esa-sceva/satcom-chunk-collection huggingface dataset:
# Generic Patient-Centered Blockchain-Based EHR Management System
## 8 Evaluation
### Practical Applications and Costs  
In the proposed system, we define the cost for the actual transactions and time taken to execute for each transaction. The cost is calculated by ether = gas used \(\times\) gas price; \"gas used\" means the constant computational cost. The network adjusts the price of gas to compensate for changes in the value of ether [8]. Thus, the total transaction costs (ether) for the accessibility of health data remain relatively constant. Regarding the paying segment, all participants must pay gas for performing an operation in SC. Therefore, the automated SC process would result in significant cost savings for the patient. In the implemented PCEHRM-SC prototype, we have set a gas limit of 30,000, with each gas unit fixed at 2 Gwei.  
Table 6 summarizes the time taken and cost of operations performed in SC. The addDoctor() function is implemented once at a cost of 0.00089442 eth with used time 10.316s. The addPatient() function costs around 0.093 eth, which is higher than other functions due to the additional input bytes that are included during function execution, such as the patient's blockchain address and usage notes. However, the used time for execute this function is not high compared to executing the getpatientdetails() function was 0.213s which requires fetch the EHR's data of the patient.  
In general, the overall costs can be further reduced by keeping the size of the input data to a minimum. However, these costs are still lower than those associated with purchasing storage space from a third party or maintaining a database through a centralized system such as ISN [29], MedBlock [30], MeDShare [31], and MedChain [11].
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 3 Electrocatalytic Nanomaterials for Promoting EET
### Metal Oxides
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Nanaterials** & **Applications** & **Performances** & **Reference** \\ \hline GO/Fe\({}_{3}\)O\({}_{4}\) & MES cathode & A CH\({}_{4}\)-producing rate of 605 \(\pm\) 119 mmol m\({}^{-2}\) d\({}^{-1}\), 24.5-fold higher than that of carbon cloth electrode & [100] \\ N-doped Fe\({}_{3}\)O\({}_{4}\)@CD & MEC additive & A 6.37-fold increase in maximum current & [101] \\ \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoarray & MFC anode & A maximum power density of 816 mW m\({}^{-2}\), 8-fold higher than that of carbon cloth electrode & [102] \\ rGO/MnO\({}_{2}\) & MFC anode & A maximum power density of 2065 mW m\({}^{-2}\), 154\% higher than that of carbon felt electrode & [103] \\  & & A maximum current density of 3.70 \(\pm\) 0.5 mA m\({}^{-2}\), 24.5-fold higher than that of carbon cloth electrode & [105] \\ MnO\({}_{2}\) & MEC cathode & A maximum power density of 1.05 \(\pm\) 0.0264 W m\({}^{-2}\), 13.0\% higher than that of the bare carbon felt & [101] \\ MnO\({}_{2}\)/rGO & MES cathode & A maximum power density of 1025 mW m\({}^{-2}\), 8-fold higher than that of carbon felt electrode & [102] \\ MnO\({}_{2}\)/rGO & MES cathode & A maximum power density of 2065 mW m\({}^{-2}\), 154\% higher than that of carbon felt electrode & [103] \\ MnO\({}_{2}\)/rGO & MFC anode & A maximum power density of 3.70 \(\pm\) 0.5 mA m\({}^{-2}\), 154\% higher than double the non-coated carbon felt & [105] \\ MnO\({}_{2}\) & MEC cathode & A maximum power density of 1.05 \(\pm\) 0.0264 W m\({}^{-2}\), 13.0\% higher than that of the bare carbon felt & [106] \\ MnO\({}_{2}\) & MFC anode & A maximum power density of 1.11 \(\pm\) 0.0224 W m\({}^{-2}\), 13.0\% higher than that of graphene and carbon & [108] \\ MnO\({}_{2}\) & MFC anode & A maximum power density of 1421 mW m\({}^{-2}\), 3-fold higher than that of bare glassy carbon electrode & [109] \\ MnO\({}_{2}\) & MFC anode & A maximum power density of 1.05 \(\pm\) 0.0264 W m\({}^{-2}\), 13.0\% higher than that of carbon felt electrode & [110] \\ MnO\({}_{2}\) & MFC anode & A maximum power density of 1.05 \(\pm\) 0.0264 W m\({}^{-2}\), 13.0\% higher than that of carbon felt electrode & [111] \\ MnO\({}_{2}\) & MFC anode & A maximum power density of 1421 mW m\({}^{-2}\), 3-fold higher than that of bare glassy carbon electrode & [112] \\ MnO\({}_{2}\) & MFC anode & A 3-fold increase in current density & [114] \\ MnO\({}_{2}\)/graphene & MFC anode & An unprecedented power density of 3220 mW m\({}^{-2}\) and a remarkable current density of 3.06 A m\({}^{-2}\) & [115] \\ MnO\({}_{2}\) & Biohybrid & An increased interfacial electron transfer rate for & [116] \\ MnO\({}_{2}\) & Re\({}_{2}\)/r_
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 3 System Topology and Principle
\[Z_{r}=\frac{(\omega M)^{2}}{Z_{S}} \tag{5}\]  
During resonance, \(Z_{r}\) and \(Z_{s}\) are substituted as follows; the fundamental part of the equivalent impedance of the primary side is  
\[R_{e}(Z_{in})=\frac{\omega^{2}{L_{R}}^{2}}{R_{P}+Z_{r}} \tag{6}\]  
where \(U_{in}\) is the input voltage of the primary coil. From the above equation, it can be concluded that the current through inductor \(L_{R}\) can be expressed as the ratio of the input voltage to the input impedance as follows:  
\[I_{R}=\frac{U_{in}}{Z_{in}}=\frac{(R_{P}+Z_{r})U_{in}}{\omega^{2}{L_{R}}^{2}} \tag{7}\]  
According to the parallel shunt principle, the current through inductor \(L_{P}\) is calculated as follows:  
\[I_{P}=\frac{U_{in}}{j\omega L_{p}} \tag{8}\]  
Figure 3:0 Circuit topology principle of contactless excitation motor. According to the above analysis and calculation, the primary side adopts the LCC compensation method, and the current of the primary side inductance \(L_{P}\) is only related to \(\omega\) and \(U_{in}\). Compared with the traditional series-parallel circuit, the change in the secondary side will not affect the current of the primary side circuit, and the anti-interference is better. Therefore, although the compensation circuit is more complex in this system, the primary side circuit is relatively stable and is not limited by the secondary side parameters. The following studies will employ this higher-order compensation method.
###### Abstract  
Design of modern antennas faces numerous difficulties, partially rooted in stringent specifications imposed on both electrical and field characteristics, demands concerning various functionalities (circular polarization, pattern diversity, band-notch operation), but also constraints imposed upon the physical size of the radiators. Conducting the design process at the level of full-wave electromagnetic (EM) simulations, otherwise dictated by reliability, entails considerable computational expenses, which is another and a serious challenge. It is especially pronounced for the procedures involving repetitive EM analyses, e.g., parametric optimization. Utilization of fast surrogate models as a way of mitigating this issue has been fostered in the recent literature. Unfortunately, construction of reliable surrogates for antenna structures is hindered by their highly nonlinear responses and even more by the utility requirements: design-ready models are to be valid over wide ranges of operating conditions and geometry parameters. Recently proposed performance-driven modeling, especially the nested kriging framework, addresses these difficulties by confining the surrogate model domain to a region that encapsulates the designs being optimum with respect to the relevant figures of interest. The result is a dramatic reduction of the number of training samples needed to render a usable model. This paper introduces a variable-thickness domain, which is an important advancement over the basic nested kriging. The major benefit demonstrated using two antenna examples is a further and significant (up to seventy percent) reduction of the training data acquisition cost. It is achieved while ensuring that the model domain covers the regions containing optimum designs for various sets of performance specifications.  
Keywords:antenna modeling; surrogate modeling; nested kriging; domain confinement; variable-thickness domain; simulation-driven design +
Footnote †: journal: Electronics Electronics
## 5 Results and Discussion
### LES Modelling in the Wake  
The previous results for \(k\) and \(\varepsilon\) indicate that the LES running in OpenFOAM is able to predict with relative accuracy not only the velocity deficit in the wake, but also the level of turbulence and its dissipation in the case where the TI in the inflow is low (\(\sim\)3%). For the high TI inflow (\(\sim\)12%), the prediction becomes more imprecise, according to the comparison with the experimental data. In the absence of disks, it is observed that the fraction of the turbulence kinetic energy that is resolved by the LES with respect to the total \(k_{res}/k_{tot}\) occurs for the most part in the resolved scales, at around 90%  
Figure 10: Vertical profiles of \(\varepsilon\) behind the disk \(C_{T}=0.45\) (**left**) and \(C_{T}=0.71\) (**right**), Ti12 case. The scale for the curves at \(x=2D\) has been doubled to accommodate the larger values.  
in both TI cases throughout the domain and only somewhat smaller close to the inlet [54]. With the introduction of the disks, this does not appreciably change except for the shear layer nearby the AD at around \(x=1D\) in the Ti3 case, where the resolved part is slightly reduced [54]. In the Ti12 case, this difference is not noticeable and the value of \(k_{res}/k_{tot}\) remains throughout.  
Figure 11 shows the ratio of subgrid dissipation with respect to the total value \(\varepsilon_{\text{scs}}/\varepsilon_{tot}\) along the wake for the Ti3 case. Note that, as the positions of comparison are no longer restricted to those of the available experimental data, profiles are shown at different longitudinal positions from other figures. In Figure 11, it is noticed that the LES has an appreciable increment in subgrid dissipation within the shear layer. Furthermore, this increase persists longitudinally even as far as when the wake appears to reach a state of transversally-uniform dissipation, i.e. at \(x=12D\) with disk \(C_{T}=0.62\). This is consistent with the hypothesis that turbulence is created at smaller lengthscales than the ambient turbulence at the disk edge. Due to the limited resolution of turbulence lengthscales in the Ti3 flow (missing in the synthetic flow as well), the increase in subgrid dissipation is produced at scales that seem absent in the incoming flow. These results also show that the wake envelope becomes the main carrier of dissipation. The subgrid dissipation part is also larger with higher thrust, yet by a small margin. It is observed that, in the absence of disks, most of the dissipation comes from the resolved fluctuations, in a proportion very similar to that shown outside the wake, at \(y\pm 1.0D\)[54]. It is seen in Figure 12 that when the inflow turbulence raises (which comprises better resolved lengthscales), the increment of subgrid dissipation in the region of the wake envelope is greatly reduced. As a result, the modelling ratio seen outside the wake is essentially conserved. From these results, it can be deduced that the LES modelling across the wake is largely determined by the ambient turbulence.
## 3 Experiments and Results
Furthermore, an experiment with increasing the total bandwidth of the transmission signal was conducted. The transmission signal was changed from a signal using 24-IF bands to a signal using 48 IF bands. As the number of IF bands used increases, the total bandwidth becomes wider, and the effects of the inter-band IMD2 and IMD3 on the signal are significantly increased. The constellation of the 48-IF signal is shown in Figure 12 after 10 km transmission. As shown in Figure 12a,b, EVM became worse from 7.1% to 10.8% using the proposed technique, but the signals satisfied the EVM requirement for QPSK. In addition, when using the proposed technique for 256 QAM from (c) to (d), the EVM was improved from 10.2% to 3.4%. It was demonstrated that the proposed technique mitigates the effects of complex inter-band IMD.
## 3 Methodology
### Maximum Detectable Deformation Rate  
In terms of the maximum detectable deformation rate (MDDR), the principle of InSAR deformation monitoring involves using two SAR images taken at different times to perform interferometric processing and obtain the relative phase \(\Delta\phi\) (\(\Delta\phi=4\pi\cdot\Delta r/\lambda\), where \(\Delta r\) is the deformation value in the radar line-of-sight and \(\lambda\) is the radar wavelength), which is then converted into deformation measurements [32]. Consequently, the radar line-of-sight deformation within the same pixel should not exceed \(\lambda/2\). Moreover, the observed phase \(\Delta\phi\) is a wrapped phase, so the deformation difference between adjacent pixels \(\Delta r^{\prime}\) must satisfy \(|\Delta r^{\prime}|\leq\lambda/4\) to meet the phase-unwrapping condition, meaning the deformation rate variation between adjacent pixels \(\Delta r^{\prime}/\Delta t\) must satisfy \(\Delta r^{\prime}/\Delta t\leq\lambda/4\) to accurately recover deformation information from the wrapped phase. To evaluate the capability of radar satellites in detecting large deformations, we assume that the straight-line distance along the slant range from a stable point to the maximum deformation position is \(L\) and the slant range resolution is \(R\). We then define the dimensionless factor \(\alpha\) as the normalized deformation rate between adjacent pixels and the normalized deformation rate \(\alpha\) is defined as:  
\[\alpha=\frac{\Delta r^{\prime}R}{\lambda L} \tag{8}\]
## 3 Accuracy and Robustness Simulation of the Improved Modular Vector Method
### Accuracy Analysis of Parameter Inversion  
The expected subsidence values, determined based on empirical values, serve as the measured data for the inversion of the predicted parameters within the probability integral method. Alongside information pertaining to the working face, the initial exploration values, as determined by the pattern recognition method in mining subsidence, are utilized to invert the parameters of the improved modular vector method.  
Upon examination of the parameter inversion results (Table 2) and the root mean square error of the fitting (Figure 3), it is evident that the root mean square error for the predicted parameters of the improved modular vector method inversion within the probability integral method is merely 1.19 mm. Additionally, the relative errors associated with the subsidence coefficient, the main influence angle tangent value, the propagation angle of mining influence, and the deviation of the inflection point do not exceed 1.2%, 1.9%, 1.7%, and 7.9%, respectively. These findings affirm the accuracy of the improved modular vector method in parameter inversion for the probability integral method.  
Figure 3: Root mean square error of fitting between measured and predicted values.  
Figure 2: Schematic representation of the position relationship between the simulated working face and the observation points.
# A Novel, Minimally Invasive Method to Retrieve Failed Dental Implants in Elderly Patients
## 3 Results  
Our participant sample was composed of 94 women and 56 men, with a mean age (SD) of 68 (3) years. They had 199 implants indicated for removal, with most (\(n\) = 134, 67.3%) being used to retain single crowns. The remaining implants were used for the treatment of fully dedentulous arches by fixed prostheses (\(n\) = 29, 14.6%) or overdentures (\(n\) = 36, 18.1%).  
The implant removal system had a success rate of 97%, i.e., 193 implants were removed without requiring flap elevation. However, six implants demanded trephination. Of these six implants, there were four cases of implant fracture during use of the removal kit, whereas two implants could not be removed even with maximum torque (i.e., 500 Ncm).  
With respect to the cause of failure, 12% were identified as iatrogenic, whereas 81% and 7% were attributed to biological and mechanical causes, respectively. There was no case with more than one cause of failure. The mean removal torque was 220 Ncm, with significantly higher torque for the anterior mandible (mean: 308 Ncm) compared to the other areas (Table 1). Removal torque achieved at least 450 Ncm in four cases. The type of connection did not influence removal torque values significantly (Table 2). There was no significant difference between the three possible causes of failure either (mean torque, iatrogenic: 219 Ncm, biological: 222 Ncm, mechanical: 250 Ncm).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Maxilla**} & \multicolumn{2}{c}{**Mandible**} \\ \cline{2-5}  & \(n\) & **Torque (Ncm)** & \(n\) & **Torque (Ncm)** \\ \hline Anterior & 49 & 184  & 56 & 308  \\ Posterior & 47 & 185  & 41 & 198  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean removal torque according to implant location.  
Figure 2: Implants removed through both methods. (**A**). Trephine bur, observing a bone ring surrounding the removed implant. (**B**). Implant removal with the counter-torque system, noting a less invasive procedure.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 2 Related Work
### Zero-Touch Network and Service Management  
The ZSM architectural framework has been designed to automate and simplify the deployment, management, and assurance of services in Beyond 5G (B5G) networks. Extensive research has been conducted on this subject, as presented in works like [12; 13], where the authors provide a comprehensive study of ZSM's background, highlighting its major advantages, architectural choices, and the most relevant standards. An in-depth study analyzing the threat surfaces introduced by the technologies constituting ZSM is documented in [14]. Taking into account various standards related to end-to-end (E2E) slicing, such as the ZSM-defined framework and the Transport Slicing solution proposed by the IETF, an abstraction model and corresponding interfaces for Transport Slicing have been introduced in [15]. Although ZSM specifies how closed loops should operate within the system, it does not define how different closed loops should communicate to ensure SLA assurance coordination. [PERSON] et al. [16] propose an intent-driven model based on delegation, describing an escalation of intents across different closed loops as a hierarchical coordination solution. Their work offers a detailed explanation of the ZSM architecture, emphasizing the definition of the intent model. However, their study focuses only on the model's definition and does not include any evaluation.  
Furthermore, in [17], a monitoring model integrated into ZSM-based service life-cycle management is defined. However, despite describing their approach as end to end, it effectively represents a single domain, as they treat each entity within the infrastructure as distinct domains. Finally, in [18], a ZSM-based architectural framework for managing slicing in 3 GPP networks is proposed. However, their approach introduces a nested structure of management domains for the same logical part of the infrastructure. This deviates from the ZSM standard definition and significantly increases the complexity of coordination between the various levels of closed loops.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 3 Modelling of the MFPG System
### Model Validation  
The proposed heat transfer model and the leakage model of in-cylinder gas were validated against the experiment data and literature data, as shown in Figures 4 and 5, respectively. The proposed simulation model was validated against the preliminary test under three different scavenging pressures \(p_{sc}\), as is illustrated in Figure 6. It is seen that the pressure prediction is generally lower than the tested data. As the \(p_{sc}\) increases, the piston top dead center (TDC) shifts farther from the cylinder head and the in-cylinder pressure trace becomes sharper indicating a higher degree of isochoric combustion. The predicted in-cylinder pressure seems not able to capture these variations of TDC and combustion process. It is because these parameters are set to be constant in the model, while these variables are strongly coupled with the piston dynamics, which hardly excludes their effects to conduct the test of a single parameter. However, the simulation well predicts the pressure increase trend and agrees with the pressure trace reasonably. The maximum relative errors were 2.9%, 5.6% and 10.5%, respectively, at \(p_{sc}\) = 1.05, 1.10 and 1.15 bar. The  
Figure 5: Comparison of in-cylinder gas leakage of micro internal combustion engines.  
results confirmed that the proposed model is able to reasonably captures the characteristics of the MFPG system, especially if it takes more parameters into consideration.
# Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
## 3 Method  
In this section, we introduce our fine-grained sentiment-controllable text-generation task together with a conditional generative model named **A**spect-**I**evel **S**entiment **C**onditioner (**A**I**Se**Cond**), which was trained with both labeled and unlabeled data to learn a fine-grained sentiment review generator with the assistance of a classifier.  
First, we give the formalization of our fine-grained sentiment-controllable text-generation task. Specifically, given the fine-grained sentiment units (i.e., aspect-polarities or aspect-opinions) as the input \(s\), the model generates a target text \(X\) that covers the input sentiments. As a straightforward approach, as other studies have used [5; 7; 8], the data-to-text modeling can be much more difficult when compared with the text-to-text modeling due to the discrete and sparsity of the input data [17]. Therefore, in this work, we consider a translation of this task to the text-to-text formulation. More conveniently, given aspect and polarity, it is effortless to retrieve opinion phrases from aspect sentiment triplets (AST [19], i.e., the triplet of aspect, opinion, and sentiment polarity) extracted from the review text. This work, therefore, set \(s=\{(a_{1},o_{1}),\ (a_{2},o_{2}),\ \ldots,\ (a_{n},o_{n})\}\) and aims to generate a review text \(X\) comprising \(m\) words (\(X=\{x_{1},\ x_{2},\ldots,\ x_{m}\}\)), which presents each aspect phrase \(a_{i}\) and its corresponding opinion phrase \(o_{i}\) (\(i\in\{1,2,\ldots,n\}\)) properly.  
In this task, we have a labeled dataset \(L\) and an unlabeled dataset \(U\). In the labeled dataset \(L\), each labeled datum \(\ell\in L\) comprises a review text and a list of aspect-opinion phrase pairs \(s\), i.e., \(\ell=\langle X,s\rangle\), while in the unlabeled dataset \(U\), each \(u\in U\) only contains a review text, i.e., \(u=\langle X\rangle\).  
In the following subsections, we first introduce our main framework for how to train a generator on both labeled and unlabeled datasets. Then, we explain our generator and classifier in detail.