esa-sceva/satcom-chunk-collection huggingface dataset:
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 3 Materials and Methods
### Feature Analysis of Data  
Cluster analysis [17] is an unsupervised learning technique that organizes data objects into distinct groups or clusters. The primary objective of clustering is to ensure that data points within the same cluster exhibit high similarity, while maintaining significant differences between points in different clusters. By uncovering the intrinsic structure of data, clustering methods facilitate visualization and provide valuable insights for explanatory analysis of feature distributions. In this study, three classic clustering techniques are employed to analyze and visualize both the original ECG signal data and the features extracted by the KAN, highlighting the advantages of the KAN in feature extraction.  
1. BIRCH (Balanced Iterative Reducing and Clustering Using Hierarchies) [18] is a hierarchical clustering method that efficiently performs clustering operations by constructing a clustering feature tree (CF Tree). The CF Tree, similar in structure to a balanced B+ tree, contains nodes composed of clustering features (CFs), which are represented as a triple (N, LS, SS). Here, N denotes the number of sample points in  
Figure 3: Comparative analysis of ECG signal data before and after variance sorting.  
Figure 2: Preprocessing of ECG signal data.  
a cluster, LS represents the linear sum of feature dimensions, and SS is the sum of squares vector. In the context of ECG signal feature clustering, BIRCH captures both the global structure and local patterns of signals through hierarchical and recursive cluster merging, making it well-suited for fast clustering of large-scale datasets. This study compares the clustering outcomes of original features and KAN-extracted features using BIRCH, aiming to demonstrate the ability of the KAN to optimize feature distribution.
2. MeanShift [19] is a nonparametric, density-based clustering method that estimates the probability density of a dataset using a kernel function. It iteratively shifts data points along the density gradient toward regions of highest density, known as mode points. Each data point converges to a high-density region, achieving automatic clustering. In this study, MeanShift clustering is applied to identify high-density feature distribution regions within ECG signals.
3. Spectral Clustering [20] is a graph theory-based method that constructs a similarity graph among data points, representing data relationships as a weighted graph matrix. The eigenvalues and eigenvectors of the graph's Laplacian matrix are then utilized for dimensionality reduction and clustering. Spectral clustering excels at handling complex, nonlinear data distributions and is particularly effective for uncovering latent high-dimensional nonlinear structures within ECG signals. This study visualizes the clustering results of both original features and KAN-extracted features through spectral clustering, highlighting the KAN's superior performance in preserving nonlinear signal characteristics and capturing intricate relationships within the feature space.
## 4 Conclusions  
The research and experimentation conducted have shown that both fly-ash additives and temperature have a significant impact on the viscosity of lubricating oils. Fly-ash additives are utilized to enhance oxidation resistance and prevent deposit formation in the oil, effectively reducing viscosity loss at high temperatures. Temperature also plays a crucial role in regulating oil viscosity, as increased temperatures tend to decrease viscosity, affecting lubrication capability and friction resistance. This can result in increased wear and damage to machine components. However, with the appropriate application of fly-ash additives, it is possible to adjust oil viscosity at different temperatures and maintain good lubricating performance.  
Experimental results demonstrated that at a temperature of 25 \({}^{\circ}\)C, the addition of a 0.5% fly ash additive led to viscosity increases of 6.2%, 8.1%, and 13.8% for oils B, C, and A, respectively, compared to the case without the additive. Similarly, at 75 \({}^{\circ}\)C, the viscosity increments were 10.2%, 11.5%, and 22.8% for oils B, C, and A, respectively, while at 85 \({}^{\circ}\)C, the corresponding increments were 11.1%, 16.6%, and 32.8%. These findings indicate that fly-ash additives effectively mitigate the impact of temperature on oil viscosity, with the highest efficacy observed in oil A.  
In summary, the combination of fly-ash additives and temperature control is crucial for maintaining stable viscosity in lubricating oils. These additives minimize viscosity loss, thereby improving equipment performance and longevity. Understanding the influences of fly-ash additives and temperature is essential for optimizing lubricant performance in various temperature conditions and selecting the appropriate additive ratios for specific applications. Continued research and implementation of this technology will significantly contribute to the advancement of lubrication science and industrial applications.  
Conceptualization, T.-A.B.; Project Administration, T.-A.B.; Supervision, T.-A.B.; Funding Acquisition: T.-A.B. and N.-T.B.; Methodology, T.-A.B.; Formal Analysis, T.-A.B. and N.-T.B.; Experimental data collection, T.-A.B. and N.-T.B.; Writing--Review and Editing, T.-A.B. and N.-T.B. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2022-PC-026.  
Not applicable.  
The School of Mechanical Engineering and Hanoi University of Science and Technology are gratefully acknowledged for providing support and conditions to carry out this research. This work was also supported by the Centennial Shibaura Institute of Technology Action for the 100 th anniversary of the Shibaura Institute of Technology to enter the top ten Asian Institutes of Technology.  
The authors declare no conflict of interest.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 3 Security, Privacy, and Confidentiality
### Interactive Technology Security  
The proper functioning of the Metaverse relies heavily on interactive technologies such as Augmented Reality (AR) and Virtual Reality (VR). These technologies play a fundamental role in shaping the Metaverse's functionalities. Despite their importance, AR and VR are susceptible to security and privacy challenges. The extent of information shared within them and the difficulties associated with identity authentication in the virtual world are among the most pressing.  
For example, traditional methods of user authentication, such as password entry, are considered unsuitable. As such, alternative authentication solutions have become imperative. Although voice recognition technology has been considered a viable option, it is susceptible to various risks, such as voice synthesis techniques. Consequently, biometric authentication mechanisms have been developed to use the unique biological traits of users for secure authentication. Of these options, facial recognition technology has emerged as an effective mechanism for verifying the user's identity. However, the reliability of facial recognition technology has been challenged by unscrupulous actors who can easily download a  
Figure 7: Comparison of key security risks in the Metaverse and MR.  
user's image and create a virtual reality representation of their face. Such manipulations can then be used to bypass the security system, thereby exposing vulnerabilities that must be addressed in order to enhance the overall security posture of an organization.  
The security challenges of interactive technologies in MR extend beyond those in AR and VR due to the unique blend of physical and digital interaction that MR entails. MR environments rely heavily on precise spatial mapping and real-time user tracking, often using biometric data and continuous environmental scanning. Facial recognition, gesture tracking, and other biometrics are essential for providing responsive interactions, yet they introduce significant privacy concerns if hacked or misused [23].
## 3 Results and Analysis
### Uniaxial Compression Test
#### 3.2.2 Peak Strain  
The peak strains for the tonalite specimens are shown in Figure 6. The peak strain \(\varepsilon_{f}\) is the strain experienced by a tonalite specimen at peak stress \(\sigma_{f}\). The average peak strain of tonalite specimens subjected to 60 days of chemical corrosion (G2) is lower compared with that of the control group (G1). This result, combined with the Young's modulus, indicates that the G2 specimens can be more easily destroyed. The average peak strain of tonalite specimens subjected to freeze-thaw cycles (G3) is shown  
Figure 3: Young’s moduli of tonalite specimens from different groups.  
to decrease with decreasing freezing temperature. In addition, the average peak strains of tonalite specimens subjected to combined effect (G4) are observed to be compared with those of G3 at \(-40\) and \(-50\)\({}^{\circ}\)C. Thus, strain softening that arises from chemical corrosion occurs in tonalite specimens subjected to the coupled effect after \(-40\)\({}^{\circ}\)C freezing temperature. Although the UCS of G3 is found to be close to that of G4, the peak strain of G4 is higher than G3 and the deformation of G4 specimens is more prominent. This may imply that silica gel is created in the process of chemical corrosion, that has a cementing effect on the samples. After the specimens dry, the silica gel does not make the peak strain of G4 decrease too much. This also makes the UCS of G4, which has a lower Young's modulus, closer to the UCS of G3. (More evidences will be given in the next sections).  
Figure 4: The variation of uniaxial compressive strength (UCS) for tonalite subjected to freeze-thaw cycles (G1 and G3).  
Figure 5: The variation of UCS for tonalite subjected to combined effect of chemical corrosion and freeze-thaw cycles (G2 and G4).
# Agriculture 4.0: Is Sub-Saharan Africa Ready?
## 2 Prospects and Challenges for Agriculture 4.0 Adoption in Sub-Saharan Africa
### Leapfrog Technology Opportunities and Digital Infrastructure Availability  
The reality for many African countries has been poorly developed physical infrastructure, including a low density of fixed telephone landlines and physical bank branches. This might historically have constrained economic development. However, increasingly African countries have proven able to leapfrog these technologies. That is, rather than follow what might be considered traditional technology adoption pathways, taken earlier by HICs, African countries may be able to skip an intermediate technology stage.  
Examples of this technology leapfrogging that have already occurred that have particularly benefitted smallholder farmers relate to telecoms and banking [61]. For example, many African countries have rapidly transitioned from having a very low penetration of fixed landlines and traditional bricks and mortar banking infrastructure to a relatively high proportion of households with access to mobile telephony and mobile banking. New and modern information and communication technologies (ICTs) have played critical roles in bridging key agricultural extension and development infrastructure gaps. The mobile money transfer service application, M-Pesa, was pioneered in Kenya by Vodafone and expanded into other SSA countries such as Tanzania and South Africa [62]. M-Pesa is transforming agriculture in SSA, in part by providing mobile payment systems to farmers that have enabled many to boost crop production and move out of subsistence agriculture [63]. Other agriculture 4.0 mobile-technology-supported financial and agricultural technologies such as M-Farm and Esoko have been widely adopted in SSA countries such as Kenya and Ghana respectively [62]. Esoko provides farmers with daily market prices for many key agricultural crops, using Short Message Services (SMS) sent to farmers' mobile phones, in addition to information on weather, market situation and farming tips [64; 65].  
Many agriculture 4.0 technologies rely on the fourth generation (4G) of broadband cellular network technology, cloud computing, and big data analytics. Around 60% of SSA smallholder farmers have access to mobile connection with an increasing network of 4G connections and Data Centres being developed in SSA [25], suggesting that Africa's farmers are, in this respect, relatively well placed to take advantage of mobile phone-driven agriculture 4.0 technologies, that could also leverage the breakthroughs already recorded in ICTs' adoption in SSA and leapfrog the conventional technologies in agricultural advisory and financial services provision for the underserved [25].
_Applied_  
_sciences_  
_Brief Report_  
**Combined Effects of Water Deficit, Exogenous**  
**Ethylene Application and Root Symbioses on**  
**Trigonelline and ABA Accumulation in Fenugreek**  
**[PERSON] 1,2,\(\dagger\), [PERSON] 1,\(\dagger\)**, [PERSON] 1,3\(\copyright\), [PERSON] 2,\(\copyright\), [PERSON] 1,\(\copyright\) and [PERSON] 1,3\(\copyright\)**  
1 National Research Council, Institute for Sustainable Plant Protection (CNR-IPSP), 10125 Torino, Italy; [EMAIL_ADDRESS] (S.I.); fabiano.sillo@ipsp.cnr.nit (F.S.); luca.nerva@crea.govit (L.N.); [PERSON] (W.C.)  
2 Department of Biology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran  
3 Council for Agricultural Research and Economics, Research Centre for Viticulture and Enology CREA-VE, 31015 Conegliano (TV), Italy  
*Correspondence: [EMAIL_ADDRESS] (R.B.); [PERSON]DDRESS] (A.G.); Tel.: 0039-011-6502927 (R.B.)  
+These authors equally contributed as first authors.  
Received: 3 March 2020; Accepted: 27 March 2020; Published: 29 March 2020  
**Featured Application: This work suggests that application of both ethephon and root symbioses in fenugreek plants can positively affect the production and accumulation of valuable compounds such as trigonelline and ABA with potentiality for future sustainable management purposes of fenugreek cultivation.**  
**Abstract:** Secondary metabolites (SMs) have high economic impact thanks to their exploitability in chemical, pharmaceutical and cosmetic industries. _Trigonella founum-garecum_ has an importance due to the production of bioactive compounds with pharmaceutical values. Among them, the alkaloid trigonelline is known for its role in the treatment of different human diseases. SM accumulation is influenced by environmental factors but is modulated by the application of exogenous compounds. Ethephon, a precursor of the phytohormone ethylene, was already used to influence SM accumulation. Our work is aimed at evaluating the accumulation of trigonelline and the phytohormone abscisic acid (ABA) when three factors were combined: i) two levels of water regimes (well-watered and water deficit), ii) ethephon treatments and iii) inoculation with an arbuscular mycorrhizal (AM)-based inoculum also leading to nodulation. The content of trigonelline and ABA was significantly affected by symbioses, showing high accumulation in AM-colonized plants irrespective of the water regimes applied. In terms of trigonelline accumulation with respect to ethephon treatments, while symbiotic plants showed a dose-dependent trend, non-symbiotic plants showed a significantly difference only when 550 ppm of ethephon was applied. In conclusion, our work provides new information on the effects of both ethephon and symbioses on plant growth and accumulation of valuable compounds, such as trigonelline, in fenugreek.  
**Keywords:** abiotic stress; AM symbiosis; fenugreek; trigonelline; water deficit
## 3 Results Analysis  
### Fabrication of Holographic Metasurface Bessel Vortex Antenna  
For the scattering experiment, a quasi-Bessel vortex wave (topological charge \(l=+1\)) was generated by a metasurface antenna, whose excellent performance was demonstrated in [14]. According to the interference hologram of the object wave and reference wave, the specific dimension of each unit cell was modulated to obtain the specific phase condition. Figure 3 shows the principle diagram of the holographic Bessel antenna and the measured reflection coefficient. It can be seen that the Bessel antenna had a good reflection performance (\(<\)\(-15\) dB) across the whole working band.  
Figure 4 illustrates the prototype and field distributions of the holographic metasurface Bessel antenna. It can be seen that the amplitude was mainly distributed as a hollow ring, and the phase was distributed as a helical wavefront that corresponded to the topological charge. In addition, with the increase in the propagation distance, the field intensity distribution gradually spread slightly. However, for this metasurface antenna, when the propagation distance was controlled within 1500 mm, the diffusion and attenuation of the wave field could be ignored, which is equivalent to the non-diffractive vortex beam that is an excellent candidate for the wave source of the Bessel vortex scattering model.  
Figure 3: Schematic diagram (**a**) and reflection coefficient of the Bessel antenna (**b**).  
Figure 5 illustrates the photographs of the scattering experiment in an anechoic chamber for several standard targets. By using a horizontal slide and an angle measuring instrument, a 45\({}^{\circ}\) reflection EM wave measuring device for two flat plates (metal and FR4) was used, as shown in Figure 5a,b, respectively. The distance between the transmitting antenna and the center of the aluminum plate was set at 800 mm, and the distance between the probe and the center of the reflective plate was 400 mm. The size of both the FR4 dielectric substrate and the metal aluminum plate was 300 mm \(\times\) 300 mm.  
Figure 4: Metasurface Bessel vortex antenna and amplitude/phase distributions. (**a**) Prototype, (**b**) observation plane was 1000/(**c**) 1500/(**d**) 2000 mm away from the antenna.  
Figure 5: Experimental scenario of the plane-near-field OAM scattering of several standard targets. (**a**) Metallic plate, (**b**) FR4 plate, (**c**) FR4 transmission, and (**d**) sphere.  
The Bessel antenna center, sphere center, and the receiving probe were assembled on the same horizontal line, forming the on-axis condition as shown in Figure 5d. Selecting 20 GHz as the working frequency, the Bessel amplitude and phase distributions presented in Figure 6 of [14] indicated that the radius of the first ring of the intensity distribution was within 150 mm. Therefore, the radius of the experimental sphere was introduced as 150 mm so that the target was sufficient to block the main energy of the wave source, resulting in little influence on the forward scattering.
## 3 Construction of Simulation Model
### Sensor Information Fusion Level  
At this level, the communication within the UAV swarm and the observation of local targets are considered, corresponding to the state space of UAV action, communication interaction, and radar/EO observation information fusion that are carried out; multiple parameters are reserved; and the limited degree of the environment relative to the task swarm is regulated.  
Figure 3 illustrates the state space composition of the mission UAV. The state space of mission UAV \(i\) consists of three parts, namely, its own state information \(O_{i}^{A}\), communication information \(O_{i,k}^{A}\), and radar observation information \(R_{i}\), which are expressed as follows:  
\[O_{i}=O_{i}^{A}+O_{i,k}^{A}+R_{i},(i,k\in N^{A},i\
eq k;j\in N^{D}) \tag{2}\]  
Self-state information \(O_{i}^{A}\) includes three spatial positional quantities and three speed quantities, which are expressed as follows:  
\[O_{i}^{A}=\left\{x_{i}^{A},y_{i}^{A},z_{i}^{A},u_{i}^{A},v_{i}^{A},w_{i}^{A}\right\} \tag{3}\]  
The communication information \(O_{i,k}^{A}\) includes three spatial position quantities and three speed quantities of all friendly UAVs except itself, and the communication radius is \(d_{O}\). When the friendly side is not within the communication radius, the information would be empty, which is represented as follows:  
\[O_{i,k}^{A}=\left\{\begin{array}{l}\left\{x_{k}^{A},y_{k}^{A},z_{i}^{A},u_{ k}^{A},v_{k}^{A},w_{k}^{A}\right\}+O_{noise},k\
eq i,d_{Oik}\leq d_{O}\\ \left\{0,0,0,0,0,0\right\},k\
eq i,d_{Oik}>d_{O}\end{array}\right. \tag{4}\]  
Radar observation information \(R_{i}\) includes three spatial position quantities and three speed quantities of defensive targets, and the observation radius is \(d_{R}\). When the enemy is not within the observation radius limit, the information would be empty, which is expressed as follows:  
\[R_{i}=\left\{\begin{array}{l}\left\{x_{j}^{D},y_{j}^{D},z_{j}^{D},u_{j}^{D},v_{j}^{D},w_{j}^{D}\right\}+R_{noise},d_{Rij}\leq d_{R}\\ \left\{0,0,0,0,0,0\right\}+R_{noise},d_{Rij}>d_{R}\end{array}\right. \tag{5}\]
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 3 Materials and Methods  
The proposed methodology consists of six steps, as shown in Figure 1, which start with dataset preparation. The second step is the implementation of preprocessing methods, which include cropping images into patches and data augmentation. Next is the building of U-Net network architecture and model training using the annotated dataset. The fifth step is the implementation of selected post-processing methods, which are [PERSON]'s thresholding using OpenCV function, and two methods of binary mask refinement, morphological operations also using OpenCV functions, and dense CRF. Lastly, all the results from the implementation of post-processing on model prediction are evaluated using suitable evaluation metrics, and the predicted mask is visually compared. Figure 1 represents the flow chart of the proposed methodology.  
The next sections will describe each step in the proposed methodology of this work in more detail.  
Figure 1: Workflow of methodology. The main steps include dataset preparation, image preprocessing, building of the shadow model, model training, post-processing, and evaluation of results.
## 3 Results and Discussion
### Cutting-Path Planning in Five-Axis CAM of Stents and Actual Stent Machining
#### 3.3.2 Cross-Section Width of the Stent  
The UX300 was employed with the following parameters: a single linear axis positioning precision of 8 \(\upmu\)m, repeatability precision of 4 \(\upmu\)m, and rotation axis precision of 4 s. The cross-section width of the cut stent structure was determined using the simultaneous motion of the \(Y\), \(Z\), and \(C\) axes combined with layer-by-layer milling. Errors of dimensions were caused by machine positioning errors, repeatability precision, and screw backlash, and were less affected by the table's center-of-rotation errors. Table 1 presents the cross-section width errors of the stent with and without compensation; the errors with compensation were within 0.01 mm, and thus within an acceptable range of machining errors.