esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Permanent Facility for Altimeter Calibration
### Regional Models and Reference Surfaces
For the crossover analysis, heights have been compared for various altimeters when they happen to fly within a short period of time (i.e., 1-3 days) at the same location at sea. Figure 23: (**Left**) Airborne surveys have been conducted in PFAC using a DeHavilland Twin-Otter aircraft owned by the Swiss Federal Office of Topography (a) equipped with an array of four GPS receivers, (b) an inertial measuring unit, (c) an Optec Laser profiler sensor, and (d) a LaCoste Romberg Sea/Air-gravimeter (University of Bergen, Norway. (**Right**) The locations of observations made for conductivity, temperature, and density around the Gavdos made via the research vessel “Philia” of the Hellenic Center for Marine Research. Figure 22: GPS buoy experiment conducted in the coast of Crete: (**left**) the boat track, and (**right**) a floating buoy with its GPS dragged by the ship. Transponder results for Jason-2, Jason-3, and Sentinel-3A have been produced at the CDN1 Cal/Val site, while along the same orbit and cycle but 4-7 s apart, calibration with sea surface techniques is determined with the Gavdos Cal/Val facility. The following section presents the latest calibration results for several altimetric missions employing the sea-surface calibration.
## 6 Conclusions  
In this study, TSEB-SM and a temporal upscaling method were used to produce spatiotemporally continuous daily ET data with a spatial resolution of 1 km across the Heihe River Basin for a lengthy time period from 2000 to 2020, combined with ground-based observations and other ET products for comparison and validation. The results were as follows:  
1. The performance of the three ET products varied at different stations in the Heihe River Basin (in the order of TSEB-SM, PML-V2, and SEBAL ET). At Arou station, the R\({}^{2}\) of the three products was 0.79, 0.76 and 0.22, respectively, with RMSE of 0.76 mm/d, 0.78 mm/d, and 0.88 mm/d, respectively. At Daman station, the R\({}^{2}\) values were 0.78, 0.73, and 0.34, respectively, and the RMSE values were 0.57 mm/d, 0.62 mm/d, and 0.74 mm/d, respectively.
2. According to our findings, there are significant spatial differences in ET in the Heihe River Basin, with the annual total ET in the study region ranging from 100 to 750 mm/year. The midstream irrigated oasis zone showed the largest yearly cumulative ET (about 700 mm/year), while the downstream Gobi Desert region showed the least ET (about 100 mm/year).
3. From the results of the TCH analysis, the TSEB-SM and PML-V2 ET models showed substantially smaller relative uncertainty than the SEBAL ET model. Most of the relative uncertainty values for TSEB-SM ET were distributed between 50 and 70%, those for PML-V2 ET were mainly in the range of 70-90%, and those for SEBAL ET ranged from 60 to 70% and were predominantly distributed in the upstream. Generally, TSEB-SM ET outperformed PML-V2 ET and SEBAL ET in arid and semi-arid regions at the watershed scale.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16030462/s1](https://www.mdpi.com/article/10.3390/rs16030462/s1)]([https://www.mdpi.com/article/10.3390/rs16030462/s1](https://www.mdpi.com/article/10.3390/rs16030462/s1)).  
This paper is a collaborative work by all of the authors. Conceptualization, L.S.; Data Curation, S.T.; Formal Analysis, S.T.; Methodology, S.T. and L.S.; Supervision, S.T., L.S., L.Z. and G.Z.; Writing\(-\)Original Draft, S.T.; Writing\(-\)Review and Editing, S.T., L.S. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (42071298) and the Anhui Province's University Science Project for Distinguished Young Scholars (2022 AH030025).  
We gratefully acknowledge all people who participated in the EC measurements, and we have shared the measured data on the platform of the National Tibetan Plateau Data Center ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/))).  
The authors declare that they have no conflicts of interest to disclose.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 4 Biomass Pretreatment to Decrease Recalcitrance
### Principal Pretreatments
#### 4.1.4 Steam Explosion Pretreatment  
This pretreatment, being recognized as one of the most efficient processes for lignocellulosic biomass from hardwood and herbaceous, consists of treating the biomass at high pressure using water vapor and at high temperature for a short period of time and then quickly depressurizing, which generates a disruption of the biomass with the partial elimination of the lignin [15; 71]. During the process, due to the acidic condition, organic compounds such as acetic acid are generated, causing the formation of acetyl groups, which allow an autohydrolysis of the hemicelluloses and a partial delignification, increasing the exposure of the cellulose to the enzymatic hydrolysis that follows [15; 186]. It can also be used in two stages for enhanced recovery of assimilable sugars, first under milder conditions to recover hemicelluloses and then under stronger conditions to destabilize the cellulose and recover higher percentages of glucose [185]. The advantages of steam explosion pretreatment are that it has a lower environmental impact and a lower production of toxic compounds, than those of acid and alkaline processes [186]. On the other hand, this pretreatment is less effective for softwoods, as they have a low content of acetyl groups, making the use of catalysts necessary, e.g., chemical compounds such as SO\({}_{2}\), H\({}_{2}\)SO\({}_{4}\) and CO\({}_{2}\) have been used to impregnate the softwood before pretreatment [71; 186]. Nevertheless, this has the disadvantages of requiring more time for impregnation and generating greater formation of inhibitors and higher degradation of sugars [185; 217]. Treatment pressure has a direct effect on reducing sugars yields, with values above 60% _w/w_[48].
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### The Machine Learning-Based Modelling
#### 3.6.2 Model Construction  
Construction of the ML model mainly involves the optimization, or tuning, of the hyperparameters. Hyperparameters are parameters that control the learning process of a ML model, the optimal set of hyperparameters to be used varies by case as it is dependent on the algorithm and the dataset involved. [PERSON] et al. (2021) [PERSON] et al. (2021) remarked that hyperparameter tuning plays a significant role in the performance and the predictive ability of an ML model. The tuning of hyperparameters is a process of trial and error, which can be done either systematically (e.g., grid search) or randomly. In this pilot study, the performances of each set of the hyperparameters considered were assessed by a five-fold cross-validation with the input dataset. The cross-validation technique reduces the bias and variance introduced by the random partitioning of the input dataset ([PERSON] et al., 2021), a five-fold cross validation involved the key steps below.  
Figure 8: Resampling of Data in the Pilot Study.  
1. Shuffling of the input dataset in a random manner.
2. Splitting of the shuffled dataset into nine groups in a stratified manner.
3. From (2), the selection of seven groups of the split data as a training dataset to fit the model.
4. An evaluation of the trained model with the validation dataset (i.e., the remaining two groups of split data) based on the area under the receiver operating characteristic (ROC) curve.
5. Repetition of (3) and (4) five times.  
Based on the two feature sets formed (i.e., FS1 and FS2 in Section 3.5.2), as well as the three ML algorithms and three combinations of NMRR durations to be taken into considerations, a total of 18 analysis cases have been identified in Table 6. For each of the analysis cases, the hyperparameters were tuned with the corresponding set of preprocessed data based on the same workflow. Afterwards, the model was formed by training using the entire input dataset without splitting.  
Scikit learn packages were used for the implementation of the Decision Tree and Random Forest algorithms, whereas the XGBoost package was adopted for the XGBoost algorithm.
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 5 Evaluation
### Complexity Analysis  
#### 5.5.1 Time Complexity  
The time complexity of our algorithm includes attribute community layout \(O(T|V_{\text{C}}|^{2})\), node layout \((T|V|^{2})\), where \(|V|\) is the number of nodes, \(|V_{\text{C}}|\) is the number of communities, and \(T\) is the number of iterations. Because the number of communities in the network is much smaller than the number of nodes, the total computational complexity of FDAC is derived as \((T|V|^{2})\). Table 5 shows the running times of our method for different data sets.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Standard Deviation of Edge Length**} & \multicolumn{3}{c}{**Maximum of Edge Length**} \\ \hline  & **Ours** & **FR** & **linLog** & **FA2** & **Ours** & **FR** & **linLog** & **FA2** \\ \hline
**VisPCNet** & 80 & 60 & 99 & 100 & 324 & 247 & 510 & 476 \\
**RCNet1** & 95 & 79 & 102 & 109 & 331 & 204 & 477 & 503 \\
**RCNet2** & 90 & 74 & 113 & 93 & 351 & 202 & 489 & 498 \\
**OMNet1** & 100 & 65 & 102 & 111 & 397 & 219 & 486 & 496 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metric comparison of edge lengths.  
Figure 8: The result of attribute community layout under the attribute ‘working years’. (**a**) FR layout; (**b**) LinLog layout; (**c**) ForceAtlas2 layout; (**d**) layout of our method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Node Number** & **Edge Number** & **Time** \\ \hline
**OMNet1** & 52 & 140 & 0.18 s \\
**RCNet1** & 176 & 425 & 0.56 s \\
**VisPCNet** & 513 & 1890 & 3.8 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Running times of our method.
# Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature
## 4 Results
### Discussion  
The literature overview presented in this section summarizes the most outstanding subjects in hyper-heuristics. We must remark that this is the first study to use a text mining approach to analyze hyper-heuristic-related literature up to the date this manuscript was submitted.  
The analysis we conducted involved different text mining methods previously discussed. Text mining played a crucial role in understanding the issues and keywords presented in the hyper-heuristic-related literature in this review. We analyzed 963 English hyper-heuristic-related publications, consisting of journal and conference articles in their final publication stage. In these documents, the most relevant terms include \"optimization\", \"scheduling\", \"genetic algorithm\", \"evolutionary algorithms\", \"timetabling\", and \"combinatorial algorithms\". Based on these terms, we extracted the problem domains preferred for developing and testing hyper-heuristics and the main techniques used for generating hyper-heuristics for such domains.  
Our findings show a preference for evolutionary algorithms to solve optimization problems when using hyper-heuristics [44,45]. Among the evolutionary algorithms, authors seem to prefer genetic algorithms to produce hyper-heuristics for domains such as vehicle routing problems [46], scheduling [15], and school timetabling [18]. Recently, many researchers have used particle swarm algorithms in hyper-heuristic applications [47,48]. The findings also unveil the interest in hyper-heuristics for different variants of scheduling problems, such as education [49], job shop [50], waiting time variance [51], and nurse rostering problems [52].  
We now discuss the major areas identified from the systematic literature review methodology. These areas are structured according to the seven areas identified through our cluster analysis.
## 4 Use Case and Simulation of the Proposed System  
### PMIPv6/AAND--Use Case  
Figure 11 describes a use-case and simulation topology of the projected design. It is supposed that the handover of inter-technology is carried out from Physical Interface 1 (PI_1) to Physical Interface 2 (PI_2) at the same time as the MN communicates with the CN via PI_1.  
The Home Network Prefix (HNP1) has the source and destination address of the concerned packets. These packets which are present in the HNP1 are transported through PI2 to CN. When the packets are received by MAG2, the packets are forwarded to the MAG2 routing table. If there is no HNP1 related routing entry in the routing table, the packets will be discarded or routed to default. The HNP1-related routing entry must therefore be maintained in the MAG2 binding update list (BUL). Here, every MAG is aware of the HNPs allocated to the MN prior to the transfer of inter-technology. The second reflection is the operation of ND in the PMIPv6 domain. This reflection relates to the multiple virtual interfaces of the architecture.
## 2 State-of-Art Review
### The Effect of Varying RFA Incorporation Ratio on Concrete Performance
#### Modulus of Elasticity  
Several investigations found a relationship between the modulus of elasticity of RFA concrete and its compressive strength. Based on these results, the authors of this study have derived another equation (Figure 7). There is a gradual increase of the modulus of elasticity of RFA concrete when its compressive strength increases for normal-strength concrete, but for high-strength concrete, the graph shows that there is only a slight increase when compressive strength increases. Hence, the regression model in Table 1 was established.  
Figure 6: Effect of incorporation of RFA in concrete on compressive strength over time.
## 2 Materials and Methods
### Data Processing  
To detect a kick event, data coming from the accelerometer were processed and the Fraden-Neuman algorithm was applied [23]. This algorithm was introduced for the detection of QRS complexes in electrocardiographic (ECG) signals and it is based on applying suitable thresholds on the amplitude and the first derivative of the signal to be analyzed. The choice of a low computational complexity algorithm is advantageous for future embedded implementation and real-time operation. In this work, the thresholds were adapted to the accelerometer signals to detect the kicks of the athletes.  
In the first step, the input signal \(x(n)\) was rectified so that:  
\[y0(n)=|x(n)| \tag{1}\]  
It is worth noting that \(x(n)\) means a generic input signal without any reference to a particular component (\(x\)-\(y\)-\(z\) axis). Indeed, the algorithm was independently applied to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Athlete Number** & **Gender** & **Age (years)** & **Height (m)** & **Weight (kg)** & **BMI** \\ \hline
1 & M & 35 & 1.84 & 74.8 & 22.1 \\
2 & M & 24 & 1.75 & 65.7 & 21.5 \\
3 & M & 26 & 1.75 & 65.2 & 21.3 \\
4 & M & 24 & 1.75 & 68.8 & 22.5 \\
5 & F & 26 & 1.66 & 53 & 19.2 \\
6 & M & 31 & 1.77 & 71 & 22.7 \\
7 & M & 28 & 1.75 & 66 & 21.6 \\
8 & M & 19 & 1.78 & 68 & 21.5 \\ \hline \hline \end{tabular} M = male, F = female, BMI = body mass index.  
\end{table}
Table 1: Gender, age, height, weight, and BMI of athletes recruited in the study.  
Figure 1: Example of set-up for data collection. The inertial sensor was placed on the right ankle and the ground truth was provided by a recorded video.  
all three components and the related results were evaluated. Then, a low-level clipper operation was applied to have:  
\[y1(n)=y0(n)\text{ if }y0(n)\geq\text{th}_{1} \tag{2}\]  
\[y1(n)=\text{th}_{1}\text{ if }y0(n)<\text{th}_{1}\]  
where th\({}_{1}\) was set to 40% of the maximum value of the input signal. This value was experimentally tuned to detect a kick event with maximum accuracy.  
Finally, the same threshold th\({}_{1}\) was applied to the _y_d(n)_ signal, which is the difference quotient of \(y1(n)\) computed over the sampling time: if _y_d was greater than th\({}_{1}\), a kick event was detected. In Figure 2 an example of the signals involved in the algorithm is shown.  
This algorithm was applied to the three axes of the accelerometer to verify which axis or which combination of axes guaranteed the highest accuracy.  
Data coming from the magnetometer were exploited to assess when an athlete changed direction during the swim (i.e., to count the laps). In addition, in this case, a threshold-based algorithm was applied. The magnetometer _x_-axis was considered since it is the one on which the contribution due to the forward movement of the athlete was most concentrated (Figure 1). The change of direction was detected when the magnetic field measured on the _x_-axis changed the sign.
## 3 Multi-Constellation Kinematic PPP Data Processing Strategy  
In this study, a self-developed PPP software was used for the data processing. Here, the data preprocessing directly affected the accuracy of the positioning results. The eliminated error and model used in this study are shown in Table 2.  
The first-order ionospheric delay errors were removed using the ionosphere-free combined observables in our self-developed PPP software. The hydrostatic (dry) tropospheric delay was corrected based on observations using the UNB3m model [32], whereas the non-hydrostatic (wet) part was estimated as a parameter. The Neill mapping functions [35] were used for projection from the slant delays to the zenith delay. The receiver and satellite antenna phase centre offsets (PCO) and phase centre variations (PCV) were collected using the parameters provided by IGS ANTEX. In addition, the MGEX of IGS investigated the new GNSSs [21]. In this study, we applied the multi-constellation GNSS precise satellite orbit and clock products from MGEX to mitigate the satellite orbit and clock errors.  
The main PPP parameters included the coordinates, the zenith tropospheric delay parameter, multi-constellation system time difference parameters, and the ambiguities. To estimate both the static and kinematic parameters, our software used the Kalman filtering model. The cut-off angle was set at 10\({}^{\circ}\). To decrease the convergence time and make full use of the satellite observation information, we regarded the satellite as a new satellite when the cycle slip occurred. The cycle slip detection and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Model and Mitigation Methods** \\ \hline Ionospheric delay & Ionosphere-free combined observables \\ Tropospheric delay(dry part) & UNB3m model [32] \\ Receiver antenna PCO and PCV & IGS ANTEX \\ Satellite antenna PCO and PCV & IGS ANTEX \\ Solid earth tides & IERS2010 \\ Ocean loading & IERS2010 \\ Polar tides & IERS2010 \\ Phase wind-up effect & Wu model [33; 34] \\ Receiver clock errors & Estimation, white noise \\ \hline \hline \end{tabular}
\end{table}
Table 2: Models used in multi-constellation kinematic precise point positioning (PPP) data preprocessing. PCO: phase centre offsets; PCV: phase centre variations.  
repair were conducted using the MW combination and geometry-free combination. The software automatically calculated the scaling matrix based on satellite movements to improve the computational efficiency. Simultaneously, the software automatically set the parameters according to the static or dynamic mode.
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments
### Baseline Models
#### 5.1.1 UNet  
The UNet [41] model is widely used for semantic segmentation. It employs an encoder-decoder architecture designed to output segmentation masks of the same size as the input image, making it suitable for pixel-wise prediction tasks. We re-implemented UNet using the segmentation models library [45], with ResNet-34 as the feature extractor. The feature extractor weights were initialized with ImageNet pretrained parameters.
# Transverse and Longitudinal Energy Flows in a Sharp Focus of Vortex and Cylindrical Vector Beams
## 4 Simulation
### Energy Flow near the Focus of a Cylindrical Vector Beam
Transverse flow in the focus plane (**d**) and profiles of the Poynting vector longitudinal projections in transverse planes (**f**). Figure 7: Intensity distribution in color and direction of energy flow (Poynting vector) in the longitudinal XZ plane (arrows) with the same parameters as before, but for the initial wave (30) with parameters \(m=0\), \(n=2\). If there is an optical vortex in the vector field (30), then the energy flows and intensity distribution will have a different form. Figure 8 shows the transverse (a-c) and longitudinal (e) intensity distributions depicted in color as well as profiles of longitudinal projections of the Poynting vector in three observation planes (d). Arrows indicate the direction of the transverse (a-c) and longitudinal (e) Poynting vectors near the focus of the initial field (21) or (30) at \(m=1\), \(n=1\). The other parameters are the same as in Figure 6.  
Figure 8: Intensity distribution (in color) and Poynting vector (arrows) near the focus for the initial field (30) with \(m=1\), \(n=1\) at a distance \(f-\lambda\) (**a**), \(f+\lambda\) (**b**) and \(z=1.41\) μm (**c**). Arrows show the direction of transverse flow in the XY plane (**a–c**). Longitudinal flow profiles in the focus (**d**). Intensity and power flow in the XZ plane (**e**). Because there is an optical vortex which has a topological charge equal to \(m=1\), the intensity is seen from Figure 8 to have the form of a transverse ring or longitudinal tube. And the energy flow before the focus not only converges to the optical axis, but rotates around it counterclockwise (Figure 8a). After focus, the transverse energy flux is directed away from the coordinate center (the beam diverges) and still rotates counterclockwise (Figure 8b). At the focus itself, the beam far from the optical axis propagates only along the optical axis, and, near it, the energy flow rotates around the optical axis. The maximum transverse energy flows are different at different distances. Before the focus, the maximum transverse energy flow is 5 a.u. (Figure 8a); after the focus, it is 9 a.u. (Figure 8b); and, at the focus itself, it is 25 a.u. (Figure 8c). The longitudinal energy flux at the focus has a maximum value in the coordinate center and is approximately 45 a.u. Figure 8e shows the intensity in the XZ plane (in color). The arrows indicate the direction of energy flow near the focus.