esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Deformation Measurement via the Geotechnical DIC System: PhotoInfor  
### Principle of the Digital Image Correlation  
DIC is developed as an optical, non-contact, correlation-based method against the measurement of material deformation. In particular, 2D-DIC has been widely applied to experimental investigations in the field of geotechnics due to its efficiency. It relies on the subset network's correlation coefficients throughout the deforming process for the displacement field and the strain [25].  
The judgment process for monitoring moving subsets is based on the correlation coefficient (CC). As shown in Figure 1, each DIC subset is created as a block with an adjustable radius. As for correlation computation, grey value \(f(x,y)\) serves as the crucial variable in Equation (1), where \(k\) is the subset radius, \((x,y)\) represents the coordinate of the center pixel, \(f_{1}\) and \(f_{2}\) refer to the grey value in reference and target images respectively, and \(R_{12}\) hints the correlation coefficient. Each subset in the target image within the searching scope will be compared in groups with the ones in the reference, and the CC can be obtained accordingly. The subset with the highest value of CC will be viewed as the target one, and the displacement can, therefore, be calculated according to the coordinate data of central pixels \(P_{i}\), \(P_{d}\).  
\[R_{12}=\frac{\sum\
olimits_{x=1}^{2k+1}\sum\
olimits_{y=1}^{2k+1}f_{1}(x,y) \times f_{2}(x,y)}{\sqrt{\sum\
olimits_{x=1}^{2k+1}\sum\
olimits_{y=1}^{2k+1 }f_{1}(x,y)^{2}\times\sum\
olimits_{x=1}^{2k+1}\sum\
olimits_{y=1}^{2k+1}f_{2} (x,y)^{2}}} \tag{1}\]  
Figure 2 describes the monitor field marked by a mesh composed of the DIC subsets where an adjacent quadrangular grid surrounds each image point. Through a series of correlation calculations, the displacement of DIC subsets can be obtained first, and the point data can be achieved through interpolation. Equation (2) describes the interpolation calculation for the displacement of each point where \(u\) and \(v\) refer to the horizontal and vertical displacement, respectively, and \(N_{i}\) denotes the interpolation function according to the coordinate relationship. The displacements can then be transformed into the strain according to Equation (3), where \(\varepsilon_{x},\varepsilon_{y}\) refer to the strain in horizontal and vertical direction, respectively, and \(\gamma_{xy}\) indicates shear strain.  
\[\left\{\begin{array}{l}u=\frac{4}{i=1}\;uiNi\\ v=\sum\limits_{i=1}^{4}\;viNi\end{array}\right. \tag{2}\]  
\[\left\{\begin{array}{l}\varepsilon x=\frac{\partial u}{\partial x}=\sum \limits_{i=1}^{4}\frac{\partial Ni}{\partial x}ui\\ \varepsilon y=\frac{\partial v}{\partial y}=\sum\limits_{i=1}^{4}\frac{ \partial Ni}{\partial y}vi\\ \gamma xy=\frac{\partial u}{\partial y}+\frac{\partial v}{\partial x}=\sum \limits_{i=1}^{4}\frac{\partial Ni}{\partial y}ui+\sum\limits_{i=1}^{4}\frac{ \partial Ni}{\partial x}vi\end{array}\right. \tag{3}\]  
Figure 1: Correlation computation for displacement within a deformation process [26].
## 5 Conclusions  
The experimental assessment to observe the effect of magnetic field induction on the fluid pressure of magnetorheological fluids (MRFs) has been conducted. The results showed that the MRFs fluid pressure could proportionally be increased with the increase of the magnetic field. The indication was observed visually with the fluid level change in the U-shaped tube and detected by the pressure sensor. Around 900 Pa of fluid pressure was recorded when the MRFs were subjected to 2.00 A current input to the electromagnet. According to the magnetic simulation, such an arrangement was estimated to produce about 0.25 T. The change of fluid pressure is positively correlated to the change of the magnetic field strentgh. Overall, the observation results indicate a strong relationship between MRFs fluid pressure and the magnetic field induction, which could pave the way to discovering a new MRFs operational mode.  
Conceptualization: P.J.W., U.U. and F.I.; Data curation: P.J.W.; Methodology: P.J.W., U.U. and F.I.; Supervision: E.P.B., U.U. and F.I.; Writing--original draft: P.J.W.; Writing--review & editing: P.J.W., E.P.B., U.U. and F.I. All authors have read and agreed to the published version of the manuscript.  
Sebelas Maret University Doctoral Program Scholarship and Penelitian Unggulan UNS.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
Figure 10: Changes in MRFs pressure at an input current of 1.00 A.
## 4 Mian Features and Challenges of IoMT and Healthcare 4.0
### Bandwidth  
Bandwidth challenges in Healthcare 4.0 systems arise due to the growing demand for data-intensive applications and the proliferation of connected devices and technologies in the healthcare industry. Also, the 5G demands for medical applications introduce many issues with the bandwidth [77].  
Healthcare 4.0 systems involve the use of data-intensive applications such as telemedicine, medical imaging, and real-time patient monitoring. These applications generate and transfer large volumes of data, putting a strain on the available network bandwidth. Moreover, With the rise of telemedicine, video conferencing has become a critical aspect of healthcare communication. High-quality video and audio streams necessitate sufficient bandwidth for smooth and uninterrupted interactions [20].  
Addressing bandwidth challenges in Healthcare 4.0 systems can be achieved using the following approaches [20; 77]:  
* Upgrading network infrastructure: Healthcare providers need to invest in high-speed and reliable network infrastructure to accommodate the increasing demands of data-intensive applications.
* Prioritizing critical data traffic: Implementing quality of service (QoS) protocols can help prioritize critical healthcare data through time-sensitive traffic, ensuring that vital patient information receives priority during peak periods.
* Edge computing: Deploying edge computing solutions can reduce the strain on centralized networks by processing data closer to the source, thereby decreasing the volume of data that needs to be transferred over the network.
* Data compression: Implementing data compression techniques can help reduce data size, easing the burden on the network and improving efficiency.
* Load balancing: Load balancing techniques distribute network traffic across multiple servers or resources, preventing congestion and ensuring even data distribution.
* Bandwidth monitoring and management: Continuous monitoring of network bandwidth usage helps identify bottlenecks and allows for proactive capacity planning and management. Also, applying AI-based methods at the core of the network can assist bandwidth management.
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 2 Background
### RUL Prediction  
The flowchart of a data-driven RUL prediction task, that is the focus of the present work, is illustrated in Figure 1. The object of the RUL prediction is an industrial machine or its components. The sensor measurements (usually, in the form of multivariate time series) indicate the state of some physical properties which are monitored by sensors installed on the machine. Then, these time-series data are fed into a black box model, which is a system that derives as output an RUL prediction.  
Such a black box model is trained based on historical data, which consist of previous sensor measurements and actual RUL values collected by run-to-failure operations. During training, the time-series data are fed in input to the model, and the actual RUL values are used as labels for calculating loss, which is defined as the difference between the predicted RUL and the actual RUL. In this context, determining an appropriate black box model is a key issue to improve the RUL prediction.
# Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump
## 2 Materials and Methods  
### Measurement Experiments  
The device under test was an axial piston pump model HSP-10V manufactured by Hydraut [31]. In total there were used two vibration sensors, three temperature sensors, three pressure sensors and one flow sensor. Figure 1 schematically illustrates the experimental setup of the axial piston pump with installed sensors. In Figure 2, a photo of the setup is presented. All of the measured variables were acquired every second. Vibration-related features, namely RMS and P2P, were calculated by a measurement module from a buffer of 4096 samples acquired with 20 kHz frequency. All the measured variables are listed in Table 1.  
Figure 1: Experimental setup scheme of the axial piston pump with installed sensors. Colors in the scheme correspond with colors on Figure 2. REDNT S.A. modules can be found on the website [[https://icm.molos.cloud/](https://icm.molos.cloud/)]([https://icm.molos.cloud/](https://icm.molos.cloud/)) (accessed on 10 September 2023).  
Figure 2: Experimental setup photo of the axial piston pump with installed sensors. Vibration sensors are marked green, pressure sensors are marked orange and temperature sensors are marked blue. These colors correspond with Figure 1.  
The pump was tested in different conditions. Measurements were carried out on the pump that operated under the following experiments:  
* Reference tests (twice)
* Reference tests with check valve
* Axial play fault
* Reassembling
* Timing plate fault
* Swash plate fault
* Bearing fault  
The same pump was used for all experiments. All of the failures were precisely performed by domain experts, so that specific failure could be isolated.  
The set point for the pump during the tests was the torque load on the pump of the braking motor. Every experiment followed the same scenario as described in Table 2.  
In the production environment there will be no measurable information about load, so it was not measured during the experiments. All of the developed approaches, model and techniques should therefore be designed in a way that does not involve information about the load.  
Figure 3 presents the time series of vibration velocity measured by sensor mounted radial and output pressure. All of the experiments followed the same scenarios, but for \"Reference tests v2\" the load was lowered by mistake in the \"Constant load\" part for a couple of minutes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Load** & **Period** & **Duration** \\ \hline Constant load & 10, 20, \(\dots\), 100\% & x & 3 min each \\ Trapezoid & 30–90\% & 180 s & 30 min \\ Sine & 75–95\% & 60 s & 15 min \\ Sine & 10–100\% & 30 s & 10 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Steps of measurement experiment. For constant load, period property is not applicable.  
Figure 3: Visualization of all experiments. Blue line is \(V_{RMS-Radial}\) (in \(\frac{\text{mm}}{\text{s}}\)) and red line is outlet pressure (in kPa).
## 3 Optimization of Controllable Rectification Based on Advanced Control Strategies
### MPSMC Control Strategy  
Traditional PWMC dual-loop control has drawbacks such as slow dynamic response, weak anti-interference capability, and complex parameter tuning. To address these issues, this paper proposes a model predictive sliding mode compound control strategy. Model predictive control (MPC), a discrete sampling control method, uses rolling optimization to mitigate the impact of uncertainties like model mismatch, distortion, and disturbances. This ensures better control performance. However, MPC has limitations, including computationally intensive algorithms and slow dynamic response due to PI control in the outer loop. To enhance system robustness, a sliding mode controller replaces the PI control in the voltage outer loop, reducing the algorithm's optimization iterations.  
The current inner loop employs MPC with online rolling optimization to minimize the impact of uncertainties, achieving better control performance. The voltage outer loop uses a sliding mode controller instead of PI control to shorten the optimization process and improve robustness. This compound control strategy effectively combines the strengths of MPC and sliding mode control, offering enhanced dynamic response and anti-interference capability while maintaining stable voltage regulation.
## 2 Vehicle Model Used in Agricultural Vehicle Path Tracking Control  
The first step in designing the path-tracking control algorithm for autonomous agricultural vehicles is to establish a vehicle model. Many studies have confirmed that accurate vehicle models are an important precondition for designing path-tracking controllers. This paper introduces the application of the kinematic and dynamic models in different operation scenes and various agricultural vehicles. It focuses on the bicycle model, agricultural tracked vehicle model, and agricultural articulated vehicle model and summarizes the difficulties of the application of the kinematic model and dynamics model in agricultural vehicle path tracking.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 5 Security Analysis
### Real-World Context for Attribute and Policy Complexity with Advanced Access Constraints
```
0: Security parameter \(\lambda\), Attribute universe \(U\), Access policy \(\mathcal{A}\), Message \(M\), Times-tamp \(t\), Location \(l\)
0: Ciphertext CTIceptrable only by authorized users with valid temporal and location attributes
1:\(\text{Setup}(\lambda,U)\)
2: Generate base matrix \(A\in\mathbb{Z}_{q}^{n\times m}\) with associated trapdoor \(\text{Trapdoor}(A)\)
3: Sample a short secret vector \(\mathbf{s}\in\mathbb{Z}_{q}^{n}\)
4:for each attribute \(\text{attr}\in U\)do
5: Generate a matrix \(A_{\text{attr}}\) and corresponding trapdoor
6:endfor
7: Output public parameters PK and master secret key MSK
8:\(\text{KeyGen}(\text{MSK},S)\)
9:for each \(\text{attr}\in S\)do
10: Generate short vector \(\mathbf{d_{attr}}\) using \(\text{Trapdoor}(A_{\text{attr}})\)
11:endfor
12: Add dynamic attributes: time < 24h, location: EU, freshness score
13: Output secret key \(\text{SK}=\{\mathbf{d_{attr}}\}\)
14:\(\text{Encrypt}(\text{PK},M,\mathcal{A})\)
15: Represent access structure \(\mathcal{A}\) as an LSSS matrix \((M,\rho)\), including temporal and spatial constraints
16: Sample random vector \(\mathbf{v}\) and compute ciphertext components using LWE encryption
17: Output ciphertext \(\text{CT}=(\text{components},\mathcal{A})\)
18:\(\text{Decrypt}(\text{SK},\text{CT})\)
19: Verify that user's attributes satisfy \(\mathcal{A}\), including checks on timestamp and geolocation
20: Use LSSS decoding vector to reconstruct the shared secret
21: Recover message \(M\) only if policy evaluation passes
```  
**Algorithm 1** Lattice-Based CP-ABE with Temporal and Geospatial Constraints
# Morphometric Analysis and Three-Dimensional Computed Tomography Reconstruction of Thai Distal Femur
## 2 Materials and Methods
### Measurement of Morphometric Parameters  
In this study, the anatomical landmarks as shown in Figure 3 were defined from previous reports for morphometric measurement of the distal femur [1,14,31] as follows: the most prominent point of lateral epicondyle (LE), the most prominent point of medial epicondyle (ME), the most anterior cortex point (AC), the most anterior point of medial condyle (MAC), the most posterior point of medial condyle (MPC), the most anterior point of lateral condyle (LAC), the most posterior point of lateral condyle (LPC), the most medial point of medial condyle (MMC), the most lateral point of medial condyle (MLC), the most lateral point of lateral condyle (LMC), the apex of intercondylar notch (DIN), the most inferior point of lateral condyle (HLC1), the most superior point of lateral condyle (HLC2), the most superior point of medial condyle (HMC1), and the most superior point of medial condyle (HMC2). Figure 1: Three-dimensional model reconstruction: (**a**) a series of raw computed tomography (CT) image, (**b**) segmentation of CT images, and (**c**) 3D model. Figure 2: The alignment of the 3D femur models: (**a**) the mechanical axis was aligned in a sagittal plane, (**b**) the most posterior point of both condyles was aligned in a coronal plane, and (**c**) the most inferior points of both condyles were aligned in a transverse plane. The abbreviation and definition of the morphometric parameters of the distal femur shown in Table 1 were investigated as follows: two lengths, six widths, one depth, two heights, and three ratios. To assess the accuracy of the measurement, the intra-rater and inter-rater reliability were evaluated before measurement [32]. Two observers measured morphometric parameters including transepicondyl axis length (TEA), mediolateral length (ML), anteroposterior width (AP), medial anteroposterior width (MAP), lateral anteroposterior width (LAP), medial condele width (MCW), lateral condele width (LCW), intercondylar notch depth (DIN), MPC, and LPC by the selection of landmark point for distance measurement with three replicates on 20 random femora, and one of the observers measured the same femur twice with a two-week interval. Measured values from two observers were analyzed by statistical software SPSS (SPSS Inc., Chicago, IL, USA).
## 3 Dynamic Multi-Hop Routing (DMR) Technique
### Phases of the DMR Protocol
#### 3.2.4 The Routing Phase of the DMR Protocol
All routes are sorted in descending order within the RT; the lowest cost route is selected first for data transmission. The cost value of each route is computed in the same manner as in the intra-cluster case. Equation (11) is used to determine the value of the route cost: the first term of this equation corresponds to the positional cost of the next-hop node (NHI),whether it is a CH or an IN, with respect to the BS, while the second term refers to the energy cost of the NHI [8]:  
\[C_{NHI}(n)=C_{NHI-BS}(n)+\alpha\cdot C_{NHIE}(n) \tag{11}\]  
Here, \(C_{NHI-BS}\) represents the distance cost of the NHI relative to the \(BS\); \(C_{NHIE}\) indicates the cost related to the remaining energy of the NHI. The variable \(n\) refers to the set of next-hop nodes, whether these are the \(CH\) or the IN. Equation (12) computes the \(C_{NHI-BS}\) as follows [8]:  
\[C_{NHI-BS}(n)=\frac{{D_{n}}^{2}}{{\overline{S}}{N{I_{D-BS}}}-\ N{H_{nD-BS}}} \tag{12}\]  
In this formulation, \(D\) refers to the distance separating the source node _SNI_, whether a CH or an IN, from the \(NHI\). \(SN{I_{D-BS}}\) represents the distance between the _SNI_ and the BS, whereas the \(NHI_{nD-BS}\) denotes the distance between the _NHI_ and the \(BS\). The second component of the cost function assigns an energy cost to each remaining energy ratio. Figure 2 illustrates an example of the energy cost values assigned to the CHs and the INs. As shown, lower cost values are given to nodes with higher energy ratios, especially those whose energy ratios exceed 50%. To reduce the burden on the CHs, the proposed approach allocates slightly lower cost values to the INs, thereby enhancing their role in forwarding data across the levels. Based on the cost values, all CHs and INs arrange their available routes in ascending order. The lowest-cost route is initially selected to transmit data to a CH or IN at a lower level. Based on the updated messages received from the CHs or INs in the lower levels, the SNI rearranges the entries in its RT. Consequently, a new route can be dynamically selected to replace the initially chosen one. Figure 2: Example of energy cost values for CH, IN, and N nodes.
## 2 Materials and Methods  
### Operating Principle and Modeling of the Dual-Sided Independent Drive System of a Tracked Vehicle  
Dual-sided independent drive steering of tracked vehicles is achieved by adjusting the speed of the drive wheels on both sides. The structure of the dual variable pump-quantitative motor hydrostatic drive system for tracked vehicles studied in this paper is shown in Figure 1.  
As shown in Figure 1, the engine transmits the power to the transmission, and the output shaft of the transmission is connected in parallel with two bi-directional variable pumps. The variable pumps and motor form a hydrostatic closed circuit, which drives the speed and steering of the hydraulic motors through the pumps, and the hydraulic motors are directly connected with the track drive wheels to drive the vehicle. For the subsequent theoretical study of the whole vehicle steering control, a mathematical model of the closed-loop system of pump-controlled motors on the tracked vehicle needs to be analyzed.  
Variable pump flow model:  
\[q_{p}=xD_{p}\omega_{p}-C_{jp}(P_{h}-P_{s})-C_{ep}P_{h} \tag{1}\]  
Figure 1: The driving system of a tracked vehicle.  
Motor working flow model:  
\[q_{m}=q_{p}=C_{im}(P_{h}-P_{s})+C_{em}P_{h}+D_{m}\frac{d\theta_{m}}{dt}+\frac{V_{ 0}}{\beta_{e}}\frac{dP_{h}}{dt} \tag{2}\]  
Motor and load torque balance equation:  
\[D_{m}(P_{h}-P_{s})=J_{m}\frac{d^{2}\theta_{m}}{dt^{2}}+B_{m}\frac{d\theta_{m}}{ dt}+T_{l}\]  
In the above Equations (1)-(3), \(D_{p}\) is the variable pump displacement; \(x\) is the variable pump displacement adjustment factor; \(D_{m}\) is the motor displacement; \(\omega_{p}\) is the pump input speed; \(\theta_{m}\) is the motor rotation angle; \(C_{ip}\) is the pump internal leakage coefficient; \(C_{cp}\) is the pump external leakage coefficient; \(C_{im}\) is the motor internal leakage coefficient; \(C_{em}\) is the motor external leakage coefficient; \(P_{h}\) and \(P_{s}\) are pressure values at high and low sides respectively; \(V_{o}\) is the total volume of high pressure side; \(\beta_{e}\) is the hydraulic oil bulk modulus of elasticity; \(J_{m}\) is the total rotational inertia of motor and load; \(B_{m}\) is the total viscous damping factor of motor and load; \(T_{l}\) is the external load torque acting on the motor shaft.