esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fluorescence Properties of Minor Compounds Present in Honey
Intensity of emission spectra is scaled and reported in arbitrary units. A derivative of caffeic acid, chlorogenic acid has also been found in monofloral sulla, acacia, and wildflower honey samples [17; 35; 55]. Its typical fluorescence emission is centered at 450 nm, by exciting with wavelengths in the intervals 270-290 nm and 320-340 nm (see Figure 7a). The o-coumaric acid (see Figure 7b), showing a maximum of emission around 500 nm (with \(\lambda_{\text{ex}}\) = 280 nm and 330 nm), was found in very small concentrations only in some of the honey samples investigated here (i.e., acacia, sulla, heather, and clover honey) [17; 47; 48]. The trans cinnamic acid, despite identification by analytical investigations in acacia and chestnut honey samples, has a very weak fluorescence [47; 48]. Among additional phenolic derivatives affecting the fluorescence of honey, 3,4-dihydroxybenzoic acid and sinapinic acid are responsible for a strong emission in the region between 320 and 460 nm, when exciting the sample with \(\lambda_{\text{ex}}\) in the interval 270-290 nm (Figure 8a). These phenolic acids were found in samples of acacia, sulla, chestnut, sunflower, heather, arbutus, and alfalfa plants [17; 47; 48]. Other polyphenols, such as the flavonoids isorhamentin, quercetin, hesperetin, naringenin, and luteolin, despite their presence in some of the investigated honey samples [17; 47; 48], are not fluorescent or present a very low fluorescence quantic yield. Quercetin compound was found in some of the honey samples and is characterized by an emission band centered at 520-530 nm, when excited at 330-350 nm. Figure 8: _Cont._  
Figure 7: FFF emission and excitation spectra of solutions of (a) chlorogenic acid in methanol (C = 0.029 mg/mL) and (b) o-coumaric acid in methanol (C = 0.041 mg/mL). Intensity of FFF spectra is reported in arbitrary units. Another class of minor compounds typical of honey is that of vitamins, including ascorbic acid (vitamin C), thiamine (vitamin B1), pantothenic acid (vitamin B5), niacin (vitamin B3), folic acid (vitamin B9), pyridoxine (vitamin B6), and riboflavin (vitamin B2) [8; 56; 57]. Ascorbic acid was found in some honey samples, but it is not fluorescent. Among fluorescence vitamins, riboflavin (vitamin B2) presents a typical emission/excitation profile with an emission band around 520-530 nm (see Figure 8b), pyridoxine (vitamin B6) has quite a sharp fluorescence peak centered at 380-390 nm (with \(\lambda_{\text{ex}}\) = 330 nm) (see Figure 8c), and folic acid (vitamin B9) has an emission band at 450 nm (with \(\lambda_{\text{ex}}\) = 330 nm) (see Figure 8d).
# Real-Time Monitoring of Passenger's Psychological Stress
## 2 Results
### Test Phase  
#### 2.2.1 User Interface  
The test phase took place two months after the learning acquisition, during a travel between the cities of Grenoble (France) and Brussels (Belgium) where our participant had to attend another meeting with partners. Throughout the journey, a homemade smartphone application continuously recorded peripheral sensor data from the same smart wristband that was used for model calibration. In the meantime, it used the personalized stress model to timely estimate and record participant's stress levels, with a 60-s time resolution Figure 1 shows four screenshots of algorithm's outputs on as many 1h time periods, displayed by means of the application user interface. For the sake of clarity, model outputs were clipped so that stress levels displayed on the GUI are contained in the [0 1] range, and the missing data were interpolated.  
Figure 1: Screenshots of the smartphone application user interface, taken the during test phase (travel from Grenoble to Brussels). On each screenshot, top chart shows the timely estimated stress levels (in red). Bottom chart displays the corresponding raw physiological signals: heart rate [bpm] in green, and skin conductance [\(\mu\)S] in orange. (**a**) First setback (7:20 to 8:20); (**b**) Airport shuttle (12:20 to 13:20); (**c**) Work in train (14:20 to 15:20); (**d**) Leisure in train (16:20 to 17:20).  
On each picture, the top chart shows the estimated stress levels, and the bottom chart shows the two main signals of interest: heart rate and the skin conductance level. During the time interval displayed in Figure 1a, an unfortunate setback happened at the working place a few hours before the shuttle departure: participant witnessed a server crash that might have compromised the main goal of the journey he was about to undertake. This resulted in a large peak of stress, which has been successfully recovered by the algorithm: it can be noticed in the middle of the top chart. The three other screenshots in Figure 1b-d are about the trip itself. The time interval considered in picture b takes place in the airport shuttle (total travel time: 83 min, mean stress reported by the user: 3/10). Picture c corresponds to the 1 st h by train (out of four), when the participant was working on his desktop (total working time: 2 h, mean stress reported by the user: 2/10). Figure 1d corresponds to his 3 rd hour in the same train, just after the participant had stopped working (total time remaining still: 2 h, mean stress reported by the user: 1/10).  
Finally, on the bottom charts of Figure 1, artefacts can be observed in the two displayed signals (heart rate and the skin conductance level). However, model outputs remained stable. This could be explained by some redundancy of information within the set of features and available signals: at a given time, the model was still able to assess a stress level in spite of isolated artefacts.
# Voltage Distribution on Transformer Windings Subjected to Lightning Strike Using State-Space Method
###### Abstract  
Transient analysis in power systems is essential for identifying deficiencies in the system, as well as for the protection and design of equipment. Transients can arise from natural events or network operations; in either case, they have the potential to cause significant damage to transmission lines, protection devices, generators, or transformers. This study examines a 20 kA, 1.2/50 us lightning strike on a distributed-parameter transmission line connected to a power transformer. The voltage distributions across the winding sections on the neutral grounded high-voltage side of a disc-structured power transformer were obtained using the state-space method. An equivalent circuit for the state-space model was also developed in the Alternative Transients Program-Electromagnetic Transients Program (ATP-EMTP), and the results from both methods were compared. Both approaches revealed that the voltage waveforms in the transformer's winding sections were consistent, with the voltage distribution decreasing linearly. Additionally, the voltage-current waves reached the transformer with a specific delay, depending on the characteristics of the transmission line and the location of the lightning strike. The impact of an increase in the grounding resistance value on the high-voltage side of the transformer on voltage distribution and peak voltage levels was examined. The proposed method effectively captures the voltage-current behavior of the transmission line and transformer windings during transient conditions. It is concluded that the state-space method serves as a viable alternative for transient analysis in power systems and can enhance the design of protection equipment and winding insulation studies.  
transmission line; lightning strike; power transformer; state-space method; transient analysis; voltage distribution Article
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Gravimetry-Based Methods  
While multispectral-based and microwave-based methods rely on remotely sensed estimates of quantities that are driven directly by irrigation (e.g., the soil water content or vegetation transpiration), gravimetric remote sensing may bear more indirect irrigation information. Specifically, gravimetric measurements have been used to derive so-called terrestrial water storage (TWS), which are temporally averaged (typically monthly) water mass change observations of anomalies of the total mass of water stored on and beneath the land surface. Temporal trends in TWS from GRACE data [104; 105], for instance, have been linked to heavily irrigated regions of the world caused by human-induced (irrigation) groundwater depletion (e.g., [106]).  
The largest caveat of gravimetric remote sensing, however, is its coarse horizontal resolution, which is about 300 km at mid-latitudes [107]. TWS retrievals, therefore, contain many other confounding factors (such as snow, surface water in lakes and rivers, etc.) that need to be removed when attempting to link TWS anomalies to groundwater depletion from irrigation.
## 6 Conclusions
* Amongst the cases studied, case (c), representing ducts embedded in the middle of the back sheet, irrespective of the cross-section, resulted in the highest rate of heat removal as suggested by the heat transfer rate flowing out from the duct \(\dot{q}_{out}\) as well as the cooling fluid temperature \(T_{out}\) at the exit of the duct. * Amongst the four duct cross-sections, the semi-elliptical duct placed in the middle of the back sheet was found to remove the heat the most within a given time. * Ducts located flush on the bottom or top on the inside of the back sheet exhibit similar cooling or heat transfer performance, with the duct flush on the top inside of the back sheet exhibiting slightly better cooling performance. * The heat flux distribution and heat transfer rates are directly proportional to the width of the contact area between the back sheet and the cooling pipe. * Amongst the configurations investigated, the semi-ellipse cross-section, configuration 3(b) in Table 2, yields the maximum increase in the cooling fluid temperature at the duct exit. * If it is desired to have the lowest possible back sheet temperature, then the triangular duct placed flush with the back sheet top, i.e., case 4(d) in Table 2, is seen to be the best candidate because it can result in an increase in the electric output power of the mono-crystalline PV modules by at least 8%. In view of the current study, a more detailed study involving the elliptical cross-section is planned that will focus on the eccentricity of the elliptical cross-section. Conceptualization, T.M., A.H. and F.S. ; methodology, T.M., A.H., F.S., A.J. and S.A.; software, T.M., A.H., F.S., A.J. and S.A.; validation, T.M., A.H. and F.S. ; formal analysis, T.M., A.H., F.S., A.J. and S.A.; investigation, T.M., A.H. and F.S. ; resources, T.M., A.H. and S.A.; data curation, T.M., A.H. and F.S. ; writing--original draft preparation, T.M., A.H., F.S., A.J. and S.A.; writing--review and editing, T.M., A.H., F.S., A.J. and S.A.; visualization, T.M., A.H., F.S. and S.A.; supervision, T.M., A.H., F.S. and S.A.; project administration, T.M. ; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data are contained within the article. The authors would like to acknowledge the support provided by Imam Abdulrahman bin Faisal University, Saudi Arabia. The authors declare no conflict of interest.
# Optimal Robust PID Control for First- and Second-Order Plus Dead-Time Processes
## 2 Control System and Design Objective
### Plant and Controller  
A controlled plant is expressed as an over-damped second-order plus dead-time system given by the following transfer function:  
\[P(s)=\frac{K}{(Ts+1)(aTs+1)}e^{-Ls}, \tag{1}\]  
where \(K\), \(L\), and \(T\) are the gain, the dead-time, and the time constant, respectively. Moreover, \(a\) denotes the ratio of two time constants, and its range is \(0\leq a\leq 1\). It is worth noting that with the specific choice \(a=0\), the FOPDT case is covered.  
This plant is controlled by a one-degree-of-freedom (1 DoF) PID control law defined as follows:  
\[U(s) = C_{1}(s)E(s)-C_{2}(s)Y(s) \tag{2}\] \[C_{1}(s) = K_{p}\left(1+\frac{1}{T_{i}s}\right),\] (3) \[C_{2}(s) = K_{p}\left(\frac{T_{d}s}{(T_{d}/N)s+1}\right),\] (4) \[E(s) = R(s)-Y(s),\]  
where \(U(s)\), \(Y(s)\), and \(R(s)\) are the control input, the plant output, and the reference input, respectively. In the PID control law, \(K_{p}\), \(T_{i}\), and \(T_{d}\) are the proportional gain, the integral time, and the derivative time, respectively. The derivative compensation just affects the feedback signal and is filtered by a first-order transfer function to prevent the noise effect, in which the range of filter constant \(N\) is usually from 5 to 33 [5]. Here, \(N\) is set to 10 without loss of generality. The design objective of the present study is to decide the PID parameters such that a performance function is minimized, subject to the prescribed robust stability.
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods
### Datasets  
The CP parameters were simulated from RADARSAT-2 quad-polarized data using the RCM simulator, which is provided by the Canadian Space Agency [12]. To develop two new wind retrieval models for RV- and RH-polarization data, we build a database consisting of simulated RV- and RH-polarized NRCSs from RADARSAT-2 fine quad-pol SAR images and collocated wind vectors measured by in situ buoys. The RADARSAT-2 quad-pol SAR images over the selected buoys are acquired. Then, these RADARSAT-2 images are converted to RCM CP mode SAR images by using the CP simulator. Finally, wind speed measured by buoy and the collocated simulated NRCS are paired. The distributions of NRCSs and wind speeds are shown in Figure 1.  
In this database, we have 267 RADARSAT-2 fine quad-pol SAR images, which are processed by a RCM CP simulator to re-construct CP mode images [1]. The results of simulated CP configurations are used as \"ground truth\" in this study. The RADARSAT Constellation Mission has several polarization configurations: linear mono-polarized, dual linear-polarized (HH/HV, VV/VH, or VV/HH); dual circular transmit-linear receive; and fully polarimetric [12]. The compact polarimetry SAR mode provides four polarimetric datasets, which are RV, RH, right circular transmit and right circular receive (RR) and right circular transmit and left circular receive (RL). In the medium resolution mode, the pixel spacings in azimuth and range directions are about 100 m and the associated noise floor is about \(-25\) dB [15]. Data from eight National Data Buoy Center (NDBC) buoys collocated with the SAR data are collected, at locations off the east and west coasts of Canada [16]. The buoy locations are shown in Figure 2. At each buoy, the mean wind speed (at 10 m reference height, hereafter \(U_{10}\)) and direction are measured by two sensors, averaged over 8-min periods and reported hourly. The wind  
Figure 1: Simulated compact-polarimetry (CP)-polarized normalized radar cross-sections (NRCSs) vs. in situ buoy-measured \(U_{10}\): (**a**) right circular-vertical (RV) polarization; (**b**) right circular-horizontal (RH) polarization.  
speeds observed by the buoys are converted to winds at 10 m reference height above the ocean surface using the Tropical Ocean and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) bulk flux algorithm [17], and the winds can be considered as neutral winds [6; 18]. The temporal separation between the SAR data and the buoy data is restricted to less than 30 min [11]. The distribution of months is shown in Figure 3. As the SAR is active microwave, the effects of weather and seasons are both almost negligible.  
We divide the collected data randomly into two data groups (2/3 and 1/3 of the total number). One contains 178 data which are used to generate the new models, and the other contains 89 data, reserved for model testing.
# Calibrating of a Simulation Model to Predict the Flexural Capacity of Pre-Stressed Concrete Beams
## 2 Materials and Methods
### Finite Element Modeling
#### 2.3.5 Model Calibration  
The calibration was performed with the two main elements that seek to address the most extreme situations of failure of an element: (a) a weakly reinforced situation where tensile failure of the reinforcement is expected (joist T18-1) and (b) a highly reinforced element, where rupture occurs due to concrete fatigue (joist TB35.5).  
The parameters used for the calibration were as follows:  
* Compressive strength. The initial compressive strength value was not modified in the tests, since, to calibrate the model, we used the actual values determined in the manufacturer's quality control process, involving the corresponding concrete compressive strength tests.
* Initial modulus of elasticity. In addition to the previous parameter, the model response requires a small calibration of the first part of the curve, which is controlled by the modulus of elasticity of the concrete and for which no real data were available to incorporate into the model. Thus, we analyzed rates of 28,000, 32,000 and 36,000 N/ mm\({}^{2}\).
* Tensile Strength and Energy Fracture. These characteristics of concrete affect the tensile strength of the elements. The tensile strengths analyzed were 2.8, 3.5 and 4.2 MPa, while the fracture energy values analyzed were 7.0, 9.5 and 13.0 GPa.
* Pre-stressing losses. The loss of the initial pre-stressing force was estimated according to the theoretical calculation results in simulations with a resistance higher than the actual resistance, both in highly reinforced and weakly reinforced elements. Thus, the pre-stressing losses were analyzed for 5 rates: 0% (no losses), 25%, 35%, 45% and 55%.
* Bond strength. As well as the response of the curve, another factor that determines the failure of the elements is the strength of the bond of the reinforcements to the concrete. Given the difficulty of experimentally determining this parameter, as in the previous case, the simulation of this factor was performed by changing variables with values of 4, 8 and 12 MPa.
## 4 Data  
In this study, we use VISSIM simulation to generate traffic trajectory data for the circular network presented in Figure 3a, which is not on scale and it is for illustration purpose only. The traffic network consists of three links, and the length of link is set to \(L\) meters. Note that there is diverge point and a merge point in Figure 3a denoted by 1 and 2, respectively. We use connectors to join the road links in VISSIM. The traffic state data of the connectors obtained from the VISSIM were not very accurate due to the following dilemma. Connectors in VISSIM (and also in other similar simulation environments) are not dimensionless points but rather a small segment with physical length. The traffic characteristics of the short connectors are then presented by VISSIM as the average along the short distance. To achieve a higher accuracy for the average value, in theory, the connectors should be as short as possible to mimic the dimensionless points so that the data at the beginning, the ending, and throughout a short segment should be the same or very close to that. On the other hand, if segments in the simulation are too short, it may lead to computational issues such as frequent zero volume and missing observation regarding speed and density. Such a dilemma shows that there is no easy way to get the instantaneous values of speed, density, and volume of a short connector accurately. Such challenges impact the fulfillment of flow conservation equations at the merge and diverge points. In this study, we balance such challenges by keeping the connectors to be reasonably short and then try to maintain the flow conservation by using the segment data of the links at their beginnings and endings.  
The traffic flows are assigned to the starting points of links \(L1\), \(L2\), and \(L3\), and the traffic is let to run in the network. The obtained vehicles' trajectory data are used to estimate the traffic speed, density, and flow. On a stretch of road with length \(L\) and time interval \(T\), the time-space is discretized into a uniform grid with cell size \(dT\times dL\). The discrete area to estimate the traffic states is illustrated in Figure 3b. The total number of cells in the time and space dimensions are \(N_{T}\) and \(N_{L}\), respectively.
###### Abstract  
In recent years, Faster-than-Nyquist (FTN) transmission has been regarded as one of the key technologies for future 6G due to its advantages in high spectrum efficiency. However, as a price to improve the spectrum efficiency, the FTN system introduces inter-symbol interference (ISI) at the transmitting end, whicheads to a serious deterioration in the performance of traditional receiving algorithms under high compression rates and harsh channel environments. The data-driven detection algorithm has performance advantages for the detection of high compression rate FTN signaling, but the current related work is mainly focused on the application in the Additive White Gaussian Noise (AWGN) channel. In this article, for FTN signaling in multipath channels, a data and model-driven joint detection algorithm, i.e., DMD-JD algorithm is proposed. This algorithm first uses the traditional MMSE or ZFinear equalizer to complete the channel equalization, and then processes the serious ISI introduced by FTN through the deepearning network based on CNN or LSTM, thereby effectively avoiding the problem of insufficient generalization of the deepearning algorithm in different channel scenarios. The simulation results show that in multipath channels, the performance of the proposed DMD-JD algorithm is better than that of purely model-based or data-driven algorithms; in addition, the deepearning network trained based on a single channel model can be well adapted to FTN signal detection under other channel models, thereby improving the engineering practicability of the FTN signal detection algorithm based on deepearning.  
FTN signaling; deepearning; detection; multipath; data-driven and model-driven combination; neural network adaptability