esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Existing Decision Tree Classification
#### 2.1.2 Decision Tree Classification via Linear Function [10]  
The Boolean output of the decision node \(D_{j}\), \(b_{j}=1(0)\) indicates that the next node is the left (right) child. For edges \(\mathsf{e}_{j,0}\) and \(\mathsf{e}_{j,1}\), respectively connecting to the left child and the right child from the decision node \(D_{j}\), we define the corresponding edge cost \(\mathsf{ec}\) as follows:  
\[\mathsf{ec}_{j,0}:=1-b_{j},\quad\mathsf{ec}_{j,1}:=b_{j}. \tag{1}\]  
From the root node of the decision tree to each leaf node, only one path is determined. We define the path cost \(\mathsf{pc}_{k}\) as the sum of the edge costs in \(\mathsf{Path}_{k}\)--the path from root node to the leaf node \(L_{k}\). The edge cost \(\mathsf{ec}_{j,0}\) and \(\mathsf{ec}_{j,1}\) are determined by \(b_{j}\), which is the comparison result of decision node \(D_{j}\). Thereafter, the path cost for each leaf node is defined by a linear function of \(b_{j}\). For example, the following \(\mathsf{pc}_{1},\mathsf{pc}_{2},\mathsf{pc}_{3}\), and \(\mathsf{pc}_{4}\) denote the path costs for leaf nodes \(L_{1},L_{2},L_{3}\), and \(L_{4}\) in Figure 1, respectively:  
\[\begin{split}\mathsf{pc}_{1}&=\mathsf{ec}_{1,0}=1- b_{1},\\ \mathsf{pc}_{2}&=\mathsf{ec}_{1,1}+\mathsf{ec}_{2,0} +\mathsf{ec}_{3,0}=b_{1}+(1-b_{2})+(1-b_{3})=2+b_{1}-b_{2}-b_{3},\\ \mathsf{pc}_{3}&=\mathsf{ec}_{1,1}+\mathsf{ec}_{2,0} +\mathsf{ec}_{3,1}=b_{1}+(1-b_{2})+b_{3}=1+b_{1}-b_{2}+b_{3},\\ \mathsf{pc}_{4}&=\mathsf{ec}_{1,1}+\mathsf{ec}_{2,1} =b_{1}+b_{2}.\end{split} \tag{2}\]  
The comparison results \(b_{j}\) indicate that the edge cost to the next node is 0, while the edge cost to the other node is 1. As a result, only the path leading to a specific leaf node \(L_{k}\) has a total cost \(\mathsf{pc}_{k}=0\); all other paths have non-zero costs. The classification result is  
Figure 1: Decision tree example.  
returned if and only if \(\mathsf{pc}_{k}=0\), meaning the output corresponds to the label stored in the leaf node \(L_{k}\).  
This can be illustrated with a concrete example (see [PERSON] in Figure 1): [PERSON]'s attributes are height \(<170\), weight \(>60\), and age \(>25\), which yield comparison results \(b_{1}=0\), \(b_{2}=1\), and \(b_{3}=1\), respectively. Substituting these into Equation (2), we compute the path costs as follows: \(\mathsf{pc}_{1}=\mathsf{pc}_{3}=\mathsf{pc}_{4}=1\), and only \(\mathsf{pc}_{2}=0\). Therefore, the output for [PERSON] is \(C_{1}\), the label held by leaf node \(L_{2}\).
## 1 Introduction
While most studies for real-time LEO satellite clock determination used real-time GNSS products either collected in good conditions or directly archived on the FTP server of the service provider, in-depth study of the impact of the continuity and latencies of the real-time GNSS orbits/clocks on real-time LEO satellite clock determination was rarely performed. [PERSON] et al. concluded a latency of about 31 s for the IGS03 stream and 28 s for the IGS01 stream [19]. [PERSON] et al. also showed the loss rates of the IGS real-time products ranging from about 3% to 45% and pointed out that the loss of GNSS products significantly impacts real-time Precise Point Positioning (PPP) applications [20]. The latencies and continuity of the real-time GNSS products would, without a doubt, lead to unignorable impacts on the real-time LEO satellite clock estimates, sometimes even greater than the precision of the real-time GNSS products themselves. In [21], the impacts of the continuity and latency of the real-time GNSS products on LEO satellite POD are presented. In this study, the focus is on the LEO satellite clocks, which are a limiting factor for the LEO satellite Signal-in-Space Ranging Error (SISRE). Different situations of the discontinuity and latencies of the real-time GNSS products, the strategies to deal with these situations, and the resulting impacts on the real-time LEO satellite clock precision are discussed in the frame of this contribution. In addition to orbit and clock products, other factors like the yaw attitude of GNSS satellites could also influence the LEO satellite clock precision [22], but this effect will not be addressed in this study. The paper starts with a general overview of the GNSS products used in the LEO clock determination in this contribution. Afterward, the methodology for LEO clock determination based on using the Kalman filter is described. Subsequently, the impacts of the latency and continuity of GNSS real-time products on the LEO satellite clock determination are discussed in detail. The conclusions and discussions are given at the end.
# An Experimental Study for Swelling Effect on Repairing of Cracks in Fine-Grained Clayey Soils
## 3 Results and Discussion
### Geo-Engineering Characteristics
The range of PI in the clay-Bentonite mixtures is shown in Figure 2. Regarding this figure, it can be inferred that a combination of clay and 10% bentonite is a suitable percentage because it does not exceed the dam core plastic index (PI = 25). \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**USCS**} & \multicolumn{4}{c}{**Particle-Size Distribution (\%)**} & \multirow{2}{*}{**Dry Density**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**C\({}_{\text{C}}\)**} \\ \cline{3-3} \cline{5-8}  & & & **D\({}_{\text{10}}\)** & & **D\({}_{\text{30}}\)** & & **D\({}_{\text{60}}\)** & **(g/cm\({}^{3}\))** & **Content (\%)** \\ \hline Bentonite & CH & 94 & 100 & 100 & 1.01 & 28.5 & 1.06 \\ Clay & CL & 58 & 94 & 100 & 1.46 & 11.7 & 1.52 \\ Kaolin & CL & 88 & 99 & 100 & 1.31 & 10.9 & 1.11 \\ Clay + 10\% Bentonite & CL & 60 & 94 & 100 & 1.48 & 15.9 & 1.47 \\ Clay + 10\% Kaolin & CL & 60 & 94 & 100 & 1.39 & 15.1 & 1.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The physiomechanical properties of the studied clayy soil. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**USCS**} & \multicolumn{4}{c}{**Afterberg Limits (\%)**} & \multirow{2}{*}{**Activity**} & \multirow{2}{*}{**Swelling Potential (\%) [30]**} \\ \cline{3-3} \cline{5-8}  & & & **LL** & & **PL** & **PI** & **Number** \\ \hline Bentonite & CH & 415 & 60 & 355 & 5.7 & 4812 & Very high \\ Clay & CL & 40 & 25 & 15 & 3075 & 0.1 & Low \\ Kaolin & CL & 46 & 27 & 19 & 0.42 & 0.004 & Low \\ Clay + 10\% Bentonite & CL & 51 & 28 & 23 & 4.5 & 0.38 & Medium \\ Clay + 10\% Kaolin & CL & 42 & 25 & 17 & 3.8 & 1.9 & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the geo-engineering description ofthe studied clayy soil. Figure 2: Effect of Bentonite additive on PI index in clayy soil. Compatibility is another geo-engineering parameter that has an impact on fine-grained clayey soils. Compaction occurs when the air in the soil is driven out and causes the grains to move closer to each other and become attached, increasing the shear strength and decreasing the soil settlement. Compaction activity plays an important part in the clay soil swelling potential by reducing the porous spacing to a minimum. The sample's moisture rate and dry density are among the parameters determining compaction, providing the optimal condition for the best condition of consolidation in soils. Nevertheless, the presented study uses the test to monitor the Bentonite and Kaolin admixture on clay behavior. The results show that mixing Kaolin with clay could not cause much change in the dry density and optimum moisture. The mixture of clay with 10% Kaolin caused only an 11% increase in the optimum moisture and a 2% decrease in the dry density of the sample. Figure 3 provides the Bentonite variation on clayey soil, which has a considerable impact on the soil's compatibility.
## 2 Materials and Methods
### Multi-Source Remote Sensing Fusion  
To generate high-resolution NDVI time series, the MODIS NDVI data for northwestern Hubei were first reprojected, resampled, and clipped to align with the Landsat NDVI data. We leveraged the high temporal resolution of MODIS data and applied a cloud removal filtering process to obtain cloud-free MODIS observations at a 16-day interval, ensuring at least one usable observation within each 16-day period throughout the year. Given the relatively low temporal resolution of Landsat imagery, cloud-free Landsat images were selected on a quarterly basis for fusion. For each fusion instance, two MODIS images were used: one served as the target image, temporally matching the Landsat image to be fused, and the other acted as a reference image with a timestamp as close as possible to the quarterly Landsat image. These images were used as inputs to the spatial and temporal non-local filter-based fusion model (STNLFFM) [40], which is well-suited for combining the high temporal frequency of MODIS with the fine spatial detail of Landsat. Input imagery with cloud cover less than 10% was selected to minimize cloud contamination. For the STNLFFM model, the following parameter settings were applied: search window size of 51, spectral parameter of 0.01, filtering parameter of 0.15, high-resolution and low-resolution error thresholds of 0.0050, and weight image patch size of 1. The spectral and filtering parameters were used to define spectral similarity criteria, guiding the computation of spatial-temporal weights during the fusion process. The resulting spatiotemporally fused NDVI data have a spatial resolution of 30 m and a temporal resolution of 16 days.  
\[F(x,y,B,t_{p})=\sum_{k=1}^{M}\sum_{i=1}^{N}W(x_{i},y_{i},B,t_{k})\times[a(x_{i},y_{i},B,\Delta t_{k})\times F(x_{i},y_{i},B,t_{k})+b(x_{i},y_{i},B,\Delta t_{k})] \tag{2}\]  
where \(F(x,y,B,t_{p})\) represents the fine-resolution reflectance at pixel \((x,y)\), band \(B\), and prediction time \(t_{p}\). \(M\) is the number of reference times, and \(N\) is the number of similar pixels. \(W(x_{i},y_{i},B,t_{k})\) is the weight assigned to each similar pixel \((x_{i},y_{i})\) at reference time \(t_{k}\). \(a(x_{i},y_{i},B,\Delta t_{k})\) is the gain coefficient, and \(b(x_{i},y_{i},B,\Delta t_{k})\) is the bias coefficient. \(F(x_{i},y_{i},B,t_{k})\) is the fine-resolution reflectance of the similar pixel \((x_{i},y_{i})\) at reference time \(t_{k}\). \(\Delta t_{k}=t_{p}-t_{k}\) represents the time interval between the prediction time and the reference time.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 8 Conclusions  
One of the core elements of the routing protocol for low power and lossy networks (RPL) is the trickle timer method. The trickle algorithm directly controls and affects the time a message takes to arrive from one node to another. It has a listen-only period, which causes load imbalance, node inconsistencies, and node slowness in the trickling algorithm. Previous studies provided positive outcomes, but there are several restrictions on trickle timer performance. The fundamental limitation of a trickle timer is a long listen-only period, leaving the node listening for an extended amount of time without the opportunity to send before the period ends, causing a high delay in the network.  
This study introduced the \"Dynamic Hop Count,\" which is a novel Trickle Timer Algorithm. It took care of the listen-only period limitations by controlling the trickle time using the number of hops between nodes (hop counter factor), then calculating the listen period according to it.  
The method was evaluated using a variety of measurement parameters, including the packet delivery ratio (PDR), power consumption, and convergence time under random and grid topologies.  
The proposed algorithm significantly improved convergence time by joining DODAGs and conserving power with a high PDR compared with the previously available trickle timer algorithms and according to the outcomes.  
In future work, we are looking to make the algorithm more secure by applying the algorithm on real testbeds and studying the performance using parallel servers.  
**Author Contributions:** Data curation, M.R.A.-H.; Formal analysis, R.M., E.M. and O.A.; Investigation, M.R.A.-H.; Methodology, R.M. and E.M.; Project administration, R.M.; Resources, R.M. and E.M.; Software, B.A.; Validation, B.A.; Visualization, O.A.; Writing--original draft, R.M. and E.M.; Writing--review & editing, B.A., M.R.A.-H. and O.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution
## 3 Method
### Single-Shot MultiBox Detector (SSD)  
To maintain the speed of detection and have higher accuracy in small object detection, we chose to use an SSD as the basis of the network. As shown in Figure 1, we can see that the SSD used VGG-16 as the backbone network and inherited YOLO's idea of regression, and a similar prior box was proposed based on the anchor frame mechanism of Faster R-CNN [10]. To have higher accuracy on both small objects and large objects, the SSD selects six feature maps with different scales as output layers, and default boxes that distribute different scales and proportions are on these feature maps. Then the SSD generates position information and category probabilities for all anchor boxes. Finally, through non-maximum suppression (NMS), the SSD excludes bounding boxes that do not meet the criteria.  
Smaller objects are detected using shallow layers with few convolutions. However, the interference of background information and the lack of context information led to low small objects detection. To solve these issues, we proposed an improved SSD called an ADSSD.  
Next, we will describe the ADSSD in detail.
# Effect of Wing Membrane Material on the Aerodynamic Performance of Flexible Flapping Wing
## 3 Data Acquisition and Deformation Measurement  
### Lift and Power Measurement  
A series of tests were conducted for these three wings under various flapping frequencies. Figure 4 presents the measurement setup. The lift force was defined as the vertical force generated by the flapping wing. During the force measurement, the flapping wing mechanism was connected to a six-axis force/torque sensor (Mini40, ATI Industrial Automation Inc., Apex, NC, USA), which was attached to the support. The force resolution of this load cell is 1/50 N. An in-house LabView program was used to record the force and torque data through a data-acquisition board (National Instruments, Austin, TX, USA), with a sampling rate of 2000 Hz. The program was designed to record the force data within the next 1 s after each trigger, and at least 10 flapping cycles' data were recorded according to the flapping frequency set in this research. Subsequently, more than 20 force measurements have been conducted during the stable flapping process at each flapping frequency. Meanwhile, the fifth-order low-pass Butterworth filter was adopted to filter the force data. The cut-off frequency is three times the flapping frequency, which has been proven to be effective in filtering high-frequency vibrations of the mechanisms while retaining reliable force data [25].  
The flapping mechanism was excited by an external DC power supply, and the output voltage of the power supply is set to a constant value of 8.4 V. Since the flapping frequency  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wing Material** & **Thickness (\(\mu\)m)** & **Elastic Modulus (GPa)** & **Density (g/cm\({}^{3}\))** \\ \hline Polyethylene terephthalate (PET) & 38 & 3.30 & 1.38 \\ Polyethylene (PE) & 40 & 1.72 & 0.92 \\ Thermoplastic urethanes (TPU) & 30 & 1.08 & 1.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the tested Wings.  
Figure 4: Flapping wing mechanism attached to the support.  
of the wing was controlled by adjusting the input current, the power input \(P_{in}\) of the mechanism can be expressed as:  
\[P_{in}=UI \tag{5}\]  
where \(I\) is the input current.
## 3 Results  
A total of six intense geomagnetic storms during 2012 were examined for any the storm-time variations in the _fo_F2 at low-latitude station, Darwin (Geomagnetic coordinates, 21.96\({}^{\circ}\) S, 202.84\({}^{\circ}\) E), a low-mid-latitude station, Townsville (28.95\({}^{\circ}\) S, 220.72\({}^{\circ}\) E) and a mid-latitude station, Canberra (45.65\({}^{\circ}\) S, 226.30\({}^{\circ}\) E), in the Australian Region. The local time of stations is given as follows: LT (Darwin) = UT + 8.7 h, LT (Townsville) = UT + 9.8 h and LT (Canberra) = UT + 9.9 h. The analysis of storms of 30 September-3 October (minimum _Dst_ = -122 nT), and 7-10 October (minimum _Dst_ = -109 nT) was presented as case studies for which _h'F_ data were also utilized. Looking at _fo_F2 variation before and after the storm, a limit of \(\pm 1\sigma\) (standard deviation) was used to show its day-to-day variability (Figures 1-6).  
Figure 6: Variations of (**a**) _Dst_ index _for_ the geomagnetic storm, (**b**) hourly values of critical frequency of F2 layer (_fo_F2) (MHz \(\times\) 10\({}^{-1}\)), (**c**) \(\Delta\)_fo_F2%, and (**d**) virtual height of F layer (_h'F_) during 7-10 October 2012 at Canberra station. The median values of _fo_F2 and _h'F_ parameters are shown by solid black curves in panels (**b**,**d**). The dashed curves in panel (**b**) show the variation of _fo_F2 by \(\pm\) 1 \(\sigma\).
## 3 Results
### Grasping with the Human Hand
#### 3.1.2 Grasp Force  
The experimental results of the grasp force under four conditions are shown in Figure 7. Figure 7a shows that the grasp force of all subjects basically decreases with increasing the disk diameter. For all disks, the average grasp force under the free condition is largest, while that under the IMRL-L condition is smallest, with a gap of 5.27 \(\pm\) 4.25 N. The differences between the free condition, the IM-L condition and the MRL-L condition are comparatively small, which are 3.10 N and 3.95 N, respectively. It should be noted that for S1, the grasp forces of the disk with a diameter of 110 mm are larger than those of the disk with a diameter of 100, which might result from when the disk is relatively small, the variation of the disk diameter is not significant for the grasp force. We can see that as the diameter increases further, the decreasing trend of the grasp force is more prominent. Figure 7b shows the frequency distribution histogram of the normalized grasp force under four conditions. The frequency distribution in the range of 0.8 to 1 is the highest under the free condition. As the number of restricted ab/d DOFs increases, the frequency distribution inclines to a lower range. The Wilcoxon test was performed on the normalized grasp force, and the results are shown in Table 2. There was a significant difference between the free condition and the IMRL-L condition.  
Figure 7: Results of the grasp force experiments. (**a**) Grasp force of 6 subjects, (**b**) frequency distribution histograms of normal grasp force under 4 conditions.
## 4 Discussion
### Interpretation of Findings
Consistent with this study's findings, parks with higher NDVI values are regularly maintained and have more shrubs planted periodically. The results of this study show that higher NDVI correlates with a higher proportion of Softscape, in line with previous research findings. NDVI represents the greenness of a park; a high NDVI indicates a greater amount of vegetation. NDVI is closely related to plant biomass and the carbon fixed through photosynthesis [63; 64; 65; 66].
# Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems
## 4 Proposed Controller Method for Coupled Tank System
### Adaptive Model-Based PID-Type Sliding Mode Control (APIDSMC)
The fundamental determinant of the stability and chattering impact of the system is the magnitude of the switching gain parameters \((\lambda_{2\text{x2}},\mu_{2\text{x2}})\). If the gain parameters of the controller are selected using the trial-and-error approach, the voltage on the control signal of the system may rise uncontrollably, perhaps resulting in significant damage to the motors. To mitigate this undesirable impact, it is necessary to estimate and adjust the switching settings of the controller properly during the operation. For the described adaptation approach, the estimated values of the switching gain parameters \((\lambda_{2\text{x2}},\mu_{2\text{x2}})\) can be derived as follows:  
\[\dot{\lambda}_{2\text{x2}}= \begin{bmatrix}\rho_{1}s_{1}(t)\dot{s}_{1}(t)&0\\ 0&\rho_{2}s_{2}(t)\dot{s}_{2}(t)\end{bmatrix} , \tag{44}\]  
\[\dot{\mu}_{2\text{x2}}= \begin{bmatrix}\gamma_{1}\big{|}\dot{s}_{1}(t)&0\\ 0&\gamma_{2}\big{|}\dot{s}_{2}(t)\big{|}\end{bmatrix}  \tag{45}\]  
Equations (44) and (45) provide the estimated switching gain parameters \((\lambda_{2\text{x2}}\) and \(\hat{\mu}_{2\text{x2}})\), and the positive constants for the adaptation speed of the switching gain parameters are denoted as \(\rho_{i}\) (\(i=1,\,2\)) and \(\gamma_{i}\) (\(i=1,\,2\)), respectively. For the purpose of controlling the system, the adaptive total feedback control law for both controllers is denoted \((\mathbf{u}_{total_{2\text{x}}})\) as follows:  
\[ \begin{split}\mathbf{u}_{total_{2\text{x1}}}=& g(\mathbf{x})_{2\text{x2}}^{-1}(\bar{q}_{d2\text{x1}}-f(\mathbf{x})_{2\text{x1}}+ \zeta(t,\mathbf{u}(\mathbf{t}))_{2\text{x1}}+\frac{k_{2\text{x2}}}{k_{d_{2\text{x2}}} }\dot{\mathbf{e}}(\mathbf{t})_{2\text{x1}}+\frac{k_{2\text{x2}}}{k_{d_{2\text{x2}}}} \dot{\mathbf{e}}(\mathbf{t})_{2\text{x1}}\\ &+g(\mathbf{x})_{2\text{x1}}^{-1}\big{\{}\hat{\lambda}_{2\text{x2}} \mathbf{s}(\mathbf{t})_{2\text{x1}}+\hat{\mu}_{2\text{x2}}\mathbf{sign}\big{(}\dot{\mathbf{s} }(\mathbf{t})_{2\text{x1}}\big{)}\big{\}}\end{split}  \tag{46}\]Moreover, the second derivative of the sliding surface can be reformulated from an alternative perspective as follows:  
\[\ddot{\textbf{s}}(\textbf{t})_{\texttt{2x1}}=-\textbf{{k}}_{\texttt{4}_{\texttt{2x2 }}}\hat{\textbf{\lambda}}_{\texttt{2x2}}\textbf{s}(\textbf{t})_{\texttt{2x1}}- \textbf{{k}}_{\texttt{4}_{\texttt{2x2}}}\hat{\textbf{n}}_{\texttt{2x2}}\textbf{ sign}\big{(}\dot{\textbf{s}}(\textbf{t})_{\texttt{2x1}}\big{)}-\textbf{{k}}_{ \texttt{4}_{\texttt{2x2}}}\boldsymbol{\zeta}(\textbf{t},\textbf{u}(\textbf{t}) )_{\texttt{2x1}} \tag{47}\]