esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulations Results and Discussions  
In this section, we present the performance of the proposed EEUCH protocol compared with the existing benchmark routing protocols, i.e., LEACH [19], LEACH-C [20], TEEN [22], SEP [21], IEE-LEACH [23], and EEECA-THWSN [24]. In simulation experiments, we consider that a WSN of \(N=200\) SNs is deployed in the FoI which is 250 m away from the BS in a 100 m \(\times\) 100 m area. For the simplicity and better comparison of EEUCH with the benchmark routing protocols, we simulate EEUCH for a single cluster. The UAV does not change its position much during the steady-state phase of a round to affect the channel conditions. Considering the mobility nature and error in the exact deployment of UAV, we assume flexibility up to 2 m on each axis of UAV deployment [29]. It is important to note that the same results are obtained for larger areas (Fols) in proportion to the number of SNs and UAVs, i.e., two UAVs will cover twice the area and twice the number of SNs, due to the cluster independence and no overlapping. MATLAB 2021b was chosen as the simulation tool for our system primarily due to the availability of toolboxes that support wireless communication and network protocols. Its user-friendly environment facilitates smooth algorithm implementation, simulation, and result analysis. MATLAB's ability to generate high-quality figures significantly enhances the visualization of simulation results. Furthermore, its computational efficiency makes it a highly favorable choice when compared to other simulation environments. We run the simulation for 3000 rounds.  
The geographical three-dimensional locations of the WSN, SNs, BS, and UAV are shown in Figure 6. The predefined network configuration parameter values are given in Table 2.  
Figure 6: Geographical locations of FoI, UAV-ABS, SNs, and BS are shown for our proposed EEUCH and other benchmark protocols simulated in MATLAB 2021b: (**a**) 3D view of EEUCH, (**b**) 2D view of SEP: the two-tier heterogeneity concept is shown, (**c**) 2D deployment of homogeneous WSNs i.e., LEACH, LEACH-C, TEEN, and IEE-LEACH, and (**d**) 2D deployment of three-tier heterogeneous WSNs, i.e., EECA-THWSN.
## 3 Applications
### Phase-change Memory (PCM)
#### 3.1.2 Multi-level Cell (MLC) Memories  
Generally, in a Phase-Change Random-Access Memory (PCRAM) device, the storage of a binary 0 or a binary 1 is attributed to the amorphous and crystalline states of GST, respectively. However, by altering the temperature and the duration induced to the PCRAM cell, it is found that the resistance can set to be set to attain values in between those of crystalline and amorphous states. Based on this fact, Multi-level Cell (MLC) memories exploit these intermediate states in-between the crystalline and amorphous to store more than one bit per cell [81], which increases the memory capacity at the cell level. Therefore MLCs can effectively double or triple the data density in a memory device without  
Figure 15: The insertion loss per unit of the latch: (**a**) as a function of the GST thickness when the waveguide width is fixed to 30 nm, (**b**) as a function of the waveguide width when the GST thickness is fixed to 50 nm. (**c**) Electrical power density and energy cost per unit of length, (**d**) total energy required by the switching-off operation as a function of waveguide width. Adapted with permission from Reference [78]. ©2013 American Institute of Physics.  
increasing the footprint; each cell can store \(b=log_{2}n\) bits of digital information, where \(n\) is the number of possible levels or states that a cell can store [82].  
GST is a binary phase change material, however, by incomplete crystallization, the whole material or only crystallizing certain part of the material, over decade researchers were able to achieve numerous intermediate states between the amorphous and crystalline states [82; 83; 84]. As shown in Figure 16, Ref. [84] reported a 7-level cell using single pulse programming. It is crucial to note here, however, that one of the drawbacks of the MLC lies in the obvious possibility of more errors due to the reduced margin separating the states and increase in power required to program the memories.
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 4 Mapping an OPM Model to a Graph
### Nested OPM Thing Flattening  
Unlike the multi-layered hierarchical structure of an OPM model visual representation, in the GDB we use a single graph for the entire model, which mandates collapsing or flattening the OPM model hierarchy altogether. Prior to converting an OPM Model to a graph, we need to collapse the multi-layered OPM model representation into a single-layered OPM model, in which all the OPDs in the OPD tree are merged into a single, \"flat\" OPD. To perform this, we use a flattening algorithm, developed as part of this research. The Flattening algorithm merges all the OPDs into a single OPD without any loss of information.  
During OPM model flattening, elements (things and links) that appear in an OPD without any relation to any one of the nested processes are relatively easy to merge; they are copied to the new flat, single-layered, single-OPD OPM model. However, any element that has a direct or indirect relation to a nested process requires extra processing by the Flattening algorithm. In some cases, the algorithm omits some of the links, and in other cases it adds new elements, as described next. Another approach that avoids flattening altogether can be an alternative option.
## 1 Introduction
The integration of lightweight, quantum-resistant cryptographic techniques using a hybrid encryption model that balances classical and post-quantum algorithms, suitable for constrained IoT systems. 3. The design and simulation of a permissioned blockchain-based identity and access management model that facilitates decentralized, tamper-resistant authentication with minimal communication and verification overhead. 4. The development of an autonomous security strategy that leverages Digital Twins and Markov Decision Processes (MDP) for predictive threat analysis and context-aware mitigation actions. 5. The proposal of a privacy-preserving data processing method based on differential privacy with dynamic budget adjustment, enabling adaptive control over data utility and confidentiality. 6. The conceptual integration of federated learning with Digital Twin updates to improve distributed threat generalization while maintaining data locality and privacy. 7. A simulation-based evaluation of the complete framework using heterogeneous IoT device profiles (ranging from 40-120 MHz CPU and 32-128 MB memory), demonstrating low processing (0.02%), memory (0.015%), and energy overhead (<1.5 mAh/day), with high detection accuracy (85-99%) and rapid response times (2 s). The remainder of this paper is structured as follows: Section 2 reports the related works. Section 3 provides a detailed overview of the IASF-IoT components and their integration. Section 4 describes the performance modeling approach and presents the results of the evaluation. Section 5 reports on the experimental results. While Section 6 discusses the implications of the findings and the potential for future research. Finally, Section 7 concludes the paper by summarising the key contributions and highlighting the significance of the proposed security framework in the context of IoT ecosystems.
# Performance Analysis of ICA in Sensor Array
###### Abstract  
As the best-known scheme in the field of Blind Source Separation (BSS), Independent Component Analysis (ICA) has been intensively used in various domains, including biomedical and acoustics applications, cooperative or non-cooperative communication, _etc._ While sensor arrays are involved in most of the applications, the influence on the performance of ICA of practical factors therein has not been sufficiently investigated yet. In this manuscript, the issue is researched by taking the typical antenna array as an illustrative example. Factors taken into consideration include the environment noise level, the properties of the array and that of the radiators. We analyze the analytic relationship between the noise variance, the source variance, the condition number of the mixing matrix and the optimal signal to interference-plus-noise ratio, as well as the relationship between the singularity of the mixing matrix and practical factors concerned. The situations where the mixing process turns (nearly) singular have been paid special attention to, since such circumstances are critical in applications. Results and conclusions obtained should be instructive when applying ICA algorithms on mixtures from sensor arrays. Moreover, an effective countermeasure against the cases of singular mixtures has been proposed, on the basis of previous analysis. Experiments validating the theoretical conclusions as well as the effectiveness of the proposed scheme have been included.  
blind source separation; independent component analysis; performance analysis; practical factors +
Footnote †: journal: Sensors
## 3 Results
### Surface Microhardness  
The results of the microhardness test are shown in Table 3. The first null hypothesis was rejected for surface microhardness (\(p<0.001\)) because the surface microhardness values varied according to the ceramic material. Crowns fabricated with HASS Rosetta showed the lowest surface microhardness (\(5.29\pm 0.13\) GPa), which was significantly different to the crowns fabricated with IPS e.max CAD (\(5.8\pm 0.21\) GPa) or VITA Suprinity (\(5.94\pm 0.32\) GPa) (\(p<0.05\)); however, the surface microhardness values were not significantly different between the crowns fabricated with IPS e.max CAD and VITA Suprinity (\(p\geq 0.05\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Groups** &
\begin{tabular}{c} **Trueness (\(\upmu\)m)** \\ **Mean (SD)** \\ \end{tabular} & **Group Comparisons** \\ \hline
**HASS Rosetta** & 26.5 (2.5) & A * \\
**IPS e.max CAD** & 33 (8.8) & B * \\
**VITA Suprinity** & 38.4 (5.4) & B * \\
**\(p\)** & \textless{}0.001 & \\ \hline \hline \end{tabular}  
* Different capital letters indicate that the difference between the groups is significant by [PERSON]’s honestly significant difference post hoc testing (\(p<0.05\)); identical capital letters indicate that the difference between the groups is not significant (\(p\geq 0.05\)).  
\end{table}
Table 2: Comparison of the trueness according to the type of lithium disilicate block.  
Figure 4: Comparison of the color difference map according to the lithium disilicate block. (**A**) HASS Rosetta. (**B**) IPS e.max CAD. (**C**) VITA Suprinity.
## 3 Results  
Both of the two new FS analyses presented good reliability when compared to the reference system built on CBCT, without any difference between growing or non-growing patients. Both reference systems showed a small difference with respect to the CBCT sagittal plane (Sagittal FS1 = 1.90 \(\pm\) 0.98\({}^{\circ}\); Sagittal FS2 = 1.80 \(\pm\) 1.13\({}^{\circ}\)) but did not show any statistically significant difference (\(p\) = 0.232). Between them, the two reference systems showed a small difference in the position of the sagittal plane (Sagittal FS1\({}^{\ast}\)Sagittal FS2 = 1.39 \(\pm\) 1.13\({}^{\circ}\)). Both reference systems showed a stable difference with respect to the CBCT coronal plane (Coronal FS1 = 25.82 \(\pm\) 4.97\({}^{\circ}\); Coronal FS2 = 25.80 \(\pm\) 4.91\({}^{\circ}\); \(p\) = 0.770; Table 2). Finally, no differences were found for any of the assessed variables between T0 and T1.  
All of the landmarks investigated in this study presented good repeatability, with a Dahlberg error ranging between 0.07 mm and 0.71 mm (Table 3). All of the Bland-Altman plots showed good intra-operator repeatability of the points (Figures 1-4 and Table 4). According to the Dahlberg formula, the landmark that showed the best repeatability on the \(X\)-axis was the Nasion (N, 0.07 mm), the landmarks that showed the best repeatability on the \(Y\)-axis were the Endocanthions (EN_L and EN_R, 0.10 mm), the landmark that showed the best repeatability on the \(Z\)-axis was the Nasion (N, 0.07 mm). All of the landmarks showed a Dahlberg value lower than 0.7 mm, except for the N on the \(Y\)-axis.  
Figure 2: (**A**) Facial scan acquisition with 3 dMD with soft tissue landmarks: Endocanthion Left and Right (EN_L-EN_R), Mid-Endocanthion (MEN), Mid-Tragyon (MTR), Nasion (N), Tragyon Left and Right (TR_L-TR_R), and Menton (M). (**B**) Three-dimensional reconstruction of a CBCT full-FOV scan; (**C**) Reconstruction of soft tissue of a CBCT full-FOV scan.  
Figure 3: Bland-Altman plots of X, Y, and Z coordinates for EN_L, EN_R, MEN, and MTR.  
Figure 4: Bland-Altman plots of X, Y, and Z coordinates for N, TR,L, TR,R, and M.  
All of the landmarks used in this analysis showed \"good intra-operator repeatability\".
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
###### Abstract  
High-voltage direct current (HVDC) transmission is preferred over high-voltage alternating current (HVAC) for long power lines for asynchronous power grid interconnection and high-level renewable energy integration. The control and protection functions associated with HVDC systems help with fast and secure clearance of faults. The control and protection challenges in the embedded HVDC network are of great concern for the stable and secure operation of an HVDC network. The DC fault current may reach an extremely high level in a rather short period because of the low impedance in a DC system, which is dangerous for converters, and disturbances in the AC network directly influence the performance of the HVDC system. Sometimes, faults on the AC side may lead to disconnection or failure of the DC link, causing reliability problems as well as huge economic losses. AC and DC protection solutions are being developed for HVDC systems to enhance their sustainability and reliability. As such, AC and DC faults should be detected and cleared at a faster rate. Therefore, in this article, the feasibility of the synchro-squeezed transform (SST) is analyzed for detection purposes. For more accurate and faster detection, the signal is first decomposed using the empirical mode decomposition (EMD) technique, and then the SST is applied. A discrete Teager energy (DTE) spectrum is obtained with the processed signal, which works as the detection index. The algorithm shows low sampling frequency requirements, with higher efficiency and reliability for the purpose. PSCAD/EMTDC version 4.6 software and MATLAB 2022a software is used for the modeling and simulation.  
AC grid; high-voltage direct current (HVDC); fault detection; voltage source converter (MMC); fault detection; synchro-squeezed transform (SST)
## 6 Conclusions  
In this work, we have developed a method for the modeling, identification, and control of nonlinear systems by means of the T-S model based on MDMFs. We considered the use of three different fuzzy clustering algorithms to adjust the MDMFs: FCM, GK, and GG.  
The proposed methodology was validated through the identification and control of a thermal mixing tank system using both one-dimensional membership functions (1 DMFs) and MDMFs. The results demonstrate that 1 DMFs do not provide an optimal control solution, as they generate fuzzy rules that occur at points far from the working area.  
The main contribution of this paper is to demonstrate that MDMFs fitted using clustering techniques such as the FCM, GK, and GG algorithms can achieve lower identification errors compared to traditional fuzzy inference methods while requiring fewer fuzzy rules. This finding has important implications for applications in control engineering contexts where the efficiency and accuracy of system modeling is crucial.  
Conceptualization, B.M.A.-H.; Methodology, B.M.A.-H. and J.G.; Software, J.G.; Validation, B.M.A.-H. and J.G.; Formal analysis, B.M.A.-H. and J.G.; Investigation, B.M.A.-H. and J.G.; Writing--original draft, B.M.A.-H. and J.G.; Supervision, B.M.A.-H.; Project administration, B.M.A.-H. All authors have read and agreed to the published version of the manuscript.  
This publication is part of the R&D project \"Cognitive Personal Assistance for Social Environments (ACOGES)\", reference PID2020-113096 RB-I00, funded by MCIN/AEI/10.13039/501100011033.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Identification Type** & **RMSE (err\({}_{\text{Q}}\))** & **RMSE (err\({}_{\text{T}}\))** & **Mean of RMSE** \\ \hline One-dimensional membership functions & 0.0026 & 0.1128 & 0.0577 \\ FCM algorithm & 0.0025 & 0.0984 & 0.0505 \\ GK algorithm & 0.0025 & 0.0865 & 0.0445 \\ GG algorithm & 0.0026 & 0.1061 & 0.0544 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Errors in the identification of the nonlinear system.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 3 Proposed Positioning Framework
### Positioning
#### 3.3.2 Camera Position  
Camera positioning, which builds a 2D visual map, is a common method [41], whereby Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF), and Binary Robust Invariant Scalable Keypoint (BRISK) features are extracted from captured images to calculate the position relative to the UAV. These feature data are stored in a dictionary map and used to match the incoming image.  
Supposing that \(p_{i}^{t,c}\) and \(p_{i}^{g,c^{t}}\) are corresponding feature points at time \(t-1\) on a global map, the transformation is formulated as:  
\[p_{i}^{g,c}=R_{t}^{c}p_{i}^{t,c}+T_{t}^{c}. \tag{11}\]  
The solution of \(R_{t}^{c}\) and \(T_{t}^{c}\) is a \(pnp\) problem. If the depth of feature points is known (associated with Equation (2)), then Equation (11) can be deduced via bundle adjustment.  
The depth map is updated with the registered LIDAR clouds, triggered by the reception of a new feature point. A key assumption is that depth value similarity correlates positively with color value similarity. The depth of each feature point is estimated using the projected laser points on the current image:  
\[d(uv_{i})=\frac{\sum_{j=1}^{k}\ \ \ L_{G}(uv_{j}^{L},uv_{i})d_{uv_{j}^{L}}}{ \sum_{j=1}^{k}L_{G}(uv_{j}^{L},uv_{i})}. \tag{12}\]
## 4 Conclusions  
In this work, the structural characteristics, phase transition, and crystallization of Fe\({}_{\rm 1-x-y}\)Ni\({}_{\rm x}\)Co\({}_{\rm y}\) alloy under the influence of various factors (such as doping concentration, number of atoms, and temperature) were investigated. The obtained results show that in the Fe\({}_{\rm 1-x-y}\)Ni\({}_{\rm x}\)Co\({}_{\rm y}\) alloy always exist 03 metals Fe, Ni, and Cu (that are distributed quite evenly according to the ratio of tap phase concentration). In Fe\({}_{\rm 1-x-y}\)Ni\({}_{\rm x}\)Co\({}_{\rm y}\) alloy, there are always six types of links, Fe-Fe, Fe-Ni, Fe-Co, Ni-Ni, Ni-Co, and Co-Co. The structural features such as length (r), height of the radial distribution function g(r) of links Fe-Fe, Fe-Ni, Ni-Co has a constant value such as r = 2.475 A, and g(r) changes (from g(r)Fe\({}_{\rm -Ni}\) = 6.42 to g(r)Fe\({}_{\rm Fe-Ni}\) = 7.32, g(r)Fe\({}_{\rm Fe-Co}\) = 6.60 to g(r)Fe\({}_{\rm Fe-Co}\) = 8.20, g(r)Ni\({}_{\rm Ni-Co}\) = 6.97 to g(r)Ni\({}_{\rm Ni-Co}\) = 7.35) with a change of doping concentration Fe, Co. The result shows that when the increase of Fe and Co doping concentrations, the crystallization process increases corresponding with Fe\({}_{\rm Ni32}\)Co\({}_{\rm 63}\), and Fe\({}_{\rm 63}\)Ni\({}_{\rm 32}\)Co\({}_{\rm 50}\) alloy. Moreover, with the increase of Ni, the crystallization process decreases corresponding with Fe\({}_{\rm 33.4}\)Ni\({}_{\rm 33.3}\)Co\({}_{\rm 53.3}\) alloy. These results correspond with the number of structural units: FCC decreases and increases, HCP increases and decreases, BCC increases and decreases, Amor increases and decreases, and I decreases from 1 = 5.34 nm to 1 = 5.33 nm, E\({}_{\rm tot}\) increases from E\({}_{\rm tot}\) = \(-\)58,189 eV to \(-\)57,932 and \(-\)58,263 eV. In particular, Fe\({}_{\rm 63}\)Ni\({}_{\rm 2}\)Co\({}_{\rm 5}\) is the alloy with the highest crystallization caused by the interaction between electronic structures. Similarly, an increase in the number of atoms leads to an increase in crystallization, and an increase in temperature leads to a decrease in crystallization. Furthermore, computational results highlighted the glass temperature of Fe\({}_{\rm 63}\)Ni\({}_{\rm 32}\)Co\({}_{\rm 5}\) alloy has the value of T\({}_{\rm g}\) = 498 K, which is in accordance with experimental results. The obtained results serve as a basis for experimental studies in developing new magnetic materials in the future.  
D.N.T.: conceptualization, methodology, validation, investigation, writing, original draft, writing--review and editing, data curation, validation. V.C.L.: data curation, writing, and editing. S.T.: data curation, writing, review, editing, funding acquisition, project administration. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data that supports the findings of this study are available from the corresponding author upon reasonable request. The authors declare no conflict of interest.
## 3 Control Hardware Architecture Using Fieldbus  
The development of fieldbus technology has facilitated the replacement of point-to-point communication and driven the development of efficient distributed systems. This is because it has simplified the hardware connectivity between devices and equipment. The aim of introducing fieldbus technology was to provide an industry standard for establishing communication among devices for easier configurability and interoperability from different vendors. However, a number of fieldbus standards have been established over time, and for industrial process fieldbus applications alone, ten different buses have been accepted by the International Electrotechnical Commission (IEC), starting with a basic analog 4-20 mA bus through to digital buses, i.e., profibus, interbus, Modbus, Controlled Area Network (CAN), devicenet, and Ethernet, to name a few.  
Among the wide range of bus standards there exist great differences and incompatibilities that restrict the interoperability of the devices [13]. Hence, selection of the right fieldbus type and devices is very important for designing the control system hardware and highly depends on the application. Specific parameters of concern to the design include data rate, noise immunity, bus faults, allowable length of transmission, number of network nodes allowed, cost, ease of adding additional nodes, and reliability. The present design was carried out with a combination of CAN bus and RS-485. This choice was mainly based on the easy availability of the devices based on RS-485 and the in-built bus arbitration and safety features for the CAN bus. Both standards include differential signaling that provides noise immunity in the industrial environment.  
RS-485 is a half-duplex multidrop bus. However, it is not a protocol. It defines the physical layer of the transmission, and only one device at a time can send a message [14]. This implies that the data transmission protocol, addressing scheme, and data collision avoidance implementation have to be done at the protocol or application software level.  
The CAN bus was originally developed for the automotive industry during the 1980s by the Bosch Company for monitoring and controlling [15]. CAN not only provides that physical layer definition but also incorporates mechanisms for addressing, failure detection, packet formation, and bus arbitration. The network management component of the CAN software can be used for configuration of the communication device parameters. During running operation, it also monitors and analyzes the network to provide diagnostic information of the communication network that provides transmission failure detection and automatic repetition of message transmission.