estimated through the Courant-Friedrichs-Lewy condition. Because of the high density and elastic modulus of the top layer, \(\Delta t\) adopted in this model is primarily controlled by the top layer elements, which are around \(5\times 10^{-4}\) s. The deflection-time histories are computed by subtracting the deflections obtained during Step II from those obtained in Step III.  
As shown in Equation (1), the back-calculation parameters are base modulus value (\(K_{1}\)) and exponent coefficient (\(K_{2}\)). Due to the complexity of Equation (1), it is difficult to obtain the analytic solution to determine the back-calculation parameters. Therefore, the method of trial-and-error was used in this study. In the calculation, the initial back-calculation parameters (\(K_{1}\) and \(K_{2}\)) were first set. A base modulus and an exponent coefficient are set by experience from the previous studies [4; 14; 29], which is also listed in Table 1. Using the initial estimated parameters of each layer, the peak deflections at the sensors are computed and compared with those obtained from the field measurements. Subsequently, the modulus values are modified by trial-and-error until the measured values of the deflections closely match the corresponding values obtained from the simulation. The difference between the calculated and measured LFWD deflections, which is known as the percentage deflection fitting error and can be calculated using Equation (4) [14], must be minimal. In this work, the value of this error is expected to be less than 3%.  
\[\varepsilon_{def}=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|d_{m,i}-d_{c,i}\right|} {d_{m,i}}\times 100\% \tag{4}\]where \(n\) is the number of sensors, \(d_{m,i}\) is the deflection measured using sensor \(i\), and \(d_{c,i}\) is the calculated deflection for sensor \(i\).
# Broadband Dual-Polarized 2 \(\times\) 2 MIMO Antenna for a 5G Wireless Communication System
## 4 Conclusions  
In this study, we proposed a broadband dual-polarized 2 \(\times\) 2 MIMO antenna for application in 5G wireless communication systems, comprising two vertically polarized circular monopole antennas, two horizontally polarized modified rectangular dipole antennas, and a ground plane. The CMAs achieved broadband characteristics owing to the distance between the driven element and the dielectric, using a parasitic element, whereas the MRDAs achieved broadband characteristics owing to the distance between the radiators. The dimensions of the proposed MIMO antenna were 242 mm\(\times\)200 mm\(\times\)40 mm, and it exhibited an impedance bandwidth (dB magnitude of \(S_{11}\)) of 101% (2.3-7 GHz). The isolation between the antennas was over 20 dB in the 5G NR n46 (5.15-5.925 GHz), n47 (5.855-5.925 GHz), n77 (3.3-4.2 GHz), n78 (3.3-3.8 GHz), and n79 (4.4-5 GHz) bands. The peak gain was observed to be over 5 dB. The results of this study verify that the proposed 2 \(\times\) 2 MIMO antenna is suitable for application in indoor 5G wireless communication systems.  
Conceptualization, C.-S.L. and J.C.; methodology, H.L. ([PERSON]); software, I.K.; validation, I.K., H.L. ([PERSON]) and H.L. ([PERSON]); formal analysis, C.-S.L.; investigation, C.-S.L.; resources, H.L. ([PERSON]); data curation, I.K.; writing--original draft preparation, C.-S.L.; writing--review and editing, J.C.; visualization, I.K.; supervision, J.C.; project administration, J.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The authors declare no conflict of interest.
## 3 Results and Discussion
#### 3.2.1 Water Vapour Permeability  
Water vapour permeability (WVP) reflects the capacity of moisture transmission, and therefore films' ability to prevent water loss during the preservation of coated products. The results (Table 3) showed that the GP film had a significantly higher WVP (1.76 \(\pm\) 0.008 g Pa\({}^{-1}\) s\({}^{-1}\) m\({}^{-1}\)) than that of the GPP edible film (1.25 \(\pm\) 0.011 g Pa\({}^{-1}\) s\({}^{-1}\) m\({}^{-1}\)) and GLO (1.34 \(\pm\) 0.015 g Pa\({}^{-1}\) s\({}^{-1}\) m\({}^{-1}\)). However, WVP of the GP film was not significantly different to that of the films incorporated with other essential oils or plant extracts. The range of WVP was similar to that in a previous study by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2018).
## 4 Practical Setup  
The test rig consisted of a motor under investigation attached with a loading machine, industrial inverters and a data acquisition set-up. The motor, with specifications given in Table 2, was tested with healthy and broken bar-based rotors. The bars were broken by drilling radial holes having a depth equal to the total rotor slot height. The motor phase currents were measured under transient intervals using the Dewetron transient recorder. The measured signal's sampling frequency was 10 kHz, which was good enough for better frequency resolution. The test set-up is shown in Figure 6. In the industrial inverter case, the variable voltage at a constant frequency could be achieved by changing the rated parameters. Since the voltage/Hz ratio in the scalar control remained constant, by changing the rated frequency with a constant rated voltage, the output voltage could be varied, as shown in Table 3. Moreover, the acceleration and deceleration time should be equal to zero to avoid the drive controller's influence on the transient interval.  
Figure 5: The time–frequency pattern of the simulated current with two broken bars at (**a**) the rated supply voltage, (**b**) the contour area of the maximum spectral density, (**c**) 10% of the nominal voltage and (**d**) the contour pattern for better legibility.
## Abbreviations  
* AdaBoost & Adaptive Boosting
* APTs & Advanced Persistent Threats
* API & Application Programming Interface
* AUC & Area Under the Curve
* BO & Bayesian Optimizer
* CAN & Controller Area Network
* CNN & Convolutional Neural Network
* CPU & Central Processing Unit
* DE & Differential Evolution
* DEA & Data Envelopment Analysis
* DL & Deep Learning
* DDoS & Distributed Denial of Service
* DNN & Deep Neural Network
* DOS & Denial of Service
* DRL & Deep Reinforcement Learning
* DrDoS & Distributed Reflective Denial of Service
* DT & Decision Tree
* EL & Ensemble Learning
* ELIDS & Ensemble Learning-Based Intrusion Detection Systems
* ET & Extra Trees
* FA-CNN & Feature Augmented Convolutional Neural Network
* FL & Federated Learning
* GA & Genetic Algorithm
* GANs & Generative Adversarial Networks
* GB & Gradient Boosting
* GBM & Gradient Boosting Machine
* GPS & Global Positioning System
* GPU & Graphics Processing Unit
* HIDS & Host-based Intrusion Detection System
* ICTs & Information and Communication Technologies
* IoT & Internet of Things
* IoV & Internet of Vehicles
* IRFC & Improved Random Forest Classifier
* ISAC & Integrated Sensing and Communication
* ITS & Intelligent Transportation Systems
* KNN & K-Nearest Neighbours
* LGBM & LightGBM
* LiDAR & Light Detection and Ranging
* LSTM & Long Short-Term Memory
* ML & Machine Learning
* NIDS & Network-based Intrusion Detection System
* NN & Neural Network
* PCA & Principal Component Analysis
* PSO & Particle Swarm Optimization
* RBF & Radial Basis Function
* RF & Recursive Feature Elimination
* RF & Random Forest
* RFR & Random Forest Regression
* RNN & Recurrent Neural Network
* ROC & Receiver Operating Characteristic
* SE-LIDS & Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme
* SVM & Support Vector Machine
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 4 Positioning and Communication for a Fleet of UAVs
### Practical Implementation Aspects  
Implementation aspects are related to the computational load that each UAV has to support and to the amount of control information exchanged among the UAVs and the ground station. As reported in [18], it depends on the network architecture, which can be decentralized (cellular and ad hoc) or centralized (direct-link and satellite).  
The proposed strategy uses a gradient estimation that depends on the position of a certain UAV \(i_{1}\), the position of its neighbors and the spatial density of users \(\lambda(x,y)\). To implement this strategy in a decentralized fashion, each UAV has to acquire three pieces of information: (i) the selected UAV \(i_{1}\) (it is the UAV corresponding to the lowest rate in case that the constraints are fulfilled, and the UAV associated to the randomly selected constraint in case that any of the constraints in Equation (24) is not fulfilled and is selected); (ii) the positions of all its neighboring UAVs; and (iii) the distribution of the users \(\lambda(x,y)\) or the positions of the user terminals (in that case, integrals of previous equations should be substituted by summations). This distribution can be calculated by each UAV for the users in its serving region as it is expected that each UAV will know the positions of the users assigned to it.  
Then, this information can be exchanged among UAVs in order to obtain the entire spatial distribution of users. Thus, a lot of information should be continuously sent among all the UAVs, which is the focus in [18], where different routing algorithms are analyzed, that is, different ways of making this information flow among the UAVs in the network. Note that the analysis of the routing strategies is out of the scope of this paper and, therefore, we just assume that this information is available. According to all previous comments, we propose a centralized system to implement this strategy to alleviate the network load at the expenses of less robustness against failures of the central entity.  
In a centralized implementation of Algorithm 1, all computation is executed in the backhaul ground station, where the positions of all the UAVs are known and the positions of the users, which are computed by each user terminal, are reported through each UAV to the ground station. Thus, the complete Algorithm 1 is implemented in the backhaul ground station and the new positions of the UAVs are reported back to each UAV. It is important to take into account that this exchange of information spends a small fraction of the backhaul bandwidth \(B_{i}^{BH}\) dedicated for each link. This fraction is so small that it almost does not affect the user transmission rate. Note that, if some hovering restrictions apply, due to air safety regulations, they could be easily applied by limiting the value of the gradient associated to the position of the UAV.
## 4 Mechanical Design and Thermal Modelling of 6U CubeSat  
### Mechanical Design Considerations  
Figure 10 shows a generic 6U satellite configuration with most heat-dissipating internal components such as batteries, avionics, actuators, communication module, payload and a cryocooler. A deployable radiator designed for both of these configurations are analyzed numerically. Radiator panels are mounted to one of the larger satellite surfaces in these configurations.  
The most heat dissipating elements are directly attached to a baseplate manufactured from aluminum 6061-T6. For the deployable radiator, the hinge mechanism keeps the radiator at stowed position below the deployable solar panel. Radiator is deployed into deep space after the solar panels are released from Hold Down and Release mechanism (HDRM). The base of the radiator is connected to the heat exchanger using high thermal conductive links such as thermal straps and thermal interface materials in order to improve the rate of heat transfer. The baseplate in this design accommodates a micro cryocooler and payload electronics. The avionics subsystem in the satellite is separately linked to the satellite external structure to keep them under operating temperature limits.  
The proposed mass distribution for two satellite design configurations are listed in Table 4.  
Thermo-optical properties of some of the components that are considered in the proposed 6U satellite design are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **6U Mass (kg)** & **27U Mass (kg)** \\ \hline Payload & 3.5 & 6.2 \\ Avionics & 0.50 & 0.75 \\ Battery & 0.5 & 0.9 \\ Heat Exchanger & 0.40 & 0.75 \\ Solar Panels & 0.325 & 0.65 \\ Satellite Structure & 3.2 & 6.9 \\ Radiator & 0.35 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mass distribution—6U satellite.  
Figure 10: 6U—Mechanical design.
## 4 Results
### Monitoring System Performance
This can be a time-consuming and resource-intensive process, which can negatively impact the overall performance of the network. Network resilience, on the other hand, refers to a network's ability to maintain its functionality and performance even in the face of disruptions or failures. In wireless sensor networks for remote monitoring, network resilience is critical for ensuring that the nodes can continue to cooperate and collaborate effectively, even in the event of topology changes or other disruptions. By implementing robust network resilience strategies, such as redundant communication pathways and self-healing mechanisms, wireless sensor networks can minimize the impact of disruptions and maintain their performance and reliability. Figure 3 shows the logical topology change over five days. This change is outlined for each scheme studied. We observe that, under the cooperative scheme, the network shows fewer links (30% approximately) concerning the collaborative scheme. In both cases, the system increases the number of logical links per node as the days go by. This is because the algorithm knows more routes, which have a lifetime in the routing table. Some of them are still valid and others have become obsolete. The greater the number of links (routes), the greater the redundancy and congestion. However, protocol management and the number of control packets can lead to more collisions. With fewer links in the nodes, the information delay and the network's power consumption can decrease.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Diffusing Redox Probes
#### 3.3.2 Enzyme-Based Immunosensors
Upon addition of H\({}_{2}\)O\({}_{2}\) and using DPV for Thi\({}_{\text{ox}}\) reduction, the electrochemical immunosensor exhibited linearity between 0.015 and 150 U mL\({}^{-1}\) and a LoD of 6 mU mL\({}^{-1}\). At last, there is one example of miRNA immunodetection. [PERSON] et al. [103] reported a composite of RGO and MWCNTs as substrate for immobilization of single-stranded oligonucleotide probes. Upon hybridization of the target miRNA on this probe, HRP-labelled anti-DNA/RNA hybrids were added. Upon addition of HQ and H\({}_{2}\)O\({}_{2}\) in solution, HQ was oxidized into BQ, which was then re-reduced into HQ at the electrode (Figure 18). Using SWV, the authors reported a LoD of 10 fM miRNA and a linear range between 10 fM and 0.1 nM. Figures of merit discussed in this section are summarized in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **Probe** & **LoD** & **Method** & **Reference** \\ \hline IgG & Ab & 0.2 ng mL\({}^{-1}\) & RGO/HRP/HQ/Sandwich & [90] \\ IgG & Ab & 10 pg mL\({}^{-1}\) & RGO/AuNPs/HRP/Anilin & [91] \\ PSA & Ab & 2 pg mL\({}^{-1}\) & GR/AuNPs/HRP & [92] \\ CEA & Ab & 0.35 pg mL\({}^{-1}\) & GR/AuNPs/Thi/HRP/DPV & [93] \\ CEA & Ab & 90 pg mL\({}^{-1}\) & GR/Lectin/Fe(CN)\({}_{6}\)\({}^{-7/4}\)–/DPV & [94] \\ CEA & Ab & 0.01 ng mL\({}^{-1}\) & N-doped GR/Thi/HRP/DPV & [95] \\
17\(\beta\)-Estradiol & Ab & 0.02 ng mL\({}^{-1}\) & GO/HQ/HRP/DPV & [96] \\ Ethynylestradiol & Ab & 65 pg mL\({}^{-1}\) & AgNPs/SiO\({}_{2}\)/GO/HQ/HRP & [97] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Figures of merit of selected enzyme-based immunosensors using graphene.