esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 3 Model Construction  
The energy demand forecasting model in the multi-energy coupling scenario is mainly composed of three parts, including the minimal redundancy maximal relevance (mRMR) model for the selection of key influencing factors, the adaptive fireworks algorithm (AFWA) algorithm for the optimization of key parameters, and the least squares support vector machine (LSSVM) model for the energy demand prediction in the multi-energy coupling model [28; 36; 37].
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 3 Multipath Routing and Spectrum Allocation Algorithm
### Primary Path Spectrum Allocation Algorithm
If the link (\(u\),\(v\)) is not allocated a channel, then the following strategy is used to allocate the channel. Channel \(k\) is selected according to Equation (10), and the residual bandwidth \(\mathrm{B}_{\varphi}^{k}(u,v)\) is calculated according to Equation (6). If \(\mathrm{B}_{\varphi}^{k}(u,v)\) is larger than \(b_{i}\), then the channel \(k\) is allocated to link (\(u\),\(v\)). If there is a link that cannot meet the QoS constraint, then the tag variable flag is set to 0, which means that the request cannot be accepted. ```
Input: \(G=(V,E)\), \(\gamma_{i}=(s_{i}d_{i}b_{i})\), \(\alpha(s_{i},d_{i})\) Output: \(x(u\),\(v)\) (1) Flag \(\gets\) 1; (2) while (\(\forall(u,v)\in\alpha(s_{i},d_{i})\)){ if (\(x(u\),\(v)=0\)){ Calculate \(x_{1}^{*}(u,v)\) according to Equation (9); \(k\gets x_{1}^{*}(u,v)\); (6) Calculate \(\mathrm{B}_{\varphi}^{k}(u,v)\) according to Equation (6); (7) if(\(b_{i}>\mathrm{B}_{\varphi}^{k}(u,v)\)){ Sort \(f_{1}^{k}(u,v)\) ascending; (9) while(\(\forall k\in K(u,v)\)){ if(\(b_{i}<\mathrm{B}_{\varphi}^{k}(u,v)\)){ if(\(u\),\(v\)) \(-k\); (12) break; (13) //end if (14) //end while (15) |else{ \(x(u\),\(v)\gets x_{1}^{*}(u,v)\); (17) //end if (18) |//end if if(\(x(u\),\(v)=0\)){ flag \(\gets\) 0; (21) break; (22) |//end if (23) |//end while (24) return flag
```
**Algorithm 3** PPSR
## 2 Data  
We utilized polarimetric observations from an operational dual-polarization S-band weather radar network operated by the KMA from 1 May to 31 July 2020. The KMA replaced single-polarization radars with dual-polarization radars from 2014 to 2019. The KMA constructed a homogeneous S-band dual-polarization radar network with the same specifications. The KMA's radar network was mainly installed along the border line and coastal area to support wide network coverage over the Korean Peninsula (Figure 1). The scanning strategy performs a three-dimensional observation consisting of nine elevation angles every 5 min. Table 1 presents the scan strategy of each radar within the KMA network. Three radars (KWK, MYN, and PSN) located at high altitudes (>600 m mean sea level (MSL)) include the negative (\(-\)) elevation as the lowest elevation angle. All elevation angles use the dual-PRF mode (5:4) to increase Nyquist velocity to alleviate velocity aliasing. Table 2 shows the specific radar parameters of GSN radar. A long pulse with a pulse width of 2 \(\upmu\)s was transmitted at low elevation angles (less than 3\({}^{\circ}\)) to improve the sensitivity of polarimetric observations [28], while a short pulse with a pulse width of 1 \(\upmu\)s was used at high elevation angles (>3\({}^{\circ}\)) to increase the pulse repetition frequency (PRF). The azimuth resolution and bin size were 1.0\({}^{\circ}\) and 250 m, respectively, for all elevation angles. To monitor the Z\({}_{\rm C}\), unfiltered Z\({}_{\rm H}\) (Z\({}_{\rm H}\) before applying the clutter filter in the signal processing procedure such as IIR and GMAP) must be recorded. Z\({}_{\rm H}\) before quality control process was used for the GC monitoring. The Z\({}_{\rm H}\) after the quality control process developed by [PERSON] et al. [29] was used for SC and intercomparison among Z\({}_{\rm H}\) across the network.  
Figure 1: Deployment of KMA’s S-band dual-polarization weather radar network. The ten circles indicate the 240 km radius coverage of each radar within the KMA network.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
###### Abstract  
The identification of branches and bud points is the key to intelligent pruning of dormant grapevine branches and precise positioning of the pruning point on the branch is an important prerequisite for robotic arm pruning. This study takes Cabernet Sauvignon wine grapes as the experimental object and proposes a depth image-based pruning point localization algorithm based on pruning rules. In order to solve the problem of bud recognition in complex backgrounds, this study adopts a detection method that combines semantic segmentation and target detection. Firstly, the semantic segmentation algorithm PSP-net is used to separate the branches and the main stem from the background and the separated image undergoes two kinds of processing: one is to skeletonize it using the Zhang-Suen thinning algorithm and the other is to identify the buds and obtain the center coordinates of the buds using the target-detection method YOLOv5; finally, combining with the depth information of the depth image, we use the coordinates of the buds to determine the location of the pruning point located on the skeleton image. The results show that PSP-net has better results in segmentation performance with mIoU reaching 83.73%. YOLOv5 performs better in target detection with mAP reaching 81.06% and F\({}_{1}\) reaching 0.80. The accuracy of this method in determining the location of pruning points reaches 82.35%. It can provide a method for fruit tree pruning robots to determine the location of pruning points.  
deep learning; agriculture; machine vision; target detection; grapevine pruning +
Footnote †: journal: Applied sciences
## 4 Discussion
### Basin-Scale Factors Hold Enough Information to Predict Potential Metal Pollution  
Differing degrees of accuracy for different chemical elements are common and have been observed elsewhere when using R.F. or other models [35,80]. Several reasons for differing performance come to mind: (1) Differences in the quality of the measured concentration data. There could be an observation bias, i.e., those specific elements have been measured under certain conditions that may not represent the general distribution of such elements. (2) Differences in required input data. It may be that necessary input variables and data, which play an important role in the processes that lead to the chemical concentrations, are not present in the selection of variables created for this study. In addition, spatial effects could play a role in the determination of metal concentrations. The concentration of Hg, for example, could be stronger affected by processes and sources in the direct proximity of the sampling site rather than by factors at the basin scale. (3) Differences in origin and transport and accumulation processes. These differ based on many combinations of physical factors [81, 82, 83]. These may interact with the chemical properties and behavior of the studied elements to form complex patterns [84, 85, 86] that could not be captured with the present data or methods.  
The R.F. models were set up with data accumulated at the basin scale. They generally performed well in categorizing the sediment concentrations of the twelve metals. The best performance was found for Al, with an OOB error of 16.5%. The poorest performance was found for Hg, with an OOB error of 28.1% (Table 2). Our results indicate that the information accumulated to the basin level can be utilized to predict a potential metal concentration for most metals. Moreover, this study's finding is that the results differ greatly between the metals, even though the same input data were available for all samples. This indicates differences in the power of the basin-scale to predict metal concentrations. These differences may not have been detected in a study focused on fewer metals.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 6 Data Forwarding in Extended ERASCA Protocol (EERASCA)  
In this section--Stages 7, 8 and 9 are explained along with mathematical analysis. In EERASCA, it is the responsibility of the member node to transfer the data packet to the desired node either through multicasting or unicasting (Stage 9). For example, as shown in Figure 4, a sender wants to send data packet D to R39. However, the source is not part of the mesh and will transfer the data packet to R39 through mesh member. In this situation, R38 is in a significant position, and it is the responsibility of R38 to transfer the data from the sender/source within the mesh to the desired destination receiver. For this purpose, a routing table is maintained through SD message. The core broadcasts the SD message periodically to maintain and update the group, and all the mesh members will be able to know their connected members through a SD message. Through the routing table, R38 will be able to transfer data packet through unicasting or multicasting.  
Figure 4: Data Forwarding in EERASCA.  
Figure 3: Mesh Formation.  
Two situations arise in EERASCA (Stage 7): (1) If the destined receiver from R38 is within the three-hops distance as shown in Figure 4, then unicasting is used for data forwarding to quickly receive the data with less overhead because unicasting creates less overhead as compared to flooding. (2) If the destined receiver is at a four-hop distance or more, then multicasting is used compared to flooding. Again, multicasting is creating less overhead and sends the data only to desired nodes. Hence, unicasting and multicasting create less overheads and less delays as compared to flooding.  
Here, \(n\) represents the number of hopes, and it is equal to three. Hence, if the destined node receives within the n hopes distance (represent less distant nodes), unicasting will be used. However, if \(n+1\) (represent far away distant node) occurs, then multicasting will be used. In EERASCA, the packet is reached to R38 from a source outside the mesh, then it is flooded in the mesh, and the destined receiver receives the data. As a result, all the nodes receive the packet and resources of the group and network decreases.  
This section is not mandatory but may be added if there are patents resulting from the work reported in this manuscript.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 5 Mission Process Control Strategy  
### Control Constraints  
Prior to the solution of the control strategy, it is necessary to determine the control constraints in order to limit the solution space of the control strategy.  
**Constraint 1.** (_Mission Type Constraint_). In the case of considering only delay disturbances, the control quantity \(u_{m_{i}}(t)\) represents the change in mission duration of \(m_{i}\), and if \(u_{m_{i}}(t)>0\), it means that the mission duration of mission \(m_{i}\) must be compressible. In practice, however, it is not necessarily the case that the given durations of all missions can be compressed, due to the constraints of specific mission situations. Therefore, we define two types of missions based on the degree of controllability of the mission, namely, controllable and uncontrollable missions. A controllable mission means that the planned duration of the mission can be compressed, and an uncontrollable mission means that the planned duration of the mission is a fixed value and cannot be changed during the execution of the mission. The control quantity is only valid when applied to a controllable mission. The mission type of \(c_{m_{i}}\) is defined by \(m_{i}\) with the expression  
\[c_{m_{i}}=\left\{\begin{array}{ll}0,\ \text{uncontrollable mission}\\ 1,\ \text{controllable mission}\end{array}\right. \tag{43}\]  
For controllable missions, we need to specify the controllable range of mission durations in advance, because the controllable mission durations cannot be compressed indefinitely to eliminate delay perturbations, taking into account the actual execution capabilities of the executors. Therefore, we must determine the shortest mission duration of all controllable missions, and we use \(x_{m_{i}}^{\min}\) to denote the shortest mission duration of the controllable mission \(m_{i}\), i.e.,  
\[x_{m_{i}}(t)\geq x_{m_{i}}^{\min}\ |\ c_{m_{i}}=1 \tag{44}\]  
Therefore, the mission type constraint on the control quantity can be expressed as follows:  
\[\left\{\begin{array}{ll}u_{m_{i}}(t)=0\ |\ c_{m_{i}}=0\\ x_{m_{i}}^{\min}-x_{m_{i}}(t)\leq u_{m_{i}}(t)\leq 0\ |\ c_{m_{i}}=1\end{array}\right. \tag{45}\]  
**Constraint 2.** (_Mission State Constraint_). Equation (19) defines the three states of a mission, and only the unstarted mission is controllable at the current moment. For a mission that has already ended, it is obviously uncontrollable. As for the ongoing mission, since the execution of a mission consumes certain resources, once the mission starts, it means that the resources may have already been put into use, and we no longer adjust the ongoing mission temporarily, which also reduces the cognitive load of the executor. Therefore, the mission state constraint of the control volume can be expressed as follows:  
\[\left\{\begin{array}{ll}u_{m_{i}}(t)=0\ |\ s_{m_{i}}(t)>1\\ u_{m_{i}}(t)\leq 0\ |\ s_{m_{i}}(t)=0\end{array}\right. \tag{46}\]  
The control quantity \(\mathbf{u}(t)\) obtained at any moment must satisfy the constraints given by (45) and (46).
# Review of Passive Flow Control Methods for Compressor Linear Cascades
## 3 Discussion
### Future Directions  
Looking ahead, several promising opportunities exist for advancing passive control methods in compressor technology. One major challenge is their validation in multi-stage compressors, as most studies focus on single-stage configurations. Future research should explore how passive flow control techniques can be scaled to address the increasedcomplexities of multi-stage systems. Additionally, hybrid approaches that combine passive and active flow control could provide synergies, improving aerodynamic performance in high-load compressors.  
Emerging technologies such as artificial intelligence (AI) and machine learning (ML) offer exciting possibilities for optimizing flow control. By integrating AI-driven models with physics-based simulations, researchers can enhance real-time optimization, predictive maintenance, and design decision-making, particularly for multi-stage compressors. Advances in high-performance computing (HPC) and data-driven turbulence modelling, such as ML-enhanced RANS or LES, could further improve the accuracy and efficiency of flow simulations, enabling faster and more reliable design processes.  
To enhance robustness across different operating conditions, future research should focus on adaptive and hybrid control approaches. Advanced material technologies, including self-healing coatings and high-temperature-resistant composites, could extend the longevity of passive control elements. Additionally, high-fidelity simulations and experimental studies under realistic engine conditions will be crucial for refining these methods and ensuring their practical applicability.  
Experimental validation remains a critical aspect of future research. High-fidelity experiments, combined with uncertainty quantification, are necessary to strengthen confidence in passive control methods and confirm their effectiveness across various compressor configurations, as part of ongoing efforts such as the project 'Advanced Fundamental Re-search on the Development of High-Energy-Efficiency Microturboprop Engines for Multirole Unmanned Aerial Vehicles', developed by COMOTI under project number PN23.12.01.01, which explores innovative approaches to improve aerodynamic performance in UAV propulsion systems.  
Furthermore, bio-inspired design innovations--leveraging insights from natural systems and advanced biomimetic materials--could lead to novel solutions for stabilizing flow separation and enhancing aerodynamic efficiency.  
Finally, the practical implementation of passive control strategies in industrial compressors presents challenges related to manufacturing feasibility, durability, and integration into existing systems. Addressing these factors will be essential to ensure the successful adoption of passive flow control methods in real-world applications. By overcoming these limitations, passive control strategies can play a key role in next-generation compressor designs, optimizing performance while maintaining reliability and durability.
## 3 Results and Discussions
### Cable Stiffness Effect  
In recent years, synthetic rope has been used to replace steel wire rope in some deep-sea and renewable energy applications [28]. Stiffness of these two kinds of ropes is quite different, which may have great influence on the system's dynamic response. Three cases with different cable stiffnesses are simulated, with variable Young's modulus and but other cross-section parameters constant.  
Figure 11: Influence of the payload’s drag coefficient on the snap load of the cable–payload system in 300 m depth, with a structural damping ratio of 0.1.  
Figure 12: Influence of the structural damping ratio on the snap load of the cable–payload system in 300 m depth, with different drag coefficients (\(C_{d,p}\)) of the payload: (**a**) \(C_{d,p}=0.7\); (**b**) \(C_{d,p}=7\).  
The equipment is lowered with a velocity of 0.2 m/s to 3000 m water depth. The top of the cable is also imposed with a sinusoidal excitation (\(H_{s}\) = 0.3 m, \(T_{s}\) = 9 s).  
The motion of the equipment at a 1500 m depth is plotted in Figure 12(a). For the cable with a stiffness of 315 MN, the equipment oscillates around its equilibrium position, which is the static elongation of the cable. As the cable stiffness decreases, the static elongation of the cable increases. For the case where \(EA\) = 315 MN, the depth zone around 1500 m is in the resonant zone of the cable-payload system under excitation, with a period of 9 s, but this is not true for the other two cases. Therefore, the payload's vibration amplitude is the most significant. Correspondingly, the traction on the payload is the largest (see Figure 12(b)). It is notable that the cable stiffness also affects the phase of the payload's response.  
For cable-payload systems with different cable stiffnesses, the traction on the top of the cable is plotted in Figure 14. The resonance water depth increases with the cable stiffness. This is because that the natural frequency of the system is mainly determined by the ratio between the cable stiffness and the cable mass. The larger the cable stiffness is, the larger the resonance depth is. From Figure 13(b), it can be seen that the peak of the dynamic force is not significantly affected by the cable stiffness.  
In general, analysis shows that the present model is capable of considering effects of the rope's structural damping and lowering velocity, as well as nonlinear effects like drag forces and snap loads. In previous studies, none of the models has incorporated all these effects. The present model extends the application of lumped-mass method in continuous lowering analysis, and provides a practical tool for offshore installation design.  
Figure 14: Influence of the cable stiffness on the traction on the top of the cable: (**a**) traction envelope, (**b**) dynamic force.  
Figure 13: Comparison of the payload’s response with different cable stiffness coefficients for 1500 m depth: (**a**) motion of the payload, (**b**) traction on the payload.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 5 Conclusions  
In this work, we have addressed the random number generation issue under conditions of ionizing radiation. This condition can be found in LEO where many pico-satellites that use FPGAs among other components are deployed. The functionality of deterministic logic implemented on FPGAs under ionizing radiation has been investigated deeply by the scientific community. Nevertheless, up-to date, the influence of ionization over non-deterministic components such as cryptographic components has not been as well studied. In this vein, we have tested the influence of ionizing radiation over two well-known TRNGs based on sampling jittery signals [12; 13]. We have used two different NIST test suites [5; 18] to evaluate the randomness quality (e.g., entropy or linear complexity) of both TRNGs for different accumulated doses. During the experiment, a degradation of the statistical properties of both TRNGs was observed. It is very remarkable that deterioration occurs before the first failure in the deterministic blocks. Therefore, the non-deterministic component (TRNG in our particular case) determines the maximum level of radiation allowed in the integrated circuit. All this points out the importance about considering the non-deterministic components when radiation is at stake.  
All authors contributed equally to the work presented in this paper.  
This work was partially supported by the Spanish Ministry of Economy and Competitiveness under contracts ESP-2015-68245-C4-1-P and ESP-2015-68245-C4-4-P; by the MINECO grant TIN2016-79095-C2-2-R (SMOG-DEV - Security mechanisms for fog computing: advanced security for devices); and by the CAM grant S2013/ICE-3095 (CIBER- DINE: Cybersecurity, Data, and Risks).  
The authors declare no conflict of interest.
# Synthesis and Characterization of Highly Crystalline Vertically Aligned WSe\({}_{2}\) Nanosheets
## Materials and Methods
### Materials
#### 2.1.2 Characterization Techniques  
Scanning and transmission electron microscopy (SEM and TEM, respectively) analyses were conducted on a JEOL 7500F microscope operating at 15 kV and on a TECNAI T20 microscope working under 200 kV, respectively. The WSe\({}_{2}\) nanosheets were examined using Raman spectroscopy, photoluminescence (PL), and UV-visible spectroscopy. The Raman and PL spectra were obtained by using a micro-Raman system (Senterra Bruker Optik GmbH) with a 3 cm\({}^{-1}\) resolution, using a laser excitation laser source (532 nm wavelength), and a laser power of 2 and 5 mW, respectively. X-ray diffraction (XRD) was used to characterize the samples using a Panalytical X'Pert PRO diffractometer (comprising Cu K\(\alpha\) radiation, Bragg-Brentano geometry, a sealed tube operated at 45 mA 30 kV, and a X'Celerator linear detector). The chemical composition of the WSe\({}_{2}\) nanosheets was studied with X-ray photoelectron spectroscopy (XPS) using an Escalab 250i Thermo Fisher ScientificTM instrument (consisting of a monochromatic Al K\(\alpha\) X-ray source and a hemispherical deflector analyzer working at a constant pass energy).
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 3 HSI Classification Method in Explainable Two-Layer Mode
### Front-End Learning Layer for Data Re-Expression  
The front-end learning layer completes the band selection task, which gives HSI data a low-dimension re-expression. Our sparse SSC method combines the superpixel segmentation strategy, the \(l_{2,1}\) norm, and graph regularization to explore HSI information fully.