esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Protocol Architecture
### Addressing Scheme
#### 3.5.2 Inter-SMAG Communication under Same Border Router  
Inter-SMAG communication will be performed by using EUI-64 bits addressing. In this case the EUI-64 bits identifier plays an important role in our addressing scheme by staying in between the 16 bits short address and 128 bits global unicast addressing. This addressing scheme will ensure inter-SMAG communication under the same border router. Figure 6 represents addressing format to communicate among different SMAGs under same border router.  
In Figure 7, a sensor is moving from one SMAG domain to another SMAG domain. This type mobility is identified by the new SMAG and the sensor node when the scanning process is performed. The new SMAG identify it by the duplicate address detection or from the pool of unassigned identifier. If the node is from the different SMAG domain then communication is performed among the peer SMAGs.
# Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method
## 3 Results
### Electrical Propagation in the Niederer Benchmark Geometry  
In this example, we evaluate our proposed method by solving the benchmark problem defined by [PERSON] et al., which is the standard, accepted approach to verify cardiac tissue electrophysiology simulators [35]. The benchmark problem considers a 3D cuboid of human ventricular tissue (\(3\times 7\times 20\) mm). Cardiac fiber orientation is parallel to the Z axis and electrophysiology is described by the [PERSON] et al. model [36]. The longitudinal diffusion coefficient is set to \(d_{0}=0.00115\) cm\({}^{2}/\)ms and the transverse-to-longitudinal ratio is \(\rho=0.12\). Periodic stimulation with frequency \(f=1\) Hz, amplitude \(A=50\) mA and duration \(t_{d}=2\) ms is applied at a cubic region with dimensions \(1.5\times 1.5\times 1.5\) mm located at corner P1 (Figure 4).  
Figure 3: Convergence rate for varying support domain’s dilatation coefficient \(\alpha_{sd}\in\{2.25,\ 2.50,\ 2.75,\ 3.00,\ 3.25,\ 3.50\}\) for the MCM with MKI (continuous blue) and RPI (slashed red) trial functions. Convergence is evaluated by computing the NRMS error with respect to an FEM simulation.  
The activation time at the corners (P1 - P8) and the center (C) of the cuboid were computed for various nodal spacings, \(h=\{0.1,0.2,0.5\}\) mm, using MCM-RPI and MCM-MKI with support domain \(s_{d}=\alpha_{sd}h\), with \(\alpha_{sd}=2.8\). Integration was performed using the DAETI method with time step \(dt=0.1\) ms. The activation times obtained by MCM-RPI and MCM-MKI were compared with those of the FEM (Table 1) previously reported in [33] and validated with the reported activation times in [35].
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 4 Latent Heat Measurements of PR Data
### The LH and the Climate Response  
In the long run, the distribution of LH accompanying the rainfall modulated the large scale circulations and thus had an interaction with climate response such as the El Nino-Southern Oscillation (ENSO). The LH released during precipitation processes would change the heat budget and balance of the atmosphere, and the consequently adjusted atmosphere motions would in turn impact the water vapor evaporation and transportation and thus the precipitation distribution. This was a complex feedback effect and some studies made pioneering works and preliminary analysis by PR  
Figure 5: Retrieved distribution of the mean unconditional LH at (**a**) 7-8 km; (**b**) 4-5 km; and (**c**) 2-3 km levels during 1998–2011. ([94] © American Meteorological Society. Used with permission.)data. [PERSON] et al. (2019) calculated the four-dimensional distribution of the LH and modeled the response of the atmosphere by an idealized model with derived LH as initial values. Results showed that during El Nino years, the gradient in stratiform precipitation across the Pacific became more pronounced. This led to major changes in the LH field and the circulation response to the LH was stronger than the other years. [PERSON] et al. (2018) and [PERSON] and [PERSON] (2019) investigated the variation of the LH associated with the summer and the winter monsoon over the South China Sea (SCS). They found that the interannual variation of the LH over the SCS depended on El Nino or La Nina conditions, and the LH over the SCS was much smaller during El Nino years than during La Nina years. By contrast, [PERSON] et al. (2019) indicated that the monsoonal interannual variation of the LH could not be explained by the ENSO when they examined the four-dimensional LH structure over the South Asian monsoon region.  
The Madden-Julian oscillation (MJO) is a quasi-periodic variability in tropical atmosphere. It turns out to be the main intra-annual fluctuation that explains weather variations in the tropics and has significant impacts on the global climate including ENSO events. Some studies analyzed the MJO signal from the perspective of LH, and they found a double peak structure of LH profiles with peaks at about 3 km and 7 km and the largest amplitudes appeared over the Indian Ocean and over the western Pacific Ocean during MJO stages ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019).
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 5 Conclusions  
In this paper, we design a computation offloading framework for GEO/LEO hybrid satellite networks and construct the energy-efficient and QoS-aware constrained optimization model. After that, we transform the computation offloading problem into an MCMF problem and propose a successive shortest path algorithm-based solution method to obtain the offloading decision in polynomial time. Finally, a set of numerical experiments are conducted to demonstrate the effectiveness of the proposed SSPCO. The results show that SSPCO can effectively reduce the total energy consumption of the satellite networks while guaranteeing the QoS constraints of the on-board missions.  
Conceptualization, W.L. and P.Y.; methodology, W.L. and Y.D.; software, W.L. and Y.D.; validation, W.L. and Y.D.; formal analysis, P.Y.; investigation, W.L. and Y.D.; resources, P.Y.; data curation, C.L. and Z.W.; writing--original draft preparation, W.L. and Y.D.; writing--review and editing, W.L., Y.D., P.Y. and Q.W.; visualization, Z.W. and C.L.; supervision, P.Y. and Q.W.; project administration, P.Y. and Q.W.; funding acquisition, P.Y. and Q.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61972302 and Grant 61962019, in part by the Shaanxi Key Technology R&D Program under Grant 2021 ZDLGY07-01, and in part by the Fundamental Research Funds for the Central Universities under Grant 20199236325.  
Not applicable.  
The authors declare no conflict of interest.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics
#### 2.5.2 Prediction Interval Width (PIW)  
The first index for estimating PI is the PIW. It is estimated using lower and upper bound estimates (LUBE), as seen in [21]. The difference between the content of \(Q^{\alpha}(x_{i})\), i.e., the upper limit and the lower limit, gives PIW, as expressed in Equation (20) [31].  
\[PIW_{i}=U^{\alpha}(x_{i})-L^{\alpha}(x_{i})\qquad i=1,\cdots,m \tag{20}\]
## 9 JADEDO Sensitivity Analysis  
The sensitivity analysis heatmaps for the JADEDO algorithm applied to different functions (F1-F12) reveal distinct patterns in the performance of the optimization process under varying numbers of search agents and maximum iterations, as we can see in Figure 8. For F1, the algorithm performs best with a higher number of search agents (40 or 50) and max iterations beyond 300, where the fitness score converges to lower values, indicating better optimization results. F2 shows a more consistent performance across different  
Figure 7: Box-plot analysis over CEC2022 (F1–F12).  
settings, though the best results are achieved with 20 search agents and lower max iterations around 100. Function F3 illustrates that increasing the number of iterations leads to slight improvements, with the performance stabilizing around 500 iterations and 30-50 search agents. For F6, a complex pattern emerges where intermediate numbers of search agents (20-30) yield better results with fewer iterations, while more iterations tend to degrade the performance slightly. F7 demonstrates a similar trend with the best results clustering around lower iterations and fewer search agents, indicating that excessive iterations might lead to stagnation. The heatmap for F8 shows minimal sensitivity to both the number of agents and iterations, with consistently similar outcomes across different configurations. F9 reveals an optimal region at 20 agents and 200 iterations, where the fitness value is minimized, though higher agent numbers do not significantly affect performance. The heatmap for F10 shows that a moderate number of agents and iterations yields better results, with performance peaking around 20 agents and 200 iterations. For F11, performance improves with higher agent counts and intermediate iterations, highlighting the need for a balanced approach to maximize efficiency. Finally, F12 displays minimal variation across different parameters, indicating that the algorithm is relatively insensitive to these settings for this particular function. Overall, these heatmaps suggest that the effectiveness of the JADEDO algorithm is highly dependent on the specific function being optimized, with the best configurations varying considerably across different test cases.  
Figure 8: Sensitivity analysis over CEC2022 (F1–F6).
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 4 Advanced Methods for Extraction of Bioactive Compounds from Avocado Peel
### Enzyme-Assisted Extraction (EAE)  
The enzyme-assisted extraction (EAE) technique utilizes hydrolytic enzymes to deteriorate the structural integrity of cell walls, exposing intracellular components for increased extraction yield. Cellulose and pectinase enzymes are usually used as a pre-treatment step before solvent extraction. Cellulose breaks down the 1,4-d-glycosidic bonds in cellulose in plant cells' major cell walls. Pectinase, likewise, destroys pectic compounds and pectin in the middle lamella and primary cell walls [26]. The use of enzymes is an exciting alternative to environmentally damaging methods. One of the most valuable advantages of this technique is the possibility of reusing the enzymes, which makes the technique somewhat economical and facilitates the application of enzymes in medical analysis [27].  
The enzymatic extraction of bioactive compounds has been used for a long time for some materials, but its use for extracting bioactive compounds from the avocado peel is scarcely reported. So far, [PERSON] et al. [21] reported that EAE was less effective than UAE. Therefore, it has to be improved or integrated with other techniques to boost efficiency, reduce processing time, and make the procedure competitive. Also, the most prevalent enzymes are unspecified enzymes like proteases, which might result in the extraction of other components [28]. EAE can be enhanced by combining it with other environmentally friendly approaches such as ultrasonic extraction, supercritical fluid extraction, ionic liquid extraction, three-phase partitioning, and microwaves.
## 3 Results and Discussion
### Experimental Verification  
To verify the accuracy of the proposed dynamic model, the experimental model of the feed system was established, as shown in Figure 7. The harmonic exciting force along the \(z\)-axis direction is generated by using a modal shaker (JZK-50); a piezoelectric force sensor (Sinocera CL-YD-331A) and acceleration sensor (Sinocera CA-YD-189) are used to measure the excitation force and acceleration of the worktable along the \(z\)-axis direction (screw shaft direction). The excitation amplitude and frequency acting on the worktable can be adjusted by the signal generator (Sinocera YE1311). When the excitation amplitude and frequency are fixed at 30 N and 270 Hz, the comparison of acceleration between theoretical and experimental results is shown in Figure 8. It can be seen that the experimental result is slightly larger than the proposed model result, which may be caused by the frame deformation, but the error is within the acceptable range.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Ball diameter & 7.14 mm \\ Initial contact angle & 45\({}^{\circ}\) \\ Length of screw shaft & 1000 mm \\ Number of loaded balls & 46 \\ Shear modulus & 206 GPa \\ Preload & 300 N \\ Mass of screw shaft & 10.6 kg \\ Pitch diameter & 42 mm \\ Lead & 16 mm \\ Circle of loaded balls & 2.5 \\ Mass of worktable & 58 kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of ball screw.  
Figure 7: Vibration experimental device of ball screw feed system.
## 3 Data and Methods
### Gridding of Gravity Data  
The study area is the hill region and the region along the river. After being adjusted for the whole area, the gravity data were interpolated using three interpolation algorithms, Kriging, Radial Basis Function (RBF), and Triangulation with Linear Interpolation (TLI) [21].  
First, the calibrated gravity data of the two areas were removed, indicated by the rectangles, as shown in Figure 2. Then, the gravity data outside the rectangles were used in the interpolation. The adjusted gravity data were the true values and were compared with the interpolated values. Therefore, the algorithms' performance was determined by the differences divided by the true values, as shown in Table 1. Appendix A gives a further explanation of gridding and several intermediate diagrams using the example of the Kriging interpolation algorithm of the dynamic changes of the gravity field from October 2002 to October 2003, around the 135 m water level stage.  
After gridding of gravity data, accidental errors, superficial crust, and local anomaly sources were eliminated. The deep crust and regional anomaly sources are highlighted. The point data that did not show the continuity and trend of gravity changes were also eliminated so that the differential image of the gravity field can have a greater accuracy, reflecting the characteristics of the gravity field changes in the study area during the TGR's impoundment process.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 2 Related Work and Materials
### Dataset  
As a pioneer, the Kelvins SpotGEO Challenge organizers have released their SpotGEO dataset, which was collected for the study of space object detection problem. All the image sequences are captured by a low-cost optical CMOS camera carried on one ground-based telescope, with an angular pixel size of about 4.5 arc seconds. The camera remains stationary relative to the earth's surface during the 40 s exposure time. Five consecutive frames are taken as a sequence imaging some portions of the sky at the geostationary ring. The cameral motion is constant between any two consecutive frames within a sequence.  
Under this observation strategy, background stars are relative moving during the exposure time and eventually the motion trajectories of stars are streak-like. While the GEO objects appears as the form of spots or short streaks in the image, the motion trajectory within one sequence appears like uniformly distributed discrete points on a straight line.  
In order to better demonstrate the SpotGEO dataset, two groups of sequences are selected for display, containing the number of targets of 3 and 5 respectively, as described in Figures 2 and 3. The visualizations of example images annotated with the corresponding annotation are shown in Figures 2a and 3a-e. It is evident that some space targets are too faint and dark to be seen through human eyes. Possible causes include the cloud over, atmospheric/weather effects, star occlusion and sensor noise/defects. All these terrible factors indeed increase the difficulty of GEO space object detection problem. Furthermore, all the coordinates of objects in five consecutive frames are gathered together, explicit motion trajectories can be seen in Figures 2b and 3f.  
Figure 2: An example image sequence of the SpotGEO dataset. (**a**) The typical sequence that captures three objects. (**b**) Object detection in a sequence of 5 consecutive frames. The source positions are shown by the circle symbols in different colors.  
It is specifically observed that the SpotGEO dataset contains 1280 sequences, 6400 grayscale images with annotations for training and 5120 sequences, 25,600 grayscale images with annotations for test, respectively. All the images have the resolution of 640 \(\times\) 480. As a matter of fact, there is only one space object category annotated with point coordinates given by (x, y).
## 3 Feature-Selection Method and Application
### Symmetrical Uncertainty (SU)
#### 3.2.2 Symmetrical Uncertainty Method  
For variables, the correlation and degree of influence between them is usually the most direct and fastest method of judgment. By calculating the correlation coefficient, it is usually possible to quickly obtain the correlation between the two, but if the correlation is used to select features, it usually results in a tendency to select features with larger values. Therefore, this research uses the method of SU to calculate the correlation between features and targets [PERSON] (1948). Calculation of SU is shown in (7).  
\[SU(X,Y)=\frac{I(X)}{H(X)+H(Y)} \tag{7}\]  
It can also be explained from the definition that it is a form of information gain normalization. Nonlinear related information variables defined based on information entropy, used to reconstruct the degree of correlation between nonlinear random variables. Among them, the symmetric uncertainty is calculated using the SU value, which effectively corrects the bias about the selected feature. After normalizing the information gain, the SU value is between 0 and 1. Make the two different types relatively fair when comparing and explain that when \(SU(X,\ Y)=1\), it means that X and Y are completely related; otherwise, when \(SU(X,\ Y)=0\), it can be judged that X and Y are completely independent individuals.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### Controlled-Bit-Flip (\(\Lambda_{f}(X)\)) Mixers
#### a.2.5 MinSetCover  
**Problem:** Given a universe \([n]\) and \(m\) subsets \(\mathcal{S}=\big{(}S_{j}\big{)}_{j=1}^{n}\), \(S_{j}\subset[n]\), find the minimum cardinality subcollection \(\mathcal{S}^{\prime}\subset\mathcal{S}\) of the \(S_{j}\) such that their union recovers \([n]\).  
**Approximability:** There exists a \(1+\ln n\) algorithm [77]. Cannot be efficiently approximated to \((1-o(1))\ln n\) unless \(P=NP\)[78]. APX-complete with the restriction \(|S_{j}|\leq k\) (Max-\(k\)-Set-Cover) [50].  
**Constraint graph:** Vertices \([m]\) corresponding to subcollections in \(\mathcal{S}\); two vertices \(\{i,j\}\) are adjacent if and only if their corresponding sets intersect, \(S_{i}\cap S_{j}\
eq\emptyset\).  
**Domain:** Set covers, \(\left\{\mathbf{x}:\bigcup_{j\in[m]:x_{j}=1}S_{j}=[n]\right\}\).  
**Objective:** min \(|\mathcal{S}^{\prime}|=\sum_{j=1}^{m}x_{j}\).  
**Initial state:**\(|s\rangle=|1\rangle^{\otimes m}\), i.e., \(\mathcal{S}^{\prime}=\mathcal{S}\).  
**Mixing rule:** Swap set \(S_{j}\) in or out of \(\mathcal{S}^{\prime}\) if \(\mathcal{S}^{\prime}\setminus S_{j}\) covers \([n]\).  
**Partial mixing Hamiltonian:**  
\[H_{\text{CX},j}=\left(\prod_{i=1}^{n}\left(1-\prod_{\ell\in[m]:\ell\
eq j,i \in S_{\ell}}\frac{I+Z_{i}}{2}\right)\right)X_{j}=X_{j}H_{\text{XMSC},j}, \tag{1}\]  
where \(\chi_{\text{MSC},j}(\mathbf{x}_{\text{nbhd}(j)})=\bigwedge_{i\in S_{j}} \bigvee_{\ell\in\text{nbhd}(j):i\in S_{\ell}}x_{\ell}\).  
**Phase separator:**\(U_{\text{P}}(\gamma)=\exp(-i\gamma\sum_{j=1}^{m}Z_{j})\).  
**Resource count:**  
* **Controlled-bit-flip mixers:** For each \(H_{\text{CX},j}\), use \(|S_{j}|\) ancilla qubits. Use each ancilla qubit \(i\) to compute \(\bigvee_{\ell\in\text{nbhd}(j):i\in S_{\ell}}x_{\ell}\) using a controlled NOT gate with \(|\{\ell\in\text{nbhd}(j):i\in S_{\ell}\}|\leq|\text{nbhd}(j)|=D_{j}\) control qubits. Then implement \(H_{\text{CX},j}\) using a controlled X gate on qubit \(j\) with the \(|S_{j}|\) ancilla qubits as the control. Finally, uncompute the ancilla qubits using the same \(|S_{j}|\) controlled NOT gates as in the first step. Depth at most \(2D_{j}+1\) per partial mixer.
* **Phase separator:**\(m\) single-qubit \(Z\)-rotations. Depth 1.
* **Initial state:** Depth 0.  
**Variants**: Equivalent to minimum hitting set [50] and under \(L\)-reductions equivalent to minimum dominating set (which is a special case of minimum set cover).
# Stability Control of Multilayer Roof Strata in the Large Mining Height Gob-Side Entry: A Case Study
## 4 Application to the 22301 Head Entry
### Support Scheme in the 22301 Head Entry  
For reinforcement support within the 22301 head entry, the coal pillar side is supported with bolts and cables. The bolts have a diameter of 20 mm and a length of 2400 mm, with a spacing of 900 mm between rows. The cables have a diameter of 21.6 mm and a length of 5000 mm, positioned 700 mm and 2300 mm from the roof. In addition, every two rows of bolts are supplemented with three additional cables in the roof. These cables have a diameter of 21.6 mm and a length of 8300 mm, with a spacing of 1500 mm \(\times\) 1800 mm between the cables, and 750 mm away from both sides of the entry. The levels of pre-tensioning for bolts and cables were 100 KN and 280 KN, respectively. The support plan is shown in Figure 10. It is noted that the specific selection of support scheme parameters is not the focus of this paper; we only present the effect of industrial application after the proposed support scheme is applied to the 22301 head entry, as would be validated in Section 4.2. Thus, the detailed reason of selecting support parameters is neglected in the study.  
Figure 10: _Cont._  
To prevent roof collapse and maintain its integrity above the backfill area, as well as to facilitate smooth backfilling operations, a roof support plan is proposed based on roof deformation and stability conditions, as illustrated in Figure 11. The plan involves laying a 7-m-wide flexible plastic mesh above the backfill area between the 3 rd and 6 th hydraulic supports and installing five roof bolts or cables after each coal mining cutting operation. The specific parameters are as follows: threaded steel roof bolts with a diameter of 20 mm and a length of 2400 mm are used, with a row spacing of 860 mm. Furthermore, the bolt spacing is adjusted based on the position of hydraulic supports. In every two rows of bolts, the 1 st and 3 rd bolts are replaced with cables, each with a diameter of 21.6 mm and a length of 6300 mm. A steel ladder beam with a diameter of 16 mm is used to protect the roof, with ladder beam dimensions of 3700 mm \(\times\) 50 mm.
## 2 UTM Monitoring Framework  
This section explains the concept of a UTM monitoring system, including the network Remote ID, conflict detection algorithm, flight data measurement, and machine learning algorithm used for analysis.