esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Optical Data Preprocessing  
The level of processing used for Landsat data was the Precision and Terrain Correction, L1 TP, which for the Canadian Arctic results in between one and two pixels of accuracy (30-60 m) [38]. Landsat data were converted to Top of Atmosphere (ToA) reflectance using the scaling factors stored in the image metadata and dividing by the cosine of the solar zenith angles.  
The level of processing used for Sentinel-2 data was the Level-1C, which offers an accuracy between 12 m for 95% of tiles [39]. Since the aim of this study is the characterization of coastal change, the quantitative retrieval of surface spectral information, such as biophysical parameters, was not necessary. As such, further processing to Level-2A, bottom of atmosphere reflectance, was not required.
## 5 Conclusions  
To further mitigate flood disasters, there is a need to regulate the downstream flood, as dams can only control the flood from upstream of the dam. In this study, we examined the feasibility of an innovative flood control scheme (IFCS) to mitigate the excess floodwater in Dongting Lake, which is located downstream of the Three Gorges Dam. The highest water level in a major flood is specially modelled and compared while using a 2D depth-averaged hydrodynamic model, i.e., MIKE 21 FM, which was applied to model the Dongting Lake. The following conclusions can be drawn from the current study:  
1. The numerical simulations agree reasonably with the observations overall with the NSE over 0.87, the maximum water level difference between the measured and simulated data is within 0.16 m at all selected stations, which indicates that the current MIKE 21 FM model can dynamically simulate the flooding process in Dongting Lake.
2. The effective flood control capacity could be greatly increased by using the IFCS due to the fact that the safe floodwater (small/middle scale flood) is not allowed to enter the inner lake. Therefore, the effective flood control capacity increased by 4.7 times, 2.8 times, and two times during the first, second, and third flood wave, respectively, after the application of the IFCS in Dongting Lake for the 1998 type flood, which is one of the largest floods in the 20 th century.
3. The operation of the IFCS plays a very important role in floodwater mitigation, which still needs further study to achieve the optimized water level reduction, especially in a complex system, which includes several gates in the flood control scheme (IFCS).
4. The IFCS also has great potential to increase the flood control capacity of the lake and thereby to mitigate the flood disaster in the flood-prone lake area according to the feasibility analysis for another large lake.  
Conceptualization, S.-Q.Y.; Formal analysis, Y.L. ([PERSON]) and U.K.; Supervision, C.J., S.-Q.Y., and M.S.; Validation, Y.L. ([PERSON]), B.D., and L.Y.; Writing--Original draft, Y.L. ([PERSON]); Writing--Review & editing, S.-Q.Y., C.J., M.S., and K.E.  
This research was funded by the China Scholarship Council, the National Natural Science Foundation of China (Grant No. 51839002) and the Natural Science Foundation of Hunan Province, China (Grant No. 2018 JJ3535).  
We would like to thank Dr. [PERSON] and [PERSON] for providing the Figure 2.  
The authors declare no conflict of interest.
## Numerical Simulations  
### Validation Test  
To confirm the accuracy of the present numerical schemes and methods for the mixed flow of exhaust smoke and wind, a CFD simulation of coaxial and confined jet flows was performed [17]. The mixed flow is related to the velocity ratio, i.e., \(Ru=U_{out}/U_{in}\), where \(U_{in}\) is the jet velocity from the circular pipe, and \(U_{out}\) is the co-flow velocity, as shown in Figure 1. Because of the axisymmetric shape, an axisymmetric boundary condition was applied at the bottom. The diameter \(D\) of the circular pipe was 11.5 mm, whereas the \(U_{in}\) and \(U_{out}\) were 10 and 0.4 m/s, respectively. Hence, the velocity ratio \(Ru\) was 0.04.  
Figure 2a,b show the velocity magnitude contours in the measuring section and entire computational domain, respectively. Based on the figures, the two different velocities resulted in a shear flow, and the flow fields were mixed. The axial velocity distributions obtained from the simulation at \(x/D=3\) were compared with those measured using two-dimensional particle image velocimetry [17]. Although a minor discrepancy was indicated at approximately \(y=0.02\), consistency was demonstrated between the results, as shown in Figure 3.  
Figure 1: Computational domain, grid systems, and boundary conditions for co-flow jet mixing problem.  
Figure 2: Contour maps of velocity magnitude in (**a**) measuring section and (**b**) whole domain for co-flow jet mixing problem.
The FEST-WB is a distributed hydrological model (Flash Flood Event-based Spatially-distributed-rainfall-runoff Transformations-Water Balance) developed since 1990 at the Politecnico di Milano university in Italy [26; 27]. The FEST-WB has been implemented for simulations of hydrological processes. Over many years, the model has been subject to several improvements including new modules, and refining some processes simulations previously described by simple empirical equations, whereas nowadays they are simulated based on more sophisticated physical and numerical solutions. The model applications range from irrigation management to flood forecast in real time. The forest component in this model has always been considered as a simple input parameter, and no feedback of hydrological forcing on vegetation were taken into account. Due to the importance of including such a component for the completeness of model simulations, within the framework of INNOMED project co-funded under the ERA-NET WaterWorks 2015 Call of the European Commission, the FEST-FOREST model has been implemented. FEST-FOREST is the new version of FEST-WB including FOREST module for the simulation of the forest growth. It is classified as process-based dynamic model that allows an appropriate simulation of forest growth coupling the ecological component with the hydrological component. This paper is organized as follows. Section 2.1 presents an overview of the FEST-FOREST model followed in Section 2.2 by the model application in the study area, the Bonis catchment in southern Italy. Section 3 describes the results of the hydrological (Section 3.1) and forest growth (Section 3.2) simulations in the study area and highlights the added value of his work compared to other similar studies in different areas of the world. Finally, Section 4 presents the conclusions.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Link Evaluation Model
#### 4.1.4 Normalization Module  
From the algorithm of the jitter accumulating module, we can find that the value of the accumulated result has a strong correlation with the length of the vector, denoted by \(\dim(\mathcal{A})\), and the level of the link latency. This brings difficulty on the judgement of link quality. The Normalization module adjusts this value to a notionally common non-dimensional scale using the formula as follows,  
\[\hat{\mathcal{A}}=\frac{\mathcal{A}}{L_{R}\times\dim(\mathcal{A})} \tag{6}\]  
With the help of this module, no matter what level of link latency is and how long the vector is, the evaluation result for links with same packet loss rates should be approximately within a same range.
## 6 Experimental Evaluation
### Evaluation Metrics and Baseline Approaches
#### 6.2.1 Overall Power Consumption  
Considering overall power consumption as an essential energy efficiency metric to reduce grid energy consumption, the total power consumed by the servers for the accepted VM request is formulated in Equation (7).
# An FCM-GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
## 3 Methodology
### Number of iterations: 100.  
After the templates were clustered, a portion of the templates in each category were taken as \"training samples\" into the GABPN to determine the weights and bias values for the category. Three phases were involved at the training stage. First, the initial weight and bias were optimized according the GA. Second, the forward propagation is conducted, in which the inputs (selected attributes with bias) were multiplied with weights (weights of bias are 1), summated, and transferred to the hidden layer. The results of nodes in the hidden layer were further processed by sigmoid function and also transferred to the output layer with the same procedure. Finally, the output of GABPN was compared with the accurate scrap rate, and the accuracy of the GABPN, represented with mean squared error (MSE), was evaluated.  
Subsequently, the backward pass which propagates derivatives (error between prediction and the actual value) from the output layer to hidden layers was conducted. The backward pass for a 3-layer BPN starts by computing the partial derivative for the output node (only one node here), and the error terms \(\delta_{j}\) of nodes \(j\) in the hidden layers can be calculated according to \(\delta_{j}=eW_{j}f^{\prime}(x_{j})\), in which \(e\) is error of the output node, \(W_{j}\) is the weight connecting node \(j\) to the output node, and \(f^{\prime}(x_{j})\) is the derivative of the sigmoid activation function with the input \(x_{j}\). On this basis, adjustments were made to the connection weights and bias to reduce the \(MSE\). Network-learning stops when the iteration is greater than a given number in this study.  
The trained GABPN was tested by the remaining portion of the templates in each category with the same performance indicator, \(MSE\). Finally, the GABPN was used to predict the scrap rate of new templates that \"completely\" belonged to the clustered category. However, complete assignment of template to only a category is usually impossible. When a new template order is coming, the selected attributes associated with the new template are recorded, and the membership belonging to each category is calculated. Then, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate for the new template.
## 5 Discussion  
### Biomimetic Behavior and Movements  
To make this analysis, the vertical jellyfish displacement has been taken as a reference, since it is the movement typically performed by the species, with which we can make a comparison. Figure 17 shows in the first column the results of velocity, displacement and acceleration of the developed jellyfish actuated with SMA, while the other three columns correspond to the research developed by [PERSON] in the study of morphology, to develop a model of propulsion called \"hydromedusa\" [51].  
The displacement made by the robotic jellyfish presents oscillations for the rise and fall with each contraction. The slight downward displacements are mainly generated by the weight and recovery time of the SMAs after heating to generate the contraction, while the ascending curves of the real jellyfish show great similarity, showing a similar decrease after contraction. The velocity curves are highly similar in all four cases; the soft robot reaches an average peak velocity of 2.54 cm/s, while the  
Figure 16: Thermal maps generated from the thermic measurements with the robotic jellyfish: (**a**) map with a thermal focus on the upper central part; (**b**) map with two thermal bulbs in the upper corners. Source: Author.  
Figure 17: (Column **A**) Comparison of movements and variables of robotic jellyfish developed, compared to jellyfish species: (Column **B**) Sarsia sp.; (Column **C**) Proboscidactyla flavicirrata; (Column **D**) Mitrocoma cellularia. Sources: Author (Column **A**). Column (**B–D**) [40].  
second, third and fourth reach 2.53, 1.95 and 1.24 cm/s, respectively; acceleration behavior in the same way in all cases presents a great similarity.  
On the other hand, the use of equations for CFD and model of bell selected gave satisfactory results, showing similar values for the acceleration value that is reflected with an acceleration of 3.8 cm/s\({}^{2}\) using the CFD and 3.38 cm/s\({}^{2}\) in the calculated values. This difference is due to the tentacle presence, because CFD shows that after each contraction, the tentacles generate an additional impulse because of the positive vortices generated at the ends. This means that tentacles contribute an 11.05% of the total acceleration.  
One of the methods aimed to minimize the wiring effect. To partially reduce the influence of the cables, an experiment was carried out by introducing plastic with air to improve buoyancy and reduce the effect of the cables; however, the results were not good, since it generated instability after each movement. For this reason, there is no buoyancy compensator; it is also important to indicate that the consumption of the robot is 0.185 W for the trajectory in Figure 17A.  
A future line of research has been proposed, which by means of a ZigBee/XBee module or similar allows the jellyfish to be completely wireless, which would eliminate the influence of the cables entirely.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 5 Analysis of Fatigue Life
### Analysis of Thermal-Mechanical Coupling Fatigue  
The heat of the mirror assembly increases with vibration loads. The S-N curve of the titanium alloy used in the flexible structure decreases significantly due to high temperature. Moreover, the fatigue limit of titanium alloy decreases due to thermal stress [11; 12]. Fatigue failure will occur under the influence of thermal stress and random vibration load. Therefore, estimating the fatigue life of structures in the temperature field is necessary to ensure the reliability of the structure. The maximum temperature of the assembly during vibration is about 330 K. Amend the S-N curve of the titanium alloy according to the temperature and calculate the fatigue life of flexible structures. The results show that fatigue life of the flexible structure meets the design index at 21,790,000 cycles. Compared with the fatigue life of random vibration only, the thermal-mechanical coupling fatigue decreases sharply.
# Assessment of Self-Supervised Denoising Methods for
## 5 Conclusions  
In this study, self-supervised denoising methods were used to tackle the challenge of enhancing esophageal speech using the Only-Noisy-Training method. For this purpose, two DCUNET-based architectures were trained in different experimental scenarios. Also, for comparison purposes and to calculate the metrics, the pre-trained VoiceFixer model was used to restore clean versions of the original ES wave files. In order to obtain different pre-trained models, four scenarios were realized in the experiments. In the first scenario, the models were trained with the normal English VBD database mixed with street music. The second one consisted of the ES database mixed with white noise for training. The VF database mixed with white noise was used in the third scenario. And in the last one,the training was performed using the ES database without any additions. After having the pre-trained models, we used mismatched conditions for testing. The results show that DCUNET-cTSTM outperforms the results of DCUNET when testing with an ES sound file. The testing with a VF wave file shows that the DCUNET-based methods remove some information because those parts of the signal were considered noises. To sum up, this study has proven the effectiveness of the ONT approach along with the DCUNET-cTSTM model in denoising ES sound files. Moreover, this approach has successfully preserved the speaker's identity and proven that we can dispose of having healthy target wave files. An improved system based on these methods is under investigation, aiming for an even better enhancement for ES.  
Conceptualization, M.A. and E.H.I.E.; methodology, M.A. and E.H.I.E.; software, M.A.; supervision, E.H.I.E. and M.C.; validation, E.H.I.E. and M.C.; visualization, M.A.; writing--original draft, M.A.; writing--review and editing, E.H.I.E., M.C., K.E. and J.D.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Interaction Effects  
Interaction effects help understand if the effect of one predictor variable on the outcome variable changes depending on the level of another predictor. To analyse interaction effects, a logistic regression model with interaction terms between the different features is developed. The findings showed that terminal cleanliness \(\times\) terminal seating (coefficient: 0.0894, \(p\)-value: 0.004) interaction is significant and positive, suggesting that the combined effect of these features on recommendations is greater than their individual effects. Terminal cleanliness \(\times\) food and beverages (coefficient: \(-\)0.1078, \(p\)-value: 0.000) interaction is significant and negative. This indicates that the combined effect of terminal cleanliness and food/beverage services negatively influences recommendations. This could be due to differing passenger expectations in these areas. Terminal seating \(\times\) food and beverages (coefficient: 0.1497, \(p\)-value: 0.000) interaction is significant and positive, implying that good seating arrangements combined with food and beverage services enhance the likelihood of recommendation. Terminal signs \(\times\) airport shopping (coefficient: \(-\)0.1577, \(p\)-value: 0.000) interaction is significant and negative, suggesting that these features might detract from the recommendation when considered together. Terminal signs \(\times\) airport staff (coefficient: 0.3195, \(p\)-value: 0.000) interaction is significant and positive, indicating that clear signage combined with helpful staff significantly boosts the likelihood of an airport being recommended. Food and beverages \(\times\) WiFi connectivity (coefficient: 0.0574, \(p\)-value: 0.046) interaction is small but significant and positive, showing that these features together slightly increase the likelihood of recommendation.  
The interaction effects analysis reveals that certain combinations of features can significantly impact the likelihood of an airport being recommended. For instance, the combination of terminal cleanliness with terminal seating, and terminal signs with airport staff, are particularly important. Conversely, the negative interactions, such as between terminal cleanliness and food and beverages, highlight areas where combined improvements are necessary.
# An Innovative Parabolic Trough Collector Design with a Twin-Cavity Receiver
## 3 Results and Discussion
### Optical Analysis Results  
This subsection refers to the results coming from the optical optimization of the PTC-TC as far as the geometry of the cavities is concerned. The optimization was based on three different parameters (\(\omega\), \(\varphi\), and q), while several different combinations were examined for achieving the desired result. The optimization results are presented in Figure 7. More specifically, the specific chart depicts five different efficiency curves, each one of which corresponds to a specific (\(\omega\)) value as a function of the (\(\varphi\)) parameter. These curves were plotted considering the (q) values that ensure the maximum optical performance in each case.  
Figure 5: Verification evidence for the PTC-TC design regarding the flow and heat transfer.  
Figure 6: Verification evidence for the PTC-F design regarding the flow and heat transfer.  
Figure 7 shows that the optical performance declines with the increment of the cavity angular aperture (\(\varphi\)). This happens because the lower the (\(\varphi\)) parameter, the higher the solar radiation entrapment and vice versa. Moreover, the optical performance shows the same behavior as the (\(\omega\)) parameter also increases. Thus, according to Figure 7, the best solution seems to be a twin cavity with \(\omega=30^{\circ}\) and \(\varphi=20^{\circ}\). However, a more detailed analysis revealed that in this case as well as in the scenario where \(\varphi=30^{\circ}\), the maximization of the optical performance was observed for only a specific (q) value, and thus there was not any sustainability. The same also happened in the case where \(\omega=20^{\circ}\). For these reasons, a (\(\varphi\)) of \(40^{\circ}\) was finally selected with \(\omega=30^{\circ}\) and q = 8 mm. This configuration showed an optical performance of 79.9%. As regards the PTC-F optical performance, the ray tracing results indicated an optical performance of 72.52%. In other words, the PTC-TC configuration seemed to exceed significantly the PTC-F in optical performance, providing an enhancement of 10.14%, which is remarkable.
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
###### Abstract  
The task of tracking cooperative satellites equipped with laser retroreflectors, by means of Satellite Laser Ranging (SLR), is an issue well described in the literature. The follow-up movement of the ground-based transceiver telescope behind an orbital object is based on the positional ephemeris data. The problem of controlling the follow-up motion of the telescope's mount mostly in the \(Az/EI\) configuration in this case boils down to the interpolation of the positional ephemeris data of the orbital object, which is the information input vector for the motion control system of the orthogonal and non-coupled axes of the propulsion system. In the case of tracking and determining the position of uncooperative objects (not equipped with retroreflectors), for which we can include rocket bodies and fragmentary elements, the task of keeping track of them becomes complex. The positional uncertainty of the ephemeris of uncooperative objects obtained mainly by means of survey radar acquisition requires the use of innovative solutions and complex control systems that enable the effective implementation of the tracking process. This paper presents innovative methods for the active control loop used in the SLR technique, consisting of dynamic motion corrections based on the passive optical acquisition with object recognition and analysis of the photon trace scattered from an orbital object.  
Satellite Laser Ranging (SLR); space debris; active control loop; trajectory tracking; predictive control +
Footnote †: journal: Sensors  
S
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
###### Abstract  
Ultrasound has been proven to be a valid tool for ranging, especially in water. In this paper, we design a high-resolution ultrasonic ranging system that uses a thin laser beam as an ultrasonic sensor. The laser sensing provides a noncontact method for ultrasound detection based on acousto-optic diffraction. Unlike conventional methods, the ultrasound transmitted from the transducer is recorded as the reference signal when it first passes through the laser. It can be used to improve the accuracy and resolution of the time-of-flight (TOF) by a cross-correlation method. Transducers with a central frequency of 1 MHz and diameters of 20 mm and 28 mm are used in the experiment. Five targets and a test piece are used to evaluate the ranging performance. The sound velocity is measured by the sound velocity profiler (SVP). The repeatability error of TOF is less than 4 ns, and the theoretical resolution of TOF is 0.4 ns. The results show a measurement resolution within one-tenth of the wavelength of ultrasound and an accuracy better than 0.3 mm for targets at a distance up to 0.8 m. The proposed system has potential applications in underwater ranging and thickness detection.  
Laser sensing; ultrasonic ranging; acousto-optic effect; cross-correlation 2019 9 1483 10.3390/app9071483