esa-sceva/satcom-chunk-collection huggingface dataset:
# Industry 4.0 and World Class Manufacturing Integration: 100 Technologies for a WCM-I4.0 Matrix
## 7 Discussion: I4.0 and WCM Synergy
### Industry 4.0 Impact on Focused Improvement Pillar  
FI has the task of monitoring the costs and benefits of the active projects in the plant, by suggesting the activities to be carried out and supervising the ongoing activities in accordance with WCM theory. FI is also responsible for the definition of the tools and methodologies to be used in addressing the improvement projects of the other pillars deriving from D Matrix.  
I4.0 technologies can offer a contribution to FI on the concepts of continuous monitoring of plant efficiency and other improvement activities of this pillar, such as the reduction of microstops or the execution of SMED (Single Minute Exchange of Die, an improved approach for shortening changeover times) projects. In particular, they can:  
* Enable continuous monitoring of efficiency data: Thanks to IoT, it is possible to constantly, reliably and accurately control the Overall Equipment Effectiveness (OEE) of the plant in the Cloud, by performing real-time analysis that can be displayed on the Human-Machine Interface (HMI) or workplace devices;
* Increase MTBMS (Mean Time Between Minor Stoppages) and reduce the duration of the microstops: Machine Learning techniques allow the machine to optimally set working parameters by learning from previous stops, increasing the mean time between two microstops. HMI shows the procedures to solve the most complex anomalies; augmented reality glasses allow the display of malfunctioning components of the machine
* Significantly improve the SMED activities: Augmented reality glasses can show in real time the optimal sequence of activities of a set-up cycle on a machine; virtual training improves the performance of maintenance workers during the production changeover; Plug and Play systems allow a modular design of the machine, aiming to eliminate the set-up activities; in the same way, 3D printing requires almost zero time to set up new production.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### K-means Clustering  
K-means algorithm groups specific data into various sub-groups (clusters) by randomly selecting the \(k\) places, which are considered initial central points for remaining clusters. Euclidean distance is calculated from the rest of the nodes from the chosen initial points, which results in the creation of new set of clusters. Then, new central points are calculated by finding the mean value. Again, find the Euclidean distance and stop the iteration if no more changes occur in the central points. It can be represented by the following expression:  
\[J=\sum_{j=1}^{k}\sum_{i=1}^{n}\|\mathbf{x_{i}^{(j)}}-\mathbf{c_{j}}\|^{2} \tag{2}\]  
Here, \(\mathbf{c_{j}}\) is a central point of cluster \(j\), \(k\) and \(n\) represents the number of clusters and number of cases, respectively, and \(\|\mathbf{x_{i}^{(j)}}-\mathbf{c_{j}}\|\) represents the Euclidean distance. In network slicing, k-means can be used to group applications into different clusters according to their similar features. In [31], authors discussed that the number of network slices that can be created by using k-means algorithm for each mobile network operator (MNO) depending on the KPI performances. In Section 7, we have proposed an approach that how clustering can be done in network slicing in order to manage the resources intelligently.
# A Test Method for Acoustic Emission Properties of Natural Cellulose Fiber-Reinforced Composites
## 3 Results and Discussion
### Effects of Specim Thickness  
The effect of the thickness of the composites on the acoustic properties is evaluated for the composites reinforced with one, three and five fabric layers at 60% RH. The acoustic spectra and the acoustic vibration performance parameters are presented in Figure 5. The specimen thickness has a limited impact on the frequency of the first peak in the spectra. However, the specific dynamic elastic modulus, E\({}_{\text{d}}\), of the material is inversely proportional to h\({}^{2}\)[23]. Thus, a small increase in thickness, h, could result in a large reduction in E\({}_{\text{d}}\), which will, in turn, reduce the three acoustic vibration parameters. Therefore, to ensure consistency in the testing results, the thickness of the specimen has to be strictly controlled.  
Another influential factor is the composite-specific density. When the number of fabric layers increases, the void content could be increased, since more fiber surfaces need to be wetted by the resin, and, thus, the fabrics may not be thoroughly infiltrated. Indeed, the specific density of the single-layer composites made of both hemp and ramie fabrics was the highest, compared with those with three and five layers of fabrics.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 4 Comparison
It accurately expresses security properties using higher-order logic and supports inductive proofs to cover all possible scenarios. Based on LoET, we constructed a bidirectional authentication model that effectively captures the security features of the double ratchet encryption mechanism, ensuring protocol security and improving scalability. With its theoretical depth and formal reasoning capability, LoET serves as a powerful approach for provable security. A summary of the comparison is shown in Table 3. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Research Method** & **Analysis of Ratchet** & **Verification Efficiency** \\ \hline \multirow{4}{*}{**This paper**} & \multirow{4}{*}{**LoET (Extended)**} & Models bidirectional authentication; introduces DH and ratchet event types, AxiomE, and Clean predicate; supports full ratchet evolution. & \(\checkmark\) Structured logic; supports over three strong matching events; short reasoning paths; modular logic; suitable for pre-integration with automated tools; the time complexity is \(O(RM)\). \\ \cline{1-1} \cline{2-3}  & \multirow{2}{*}{**LoET (Extended)**} & \multirow{2}{*}{**LoET (Extended)**} & \multirow{2}{*}{**Models bidirectional authentication; introduces DH and ratchet event types, AxiomE, and Clean predicate; supports full ratchet evolution.} & \(\checkmark\) Structured logic; supports over three strong matching events; short reasoning paths; modular logic; suitable for pre-integration with automated tools; the time complexity is \(O(RM)\). \\ \cline{1-1} \cline{2-3}  & \multirow{2}{*}{**LoET (Extended)**} & \multirow{2}{*}{**LoET (Extended)**} & \multirow{2}{*}{**Models bidirectional authentication; introduces DH and ratchet event types, AxiomE, and Clean predicate; supports full ratchet evolution.} & \(\checkmark\) No formal reasoning structure; depends on source code details; verification cannot be generalized \\ \cline{1-1} \cline{2-3}  & & & \(\times\) Complex state transitions; cannot support systematic authentication; low verification efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ratchet protocol analysis methods and their verification efficiency.
## 2 Materials and Methods
### Remotely Acquired Images and Data
#### 2.3.2 Drone Survey  
The aircraft used to perform the survey was a quadcopter. The drone is equipped with an autopilot package that, in the presence of internet connection, follows a pre-programmed flight-path using the appropriate app, enabling the user to choose the number of waypoints, the photo shooting, and the desired flight altitude. The mission program for both the study areas included a detailed flight plan, including the flight direction, number of flight lines, flying altitude, and orientation of the camera respect to the flight path. The program was thought taking into account that the awaited resolution of the post-processing images strongly depends on the overlap, side lap, and ground sampling distance.  
A drone camera with sensor 1/2.3\" CMOS, 12.4 M Effective pixels, lens FOV 94\({}^{\circ}\) 20 mm (35 mm format equivalent), image size of 4000 \(\times\) 3000 was used for the survey; for more detailed resolution, a camera Sony DSC-HXSV with 10 megapixels and internal GPS sensor was also used.  
The flight was followed through a remote controller wirelessly connected with an Apple iPad Air 2.  
In the study area 1, a manually piloted flight-mode was carried out due to the lack of coverage of UMTS signal causing, in turn, the complete absence of internet connection. The manual flight paths were organized into three different acquisition methods: (a) three aerial strips at three different altitudes: 30, 60, and 120 m above ground level, shooting the photos perpendicular to the flight direction; (b) two aerial strips very close (3 and 5 m) to the fault plane, with the oblique-oriented camera at 45 degrees; (c) two aerial strips farther from the previous one, with photos shooted perpendicular to the outcrop (Figure 2a,b). A total of 268 photos were acquired, with a minimum front and side overlapping of 80%. During half of the flight, the camera orientation was maintained perpendicular to the main path, whereas, for the second half-flight, a path-parallel arrangement was set. The need to adopt different shooting angles was due to the high slope-angle characterizing the outcrops of this sample area.  
In the study area 2, the consistently good internet connection allowed to plan an automatic-mode guided flight through a dedicated application.  
In this case, the flight path was planned to maintain a constant elevation of 70 m above ground level (Figure 2c,d). In the second study area, four aerial sweeps were carried out, leading to the acquisition of 100 photos, with overlapping (both in front and side) ranging from 60% to 65%. During the flight, as this area was completely flat, the camera orientation was maintained perpendicular to the main path, and the images were acquired automatically.
## 4 PSA in a DOF: Dual Pump Configuration
### Numerical Model: Nonlinear Schrodinger Equation (NLSE)  
For an accurate numerical modeling of the propagation of the waves through the dispersion tailored fiber, we resort to the high-order NLSE given by:  
\[\frac{\partial A}{\partial z}+\frac{i\beta_{2}(z)}{2}\frac{\partial^{2}A}{ \partial T^{2}}-\frac{\beta_{3}(z)}{6}\frac{\partial^{3}A}{\partial T^{3}}- \frac{i\beta_{4}(z)}{24}\frac{\partial^{4}A}{\partial T^{4}}-i\gamma|A|^{2}A=0, \tag{40}\]  
where \(A(z,T)\) is the time domain complex amplitude of the wave propagating along \(z\), \(T=t-\beta_{1}z\) is the retarded time. The variation of \(\beta_{2}\), \(\beta_{3}\) and \(\beta_{4}\) with fiber core radius are modeled as linear functions [24] following the numerical results discussed in Section 2.1. We utilize a split-step Fourier method (SSFM) to solve the NLSE. We consider a frequency resolution of 67 MHz and a space step of size \(dz=10\) m. The step size was chosen such that at each step of the SSFM propagation, the accumulated nonlinear phase in the spectrum is much smaller than \(\pi\). Thereafter we simulate the output spectrum for different values of pump-signal frequency separation. The maximum PSA gain was obtained by scanning the input signal phase from 0 to \(\pi\).  
In this work we focus on three principal regions, one with an average dispersion \(\bar{\beta}_{2}\), close to 0 ps\({}^{2}\)/km and the other two around 5 ps\({}^{2}\)/km and \(-5\) ps\({}^{2}\)/km. In Figure 11 we show the typical regime of core radius variation, for which a 5 ps\({}^{2}\)/km variation of \(\beta_{2}\) is possible with a W-ratio clamped at 3. The three vertical dashed lines in Figure 11 indicate \(\beta_{2}=5\) ps\({}^{2}\)/km (left), \(\beta_{2}=0\) ps\({}^{2}\)/km (middle) and \(\beta_{2}=-5\) ps\({}^{2}\)/km (right). Thus a core radius variation of 0.1 \(\upmu\)m would lead to a \(\bar{\beta}_{2}\) of 5 ps\({}^{2}\)/km.  
Consequently, the variation in \(\beta_{3}\) and \(\beta_{4}\) for 1.55 \(\upmu\)m to 1.65 \(\upmu\)m core radius variation will be from 35.6 \(\times\) 10\({}^{-3}\) ps\({}^{3}\)/km to 5.7 \(\times\) 10\({}^{-3}\) ps\({}^{3}\)/km, and from 4.4 \(\times\) 10\({}^{-4}\) ps\({}^{4}\)/km to 2.3 \(\times\) 10\({}^{-4}\) ps\({}^{4}\)/km respectively.
# Performance and Effectiveness of the Passive-Compliant Citrus-Picking Manipulator
## 4 Results
### Picking Experiment in Orchard Environment  
In the experiment on the cutting force of citrus stems, [PERSON] [34] found that the maximum cutting resistance required to cut stems of different thicknesses varies significantly, with the maximum cutting force reaching 136.94 N. This makes cutting the stem for citrus-picking a relatively difficult operation. In comparison, the force separation method [25] is simple and easy to operate. Research shows that Navel oranges in Hubei Province begin to mature in November and can be harvested until March of the following year. Late-season Navel oranges begin to mature in March and can be harvested until June. The skin of Navel oranges is tightly wrapped, and the stems are relatively brittle. When manually rotating and picking, adding a pulling force makes it easy to pick the oranges. Even after the stem completely detaches, the calyx still stays tightly against the fruit's skin. Therefore, the citrus-picking robot in this paper adopts the force separation method. The gripper component of the robot grabs the fruit and breaks the stem by rotating multiple times to pick the fruit. To verify the practical feasibility of the optimized picking robot in an orchard  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Citrus Diameter/mm** & **Citrus Weight/kg** &
\begin{tabular}{c} **Maximum Gripping** \\ **Weight/kg** \\ \end{tabular} \\ \hline
65 & 0.131 & 0.8 \\
75 & 0.201 & 1.7 \\
85 & 0.292 & 2.3 \\
95 & 0.408 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gripping test results.  
Figure 10: (a) Grab citrus fruits of different sizes; (b) grasping the oval citrus.  
Figure 9: Envelope grabbing process.  
environment, a field picking test of Navel oranges was conducted in an orchard in Wuhan in January 2024, as shown in Figure 11. Based on the field orchard test, the fruit diameter range for picking was 65-100 mm, with a total of 420 samples picked. The citrus numbers and fruit damage rates were recorded for different rotation ranges, (0,1), (1,2), (2,3), and (3,4), where the number of rotation cycles refers to the number of rotations required by the picking robot to successfully twist and detach the stem. The test results are shown in Table 2.  
The picking results show that the number of rotations required to break the citrus stem varies with the size of the fruit. Without applying pulling force, the fruit can be fully detached after four rotations, with a damage rate of 2.6%. During the picking process, branches were found to contact the fruit skin, and when twisted, the branches were inserted into the skin, causing fruit damage. Furthermore, after bringing the harvested citrus back to the lab, no visible damage was observed at the stem connection, as shown in Figure 12. Upon cutting the fruit for an internal examination, no damage to the flesh was found. Therefore, in the absence of external pulling force, the main cause of fruit damage can be attributed to physical interference from branches during the twisting process.
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 5 Fault Tree Analysis
### Qualitative Analysis  
The essence of qualitative fault tree analysis is to systematically identify all minimal cut sets that lead to the top event occurrence--namely, the most simplified combinations of fault events. These minimal cut sets expose the system's potential failure mechanisms and critical design vulnerabilities, providing clear direction for reliability improvement. Analysis with AutoFTA 3.9 software reveals 54 minimal cut sets leading to NNBI beam source system failures, all being first-order cut sets, including 27 related to negative ion accelerator failures, 15 involving plasma expansion chamber failures, and 12 pertaining to RF driver failures [23].
## 2 Methods and Model
### Model
#### 2.2.2 Modeling of Red Team Defensive Agents  
In a multi-agent mixed game decision-making system based on the MCCFR algorithm, the primary task of the Red Team defensive agents is to adjust their frequency allocation strategy based on the interference caused by Blue Team attacking agents on the current communication frequency bands. This adjustment is made using a CFR algorithm and is optimized under incomplete information using an MCM algorithm. The frequency allocation process for Red Team defensive agents is illustrated in Table 1, with the input and output parameters detailed in Table 2. In addition, the space and time complexity of all approaches are also evaluated [25]. The time complexity of the MC algorithm is O (_Na + N_), where _Na_ and \(N\) is the maximum number of iterations. We can see that the MCCFR algorithm has low complexity and is suitable as a frequency selection algorithm. \begin{table}
\begin{tabular}{l c c} \hline \hline Algorithm: The frequency allocation process for Red Team defensive agents. & Time complexity & Space complexity \\ \hline Input: the input listed in Table 2. & & \\ Output: listed in Table 2. & & \\ Begin\{ & & \\ \hline Step1: initialization. & & \\ Load scene. & & \\ Read input information. & & \\ \hline Step2: Frequency selection for communication links based on **MCCFR**. & & \\ Initialization: Strategy and Regret. & & \\ for i = 1:_Na_ & & \\ for j = 1:_N_ & & \\ **rBF = MC(rF)**. & & \\ Simulated confrontation. & & \\ \hline Accumulated regret. & O(_Na+N_) & & \\ Update strategy. & & \\ \hline end & & \\ \hline Determine the final frequency by **CFR** with strategy and **rBFP**. & O(1) & & \\ \hline Step3: Communication link calculation. & & \\  Building link with other intelligent agents. & & \\  Calculate \(L\) by **ITU-R****P.525**. & & \\ \hline Step4: Output the parameters in Table 2. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The frequency allocation process for Red Team defensive agents. The spectrum allocation process is as follows:  
(1) Environment modeling  
The game environment for the Red Team defensive agents is set as a cluster communication environment with 8 agents. The agents are divided into two groups of 4: the first 4 agents form the ground communication cluster, and the remaining 4 agents form the aerial communication cluster. Each agent's main task is to manage frequency based on MCCFR strategies, considering spectrum parameters, equipment deployment locations, action trajectories, timing of actions, and frequency priorities. Each agent has five candidate frequency points, ranging from 30 MHz to 3000 MHz. (2) Frequency selection for communication links based on MCM
###### Abstract  
Malaysia's location in Southeast Asia exposes it to various weather patterns influenced by El Nino-Southern Oscillation (ENSO), monsoons, the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). To overcome the limitations of previous studies due to insufficient spatial information, this study utilizes remote sensing (RS) data from Landsat and MODIS satellites, along with the Oceanic Nino Index (ONI), to analyze the spatial distribution of temperature affected by El Nino-Southern Oscillation (ENSO). This study employs radiometric and atmospheric corrections on remote sensing (RS) data, converting them to surface temperature data. Our analysis reveals a correlation coefficient of 0.73 (MODIS) and 0.71 (Landsat) between the ONI and RS temperature data. During El Nino events, Landsat recorded temperature increases of 0-1.6 \({}^{\circ}\)C, while MODIS showed increases of 2.2-2.8 \({}^{\circ}\)C. The spatial information obtained assists in identifying affected areas and facilitating the implementation of mitigation measures by the government. By utilizing RS data, this research enhances our understanding of the ENSO-temperature relationship, surpassing previous limitations and providing valuable insights into climate dynamics.  
ENSO; remote sensing; temperature distribution Article Exploring the Impact of El Nino-Southern Oscillation (ENSO) on Temperature Distribution Using Remote Sensing: A Case Study in Study in Kuching City 2023  
[PERSON]  
1 Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia,  
Bangi 43600, Malaysia; [EMAIL_ADDRESS]  
2 Faculty of Social Sciences and Humanities, University Malaysia Subah (UMS), Kota Kinabalu 88400, Malaysia
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.7 Room Classification
Their solution grounded the processing in the Bayesian inference. It was utilized in the calculations of room-type probabilities as well as in the creation of a parse forest structure. Energy consumption models were used in two articles. In 2015, [PERSON] et al. (2017) published a paper researching the use of a neural network in room classification for office buildings. Their solution analyzed electricity consumption measurements from three main categories of devices: sockets, lights, and air-conditioners. The presented Echo State Network (ESN) was a recurrent neural network reported to achieve good effects in practical applications. The work was later continued and improved by [PERSON] et al. (1974). The extended version combined two layers of ESNs--a set of three networks for each of the modeled consumption categories and a fourth one, on top of the others, combining previous results into a single classification. Grid map was found to be processed by only one additional article. The solution of [PERSON] et al. (2014) grounded the map analysis in a semi-supervised learning algorithm. A combination of Support Vector Machine (SVM), Conditional Random Fields (CRFs), and a Generalized Voronoi Graph (GVG) resulted in three-class office room-type recognition.