esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of the Damping Ratio of Undisturbed Offshore Silty Clay in Eastern Guangdong, China
## 3 Results and Analysis  
### Damping Ratio Calculation Method  
The viscous damping ratio \(D\) (%) obtained from the resonant column test is derived from the free vibration decay curve. This curve is measured by an accelerometer mounted on the drive plate of the resonant column. A sinusoidal wave is applied to the soil, after which the excitation is stopped, and the results of the free vibration are measured.  
As shown in Figure 6, the damping ratio \(D\) (%) from cyclic direct simple shear tests is calculated based on the dissipated energy \(W_{D}\) and total input energy \(W_{s}\) over one cycle. The relevant equations are as follows:  
\[D=\frac{1}{4\pi}\frac{W_{D}}{W_{S}} \tag{1}\]  
\[W_{D}=\oint{(\tau_{cy})(\tau_{cy})d\gamma} \tag{2}\]  
\[W_{S}=\frac{1}{2}\tau_{cy}\tau_{cy} \tag{3}\]  
Figure 6: Typical Soil Hysteresis Loop and Damping Ratio Illustration.  
For cyclic direct simple shear tests under symmetric loading, the damping ratio is directly calculated using Equation (1). In cases with asymmetric loading (i.e., with a nonzero average shear stress), cumulative strain develops during cycling, causing the stress-strain hysteresis loops to remain open. In this study, we processed the test data using Equation (4) [26], and Figure 7 shows the resulting curves. Once the stress-strain hysteresis loops are obtained via this method, Equation (1) is used to calculate the damping ratio.  
\[\gamma_{i,c}=\gamma_{i,\rho}-\frac{i}{n}\Delta\gamma_{p} \tag{4}\]  
In the equation, \(\gamma_{i,c}\) is the strain data after processing, \(\gamma_{i,o}\) is the raw (original) strain data, \(i\) is the \(i\)th data point in one loop, \(n\) is the number of data points in the current cycle, and \(\Delta\gamma_{p}\) is the accumulated shear strain generated during that cycle.
# VBF Event Classification with Recurrent Neural Networks at ATLAS's LHC Experiment
## 3 A Recurrent Neural Network Using Jets to Solve an Event Level Problem
It was introduced by [PERSON] and [PERSON] as a solution to the vanishing gradient problem [10]. In order to improve the performance of the prediction across the input sequences, these networks have inner cells each with three gates, an input, an output and a forget gate; in practise, these gates control the flow of the information and, therefore, the output of the network. Finally, GRU based networks also work to address the short-term memory problem of RNN models. They have two gates in the inner cells, a reset and an update gate. Similar to the gates of the LSTMs, the reset and update gates control how much and which information to exploit. The semi-leptonic diboson event topology results in a clear experimental signature: a vector boson (\(W\) or \(Z\)) decays in a lepton pair and the other in a quark pair. The leptonic decay is used to trigger the events recorded by the ATLAS experiment, and it allows heavily reducing the huge amount of fully quantum chromodynamics (QCD) events. The jets recorded in the event bring information about both the hadronic decay of one of the two reconstructed bosons (\(W/Z\to qq^{\prime}\)) and the additional jets expected according to the vector-boson fusion production mechanism. Concurrent interactions (pile-up) and the remainder of the proton collision (underlying event) produce additional jets in the event rather than the ones coming from the Feynman diagrams (hard processes), and the final jet multiplicity in reconstructed events is relatively high. A simple experimental representation of a generic event is shown in Figure 4. The two blue arrows represent the clear signature of the boson decaying leptonically (\(Z\to\ell\ell\) in this example), while the red triangles represent all the jets reconstructed. If the event was produced via VBF, four jets coming from the hard process could be, in principle, recorded in that sequence, and they bring useful information. In order to improve the categorisation of the observed events in terms of the GGF and the VBF categories, a machine learning approach was introduced. Indeed, it was observed that for this binary classification problem, a standard data analysis, using jet identification algorithms and cut-based selection, would result in a very inefficient confusion matrix (CM) [12]. Machine learning approaches have been proven to outperform if used in looser phase spaces and using low-level input variables. Therefore, the traditional jet pair selection (aimed to identify the jets coming from the VBF selection) and the selection cuts were removed. Furthermore, the lowest level variables available at the event level were considered as input to the ML algorithm; in particular, the full four-momentum of the jets for the sequence was used. Given the variable jet multiplicity per event, an approach involving recurrent neural network layers was adopted to build a DeepNN.
## 4 Flight Control Algorithm
### Flight Control System  
The quadrotor is a typical underactuated system with only four independent inputs less than the degrees of freedom, so only the desired position and desired yaw angle can be directly tracked. The desired roll angle and the desired pitch angle are determined by the known ones. The flight control system of a quadrotor is described in Figure 13.  
The pixel values \(u_{T},v_{T}\) are the centroid of the target position in each image frame, and \(S_{T}\) is the scale change of the target, which is given by the proposed KCF tracker. The state of the quadrotor \((\Theta,\omega)\) expressed in global NED frame is given by the autopilot using an Extended Kalman Filter (EKF). The desired position \(\left[\begin{array}{cc}x_{d}&y_{d}&z_{d}\end{array}\right]^{T}\) and the desired yaw angle \(\psi_{d}\) are given by the gimbal system in the global NED using the above-mentioned tracking strategy. A cascade proportional-integral-derivative (PID) controller is designed to individually control the 6 DOF motion of the quadrotor. The attitude control loop is implemented on the microcontroller, while the outer loop for position control is implemented on the onboard computer. All PID gains have been preliminarily tuned in hovering flight tests. The outputs of the cascade PID controller are the desired force \(f_{d}\) and the desired torque \(\tau_{d}\), which are applied to the UAV body. The mixer gives the desired angular velocity of each motor to the electronic speed controller (ESC), which is expressed as a Pulse Width Modulation (PWM) signal.  
It is worth mentioning that the thrust value is not only determined by the desired position, but also by the takeoff weight of the quadrotor platform. Thus, the height control can be considered as two parts: a slightly changed base value for hovering control and a fast controller for position control.  
Figure 12: Schematic diagram showing the tracking strategy. (**a**) description of tracking the lateral LOS angle based on a pure pursuit method; (**b**) description of tracking the longitudinal LOS angle based on PN guidance law.  
Figure 13: Hierarchical control architecture of the quadrotor for target tracking.  
When the target is selected, the quadrotor will keep the distance relative to the target based on the estimation of its scale changes in the image frame, which is shown in Supplementary Materials.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 3 Duplication Suppression and Re-Queuing
### Re-Queuing to Compensate for Delivery Failure
#### 3.3.1 The Selection Criterion for Re-Queuing Frames
We present a simple example when \(\alpha=1\), \(C_{i}^{mode}=4\), and \(C_{i}^{max}=10\), as indicated in Figure 7. Assume that \(DR_{i}(2)=0.25\) and \(DR_{i}(3)=0.45\) and that node \(i\) receives frame \(F_{i,j}\) twice from its adjacent nodes at a certain time, then \(\hat{n}_{i,j}\) (\(=\lceil 2.5\rceil=3\)) is smaller than \(C_{i}^{mode}\) (=4), and so node \(i\) considers re-queuing frame \(F_{i,j}\). However, if node \(i\) receives a third duplicate frame of \(F_{i,j}\) before the observation time expires, then \(\hat{n}_{i,j}\) increases to 5 (= [4.5]) and \(\Delta_{i,j}>0\), so the frame is not re-queued. Lastly, it should be noted that once the frame is determined to be re-queued, it is rearranged in the transmission buffer according to the sequence number so that the re-queued frame can be delivered preferentially.
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 2 State of the Art
Redundancy of sensors is one option to avoid false alarms in detection and improve the quality of the measurements collected. There is the idea of long-lasting networks, with lower maintenance, and in this sense, high quality--but low cost--microphones gather together good accuracy in the measurement stage and also lower incidences while working real-time. Finally, connectivity and ensuring that no information is lost in the data gathering to enable the central node to take decisions and activate warnings if needed is a crucial focus nowadays. In this sense, the reader can find an exhaustive review of the last trends of WASN design in [20]. Several examples can be found in [21] for the well-known project Sounds of New York [22; 23], or [24], which maximizes network lifetime using coverage sets by means of designing its schedule. After the work conducted by [PERSON] et al., another contribution has appeared in the literature, which is worth mentioning for its similarity to the network proposed in this paper: [PERSON] et al. [9] present a highly-scalable wireless acoustic sensor network that can monitor urban environments by recognizing a given set of sound events or classes. The nodes are based on Raspberry Pi (RPi) devices that do not only have the capability of recording the environmental sound but also to recognize different sound events by means of Convolutional Neural Networks (CNNs). Running the classification algorithms locally in the node has not modified substantially the results of accuracy compared to cloud-based solutions, proving that the local computation of deep learning is a good and feasible option for WASNs. Despite [9] having done all their tests in an outdoor environment, their proposal could be adapted for an indoor scenario. For this purpose, the connectivity would have to be reviewed and improved, in order to guarantee the coverage in all the rooms inside a house, a hospital, an university or a library (among others). The proposal of the WASN presented by [PERSON] et. al. in [9] will be further compared to the approach presented in this paper to detail the essential contributions of the proposed WASN, especially orienting the comparison to three items: (i) low cost of the devices, (ii) acoustic quality of the microphone and raw data collection and (iii) ensured connectivity between nodes.
## 4 Results and Analysis
### Thermal Mechanical Event Tests  
Event tests are used to determine the effects of bias conditions and temperature on solid state devices over time. They simulate the devices' operating condition in an accelerated manner, primarily for device qualification and reliability monitoring. The test consists of three 24 h cycles from 30-63 \({}^{\circ}\)C and three 4 h cycles of \(-\)33 \({}^{\circ}\)C to replicate conditions found in the storage of the device in ambient air conditions. This was repeated for three 24 h cycles of 30-43 \({}^{\circ}\)C and a further three cycles of 4 h at \(-\)21 \({}^{\circ}\)C, again carried out under ambient air conditions for an operational device. The results are shown in Figure 8a-c.  
As can be seen from Figure 8a that the thermal mechanical event stress at 63 \({}^{\circ}\)C is maximized with a displacement of 2.82 mm on the auxiliary power supply sockets. As a result, the auxiliary sockets may experience, during normal operation, some increase in the contact resistance, which in turn may cause voltage to drop, thereby impacting the boost  
Figure 8: The impact of a thermal mechanical event on the PCB. (**a**) Event displacement at 63 \({}^{\circ}\)C. (**b**) Event strain at 63 \({}^{\circ}\)C.  
function of the power board. Some high traces of stress are also observable on the edges of small smoothing connectors on the left side of the heatsink. In contrast, the remainder of the PCB (multi-layered board of FR4 laminate) and the components (heatsink, toroidal inductor, large smoothing capacitors, and surface mounted devices) remain within the safe regions of stress (<2.7 mm).  
In Figure 8b, the traces of thermal mechanical event strain at 63 \({}^{\circ}\)C are minimal around most parts of the PCB, except for the maximum displacement observable at the bases of the power supply and various signal sockets and the smoothing capacitors. However, no component failure was reported. It is, however, recommended to carry out condition monitoring tests after any such thermal mechanical event to determine any anomaly within the power switching function.
# Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm
## 6 Experiments, Results and Discussions
### Comparison to State-of-the-Art Approaches  
We compared the best feature (64 CH and 25 FRFE, then reduced to four PCs) and the best classifier (Jaya-FNN), with seven state-of-the-art approaches (SVM [15], BPNN [16], LDA [21], GNN [22], FSCABC-FNN [25], SVM + WTA [28], and FSVM + WTA [28]), on the basis of ASR of 10 \(\times\) 10-fold SCV, which were used in reference [25; 28]. Table 7 shows the comparison results. The overall ASR is defined as the average ASR of three categories of teas.  
Table 7 shows that our approach achieves ASR of 97.3% \(\pm\) 0.4% on green tea, 98.9% \(\pm\) 0.3% on Oolong tea, and 97.6% \(\pm\) 0.3% on black tea, respectively. The overall ASR is 97.9% \(\pm\) 0.3%, ranking first among all algorithms. The second best algorithm is \"FSVM + WTA\" [28] with overall ASR of 97.77%. The third one is FSCABC-FNN [25] with overall ASR of 97.4%. The fourth one is SVM + WTA [28] with overall ASR of 97.23%. The GNN [22] method ranks fifth with overall ASR of 96.0%. The LDA [21] approach ranks sixth with overall ASR of 96.5%. Finally, the two worst algorithms, BPNN [16] and SVM [15], yield the least overall ASR of 95%.  
In addition, our method only used four reduced features, less than BPNN [16] of eight, SVM [15] of five, LDA [21] of 11, GNN [22] of eight, FSCABC-FNN [25] of 14, and SVM + WTA [28] of five.
## 6 Conclusions  
Building upon the GNSS/INS/OD combination positioning, this paper proposes a lane-constrained positioning method integrating odometer mileage calculation and laser radar lateral distance measurements. The constrained positioning results are used to update the measurements, achieving fusion between lane-constrained positioning within the tunnel and INS/OD combination positioning. First, the paper introduces a lateral distance measurement method inside tunnels based on laser radar. By constraining and extracting point clouds, the closest distances from the laser radar to the walls on both sides of the tunnel are calculated. Based on the horizontal rotation angle of these points, the left and right attributes of the walls are further determined. Additionally, by assuming a constant total width of the tunnel, the effects of interference from dynamic vehicles, emergency parking spaces, emergency exits, and other structural change scenarios on lateral distance measurements are resolved, enabling continuous lateral distance measurements. Furthermore, the positioning points calculated from odometer mileage and lateral distance are fused with the INS/OD combination positioning. On the one hand, this approach replaces GNSS measurement updates with positioning measurement points, effectively suppressing the divergence of the INS/OD errors. Simultaneously, the real-time calibration errors estimated by the odometer are fed back into the odometer mileage calculation, enhancing the accuracy of lane-constrained positioning. Through the fusion of these two positioning models, errors between different positioning models are mutually compensated and suppressed, thereby enhancing the accuracy and robustness of tunnel positioning.  
The vehicle test results conducted at speeds of around 60 km/h inside tunnels demonstrate that the lane-constrained GNSS/INS/OD combination positioning model significantly improves the positioning accuracy of vehicles in long tunnels compared to the GNSS/INS combination and GNSS/INS/OD combination alone. Through a statistical analysis of 61,001 positioning points collected along a 4.6 km tunnel, the average absolute error of lateral positioning was found to be 0.294 m. In terms of longitudinal accuracy, compared with the precision at the tunnel exit (approximately 9.2 km) and the RTK fixed solution, the calculated longitudinal error at the midpoint of the 4.6 km tunnel is less than 0.6 m.  
Formal analysis, H.Z.V.; methodology, H.Z.; writing--original draft, H.Z. writing--review and editing, X.Z. 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 acknowledge the tunnel administration for providing the test environment.  
The authors declare no conflict of interest.
# Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
## 2 Materials and Methods
### Experimental Setup  
Batch experiments were conducted over 11 d in 1 L flasks, with a working volume of 900 mL. Different experimental conditions were tested (Figure 1): (i) a negative control test (C\(-\)) consisting only of 1 L of aquaculture wastewater; (ii) an assay with _C. vulgaris_ in aquaculture wastewater (AW) in triplicate; and (iii) a positive control test (C+) with _C. vulgaris_ in the modified OECD medium, in duplicate.  
The optical density at a wavelength of 680 nm (OD680) was measured daily with a spectrophotometer (Spectroquant Prove 300, Merck, Darmstadt, Germany) to evaluate the microalgal growth. The initial biomass concentration in the assays in terms of dry weight (dw) was 93.0 \(\pm\) 2.7 mg\({}_{\text{dw}}\) L\({}^{-1}\). The pH and temperature were quantified daily using a pH meter (Consort's C6010, Brussels, Belgium). The defined pH was 7.53 (pH measured in the effluent on day 0), being adjusted daily to this value. The flasks were kept at an ambient temperature of 18.1 \(\pm\) 1.4 \({}^{\circ}\)C. The light was continuously supplied (light:dark ratio of 24:0) by a light-emitting diode (LED) panel with a light intensity of 77.8 \(\pm\) 4.6 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\). A radiometer (HD 2102.2, Delta OHM, Caselle, Italy) was used to determine the light intensity. Agitation and CO\({}_{2}\) were ensured by means of air pumps (Sicce Airlight 3300, Pozzoleone, Italy), delivering 1.7 L min\({}^{-1}\) of atmospheric air flow rate.  
The samples were collected throughout the study with different periodicities according to the objective. Daily samples were taken from each flask to determine the kinetic parameters. Meanwhile, to evaluate the intake of nutrients, samples were collected on days 0, 1, 2, 4, 7, 9, and 11, and centrifuged (Himac CT6E Centrifuge, VWR, Amadora, Portugal) for 10 min at 4000 rpm and stored at \(-\)20 \({}^{\circ}\)C. The quantification of nutrients TN, N-NO\({}_{3}\), P-PO\({}_{4}\) and COD was performed using the previously described methods.  
At the end of the experiment, the cultures were centrifuged at 20 \({}^{\circ}\)C for 10 min at a speed of 12,000 rpm. The resulting biomass was stored at \(-\)80 \({}^{\circ}\)C and, finally, lyophilised. Finally, the lyophilised biomass was macerated with a mortar and pestle to obtain a powder and to ensure cell rupture so that the subsequent extraction of proteins, carbohydrates, lipids, and photosynthetic pigments was facilitated. The extraction assays were done in triplicate. The proteins were quantified through the Lowry method [27]. Carbohydrates were quantified using a colourimetric method [28]. The lipids content was obtained by a modified Bligh and Dyer method [29]. Finally, the pigments were analysed in accordance with the method described by [PERSON] et al. [30] and [PERSON] [31] with some modifications.
## 4 Discussion  
### Main Cropland Use Transitions Causing GPP Loss in Guangdong Province  
China's non-agriculturalization process has experienced rapid development followed by gradual stabilization [38]. Non-agriculturalization, which sacrifices cropland, poses a significant challenge to food production [39]. In China, two-thirds of urbanization has directly contributed to cropland loss, with 37% of the lost cropland being high-quality [40]. Our study results are similar, showing that from 1991 to 2020, non-agriculturalization caused a GPP loss of about 34.1 million tons, which is significantly higher than the GPP loss of 21.5 million tons caused by cropland abandonment, with a difference of about 12.7 million tons. This indicates that cropland non-agriculturalization remains the dominant factor affecting food production (Figure 11). Using the \"400 kg per capita food security line\" proposed by the FAO to quantify food loss, we find that the annual loss could meet the needs of approximately 4.6 million people, equivalent to the population of Zhongshan in Guangdong province. Notably, from 1991 to 2020, the per-unit area production potential of abandoned cropland was generally lower than that of non-agriculturalized cropland, indicating lower quality of abandoned cropland during this period. This is because abandonment typically occurs in hilly or mountainous areas with poor natural conditions, such as northern Guangdong, where cropland is fragmented, slopes are steep, soil fertility is low, and agricultural infrastructure is weak, severely restricting regional grain production capacity [41]. Compared to flatlands, hillside cropland demands greater labor input, increasing costs and reducing marginal profits. This makes cropland abandonment more likely and decreases the likelihood of its reuse after abandonment [42]. By contrast, non-agriculturalization often occurs alongside urban expansion. Cropland in these areas have been cultivated for a long time, are conveniently located, and have fertile soil. Only in 2001, 2017, and 2018 did the production potential per unit area of supplemented cropland exceed that of the reduced cropland.  
Figure 10: Change in spatial mismatch index from 1991 to 2020.
## 4 Discussion
### Limitations and Future Research
Some studies did not report the ethnicities of participants (eHealth users) within their sample, which means some groups of people may not be well represented. Therefore, findings may not accurately represent the needs and responses of under-reported ethnic groups. Future studies should aim to enhance the inclusivity and representativeness of participant samples to contribute to a more robust and generalizable knowledge base within the context of transplantation. Lastly, many studies focused on mHealth (\(n=22\)) and telehealth (\(n=19\)) as digital interventions for transplantation. Emerging digital interventions (e.g., AI, virtual reality, augmented reality, wearables) did not receive much attention. Considering the growth and importance of these emerging technologies for further improving patient care, and outcomes and streamlining healthcare processes in innovative ways, it is suggested that future studies investigate emerging eHealth interventions and measure their impact to help health facilities deploy the most beneficial solution to improve patient outcomes.