esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurements with the SCNAALL System  
In each investigational protocol, pulse wave detection and analysis were performed by the 1.0 version of the SCN4 ALL telemedicine system (E-Med4 All Europe Ltd., Budapest, Hungary). Pulse wave was recorded as DVP detected by a commercially available transmission pulse oximeter (Berry Pulse Oximeter, Shanghai Berry Electronic Tech Co., Ltd., Shanghai, China; hardware: 32-bit AD converter, 200 Hz sampling rate). The device emits light to the tissues of the finger from an LED light source and detects the transmitted light by a photodiode. Vessel diameter and blood volume in the arteries change with pulsation, and so does the amount of transmitted light, enabling the detection of a continuous DVP. The pulse oximeter device communicates via Bluetooth connection with a mobile application that initiates and terminates a 2-min-long data acquisition and transmits the recording to a cloud-based automated algorithm that was developed by our research group (Figure 2).  
Signal preprocessing by the algorithm starts with upsampling the 200 Hz sampling frequency of the device to 1 kHz. In order to condition the PPG signal, a digital bandpass filter--fourth-order Butterworth--with \(-\)3 dB points at 0.1 and 10 Hz is applied. Then, the algorithm identifies the pulse cycles. Afterward, within each cycle, particular distinct points of the DVP (primary curve, first and second derivatives) are identified. Then, contour parameters are computed for every individual cycle. Afterward, the means of all cycles are calculated and displayed on an internet platform for the physician. In this study, these averages were exported as spreadsheets for further analysis. The measurement data are stored at a cloud-based server (Amazon Web Services, Amazon Web Services EMEA SARL, 1855 Luxembourg, Luxembourg) equipped with safe data protection that conforms to the applicable regulations ((EU) 2016/679).  
The automatically calculated pulse contour parameters that this study focuses on are as follows: mean interbeat interval (IBI, ms), heart rate (HR, 1/min), stiffness index (calculated as the height of the subject over pulse transit time (PTT), m/s [4,6]), reflection index (the ratio of the amplitude of the diastolic peak to the amplitude of the systolic peak), left ventricular ejection time index (LVETI, ejection time (ET) normalized for heart rate using the formulae LVETI = 1.7 \(\times\) heart rate + ET and LVETI = 1.6 \(\times\) heart rate + ET in males and females, respectively [13]), b/a (parameter relating the amplitude of the second wave of the DVP second derivative to the first wave), d/a (ratio of the fourth and first inflection points of the second derivative of the DVP), aging index (a parameter derived from the amplitudes of inflections of the second derivative of the DVP as (b \(-\) c \(-\) d \(-\) e)/a [31]), and c-d point detection ratio (a value that specifies the percentage of those pulse cycles in the 2-min recording in which c and d points of the second derivative were successfully identified by the algorithm) (Figure 2).
# Investigation of Cavitation Noise in Cavitating Flows around an NACA0015 Hydrofoil
## 4 Results and Discussion
### Sheet Cavitation
#### 4.1.3 Influences of Different Sheet Cavitation Number  
In the comparably steady flow condition of sheet cavitation, three different cavitation numbers, \(\sigma\) = 0.975, \(\sigma\) = 1.075, and \(\sigma\) = 1.175, were investigated and their similarities and disparities are discussed in this part.  
Cavitation morphology for \(\sigma\) = 0.975 shared several features with cloud cavitation because of its relatively lower outlet pressure. It can be seen from Figures 8-11 that cavitation clung to the leading-edge, instead of shedding from the leading edge periodically, but cavitation developed and extended from the leading edge towards the flow direction dramatically. Thereafter, cavitation retreated and collapsed. As a contrast, cavitation for \(\sigma\) = 1.075 and \(\sigma\) = 1.175 stayed constant and cavity stretched from the leading-edge to approximately 1/3 \(C\). Additionally, the rule that the distance from locations of peak value in specific times is 0.2-0.4 C away from cavitation growth and the collapsing point was again verified.  
Frequency spectrograms of leading noise for three different cavitation numbers are shown in Figure 12, and the same point was selected for analysis.  
Figure 6: Acoustic power spectral density at U7 for \(\sigma\) = 1.075.  
Figure 7: Acoustic pressure pulsation at different locations for \(\sigma\) = 1.075.  
Figure 8: Bubble volume pulsation and numerical results for \(\sigma=1.175\).  
Figure 10: Acoustic pressure at different flow times for \(\sigma=1.175\).  
Figure 9: Bubble volume pulsation and numerical results for \(\sigma=0.975\).  
The frequency of the first peak value for \(\sigma=1.175\) was 7.28 HZ, and the frequency of the first peak value for \(\sigma=1.075\) and \(\sigma=0.975\) was 10.49 HZ. Therefore, the cycle lengths for \(\sigma=0.975\), \(\sigma=1.075\), and \(\sigma=1.175\) were 95.33 ms, 95.22 ms, and 137.36 ms, respectively. In addition to the period length, there was also a difference in the magnitude of acoustic power spectral density. The magnitude of acoustic power spectral density for \(\sigma=1.075\) and \(\sigma=1.175\) were the same, whereas that for \(\sigma=0.975\) was significantly higher. The results of PSD had good agreement with the experimental research from [PERSON] [19], that the magnitude of multimodal behavior increases with decreasing cavitation number. The difference was largely due to its drastic pressure fluctuation caused by extending and retreating of cavitation on the leading edge.
## 1 Introduction
Since the main field of application of lanthanide-doped oxides is luminescence, a major goal is the enhancement of the intensity of the luminescence emission. There are two basic approaches to improving the luminescent properties of RE-doped materials. The first consist of the use of a suitable sensitizer, a very effective radiation absorber that can transfer energy to the lanthanide ion. The second way is to modify the host lattice so that the transitions at lanthanide ions are favored; commonly, Li\({}^{+}\) is used for this purpose [20, 24, 26]. Nevertheless, due to the mismatch between ionic radii (Zn\({}^{+2}\)\(\sim\)0.74 A; and Eu\({}^{+3}\)\(\sim\)0.94 A) and charge unbalance (the most frequently used are the RE trivalent ions), effective doping of ZnO with rare elements is still challenging. In this regard, co-doping with alkali metals, particularly Li, is a very good option [24, 27, 28, 29, 30]. Figure 1: Scheme of the most common faces that appear in the growth of ZnO. In the current work, ZnO nano- and microstructures co-doped with Eu and Li were obtained by a solid vapor method. To gain better information on the role of the Li ions as co-dopants, the amount of Eu was kept constant, and Li precursor content was varied. This allowed us to observe the evolution of the morphology and relate it to the lattice deformation due to the incorporation of dopants and the consequent defect structure. The amount and morphologies of structures obtained depend strongly on the initial lithium content. XRD and Raman experiments were performed to assess the crystal structure, and luminescence investigations were conducted to obtain information about the optical properties and local defect structure. Microphotoluminescence measurements (\(\mu\)-PL) showed that the structures obtained support resonant modes and hence can be used as lightguides and resonant cavities with good quality factors. The study of this kind of structure has received much attention in recent years, and comprehensive reviews can be found in works by [PERSON] et al. [31] and [PERSON] et al. [32]. A good analysis of the influence of geometry on the establishment of the different resonant modes can also be found in work by [PERSON] et al. [33].
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 5 Numerical Simulation and Analysis
### Design Parameters Analysis
#### 5.2.2 Transfer Duration  
In this paper, the method of numerical iteration is adopted to adjust the re-entry velocity, so that the duration of the Moon-to-Earth transfer can precisely meet the preset value. At the same time, the shorter the transfer time, the larger the velocity of the probe  
Figure 8: Relationship between longitude of landing field and optimal re-entry moment in a day.  
Figure 9: Opportunity distribution about several latitudes of landing field in the first three months of 2030. (**a**) Variation in the natural perilune altitudes at different latitudes; (**b**) Opportunity distribution for different latitudes.  
during the transfer. Given the fact that the magnitude of the relative distances and velocity of the Earth and the Moon vary little, a larger transfer velocity results in greater Moon capture maneuver. Figure 10 shows the change curves of the transfer duration and the total velocity increment \(\Delta V\), as well as the atmospheric re-entry velocity. Design parameter settings are the same as in the single transfer trajectory example except for the transfer duration.
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
###### Abstract  
Weighted belief propagation (WBP) for the decoding of linear block codes is considered. In WBP, the Tanner graph of the code is unrolled with respect to the iterations of the belief propagation decoder. Then, weights are assigned to the edges of the resulting recurrent network and optimized offline using a training dataset. The main contribution of this paper is an adaptive WBP where the weights of the decoder are determined for each received word. Two variants of this decoder are investigated. In the parallel WBP decoders, the weights take values in a discrete set. A number of WBP decoders are run in parallel to search for the best sequence- of weights in real time. In the two-stage decoder, a small neural network is used to dynamically determine the weights of the WBP decoder for each received word. The proposed adaptive decoders demonstrate significant improvements over the static counterparts in two applications. In the first application, Bose-Chaudhuri-Hocquenghem, polar and quasi-cyclic low-density parity-check (QC-LDPC) codes are used over an additive white Gaussian noise channel. The results indicate that the adaptive WBP achieves bit error rates (BERs) up to an order of magnitude less than the BERs of the static WBP at about the same decoding complexity, depending on the code, its rate, and the signal-to-noise ratio. The second application is a concatenated code designed for a long-haul nonlinear optical fiber channel where the inner code is a QC-LDPC code and the outer code is a spatially coupled LDPC code. In this case, the inner code is decoded using an adaptive WBP, while the outer code is decoded using the sliding window decoder and static belief propagation. The results show that the adaptive WBP provides a coding gain of 0.8 dB compared to the neural normalized min-sum decoder, with about the same computational complexity and decoding latency.  
belief propagation; neural networks; low-density parity-check codes; optical fiber communication 20252795
## 7 Cross-Layer Design Schemes
### Tightly Coupled--Multi-Layer Cross-Layer Schemes  
In a network with multiple nodes all contending for resources, if no arbitration is enforced, QoS constraints can be breached. The traditional solution to this problem is to use the transport layer to perform congestion control, find the best path via an effective routing algorithm at the network layer, and use of the MAC protocol to access the channel to give the best single hop performance [79]. However, this type of mechanism can never deliver optimal performance as the algorithms in the various layers are not optimized together. Joint optimization between the full protocol stack is the only way to deliver full network wide scheduling and congestion control. [PERSON] _et al._[91] presented a Cross-layer Module (XLM) that replaced the entire layered architecture by a single protocol where the objective is reliable communication with minimal energy consumption, adaptive communication, and local congestion avoidance. [PERSON] and [PERSON] [99] developed a similar scheme studying the performance bounds of cross-layer congestion control. XLM is governed by the Initiative Determination concept where the cross-layer protocol performs receiver-based contention, local congestion control, and distributed duty cycle operation to deliver efficient and reliable communications. Each node has the freedom to decide on participating in communication and initiate transmission by sending an RTS packet to indicate that it has a packet to send. Upon receipt the node will decide whether to participate in the communication. Results show that XLM outperforms the traditional layered protocol stack in terms of performance and implementation complexity. One of the major advantages of cross-layer design is implementation efficiency. In a traditional layered architecture, each layer has clear boundaries and packet overhead leading to computation delays due to the sequential handling of a packet through the protocol stack. Cross-layer Protocol (XLP) [100] extends XLM and merges the functionalities of traditional MAC, routing, and congestion control into a unified cross-layer module by considering physical layer and channel effects, avoiding the need for end-to-end congestion control. A summary of the protocols discussed above is shown in Table 5.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### U-Net Architecture  
The U-Net module utilizes an encoder-decoder architecture frequently used in segmentation tasks. The encoder path extracts hierarchical features from the input image, while the decoder reconstructs the image or generates high-level representations. Comprising multiple blocks, the encoder contains convolutional layers, which are followed by batch normalization and ReLU activation. As the blocks progress, they reduce the spatial resolution of the feature maps, capturing more abstract representations. At the end of each encoder block, max-pooling layers downsample the feature maps. Conversely, the decoder path employs transposed convolutions to enhance the resolution of feature maps, along with concatenation with corresponding encoder features via skip connections. This methodology enables the model to utilize fine-grained spatial information from earlier layers, improving prediction accuracy. In both the encoder and decoder paths, each convolutional block features two convolutional layers with 3 \(\times\) 3 kernels, followed by batch normalization and ReLU activation. Ultimately, the U-Net decoder path produces a high-level feature representation of the input image.
###### Abstract  
We report a direct tunable diode laser absorption spectroscopy (dTDLAS) instrument developed for NO2 concentration measurements without chemical pre-conversion, operated as an Optical Gas Standard (OGS). An OGS is a dTDLAS instrument that can deliver gas species amount fractions (concentrations), without any previous or routine calibration, which are directly traceable to the international system of units (SI). Here, we report NO2 amount fraction quantification in the range of 100-1000 umol/mol to demonstrate the current capability of the instrument as an OGS for car exhaust gas application. Nitrogen dioxide amount fraction results delivered by the instrument are in good agreement with certified values of reference gas mixtures, validating the capability of the dTDLAS-OGS for calibration-free NO2 measurements. As opposed to the standard reference method (SRM) based on chemiluminescence detection (CLD) where NO2 is indirectly measured after conversion to NO, titration with O3 and the detection of the resulting fluorescence, a dTDLAS-OGS instrument has the benefit of directly measuring NO2 without distorting or delaying conversion processes. Therefore, it complements the SRM and can perform fast and traceable measurements, and side-by-side calibrations of other NO2 gas analyzers operating in the field. The relative standard uncertainty of the NO2 results reported in this paper is 5.1% (\(k=1\), which is dominated (98%) by the NO2 line strength), the repeatability of the results at 982.6 umol/mol is 0.1%, the response time of the instrument is 0.5 s, and the detection limit is 825 nmol/mol at a time resolution of 86 s.  
gas analysis; laser absorption spectroscopy; dTDLAS; nitrogen dioxide (NO2); metrology 2021  
1
# Online SOPDT Model Identification Method Using a Relay
## 2 Description of the Method
### Measurement of the Time-Delay Constant  
In most online identification methods for SOPDT models, the dead time is calculated computationally on the basis of the measurements of other physical variables. If the model parameter \(\theta\) is treated as pure dead time, its measurement can be performed with high accuracy. After determining the steady-state gain, the hysteresis must be reduced, thus changing the state of the relay output from \(B\) to 0. This allows for the measurement of the dead time, which is equal to the time from switching the relay from the high to low state to the process response to this change. After returning to the steady state for a given operating point, the time that elapses from the negative edge of the control signal to the plant's response to this change is measured. This is illustrated in Figure 2. However, under industrial conditions, technical difficulties might appear during such measurements due to disturbances and measurement noise.  
On the other hand, the determination of the dominant time constants of inertia requires the phase lag resulting from the remaining terms of the plant dynamics to be obtained. If we are indeed dealing with a second-order plant with pure dead time only, or the model uncertainty is very small, its precise measurement will make it possible to obtain an accurate model.  
In real systems, the distinction between the time in which the plant's response is faint and the pure dead time depends on the adopted criterion. It can be difficult to make any generalizations about this distinction, but in the case of high-order plants, it is necessary. The determination of the time delay is made by zero-crossing detectors, except that this procedure is carried out during the measurement of the limit cycle parameters, leadingto results that differ from those obtained during the step response. This is because the measurement of the time delay continues until the output derivative \(y^{\prime}(t)\) crosses zero. If the value measured in this way is at least 30% greater than the dead time, it means that we are dealing with high-order inertia and this should be taken as the parameter \(\theta\).
## 2 Effect of Floating DC Bus on System Stability
### Definition of Hard Lower Bound for DC Load Voltage  
The analysis in Reference [33] introduced the stability problem of a DC floating bus. In particular, an islanded power system supplied only by batteries may be affected by voltage instability in the presence of a perturbation capable of moving the system state outside the BA. This issue becomes critical when the battery has a low SoC and, consequently, its voltage output is low. In this case, the BA shrinks as the bus voltage decreases. This limits the operating margin of the power system.  
To introduce the concept of hard lower bound for voltage stability, in this section, the case study was based on the data reported in Reference [33]. The adoption of per unit (p.u.) notation [34] allows performing a numerical continuation analysis [32,33], following the hypothesis of neglecting the presence of the CL (i.e., \(R_{L}=\infty\)). Indeed, the equivalent linear resistance \(R_{L}\) determines an increase in the damping factor, thus enhancing the system's voltage stability. Conversely, by neglecting CL, it is possible to assess the stability degradation in the worst case [35], thereby making the negative effect of the floating bus more apparent. The method used here to assess voltage stability relies on the evaluation of the basins of attraction (BAs) in the regions close to the stable operating point (Figure 3). Each stable operating point is defined by the couple of variables (\(v_{0}\), \(i_{0}\)), where \(v_{0}\) is the steady-state voltage on the capacitor (in p.u.), and \(i_{0}\) is the steady-state nonlinear current in the filter inductor (in p.u.). The progressive reduction in the battery voltage \(e\) (due to the battery SoC decrease) causes the shift of the equilibrium point toward the upper left-hand side of Figure 3 (i.e., lower \(v_{0}\) and higher current \(i_{0}=p/v_{0}\)), with a consequent shrinking in the BA. In particularly, it can be noted that the progressive reduction in BA area (which can be assumed as a measure of system stability) becomes significantly faster for \(v_{0}\) below 0.8 p.u. (refer to the area with a red boundary in Figure 3). Based on this consideration, it is possible to consider \(v_{0}=0.8\) p.u. as a sort of hard lower bound for the steady-state voltage on the load bus (blue basin of Figure 3). Clearly, this limit on \(v_{0}\) corresponds to a lower bound also for the battery voltage \(e\), whose value depends on the voltage drop in the filter resistance component. Consequently, for each set of input data (i.e., filter components, CPL power, and rated voltage of the CPL), it is also possible to define a lower limit for the battery SoC, using the voltage limits and the battery specifications.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 1 Introduction
### Multimodal Warning Systems and Related Theories
#### 1.3.2. Yerkes-Dodson Law  
While it is commonly believed that multimodal warnings can better facilitate drivers' response time as compared to unimodal warnings [37], some studies have discovered that shorter response time was found in bimodal warning conditions rather than trimodal warning conditions [29]. These findings are similar to the relationship between performance and arousal level as illustrated by the Yerkes-Dodson law (YDL). Researchers have proven that both individuals' arousal level as well as task difficulty influence individuals' task performance; when the task is neither too simple nor too difficult, the best performance is found when individuals' arousal is at a medium level [38]. Thus, the multisensory integration effect (MIE) could potentially peak only when the optimal number of applied modalities is used, similar to the trend as YDL suggested (see right panel Figure 1).