esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Non-Equilibrium Thermodynamic-Based Model Framework
### Soil-Water Characteristic  
The soil-water characteristic curve (SWCC) generally takes the form of the vG model [37], \(s_{r}=\Big{[}1+\Big{(}\frac{s}{a}\Big{)}^{n}\Big{]}^{-m}\), where \(a\), \(m\), and \(n\) are the parameters associated with the curve. Since the saturation is also affected by the void ratio \(e\), some authors [38; 39] proposed modified SWCC models by adding the deformation factor into the vG model. For example, [PERSON] [39] proposed a modified SWCC model to describe the effect of void ratio on soil-water characteristic behavior. He found that the pore water void ratio \(e_{w}\) can be described by a power function of suction \(s\), \(e_{w}=a_{1}s^{b_{1}}\), where \(a_{1}\) and \(b_{1}\) are related to the intercept and slope of the \(\mathit{lns}\sim\mathit{lne}_{w}\) curve. The factor \(\Big{(}\frac{c}{a_{1}}\Big{)}^{1/b_{1}}\) is added to indicate the effect of deformation, and the modified SWCC model is given as:  
\[s_{r}=\left\{1+\left[\Big{(}\frac{e}{a_{1}}\Big{)}^{1/b_{1}}s\right]^{n} \right\}^{-b_{1}/n} \tag{24}\]  
where \(a_{1}\), \(b_{1}\), and \(n\) are the fitting parameters and \(m=\)\(b_{1}/n\) is equal to the parameter \(m\) in vG model.  
Figure 2 shows the main drying and wetting surfaces of Speswhite kaolin in \(e\sim\mathit{lns}\sim\mathit{s}_{r}\) space based on the above modified SWCC model (Equation (24)). When the suction remains constant, the degree of saturation will decrease with the increase of void ratio, and when the degree of saturation  
is constant, the suction will increase with the increase of void ratio. Equation (24) can also be reduced to the vG model without considering the change of void ratio.
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.6 Delay  
In VANET, various applications have different requirements. For example, an application such as safety warning applications may not need excellent data rate, but it is severely restricted in terms of delay. In this case, if an alarm message reaches the destination, too late and after a long period of time, it may not be effective to keep vehicles from happening accidents or overturn. Therefore, it is very important to provide acceptable time delay when designing routing protocols [28].
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
###### Abstract  
Energy efficiency is a key requirement in the design of amplifiers for modern wireless applications. The use of quasi-floating gate (QFG) transistors is a very convenient approach to achieve such energy efficiency. We illustrate different QFG circuit design techniques aimed to implement low-voltage, energy-efficient class AB amplifiers. A new super class AB QFG amplifier is presented as a design example, including some of the techniques described. The amplifier has been fabricated in a 130 nm CMOS test chip prototype. Measurement results confirm that low-voltage, ultra-low-power amplifiers can be designed, preserving, at the same time, excellent small-signal and large-signal performance.  
amplifiers; analog integrated circuits; CMOS integrated circuits; energy-efficient amplifiers; class AB circuits +
Footnote †: journal: Applied
# Using Canola Oil Biodiesel as an Alternative Fuel in Diesel Engines: A Review
## 3 Properties of Canola Oil Biodiesel
### Density and Viscosity  
The density and viscosity of biodiesel fuel are important factors in engine operation. Density will directly affect the mixing of biodiesel and diesel, and excessively high density will lead to stratification of mixed oils. In addition, the density is also related to the cetane number, viscosity, and heating value of the fuel. Viscosity is an important factor that directly affects the combustion performance and exhaust characteristics of an engine. High viscosity deteriorates the atomization efficiency, extends ignition time, reduces injection pressure, and hinders full combustion of the fuel [58; 59; 60]. As a result, carbon deposition is increased, engine wear is increased, service life is shortened, and exhaust emissions are increased. Table 1 shows that the density and viscosity of pure diesel and canola oil biodiesel blended with diesel fuel (BD) 100 are 836.8 kg/m\({}^{3}\), 2.719 mm\({}^{2}\)/s and 880 kg/m\({}^{3}\), 4.29 mm\({}^{2}\)/s, respectively. Their density difference is not very large compared with the viscosity difference, which indicates that they can mix well. [PERSON] et al. [61] tested the density of biodiesel blended with diesel, with different amounts of biodiesel at different temperatures; these test results are presented in Figure 6, showing that most biodiesel density values fell between 838 kg/m\({}^{3}\) and 896 kg/m\({}^{3}\). On the other hand, in Table 1, the viscosity of BD10 and BD20 were reduced to about the same as that of pure diesel; these results are the same as [PERSON] et al. [62] (see Figure 7). Many other researchers [63; 64; 65; 66; 67] have studied a variety of biodiesel blended fuels (diesel blended with sunflower, cottonseed, soybean, corn oils, and waste palm oil) and have also discovered that the viscosities of the 20% blend fuels are very close to that of pure diesel fuel.  
Figure 6: The density of biodiesel blended with diesel fuels.  
Figure 7: The viscosity change of canola oil biodiesel blended with diesel fuel at different temperatures.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Performance Analysis
#### 3.3.2 Effects of the Depth and Time Window  
As for the models applied to the FD001 dataset, the depth of the network would be gradually increased from one to eight while other parameters remained unchanged to explore the influence of the depth of the CNN on the results. At the same time, the effect of the size of the TW on the results is also discussed.  
With the evaluation function RMSE as a criterion, Figure 9a depicts the prediction results of models with different time windows and depths. When the size of the TW was selected as 10, the value of RMSE grew gradually to 17.64 at the five layers, after which it fluctuated between 17.55 and 17.92. For the algorithm with the TW of 20, the value of RMSE fluctuated between 15.61 and 16.48 from one layer to eight layers, ending at 16.32. Regardless of the TW of 10 or 20, the prediction accuracy of the model did not get better as the depth of the neural network increased. However, when the TW was set to 30, the performance of the model became better as the depth of the layer increased. The increase in the depth of the model after seven layers could not lead to the improvement of the result and would have increased the burden on the network. In this experiment, the seven-layer model with the TW 30 achieved the best result in the FD001 testing set.  
Figure 9a shows that the increase of the TW could improve the prediction result because the larger time window meant that more raw information could be covered, which could better reflect the degradation of the aircraft engines [27]. However, the TW could not be increased indefinitely, the maximum allowable TW was determined by the smallest operating cycles present in the testing set. For example, for an engine with an operating history of 20 cycles, it was im  
Figure 8: Part of prediction results on FD001 testing set: (**a**) Aircraft engine No. 17; (**b**) aircraft engine No. 24.  
Figure 9: Prediction results: (**a**) FD001?(**b**) FD002, FD003 and FD004.  
window of 30 cycles [22]. Therefore, the TWs selected for FD001, FD002, FD003, and FD004 were 30, 20, 30 and 15, respectively.  
As shown in Figure 9b, for the FD002 dataset, as the depth of the model increased from one to three, the prediction accuracy was improved, but if the depth of the model further increased, the prediction result would fluctuate. For the FD003 and FD004 datasets, as the depth of the corresponding model increased, the optimal prediction results were obtained when the third layer and the second layer were respectively reached. If the model depth was continuously increased, the prediction result of the model would fluctuate and increase computational costs.
## 3 Design, Implementation and Test of the DXN Engine
### DXN Tests Scenarios and Experiments
#### 3.4.4 Overall Evaluation of DXN  
DXN has shown that it is possible to enhance the network performance by implementing a dynamic AI-based engine that is constantly analysing the collected data from the actual deployed SNs and giving live feedback to adjust the SNs' behaviour. This was achieved by using the DNN model with 98% accuracy in predicting. As for the Time Series model, it achieved a Mean Squared Error (MSE) of 0.11.  
By using the network server resources located in the cloud (assumed to be unlimited in terms of power source, computational capabilities, storage, etc.), DXN was able to perform all the heavy calculations, leaving the SNs with minimum tasks and eliminating any additional overhead when compared to solutions such as [14] and [18] that are proposing the implementation of AI algorithms onboard the deployed SNs. Furthermore, no additional hardware equipment/capabilities were required other than the typical LoRaWAN  
Figure 9: Waiting time reduction using DXN.  
compatible network. Therefore, we envisage that more enhancement is achievable by including extra (simple) information within the SNs' data messages.
## 2 Zero-Trust Overview
### Zero-Trust Network Access (ZTNA)  
Google implemented a zero-trust framework for its internal network called BeyondCorp in 2015 [13]. This implementation includes a Device Inventory Database, which uses a concept called \"Managed Device\" and only these devices are allowed to access the enterprise resources. Device certificates are issued during enrolment to uniquely identify them in the database and are also used as one of the factors in the authentication. BeyondCorp also implemented a Single Sign-On system for authentication, which issued a session token for the access of a specific resource. An internet-facing proxy in their network is also included, which delegates requests from internal and external clients to the appropriate back-end resource. All the public DNS registrations of Google's applications point to this access proxy.  
Based on the concept of service-initiated architecture by BeyondCorp, Gartner 2019 introduced Zero-Trust Network Access (ZTNA) in [14]. According to this, an additional component called Connector is implemented in the internally protected network to accept incoming requests from users or devices after authentication. The connector then processes the request to verify access and connects the requestor to the service. ZTNA is rapidly replacing VPN-based access to critical services and will continue to phase out 60% of VPN usage by 2023 as per Gartner's prediction [12].
## 3 Experiments and Results
### The Road Dataset Experiments  
This paper uses the above method to evaluate the image generated by the fusion of the model proposed in this paper. At present, the more advanced algorithms in the field of image fusion, IFCNN,DenseFuse [12] CBF [31], CNN [8; 32], DeepDecFusion [23], MEFGAN [33], FusionGAN [9], DualBranchFusion [14], are compared, and the experimental results are shown in Table 3 From Table 3, it can be seen that the network model proposed in this paper is the best in 3 of 10 evaluation indexes. Another 4 indicators are suboptimal. It can be seen from Figure 5 that the part of the red frame in the Infrared Image image cannot see the texture of the wall, but the texture of the image can be clearly seen in the Visible Image. Through the experimental comparison, it is found that in addition to the overexposure of the images obtained by the MEF-GAN model, the proposed model, and other comparable models can retain more texture information of the background wall. However, the brightness of the images after the fusion of the DeepDecFusion and Dense fuse models is not as high as that of the proposed model. In addition, in the blue frame, the model proposed in this paper retains the contour information of flowerbed plants to the greatest extent.  
Besides, in the blue rectangle, the car outline in DeepDecFusion and FusionGAN is not clear, and the person's outline is not clear. Densefuse, IFCNN, CNN, DeepDecFusion, and Ours have clear car outlines. In the purple rectangle, the Infrared image can see a crack. In contrast, in the Visible image it is almost invisible, MEF-GAN, closer to the Visible image, Densefuse, DeepDecFusion, IFCNN, and FusionGAN can see the crack inconspicuously, but CNN and Ours can clearly see the crack.  
In summary, the network model proposed in this paper retains the various information of Infrared Images and Visible Images to a certain extent.
## 3 Results
### In-Orbit Results
#### 3.2.2 DLP Instrument  
During phase 1 of the mission (LEOP or early operations), the DLP was operated in the real-time mode similarly to the CPD. The data were transmitted to the ground station at Kyutech when the satellite was in view and after it had received the command for the DLP real-time operation. During these tests, the three DLP resolution modes were selected. However, in all cases, most of the received data packets contained zeros. Certain data packets containing data from the DLP instrument were received among the zero values packets. However, the received samples were not sufficient to reconstruct a single I-V curve. A problem with the DLP power line, which would prevent the instrument for switching on for the required duration was suspected. This is presented in more detail in the discussion section.
## 3 Elastic Equilibrium Problem  
Let us consider a FG nano-rod subject to a distributed axial load \(q(x)\) per unit length in the interval \([0,L]\) and to concentrated axial forces \(\mathcal{F}\) at the end cross-sections \(x=0\) and \(x=L\).  
Differential condition of equilibrium can be written as  
\[\partial_{x}N\left(x,L_{c},l\right)=-q(x) \tag{22}\]  
with the boundary conditions \(N\left(x,L_{c},l\right)=\mp\mathcal{F}\) at \(x=0\) and \(x=L\).  
The axial displacement at the abscissa \(x\) along the nano-rod axis is denoted by \(u(x)\) and the kinematically compatible axial strain has the form  
\[\varepsilon(x)=\partial_{x}u(x). \tag{23}\]  
In the sequel, elastic \(\varepsilon_{el}\) and kinematically compatible \(\varepsilon\) strains are assumed to be coincident.  
Exact solutions according to the proposed modified nonlocal strain gradient (NSG) model for FG nano-rods can be performed by the following steps.  
* **Step 1**: Solve the equilibrium Equation (22) to get the expression of the axial force \[N\left(x,L_{c},l\right)=-\int_{0}^{x}q\left(s\right)ds+a_{1}.\] (24)
* **Step 2**: Solve the second-order differential Equation (6) in the form \[\begin{array}{l}\left(A_{E}\;\varepsilon_{el}\right)\left(x\right)-l^{2} \partial_{x}^{2}\left(A_{E}\;\varepsilon_{el}\right)\left(x\right)=\\ \quad=-\int_{0}^{x}q\left(s\right)ds+a_{1}+L_{c}^{2}\;\partial_{x}q(x)\end{array}\] (25) obtaining the expression of the elastic axial strain \(\varepsilon_{el}\) of the nano-rod in terms of three integration constants (\(a_{1}\), \(a_{2}\), and \(a_{3}\)) to be determined.
* **Step 3**: Solve the first-order differential in Equation (23) in terms of the axial displacement \(u\) of the nano-rod to get the expression of \(u\) in terms of four integration constants (\(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\)) to be determined.
* **Step 4**: Determine the four integration constants (\(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\)) by imposing the two CBC given by Equation (7) in terms of the axial displacement \(u\) \[\begin{array}{l}-q(0)=\dfrac{1}{L_{c}}a_{1}+\dfrac{l^{2}}{L_{c}^{2}}A_{E}\; \left.\partial_{x}^{2}u\left(x\right)\right|_{x=0}\\ -q(L)=\dfrac{1}{L_{c}}\left(\int_{0}^{L}q\left(s\right)ds-a_{1}\right)+\dfrac{ l^{2}}{L_{c}^{2}}A_{E}\;\left.\partial_{x}^{2}u\left(x\right)\right|_{x=L}.\end{array}\] (26) and the two classical boundary conditions at the nano-rod end points \(x=0\) and \(x=L\) by specifying \[u\;\;\;\text{or}\;\;\;N.\] (27)  
It is worth noting that, in statically determinate rods, the axial force \(N\) can be obtained by Equation (24) by imposing the classical static boundary conditions.
## 2 Actively Tunable Topological Transition in Graphene/Dielectric Multilayers
Hence, the actively tunable IFCs in graphene/dielectric multilayers are realized by tuning the chemical potential of graphene layer. We further study the influences of the topological transition of dispersion on the scattering properties of structures if the PEC or PMC defect is added. According to Equation (5), the sign of \(\varepsilon_{\perp}\) also can be controlled with the varied \(\mu_{c}\), which is shown in Figure 4a. The effective parameters of \(\varepsilon_{\perp}\) and \(\varepsilon_{//}\) are marked by green dashed line and black line, respectively. We can clearly see that the sign of \(\varepsilon_{\perp}\) will change at the critical point 18 eV as the value of \(\mu_{c}\) increases. At the critical state \(\mu_{c}=18\) eV, the IFC is a very flat elliptic curve along the horizontal direction (marked by orange line in Figure 4b), which can be regarded as one kind of anisotropic ENZ medium. As another kind of anisotropic ENZ medium for \(\mu_{c}=0.439\) eV in Figure 3a, the IFC is a very flat elliptic curve along vertical direction (marked by a purple line in Figure 4b). So far, based on a passive system, unusual transportation properties of the light in anisotropic ENZ metamaterials have been demonstrated, including collimation [41], flux manipulation [42], and total transmission (reflection) [43]. Now, with the aid of active control, we study the actively tuned transmissions of two kinds of anisotropic ENZ media based on the graphene/dielectric multilayers. For a type I anisotropic ENZ medium (\(\mu_{c}=18\) eV), when a plane wave is incident on this structure embedded with a tiny PEC defect, the wave can perfectly pass through this defect without any influence, as is shown in Figure 4c. The transmission behavior will change once the PEC defect is replaced by a PMC defect and the incident wave will be scattered by the defect (Figure 4d). Similar to the type I anisotropic ENZ medium, for the type II anisotropic ENZ medium (\(\mu_{c}=0.439\) eV), Figure 4e shows that the incident wave also will not be affected by the PEC defect, as in the case of Figure 4c. However, the transmission will dramatically change for the structure containing a PMC defect. In this case, the incident wave will be completely blocked by the tiny defect (Figure 4f), which is similar to the total reflection effect realized in the double near-zero index material [44]. So, inspired by the demonstration of the topological transition of dispersion, we present the novel transportation behavior that can be controlled in an active manner.
## 3 Materials and Methods
### Anomaly Detection System
#### 3.5.2 Training and Implementation  
The anomaly detection system was implemented with the following methodology:  
* **Data Collection and Organization**: Sensor data was collected from multiple driving scenarios under normal conditions and organized into training and testing datasets with separate directories for normal and anomalous data.
* **Data Preprocessing**: All sensor data underwent standardized preprocessing:
* Images resized to 128 \(\times\) 128 pixels for computational efficiency,
* Normalization using ImageNet statistics (mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]), and
LiDAR point clouds converted to 2D bird's-eye-view images for compatibility with the image-based model.
* **Model Training Configuration**:
* Training performed using PyTorch 1.13.1 with CUDA 11.7 acceleration
* Batch size of 1 with gradient accumulation over 4 batches for memory efficiency
* Training duration of 30 epochs with validation every epoch
* Separate models trained for each sensor type (RGB, DVS, depth, and LiDAR). The detailed training process and loss behavior for the DVS anomaly detection model are illustrated in Figure A1 in Appendix A.
* **Real-time Analysis**: The trained models are used to analyze sensor data during autonomous driving episodes, generating anomaly scores that indicate the likelihood of sensor attacks. The system can process sensor data in real-time and provide continuous monitoring of sensor integrity.  
The anomaly detection system operates independently of the traditional filtering defenses, providing an additional layer of security that can detect novel attack patterns and complement the rule-based defense mechanisms.
## 2 Data Collection and Quality Evaluation  
High-quality GNSS observations are the prerequisite for achieving high-precision relative orbit. Therefore, it is necessary to analyze the quality of the onboard GNSS observations before performing PROD. In this section, the information of TH-2 satellite formation system is introduced, and the quality of the observation data is analyzed from the available data, code multipath, and carrier phase noise.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 1: Processing Information of Power Distribution System and Substation Fault Records  
In this step the information of the power distribution system is added, such as its topology and the electrical parameters of loads and distribution lines. Also, the substation fault voltage and current phasors are estimated by the application of the Fourier transform [3].
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 6 Results and Discussion  
In this section, we will present and discuss the results of the three experiments described in the previous section. Every evaluation scenario was run three times resetting all testbed components after each iteration to ensure an identical operating environment. The results were then collected and averaged using client-side scripts that were developed specifically for the testbed. In the following, we first define the performance metrics that were collected in the evaluation and then discuss the results of each experiment.
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 3 Methodology
### Apparatus and Materials  
Participants in the usability study were required to prepare their own computer to perform collaborative activities. Their computer should be equipped with a web camera, microphone, keyboard, and mouse, and the laptop should be set up on a desk. Each participant required a stable internet connection in order to complete the remote collaboration task that had been set. The participants were also required to install a virtual conference application (Zoom) to perform most of the collaborative activities.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Materials
#### 2.1.3 Conducting Polymers  
Conducting polymers can have flexibility and stretchability that are close to those of traditional plastics, also they have fast carrier transport close to silicon electrons [53]. Poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is one of the most extensively studied conducting polymer for flexible and wearable electronics [37; 54; 55]. PEDOT partially aggregates and forms a \"hard\" conductive network in the soft PSS matrix due to the weak electrostatic interaction between PSS and PEDOT. Therefore, additives such as STEC enhancers are added to soften the PSS domains, which makes it more applicable in wearable devices. Figure 2g shows conductive core/shell polymer nanofibers that are composed of poly (vinylidene fluoride-co-hexafluoropropene) (PVDF-HFP)/poly(3,4-ethylenedioxythiophene) (PEDOT) [38]. A sponge-like 3D membrane made of nanofibers were fabricated through the electrospinningning of PVD-HFP and the following vapor deposition polymerization of PEDOT. The 3D membrane displayed a sheet resistance of approximately \(7\times 104\)\(\Omega\)/sq. Synergized with the well-dispersed spherical bumps on the PEDOT shell that lead to the formation of a hierarchical conductive surface, the sponge-like 3D mats exhibited an excellent pressure sensitivity.