esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resonant Converter Families
### Structure of Resonant Power Converter
#### 3.1.3 Rectifier Network with Low-Pass Filter  
\(DR\)-\(LPF\) is mainly employed to resolve and filter the \(AC\) waveform as the last stage of the process of the structure of the resonant network converter, after generating sinusoidal current and voltage waveforms by the \(RT\) network, to attain the required \(DC\) output waveform. There have been many studies on resonant power converters that considered the \(DR\)-\(LPF\) as a center-tapped or full-bridge rectifier. The high voltage stress on the diodes restricts the practical suitability of center-tapped rectifiers; therefore, a low-pass channel has been preferred for two instances of capacitance or inductance [86, 97, 98].  
* Diode rectifier network with capacitive low-pass filter (\(DR\)-\(LLPF\)): The evaluation of the \(DR\) action when entering the current \(i_{R}(t)\) can serve as a basis for testing the corresponding circuit of DR with a capacitive output filter and in-parallel load resistor; this is expressed in Equation (7) due to the series correlation [10].  
Figure 20: Three-element and four-element resonant tank network topologies: (**a**) \(LCC\); (**b**) \(LLC\); (**c**) \( LCL\); (**d**) \(LCLC\)[80].  
\[i_{R}(t)=I_{p}sin(\omega_{s}t-\varphi_{s}) \tag{7}\]  
Consequently, this current \(i_{R}(t)\) is rectified by \(DR\), as described in Figure 21. Due to the condenser \(C_{f}\) filtering operation, only the \(DC\) portion flows into the load, generating the current \(Io\) and the related \(DC\) voltage \(Vo\). The \(DC\) portion must be equivalent to the current Io at a steady state. Voltage \(V_{R}(t)\) at the \(DR\) input can be obtained by noting that the conducting diodes of the \(DR\) change when current \(i_{R}(t)\) crosses null. The value of \(V_{R}(t)\) is equal to \(Vo\) when \(i_{R}(t)\) is positive, and equal to \(-V_{o}\) at negative \(i_{R}(t)\)[10].  
\[V_{R}(t)=\frac{4V_{o}}{\pi}\sum_{n=1,3,5,...n}\frac{1}{n}sin(n\omega_{s}t- \varphi_{s}) \tag{8}\]  
\[V_{R1}(t)=\frac{4V_{o}}{\pi}sin(\omega_{s}t-\varphi_{s}) \tag{9}\]  
\[I_{out}=\frac{2}{T_{s}}\int_{0}^{\frac{T_{s}}{2}}i_{R}(t)dt=\frac{2}{\pi}I_{R} \tag{10}\]  
* Diode rectifier with inductive low-pass filter (\(DR\)-\(CLPF\)) (Figure 22): Here, a sinusoidal voltage provides the \(DR\), and the \(i_{R}(t)\) input current exhibits a square waveform that is equivalent to \((-I_{0})\) when \(V_{R}(t)\) is negative and \((+I_{0})\) when \(V_{R}(t)\) is positive. Since the \(DR\)'s conductive diodes are adjusted when \(V_{R}(t)\) voltage passes 0, the current \(i_{R}(t)\) basic component \(i_{R1}(t)\) is in phase with \(V_{R}(t)\)[10; 16]. \[V_{R}(t)=V_{P}sin(\omega_{s}t-\varphi_{s})\] (11) \[i_{R}(t)=\frac{4I_{o}}{\pi}\sum_{n=1,3,5,...n}\frac{1}{n}sin(n\omega_{s}t- \varphi_{s})\] (12) \[i_{R1}(t)=\frac{4I_{o}}{\pi}sin(\omega_{s}t-\varphi_{s})\] (13) \[V_{o}=\frac{2}{T_{s}}\int_{0}^{\frac{T_{s}}{2}}V_{R}(t)dt=\frac{2}{\pi}V_{R}\] (14)  
Figure 21: Diode rectifier with capacitive output filter [10].
## 3 Background
### AirQKD Project Partners  
A substantial portion of the AirQKD project involved the design of new semiconductors, electronics, and control systems for the FSO-QKD layer. This was then coupled to higher system layers to allow applications to consume the generated keys. These requirements meant that the AirQKD project partners had to encompass a diverse array of expertise. The fifteen partner organisations are listed in Table 1. Their expertise ranges from semiconductor design and packaging to electronics, software, engineering, cryptography, telecommunications, and vehicle systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Project Expertise** & **Type** \\ \hline Angoka Limited & Security and comms protocols & Commercial \\ \hline Arqit Limited & Quantum security technology & Commercial \\ \hline Bay Photonics Limited & Semiconductors & Commercial \\ \hline University of Bristol & QKD and quantum photonics & Academia \\ \hline British Telecommunications PLC & Project lead and telecommunications systems & Commercial \\ \hline Compound Semiconductor & Semiconductors & Commercial \\ Applications Catapult Limited & & \\ \hline Duality Quantum Photonics & & \\ Limited (subcontractor) & Quantum photonics fabrication & Commercial \\ \hline University of Edinburgh (The & & \\ School of Informatics) & Cryptography & Academia \\ \hline Fraunhofer Centre for Applied & Optical and photonic systems & \\ Photonics & engineering & Commercial \\ \hline Heriot-Watt University & QKD research and development & Academia \\ \hline National Physics Laboratory & & \\ Management Limited & Verification and validation & Commercial \\ \hline NU Quantum Limited & Quantum photonics system & Commercial \\ \hline OpenLightComm Limited &
\begin{tabular}{c} Engineering the \\ telecommunications software \\ \end{tabular} & Commercial \\ \hline University of Strathdyde & Quantum photonics simulation & Academia \\ \hline WMG at the University of Warwick & Cybersecurity and vehicular & Academia \\ \hline \hline \end{tabular}
\end{table}
Table 1: Organisations within the AirQKD project.  
Figure 2: A quantum key distribution (QKD) system [13; 21] using different photon polarisations to generate secure encryption keys, offering a statistical guarantee even in the presence of eavesdropping. The keys generated by the QKD system are then used to secure the classical channel communications, in our case, a V2I network.
## 4 Prevention Mechanisms of the Proposed Scheme
### Fog Computing (FC) Storage Preventive Model
```
1.for\(d_{p}\) generating or receiving;
2. filter \(Id_{data}\) and req_data;
3.if(\(Id_{data}\), req\({}_{data}\)) \(e\) (CL, vInf) then
4. broadcast \(LTE_{dp}\) to eNodeB-fog then
5. broadcast \(LTE_{dp}\) to CL then
6. send to server-fog then
7. broadcast \(LTE_{dp}\) to eNodeB then
8. broadcast \(LTE_{dp}\) to CL
9. Update eNodeB;
10.endfor
11.endif
12.end
```  
**Algorithm 5** IEEE 802.11p-LTE eNodeB. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Descriptions** \\ \hline \(d_{p}\) & Data packet \\ CM & Cloud member \\ LTE & Long term evolution \\ CL & Cloud leader \\ \(pr_{c}\) & Parent cloud \\ encode-B & Element of LTE network \\ LTE\_Datapacket & LTE Data packet \\ T & Time of transmission \\ Rv & Receiving vehicle \\ Sv & Sending vehicle \\ (rv\&ap) & Receiver with access point \\ (ap\&s) & Access point and server \\ (s\&frv) & Server with feedback reporting from vehicle \\ t(p) & Server processing time \\ CLvInf & Cloud leader vehicle information \\ \(LTE_{Datapacket}\) & LTE data packet \\ \(Id_{data}\) & Identified data packet \\ \(req_{data}\) & Requested data packet \\ VInf & Vehicle information \\ VANET & Vehicular ad hoc network \\ VCC/VCF & Vehicular cloud computing/Fog computing \\ SUMO & Simulation of urban mobility \\ SDN & Software define network \\ FCM & Fuzzy-c mean \\ IoE & Internet of everything \\ MGA & Modelling to generate alternative \\ V & Velocity \\ F & Frequency \\ \(\lambda\) & Road characteristic coefficient \\ Vmax & Maximum speed \\ ROI & Region of interest \\ Alg(r) & Algorithm request \\ FFBPNN/GA & Feed forward back propagation neural network \\  & utilizing genetic algorithm \\ DoS & Denial of service \\ AKA & Authentication and key agreement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations and descriptions utilized in the algorithms and models. In order to investigate HDSA and DoS JSA, including other attacks, using the PESATRM model, the fog server (FS) and fog level (FL) authentication preventive mechanism is important, which should be utilized in the Elliptical Segment Area Transmission Range Model (ESATRM), as explained below.
## 3 Results and Discussion  
### Water Loss of GO/CCs  
Figure 3 shows the WL curves of GO/CC specimens under the same drying time. The GO/CC specimens were classified into six states based on their drying times: I, II, III, IV, V, and VI. Table 4 demonstrates the average water loss of specimens at each state. With the same drying time, GO/CCs with high GO concentrations always lost water faster than GO/CCs with low GO concentrations. In stages I-III, the difference in WLs between specimens with different GO concentrations is small. With the increase in drying time, the difference in the WL of specimens with different GO concentrations was larger in stages IV-VI. At stage VI, the difference in water loss between the specimens with the lowest and highest GO concentration was the highest, with a difference of 1.1%. A previous study found that the addition of carbon nanomaterials to cement can promote the hydration reaction of cement, reduce cement porosity, and form a denser microstructure. It can be assumed that the addition of GO increased more continuous micropores, which promoted the diffusion of moisture from the center to the edges [34]; the drying rate was determined to be related to microstructure change.  
At the same drying state, GO/CCs with a high GO concentration almost always had higher water loss than those with a low GO concentration. As shown in Table 4, the water content decreased from 7.963 to 7.868 when GO increased from 0.075% to 0.1% (drying state V). This result is because the WL was calculated before the start of the specimen conductivity experiment. During the experiment it was found that if the specimen was dry, it slowly absorbed water from the environment, which resulted in a small decrease in WL; however, the decrease was insignificant. When measuring the 0.075% specimen, the 0.1% specimen absorbed moisture slightly during the waiting process; therefore, the WL of the 0.1% specimen tested was slightly smaller than that of the 0.075% specimen. This occurred in some of the specimens because the difference in WL was small; thus, the absorption of moisture resulted in only a small decrease in WL.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**GO Concentration**} & **Drying** & **Drying** & **Drying** & **Drying** & **Drying** & **Drying** \\  & **State I** & **State II** & **State III** & **State IV** & **State V** & **State VI** \\ \cline{2-7}  & \multicolumn{6}{c}{**Water Loss**} \\ \hline
0.000\% & 0.000\% & 1.358\% & 3.600\% & 5.552\% & 7.188\% & 8.830\% \\
0.025\% & 0.000\% & 1.67\% & 3.590\% & 5.230\% & 7.130\% & 8.813\% \\
0.050\% & 0.000\% & 1.737\% & 3.803\% & 5.477\% & 7.477\% & 9.223\% \\
0.075\% & 0.000\% & 1.923\% & 4.090\% & 5.870\% & 7.963\% & 9.867\% \\
0.100\% & 0.000\% & 2.016\% & 4.152\% & 5.936\% & 7.868\% & 9.912\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Water loss of GO/CC specimens.  
Figure 3: (**a**) Specim weight variation with drying time; (**b**) variation in water loss with drying time.
## 5 FSK Modulation and Demodulation
### FSK Modulation for Biological Implants  
It is usually necessary for a nurse to be near a patient all the time for monitoring and watching the patient's condition. To avoid this situation, a new kind of low cost monitoring system is developed [56] that consists of a transmitter and receiver. The transmitter is located near the patient or can be attached to him and the receiver located in the monitoring room to be watched and monitored by the physician and the nurses. The multi-signal of the system is modulated by analog FM modulation and FSK carrier frequencies at 447 MHz. However, the system suffers from interfacing between the signals and noise. To address the above issues, a low-power FSK modulator and demodulator for biological signals can be used to reduce the noise. The transmitter side of the system can also be used a four stage differential ring VCO with tone frequency located at 402-405 MHz carrier frequency.
## 4 Sensor Signal Analysis
### Sensor Signal Pre-Processing  
The raw thrust force and torque sensor signals acquired during the drilling tests included signal portions corresponding to time periods before and after real machining. With the aim to extract sensorial information only when the tool is actually removing material, a signal segmentation procedure was performed on the thrust force signals and synchronically extended to the torque signals. The identification of the start and end of the actual machining portion of the signal was carried out on the basis of thresholds set on the moving average of the thrust force signals.  
In Figure 10, the thrust force signal segmentation for hole n\({}^{\circ}\) 6 with drilling parameters 6000 rpm and 0.15 mm/rev is reported, showing a drop of thrust force at the interface between the two laminates which are in contact with their bag sides characterized by very irregular surfaces.  
In Figure 11, the thrust force signals are reported versus the increasing number of holes (from 1 to 60) for the operating conditions 2700 rpm-0.15 mm/rev showing that the thrust force and its variance significantly grows with increasing number of holes (ranging from 50 N for hole n.1 to over 200 N for hole n.60). Figure 12 shows the torque signals versus the number of holes for 2700 rpm-0.15 mm/rev drilling conditions highlighting the same previous behaviour.  
Figure 11: Segmented thrust force signals vs. number of holes (2700 rpm–0.15 mm/rev).  
Figure 10: Thrust force segmented signal (6000 rpm–0.15 mm/rev, hole n\({}^{\circ}\)6): no real machining occurs before and after the segmented signal [27].  
Figure 9: Diverse simultaneous fiber orientations along the cutting edge.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 2 Materials and Methods
### Design of the Grid Array Antenna for Radar Sensing
#### 2.3.1 Impedance Matching of the GAA  
The S\({}_{11}\)(dB) obtained in simulation for the optimized dimensions is shown in Figure 5. Very good impedance matching is achieved for the designed GAA in the target 24.05-24.25 GHz band, with close to 4% bandwidth (see Table 1).
## 5 Thermal Characterization
### Dynamic Self-Heating and Thermal Impedance Modeling in InP DHBTs  
The Low-Frequency S-parameter measurement setup consists of a semiconductor parameter analyzer, HP 4155, for DC biasing and a vector network analyzer, Agilent E5061B (5 Hz-3 GHz). In order to couple RF and DC bias, bias tees (bandwidth of 30 kHz to 3 GHz) were used. The dynamic self-heating is observed in the 30 kHz-300 MHz range where thermal impedance is extracted. A standard short-open-load-thru (SOLT) calibration was used with an RF input power of \(-\)28 dBm, followed by open-short de-embedding. DC bias points were chosen with higher VBE and VCE, where self-heating effects are dominantly visible.
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 4 Noble Metal-Based Heterogeneous Catalysts
### Alloying Strategy
#### 4.1.3 Other Pt Group Metal Alloys  
In addition to Pt-based alloys and Pd-based alloys, Ir-based alloys have attracted attention in the field of water electrolysis due to their superior intrinsic catalytic activity. At present, Ir alloys with both noble and non-noble metals have been successfully designed, especially with non-noble metals (such as Fe [73], Co [74] and Ni [75]). Alloying not only ensures superior catalytic performance but also has a competitive advantage in cost. Taking IrFe alloy as an example, the overpotential is only 850 mV at the current density of 1000 mA cm\({}^{-2}\) in 1 M KOH, which is better than commercial Pt/C (\(\eta_{1000}\) = 1.17 V). Due to the difference in electronegativity, the incorporation of Fe significantly modulates the Fermi level of Ir through electron transfer, bringing \(\Delta\)G\({}_{\text{H}^{*}}\) closer to 0 and thus enhancing the HER activity [73].  
Fine-tuning the composition is also crucial to the catalytic activity of the alloy. Researchers usually choose economical iron elements to form an alloy with precious metals. [PERSON] et al. [76] found that the strength of the HER catalytic activity of RuIr alloy doped with iron elements was Co-RuIr \(>\) Ni-RuIr \(>\) Fe-RuIr \(\approx\) RuIr. In addition, some research groups have synthesized RhCo alloy nanosheets [77] and RuNi alloy multilayer nanosheets [78]. The RuNi alloy shows excellent HER catalytic activity (\(\eta_{10}\) = 15 mV, Tafel slope: 28 mV dec\({}^{-1}\)) in 1 M KOH, attributing to the optimization of H\({}_{2}\)O dissociation and hydrogen adsorption and desorption during the HER process by RuNi alloying effect [78].  
Figure 5: (**a**) HAADF-STEM and EDS mapping images of a dealloyed PtCo sample and PtCo with Pt skin. (**b,c**) The experimentally acquired relationship between the d–band vacancies and \(j_{0}\) for a series of Pt–based materials in (**b**) 0.1 M KOH and (**c**) 1 M KOH solutions [68]. Copyright 2019 Royal Society of Chemistry.
## 4 Results  
A practical data for a large customer (Class-A) is employed to check the performance of the developed model. The energy market price including, global adjustment cost, demand charges, hourly energy price are adopted from the available data of IESO in Ontario, Canada [24; 25; 26]. It is important to mention here that the uncertainty in the load demand and market prices is fully considered in the data processing step. This analysis is based on the long-term planning forecasted data of IESO, which is available in [26]. This study has considered the average forecasted data for the prices and the global demand load profile over the study horizon (5 years). For the class-A customer data, we have used the available historical data to forecast the average customer load over the study horizon (5 years).  
The proposed mixed integer nonlinear mathematical formulation was coded in the AMPL optimization platform [27] and solved using the KNITO commercial solver. Figure 3 shows the daily peak demand power for a class-A customer in Ontario during 2016 and Figure 4 shows a typical hourly maximum demand for 24 hours. As per the IESO regulations the analysis period should start from 1 st May of current year to 31 st April of the next year.  
Figure 4: Typical hourly maximum demand for a class-A customer in Ontario (2016).  
Figure 3: The daily maximum demand for a class-A customer in Ontario during the analyses period (2016).  
In Figure 5 the one line diagram of the considered class-A customer is presented. As per IESO regulations the BESS is connected behind the customer meter. The main parameters for the base case are assumed as follows. The maximum allowable budget for the BESS is considered to be $2 million. The horizon (the planning timeline for the study) is assumed to be 5 years. As per the market price, the battery energy and power cost coefficient are considered to be $344/kWh and $125/kW, respectively. As the proposed analysis is annual, the annual interest rate is set to be 6%. The limit of the C-rate is assumed to guarantee a two-hour discharging during the enforced deployment of the peak hours. Finally, the loading factor (KD) is considered to be 100%. The assumptions for obtaining the base case results are summarized in Table 1.
# The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
## 4 Results  
### InSAR Time Series  
As mentioned above, Sentinel-1 SAR images are optimal data for this study as the time series of the PS network covered a two-year period with 72 images in descending mode as shown in Figure 3. Unfortunately, Sentinel-1 ascending mode coverage over Jeju Island only has a few pairs, which are not adequate for any time series analysis; thus, ascending mode InSAR time series data is not available in this study. The basic characteristics of Sentinel-1 imagery are summarized in Table 1.  
Although InSAR techniques have been effectively used for natural and artificial surface deformation observations, land surface monitoring caused by instability on the subsurface cavity would be a challenge due to the expected low deformation rate as long as the observation does not cover the collapsing phase of the lava tube.  
Figure 4a shows the LOS displacements extracted via the PS time series analysis in the target area together with the corresponding average phase coherences (Figure 4b). The stable reference point of InSAR deformations was carefully chosen based on the stability and dispersion of phase coherence and deformation rate. The time series LOS surface deformation demonstrates that the displacements on a large number of PS points are quite tiny (<1 mm/year). However, there are a few obvious strong regional deformations. We may consider such regional LOS migrations as either different thermal dilations of bedroks  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **SAR Image Parameters** \\ \hline Image number & 72 \\ Time coverage & 1 January 2016–22 December 2018 \\ Heading angle (deg) & \(-\)167.107 \\ Incidence angle (deg) & 44.0 \\ Relative Orbit & 134 \\ Acquisition time & 06:32 KST (GMT + 9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of employed Sentinel-1 images.  
Figure 3: Connection graphs of employed Sentinel-1 InSAR pair as shown in 72 descending images employed for PS time series analysis.  
induced by weather change or different brittle deformations of bedrocks responding to large-scale dynamic stress [54; 55] in geological units. However, the ground subsidence in Seogwipo sediment was interpreted as an effect of loading by ongoing heavy construction projects in Seogwipo. As PS observations certainly come from localized scatterers such as artificial structures or significant rocky objects, it implies that there is plausible surface migration induced by various localized instabilities, including potential LTDPs.  
Figure 4: PS processing results over Jeju Island. (**a**) LOS deformation velocity and (**b**) average phase coherence.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 5 Discussion  
During our study, we found that the performance of RD-SLAM depends on the quality of RGB-D images, and the system's effectiveness may be compromised with poor camera quality or in environments where high-quality images are unavailable. Methods such as [40, 41] can quickly reconstruct high-quality images with their lightweight and efficient modeling architecture. We plan to construct image pyramids that are first optimized for 3D Gaussian using low-resolution images captured by the camera and then supervised using reconstructed high-resolution images as truth values. We hope to use the above methods in future work to reduce the effect of low-quality input images and improve the robustness of the system.  
In addition, we plan to extend RD-SLAM to make it more adaptable to large-scale scenes. The two main challenges that we will face are the large number of 3D Gaussians that need to be optimized and the enormous amount of memory required to store the parameters. To address the problem of optimizing too many 3D Gaussians, we propose using a fixed number of keyframes to compose the sub-map. The efficiency and real-time performance of the system are ensured by expanding and optimizing only one active sub-map at any given time. To address the problem of excessive memory consumption, we plan to implement more accurate initialization and pruning strategies so that a single Gaussian better fits the object surface without the need for multiple overlapping Gaussians, thus reducing memory costs. These improvements can adapt the system to increasingly complex downstream tasks.