esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
\(A_{1}\) cross section at the impeller inlet  
\(a_{\rm c}\) Coriolis acceleration  
\(c\) absolute velocity  
\(c_{\rm 1,m}\) absolute velocity mean meridian streamline impeller inlet  
\(c_{\rm m}\) meridional component of absolute velocity  
\(c_{\rm p,RSR}\) pressure reduction coefficient  
\(c_{\rm u}\) circumferential component of absolute velocity  
\(c_{\rm u1,m}\) circumferential component of absolute velocity mean meridian streamline impeller inlet  
\(c_{2}\) absolute velocity impeller outlet  
\(c_{\rm u2}\) circumferential component of absolute velocity impeller outlet  
\(c_{\rm u2\infty}\) circumferential component of absolute velocity impeller outlet blade-congruent flow  
\(d_{\rm 1a}\) outer diameter impeller inlet  
\(d_{\rm 1,m}\) mean diameter at impeller inlet  
\(d_{\rm 1i}\) inner diameter impeller inlet  
\(b_{2}\) width impeller outlet  
\(d_{2}\) diameter impeller outlet  
\(f_{\rm M}\) motor frequency  
\(f_{\rm s}\) sampling rate  
\(g\) acceleration due to gravity  
\(H\) pump head  
\(H_{\rm exp}\) experimental measured pump head  
\(H_{\rm phys}\) pump head determined by physical model  
\(H_{\rm poly}\) pump head determined by polynomial  
\(H^{*}\) head at BEP  
\(\Delta H_{\rm pb}\) head loss prewhirl body  
\(\Delta h\) geodetic height between pressure transmitter and suction pipe  
\(k\) turbulent kinetic energy  
\(k_{\rm s}\) equivalent sand grain roughness  
\(M_{\rm exp}\) measured torque  
\(in\) mass flow rate  
\(n\) speed  
\(n_{\rm exp}\) measured speed  
\(n_{\rm N}\) nominal speed  
\(n_{\rm q}\) specific speed  
\(P_{2}\) pump power consumption  
\(P_{2}^{*}\) pump power consumption at BEP  
\(P_{2,\rm exp}\) measured pump power consumption  
\(P_{\rm loss}\) power loss  
\(P_{\rm loss,St}\) power loss due to shock loss  
\(P_{\rm mech}\) mechanical power  
\(p\) static pressure  
\(p_{2}\) static pressure impeller outlet  
\(p_{\rm PS,exp}\) measured static pressure at pressure side  
\(p_{\rm RSR}\) static pressure in impeller back side gap  
\(p_{\rm RSR,exp}\) measured static pressure in impeller back side gap  
\(p_{\rm ss,exp}\) measured static pressure at suction side
# Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies, and Human Health
## 3 Biomimetics and Artificial Intelligence
### Combined Applications of Biomimetics and AI
#### 3.2.1 Autonomous Robotics  
Autonomous robotics has seen significant development through bioinspiration, taking natural organisms as a reference to optimise the navigation, adaptability, and energy efficiency of robots. From insect-inspired flying microrobots to bird-inspired drones and underwater robots that emulate the biomechanics of aquatic species, these advances have made it possible to explore new environments and improve the autonomy of robotic systems in various scenarios.  
Significant progress in autonomous robotics has been achieved by emulating the navigation and adaptability skills of insects. The RoboBee project, for instance, has developed micro-flying robots powered by soft muscles based on dielectric elastomers, enabling highly efficient interactions with the environment [45]. Moreover, algorithms inspired by insect intelligence have demonstrated superior resource efficiency for small mobile robot autonomy when compared with classical methods [46; 47; 48; 49; 50]. In the marine domain, the SILVER2 robot--modelled on benthic animals--uses limb-like appendages to traverse the seabed with minimal energy consumption, facilitating exploration of hard-to-reachareas [50], while bioinspired soft robots have been designed to withstand the high pressures of the deep ocean, opening up previously inaccessible regions for study [51].  
Drones have similarly benefited from avian inspiration, with recent designs incorporating morphing wings and tails that adapt their shape in flight to enhance stability and energy efficiency under changing atmospheric conditions [52]. Studies of Australian budgetigar flight have provided further insights, informing UAV control algorithms that enable safer and more effective navigation in confined or cluttered spaces [53].
## 4 Simulation Results
### Acute Weather Condition  
Figure 10b shows the simulation of power waveforms for conventional INC, P&O, and proposed algorithms under uniform solar insolation on the PV array. In this dynamic weather condition, the maximum power of the PV array is measured at 1497 W. Power oscillations considered in both the INC and P&O algorithms are much higher than the proposed MPPT scheme (zero oscillation). The power fluctuation in the former two algorithms during the steady-state condition is approximately 30 W and 25 W, respectively, whereas no considerable power loss was found in the proposed algorithm. The percentage of MPPT capability is 98.2%, 98.3%, and 99.99% in the INC, P&O, and proposed algorithms, respectively, indicating that the proposed algorithm is superior in terms of tracking performance.  
Similarly, no oscillation occurred in the current and voltage graphs and running of the proposed algorithm under this condition, as demonstrated in Figure 10c,d. Coordinated PI-MPPT achieved 0.13 and 0.11 s faster tracking speed than the conventional INC and P&O techniques. Figure 10e shows how the step change of the duty cycle occurs during the simulation time (from 0 to 1 s). A decreasing and increasing trend of the duty cycle appeared until 0.14 s of the simulation time in the INC, P&O, and the proposed algorithms. It is estimated that the proposed algorithm stops the iterations of the duty cycle after passing transient time (0.15 s) and tracks GMPP whereas the other algorithms continue the  
Figure 9: P-V and I-V characteristic curves of the four weather conditions are showed as: (**a**) uniform weather; (**b**) shading pattern 1; (**c**) shading pattern 2; and (**d**) shading pattern 3.  
perturbation to the next level. The changes in the duty cycle perturbation during the SSC observed in the INC and P&O algorithms are 0.01 and 0.02, respectively, whereas that for the proposed system has a constant value.
# SGM-EMA: Speech Enhancement Method Score-Based Diffusion Model and EMA Mechanism
## 2 Methodology
### Numerical SDE Solver
(2024) [PERSON]; [PERSON]; [PERSON] Cmgan: Conformer-based metric-gan for monaural speech enhancement. _IEEE/ACM Trans. Audio Speech Lang. Process._**2024**, _32_, 2477-2493. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Unsupervised speech enhancement using dynamical variational autoencoders. _IEEE/ACM Trans. Audio Speech Lang. Process._**2022**, _30_, 2993-3007. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Conditional diffusion probabilistic model for speech enhancement. In Proceedings of the ICASSP 2022--2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Online, 7-13 May 2022; pp. 7402-7406. * [PERSON] et al. (2001) [PERSON]; [PERSON]; [PERSON]; [PERSON] Perceptual evaluation of speech quality (PESQ)--A new method for speech quality assessment of telephone networks and codecs. In Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, UT, USA, 7-11 May 2001; Cat. No. 01 CH37221. Volume 2, pp. 749-752. * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] An algorithm for predicting the intelligibility of speech masked by modulated noise maskers. _IEEE/ACM Trans. Audio Speech Lang. Process._**2016**, _24_, 2009-2022. [CrossRef]
* [PERSON] et al. (2019) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] SDR-half-baked or well done? In Proceedings of the ICASSP 2019--2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12-17 May 2019; pp. 626-630. **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
## 3 D-V Profile Method Preliminary
### Bank Reversal Logic  
In this subsection, a lateral planner will be designed to plan the lateral movement of the vehicle. It is assumed that the vehicle's current location is \((\lambda,\,\phi)\) and that the line-of-sight azimuth with respect to the target point \((\lambda^{*},\phi^{*})\) is \(\psi_{LOS}\). Without confusion, the line-of-sight azimuth \(\psi_{LOS}\) indicates the angle between the direction of the line of sight (from the mass center of the vehicle pointing towards the target point) and the local north. Therein, it is obtained by:  
\[\tan(\psi_{LOS})=\frac{\sin(\lambda^{*}-\lambda)}{\cos(\phi)\tan(\phi^{*})- \sin(\phi)\cos(\lambda^{*}-\lambda)} \tag{15}\]  
The error of the azimuth is defined as:  
\[\Delta\psi=\sigma-\psi_{LOS} \tag{16}\]  
As shown in Figure 2, the artificially designed error corridor is symmetrical about the line \(\Delta\psi=0^{\circ}\) and is separated into three speed intervals from right to left: a high-speed interval, a medium-speed interval, and a low-speed interval. The precision of the range can be ensured by a well-conditioned error corridor design.  
The bank reversal logic of the vehicle in the gliding phase is designed similarly to the lateral steering logic of a shuttle and consists of the following main scenarios: when the azimuth error value lies within the error corridor, \(|\Delta\psi|\leq|\Delta\psi_{\rm max}|\), the sign of the bank angle is unchanged; however, when it exceeds boundary of the designed error corridor, the sign of the bank angle changes. To further improve guiding accuracy, the azimuth error corridor can then be adjusted appropriately for the specific interface of the entry task.  
Figure 2: The designed error corridor of the azimuth schematic.
## 2 Methods
### Kernel-Based Relevance Analysis
#### 3.1.3 Narrowband Bidirectional Interactions  
In this experiment, we aim to evaluate how our proposal deals with bidirectional interactions of localized frequency content. Particularly, we want to assess its performance for signals \(\mathbf{x}\) and \(\mathbf{y}\) containing a directed interaction from \(\mathbf{x}\) to \(\mathbf{y}\) at 10 Hz and an interaction in the opposite direction, from \(\mathbf{y}\) to \(\mathbf{x}\), at 40 Hz. To generate such signals, first, we modify the model parameters of Area 2 so that it produces a signal \(\mathbf{y}\) with a power spectrum peaking in the \(\gamma\) band [39]. The power spectrum of \(\mathbf{x}\) remains as before. The coupling coefficient \(C_{12}\) is again held constant at a value of 0.5. The change in the parameters of Area 2 leads to strong directed interactions from \(\mathbf{x}\) to \(\mathbf{y}\) around 10 Hz and 40 Hz. Then, we use a Morlet wavelet (Equation (10)) to filter both \(\mathbf{x}\) and \(\mathbf{y}\) at those frequencies (10 Hz and 40 Hz). The obtained real-valued narrowband time series are then combined as follows: \(\mathbf{x}_{*}=\mathbf{x}_{10\;Hz}+\mathbf{y}_{40\;Hz}\) and \(\mathbf{y}_{*}=\mathbf{y}_{10\;Hz}+\mathbf{x}_{40\;Hz}\). Next, \(\mathbf{x}_{*}\) and \(\mathbf{y}_{*}\) are added to broadband noise generated following the same approach described in Section 3.1.2, with an SNR of 6.
## 2 Materials and Methods
### Knockout of EGFP Gene in Reporter Cells  
EGFP reporter cells were constructed by transforming cells with an EGFP-coding plasmid and selecting cells using hygromycin for 1 month [25]. The reporter cells (1 \(\times\) 10\({}^{4}\) cells) were seeded on 3 mm square area in a 40 mm culture dish (TPP Techno Plastic Products AG, Trasadingen, Switzerland) and cultured overnight. Next, 15 pmol Cas9 and 10 pmol sgRNA were used for RNP complex formation. The preparation of RNP-adsorbed NNA was as described above. The RNP-adsorbed NNA was moved towards the cells while being observed via an inverted microscope (GX71, Olympus). Following insertion into the cells, the NNA was oscillated at an amplitude of 1.0 \(\upmu\)m and a frequency of approximately 5 kHz for 1 min. The cells were then rinsed with PBS twice and cultured in fresh DMEM for 48 h. In order to introduce RNPs via lipofection, 1 \(\times\) 10\({}^{4}\) reporter cells were seeded on a 48 well plate in DMEM containing 10% FBS and penicillin/amphotericin. Then, 15 pmol Cas9 protein and 10 pmol sgRNA were mixed in 10 \(\upmu\)L Cas9 buffer and incubated at RT for 10 min. Next, 1 \(\upmu\)L of Lipofectamine 2000 (Thermo Fisher Scientific) was added to the RNP solution and the final volume was adjusted to 25 \(\upmu\)L with Opti-MEM (Thermo Fisher Scientific). Following 15 min of incubation at RT, the lipofection solution was added to each well. The medium was replaced with fresh medium following 16-20 h of incubation. The cells were incubated for 48 h before being subjected to fluorescence imaging.  
To evaluate EGFP knockout efficiency, we determined an area of cells from bright field images and that of EGFP expressing cells from fluorescent images. The fluorescence intensity in an area due to the absence of EGFP expressing cells was used as a threshold value to determine areas of EGFP expressing cells. The EGFP knockout efficiency was calculated based on the area of EGFP disrupted cells relative to the total area of cells as seen in the bright field image, using Image-Pro Plus (Media Cybernetics). At least 2 \(\times\) 10\({}^{3}\) cells were present in the area approached by the Si-NNA.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 7 Discussion
Many algorithms and filters have been used to reduce position errors caused by measurement errors in ultra-wideband technology, especially when passing through obstacles in non-line-of-sight environments. These algorithms and filters are also used to fuse data from more than one source when using indoor positioning systems operating with different types of technologies and sensors. Figure 16 explains the distribution of the used algorithms and filters in the proposed systems. Indoor positioning and tracking systems can be used in many applications, whether for personal uses such as tracking and monitoring systems for the disabled and the elderly in the health field, or to track the movement of valuable private property. They can also be used for industrial applications, resource management, and in warehouses, e.g., for tracking the movement of goods, forklifts, robots, and unmanned ground and air vehicles. Figure 17 explains the most important applications for the included references. The accuracy data in Table 3 show that the accuracy varies between the different systems. This is logical because the testing environments and conditions differ in terms of  
Figure 16: Distribution of the used algorithms and filters. Figure 17: The main applications of the proposed references. the numbers of anchors and tags used, as well as the types of algorithms used to calculate the distances between the system units or to reduce and filter errors during the identification and tracking of moving objects. The largest error rate recorded was 89.2 cm. Figure 18 shows the percentage range of errors recorded for the systems included in this study. Despite the importance of monitoring programs that convert ultra-wideband unit measurements into distances and plot the coordinates and locations of moving objects, many works lack information on how this part was implemented and which software or programming languages were used. The use of MATLAB was noted in 7/147 works, while Python was used in 12/147 works. Robot tracking systems and SLAM systems were used in 16 of the works presented here, while various programs were used in 6/147 works. The type of program or language used was not mentioned in the remaining works.
## 1 Introduction
### How to Measure Gas Fluxes?
The gradient-flux method or gradient method represents an alternative for measurements of soil-atmosphere fluxes. Assuming gas diffusion as the dominant transport mechanism, the flux of gas in the soil can be calculated based on the soil profiles of gas concentrations and soil gas diffusivity [44; 50]. While chamber methods measure the soil-atmosphere flux of gas more or less directly, the GM is based on a \"passive observation\" of the driving gradient in the soil gas concentration and the respective exchange coefficient. The GM approach is similar to the approaches used for modelling matrix flow of water in soils. Here, gradients in the soil water potential are the drivers behind the flow of water through the soil pore matrix [59]. The exchange coefficient here is called hydraulic conductivity, and is strongly dependent on the pore structure of the water saturated pores. The exchange coefficient for gas in soil is called soil gas diffusion coefficient, \(D_{\text{S}}\), (m\({}^{2}\) s\({}^{-1}\)) and is strongly depended on the pore structure of the air-filled pores and the gas species. The GM allows calculating the gas flux across the soil surface, but it can also provide additional valuable information about the depth profile of sources and sinks of gases [11; 16; 60]. As with chamber measurements, the GM can be used for different gases as long as the soil gas profiles can be measured, i.e., the gas concentrations are within the detection range of the measuring instruments, and the specific \(D_{\text{S}}\) of the respective gas species is considered. Most studies focused on CO\({}_{2}\)[60; 61; 62; 63; 64; 65], but there are also many studies that investigated CH\({}_{4}\) and N\({}_{2}\)O and other gases [66; 67; 16] including the isotopic composition of soil gases [68; 22].
# Long-Wavelength VCSELs: Status and Prospects
## 4 Monolithically Grown VCSELs on GaAs Substrates in the 1300-1550 nm Spectral Range
The current aperture was formed by lateral oxidation. The maximum data transfer rate amounted to 12 Gbit/s [13] for an output power of 0.6 mW. The first VCSEL based on GaInAsNbSb QWs with electric injection operating beyond the 1310 nm spectral range was presented in 2003 [104; 105]. The device design consisted of a bottom DBR based on 29 AlGaAs/GaAs pairs, an active region based on GaInNAsSb/GaNAs QW, and a top DBR based on 24 p-doped AlGaAs/GaAs pairs. A VCSEL with a 1460 nm emission wavelength operated in pulse mode only when cooled to \(-10\)\({}^{\circ}\)C. In 2006, an extension of GaInAsNSb-based VCSEL spectral range to a still longer wavelength was achieved when cooled to \(-48\)\({}^{\circ}\)C [106]. In 2009, a monolithic GaInNAsSb-based VCSEL was demonstrated, which operated in pulse mode at 1530 nm wavelength at room temperature in CW mode, but the output power was less than 1 \(\mu\)W [107]. In 2019, GaInNAsSb-based VCSELs with an 8 \(\mu\)m aperture operated up to 4.3 GHz at a 3.9 mA threshold current. The output power again did not exceed 0.3 mW at room temperature in CW mode, and the maximum emission wavelength was 1490 nm [108]. Note that the growth of Sb-containing QWs is associated with additional technological difficulties due to the volatility of Sb. An alternative approach is using layers of GaAsN/InAs superlattices [109], which makes it possible to minimize the effect of nitrogen diffusion from QWs [110]. The presence of the mini bands, being both in the QW region and in the barrier region, enables a significant increase in the overlap factor and, therefore, a significant increase in the modal gain as compared to QW-based active regions.
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 5 Simulation Results
### Performance Evaluation
#### 5.2.1 Improved Calibration Method
However, in the remaining SNR ranges, the proposed calibration method achieved high system throughput performance of at least 0.18 kbps and up to 9.17 kbps.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 4 DMS Architecture and Functional Overview
### Data Processing
#### 4.2.5 Communication  
A DMS requires robust and efficient communication among its various components to ensure accurate and timely safety interventions. These systems rely on multiple communication interfaces to facilitate high-speed and reliable data exchange between sensors, processing units, and actuators. A key element is the integration of the Calculation Unit (CU) with other ECU components via the Controller Area Network (CAN) bus, which serves as a standardized backbone for real-time data transfer in automotive environments. This architecture enables the coordination of safety-critical tasks, ensuring consistent communication between modules. In addition to CAN, specialized interfaces are used to handle high-bandwidth visual data. The Camera Serial Interface Receiver (CSI-RX), for instance, supports the reception of RGB-IR camera streams. This separation enables parallel processing: infrared (IR) data are used for real-time driver attentiveness analysis (e.g., eye-blink rate and yawning frequency), while RGB data feeds deep learning models for occupant detection and classification, which operate at lower sampling rates [13]. The processed output, such as alerts or diagnostic messages, is transmitted over high-speed interfaces like the Reduced Gigabit Media Independent Interface (RGMII) or Reduced Media Independent Interface (RMII), which are used to forward encoded images or alerts to external modules or data loggers [13]. Additionally, GPIO interfaces support low-level tasks such as sensor triggering and data relay to the CU or Vehicle Safety Application (VSA) unit [31]. UART interfaces support low-latency, serial communication between lightweight processing units within the DMS, enabling the consistent transmission of biometric signals such as heart rate or eye-blink metrics at synchronized baud rates [17]. Additionally, USB interfaces offer a dual function by combining data transmission with power delivery over a single cable, reducing wiring complexity and supporting compact, energy-efficient designs suitable for embedded automotive applications [17]. Lastly, Ethernet may be employed as a high-throughput link between in-cabin cameras and processing units, supporting continuous image transfer with low latency and high reliability. This is particularly useful for real-time driver behavior monitoring and video streaming to centralized systems [17].
## 2 Background Knowledge
### Rule-Based Representation and Reasoning  
Symbolic rules are the oldest and most popular KRR method used by experts and generally in intelligent diagnostic systems. Their popularity comes from the fact that they are natural representations of human knowledge, which makes it easy to comprehend the represented knowledge. The basic structure of a diagnostic rule is the following:  
\[\begin{array}{l}\textbf{if }\textbf{e}_{1}\text{ and }\textbf{e}_{2}\text{ and }\ldots\textbf{e}_{n}\\ \textbf{then h}\end{array}\]  
where each \(e_{i}\) represents an evidence statement (e.g., symptom presence) and \(h\) represents the hypothesis (e.g., a disease). The evidence statements of a rule are connected to each other with logical connectives, commonly with \"and\". When the evidence statements of a rule hold (or are observed), the hypothesis is derived, and the rule is said to be fired. Rules represent general knowledge regarding a domain. The following is an example rule from the domain of fish disease diagnosis:  
R: **if** fish-weight > 15 **and** mouth-lower-jaw is deformed **and** anorexia is yes  
**then** fish-disease is ceratotho  
In such systems, there are two basic inference strategies: forward chaining and backward chaining. The first is more common and natural for such cases; it starts from the evidence statements (known facts) and goes towards the hypotheses (derived facts), eventually ending up with the searched disease(s). Technically, the rules that can fire (i.e., their evidence statements hold) are found and produce their hypotheses, until a disease related hypothesis is reached. For an extensive treatment of rule-based reasoning see [5] (ch. 7) or [13] (ch. 2).