esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 IoT Standard Architectures
### IWF Reference Architecture  
The IoT World Forum (IWF) Reference Architecture (as shown in Figure 12), released in October 2014 contains seven layers from edge level to a central cloud [42]. The IWF model is mainly concerned with developing the applications, middle-ware, and support functions of enterprise-based IoT. The characteristic of the IWF reference model is it simplifies, clarifies, identifies, standardizes, and organizes the IoT functionalities. This reference model defines the integration of traditional IT and OT parts. Table 1. depicts the IWF layers and their respective responsibilities.  
Figure 11: Multi-IoT Architecture.  
Figure 12: IWF 7-layered reference architecture.  
Advantages of IWF Reference Model over Other Architectures
* The IoT system topology is depicted clearly in the IWF model.
* It boosts the operational efficiency of the IoT ecosystem by effective decision-making on resource and service management.
* All seven layers of the IWF model are responsible for the optimal flow of IoT data, so this architecture minimizes the cost and downtime by enabling preventive maintenance.
* The business layer of the IWF model is responsible for collaboration and business process handling. This improves the IoT products and services through user or customer satisfaction. This model opens up new business opportunities.
* Realizing the scalability is easy with the IWF model compared to other architectural standards,
* IWF Architecture is the best model for data management. It defines the data accumulation and abstraction capabilities separately for exhaustive data processing.
* Instead of being conceptual, the IWF reference model is a real and approachable system applicable to any IoT application.
* This model ensures modularity and interoperability by enabling the technologies to be compatible with Industry 4.0  
After an extensive analysis of the above nine standard IoT architectures, IWF Architecture has better advantages for designing a reference architecture for any IoT application. This motivated us to consider the IWF model the best model for IoT research.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 4 Results and Discussion  
In this section, we will outline the results of training the models with the proposed architectures and quantization techniques for on-edge classification. We will begin with an examination of the actual performance of the models on testing data and then look at the sizes of the different models before and after quantization. We will combine the two (i.e., F1-score and size) to obtain a better understanding of how well a model performs relative to its size. This is achieved by a simple performance-to-size ratio. Based on the various analyses conducted in this section, we select a model that works best for on-edge deployment. This model will also go into further analysis by considering its performance  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Technique** & **Size Reduction** & **Accuracy** \\ \hline Post-training float-16 quantization quantization quantization-aware training & Up to 50\% & Insignificant accuracy loss \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantization methods in TFLite [36].  
by running inference on a normal CPU, imitating on-edge devices. This is needed as ViT operators are poorly supported in the current existing TFLite development frameworks [11]. Based on the performance of some models, several versions of the same architecture are used to attempt to obtain a better performance-to-size ratio.  
The general consensus across all models, architectures, and quantizations for on-edge classification is that models are faster in terms of inference time and smaller in terms of the actual size in memory, following extensive literature attesting their performance [37]. However, the performance of each model varies, which will be analyzed in the following subsections.
## 4 Cyber Physical System Threat Modeling
### Denial of Service Attack  
Since denial-of-service attacks are amongst the biggest risks to collected data, they are primarily taken into consideration. Communication is often disrupted during a DoS attack, which could prevent measurement or modification information from accessing their appropriate locations. An attacker can use a variety of tactics to initiate DoS attacks. For instance, the attacker can saturate the network with traffic, target the routing protocols, infiltrate devices and stop them from providing data, and jam the communication channels [10].  
As illustrated in Figure 7, denial-of-service attacks that utilize a fictitious time stamp include timing attacks to prevent communications among data both senders and recipients [28]. Inside the broad monitoring system for power grids, synchronized global time across PMUs apply a standard time reference from a satellite that contains time synchronization protocols, which might be hacked by either falsifying the time stamp or inserting a planned delay for malign objectives. Even if synchronization communications are encrypted and/or authenticated, the timing attack, which might just delay messages, may still endanger the controllers [29]. In this investigation, it was presupposed that attackers would purposefully lag behind the readings that the damping controller would receive. In particular, the attacker would alter the time stamp and induce a planned delay for the measurement of the HVDC transmission from the distant terminal. Furthermore, missing data attack operations are almost similar to false data injection attacks in terms of the malicious availability of data. However, the remarkable difference could be described through the timing attack operation itself.
## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.3.2 The Impact of Sensor Mobility
Figure 22: Comparison of throughput between Static and Mobile-SensorAnt with different periods of time. see from the same figure that with increasing the time the consumption of the energy increases too for both cases due to the packet's transmission. In Figure 24, the average remaining energy of the sensor nodes in both the Static and the Mobile-SensorAnt are shown. Here, the static case shows better results compared to the mobile case. This is because in the mobile case, most of the sensor nodes move away from their current routes with time, which imposes more computational complexity on the algorithm in order to discover new paths, resulting in the dissipation of more energy; thereby resulting in the exhaustion of the average residual energy in the WSN. This is in fact affects the energy efficiency of the Mobile-SensorAnt as shown in Figure 25 where energy efficiency of Static-SensorAnt outperforms Mobile-SensorAnt with increasing simulation time. From Figure 26, it can be seen that the energy balanced at the 1,000 s of time in the static scenario compared with the mobile in our proposed method, nevertheless the result still comparable. This is because the route selection in the static case is more stable by causing the distribution of the available energy among the neighboring sensor nodes with fixed locations. In this way, the energy consumption is fairly balanced over some sensors in the WSN compared to the Mobile-SensorAnt. Figure 24: Average energy of Static and Mobile-SensorAnt with different periods of time. Figure 23: Total energy consumption between Static and Mobile-SensorAnt with different periods of time.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Trust-Aware Evidence Reasoning
#### 4.1.1 Information Dispersion Network Construction  
The comment credibility ranking was based on an information dispersion network aimed at one claim (source information). We first constructed an information network \(G(\mathbf{S})=\langle V,E\rangle\) based on source information along with its related comments, as well as the authority of users participating in the discussion, as shown in Figure 4.  
Figure 3: Framework of the proposed TRSA model.  
The node at the top level of the network represents the source information, and the other nodes represent comments. As described in Section 3, each node is represented as \(v_{j}=(uj,\ cj)\in V\), where \(u_{j}\in R\) represents the authority of the user who posted comment \(j\), and \(c_{j}\in R^{d}\) is a d-dimensional vector obtained by a pre-trained BERT model that can be used to characterize the comment content. The initial weight of node \(i\) is represented by the corresponding user's authority, \(u_{j}\). Each edge \(e_{ij}\ =\ \left\langle v_{i},v;\omega_{ij}\right\rangle\in E\) represents the interaction between posts \(i\) and \(j\). Edge weights \(w_{ij}\) indicate the recognition degree of post \(i\) relative to the content of post \(j\). Its calculation process is as follows:  
\[\omega_{ij}=\text{sign}\left(\text{c}_{i},\text{c}_{j}\right)*\text{similar} \left(\text{c}_{i},\text{c}_{j}\right), \tag{1}\]  
where \(\text{sign}\left(\text{c}_{i},\text{c}_{j}\right)\) represents the emotional difference between posts. \(\text{sign}\left(\text{c}_{i},\text{c}_{j}\right)=1\) if the emotional polarity of the two posts is the same, and \(\text{sign}\left(\text{c}_{i},\text{c}_{j}\right)=0\) otherwise. \(simlar(content_{i},content_{j})\) represents the semantic similarity; its value is in [0, 1]. We adopted the interface provided by the Baidu AI platform for calculating emotional difference ([[https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify](https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify)]([https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify](https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify)) (accessed on 8 August 2022)) and adopted the soft cosine measure [41] between embeddings of comments (or the source post and its comments) as semantic similarity. In Equation (1), although there is interaction between posts I and \(j\) (e.g., \(i\) is a comment to \(j\)), \(w_{ij}\) may still be 0. This is because there is no emotional resonance which means \(\text{sign}\left(\text{c}_{i},\text{c}_{j}\right)=0\), or the content of the two posts is irrelevant, which means \(\text{sign}\left(\text{c}_{i},\text{c}_{j}\right))=0\).
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 2 Related Works  
In 2018, [PERSON] et al. [13] proposed the MVSNet algorithm, which represents the first end-to-end multi-view stereo network model that applies deep learning to estimate depth. The core of the network is a differentiable homography module that creates a matching cost volume through mapping source images to target images via depth sampling. The cost volume undergoes regularization and soft argmax to produce the final depth map. Subsequent improvements concentrate on two primary aspects.
# Different Formulations to Solve the Giesekus Model for Flow between Two Parallel Plates
## 4 Results
### Agreement Region  
In the present section, we present a comparison of the results obtained with the three techniques in a range of parameters, where all are in agreement. In the range of dimensionless parameters adopted in the present section, all of the formulations showed good agreement. The range of parameters adopted here is given by:  
* Reynolds number--\(0<Re<10\),000;
* Weissenberg number--\(0<Wi<10\);
* \(\beta\) parameter--\(0.01<\beta<1\);
* \(\alpha_{G}\) parameter--\(0<\alpha_{G}<0.5\).  
Some results were chosen to show the range mentioned above. These results are presented showing the variation of the streamwise velocity \(U(y)\) and the three components of the extra-stress tensor \(T_{xx}\), \(T_{xy}\) and \(T_{yy}\) in the wall-normal direction \(y\).  
Figures 1-3 show the comparison among the formulations presented for different values of the dimensionless numbers \(Re\), \(\beta\), and \(\alpha_{G}\) in the range of the agreement region.  
It is possible to observe very good agreement between the results obtained by different formulations, both for \(\beta\) close to zero (close to polymeric fluid) and for \(\beta\) close to one (close to Newtonian fluid).  
Figure 1: Streamwise velocity \(U(y)\) and the components of the extra-stress tensor \(T_{xx},T_{xy}\) and \(T_{yy}\) variation in the wall-normal direction \(y\). Dimensionless numbers: \(Re=2000\), \(\beta=0.25,\alpha_{G}=0.1\) and \(Wi=2\).  
Figure 2: _Cont._  
Figure 3: Same as in Figure 1 for dimensionless numbers: \(Re=8000\), \(\beta=0.75,\alpha_{G}=0.2\) and \(Wi=2\).  
Figure 2: Same as in Figure 1 for dimensionless numbers: \(Re=5000\), \(\beta=0.5,\alpha_{G}=0.2\) and \(Wi=2\).  
For these simulations, different values for the dimensionless parameters of the flow were considered, where all the formulations converge to a solution. It is noteworthy that the formulations have certain limitations for some values for the dimensionless numbers. Out of the agreement region, the results using the first and the second formulation diverges or appears with oscillations on their field. The exploration of these bounds are shown below.
## 4 Proposed Hybrid FVIMDE Algorithm
### Hybrid FVIMDE Algorithm Stages
#### 4.5.2 Stage 2: Four Vector Intelligent Mechanism (FVIM) Phase  
The second stage is the Four Vector Intelligent Mechanism (FVIM) phase, which is designed to exploit the most promising regions identified during the DE phase. The best solution obtained from the DE phase is used as the initial position for the alpha particle, a key element in the FVIM. The FVIM phase begins by reinitializing the population with the alpha particle as a reference. Each iteration involves updating the positions of the population based on the influence of four key particles: Alpha, Beta, Gamma, and Delta. These particles represent different levels of solution quality, with alpha being the best. The position updates are governed by a coefficient that decreases linearly over time, encouraging convergence as the algorithm progresses. The updated positions are evaluated, and the best positions are continuously refined. This phase focuses on intensively searching around the best-known solutions, aiming to improve them further. The process concludes when the maximum number of iterations is reached, and the algorithm returns the best solution found during both stages.
## 2 Materials and Methods
### Cell Culture and Cell Treatment  
Human OSCC cells (SCC4) were maintained in DMEM/F12 medium (Thermo Fisher, Waltham, MA, USA), which was supplemented with 10% fetal bovine serum (FBS; Peak Serum, Wellington, CO, USA) and antibiotic-antimycotic (Thermo Fisher, Waltham, MA, USA), under a humidified atmosphere of 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. The cells were subjected to a medium change every 48 h and grown until 70-80% confluence was achieved for subsequent experiments. To evaluate the response of SCC4 cells to monotherapy or combination therapy, the cells were treated with varying concentrations of 5-FU (50, 100, 200, and 400 uM) for 24 h, with or without a 1-h pretreatment of Refametinib (50 nM).
## 4 Experimental Section
### Uncertainty Assessment of Experimental and Simulation Results  
Uncertainty in Experimental Data:  
(1) Measurement Errors  
Thermocouple accuracy: \(\pm\)0.5 \({}^{\circ}\)C (Manufacturer's calibration value), DAS (Data Acquisition System) error: \(\pm\)0.1% FS (Full Scale).  
(2) Repeatability Verification  
Three tests were conducted on the mold under identical operating conditions, revealing fluctuation ranges of \(\pm\)1.3 \({}^{\circ}\)C for temperature measurement points and \(\pm\)0.2 MPa for stress measurement points.  
(3) Combined Uncertainty  
Temperature uncertainty range: \(\pm\)1.5 \({}^{\circ}\)C, Stress uncertainty range: \(\pm\)0.3 MPa.  
Uncertainty in Simulation Data:  
(1) Fluctuation ranges of key parameters are listed in the Table 3 below.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Standard Value** & **Fluctuation Range** & **Basis** \\ \hline Steam Temperature & 180 \({}^{\circ}\)C & \(\pm\)2 \({}^{\circ}\)C & Vulcanizer control accuracy \\ Thermal Conductivity & 50 W/m\(\cdot\)K & \(\pm\)5\% & Material handbook tolerance \\ Molding forces & 80 T & \(\pm\)3\% & Hydraulic system error \\ \hline \end{tabular} (2) Simulation of key parameters  
Temperature uncertainty range: \(\pm\)1.6 \({}^{\circ}\)C, Stress uncertainty range: \(\pm\)0.2 MPa.  
Reliability of Results Comparison:  
Temperature Field Validation  
\end{table}
Table 3: Key simulation parameters and their uncertainties.  
Figure 15: Force Testing System.  
For all seven temperature measurement points, the errors between simulation and experimental results were less than 8%. Under thermally stabilized conditions, the simulation uncertainty range (\(\pm\)1.6 \({}^{\circ}\)C) fully encompassed the experimental uncertainty range (\(\pm\)1.5 \({}^{\circ}\)C).  
(2) Stress Validation  
The errors were below 6% at six stress measurement points. The simulation uncertainty range (\(\pm\)0.2 MPa) was narrower than the experimental uncertainty range (\(\pm\)0.3 MPa).
# Synergistic Effects of CuO and ZnO Nanoadditives on Friction and Wear in Automotive Base Oil +
## 3 Results
### Friction Results  
Frictional behavior was first evaluated using the friction absolute integral (FAI), representing the cumulative dynamic friction over the entire test duration (see results in Figure 6). The results indicate that adding CuO nanoparticles significantly reduced friction compared to the additive-free reference. Even the lowest tested concentration of CuO led to a marked reduction, with a 0.3 wt% CuO addition decreasing the FAI by approximately 19%. This effect was further enhanced by partially substituting CuO with ZnO nanoparticles. A 2:1 CuO-ZnO mixture resulted in the highest observed reduction of 27%, while a 1:2 CuO-ZnO ratio achieved a 24% decrease. In contrast, ZnO nanoparticles alone had minimal effect, reducing the FAI by only 1%, indicating their limited friction-reducing potential in this configuration.  
A similar curved trend was observed in static friction measurements. The pure CuO nanoadditive reduced static friction by 15%, while ZnO caused a slight 1% increase. However, the combined application of both oxide nanoparticles also proved beneficial. The 2:1 CuO-ZnO mixture showed a synergy, achieving the most favorable result, yielding a 17% reduction in static friction relative to the reference.  
These findings confirm that CuO nanoparticles consistently exhibit the expected friction-reducing effect in this tribological system, whereas ZnO nanoparticles alone do not significantly influence frictional behavior. Importantly, a partial substitution of CuO with ZnO--specifically, a one-third replacement--improved performance in static and dynamic friction scenarios. This suggests a synergistic interaction between the two oxides, enabling optimized tribological characteristics through careful compositional tuning.
When aggregating wear data through the total wear volume (TWV)--calculated as the sum of BWV and DWV--a comprehensive picture of system-level wear performance emerges. The pure CuO formulation showed the most substantial wear reduction, achieving a 33% decrease in TWV relative to the reference. The 2:1 and 1:2 CuO-ZnO mixtures also offered measurable benefits, with 16% and 12% reductions, respectively. However, no apparent synergistic effect on wear behavior could be identified in these combinations. Although frictional improvements were observed when ZnO was partially introduced, the overall wear performance remained superior with CuO alone.  
These findings underscore that CuO nanoparticles are highly effective in reducing wear across both components, while ZnO requires careful control due to its abrasive potential, particularly in systems lacking sufficient film-forming additives. The absence of wear synergy in the nanoparticle mixtures highlights the complexity of multi-nanoparticle systems and the importance of material pairing and surface compatibility in achieving tribological performance improvements.
## 4 Results and Discussion
### Operation Stage
With its 70 acres of agricultural land, 500+ residential structures, and 3000+ inhabitants, Baradam relies heavily on rainfall for farming and must travel long distances to secure drinking water. To meet the United Nations' benchmark of 50 L per day per person for domestic usage with the available water supply, a comprehensive and efficient solution is required. In the winter regions of Pakistan, wheat and maize account for 95% of crop production, and require 180 mm of water and 4 months of growth time [58, 59]. The proposed solution is to use the water flow of the Lowari Tunnel to satisfy the irrigation requirements for these essential  
Figure 6: Lowari Tunnel water suitability for drinking and irrigation purposes: (**a**) Piper Diagram for Lowari Tunnel, (**b**) Wilcox Plot for Lowari Tunnel. crops. Future studies should be conducted to assess the presence of organic and microbial contamination in the water of the Lowari Tunnel. The focus can be on understanding how water quality changes over time, its impact on the tunnel structure, and the health effects of low pH levels on workers and residents. Additionally, comparing the water quality of the Lowari Tunnel with other tunnels in the region would be valuable. The findings from these studies can be used to develop a predictive model for water quality in tunnels, aiding in the design and management of tunnels to minimize water ingress and maintain optimal water quality.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM
### ML Methods
#### 3.4.4 KNN  
This algorithm is lazy; it does not immediately begin learning from the data [22]. Instead, it saves it and performs the action when it is time to classify. For a new instance \(\mathbf{H}_{T}^{new}\), its feature vector \(\mathbf{f}^{new}\) is acquired. When this feature vector is normalized, we obtain \(\mathbf{f}^{new}\). The KNN classifier finds the \(K\)-closest data points amidst \(N\) data points. The KNN classifier performs the following main steps:  
Step 1: Determine \(K\) through \(k\)-fold cross-validation for which the model has low variance and reasonably good accuracy.  
Step 2: Calculate the FD of a new observation \(\mathbf{f}^{new}\) for each data point in the dataset.  
\[d(\mathbf{f}^{n},\mathbf{f}^{new})=\|\mathbf{f}^{n}-\mathbf{f}^{new}\|^{2}. \tag{35}\]  
Step 3: Find out the \(K\)-nearest neighbors.  
Step 4: For each \(K\)-nearest neighbor, count the data samples.  
Step 5: Allocate the test data sample \(\mathbf{H}_{T}^{new}\) to the class label, which obtains the maximum number of votes (\(l^{KNN}\)).  
Despite its apparent simplicity, the cost of computing the FD between data samples and the time complexity increases with the dataset size. One of the disadvantages of KNN is that it works well for small datasets but becomes more difficult and time-expensive withlarger datasets. Identifying the value of \(K\) is always necessary, although it might be difficult at times.