esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture
As network coverage, 5G technology is ideal for IoT applications that need reliable and high-speed internet access connectivity in order to send sensor information from hard-to-reach areas. The substantial bandwidth provided by the 5G network ensures that the 5G link will not be the bottleneck for the transmission of our system's data. Specifically, in our system we transmit up to 255 bytes per packet, with each packet transmitted every 15 s. This makes the 5G-enabled Raspberry Pi configuration a versatile and dependable option for IoT applications that need sustained data transfer in remote locations.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Geographical Routing ICN Procotol
#### 3.2.3 Content Store (CS)  
The content store contains the data, the sequence number, and the current hop limit associated with the data. Before storing data, a mobile node checks if the number of the hop is higher than 1, else it is the last hop. Additionally, before updating the content store with received data, the mobile node checks if the current sequence number is lower than the received one. If these two conditions are fulfilled, the received data are stored with the sequence number and the hop limit decremented by one (Figure 5). For an RSU, the hop limit has to decrease over time, because the longer the data has been stored, the closer they are to expiration. Additionally, an RSU will always update the data coming from C-ITS-C, because C-ITS-C only broadcast the data when they change. However, if the data are coming from the mobile nodes, they will not update.
## 4 Experiments and Analysis
### Analysis of Algorithm Security  
The security of perceptual hash algorithm mainly refers to the unidirectionality, that is, no valid content information of the image can be obtained from the perceptual hash value without the key. For our algorithm, the security relies on the security of the encryption algorithm, and we adopt the advanced encryption standard (AES-128) algorithm in this paper. As the security of the AES-128 algorithm has been widely studied and recognized, our algorithm has enough security. Moreover, if there is a higher requirement for the security of the perceptual hash value, a stronger encryption algorithm such as aes-256 can be used.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
## 2 Lumped-Parameter Modeling of Piezoelectric Metamaterial
### Piezoelectric Metamaterial with Cavity
From Equation (20) it can be observed that, fundamentally, the dispersion relation is decided by both the circuit resonant frequency and the system level electro-mechanical coupling coefficient. The solutions to the dispersion equation under the parallel-connection configuration, can be easily obtained as  
\[\omega_{1,2}=\frac{\sqrt{2}}{2}\sqrt{\omega_{0c}^{2}+\omega_{LC}^{2}\mp\sqrt{ \left(\omega_{0}^{2}-\omega_{LC}^{2}\right)^{2}+8k_{cc}^{2}\omega_{0}^{2} \omega_{LC}^{2}}} \tag{21}\]  
Consider that the frequency of LC resonance is a chosen value to fit specific design requirements. The bandgap width of the piezoelectric metamaterial fundamentally hinges upon the term \(k_{cc}^{2}\) in Equation (21). Choosing piezoelectric transducers with a larger coupling constant \(k_{1}\), e.g., piezoelectric single crystal (e.g., PMN-PT), may certainly help in the improvement of bandgap characteristics, but this type of material costs much more than piezoelectric ceramic. Many studies employ a negative capacitance element integrated to the piezoelectric transducer, to reduce the equivalent local stiffness of a unit-cell [38], thereby tuning the location of bandgap or influencing its bandwidth. Introducing a cavity in the substrate can mechanically reduce the stiffness of the unit-cell and broaden the bandgap width of the metamaterial, as qualitatively indicated in Equation (21). It should be noted that in practice the cavity geometry does not have to be rectangular in shape. In fact, owing to the rapid advancement of additive manufacturing technology, in future one may create complex shapes of cavity to fit the specific applications, and even perform topology/shape optimization.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 4 Problem Description  
Figure 1 depicts the NOMA downlink system for reference. As seen in Figure 1, each sub-channel \(v\) serves users that are close and far, with channel and power conditions described in (7) and (8), respectively.  
The NOMA energy efficiency is determined as the ratio of the total data transmission rate to the system's total power consumption. The total power consumption includes the base station transmission power and the circuit consumption of the wireless devices [34]. Thus, from (11), the energy efficiency of the sub-channel can be expressed as:  
\[E_{v}=\frac{R_{v}}{P_{fixed}+P_{v}} \tag{13}\]  
where \(P_{fixed}\) is the power of fixed circuit in simulator, and:  
\[P_{v}=\sum_{j=1}^{n}P_{j,v} \tag{14}\]  
The energy efficiency of the system is:  
\[E=\sum_{v=1}^{V}E_{v} \tag{15}\]  
Hence, we can achieve the goal of energy saving by optimizing the problem via maximizing the data rate that can be transferred per unit of energy. Therefore, to increase the energy efficiency of the system, our objective function is:  
Figure 1: User pairing diagram.  
\[\max_{P_{v}>0}\sum_{v=1}^{V}\frac{R_{v}(\alpha_{n})}{P_{fixed}+P_{v}}\ \,\ 0< \alpha_{n}<1\] (16) subject to \[C1:R_{j,v}>R_{min}\] \[C2:\sum_{v=1}^{V}P_{v}=P_{BS}\]  
where \(\alpha\) is the coefficient of the power allocation of the worst channel gain. Constraint C1 guarantees the user's minimum data rate, where \(R_{min}\) is the minimum data rate required by \(QoS\). Constraint C2 means that the transmit power at the base station is constant.  
The global optimal solution suffers from a high complexity. To obtain sub-optimal solutions, we have divided the objective optimization function into two sub-problems: the user allocation sub-problem and the power allocation sub-problem. These will be discussed in the following section.
# Fully Automated Electronic Cleansing Using CycleGAN in Computed Tomography Colonography
## 2 Methods
### Network Structures  
In this study, CycleGAN was employed to generate cleansed CTC images. An outline of the CycleGAN architecture used in this study is shown in Figure 5. The datasets fed to it are the original CTC and MC images that were manually cleaned with barium. Generator G is trained to generate images that fool the discriminator from the original input CTC images, and discriminator Dy is trained to discriminate between real virtual MC and nonreal MC images. By alternately training the generator and discriminator, the generator can generate images that resemble real original CTC images. Simultaneously, generator F transforms the MC image into a CTC original image with a barium residue, and discriminator Dx identifies it from the real CTC original image. In addition, CycleGAN transforms one set of images to another set of images, restores them to the original set of images, compares the differences, and learns. Figure 6 shows the network structure of CycleGAN used in this study. ResNet [20] with nine residual blocks was used as the generator of CycleGAN, and the mechanism of PatchGAN [24] was employed as the discriminator.  
Figure 4: Manual cleansing image creation.  
Figure 3: Evaluation distribution of pretreatment at each site.  
To train CycleGAN, we set the number of training cycles to 100 and the batch size to 1. Adam was used as the optimization algorithm. The learning rate was set to 0.0002, and the loss function was set to the mean absolute error. Regarding the batch size, the evaluation was conducted while changing the parameters to 1, 4, and 16 during the preliminary experiments. Consequently, a batch size of 1 was adopted, which produced the best output images.  
Figure 5: Outline of the CycleGAN architecture.  
Figure 6: Network structure of CycleGAN. (**a**) Generator; and (**b**) discriminator.  
This study was conducted using an in-house Python program with the Keras and TensorFlow software libraries. Three types of images were used for evaluation: original CTC images, conventional cleansing images (hereafter referred to as \"conventional method\"), and images created using the proposed cleansing method (hereafter referred to as \"proposed method\"). The conventional cleansing method has no parameters for the cleansing process, and all processing was automatically conducted.
## 4 New Cascaded Framework
### Battery OCV Estimation  
The estimated battery OCV plays an important role in the new cascaded framework. As can be noticed in Figure 7, this module makes the connection between the estimated SOC and ECM parameters and the estimation of the battery usable capacity.  
Thanks to the zero-time derivative condition, the estimation of the battery OCV can also be done similarly based on the extended model (11). The added noises \(\varepsilon_{1\sim 3}\) and \(\psi\) are also AWG. Furthermore:  
\[\begin{split} F(k)&=\left[\begin{array}{cc}\mathrm{e }^{\frac{-T}{\mathrm{R}_{1}(k)\zeta_{1}(k)}}&0&0\\ 0&\mathrm{e}^{\frac{-T}{\mathrm{R}_{2}\zeta_{2}}}&0\\ 0&0&1\end{array}\right],\qquad H=[-1,-1,1]\\ G(k)&=\left[\begin{array}{c}\mathrm{R}_{1}(k)\cdot(1-\mathrm{e}^{\frac{-T} {\mathrm{R}_{1}(k)\zeta_{1}(k)}})\\ \mathrm{R}_{2}\cdot(1-\mathrm{e}^{\frac{-T}{\mathrm{R}_{2}\zeta_{2}}})\\ 0\end{array}\right],J(k)=-\mathrm{R}_{0}(k)\end{split}\]  
\[\begin{split}\left[\begin{array}{c}V_{1}(k)\\ V_{2}(k)\\ V_{\text{OC}}(k)\end{array}\right]&=F(k)\left[\begin{array}{c}V_{1}(k-1)\\ V_{2}(k-1)\\ V_{\text{OC}}(k-1)\end{array}\right]+G(k)I_{\text{batt}}(k-1)+\left[\begin{array} []{c}\varepsilon_{1}(k-1)\\ \varepsilon_{2}(k-1)\\ \varepsilon_{3}(k-1)\end{array}\right]\\ V_{\text{batt}}(k)&=H\left[\begin{array}{c}V_{1}(k)\\ V_{2}(k)\\ V_{\text{OC}}(k)\end{array}\right]+J(k)I_{\text{batt}}(k)+\psi(k)\end{split} \tag{11}\]  
Note that based on our former research work, although the regression-based OCV estimation algorithms can provide an independent OCV estimation, the observer-based method exhibits better OCV estimation performance from the perspective of accuracy [34]. Hence, observer-based OCV estimation is employed here in order to connect the other two parts. Besides, the estimation of the polarization voltages \(V_{1}\) and \(V_{2}\) cannot be simply considered as done two times, i.e., during the estimation of SOC and parameters, as well as the estimation of OCV. Because in the \(k\)th step, the battery SOC and parameter estimation module use the information of the (\(k\)\(-\)1)th step; while the state transfer matrix \(F(k)\) and the so-called control input matrix \(G(k)\) of (11) are calculated with the latest information of the \(k\)th step, namely the estimation is executed with the updated battery ECM parameters.  
Obviously, the full rank characteristic of the observability matrix for this linear model can be easily verified. Therefore, the classical linear observer KF is used in this module.
###### Abstract  
The need for adaptivity and scalability in telecommunication systems has led to the introduction of a software-based approach to networking, in which network functions are virtualized and implemented in software modules, based on network function virtualization (NFV) technologies. The growing demand for low latency, efficiency, flexibility and security has placed some limitations on the adoption of these technologies, due to some problems of traditional virtualization solutions. However, the introduction of lightweight virtualization approaches is paving the way for new and better infrastructures for implementing network functions. This article discusses these new virtualization solutions and shows a proposal, based on _serverless computing_, that uses them to implement container-based virtualized network functions for the delivery of advanced Internet of Things (IoT) services. It includes open source software components to implement both the virtualization layer, implemented through Firecracker, and the runtime environment, based on Kata containers. A set of experiments shows that the proposed approach is fast, in order to boost new network functions, and more efficient than some baseline solutions, with minimal resource footprint. Therefore, it is an excellent candidate to implement NFV functions in the edge deployment of serverless services for the IoT.  
Internet of Things; micro virtual machines; network function virtualization; serverless computing; performance evaluation +
Footnote †: journal:  
0000-0001-4224-3300][PERSON]. [PERSON]  
1 Department of Engineering, University of Perugia, via G. [PERSON] 93, 06125 Perugia, Italy;  
[EMAIL_ADDRESS]
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 3 Case Study
### Information Fusion Diagnosis Analysis
#### 3.3.1 Information Fusion Based on Bayesian Network
Such an adjustment is consistent with the data characteristics in Figures 5 and 6; that is, the parameter changes faster when the fault occurs. The diagnosis results for information fusion based on a Bayesian network are shown in Figure 8. It can be seen from the figure that the probability of diagnosis results after the fusion changes and increases quickly about 1756 s afterwards. Without prior knowledge, the highest probability is 87.22% at 1760 s. In the case of prior knowledge, due to the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fault Feature**} & \multicolumn{2}{c}{**Without Prior Knowledge**} & \multicolumn{2}{c}{**With Prior Knowledge**} \\  & **A1 = 0** & **A1 = 1** & **A1 = 0** & **A1 = 1** \\ \hline Vic & N(0, 0.5) & N(220, 0.5) & N(0, 0.8) & N(220, 0.01) \\ DGF & N(0, 0.5) & N(\(-\)0.05, 0.5) & N(0, 0.8) & N(\(-\)0.05, 0.01) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Child nodes’ probability distribution settings. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Node**} & \multicolumn{2}{c}{**States**} \\  & & **0 (Healthy)** & **1 (Faulted)** \\ \hline Fan & A1 & 0.97 & 0.03 \\ HPC & A2 & 0.99 & 0.01 \\ HPT & A3 & 0.99 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The prior probability setting of the parent nodes. adjustment of the standard deviation, the probability of fan failure in the few seconds before shutdown is generally higher than that without prior knowledge. In addition, the highest probability of failure with prior knowledge also occurs in the last second, which is 91.16%. This shows that the introduction of prior knowledge and the adjustment of the probability distribution can improve the reliability of diagnosis. Compared with the characteristic diagnosis result of a simple gas path fault, the information fusion diagnosis method of the Bayesian network can eliminate the fluctuation when the fault does not occur by considering the vibration signal. However, in practical engineering applications, the acquisition of prior knowledge is more difficult, which may lead to obstacles in applications based on Bayesian networks.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development
### Semi-Empirical Model
#### 3.2.1 Small Molecular Substances  
In the SCWO process, complex organic compounds are decomposed to various small molecular substances, whose transformation into harmless inorganic substances such as CO\({}_{2}\) and N\({}_{2}\) marks the completion of pollutants' degradation. Hence, it is important to study the transformation of small molecules in SCWO for the complete degradation of organic matters.  
[PERSON] et al. [99] established a kinetic model for the oxidation of CO in SCW, by which the oxidation of CO was divided into a direct oxidation reaction with O\({}_{2}\) and a water-gas conversion reaction with H\({}_{2}\)O. [PERSON] et al. [70] developed a global mechanism for methane oxidation in SCW on the basis of such a kinetic model. Methane was converted to CO and H\({}_{2}\)O first, and then CO directly oxidized with O\({}_{2}\) to generate CO\({}_{2}\), or reacted with H\({}_{2}\)O to generate CO\({}_{2}\) and H\({}_{2}\) through water-gas conversion. The model well predicted the changes of O\({}_{2}\) and CO\({}_{2}\) concentrations during the reaction, while CO concentration was underestimated and H\({}_{2}\) concentration was overestimated due to the rapid water-gas conversion reaction. By comparing the yield changes of formaldehyde, CO and CO\({}_{2}\) at different methanol conversion rates, [PERSON] et al. [66] considered that formaldehyde was the initial reaction product and then converted to CO and CO\({}_{2}\). The concentration distribution expressions of each compound were obtained by solving the governing differential equation, which well illustrated the basic characteristics of methanol SCWO. The calculated reaction rate constant indicated that formaldehyde was easier to react than methanol, while CO was not as easy to react as methanol.
## 3 Results
### Experiments and Evaluation Scenarios
#### 3.5.2 Second Evaluation Scenario: Multiple Clients Simultaneously Interact with the Holo-Cloud
The memory utilization trend in Figure 10 shows a similar behavior to the first test scenario. Memory usage spikes only happen during the process of loading the study data. Serving clients' requests to register with the server occupies minimal memory on the server system. In this scenario, the server software process used a maximum of 5% of the total 4 gigabytes of memory provided by the Holo-Cloud virtual machine to keep the study data loaded. In Figure 11, the TCP transmission rate of the FI3D software is demonstrated. A huge spike occurred when the server started animating the 3D model, and FIs simultaneously received the animation data. \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Average Receive Speed (Kbps)** & **Average Send Speed (Kbps)** \\ \hline FI3D server internet & 251,300.00 & 891,900.00 \\ Client software internet & 242,800.00 & 201,260.00 \\ FI3D-clients end-to-end speed & 41,900.00 & 41,900.00 \\ Server network utilization & 1.63 & 1198.48 \\ \hline \end{tabular}
\end{table} 
Table 2: Network utilization of the Holo-Cloud platform components in the second experiment. Figure 10: Memory utilization of FI3D software in the second experiment. Spikes in memory utilization are observed when clients load the study data and the animation sequence starts. The blue solid line is memory utilization and the red horizontal dashed line is the maximum memory utilization. Figure 9: CPU utilization of the FI3D software in the second experiment. The blue solid line is % CPU utilization and the red horizontal dashed line is the maximum CPU utilization.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Simulation Environment and Setup
#### 4.1.1 DTIM Beacon  
We implemented the DTIM beacon by sending the bitmap that indicates which TIM groups have DL data pending at the AP. Stations calculate the duration of the sleep time of their radio upon reception of the beacon. In reality, switching the radio from sleep to active mode takes time so we reduced the duration of the sleep time by 4 ms in order to provide the radio with enough time to switch from an off state to on and actively listening state. Furthermore, 4 ms is large enough to provide enough time even for radios with very long switch-on times, in the worst case. The time needed for switching from off to on state is configurable in the simulator to enable simulation of different radios.  
To assess the energy consumption, we measured sleeping time and awake time of each station. Stations know if they can sleep through the entire TIM beacon period by checking the bitmap in the DTIM beacon and their own pending transmission queues. The radio is turned off at the physical layer and cannot sense any signal when stations enter sleep mode. Stations only wake up for the DTIM beacon, their TIM group beacon if their TIM group is paged in DTIM and their assigned slot in the RAW period (if they are paged in TIM or have data to transmit). This allows us to accurately assess the total sleep duration of the stations and prevent frames from being processed when they should not be
# Biohydrogel Based on Dynamic Covalent Bonds for Wound Healing Applications
## 3 Results and Discussion
### Formulation and Relevant Characterization of Hydrogels
#### 3.1.4 Self-Healing Properties  
In agreement with our previous study [13], the 3% \(w/v\) hydrogels showed complete self-repair or closure within 7 min after the bulk soft gel was cut into two pieces and placed back in contact. A gradual and visual disappearance of the interface was observed, until the healing process concluded with no visible demarcation. A gradual reduction in the area of punched holes was also observed, with complete closure after approximately 10 min. Previous rheological studies also demonstrated the thixotropic nature of these gel networks,  
Figure 1: (**a**) Chemical structure of PBA-Alg conjugate used in this study. (**b**) Schematic representation of the composites. Images of formulated nanocomposite hydrogels labeled NH-20 (**c**), NH-80 (**d**), NH-140 (**e**), and NH-200 (**f**) representing composites with 20 mg, 80 mg, 140 mg, and 200 mg of magnesium aluminometasilicate, respectively, in combination with 50 mg of PBA-Alg.  
in good agreement with the macroscopic self-healing phenomenon [13]. It is desirable for these biomimetic materials to have the ability to repair damage and consequently restore lost or degenerated properties and functions by utilizing capabilities inherently available in the system, in this case, a reversible cyclic reaction involving the boronic acid groups and vicinal diols present.  
In the case of the composite hydrogels, samples prepared with 20 mg and 80 mg silicate self-repaired completely after 20 min and 30 min, respectively, whereas it required 60 min for those prepared with 140 mg and 200 mg. Thus, the mineral seems to interfere with the dynamic covalent interactions which is the primary mechanism of self-healing in PBA-Alg conjugates.
## 2 Materials and Methods
### Second Test: Horizontal Inclinometer Equipped with GG Transducer  
The tested specimen consisted of an inclinometer casing tube equipped with standard displacement transducers. The tube (with the same cross-section as the previous experiment) consisted of three segments joined to form the whole cylinder. The three segments were fastened, employing two collars that caused a little disturbance to the alignment of the transducer. The optical fiber-based transducers instrumented the inclinometer casing. The transducer was fastened to the cylinder along its generatrix. Table 3 below reported the geometry data of the structure used for the test and the experimental set-up is described in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Length [mm]** & **Radius [mm]** & **Thickness [mm]** & **Inertia [cm4]** \\ \hline
7500 & 80 & 3 & 56.4871 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Second test: geometrical parameters.  
The GG transducer is made according to the scheme reported in Figure 6. The inclinometer measures, in terms of displacements, have been converted into strains by numerical derivation, then they were compared with the transducer output. The inclinometer, indeed, furnishes displacement measures that must be elaborated in order to convert them into strain by means of derivation with respect to the length coordinate.  
The experiment consisted of the application of a constrained displacement at the first end of the structure, while the opposite end was kept clamped; namely, neither displacement nor rotations were allowed. The applied displacement ran from 0 to 0.014 m.