esa-sceva/satcom-chunk-collection huggingface dataset:
# Updates on Impact Ionisation Triggering of Thyristors
## 1 Introduction
The trigger voltage required for the shock ionization wave mode is generated using an avalanche transistor Marx generator of 60 stages. Previous studies [15] have explored the usage of components from the fast opening switches family (Drift Step Recovery Diodes--DSRD and Semiconductor Opening Switch--SOS diodes) as triggering generators for impact ionization. However, these components present challenges such as unavailability on the commercial market, bulky designs and relatively long pre-charging phases in the hundreds of nanoseconds range--resulting in a pulse triggering delay of up to more than 500 ns [16]. Both types are categorized as opening switches, operating on the principle of rapidly recovering the blocking capability after the removal of the pre-charged plasma from the component structure (equivalent to reverse recovery in diodes) [17]. This involves the generation and maintenance of plasma prior to its application for triggering functions, a process that demands considerable effort. The spiral generator, used in the study from [18] to trigger thyristors in impact-ionization wave mode, features a compact design based on the spiral/vector inversion principle. It charges a spiral stripline to a high voltage that is rapidly discharged upon activation, reversing the electric field direction and effectively doubling the output voltage. Although the spiral stripline's design is compact compared to other high-voltage generators, it remains bulkier than solid-state electronics. Scaling this design for different sizes or enhanced performance without losing efficiency or enlarging the footprint poses significant challenges. Our approach emphasizes utilizing commercial off-the-shelf (COTS) components while maintaining a compact and scalable design. This paper details our efforts to enhance the output voltage, slew rate, and current of the super-boosting driver. These enhancements are pivotal in minimizing the commutation time of SiC MOSFETs and preparing the driver for increased MOS gate loads. Our aim is to empirically showcase the potential benefits of employing COTS components within a compact configuration. This approach is expected to result in reductions in size and weight, enhanced reliability and stability, lowered manufacturing costs, and improved performance. Building upon our previous development of an ultra-fast gate-boosting driver for HV SiC MOSFETs that achieved a significant reduction in rise times, an output voltage slew rate exceeding 1 kV/ns and an amplitude greater than 1 kV ([19; 20; 21; 22]), the current paper outlines the enhancements made to the previous driver design. These enhancements are aimed at improving the super-boosting driver's output voltage, slew rate, and current.
# Localized Necking Model for Punching Fracture Simulation in Unstiffened and Stiffened Panels
## 5 Conclusions  
The applicability of fracture assessment based on shell elements using a ductile fracture model with localized necking was demonstrated through a structural test for unstiffened and stiffened panels. Tensile tests were carried out to calibrate the plasticity and fracture model parameters of JIS G3131 SPHC steel. The results showed that ductile fracture is more sensitive to the Lode angle parameter than stress triaxiality. For the localized necking envelope, JIS G3131 SPHC showed lower ductility under plane stress tension and proportional loading. It was proved that the presented model showed good accuracy in the prediction of fracture initiation for punching tests. Furthermore, fracture simulations could verify the experimentally obtained post-fracture shapes. Local necking occurs in the local area of the material and structure, and dense elements can simulate the exact necking initiation of the necking in the numerical analysis. Although the HC-DSSE model considers the occurrence and flexible fracture displacement of the necking to be highly approximate,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Case** & **d\({}_{1}\) (mm)** & **d\({}_{2}\) (mm)** & **d\({}_{3}\) (mm)** & **d\({}_{4}\) (mm)** \\ \hline \multirow{2}{*}{Unstiffened} & Test & 7.3 & 5.1 & 7.1 & 8.7 \\  & Simulation & 7.32 & 5.2 & 6.63 & 8.04 \\  & Error (\%) & 0.27 & 1.96 & 6.62 & 7.59 \\ \hline \multirow{2}{*}{Stiffened} & Test & 12.0 & 9.9 & - & - \\  & Simulation & 11.7 & 9.73 & - & - \\  & Error & 2.5 & 1.72 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement of failure surface of experimental and numerical results.  
Figure 16: Comparison of experimental and numerical post-fracture shapes.  
it is difficult to know exactly when the actual fracture occurs after the necking. Therefore, by using dense elements, the precise timing of the necking can be considered fracture, underrating the fracture displacement. This effect can be corrected by making the element size sufficiently large; in this study, structural experiments demonstrated that element sizes ranging from approximately two to five times the thickness of the structure can be used. In the future, it will be necessary to address the element sensitivity part in the fracture model by conducting studies, such as correction of fracture strain, according to the size of the element.  
Conceptualization, S.-J.P.; formal analysis, S.-J.P.; investigation, S.-J.P. and K.K.; methodology, S.-J.P.; software, S.-J.P.; validation, S.-J.P.; visualization, S.-J.P. and K.K.; writing--original draft, S.-J.P.; writing--review and editing, K.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant from Tongmyong University Innovated University Research Park (I-URP) funded by Busan Metropolitan City, Republic of Korea.  
The authors declare no conflict of interest.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 3 Theory and Methods
### Sea Ice Classification Method
#### 3.2.3 Sea Ice Classification Based on RF and SVM
Finally, the OW-Sea Ice and FYI-MYI classification results are evaluated against the OSISAF SIT. set as 1. Then the _F-value_ is the _F1_ score, which is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. _G-mean_ can be used to evaluate the overall classification performance. _G-mean_ measures the balance between classification performances on both the majority and minority classes. In addition, the _kappa_ coefficient is also used to measure inter-rater reliability for classifiers. The _kappa_ coefficient measures the agreement between two raters who each classify N items into C mutually exclusive categories.
## 2 Principles and Methods
### Particle Swarm Optimization  
During the parameter optimization process, if one influencing parameter is kept constant while optimizing another, the interaction between these two parameters may be overlooked, leading to results that are only relatively optimal. This limitation of local optimization has prompted researchers to seek more effective parameter optimization methods. PSO, as a collective intelligence optimization algorithm, possesses strong global search capabilities by simulating the flocking behavior of birds to find the best solution. The PSO algorithm can be described as follows: a population consists of M particles, with each flying at a certain speed in a d-dimensional search space. Each particle records its individual historical best position and compares it with the historical best positions of all the other particles in the population. If a particle has not yet found the optimal solution, it continuously iterates to update its position until the optimal solution is obtained. The global search characteristics of the PSO algorithm reduce the risk of becoming trapped in local optima, particularly when optimizing the parameters of the VMD algorithm, enabling the discovery of parameter combinations that better suit the data characteristics, thus achieving better signal decomposition results [31]. Therefore, this paper employs the PSO algorithm to perform parallel optimization of the two influencing parameters in the VMD algorithm, avoiding the interference of subjective human factors and automatically selecting the optimal parameter combination for K and \(\alpha\).  
The updated expressions for particle velocity and position are as follows:  
\[v_{i}^{k+1}=\omega v_{i}^{k}+c_{1}r_{1}\left(p_{i}^{k}-x_{i}^{k}\right)+c_{2}r_ {2}\left(p_{g}^{k}-x_{g}^{k-1}\right) \tag{9}\]  
\[x_{i}^{k+1}=x_{i}^{k}+v_{i}^{k+1} \tag{10}\]  
where \(\omega\) represents the inertial weight; \(k\) is the current number of iterations; \(c_{1}\) and \(c_{2}\) are the individual learning factors; \(r_{1}\) and \(r_{2}\) are arbitrary numbers in the range \([0,\ 1]\).  
After k iterations, \(x_{i}^{k}\) is the position of particle, \(v_{i}^{k}\) is the velocity of particle, \(p_{i}^{k}\) is the current optimal position of the particle, and \(p_{g}^{k}\) is the current optimal position of the entire particle swarm.
## 3 Smart Inverter Control Architecture
### The Voltage-Power Control Technique for the Smart Inverter  
When connecting the smart inverter to the mains system, the grid-connected specifications of the power company must be complied with. Since connecting a massive amount of renewable energy to the mains grid would cause voltage fluctuation, the voltage-power control technique must be applied, where attention is required to the allowable voltage scope in grid-connected specifications. When the photovoltaic smart inverter starts voltage-power control, should the mains voltage and frequency still fail in steady parallel operation and exceed the normal range, it must automatically trip to avoid damage.  
Figure 9 displays the reactive power control architecture for the smart inverter [20]. The errors for actual mains voltage and reference voltage were acquired first; through the P-I controller (the proportional and integral constants were \(K_{Pg}=0.1\) and \(K_{lg}=0.13\), respectively) and reactive power limiter, a command value of reactive power could be obtained. By entering the value in Figure 8, the smart inverter could be controlled to provide or absorb the reactive power for achieving the purpose of stabilizing mains voltage.  
In order to be easy to implement, all the controllers in this paper adopt the traditional proportional-integral (P-I) controller, whose proportion and integral constant are obtained by the \"trial and error\" method.  
Figure 10 shows the voltage-power control flow chart [20]. Firstly, the voltage at the grid-connected point of the inverter is detected to control the reactive power according to the per-unit value of mains voltage. When such value equals 1, the power factor (PF)  
Figure 8: The control scheme for the phase angle of the smart inverter.  
Figure 9: The reactive power control architecture for the smart inverter.  
also stays at 1. Since no reactive power occurs then, only the provision of real power is required. When the per-unit value of mains voltage is between 1 p.u.-1.03 p.u., the smart inverter will absorb the reactive power, where PF is controlled and gradually reduces to 0.9 (lagging). When the per-unit value of mains voltage is between 0.97 p.u. and 1 p.u., the smart inverter will provide the reactive power, where PF is controlled and gradually reduces to 0.9 (leading). The above control brings the mains voltage back to the normal range by adjusting the magnitude of the reactive power. However, should PF be adjusted to 0.9 leading or lagging and the mains voltage still cannot effectively restrain its rise or fall, the \"technical highlights for parallel connection of renewable energy power generation system, Taiwan Power Company\" [25] will be followed. When the voltage is greater than 1.03 p.u. or less than 0.97 p.u., the smart inverter will be disconnected from the mains system.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 3 Signal Generation and Detection  
Traditionally, a chirp signal can be generated with analog circuitry via a voltage-controlled oscillator (VCO). Recent advances in SAW devices have made it easier to generate chirp in a low-cost way. Chirp can also be generated digitally by using a direct digital synthesizer (DDS) [14].  
Since the modulation principle of ChPN is similar to BOC, the ChPN signal can be generated by replacing the BOC sub-carrier generating module with the chirp generating module, as shown in Figure 2a. In the figure, the data waveform \(d(t)\) is modulated by the PRN code signal \(c(t)\) and the chirp sub-carrier \(p(t)\) to form a baseband signal \(s(t)\). After passing through the band-pass filter (BPF) and being modulated by the radio frequency (RF) carrier, \(s(t)\) is up-converted to the RF signal \(s_{RF}(t)\). The clock signals driving PRN code and chirp generation modules should be derived coherently from the same oscillator source.  
A regular acquisition scheme of ChPN signals is shown in Figure 2b, which is similar to that of BOC signals. First, the RF signal \(s_{RF}(t)\) was mixed with the local oscillator (LO) signal, down-converted to an intermediate frequency (IF); then, the IF signal \(S_{IF}(t)\) is down-converted by the local carrier and cross-correlated by the local ChPN reference signal, respectively; next, after coherent integrations and square operations, the decision variable can be obtained by adding the outputs of I and Q channels as the following:  
\[\left\{\begin{array}{l}S_{I}=\sqrt{C/2}R(\tau)\cos(\theta)\text{sinc}\big{(} \pi F_{D}T_{p}\big{)}+n_{I}\\ S_{Q}=\sqrt{C/2}R(\tau)\sin(\theta)\text{sinc}\big{(}\pi F_{D}T_{p}\big{)}+n_{ Q}\end{array}\right. \tag{16}\]  
where \(C\) is the signal power, \(\tau\) is the delay difference between the local code and the incoming code, \(\theta\) is the phase difference between the local carrier and the incoming signal carrier, \(F_{D}\) is the frequency difference between the local carrier and the incoming signal carrier, \(T_{p}\) is the pre-detection integration time, \(n_{I}\) and \(n_{Q}\) are the Gaussian noises in the correlator outputs. The decision variable \(S(F_{D},\tau)\) can be expressed as:  
\[S(F_{D},\tau)={S_{I}}^{2}+{S_{Q}}^{2} \tag{17}\]  
which is a random process with a chi-square distribution and two degrees of freedom, and its non-centrality parameter is \(\lambda=\
icefrac{{C}}{{2}}\text{sinc}^{2}\big{(}\pi F_{D}T_{p}\big{)}R^{2}(\tau)\).
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
A tunnel detection method combining photogrammetry and laser scanning [23] is susceptible to the set position of the equipment; when the equipment is set deviating from the tunnel axis, the detection error increases obviously [24]; this indicates that the method is not suitable for a tunnel during construction, since the set position has a conflict with tunnel construction. This paper presents a real-time and automatic tunnel deformation monitoring system to realize real-time monitoring and early warning for dangerous tunnel deformation. Considering the dusty and dark tunneling environment, a fixed laser beam is used to transmit the monitoring reference from a stable point to a measured point through the dust and darkness in low visibility, and a shielded image acquisition module mounted on the measured points is used to acquire the laser spot image regardless of the dust and darkness. However, because of the adverse tunnel environment, the measurement result is affected by laser beam feasibility, temperature, air refraction index, and wireless antenna attitude. These factors are carefully examined in this study, and compensation methods are proposed to reduce the measurement errors caused by them. The results show that the proposed system not only achieves good detection accuracy, repeatability and resolution, but also compensates for the shortcomings of the above methods, such as the low degree of automation, the lack of real-time measurement function and overcoming the influence of dusty and dark environments in low visibility.
## 5 Remote Monitoring Technologies
Unauthorized scanning of people to determine the presence and type of implantable medical device, such as a cochlear implant or implantable cardiac device, is a potential threat, as such a discovery may result in people being discriminated against, with negative economic and social consequences [132]. A tempered cochlear implant system can perform as a sound recording and transmitting device, thus infringing the privacy of the user and those surrounding the bearer of the implant. The implantable system can also be reconfigured to act as a tracking device. A sophisticated authentication system that integrates all the implant components may aid in ensuring the integrity of the communication and limiting the unauthorized access to the device. In doing so, however, it may render the device less accessible by medical practitioners at clinics outside of the patient's clinical team [131]. For instance, in a time-sensitive situation when a patient loses consciousness and is treated by an emergency unit in a country other than their own, an open access device may allow rapid medical response and potentially save the life of the patient. To handle the dynamic nature of the emergency response, an elliptic curve cryptography-based public key encryption scheme can be employed for authentication, such as in the CodeBlue project [133]. Yet, this authentication does not ensure the security of the data stored within the network, or the control to these data. Another consideration is that the advanced security system is likely to increase the power budget of the device and require some changes to the electronics design of the indwelling module. [PERSON] _et al_. reported on a system of zero-power defense and prevention mechanisms that reside at the interface between an implantable cardioverter defibrillator and the external components [134]. These mechanisms are powered by externally delivered RF energy and not the primary battery of the indwelling module, and can effectively mitigate the attacks from adversaries using custom and/or commercial programmers. Upon an encounter of a security-sensitive event, the notification mechanism sounds a warning to the implant bearer whilst symmetric encryption and authentication prevent unauthorized access. The limit memory resources available to software developers necessitate the use of lean, event driven concurrency models, significantly different to the conventional operating system designs [117]. The limited computational power and restricted bandwidth result in the sensor nodes engaging in a limited on-board processing to minimize information transmission requirements.
Physicochemical Aspects, Bioactive Compounds, Phenolic Profile and In Vitro Antioxidant Activity of Tropical Red Fruits and Their Blend  
[PERSON]  
1  
[PERSON]  
2 Department of Agricultural Engineering, Federal University of Campina Grande, Campina Grande 58429-900, Brazil; [EMAIL_ADDRESS] (R.M.E.F.)  
2 [EMAIL_ADDRESS] (A.J.M.Q.; l.mourar  
Brazil is the largest producer and exporter of acerola (_Malpighia emarginata_) in the world. This fruit stands out for its high content of ascorbic acid, as well as amino acids, minerals and bioactive compounds, such as carotenoids and phenolic compounds, which give it high antioxidant activity [3].  
Brazil is also the world's largest producer of guava (_Psidium guajava_ L.), producing 424 thousand tons per year [4]. This fruit has large amounts of phytochemicals with antioxidant activity, as well as high levels of several vitamins and minerals [5]. Its high acceptance is due to its excellent sensory characteristics, mainly due to volatile compounds related to aroma [6].  
Pitanga (_Eugenia uniflora_ L.) is a native Brazilian species, rich in calcium, phosphorus, flavonoids, carotenoids and vitamin C, giving it high antioxidant activity. It has a predominantly acidic flavor, with variations observed in fruits with low acidity and specimens with high acidity, in addition to some astringency with a characteristic aroma [7].  
Popular among consumers and indicated by health professionals, foods with properties considered functional have been used with the promise of preventing chronic diseases [8]. The combination of fruits of different species, complementing each other in the content of bioactive compounds, antioxidants and dietary fibers, presents itself as a food alternative with great functional potential, in addition to allowing the modulation of sensory attributes, offering unprecedented flavors and aromas.  
Therefore, the objective of this study was to evaluate and compare the physicochemical characteristics, bioactive compounds, profile of phenolic compounds and in vitro antioxidant activity of pulps of three species of tropical red fruits (acerola, guava and pitanga) and of the blend produced from the combination of these pulps.
# Multi-Input Logic-in-Memory for Ultra-Low Power Non-Von Neumann Computing
## 2 Materials and Methods
### Logic-in-Memory with RRAM Devices and the Material Implication Logic  
The material implication logic is a functionally complete logic that can be effectively implemented with RRAM devices [11]. In fact, RRAMs enable its efficient implementation using a circuit architecture like the one in Figure 3a. A control logic equipped with analog tri-state buffers is needed to deliver appropriate voltages at the top electrode (TE) of each RRAM device of the array, with the devices in the array having their bottom electrodes (BE) connected to the same resistor R\({}_{\text{G}}\). Differently from traditional logic gates, in RRAM-based LIM schemes, the logic values are not encoded as voltages at circuit nodes, but as nonvolatile resistive states of RRAMs (HRS for logic 0, LRS for logic 1). All possible logic gates can be defined with two fundamental operations [32; 33], namely the IMPLY (2-input 1-output operation, truth table in Figure 3d) and the FALSE (1-input 1-output operation always yielding logic 0). The COPY function (realized with IMPLY and FALSE) allows cascaded operations [12]. FALSE is executed by applying a negative voltage pulse to the RRAM (i.e., the \"classical\" reset operation), see Figure 3c. IMPLY between two logic values (stored in RRAMs P and Q) is executed by simultaneously applying two different positive voltage pulses at P (V\({}_{\text{COND}}\)) and Q (V\({}_{\text{SET}}\)), see Figure 3b. The result is stored in Q, while P must preserve its state. These requirements introduce several constraints that make the design space for the definition of V\({}_{\text{SET}}\) and V\({}_{\text{COND}}\) values very narrow [12; 18], requiring a fine control down to the tens of mV, which is hard to achieve without huge overheads [12; 18]. Further, the choice of R\({}_{\text{G}}\) suffers from trade-offs [12]. Moreover, the repeated execution of IMPLY causes state drifts in P and Q, eventually causing bit corruption, as reported in [12; 18]. Upon this, a refresh is needed that requires moving all data back and forth to a new memory location, wasting energy and time [12; 18].
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
In Section 2, the characteristics of the spaceborne SAR operating in stripmap mode are analyzed, the model of the area observed by the spaceborne SAR operating in boresight stripmap mode is established, and the time-varying observation area is parameterized into a fixed look angle of the spaceborne SAR's antenna. In Section 3, an ISAR imaging model for spaceborne SAR is established and an objective function to estimate the look angle of the spaceborne SAR operating in boresight stripmap mode is constructed. In Section 4, simulation experiments are designed to validate the feasibility and robustness of the proposed algorithm. In Section 5, some conclusions are drawn.