esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Model Trade-Offs  
The computational complexity analysis of the ViT and base neural network models is summarized in Table 13 and is summarized as follows: **Architecture-Specific Trade-offs**: **CNN**: fastest inference (0.43 ms) but highest memory usage (1027.84 MB), likely due to layer-wise activation storage. **ViT**: highest accuracy but also the most computationally expensive (5.15 ms inference, 332.34 MB). **VGG19**: despite a large parameter count, it maintains relatively low memory usage (36.14 MB), making it suitable for constrained environments. **DenseNet**: deepest model (718 layers) with 87% non-trainable parameters, reducing fine-tuning flexibility.  
**Parameter Utilization:** models with higher proportions of trainable parameters (CNN, ViT) generally contribute more to ensemble performance than those with many non-trainable parameters (DenseNet, VGG19).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Size (MB)** & **Parameters (M)** & **Layers** & **Avg. Inference Time (ms)** & **Memory Usage (MB)** & **Trainable/Non-Trainable** \\ \hline CNN & 27.62 & 7.24 & 20 & 0.43 & 1027.84 & 7.24 M/0 \\ DenseNet & 79.93 & 20.95 & 718 & 2.92 & 222.47 & 2.63 M/18.33 M \\ EfficientNet & 22.99 & 6.03 & 249 & 1.55 & 40.95 & 3.32 M/2.70 M \\ MobileNet & 16.12 & 4.23 & 162 & 0.97 & 766.00 & 1.97 M/2.26 M \\ ResNet & 100.49 & 26.34 & 183 & 1.64 & 83.51 & 11.69 M/14.66 M \\ VGG19 & 127.04 & 33.30 & 39 & 2.18 & 36.14 & 13.26 M/20.04 M \\ ViT & 332.34 & 87.12 & 27 & 5.15 & 71.95 & 87.12 M/0.003 M \\ \hline \hline \end{tabular}
\end{table}
Table 13: Computational complexity analysis of the neural network models.  
Figure 13: Ablation studies of the ViT and base models.  
**Memory vs. Size:** there is no clear correlation between model size and memory usage during inference (e.g., CNN uses 37\(\times\) more memory than VGG19 despite being 4.6\(\times\) smaller).  
**Ensemble Design Implications:** the data suggest a more optimal ensemble could be created using only **ViT**, **CNN**, and **ResNet**, potentially with **EfficientNet** if robustness to noise is critical. This would reduce computational overhead while maintaining or improving accuracy.  
**Deployment Strategy:** for resource-constrained environments where a single model must be chosen, the optimal selection would depend on specific priorities: for raw accuracy: **ViT**; for noise robustness: **EfficientNet**; for inference speed: **CNN**; for efficiency (accuracy/cost): **VGG19**; and for balanced performance: **ResNet**.
## 2 Tunnel-Soil-Pipeline Interaction
### Tunnel-Soil-Pipeline Interaction Based on Subgrade Reaction Analysis  
The subgrade reaction analysis is a primary method to study the tunnel-soil-pipeline interaction. This method regards the existing pipeline as a Euler-Bernoulli beam or Timoshenko beam resting on an elastic foundation model. The foundation model is commonly composed of the simplest Winkler model [39] and the two-parameter Pasternak model [40]. The closely spaced linear springs in the Winkler model represent the subgrade reaction forces. The subgrade reaction forces on the springs are expressed as:  
\[q_{k}=k_{k}(u_{z}(x)-w(x)) \tag{27}\]  
where \(k_{\text{k}}\) is the coefficient of subgrade reaction in the Winkler model; \(w(x)\) is the deformation of the existing pipelines.  
Compared with the Winkler model, the two-parameter Pasternak model is more accurate because a shear layer on the spaced springs can model the interaction between the spaced springs. The subgrade reaction forces on the springs are expressed as:  
\[q_{p}=k_{p}[u_{z}(x)-w(x)]+G_{c}\frac{d^{2}w(x)}{dx^{2}} \tag{28}\]  
where \(k_{p}\) is the coefficient of subgrade reaction in the Pasternak model; \(G_{c}\) is the shear stiffness of the shear layer.  
Some related researchers have proposed the governing equations of the subgrade reaction analysis for the Euler-Bernoulli beam on the Winkler model (EW) and the Euler-Bernoulli beam on the Pasternak model (EP).  
\[\begin{array}{ll}EI\frac{d^{2}w(x)}{dx^{2}}+k_{k}w(x)=k_{k}u_{z}(x)&\text{ for ET}\\ EI\frac{d^{2}w(x)}{dx^{2}}-G_{c}\frac{d^{2}w(x)}{dx^{2}}+k_{p}w(x)=k_{p}u_{z}(x)& \text{for EP}\end{array} \tag{29}\]  
Substituting \(u_{z}(x)\) calculated in Sections 2.1 and 2.2 into the Equation (29), the deformation of the existing pipeline can be predicted with the shield tunnel construction process.  
The coefficient of subgrade reaction \(k\) and the shear stiffness of shear layer \(G_{c}\) are two main parameters. Many published papers estimated \(k\) using different methods [41; 42; 43; 44]. The solution proposed by [PERSON] et al. [45] is commonly used because it considers the buried depth.  
\[\begin{array}{l}k=\frac{3.08}{\lambda}\frac{E_{\text{s}}}{1-\mu^{2}}\sqrt{ \frac{E_{\text{s}}B^{4}}{EI^{4}}}\\ \lambda=\left\{\begin{array}{ll}2.18&\text{when }\frac{z_{0}}{B}\leq 0.5\\ 1+\frac{1}{1.72\rho_{0}/B}&\text{when }\frac{z_{0}}{B}>0.5\end{array}\right.\end{array} \tag{30}\]  
where \(E_{\text{s}}\) is the elastic modulus of the soil; \(B\) is the diameter of the existing pipeline; \(z_{0}\) is the buried depth of the existing pipeline.  
[PERSON] [46] proposed an empirical solution to estimate \(G_{c}\):  
\[G_{c}=\frac{E_{\text{s}}h_{t}}{6(1+v)} \tag{31}\]  
where \(h_{t}\) is the elastic layer' depth, approximately \(2.5B\) suggested by [PERSON] [47].
## 4 Numerical Modeling  
To assess the seismic behavior of the central pier of the considered viaduct, and compare the different responses obtained by including or ignoring deformable soil, two different numerical models were developed: (i) an FE model of the pier perfectly embedded to the ground using the SAP2000 code [43], which ignores the SSI; and, (ii) a 3D FE model of the pier and the foundation soil using the PLAXIS 3D code [44], which considers the SSI effects. For both the models, the following analyses have been performed:  
* Application of a lumped load at the top of the pier (in the direction of the system's highest stiffness, i.e., orthogonal with respect to the deck direction) and analysis of the system free vibration, to determine the structure's natural frequency at small strains;
* Application of the selected earthquake in the direction of the system's highest stiffness (orthogonal with respect to the deck direction) and dynamic analysis of the system response.  
In both the FE models described above, the ground surface has been considered horizontal and the mutual interaction between the pier has been ignored by assuming the loading condition orthogonal to the deck.
## 2 Materials and Methods
The temperature stabilizers are available in three versions, for transport at +4 \({}^{\circ}\)C, +22 \({}^{\circ}\)C and \(-\)30 \({}^{\circ}\)C, with the possibility of maintaining \(-\)80 \({}^{\circ}\)C using dry ice. The possibility for the Smart Capsule to be integrated into different already-certified containers allows to cover the requirements for blood, medicine, and organs transportation by simply integrating suitable containers. While these stabilizers correctly maintain the temperature in the correct ranges during traditional car transportation in only 60% of cases, the possibility of maintaining temperatures during drone flight has been demonstrated in this study. In any case, the Smart Capsule will meet the good distribution practice (GDP) (both for the European Medicines Agency and the Food and Drugs Administration) in order to respect the minimum standards for ensuring the quality and integrity of medicines. These requirements are met by the Smart Capsule in this preliminary phase by integrating certified containers inside the main one. Although the integration of additional containers will be not convenient in the future due to their bulkiness and payload reduction, this  
Figure 1: Smart Capsule 3D view with UN3373 container for blood sample delivery. solution was preferred in this phase in order to accelerate the use of the system in a real medical scenario. The Smart Capsule mechanical structure was fabricated using CNC (computerized numerical control) techniques starting from a single part of polyurethane. Metal parts were also fabricated using traditional techniques. During series production, the costs of manufacturing will be drastically reduced through injection molding of plastic materials, improving both the mechanical and thermal characteristics of the device. The Smart Capsule is designed to be compatible with any drone with the following features:  
- Multicopter or VTOL (vertical take off and landing) drone  
- Electric power  
- LiPo or Li-ion batteries, plug and play charge  
- IP53 protection from liquids minimum  
- Resistant to 10-15 m/s winds  
- Cruise speed 10-15 m/s  
- 10 kg payload, MTOW (maximum take off weight) < 25 kg  
- 20-30 or more energy autonomy, 12 km minimum distance one-way  
- GNSS  
- Pixahawk 2.1 optionally, including ADS-B  
- Certified parachute  
- Serial wired connection for the capsule  
- Dimensions compatible with the Smart Capsule volume  
The Smart Capsule is interfaced with the drone with a proprietary mechanical interface that allows electric connection between the drone flight controller and Smart Capsule electronics.
## 4 Discussion  
According to the analysis of this study, there are significant differences in climate factors between 1970-1996 and 1997-2018 (Figures 5 and 6; Table 1). The spatiotemporal dynamic of the four climate factors in this study is basically consistent with a lot of research on climatic change. Before 1997, the wind speed decreased significantly, but then increased significantly. The weakening of the Asian monsoon system may explicate the decrease of \(U_{2}\) on the TP, while the strengthening of zonal wind may have a significant contribution to the increase of \(U_{2}\) after 1997 [44]. An insignificantly decreasing trend of \(RS\) was found during 1970-1996, but a significantly decreasing trend was found during 1997-2018. The solar dimming over the TP is mainly caused by the increase in water vapor and deep cloud cover [3]. \(RH\) also decreased significantly after 1997. Most areas of TP belong to the arid region and the amount of water is limited. The rise of air temperature makes the saturated vapor pressure rise, which may be the reason for the decrease in \(RH\)[45]. The climatic factors and \(ET_{0}\) have complex mutual feed mechanism and give rise to an abrupt change in \(ET_{0}\) in the meantime.  
\(ET_{0}\) decreased significantly before 1997 while increased significantly after 1997 on the TP in this study (Figures 3 and 4; Table 1). The change of \(ET_{0}\) is influenced by climate factors, and the weight of each climate factor is different. The dominant factor causing the decrease of \(ET_{0}\) on the TP was \(U_{2}\) before 1997(Figure 8; Table 2), and this is similar to many studies on the TP. For example, [PERSON] et al. [46] found that wind speed is the dominant meteorological factor affecting the \(ET_{0}\) of the TP. [PERSON] et al. [26] considered that wind speed caused changes in the \(ET_{0}\) of the TP, especially in the northern part of the TP. [PERSON] et al. [47] have shown that the decrease of wind speed can explain the variation of the potential annual evapotranspiration in the temperate zone of the north of China and the TP. In this study, the dominant factor that caused the increase of \(ET_{0}\) on the TP after 1997 is \(RH\), which is consistent with [PERSON]'s [27] study that relative humidity may be an important factor affecting \(ET_{0}\). However, this is not consistent with the view of [PERSON] [48] that relative humidity is considered to have little effect. This is due to the difference in the research periods and methods.  
Some uncertainties remain for the estimation of \(ET_{0}\) in this study. Some of the parameters in the P-M equation are empirical and local. Therefore, regional optimization of parameters is an important means to improve the accuracy of \(ET_{0}\) calculation. Additionally, the number of meteorological stations used in this study is limited and unevenly distributed. The TP has large area and complex natural environment. It is difficult to fully describe the spatiotemporal dynamic of the climatic factors and \(ET_{0}\). More data from different sources and interpolation methods should be introduced in the future.
# Challenges of PBFT-Inspired Consensus for Blockchain and Enhancements over Neo dBFT
## 2 Background  
### Twenty Years of Practical Byzantine Fault Tolerance  
Over 20 years ago, the groundbreaking work on PBFT by [PERSON] and [PERSON] [7] had finally demonstrated the practical feasibility of dealing with systematic byzantine (The term Byzantine was coined by [PERSON] [6], on the Byzantine Generals Problem, which simply represents a faulty machine that shows arbitrary behavior, thus invalidating or even attacking the protocol itself.) faults in a distributed system, even in face of a very strong adversary. The resiliency of such system was proven to be optimal, resisting up to \(f\) faulty/byzantine nodes, from a total of \(N=3f+1\) replicated state machines [11]. The reason \(3f+1\) nodes are required is also straightforward due to the hostile nature of the considered network: messages can be delayed, delivered out of order, duplicated, or even lost. Since \(f\) nodes can be faulty, you need to be able to decide after communicating with the other nodes (\(N-f=2f+1\)). However, even when you receive \(f\) responses from them, you still cannot know if those are the faulty ones or not. Maybe the others were just delayed, so you need extra \(f+1\) confirmations to be certain of the truth.
## 4 Test on Beams  
In general, experimental results available from the literature on RC beams retrofitted by means of EB-CFRP are based on simply supported beam specimens under point loads. Then, the obtained results are analytically and/or numerically elaborated to take into consideration more realistic static behavior given in the case of framed structures.  
In the present research, a three-point loading scheme (simply supported beam) was selected to test the extracted beams before and after retrofitting. In this way, it is possible to compare the results of the \"real\" beams with the ones given in the literature.  
The existing steel reinforcement in the extracted beams was designed considering a frames/continuous beam configuration (the beam in the structure) with negative moments at beam ends and positive moments at beam midspan. This steel reinforcement configuration is different with  
Figure 4: Beam retrofitting: (**a**) rebars cleaning and protection, addition of new bottom rebars for beam flexural strengthening; (**b**) concrete cover restoration; (**c**) CFRP U-jacketing application for beam shear strengthening; (**d**) Details of U-jacketing anchorage by pultruded composite bar.  
respect to the one obtained considering a simply supported beam. However, elastic three-point loading tests were performed on the extracted beams before retrofitting to compare the deflection and stiffness of two beams with very similar rebar configurations and the same design materials. This is interesting because:  
1. The material mechanical characteristics showed variability along the beams (SS2);
2. The preexisting concrete cracking may be different for the two beams.  
Finally, failure of three-point loading tests were carried out after the addition of new longitudinal rebars along the beam intrados, which increased the flexural strength at midspan and assured the beam failure due to shear at the beam ends. In fact, the moment values increase at the beam intrados when the beam is tested considering a simply supported beam with respect to the ones of the beam in the structure.
# A Prospective Study on Accuracy of Computer-Based Fully Guided Versus Pilot-Guided Implant Surgery
## 3 Results
No significant difference was found between the pilot-guided and fully guided group in comparing the accuracy of the tooth-supported guide in the dental arch and the bone-supported guide in the dentate area. Between the tooth-supported guide and the bone-supported guide, the tooth-supported guide showed more accurate value, but it was not statistically significant (Table 6.). Accuracy comparisons for anterior and posterior parts also showed no difference between the experimental and control groups. There was a statistically significant difference between the anterior and posterior parts in the dy values of angulation and shoulder area (Table 7.). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tooth-Supported (\({}^{\circ}\), mm)** & **Bone-Supported (\({}^{\circ}\), mm)** & \(p\)**-Value** \\ \hline Angulation & \(3.14\pm 2.19\) & \(4.47\pm 3.95\) & 0.338 \\ Shoulder area & & & \\ dx & \(0.42\pm 0.38\) & \(0.70\pm 0.60\) & 0.328 \\ dy & \(0.47\pm 0.46\) & \(0.51\pm 0.55\) & 0.952 \\ dz & \(0.74\pm 0.66\) & \(0.83\pm 0.64\) & 0.461 \\ Mean & \(1.10\pm 0.72\) & \(1.44\pm 0.62\) & 0.061 \\ Apical area & & & \\ dx & \(0.71\pm 0.59\) & \(1.16\pm 1.12\) & 0.323 \\ dy & \(0.53\pm 0.40\) & \(0.58\pm 0.54\) & 0.944 \\ dz & \(0.78\pm 0.54\) & \(0.99\pm 1.01\) & 0.614 \\ Mean & \(1.35\pm 0.59\) & \(1.94\pm 1.17\) & 0.196 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tooth-supported vs. bone-supported (mean \(\pm\) S.D. ). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Deviation (\({}^{\circ}\))** \\ \hline Pilot-guided & \(3.33\pm 3.23\) \\ Fully guided & \(3.90\pm 1.85\) \\ [PERSON]’s test \(p\)-value & \(0.013\)\({}^{\mathrm{a}}\) \\ \(p\)-value & \(0.593\) \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) There is a statistically significant difference (\(p<0.05\)) ([PERSON]’s test for equality of variances).  
\end{table} 
Table 4: Deviations of angle (mean \(\pm\) S.D. ). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mesial(mm)** & **Distal(mm)** & **Mean(mm)** \\ \hline Pilot-guided & \(0.50\pm 0.40\) & \(0.50\pm 0.43\) & \(0.50\pm 0.29\) \\ Fully guided & \(0.50\pm 0.26\) & \(0.50\pm 0.29\) & \(0.50\pm 0.24\) \\ \(p\)-value & \(0.870\) & \(0.691\) & \(0.732\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Marginal bone loss after 1 year (Mean \(\pm\) S.D.). There was no statistically significant difference between flap and flapless surgery (Table 8). The mean deviation showed that flap surgery was more accurate in the shoulder area, but flapless surgery has less deviation in the apical area. These differences are not statistically significant.
## 1 Introduction
In [40], the authors used the concept of entropy in the network selection in heterogeneous network architectures. The authors used UE-side requirements and network-side requirements for an efficient network selection. To this end, the authors defined weights of multiple criteria of UE-side and network-side requirements using an analytic hierarchy process. Entropy was also used to define weights of network-side requirements. Although the authors in the work in [35; 36] developed an efficient call admission control scheme, they did not consider separate control and data planes. Also, context information on data type of a service, i.e., low rate data traffic or high rate data traffic, was not considered for handover decisions. To extend the previous works on CAC-based handover [35; 36], we consider a new heterogeneous network architecture in 5G networks with separate data and control planes, where a macro cell BS manages control signals and a small cell BS manages data. In addition to previously used context information such as RSRP, speed of UE, traffic load of target cell, and admission control level, we newly consider the data type of  
Figure 2: Handovers in a traditional approach and a CAC-based approach. a service, i.e., low rate data traffic and high rate data traffic. Then, we propose an optimized handover scheme considering the mentioned context information. An optimization problem is formulated and the solution is obtained by particle swarm optimization, since the optimization problem is non-deterministic polynomial-time hard (NP hard). The basic idea of the proposed optimized handover scheme is similar to the work presented in our preliminary work [41] but the proposed work is significantly extended from the previous work according to the following aspects:  
* The proposed optimized handover scheme is extended and more elaborated from our previous work so a UE with low speed and low rate data traffic can be handed over to a small cell BS, depending on the traffic load condition of neighboring small cell BSs. * A thorough optimization problem for the proposed handover scheme is formulated, from the aspect of the minimization of the number of handovers and energy consumption of BSs, for the performance analysis based on context information such as RSRP, speed of UE, traffic load, call admission control level, and data type. * The particle swarm optimization is applied to solve the formulated optimization problem heuristically. * Extensive numerical examples are obtained through simulation based on the assumption of specific probability distributions of various random variables defined in this paper, and the performance of the proposed scheme is evaluated in detail. The remainder of this paper is organized as follows: Section 2 proposes an optimized handover scheme based on context information and formulates an optimization problem. In Section 3, numerical examples are presented. Finally, Section 4 concludes this work.
# Unsteady Aerodynamic Design of a Flapping Wing Combined with a Bionic Wavy Leading Edge
## 2 Aerodynamic Characteristics Analysis of Baseline Flapping Wing  
### Flapping Wing Motion Model  
The flapping wing motion can be decomposed into two basic motion forms: flapping and pitching, as shown in Figure 1. The cross-sections of bird's wing like thin and cambered airfoil. NACA0014 airfoil has been experimented [34] and numerically simulated [34; 35] as a flapping wing. Therefore, the flapping wing model adopted in this paper is a rectangular straight wing with a finite span and the wing section of NACA0014 airfoil. The chord length of the wing is \(c\), the span length is \(L\) and aspect ratio is \(AR~{}=~{}\frac{L}{c}\). The set flapping wing motion law is expressed as Equation (1) [35], where, \(f\) is the flapping frequency, \(\psi(t)\) represents the flapping motion, \(\psi_{1}\) is the amplitude of the flapping motion, \(\alpha(t)\) represents the pitching motion, \(\alpha_{0}\) is the pre-installation angle of the wing, \(\alpha_{1}\) is the amplitude of the pitching motion, \(\phi\) is the phase difference, and the center of the pitching motion is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
the quarter chord length of each section of the wing. Figure 2 shows the time history plot corresponding to Equation (1).  
\[\left\{\begin{array}{lcl}\psi(t)&=&\psi_{1}\cos(2\pi ft)\\ \alpha(t)&=&\alpha_{0}+\alpha_{1}\cos(2\pi ft+\phi)\end{array}\right. \tag{1}\]  
According to the flight speed and flapping frequency of small birds such as pigeons, the approaching velocity \(U_{\infty}\) is 15 m/s, the flapping frequency \(f\) is 8 Hz, the chord length \(c~{}=~{}0.12\)m, the aspect ratio \(AR=8\), the Reynolds number is \(\mathrm{Re}~{}=~{}\frac{\rho U_{\mathrm{loc}}}{\mu}~{}=~{}1.2\times 10^{5}\), the reduced frequency is \(k~{}=~{}\frac{\pi fc}{U_{\infty}}~{}=~{}0.2\). The specific values of each parameter are shown in Table 2.  
Figure 1: Motion of flapping wing.  
Figure 2: The time history plot corresponding to Equation (1).