esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 1 Introduction  
As an indispensable processing technology in agricultural engineering, cutting is widely used to peel, slice, trim, dice, and harvest agricultural materials [1, 2]. Agricultural products include edible and flammable materials, so clean and pollution-free operation is required. Existing cutting methods can be divided into traditional cutting technologies represented by blade cutting and non-traditional cutting technologies involving multiple energy sources and processes (i.e., wire electrical discharge machining, plasma arc cutting, laser cutting, flame cutting, water jet cutting, etc.) [3, 4]. Among them, water jet cutting technology, as a cold cutting method, is suitable for cutting agricultural materials because of its no-heat and spark-free processing characteristic and non-contact cutting way [5].  
At present, water jet cutting technology has been widely applied in industry, medicine, and other fields, involving ores, metals, plastics, and other materials, accompanied by a variety of technical and theoretical supports [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. There are many specific advantages that have been found. For example, the water jet cutting process will not cause problems such as a heat-affected zone and thermal deformation, which can avoid fire hazards [16]. Unlike traditional cutting tools contacting objects directly, which need to sharpen, clean, and change blades in time, water jet cutting has a non-contact way and can save time and labor by less often replacing worn nozzles [17, 18, 19]. Water is the primary medium for water jet cutting, which is green and recyclable [20]. These advantages are conducive tocut agricultural materials by water jet cutting technology. However, the research depth of water jet cutting technology in agriculture engineering is low due to the variety of agricultural materials [21; 22]. Most processed products become foods, which require high quality and safety [23; 24]. Some cutting operations are carried out in complex field environments rather than factories, such as cutting stems and soils, whose applications are more challenging [22; 25]. Moreover, there are many outcomes of water jet cutting (i.e., cutting depth, kerf width, surface roughness, etc.) and post-cutting influences on agricultural materials (i.e., microbial counts, brownish cut edges, the loss of raw materials, etc.), which are the major indices to judge the cutting quality [26].  
Therefore, this paper reviews the application status of water jet cutting technology in agricultural engineering, including six kinds of agricultural materials (i.e., vegetables, fruits, meats, woods, stems, and soils). How to cut each material to complete different cutting operations, improve the cutting ability, or control post-cutting influences is analyzed. Then, suggestions for future directions of water jet cutting technology are put forward, including deepening mechanism research, optimizing agricultural machinery, and adopting intelligent control.
## 3 Result Discussion and Assessment
### Event-Based MCS for Reliability Evaluation  
The MCS method has been effectively employed for the reliability evaluation of stochastic systems, as it provides a straightforward and efficient approach [54]. The non-linear dynamics associated with GRC and speed governor dead bands complicate the formulation of explicit reliability functions for multi-area LFC systems. Therefore, MCS must be used to model the operating environment of these systems, incorporating various degraded components to assess their reliability. The event-based MCS method is utilized to assess the reliability of control systems incorporating a PI controller [16]. The following Algorithm 1 is used to evaluate the reliability of the multi-area LFC system in accordance with the operational requirements.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Domain Requirements** & **Descriptions** \\ \hline \multirow{7}{*}{Operational} & **Maximal peak time (PT\({}_{max}\))** \\  & The time required for the result of the control process to reach its maximum or minimum value. \\ \cline{2-3}  & **Maximal percentage overshoot (PO\({}_{max}\))** \\  & The absolute value of the difference between the expected output and its maximum or minimum output, divided by the expected output. \\ \cline{2-3}  & **Maximal settling time (ST\({}_{max}\))** \\  & The duration from the initiation of the control process to the point when the output reaches and stays within a specific range. \\ \hline \multirow{2}{*}{Nonfunctional} & **Reliability** \\  & The capacity of the LFC system to maintain its anticipated performance despite experiencing component degradation and cyber-attacks. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time-domain requirements of LFC for multi-area systems.  
```
0: The PI controller's parameters \(K_{pi}\) and \(K_{li}\) for area \(i\) (\(i=1,2,\ldots,N\)); the degradation model for a multi-area LFC system subject to FDIA; the operational requirements; the total runtime \(T_{0}\).
0: The reliability estimation \(R^{*}(T_{0})\).
1: Set \(m\gets 0\).
2: Initialize \(H\), \(I\), \(N_{T}\), \(N_{F}\), \(N_{E}\), \(i\gets 0\).
3:while\(m<M\)do
4: Perform a simulation using the dynamic model
5:if simulation fails (Equation (7) or Equation (8)) then
6:\(N_{F}\gets N_{F}+1\)
7:\(R_{i}^{*}(T_{0})=1-\frac{N_{F}}{i}\)
8:endif
9:if\(\big{|}R_{i}^{*}(T_{0})-R_{i-1}^{*}(T_{0})\big{|}/R_{i-1}^{*}(T_{0})\leq H\)then
10:\(N_{E}\gets N_{E}+1\)
11:endif
12:if\(N_{E}/i\leq l\)or \(i<N_{T}\)then
13:\(i\gets i+1\)
14:else
15:\(R^{*}(T_{0})=1-N_{E}/N_{T}\)
16:break
17:endif
18:\(m\gets m+1\)
19:endwhile
20:\(R^{*}(T_{0})=\sum_{m=1}^{M}(R_{m}^{*}(T_{0})/M)\)
```  
**Algorithm 1** Reliability estimation for multi-area LFC system.
## 3 Proposed Method
### LRLQ Depth Generation for Training  
In order to simulate the LRLQ depth maps obtained from MR glasses for training, we introduce a depth sampling method that generates low-resolution depths randomly from the ground truth. Figure 8 shows the results of LRLQ depth generation with \(p=8\) by our proposed simulation strategy, where the proposed two-step LRLQ depth generation is stated as follows:  
1. Subblocking: To obtain a \(p\times p\) low-resolution depth map \(\mathbf{d}_{p\times p}\) from the GT depth map \(d\), we first divide the GT depth map into \(p\times p\) subblocks.
2. Median pooling: We simply take the median value of each sub-block as the LP depth value to obtain the simulated LRLQ depth map.
3. Max-depth filtering (optional): In practice, the depth range on most MR glasses depth sensors is limited to a short distance range. To simulate the short distance range depth during training, we apply max-depth filtering on the sampled low-resolution depth map. As the depth value is greater than a max-depth threshold, max-depth filtering will reset the depth value to zero (i.e., invalid pixel).
## 6 Weakening Mechanism of Shale Anisotropy
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Bedding** \\ **Dip** \\ **Angle \(\theta^{c}\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Confining** \\ **Pressure/MPa** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Triaxial Monotonic Loading** \\ **and Unloading** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Triaxial Incremental Cycle Loading** \\ **and Unloading** \\ \end{tabular} } \\ \cline{3-4}  & & & **Destruction Mode** & & **Destruction Mode** & **Main Control** \\ \hline \multirow{4}{*}{0} & \multirow{4}{*}{20} & \multirow{4}{*}{20} & \multirow{4}{*}{\
\begin{tabular}{c} **Tensioning failure through** \\ bedding planes** \\ \end{tabular} } & \multirow{4}{*}{Matrix body} & \multirow{4}{*}{
\begin{tabular}{c} **Tensioning failure** \\ **through bedding planes** \\ \end{tabular} } & \multirow{4}{*}{Matrix body} \\  & & & & & \\ \cline{3-4}  & & & & & \\ \cline{3-4}  & & & & & \\ \cline{3-4}  & & & & & \\ \cline{3-4}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Failure modes and main control factors of shale with bedding inclination angles of 0\({}^{\circ}\) and 60\({}^{\circ}\) under two loading conditions. The changes in the failure mode and main control factor of shale that is subjected to incremental cyclic loading and unloading, which lead to weakening of the anisotropy, occur for two main reasons: (1) during triaxial incremental cyclic loading and unloading, the microstructure between the shale layers and unoriented mineral particles is subjected to periodic stretching and compression, which gradually destroys the bedding structure, rendering the internal structure of the shale more uniform and the location of cracks more random and weakening the anisotropy of the shale; (2) after unloading, the stress distribution in the shale specimens is redistributed, thereby relieving the local stress concentration that results from the loading. After multiple loading and unloading cycles, the stress distribution inside the specimen becomes more uniform, weakening the anisotropy. In addition, each loading and unloading cycle produces an irreversible strain, which is also a mechanism by which the anisotropy can be weakened.
# Effect of Cement Content on the Deformation Properties of Cemented Sand and Gravel Material
## 3 Results and Discussion
### Initial Tangent Volumetric Ratio  
[PERSON] et al. directly assumed that there was a linear relationship between volumetric strain and axial strain in the nonlinear elastic model of CSG material [24]. However, the volumetric strain and axial strain of CSG material had obvious nonlinear characteristics when the cement contents were low, so the feature of the curves was difficult to represent as a straight line. The cubic polynomial adopted by [PERSON] et al. can represent the relationship of the volumetric strain and axial strain well, but this relationship is more complicated [25]. The volumetric-strain-axial-strain relationship of the rockfill material that can adequately reflect the dilatancy characteristics of CSG material is expressed by a quadratic function that passes through the origin (0,0) as follows:  
\[\varepsilon_{\mathrm{v}}=A\varepsilon_{\mathrm{a}}^{2}+B\varepsilon_{\mathrm{a}}, \tag{18}\]  
Figure 14: The relationship between the parameter \(\lambda_{2}\) and cement content \(Cc\).  
Figure 15: The relationship between the parameter \(d_{2}\) and cement content \(Cc\).  
where \(A\) and \(B\) are the material parameters.  
To reflect the physical significance of parameters in the volumetric-strain-axial-strain relationship of CSG material, Equation (18) can be rewritten as  
\[\varepsilon_{\mathrm{v}}=A(\varepsilon_{\mathrm{a}}-\varepsilon_{\mathrm{n}})^{ 2}+\varepsilon_{\mathrm{vd}}, \tag{19}\]  
where \(\varepsilon_{\mathrm{n}}\) is the axial strain of the peak point, which can be obtained from Equation (19), \(d\) is the fitting coefficient, and \(\varepsilon_{\mathrm{vd}}\) is the peak volumetric strain.  
Equation (19) can reasonably reflect the shear shrinkage and dilatancy characteristics of CSG material. The tangent volumetric ratio \(\mu_{\mathrm{t}}\) is derived as follows:  
\[\mu_{\mathrm{t}}=\frac{\mathrm{d}\varepsilon_{\mathrm{v}}}{\mathrm{d} \varepsilon_{\mathrm{a}}}{=}2A(\varepsilon_{\mathrm{a}}-\varepsilon_{\mathrm{ n}}). \tag{20}\]  
As the origin (0, 0) in the volumetric-strain-axial-strain curve is drawn according to the triaxial shear test results, the point is substituted into Equation (19), and \(A\) can be expressed as follows:  
\[A=-\varepsilon_{\mathrm{vd}}/\varepsilon_{\mathrm{n}}{}^{2}. \tag{21}\]  
When the axial strain \(\varepsilon_{\mathrm{a}}\) is 0, \(\mu_{\mathrm{t}}\) is the initial tangent volumetric ratio is as follows:  
\[\mu_{\mathrm{t}}{=}2\varepsilon_{\mathrm{vd}}/\varepsilon_{\mathrm{n}}. \tag{22}\]  
According to Equation (19), the initial tangent volumetric ratio \(\mu_{\mathrm{t}}\) for CSG material is related to the peak volumetric strain and its corresponding axial strain.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 2 Related Work
### Research Projects
Thirdly, the Self-organized Network Management in Virtualized and Software Defined Networks (SELFNET) project [25] will design and implement an autonomic network management framework to achieve self-organizing capabilities, such as self-protection, self-healing and self-optimization functionalities to deal with major network management problems, which are currently still being manually addressed by network operators, thereby significantly reducing operational costs and improving user experience. Fourthly, the End-to-End Cognitive Network Slicing and Slice Management Framework in Virtualised Multi-Domain, Multi-Tenant 5G Networks (SLICENET) project [26] aims to maximize the potential of the future 5G infrastructures and their services based on advanced software networking and cognitive network management in SDN/NFV-enabled 5G networks. One scenario considered by SLICENET is a smart city use case, the goal of which is to implement a remote water metering and an intelligent public lighting system in the city of Alba Iulia, in Romania. Finally, the Small cEllS coordinAtion for Multi-tenancy and Edge services (SESAME) [27] project' intention is to develop a new multi-operator-enabled small cell that integrates a virtualized execution platform for deploying VNFs and supporting self-management capabilities. Although the existing and ongoing research projects cited address some of the requirements of MANO functionalities for 5G networks, they have not yet delivered an integrated and autonomous MANO solution. Therefore, in this article, a fog-based MANO framework is proposed that goes beyond the current state-of-the-art by introducing a fully-integrated and autonomous FN management system, which combines monitoring and data analysis operations alongside management and orchestration decisions for the resource provisioning issue in smart cities.
## 3 Methodology
### Threat Model: Addressing Security Attacks on ECUs
#### 3.1.2 Sensing Layer and Camera Attacks  
CAVs' wireless sensors, like LIDAR, RADAR, cameras, and remote keyless entry (RFID), can be exploited within short range. Depending on the channel distance, hackers can execute such assaults by placing a wireless transmitter next to the receiver (sensor) [40]. Sensing layer attacks can be classified into two categories: wireless sensor attacks (including RADARs, ultrasonic sensors, and cameras) [18; 19] and analog sensor attacks, such as anti-lock braking system attacks [20].
## 4 Discussion and Results
### Regional Radioactive Heat Production  
Radioactive heat production (RHP), a key indicator of geothermal energy potential, was estimated using the first expression in Equation (4), as differences among the available formulations were determined to be negligible. The estimation was based on the measured spatial distribution of K, U, and Th concentrations. The resulting spatial distribution of RHP is presented in Figure 5, highlighting zones of elevated radiogenic heat generation.  
Figure 4: Spatial variations of magnetic susceptibility: (**a**) measured values at low frequency, (**b**) measured values at high frequency, (**c**) computed values at low-frequency susceptibility using Equation (1), (**d**) computed values at high-frequency susceptibility using Equation (1), (**e**) percent frequency-dependent susceptibility values calculated with Equation (2), and (**f**) mass-specific, frequency-dependent susceptibility values derived from Equation (3).  
RHP values in the study area range from 0.291 to 6.784 \(\upmu\)Wkg\({}^{-1}\). RHP values exceeding 2.0 \(\upmu\)Wkg\({}^{-1}\) are generally considered indicative of significant heat producing rocks [17; 19]. In this context, migmatiites with RHP values exceeding this threshold are predominantly concentrated in the eastern part of the region, in close proximity to seismically active zones. This spatial association suggests that these migmatiites represent the principal heat-producing lithology in the area that contributes to the geothermal energy potential of the region. The combination of high RHP and tectonic activity further underscores the geothermal energy potential of this part of the study area.  
Moreover, RHP-based assessment of geothermal energy potential in Equation (4) does not account for the effect of the primary seismic wave (P-wave) velocity, \(V_{p}\). However, the relationship between \(V_{p}\) and RHP, as demonstrated by [PERSON] and [PERSON] [19], provides valuable insights for both geothermal system identification and the interpretation of seismotectonic processes. Low \(V_{p}\) values are typically associated with increased fracture density, high porosity, and advanced hydrothermal alteration, features commonly found in high-RHP geothermal regions and seismically active fault zones. In geothermal environments, elevated heat flow and hydrothermal alteration reduce the elastic properties of rocks, resulting in lower \(V_{p}\) values and seismic wave anomalies. These low \(V_{p}\) zones also influence the mechanical behavior of fault systems, potentially facilitating earthquake occurrence. Therefore, the frequent seismic activity observed in geothermal areas with low \(V_{p}\) values may reflect elevated geothermal potential and active tectonic deformation that facilitates the upward migration of hydrothermal fluids. Consequently, \(V_{p}\) anomalies serve as valuable geophysical indicators for delineating brittle zones in the crust and assessing geothermal energy potential, as they are often associated with regions of high thermal output and increased seismicity.
# User Visit Certification and Visit Trace System Using Inaudible Frequency
###### Abstract  
This study proposes a user visit certification and visit trace system using inaudible frequencies in the range of audible frequencies but not those audible to people. The signal frequency consists of inaudible frequencies in the range of 18 kHz to 20 kHz, which can be generated by normal speakers. This system recognizes the signal frequency and sends signal values, users' IDs, and location information to a server to certify the current user's location. The server categorizes and stores the user's visit history by individual, and the user can check their personal visit trace information in the application. To verify the utility of the proposed system, we developed an application for user certification and tracing based on a smart device and a built server system. We conducted user certification and trace experiments using the proposed system, resulting in 99.6% accuracy. As a comparative experiment, we conducted a visit certification experiment using a QR code and the proposed system and found that the proposed system performed better. Thus, the proposed system will be a useful technology for epidemiological surveys for individual users and electronic entry lists to restaurants and facilities in the age of COVID-19.  
v 2025 10.3390/signals6020024  
1
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions
### General Results
For both the ML and TL models, Scenario Zero usually achieved the highest accuracy. However, the TL model surpasses the baseline of the ML model in several cases, demonstrating its effectiveness in transferring a model trained on lab-recorded data to an emulated workshop environment.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Advanced Oxidation Processes (AOPs)  
Numerous technological methods for water and wastewater treatment have been put forward and, among these, AOPs have garnered growing acknowledgement for their effectiveness in water disinfection and decontamination [35]. Traditional AOPs have shown an efficient removal of chemical and microbial pollutants from water. Furthermore, AOPs provide a way to combine two distinct processes into a single treatment step, water disinfection and decontamination, thereby improving the cost-effectiveness of treatment [36].  
The primary mechanism in most AOPs involves the in-situ generation of highly reactive species, such as the hydroxyl radical (-OH), the superoxide radical (O\({}_{2}\)-), and the sulfate radical (SO\({}_{4}\)-), which can degrade organic pollutants and pathogens. Currently, hydroxyl radical-based processes are extensively studied among various AOPs. The hydroxyl radical has a higher oxidation potential (approximately 2.72 V) than other common disinfectants/oxidants like ozone, chlorine, and chlorine dioxide. It is non-selective in attacking target molecules, making it suitable for treating contaminated water. Hydroxyl radical-based AOPs can be classified into Fenton or Fenton-like processes, ozonation, photolytic-like processes, and photocatalytic-like processes, depending on the method used to generate the radical species.  
Nevertheless, the effectiveness of hydroxyl radical-based AOPs can be restricted in intricate water matrices containing hydroxyl radical scavengers, like the carbonate/bicarbonate anion and NOM. As a result, there has been an increasing interest in utilizing reactive species with greater selectivity, such as the sulfate radical [9].
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 1 Introduction
### Research Motivation and Significance  
This study is driven by the objective of transforming data management practices in the construction industry through the development of a resilient digital system. The paramount concern for the welfare of construction workers arises from the sensitive nature of their personal information, which encompasses details vital for administrative and operational purposes, such as employment, compliance, and safety. However, the conventional approaches leave a yawning gap, thus rendering these valuable data susceptible to breaches, identity theft, and misuse. The consequences, which extend beyond legal and ethical dimensions, manifest in compromised worker safety, eroded trust, and potential harm to individuals. Through the implementation of a blockchain-based solution, the authors seek to introduce a solution that significantly improves the efficiency, trustworthiness, and security of information management within the construction industry.  
This study holds paramount significance for the construction workforce. The protection of personal information directly contributes to the well-being and trust of construction workers. In a sector known for its physically demanding tasks and safety risks, the social implications of ensuring privacy resonate deeply. According to [PERSON] et al. [35], addressing the social dynamics involved in safeguarding worker information fosters a positive work environment, which ultimately impacts the morale and productivity of the workforce. Moreover, the proposed blockchain model facilitates collaboration among diverse stakeholders in the construction industry. This collaboration extends to workers, project managers, and regulatory bodies, thus emphasizing the social dynamics of trust and transparency within the construction ecosystem. As discussed by [PERSON] and [PERSON] [36], the collaborative nature of blockchain is transforming industries by providing shared, secure, and transparent information, thereby fostering a sense of shared responsibility and accountability among stakeholders.