esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Modeling Performance  
The developed model predicted the average skiing speed of 6.56 \(\pm\) 0.96 m/s, which was very close to the actual observed average speed of 6.51 \(\pm\) 1.02 m/s, showing a strong agreement between the predicted and real values. The mean absolute error (MAE) between the predicted and actual speeds was 0.254 \(\pm\) 0.183 m/s, corresponding to an error margin of 4.20% \(\pm\) 3.46% relative to the actual observed speeds. This strong congruence between the predicted and actual speeds is visualized in Figure 2, where the comparative analysis clearly demonstrates a high correlation coefficient of 0.953 (\(p\) < 0.001), indicating the accuracy and reliability of the model in predicting skiing performance.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Parameter** & **Relative Importance** \\ \hline Gross cycle kinematics & Cycle length on uphill terrain & 0.66 \\  & Cycle length on flat terrain & 0.16 \\  & Cycle rate on uphill terrain & 0.013 \\  & Cycle rate on flat terrain & 0.008 \\ Kinematics of the CoM & Vertical displacement during the poling phase on uphill terrain & 0.052 \\  & Vertical displacement during the poling phase on flat terrain & 0.011 \\ Physiological variables & Maximal oxygen uptake & 0.034 \\  & 30 s anaerobic capacity & 0.028 \\  & Length of standing long jump & 0.017 \\  & Number of pull-ups & 0.014 \\ Categorical variables & Sex & 0.001 \\  & Technique (i.e., classical or freestyle) & 0 \\  & Cycle rate & \textless{}0.01 \\ \hline \end{tabular}
\end{table}
Table 4: The relative importance of kinematic and physiological parameters for skiing speed.  
Figure 2: Comparative analysis of XCS speeds: model predictions versus actual measurements.  
Furthermore, the Bland-Altman analysis shown in Figure 3 provides a detailed assessment of the prediction accuracy by comparing the predicted and actual values. This analysis revealed that 97.1% of the predictions fell within the 95% agreement limits, which are represented by the dashed lines on the plot. Only one data point fell outside these limits, further confirming the exceptional precision and reliability of the model's speed predictions. The Bland-Altman plot underscores the model's capability to provide accurate and consistent estimates, with minimal bias in the predictions.
## 3 Background on YANG and OPC UA
### Opc Ua
#### 3.2.1. OPC UA Address Space Model  
The address space model serves as the OPC UA's meta-model, laying the groundwork for OPC UA information models. Its main goal is to provide servers with a standardized way to represent objects to clients. Figure 5 summarizes all of the elements of the OPC UA address space model. The address space comprises a collection of nodes, each described by its attributes and references, which define the set of objects and their relational information [39]. Each OPC UA information model is identified using a namespace URI, and each OPC UA node is identified using an identifier. An OPC UA node belongs to a node class defined in terms of the attributes and references instantiated when a node is defined in the address space. Attributes are data elements that describe nodes, and OPC UA clients can access attributes using read, write, query, and subscribe functions. References establish connections of the OPC UA node to other OPC UA nodes. OPC UA defines node classes, which fall into three categories: node classes used to define instances, node classes used to describe types, and data types.
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method
#### 5.3.4 Convolutional Neural Networks (CNN)
The fault diagnosis method based on artificial intelligence does not require an accurate mathematical model, and the accuracy of the diagnosis is high. However, the model has high requirements for calculation, storage, and other resources and requires a large amount of historical data. However, In daily agricultural activities, many quantity monitoring systems tend to collect and store real-time data, and insufficient amounts of historical data may reduce the accuracy of the model. The fault diagnosis method based on deep learning overcomes the flaw of the manual feature extraction method based on machine learning, and the model accuracy is higher; however, the method for fault diagnosis based on deep learning has flaws. To ensure the accuracy of the fault diagnosis model, it must be debugged and simulated multiple times, which wastes a significant amount of human resources and requires more resources such as computation and storage. Table 4 displays the advantages and disadvantages of each method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Advantages** & & **Disadvantages** \\ \hline Model- & & & & \(\bullet\) & Poor universality \\ \multirow{2}{*}{based} & \(\bullet\) & Strong interpretability & \(\bullet\) & Struggles to establish accurate models \\  & \(\bullet\) & Good real-time performance & \(\bullet\) & The amount of data processed is limited \\ \hline \multirow{4}{*}{AI-based} & & & \(\bullet\) & Poor real-time performance \\  & \(\bullet\) & Good interpretability & \(\bullet\) & It is easy to misjudge when the data \\  & \(\bullet\) & No need to understand the structure & & fluctuates greatly \\  & & and principle of the sensor system & \(\bullet\) & Suitable only for working with \\  & & & & univariate data \\ \hline \multirow{4}{*}{AI-based} & & \(\bullet\) & Strong fault tolerance & \(\bullet\) & Lack of versatility and flexibility \\  & \(\bullet\) & Autonomous learning ability & \(\bullet\) & Systems are independent of each \\ \cline{1-1}  & \(\bullet\) & Good performance in nonlinear systems & & other, making it difficult to handle \\ \cline{1-1}  & & & & multivariate data \\ \hline \multirow{2}{*}{SVM [81]} & \(\bullet\) & Strong ability to handle small samples & \(\bullet\) & Weak multi-classification ability \\  & & and nonlinear data & \(\bullet\) & Poor real-time performance \\ \hline \multirow{2}{*}{NN [83]} & \(\bullet\) & Good universality & \(\bullet\) & High requirements for data volume \\  & \(\bullet\) & High diagnostic accuracy & \(\bullet\) & High requirements for data volume \\ \cline{1-1}  & \(\bullet\) & Be able to handle multivariate data & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of advantages and disadvantages of intelligent fault diagnosis methods.
# Decentralized Blind Spectrum Selection in Cognitive Radio Networks Considering Handoff Cost
###### Abstract  
Due to the spectrum varying nature of cognitive radio networks, secondary users are required to perform spectrum handoffs when the spectrum is occupied by primary users, which will lead to a handoff delay. In this paper, based on the multi-armed bandit framework of medium access in decentralized cognitive radio networks, we investigate blind spectrum selection problem of secondary users whose sensing ability of cognitive radio is limited and the channel statistics are a priori unknown, taking the handoff delay as a fixed handoff cost into consideration. In this scenario, secondary users have to make the choice of either staying foregoing spectrum with low availability or handing off to another spectrum with higher availability. We model the problem and investigate the performance of three representative policies, i.e., \(\varrho^{\text{PRE}}\), SL(K), \(k\)th-UCB1. The simulation results show that, despite the inclusion of the fixed handoff cost, these policies achieve the same asymptotic performance as that without handoff cost. Moreover, through comparison of these policies, we found the \(k\)th-UCB1 policy has better overall performance.  
cognitive radio networks; opportunistic spectrum access; multi-armed bandit; handoff cost; distributed algorithms +
Footnote †: journal:  
_Future Internet_
## 1 Introduction  
Unmanned Aerial Vehicles (UAVs) have gained popularity in the last decades due to their capability for moving in three-dimensional space. UAVs were first used for military purposes. However, they are now used for surveillance, research, monitoring, and search and rescue activities [1]. These kinds of vehicles are suited for situations that are too dangerous and hazardous where direct monitoring is not humanly possible [2].  
One of the challenges of UAV is the loss of communication with the remote pilot. For this reason, it is necessary to provide the vehicle with a certain level of autonomy to maintain flight in such scenarios. A UAV must be able to adapt and change its path according to ground position and weather conditions, since sustaining an aircraft when flying through a dense cloud is not possible [3].  
Given weather indicators that allow the detection of clouds, can be seen from long distances [4]; it is possible to develop an intelligent system capable of avoiding them.  
Cloud detection is a very challenging task; each big water cluster has a unique amorphous shape, which is continuously changing; making it impossible to extract characteristic features to be tracked with some descriptor such as with Speeded Up Robust Features (SURF) [5], then, other methods to extract information are needed, such as segmentation based on color, texture, and illumination [6; 7; 8; 9].  
In Reference [10], several simple-to-implement descriptors with linear computational costs are presented, showing a good training and generalization. Results from a video camera mounted on a UAV reported satisfactory results for two and three class classification in real-time.  
Our proposed scheme describes and implements an approach to classify three elements of the environment (ground, sky, and clouds), using Superpixel Segmentation (SPS) and Support Vector Machine (SVM) to pre-train a Convolutional Neural Network (CNN), which is a form of deep learning model, trained end-to-end from raw pixel intensity values to classifier outputs. The spatial structure of the images makes it suitable to work with this kind of networks, setting connectivity between the filters (or layers) and the parameter sharing, and discrete convolutions [11].  
The used images in this work were captured by a camera mounted on a UAV provided by Hydra Technologies of Mexico(r); an example of an obtained image is shown in Figure 1.  
The outline of this papers is as follows: In Section 2, related work is presented. Section 3 presents a brief description of the used SVM whose output is used to pre-train the CNN. Section 4 presents the descriptors based on SPS methodology. In Section 5, the CNN architecture is described. Experimental results are presented in Section 6 and important conclusions are discussed in Section 7.
## 6 Proposed Interaction Diagram for Plugging
### Diameter Criterion  
Using both interaction diagrams resulted in the same accuracy of 74.3%, which means that the plugging condition of the pile could be forecasted approximately 3 out of 4 times. This occurs because no interaction diagram was able to increase the rate of positive identification of plugging while reducing the rate of false identification. A much larger dataset is likely needed to overcome this challenge. At any rate, interaction diagrams do not represent an improvement over using the straightforward diameter criterion (Figure 4a). Hence, the authors chose the original diameter criterion proposed in Figure 4a for a couple of reasons. First, the diameter criterion is a more discreet and intuitive concept, and easier to comprehend. Second, the diameter criterion resulted in a higher accuracy, rendering the Diameter-L/D interaction diagram inferior to the diameter criterion.
## 3 Results and Discussion
### Spatiotemporal Variation of Land Use/Land Cover
#### 3.2.2 LULC Variation during 1987-2016  
Figure 5 and Table 7 illustrate the LULC dynamics based upon type to transition for the four study periods. Generally, the most dominant land transition was from farmland to construction land in all cases with transition areas of 90.4 km\({}^{2}\), 92.4 km\({}^{2}\), 88.1 km\({}^{2}\) and 218.3 km\({}^{2}\) during 1987-1996, 1996-2007, 2007-2016 and 1987-2016, respectively. However, the transition directions and spatial extents varied significantly over different sub-periods.  
In the period 1987-1996, the outward transfer of farmland (124.8 km\({}^{2}\)) was much more than its inward transfer (32.2 km\({}^{2}\)). The area loss of farmland was mainly returned to construction land (72.4%) and water bodies (22.9%). Farmland was the major contributor to the inward transfer of construction land (78.1%). It was mainly distributed along the newly built roads (Figure 5a).  
In the period 1996-2007, the area of construction land increased by 127.0 km\({}^{2}\) (shown in Table 6) About 62.2%, 32.6% and 5.18% of the inward transfer of construction land (148.5 km\({}^{2}\)) were from farmland, water body and greening land, respectively. Besides farmland (102.5 km\({}^{2}\)), water body had a significant outward transfer with an area of 90.3 km\({}^{2}\). Newly expanded built-up area was at the expense of the farmland and water bodies around the city core (Figure 5b).  
Figure 5: LULC transition maps during 1987–1996 (**a**), 1996–2007 (**b**), 2007–2016 (**c**) and 1987–2016 (**d**).  
In the period 2007-2016, the inward transfer of construction land was 105.1 km\({}^{2}\), including 88.1 km\({}^{2}\) from farmland, 10.8 km\({}^{2}\) from green land and 6.1 km\({}^{2}\) from water bodies. The outward transfer of farmland was 94.0 km\({}^{2}\), 93.2% of which was converted into construction land. As Figure 5c shown, farmland near the third ring-road in Wuhan was almost completely replaced by construction land.  
In the entire period 1987-2016, 218.3 km\({}^{2}\) of farmland was transferred to construction land. It accounted for 95.4% of the outward transfer of farmland and contributed to 69.2% of the inward transfer of construction land. In addition, the transition of construction land from water bodies and greening land was 78.9 km\({}^{2}\) (25.0%) and 18.4 km\({}^{2}\) (5.8%), respectively. Figure 5d indicates that the majority of farmland area in the study area was occupied by construction land due to urban expansion. Several large urban lakes, such as Huangtang Lake, East Lake, Shahu Lake and South Prince Lake, were partly occupied by construction land.
## 6 Conclusions  
In this paper, a blind robust 3D mesh watermarking method based on visual saliency and wavelet coefficient vectors for copyright protection is proposed. The proposed method takes the full advantage of jointing mesh saliency and QIM quantization of wavelet coefficients to ensure both high imperceptibility and robustness. The robustness requirement is achieved by quantifying the wavelet coefficients using the QIM scheme, while the imperceptibility performance is ensured by adjusting the embedding process according to the visual saliency. The experimental results demonstrate that the proposed scheme yields a good tradeoff between the imperceptibility and robustness requirements. Moreover, experimental simulations show that the proposed method outperforms the existing methods against the majority of the attacks. Future work will focus on improving the robustness against severe attacks such as cropping and remeshing using the weights of the saliency mesh in order to embed more data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack** & **Parameters** & [14] & **Our Method** \\ \hline \multirow{3}{*}{Noise} & 0.1\% & 0.87 & 0.99 \\  & 0.3\% & 0.51 & 0.90 \\  & 0.5\% & 0.18 & 0.84 \\ \hline \multirow{3}{*}{Quantization} & 9-bit & 0.94 & 0.99 \\  & 8-bit & 0.88 & 0.95 \\  & 7-bit & 0.39 & 0.89 \\ \hline \multirow{3}{*}{Smoothing} & (10, 0.03) & 0.75 & 0.92 \\  & (30, 0.03) & 0.57 & 0.88 \\  & (50, 0.03) & 0.46 & 0.71 \\ \hline \multirow{3}{*}{Simplification} & 32.11\% & 0.94 & 0.98 \\  & 51.44\% & 0.77 & 0.85 \\ \cline{1-1}  & 70.79\% & 0.58 & 0.71 \\ \cline{1-1}  & 90.65\% & 0.38 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Results of robustness comparison with [PERSON] et al.’s [14] method measured in terms of the Bunny model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack** & **Parameters** & [10] & **Our Method** \\ \hline \multirow{3}{*}{Noise} & 0.1\% & 0.75 & 0.99 \\  & 0.3\% & 0.5148 & 0.90 \\  & 0.5\% & 0.111 & 0.84 \\ \hline \multirow{3}{*}{Quantization} & 9-bit & 0.77 & 0.99 \\  & 8-bit & 0.61 & 0.95 \\  & 7-bit & 0.32 & 0.89 \\ \hline \multirow{3}{*}{Smoothing} & (10, 0.03) & 0.73 & 0.92 \\  & (30, 0.03) & 0.55 & 0.88 \\ \cline{1-1}  & (50, 0.03) & 0.39 & 0.71 \\ \hline \multirow{3}{*}{Simplification} & 32.11\% & 0.80 & 0.98 \\ \cline{1-1}  & 51.44\% & 0.61 & 0.85 \\ \cline{1-1}  & 70.79\% & 0.33 & 0.71 \\ \cline{1-1}  & 90.65\% & 0.17 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 22: Results of robustness comparison with [PERSON] et al.’s [10] method measured in terms of NC for the Bunny model.  
**Author Contributions:** Conceptualization, A.C.; Methodology, H.C.; Software, M.H.; Supervision, M.E.H. ([PERSON]), M.E.H. ([PERSON]) and H.C.; Writing--original draft, M.H.; Validation, A.C.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Decoupling Control of Multi-DOF Supporting System of MLDSB
## 6 Experimental Result of Decoupling  
### Brief Introduction of the MLDSB Testing System  
The MLDSB Testing System is composed of the electronic control system, hydrostatic system, and bearing body as shown in Figure 16.  
Figure 16: _Cont._  
Figure 14: Control block diagram.  
Figure 15: Output response curve of the rotor.  
The MMDSB Testing System uses LabVIEW software to process the acquired signals, and the PC Collecting Interface is shown in Figure 17.  
The hydrostatic system is a constant pressure supporting model, and its flow is adjusted by a needle valve. The electronic control system is a closed-loop position control system, and its current is adjusted by a PID controller.  
The component's parameters of the MLDSB Testing System are shown as follows.  
1. Hydraulic pump, model TGPVLA4-200 SH, manufacturers for Taixing Hongsheng Hydraulic Co., Ltd., Taixing, China. Pressure 14 MPa, flow 16 L/min, rotation speed 1450 r/min.
2. Relief valve, model DBD-H-6-P-10-B-NG10, manufacturers for Beijing Huade Hydraulic Industrial Group Co., Ltd., Beijing, China. Pressure 10 MPa, size 6 mm.
3. Nozzle valve, model A7-2-KL2-0 KL20-PTFE, manufacturers for Shanghai Hawk Fluid Control Co. Ltd., Shanghai, China. Size 6 mm.
4. Flow gauge, model LWGY-S, manufacturers for Nanjing Detair Instrument & Electromechanical Equipment Co. Ltd., Nanjing, China. Pressure 2 MPa, flow 120-2400 mL/min, accuracy 2%.
5. Pressure gauge, model HSTL-802, manufacturers for Beijing Huakong Xingye Technology Development Co., Ltd., Beijing, China. Pressure 10 MPa, accuracy 0.25%.
6. Displacement gauge, model VB-Z9900, manufacturers for Shanghai Ann Electronic Technology Co., Ltd., Shanghai, China. Range of 4 mm, accuracy 1.5%.  
Figure 16: MLDSB Testing System.  
Figure 17: PC Collecting Interface of the MLDSB Testing System.  
Coil, manufacturers for Sanhan Tin Co., Ltd., Kunshan, China. Materials for Cu, diameter 1.0 mm, electrical resistivity 0.02240 \(\Omega\)/m, length 25 m.
* PC, model IPC-610L, manufacturers for Taiwan Advantech Co., Ltd., Taiwan, China. Mainboard AIMB-705 BG, CPU I5-6500.
* Output card, model NI6723, manufacturers for America NI instruments Co., Ltd., Texas, America. 8 channels output.
* Input card, model PCI1716, manufacturers for Taiwan Advantech Co., Ltd., Taiwan, China. 16 channels input,
* 12V Power, model S1500-12, manufacturers for China Meanwhilel Group Co., Ltd., Guangzhou, China. Voltage 12V, current 125 A, power 1500 W.
* Power amplifier, model AQMD3620 NS-A2, manufacturers for Chengdu Aikong Electric Technology Co. Ltd., Chengdu, China. Power 400 w, input voltage 36 V, current 16 A.
## 2 Experimental Study
### Description of the Slab Types  
The same steel decking profile of 1 mm with embossments and three types of LWC as well as one type of NC, reinforced with polyolefin fibers, were used to manufacture unprotected composite slabs. A cross section of the composite slab profile is shown in Figure 1. In the upper part of the slabs, a reinforcement mesh with steel bars of 2 mm in diameter was used to minimize shrinkage cracks in the concrete. The profile steel decking has a total width of 1120 mm and 2030 mm length. The webs are inclined at a 71\({}^{\circ}\) angle and have several embossments to guarantee a good connection between the deck and the concrete. The shape of the profiles is shown in Figure 1.  
Steel decking is made of cold-rolled steel. To study the mechanical properties of steel decking, three steel specimens were taken from the same batch of materials during the fabrication. Tensile tests following ISO 6892-1 [27] were carried out on these specimens and the averaged results are shown in Table 1.  
Figure 1: Steel decking profile (dimensions in mm): (**a**) Trapezoidal steel decking in the composite slab. (**b**) Composite slab cross section.  
Three types of LWC and one NC were manufactured for designing concrete slabs with compressive strength between 25 to 37 MPa. Portland cement (CEM II/A-V 42.5), siliceous aggregates, two sizes of expanded clay, water, and polyolefin fibers were used to manufacture concrete samples. In addition, siliceous aggregates with a diameter of 0-2 mm, ECF with diameters of 1-5 mm and a density of 430 kg/m\({}^{3}\), and ECC with diameters of 2-10 mm and a density of 350 kg/m\({}^{3}\) were used to manufacture the LWCs. In order to avoid shrinkage cracking, polyolefin fibers 48 mm in length and with a density of 910 kg/m\({}^{3}\) were used. The mixing proportions used for the manufactured LWCs are shown in Table 2.  
To achieve the compressive strength and elastic modulus of concrete, three cylinders of \(\Phi\)100 mm \(\times\) 200 mm for each concrete were cast and cured for 28 days. The average bulk density, compressive strength, and the elastic modulus of concrete are shown in Table 3. The modulus of elasticity was determined according to EN 12390 [28], while compressive strength follows UNE 83507 [29].  
The thermal conductivity and specific heat of each LWC were measured in previous experimental studies [11]. The values at ambient temperature of thermal conductivity and specific heat are shown in Table 3.
# A Perturbation and Symmetry-Based Analysis of Mobile Malware Dynamics in Smartphone Networks
###### Abstract  
In this paper, we present a mathematical model, \(M_{sigr}\), to analyze the dynamics of mobile malware propagation in smartphone networks. The model segments the mobile device population into susceptible, exposed, infected, quarantined, and recovered compartments, integrating critical control parameters such as infection and quarantine rates. The analytical results include the derivation of the basic reproduction number, \(R_{0}\), along with equilibrium and stability analyses that provide insights into long-term system behavior. A focused scenario analysis compares the baseline dynamics with a more aggressive malware variant and a more effective quarantine response. The results show that increased infectivity sharply escalates the peak of infection, while enhanced quarantine measures effectively suppress it. This highlights the importance of prompt containment strategies even under more virulent conditions. The sensitivity analysis identifies the infection rate as the most influential parameter driving peak infection, while the quarantine rate exerts the most significant dampening effect. Monte Carlo simulations of parameter uncertainty reveal a consistently high epidemic potential across varied conditions. A parameter sweep across the infection-quarantine space further maps out the conditions under which malware outbreaks can be mitigated or prevented. Overall, the model demonstrates that mobile malware poses sustained epidemic risk under uncertainty, but effective control parameters--particularly quarantine--can drastically alter outbreak trajectories.  
disease-free equilibrium; endemic equilibrium; stability analysis; Lyapunov function; basic reproduction number; sensitivity analysis; PRCC; symmetry analysis; cybersecurity; perturbation +
Footnote †: journal: Applied Sciences