esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### MODIS LAI and fAPAR
Sites from the FLUXNET database were selected, as these are well-characterized and studied over significant time periods. GlobAlbedo-derived LAI and fAPAR estimates were obtained through the GlobAlbedo website (see [39]) or direct via the GlobAlbedo team ([PERSON], _pers. comm._). The GlobAlbedo portal provides a web interface allowing the user to specify a site location (lat, lon), an irregular area using a GoogleMaps mashup, or MODIS tile location (horizontal and vertical tile ID, and sample and line within the tile) along with a region surrounding this pixel (from 1 \(\times\) 1 km to 25 \(\times\) 25 km), and a date range between any two DOYs. The user interface queries the GlobAlbedo data, generates text file output, plots the corresponding time series and provides scatter plots of GlobAlbedo LAI and fAPAR against the corresponding MODIS 4 or 8-day LAI, fAPAR products and then emails the user with URLs pointing to the plot and ASCII text result files. For each time series of observations, the TIP and MODIS values are plotted, as well as the uncertainty (\(\sigma\)) of the retrievals. For the GlobAlbedo TIP-derived products, uncertainty values are a by-product of the inversion process and are a true representation of the uncertainty in the underlying observations and retrieval process. Well-characterized uncertainty is a key advantage of the TIP process, as it depends only on the observations themselves. Retrievals that require a priori model and retrieval assumptions may have more detailed representations of the canopy (e.g., 3D RT models), but at the expense of assigning realistic uncertainty values to retrievals. This is due to the difficulty of assessing the contribution to uncertainty of a priori assumptions (e.g., biome choice, model structure) and how the uncertainty in underlying reflectance data feed through. In the MODIS retrievals for example, an imposed band- and biome-specific threshold on allowable discrepancies between RT-model simulated and MODIS surface reflectance was introduced into the processing chain in collection 5 (see [40]). Therefore, whilst this is an improvement over previous versions, this results in uncertainty estimates that are both constrained (fixed upper limit) and quantised. As a result, their accuracy/validity is hard to assess, unlike the TIP uncertainty values. Here, we avoid using the MODIS uncertainty, and instead plot the standard deviation of the spatial average of the 3 \(\times\) 3 pixels surrounding the flux tower in each case.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 6 Future Research Directions  
### SDN/NFV-Enabled HTS Systems  
Leveraging the concepts of software and virtualization, software-defined networks (SDN) and network function visualization (NFV) are expected to be attractive paradigms for satellite communications. The introduction of SDN and NFV paradigms into the current evolution trend of HTS could improve key system characteristics such as flexibility, customization, scalability, etc. However, there are several technical challenges which remain to be solved when turning the SDN-/NFV-enabled HTS systems to practical implementation. Firstly, an efficient and cost-effective deployment strategy for SDN controllers and SDN-enabled smart gateway diversity architecture should be carefully designed. Moreover, automatic and programmable handover operation between gateways is also the cornerstone of SDN-enabled HTS systems. In addition, orchestrating the network resources through network slicing according to the network QoS indicators is another main challenge.
# Containment Control-Guided Boundary Information for Semantic Segmentation
###### Abstract  
Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accuracy of semantic segmentation. The proposed model introduces the concept of containment control in a pioneering way, which treats image interior elements as well as image boundary elements as followers and leaders in containment control, respectively. Based on this, we utilize two learnable feature fusion matrices in the high-level semantic information stage of the model to quantify the fusion process of internal and boundary features. Further, we design a dedicated loss function to update the parameters of the feature fusion matrices based on the criterion of containment control, which enables fine-grained communication between target features. In addition, our model incorporates a Feature Enhancement Unit (FEU) to tackle the challenge of maximizing the utility of multi-scale features essential for semantic segmentation tasks through the meticulous reconstruction of these features. The proposed model proves effective on the publicly available Cityscapes and CamVid datasets, achieving a trade-off between effectiveness and speed.  
semantic segmentation; containment control; features fusion +
Footnote †: journal: Applied sciences
## 4 Parameter Sensitivity Analysis
### The Effect of Thermal Conductivity of the Pipeline Soil Layer on Frost Heave  
By varying the thermal conductivity of the silty clay surrounding the pipeline, the effects on frost heave displacement and the recovery of displacement after thawing were analyzed, as shown in Figure 18. The results indicate that as thermal conductivity increases, frost heave displacement gradually decreases, and more displacement recovery occurs after thawing. When the thermal conductivity of the silty clay is higher, heat is transferred more quickly within the soil, resulting in a more gradual temperature gradient surrounding the pipeline during the winter cooling process, thereby reducing frost heave displacement. During the thawing phase, soil with high thermal conductivity can absorb heat from the pipeline more efficiently and distribute it evenly, promoting the complete thawing of ice crystals and the redistribution of moisture, which aids in the soil's return to its unfrozen state. In contrast, soil with low thermal conductivity experiences a significant temperature lag during the thawing phase, leading to incomplete recovery in certain areas and resulting in a greater amount of residual displacement.  
Figure 16: Time-dependent variation of surface and pipeline top displacements for different pipeline radii.  
Figure 17: Variation of surface displacement with changes in the specific heat capacity of the soil layer surrounding the pipeline.  
Figure 18: Time-dependent displacement of surface point 4 under different thermal conductivity coefficients.
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 6 Conclusions  
The vibroseis truck is in danger of rollover during the steering process, which severely threatens the safety of the drivers and the equipment. This paper researched the rollover stability of and anti-rollover warning systems for vibroseis trucks. The main findings are as follows.  
(1) According to the structure characteristics and working conditions of the vibroseis truck, the analysis of the influence of different factors on the rollover stability of the vibroseis truck was carried out. The results indicated that the rollover stability of the vibroseis truck tends to decrease when the roll angle, steering angle, slope angle, and steering velocity increase. This study lays the foundation for establishing a valid anti-rollover warning system for vibroseis trucks.  
(2) Based on the analysis of a vehicle warning indexes, a new warning index delivering four levels of alert was developed. The warning index considers the absolute quotient of vehicle speed to critical steering speed in the slope steering process as an evaluation index.  
(3) A warning system simulation analysis of the vibroseis truck was carried out by using MATLAB/Simulink software for the flat-road steering process and the slope steering process. It was concluded that the warning index \(A\) can realize an accurate evaluation of the driving state in the two conditions, activating the warning system, which sets off the corresponding level of sound-light alarm. Meanwhile, the result indicated that the vibroseis truck had lower roll stability in the slope steering process. Maintaining a reasonable vehicle speed and steering angle is the most effective way to ensure the stability and safety of a vibroseis truck. This warning system can lay the foundation for developing anti-rollover warning systems for vibroseis trucks and other articulated trucks.  
Conceptualization, M.D.; Software, M.D.; Investigation, M.D.; Data curation, M.D.; Writing--original draft, M.D.; Writing--review & editing, Z.H.; Supervision, Z.H.; Project administration, Z.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nanchong City and Southwest Petroleum University Science and Technology Strategic Cooperation Special Fund (Grant No. SXHZ048).  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable comments to improve the manuscript.  
The authors declare no conflict of interest.  
Figure 18: Comparison of the warning index \(A\) between flat-road steering and slope steering.
## 2 Mathematical Modeling  
### The Piezoelectric Pump Analytical Modeling  
The insulin rate that needs to be pumped for an adult is defined as a minimum of 1.67 \(\mu\)L/min [27]. To pump this amount with piezoelectric disk deflection, the valve selection of the pump is one of the most sensitive parts. Neither active nor passive valves were used in this study because the opening-closing speed and back-pressure constraints of the active valves were inadequate for the designed pump, and passive valves are not only difficult to manufacture but also inapplicable in terms of numerical implementation. Instead, a valveless design was desired to be used. In this valveless design, literature reviews of 3D conical, 2D cone-shaped, or planar cone projected options were examined [16]. When the voltage sent to piezoelectric disk is positive, and it does an upward displacement, insulin is pumped from both inlet and outlet, but more from the outlet. When the voltage is negative, the piezoelectric disk will be in downward displacement, and it will pump insulin from the reservoir from both the inlet and outlet, but more from the inlet. According to the difference in pumping frequency, the speed difference between inlet and outlet can be defined. This is illustrated in Figure 1 below. Nozzle and diffuser cone angle, in other words, divergence angle (\(2\theta^{\circ}\)), differs from \(5^{\circ}\) to \(70^{\circ}\). Optimum angle needs to be calculated for each system, but studies have searched for angle-length variances as well [28].  
The deformation of the piezoelectric pump \(d_{z}\) was calculated using [PERSON]'s theory of thin circular plates with small deflection [29].  
\[d_{z}(r,t)=A_{0}(t)\left[1-\left(\frac{r}{R}\right)^{2}\right]^{2} \tag{1}\]where \(r\) is the radial coordinate, \(R\) is the radius of the piezoelectric disk, and \(A_{0}\) is the time-dependent deflection of the piezoelectric disc. Maximum of \(d_{z}\) is used to calculate the volume. The elliptic conical form is used to calculate the approximate delivered insulin volume analytically. This volume is considered a quarter of one period.  
\[V=\frac{\pi 4R^{2}h}{6}, \tag{2}\]  
where \(h\) is the maximum deflection (\(d_{z}\)) of the piezoelectric disk. The total flow rate from both inlet and outlet can be calculated as follows:  
\[Q_{total}=\frac{V}{t_{quarter}},\text{ where }Q_{total}=Q_{inlet}+Q_{outlet}, \tag{3}\]  
and \(t_{quarter}\) represents time (e.g., 1/400 s for 100 Hz). In order to validate numerical results easily, \(Q_{total}\) equation is used to compare total flow rates in a quarter of one period. Therefore, Equation (3) does not calculate the net flow. The net flow occurs from the difference of \(Q_{inlet}\) and \(Q_{outlet}\) instead. The following net flow rate equation is calculated in Section 3 using summation of inlet and outlet flow rate difference [30].  
\[Q_{net}=\frac{1}{2}\sum_{0}^{1/FR}Q_{inlet}-Q_{outlet}, \tag{4}\]
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 4 System Integration and Experiment
The communication algorithm serves to report to the logistics system the current state of the robot arm and obtain the data from it. The whole process can be summarized as follows: After receiving information from OPIL that the input pallet is present and how many crates are on it, the robot is positioned above the pallet. The robot picks the industrial crate and transfers it to the RFID sensor for identification. Depending on the identification, the robot arm places the crate in a determined position on the corresponding output pallet. When the input pallet is unloaded, the robotic arm returns to the starting position to wait for the next pallet. ```
Input: pallet information and industrial crates identifiers; Result: Sorts crates onto appropriate pallets depending on their identifier;  Initialization loop  goto_position(home)  publish_state (waiting_input_pallet)  wait(input_pallet)  read(input_pallet)  publish_state(input_pallet_arrived) for all crate on input pallet do  publish_state(picking_up_crate)  goto_position(input_pallet)  goto_position(crate)  pick_up_crate()  publish_state(crate_picked_up)  goto_position(RFID_scanner)  publish_state(scanning)  pallet, drop_crate_position := read(crate)  publish_state(scanned)  goto_position (pallet)  goto_position(drop_crate_position)  publish_state(dropping_down_crate)  drop_crate()  publish_state(crate_dropped_down) endloop
```  
**Algorithm 2** Sorting/palletizing module realization. The algorithm for communication (Algorithm 3) has the role of informing the OPIL system of the state in which the robotic arm is currently found and to take data for palletizing task. The states in which the robot can be found are predefined states depending on the task that it is currently performing. With the help of this module, the robotic arm receives information about the environment around it through a logistics system. ```
Data: The identifier of the task that the robot performs and data about pallets and industrial crates Result: Reports the robot arm state to the logistics system and communicate sorting data to robot initialization loop wait(new_message) robot_state := read(new_message) switch(robot_state) case\(waiting\_input\_pallet\): wait(input_pallet) case\(input\_pallet\_arrived\): read(input_pallet) publish(input_pallet) case\(picking\_up\_crate\): pass; case\(rate\_picked\_up\): write(time&date) case\(scanning\): wait(crate) case\(scanned\): read(crate); publish(crate) case\(dropping\_down\_crate\): pass; case\(rate\_dropped\_down\): write(time&date) endswitch reset robot_state endloop
```  
**Algorithm 3** Communication with OPIL module.
## 2 Finite Element Analysis of the Electromagnetic Track Launcher
### Finite Element Calculation Results
#### 2.2.3 Temperature of Armature and Track  
In the armature launching process, the high frequency current will bring about a high temperature rise gradient. The temperature distributions at different times are calculated by LS-dyna as shown in Figure 9.  
Figure 8: Inductance gradient curve with time at different conductivities. (**a**) Inductance gradient at different armature conductivities \(\sigma_{\mathrm{d}}\); (**b**) inductance gradient at different track conductivities \(\sigma_{\mathrm{g}}\).  
Figure 9: Temperature distribution at different time points. (**a**) \(t\) = 0.10 ms; (**b**) \(t\) = 0.25 ms; (**c**) \(t\) = 0.40 ms; (**d**) \(t\) = 0.55 ms.  
It can be seen that the uneven distribution of the current density leads to a larger temperature of the armature at the current aggregation, and the heat is mainly concentrated in the armature center and the contact of the armature tail and track. This is consistent with the distribution of the current density in Figure 6.  
The temperature curves of the armature and track are calculated by LS-dyna as shown in Figure 10.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 3 Methods
### Identifying Local Modularity Structures  
To identify local modularity structures, we consider seed proteins calculated by Equation (13) as initial nodes to generate clusters by first computing the support function, followed by the local modularity function. Hence, using Equation (4) first, we calculate the similarity score between a seed protein \(v\) and its immediate proteins gradually adding the neighboring proteins with the help of the support function and the local modularity function in order to generate cluster \(K\) as sub-network. To prioritize each neighboring protein \(u\), first, we calculate the support function to measure how close the protein \(u\) is to the cluster \(K\) using the formula  
\[supp(u,K)=\frac{\sum_{u^{\prime}\in K\cap N(u)}w(u,u^{\prime})}{\sum_{u^{\prime} \in N(u)}w(u,u^{\prime})}, \tag{14}\]  
where \(supp(u,K)\) is the support function in the range \([0,1)\), \(u\
otin K\), and \(\sum_{u^{\prime}\in K\cap N(u)}w(u,u^{\prime})\) is the summation of the edge weight linking protein \(u\) to \(K\), and \(\sum_{u^{\prime}\in N(u)}w(u,u^{\prime})\) is the total degree weight of protein \(u\). The above-prioritizing approach can be extended iteratively for the neighbors of any initial cluster \(K\). Thus, in each iteration step, according to the priority of the neighbors, the decision to join the cluster is made by the local modularity function.  
Given subnetwork \(K\) of \(\mathcal{G}\), we can define weights in-degree as the sum of the weight of edges linking protein \(u\) to other proteins in \(K\) denoted by \(w_{in}(K)\) and weighted out-degree as the sum of the weight of edges linking protein \(v\) to proteins in the rest of \(\mathcal{G}-K\) denoted by \(w_{out}(K)\). Thus, we can define \(w_{in}(K)\) and \(w_{out}(K)\) by  
\[w_{in}(K)=\sum_{\begin{subarray}{c}u,u^{\prime}\in K\\ w(u,u^{\prime})\in W\end{subarray}}w(u,u^{\prime}), \tag{15}\]  
and  
\[w_{out}(K)=\sum_{\begin{subarray}{c}u^{\prime}\in K,u\
otin K\\ w(u,u^{\prime})\in W\end{subarray}}w(u,u^{\prime}), \tag{16}\]  
where \(w\) represents the weight of the edges in sub-network \(K\). To determine the local modularity structure in sub-network \(K\), we defined modular uncertainty correction threshold value \(\eta\) in the interval of \([0,1)\). Using Equations (15) and (16), we can define the local modularity of sub-network \(K\) by  
\[\mathcal{Q}(\eta,K)=\frac{w_{in}(K)}{\left(w_{in}(K)+w_{out}(K)+\eta\cdot|V_{ K}|\right)^{\alpha}}\,, \tag{17}\]  
where \(\mathcal{Q}(\eta,K)\) takes a value \((0,1)\); \(|V_{K}|\) is total number of proteins in \(K\), \(\eta\) is predefined modular uncertainty correction parameter in the range of \((0,1]\), \(\alpha\) is the ratio of the internal interaction to the total interaction in the community. We set \(\alpha=1.0\) in order to detect high \(w_{in}(K)\) and a low \(w_{out}(K)\) which makes it efficient in the detection of local modularity structure. A neighboring node is added to \(K\), if extending \(K\) by the given node, the value of the local modularity function would increase.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 3 Validation Methodology  
The validation approach has three main steps (Figure 2): (1) Validation of the SL2P-based Sentinel-2 LAI product and generation of reference LAI maps after recalibration with field measurements; (2) Validation of the hectometric and kilometric MODIS, GEOV2, GEOV3, EPS and GLASS and C3S LAI products; and (3) Validation of the phenological metrics derived from time series of LAI.
## 3 Discussions
### Mechanism of AE Events Neighboring to the 3D Internal Flaw before UCS Status  
Figure 7 illustrates that many AE events initially gather around the 3D internal preset flaw before reaching the UCS status, and the detailed mechanism of AE events is explained by the Moment tensor inversion method [45]. Based on this method, the moment tensor of an AE event can be separated into three components, isotropic (ISO), double couple (DC), and compensated linear vector dipole (CLVD) [46], and the corresponding calculation formula is expressed as follows:  
\[\mathrm{m}=\begin{bmatrix}\mathrm{M}_{1}&0&0\\ 0&\mathrm{M}_{2}&0\\ 0&0&\mathrm{M}_{3}\end{bmatrix}=\mathrm{m}^{\mathrm{ISO}}+\mathrm{m}^{\mathrm{ Deviatoric}}=\mathrm{m}^{\mathrm{ISO}}+\mathrm{m}^{\mathrm{DC}}+\mathrm{m}^{ \mathrm{CLVD}} \tag{3}\]  
\[\mathrm{ISO}=\frac{100 tr(m)}{|tr(m)|+\sum_{i=1}^{3}\bigl{|}m_{i}^{*}\bigr{|}} \tag{4}\]  
\[\mathrm{DC}=\frac{m_{3}^{*}(1-2F)}{\bigl{|}m_{3}^{*}(1-2F)\bigr{|}+\bigl{|}2m_ {3}^{*}F\bigr{|}}\bigl{(}100-\mathrm{ISO}\bigr{)} \tag{5}\]  
\[\mathrm{CLVD}=\frac{2m_{3}^{*}F}{\bigl{|}m_{3}^{*}(1-2F)\bigr{|}+\bigl{|}2m_ {3}^{*}F\bigr{|}}\bigl{(}100-\mathrm{ISO}\bigr{)} \tag{6}\]  
\[F=-\frac{m_{3}^{*}}{m_{3}^{*}} \tag{7}\]  
where \(tr(m)\) is the trace of the matrix; \(m_{i}^{*}\)(\(i\) = 1,2,3) are the three eigenvalues of the deviatoric component of the moment tensor.  
[PERSON] [47] sets the DC percentage (%DC) to the standards, and the AE event is defined as a shear failure when the %DC is more than 70. The typical tensile failure occurs as the %DC is less than 40, while events accompanied by %DC between 40 and 70 represent mixed tensile-shear failures.  
Figure 12 presents the event mechanism by the beach ball, and the selected events are neighboring to the internal 3D flaw. The %DC of all events in the Design-1 sample are less than 70 (tensile failures play the dominant role), and 50% of events belong to the typical tensile failure (%DC is less than 40). In the Design-2 and 3 samples, events that are located at the middle position in the projection view belong to the pure tensile or tensile-shear failures, and events that occur near the flaw boundary present as the typical shear failures.  
Based on previous theoretical analysis results [48], it can be concluded that the relatively large non-uniform deformation occurs in the upper and lower free surfaces of the internal 3D flaw, and its central position is more prone to tensile failure; as the inclination angle of the preset flaw increases, its boundary position is more prone to shear failure.  
Figure 12: Location and beach ball figures of AE events neighboring to the internal 3D flaw before reaching the UCS status: (**a**) calculation results of the Design-1 sample; (**b**) calculation results of the Design-2 sample; (**c**) calculation results of the Design-3 sample.