esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Parametric Study
### The Effect of Lateral Boundary Distance  
In groundwater calculations affected by excavation dewatering, the distance of lateral boundary conditions represents the external recharge condition for the excavation's dewatering area, significantly impacting its effectiveness. To investigate this influence, Figure 10 illustrates the effect of different lateral boundary distances on the depth of water level drawdown at \(z_{\rm D}=0.67\) near the excavation pit when dewatering reaches stability. It can be observed from the figure that, when other parameters remain unchanged, with the increase in the lateral distance, the supply from the constant-head boundary becomes farther away from the excavation. This results in a greater drawdown of the water level inside the excavation, thus leading to improved dewatering effects. Conversely, as the lateral distance increases towards the excavation, the dewatering effect diminishes. Therefore, to meet specific drawdown requirements, an increase in the pumping rate or embedding depth of cut-off walls becomes increasingly necessary. In other words, when the lateral distance to the excavation is closer, the excavation is more easily replenished from external sources, necessitating a greater pumping rate to achieve the desired drawdown depth.  
Figure 10: The influence of different lateral boundary distances \(R_{\rm D}\) on the groundwater drawdown as the pumping time approaches the steady state (\(t_{\rm D}=\infty\)).  
Figure 9: The influence of the penetrating depth ratio \(n_{1}\) on the dimensionless drawdown at different locations for \(x_{\rm D}=1.0\), \(1.1\), \(1.2\) and \(1.3\) along the top of the aquifer (\(z_{\rm D}=0\)).
## 4 Validation Through Sea Trials
### Processing and Analysis of Sea Trial Data with Added Ocean Noise  
After processing and analyzing the data from the three groups of trials, we can conclude that, under conditions where noise interference is not significant, the decoding performance of the four reception methods is relatively similar during low-speed operation. During high-speed operation, the decoding performance of WDE-MSS deteriorates significantly, while the performance of the other three reception methods remains relatively comparable. In situations with significant acceleration variations, only CCF-MSS is able to maintain good decoding performance, while the performance of the other three reception methods declines substantially. When there is significant noise interference and high-speed operation, only CCF-MSS can maintain good decoding performance. In summary, CCF-MSS exhibits superior resilience to speed variations, acceleration, and noise interference. The following section will process different portions of the three groups of sea trial data, incorporating measured ocean noise, to supplement the aforementioned analysis results.  
Figure 16: (**a**) is the CFO compensation estimated by CCF-MSS, (**b**) is the CFO compensation estimated by BDS output speed, (**c**) is the CFO compensation estimated by FDE-MSS (third group).  
Figure 17: (**a**,**c**) is despreading correlation peaks of symbols 21 and 31 by CCF-MSS, (**b**,**d**) is despreading correlation peaks of symbols 21 and 31 by FDE-MSS (thirteen frame of third group).
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods
### Fracture Network Simulation
#### 2.4.2 Input Data Acquisition  
For the microfracture network analysis, 5-mm-thick disks were cut from the 16 specimens perpendicular to the drillhole axis, and polished after impregnation with UV fluorescent epoxy. The specimens were then illuminated with refracted UV light and photographed with an Olympus DP73 camera mounted on an Olympus BX41 microscope with 1.25\(\times\) magnification. The microscope was used in reflected-light mode with a 100-Watt U-LH100 HG mercury vapour lamp as the light source and an Olympus U-MWBV2 filter cube installed (excitation range between 400 and 440 nm).  
Images were processed using the Trainable Weka Segmentation plug-in of the ImageJ-based Fiji environment [31], which allowed the images to be simplified and reduced to two types of pixels: fractures and host rock. Examples of a fluorescent image, segmented image, and then skeletonised image are shown in Figure 3. The images were further processed in MATLAB, and then geometric parameters of the microfracture networks were extracted from the images using an algorithm that was developed for this purpose (Figure 4). The algorithm detects the branch points of the intersecting fractures, and based on the dip of the fractures to a reference line, it divides them into separate linear objects. This allows the end and centre points of the individual fractures to be detected. Based on the data sets of these points, the algorithm calculates the length and orientation of the fractures. The geometric parameters of a fracture set, such as the fracture length index and fractal dimension, were then determined as shown in Section 2.4.1. A basic overview of the process workflow is shown in Figure 5.  
Figure 3: Example of (**a**) a fluorescent image of the microfracture network; (**b**) the resulting segmented image; and (**c**) a close up of the skeletonised image. Adapted from [PERSON] et al. [5; 13].  
Figure 4: Examples of: (**a**) schematic skeletonized image; (**b**) branch point detection of the intersecting fractures; and (**c**) division to individual objects. Identification of the end points (marked with yellow) and centre points (marked with orange). Adapted from [PERSON] et al. (2020).  
Figure 5: The microfracture network parameterisation process workflow and steps taken, including the used algorithms. Adapted from [PERSON] et al. (2020).
## 3 Routing Protocols for UWSN
### Regional Sink Mobility (RSM) and Vertical sink Mobility (VSM)
#### 3.7.2 Vertical Sink Mobility (VSM)  
Data transmission of VSM is depicted by Figure 13 which shows that the whole network area is divided into 10 equal parts. Sensor nodes are placed arbitrarily over the whole volume. Three mobile sinks are employed for the whole volume of the area with a predefined distance from each other to maximize the coverage area. These sinks take a new position in every round in such a way that they maintain this predefined distance from each other. When one round ends sink one take a new position by traveling a preset distance in X or Y axis. Sink two traverse the same distance in same direction of the same coordinate to maintain that predefined distance. Similarly Sink three take position depending on the position of the sink two. To transmit data packet, every node calculates its distance with the sink and neighboring nodes. If it finds one or more sinks within its transmission range, the node directly transmit to the sink. If all of the sinks are out of the range, the sending node tries to find a neighboring node who has a sink within its range. In this case, the neighboring node transmit the received data from sending node to the sink. But unfortunately, if there is no eligible neighboring nodes even, then the data packet has to be discarded.  
Both RSM and VSM either transmit directly or by two hop transmission method to the sink. This improves the throughput, the data received rate and initial network stability of both the schemes and have better performance than SEEC. Throughput of VSM is twice and that of RSM is thrice of the throughput of SEEC. But these two schemes possess some limitations also. In both the schemes every node take part in transmission, which eventually decrease the residual energy. So the energy of the nodes decrease fast causing their death. Death of the nodes results in a network hole disrupting the network stability. Stability of SEEC is not that prominent at the beginning compared to RSM and VSM but after 800 rounds, SEEC shows more stability than RSM and VSM. The low residual energy causes the death of the nodes earlier which affects the network lifetime of both RSM and VSM. SEEC has longer network lifetime than these two schemes. Even though RSM and VSM gives high performance in throughput and data received rate, their network stability and network lifetime is less than that of SEEC.
## 3 Results and Discussion
### Dry Matter Content and Water Activity  
The average content of dry matter in the fresh apple was 16.00 \(\pm\) 0.01%. A similar result was obtained by [PERSON] et al. [44], who obtained a value of 15.10 \(\pm\) 0.80% for a raw apple (variety 'Idared'). Figure 3a shows the impact of US pre-treatment time and US power applied during drying on the dry matter content of hybrid dried apples (Figure S2a). Dry matter content changes in the apple tissue were linked to US pre-treatment, which occurred in the water surrounding it. Drying and pre-treatment with the US resulted in a significantly increasing dry substance content (to above 90%). The response surface plot showed that the highest dry matter content was at the average US pre-treatment time (20 min) and the highest US power applied during drying (200 W). The significant influence of the US power during drying on the dry matter content results was confirmed by a two-factor analysis of variance (\(p<0.05\)). [PERSON] et al. [44] and [PERSON] et al. [45] found that extending the time of US pre-treatment from 10 to 30 min resulted in a higher content of dry matter. Changes occurring in plant tissue after US pre-treatment may be explained by transferring soluble substances from dry matter to the water environment, in which the US processing is conducted [19].  
Figure 3b shows the impact of US pre-treatment time and US power applied during drying on the water activity of hybrid-dried apples. The water activity of dried apples ranged from 0.24 to 0.31. This means that the obtained materials were microbiologically stable because all samples were below the water activity of 0.6, which prevents the microorganism's growth [46]. The lowest water activity values were obtained for the average US pre-treatment time. A correlation between water content and water activity was noticed--the lowest water content was also recorded for materials after the average US pre-treatment time. A significant effect of the ultrasonic pre-treatment time on the water activity content was found by the Pareto graph (the relationship was square, Figure S2b). [PERSON] and [PERSON] [47], using US pre-treatment with a frequency of 40 kHz for 20 min and 30 min for pineapple prior to convective drying, found a significant decrease in water activity only when the US pre-treatment was given to pineapple slices for 20 min. In turn, [PERSON] and [PERSON] [48] studied the effect of US treatment with a frequency of 28 kHz for 10 min,  
Figure 3: Effects of the US treatment time and the US power applied during drying (**a**) on the dry matter content (d.m.) of hybrid dried apples and (**b**) on the water activity (\(a_{w}\)) of hybrid dried apples. Blue dots on the figure shows research data.  
20 min, and 30 min for quince before freeze-drying. Although they showed an important decrease in water activity as a result of the US application, they noted that the duration of pre-treatment did not significantly affect this parameter.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 1 Introduction
Apart from optical data, recent studies have started exploiting the potential of SAR (synthetic aperture radar) data to map different agricultural irrigated areas ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). The assumption established for using SAR data relies on the fact that radar remote sensing is sensitive to the water content of soil due to the increase in the dielectric constant with the increase of the soil water content. In fact, several studies have reported that the SAR signal is highly affected by soil moisture content ([PERSON] et al., 2017; [PERSON] et al. time series. Recently, deep learning (DP) techniques [32; 34; 35] have shown that neural network models are well adapted tools to automatically produce land cover mapping from information coming from both optical [36] and radar [37] sensors. The main characteristic of these models is the ability to simultaneously extract features optimized for image classification. However, only few studies have started exploiting the potential of S1 time series for land cover classification using the DP method. In [PERSON] et al. [37] the authors proposed to leverage DP to perform winter quality mapping over a zone in West France. In [PERSON] et al. [31] another DP approach was proposed to perform land cover mapping considering several agricultural classes. Concerning the joint exploitation of radar/optical satellite images based on deep learning techniques, some approaches were recently proposed considering really precise tasks such as image simulation [38] or change detection [39]. Unfortunately, the same opportunity has not been yet fully exploited in the context of land cover mapping. This study proposes an innovative approach to map irrigated areas at plot scale using the Sentinel-1 and Sentinel-2 (S2) time series. The potential of S1 and S2 acquired at high spatial resolution and high revisit time for irrigation mapping did not yet receive enough attention by the scientific committee. The approach is based on the use of S1 and/or S2 time series combined with statistical and mathematical functions such as the principal component analysis (PCA) and the wavelet transformation (WT) to map irrigated areas. The novelty of this study resides in eliminating the ambiguity between irrigation and rainfall through the comparison between the SAR backscattering signal of each plot and that of the grid (10 km \(\times\) 10 km) including this plot. To map irrigated areas, the PCA and WT transformation were applied to the S1 time series prepared over a study site located in west of Catalonia, Spain. The PCA was also applied to the NDVI time series derived from the S2 images. Then, random forest (RF) and the convolutional neural network (CNN) approaches were used to build up classification models in three different scenarios: The first using only S1 data, the second using only S2 data, and the third using both S1 and S2 data. Finally, the two classification approaches (RF and CNN) were inter-compared.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 3 Materials and Methods
### SCP-VO Method
 \tag{14}\]  
where \(\delta I(u,v)\) represents the intensity difference between the adjacent images at the position \((u,v)\). Eventually, the prediction of the feature pixel in the next frame can be predicted as below. According to the matched feature points between consecutive frames, the camera's motion can be estimated by minimizing the photometric error. \[(u,v)_{k}=(u,v)_{k-1}+\bar{\eta} \tag{15}\]  
As the spherical image \(\tilde{\mathcal{S}}_{L}\) is divided into six subregions, the feature points may shift between regions, leading to the \"cross-domain\" phenomenon, particularly near the north and south poles where subregions intersect. Assume that the coordinate of the feature point in the subregion \(\tilde{\mathcal{S}}_{RR1}\) is \((u_{t},v_{t})\) at time \(t\), and the predicted coordinate can be calculated as \((u_{t+1},v_{t+1})\) based on Equation (15). If \((u_{t+1},v_{t+1})\) exceeds the boundary of \(\tilde{\mathcal{S}}_{RR1}\), the corresponding spherical coordinate \(\tilde{P}\) in \(\tilde{\mathcal{S}}_{L}\) can be calculated based on the Scaramuzza camera model. Then, project \(\tilde{P}\) onto the SCP image, and identify in which subregion the projected pixel resides. The projected pixel represents the location of \((u_{t+1},v_{t+1})\) after \"cross-domain\" and participates in optical flow tracking with the newly extracted feature points in the next frame. As the pixel correspondence between \(\tilde{\mathcal{S}}_{L}\) and subregions remains fixed, a link table can be generated in the preprocessing stage and retrieved during the practical stage. This approach eliminates the motion prediction procedure when exceeded pixels cross the subregion boundary, thereby enhancing the efficiency of tracking such boundary-crossing pixels.
environment and combines it with SFC routing [8]. In order to overcome the network constraints, we developed a customised virtual switch by the use of P4 [9] in order to support a new SFC packet header based on Network Service Headers (NSH). Accordingly, we aim to verify that this implementation fulfils the requirements we have developed in our previous work.  
Section 2 summarises the related work to this research. Section 3 presents the operational context under which the developed architecture was designed. Sections 4 and 5 present the architecture and implementation, while Section 6 gives a verification of the presented scenarios for a closing demonstration. Through defining three episodes in this scenario, we seek to highlight how the elements presented in this paper are supporting a secure SFC implementation in NFV.
## 3 Loss Calculation
### Iron Loss Online Calculation  
Figure 5a shows the equivalent circuit model with the equivalent iron loss resistance of PMSMs in the \(d\)-axis and \(q\)-axis. Compared with the general equivalent circuit model, this circuit model is connected with the iron loss equivalent resistance in parallel.  
The iron loss can be expressed as:  
\[P_{Fe}=\frac{3R_{c}\Big{(}\frac{i_{cd}^{2}+i_{cq}^{2}}{2}\Big{)}}{2}=\frac{3 \omega_{e}{}^{2}\left(\psi_{f}+L_{d}i_{md}\right)^{2}}{2R_{c}}+\frac{3\omega_{ e}{}^{2}\ L_{q}{}^{2}i_{mq}{}^{2}}{2R_{c}}, \tag{6}\]  
where \(R_{c}\) represents the iron loss equivalent resistance; \(i_{cd}\) and \(i_{cq}\) are the iron loss current in the \(d\)-axis and \(q\)-axis, respectively; \(i_{md}\) and \(i_{mq}\) represent the magnetization current in the \(d\)-axis and \(q\)-axis, respectively.  
However, in the conventional equivalent resistance model, the thermal resistance of the stator and the rotor is not distinguished. In the estimation of the motor temperature, the rotor iron loss and the stator iron loss belong to different nodes. Therefore, the equivalent iron loss resistance is regarded as the parallel connection of the stator iron loss equivalent resistance and the rotor iron loss equivalent resistance, respectively:  
\[\frac{1}{R_{c}}=\frac{1}{R_{cr}}+\frac{1}{R_{cs}}, \tag{7}\]  
where \(R_{cr}\) and \(R_{cs}\) are the stator iron loss equivalent resistance and the rotor iron loss equivalent resistance, respectively.  
The models of the equivalent iron loss separation for the stator and rotor are shown in Figure 5b.  
The stator iron loss power, \(P_{Fe}\), and rotor iron loss power, \(P_{Fe}\), of the motor can be expressed as:  
\[P_{Fe}=\frac{3\omega_{e}{}^{2}\left(\psi_{f}+L_{d}i_{md}\right)^{2}}{2R_{cs}} +\frac{3\omega_{e}{}^{2}\ L_{q}{}^{2}i_{mq}{}^{2}}{2R_{cs}}, \tag{8}\]  
Figure 5: Dynamic equivalent circuit of PMSM in \(d\)-axis and \(q\)-axis: (**a**) considering iron loss; (**b**) iron loss separation.  
\[P_{Fer}=\frac{3\omega_{e}{}^{2}\left(\psi_{f}+L_{d}i_{md}\right)^{2}}{2R_{cr}}+ \frac{3\omega_{e}{}^{2}\,{L_{q}}^{2}{i_{mq}}^{2}}{2R_{cr}}. \tag{9}\]  
Under no-load conditions, the iron loss current and the magnetization current become 0. According to Equations (8) and (9), the relationship between the equivalent iron loss resistance and the speed can be expressed as:  
\[\begin{array}{l}R_{cs}=\frac{3\omega_{e}{}^{2}\psi_{f}{}^{2}}{2P_{esc}}\\ R_{cr}=\frac{3\omega_{e}{}^{2}\psi_{f}{}^{2}}{2P_{cr}}\end{array} \tag{10}\]  
With the help of the finite element simulation method, the iron loss of the motor at different speeds can be simulated and calculated. According to the calculation result and Equation (10), the iron loss resistance of the motor can be estimated. After obtaining the equivalent iron loss resistance, the stator iron loss power and rotor iron loss power can be calculated online according to Equations (8) and (9).
## 4 Validation
### Simulation Input
#### 4.2.2 Ship Performance Data  
The considered maritime traffic data covers ships representing some 16 different models. For each ship model, as per Figure 5, the achievable speed in ice is determined in terms of ship model-specific hv curves representing three different modes of operation: (a) IB assistance at distance, (b) operation in a brash ice channel, and (c) independent operation in level ice. All hv curves were determined within the research project WINMOS II [26] based on ice resistance formulas and design particulars of the corresponding real-world ships.
## 2 Materials and Methods
### Data Reduction and Statistical Analysis  
All waveforms were imported, processed, and quantified in the Origin 2018 software (OriginLab Corp., Northampton, MA, USA). Data from experiments performed at different experimental conditions (shock wave intensity, headform orientation, or helmet type) were pooled together in 3 subsets according to blast intensity. Multiple comparison two-tailed \(t\)-test with a post-hoc Bonferroni correction was performed, and \(p<0.003\) was considered statistically significant. All data are presented as mean and standard deviation.  
Principal component analysis (PCA) was performed in RStudio 1.2.5 using R version 3.6.3 [34]. The pressure waveform characteristics (peak overpressure, rise time, duration, and impulse) recorded by the headform sensors were first reduced to non-dimensional amplification factors: their respective values were divided by corresponding incident pressure characteristics. In the next step, the amplification factors were natural log-transformed and subjected to PCA.
# Incremental Formula-Based Fix Localization
## 6 Related Work
### Automatic Program Repair  
Automatic program repair (APR) [35; 36; 37; 7] is a hot research topic in software engineering currently. These techniques try to provide the developer with actual patches that can make the buggy program pass a given test suite, which it originally fails. Automated program repair techniques usually start by using a fault localization or a fix localization [37; 38] to identify a subset of code elements at which a patch can be applied. The effectiveness of the fix localization task is critical important to the effectiveness, as well as the reliability of automatic program repair [39; 40]. The fix localization components in the semantic-based APR approaches (such as Angelix [37] and Nopol [36]) share the same objective with our approach that is finding angelic execution paths that make the failing test case to be a pass. Our method differ to these techniques in several points. The angelic fix localization in Nopol find angelic values only for conditional expression, they assume a single modification, and they do not use solver. Our approach similar to the angelic forest extractor component in the Angelix, as they find angelic values for both assignments and conditional expressions. However, our method produce angelic execution path by modifying minimal locations, while Angelix does not constraint the size of the fix locations.
# Tailorable and Broadband On-Chip Optical Power Splitter
## 5 Conclusions  
A 1 \(\times\) 2 tailorable and broadband beam splitter with low insertion loss is proposed for the silicon-on-insulator platform. The designed structure is based on 1 \(\times\) 2 interleaved tapered waveguides with a coupling length of 15 \(\upmu\)m, and the power split ratio can be determined by the asymmetric gap size between the upper and lower output channels. The relation between the split ratio, R, and the upper gap size, d\({}_{1}\), is extracted from the simulation results with a fixed lower gap size of 150 nm. With this equation, a tailorable beam splitter of a desired coupling ratio can be designed. The proposed beam splitter scheme has most of the performance requirements of on-chip optical beam splitters and can be the most promising on-chip optical power divider in various photonic applications of classical and quantum photonic integrated circuits.