esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduction of Bias and Light Instability of Mixed Oxide Thin-Film Transistors
## 4 Discussion  
### \(V_{\rm TH}\) Dependency on Channel Thickness  
[PERSON] et al. noted that the last term in the TFT \(V_{\rm TH}\) expression:  
\[\begin{array}{cc}V_{\rm TH}=(\Phi_{\rm M}-\Phi_{\rm S})&+\frac{q\times N_{\rm bulk }\times(E_{\rm F}-E_{i})_{\rm threshold}}{C_{\rm OX}}-\frac{Q_{f}}{C_{\rm OX} }+\frac{q\times N_{\rm int}\times(E_{\rm F}-E_{i})_{\rm threshold}}{C_{\rm OX }}-\frac{Q_{\rm m}}{C_{\rm OX}}\\ &-\frac{q\times(D_{\rm D}-D_{\rm A})}{C_{\rm OX}}\end{array} \tag{1}\]  
suggests that carrier concentration in the channel layer influences the \(V_{\rm TH}\)[24]. In the \(V_{\rm TH}\) expression, \(C_{\rm OX}\) is the gate-insulator capacitance per unit area, \(Q_{f}\) is the oxide charge density, \(N_{\rm bulk}\) and \(N_{\rm int}\) are the shallow bulk trap density and the interface trap density, respectively, and \(\Phi_{\rm M}\) and \(\Phi_{\rm S}\) are, respectively, the metal and semiconductor work functions. \(D_{\rm D}\) and \(D_{\rm A}\) are respectively the donor and acceptor concentrations per unit area. In an n-type oxide semiconductor such as a-IGZO, \(D_{\rm A}\) is negligible and the product of the donor concentration per unit volume (\(N_{\rm D}\)) and the semiconductor thickness obtain \(D_{\rm D}\). For a-IGZO TFTs with varying \(t_{\rm IGZO}\), \(V_{\rm TH}\) thus varies with \(q\times N_{\rm D}\times t_{\rm IGZO}/C_{\rm OX}\), given that all other terms in the \(V_{\rm TH}\) expression are independent of the semiconductor thickness. For a 250 nm-thick SiO\({}_{2}\) gate-insulator, Figure 6 shows how \(V_{\rm TH}\) varies with \(t_{\rm IGZO}\) for \(N_{\rm D}\) ranging from \(10^{15}\)-\(10^{17}\) cm\({}^{-3}\). As the carrier concentration of the a-IGZO TFTs studied herein lies between \(10^{16}\) and \(10^{17}\) cm\({}^{-3}\), simulated results in Figure 6 are consistent with the experimental results in Figure 4a. [PERSON] et al. argued that this phenomenon is generally disregarded in a-Si:H TFTs, given that their carrier concentration is 2-3 orders of magnitude lower than that of the oxide TFTs, making the \(V_{\rm TH}\) sensitivity on the channel thickness of a-Si TFTs negligible [24]. This is confirmed in Figure 6, where \(V_{\rm TH}\) becomes insensitive to semiconductor thickness when \(N_{\rm D}\leq 10^{15}\) cm\({}^{-3}\).
## 4 Performance Evaluation
### CQI Quantization  
This section discusses the relationship between the CQI-quantization performance and the prediction power of the strongest beam. First, as described in Section 2.3, we quantize the continuous power and restore the quantized power to continuous power. The parameters \(r_{CQI}\), \(P_{u}\), and \(P\) are used by us in different combinations to evaluate the predicted RMSE. It can be seen from the observation results of the dataset that, among all pairs of beams, the maximum beam power is \(P_{\text{max}}=44.23\) dBm, and the minimum beam power is \(P_{\text{min}}=-15.15\) dBm. After multiple experimental validations, we chose to use a combination of \(P_{u}\) and \(P_{\ell}\) to evaluate the RMSE, as is shown in Figure 8.  
The experimental results show that the change in the RMSE is usually accompanied by a large change, and, within a small range of change, the RMSE is basically unchanged, which indicates that the RMSE presents a certain step shape in the change granularity of this experiment. In addition, when the value of \(P_{u}\) is larger, the predicted result is more accurate, but the change in the value of \(P_{\ell}\) has little effect on the predicted result. The reason for this phenomenon is that we predict the value of pairs of beams with the strongest power. When the value of the upper bound (\(P_{u}\)) is not large enough, it is easy to cause the beam power to be outside the interval (\([P_{\ell},P_{u}\ ]\)), which thereby causes a large quantization error. Although the lower bound has little effect on the prediction results, we should also carefully design the upper and lower bounds. Only in this way can all the data in the dataset be distributed in the interval (\([P_{\ell},P_{u}\ ]\)) as much as possible, which thereby minimizes the quantization error.  
Figure 7: Comparison of alignments with different degrees of situational awareness.  
Figure 9 further discusses the cumulative density function (CDF) between the different quantization granularities and the regression errors. The three points on the blue, black, and red lines stand for the probabilities of regression error under 1 dBm for the three \(r_{CQI}\) conditions. The experimental results show that when \(r_{CQI}\) is 1 or 2 dBm, the predicted error less than 1 dBm can exceed 50%. Moreover, when we use 1 dBm as the quantization precision, the prediction result is not much different from the prediction result without quantization. The reason for this phenomenon may be due to quantization error. When we use actual data, there is no quantization error. In addition, when we use CQI = 1 dBm and 2 dBm, the quantization error is so small that it has little effect on the result.
## 6 Experimental Results
### The Sensitivity Analysis of the Noise  
We added different noises, such as salt and pepper noise, uniform noise, and Gaussian noise, in our experiments. Table 5 gives the corresponding statistical results of the model classification accuracy on dataset D1 under different noises. As shown in Table 5, when salt and pepper noise, uniform noise, and Gaussian noise are added, the corresponding classification accuracies can reach 88.51%, 87.36%, and 89.45%, respectively. Among them, the classification accuracy obtained by the Gaussian noise method is higher than with the other methods, which indicates that the Gaussian noise method can effectively reduce the data noise and extract less redundant information.  
In the case of adding noise, we analyze in detail the effect of the proposed CDAENet model, and the experimental results are shown in Table 6. Signal-to-noise ratios of 0 dB, 1 dB, 5 dB, 10 dB, 20 dB, 30 dB, and 40 dB Gaussian noise are added to the BeiDou satellite data to simulate the noise interference that accompanies the data in practical applications. The generalization and robustness of the features extracted by the DAE are slightly weaker when the noise signal has smaller values, such as 0 dB, 1 dB, and 5 dB, compared to the 10 dB noise, but the overall recognition rate is still maintained at a high level. As the noise  
Figure 14: The BDS classification accuracy of different algorithms: (**a**) D1, (**b**) D2, (**c**) D3, (**d**) D4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Noise** & **Accuracy** \\ \hline Salt and Pepper Noise & 88.51\% \\ Uniform Noise & 87.36\% \\ Gaussian Noise & **89.45\%** \\ \hline \hline \end{tabular} The number in bold denotes it is the best result.  
\end{table}
Table 5: Model under different types of noise components.  
signal increases further, it destroys the distribution of the original data to a certain extent, resulting in a decrease in the overall recognition rate. This shows that too much or too little noise can affect the performance of the DAE. Thus, adding a suitable noise signal can enhance the robustness of the model feature extraction.
## 3 AGC Power Allocation Strategy of Charging Stations Based on Consensus Algorithm
### Overall Design Flowchart  
As shown in Figure 2, the AGC power allocation with EV mainly consists of two steps; that is, (a) the EV control center allocates the total power instruction \(\Delta P_{\text{EV}\sum}\) to each charging station based on the cost consensus algorithm; and (b) it allocates the power instruction to each EV battery based on the SOC proportional allocation method.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 2 Resilience in Smart Grids  
Amid desired functionalities for smart grids lays the need for capabilities like: self-healing, high reliability, power quality, and resistance against various disasters and attacks [50]. Resilience represents a promising approach to meet such requirements, by being able to address network circumstances not handled by widely adopted principles of reliability and quality of service.
## 4 Development of Predictive Models  
### Data Pre-Processing and Exploring Machine Learning Models  
In this study, machine learning regression models were developed using the Python v.3.9.12 programming language, and the sci-kit learn and Matplotlib libraries were primarily used. A stratified sample of 31 datapoints was meticulously created to serve as a separate testing dataset. This was carried out to obtain representative testing instances from the imbalanced data obtained due to significant differences in the geotechnical and geological properties of the rock masses at the sites studied. The remaining 200 instances were utilized for training the models. Standard scaling was used to address the issue of different scales or ranges of features by transforming the data such that it had a mean of 0 and a standard deviation of 1. A five-fold cross-validation approach was used for training and validation of the models [55]. Additionally, the effect of different kernels on the SVR [39] was also assessed by running it with default hyperparameters while changing the kernel type in each run. The results of the kernel experiment have been summarized in Table 4.  
Based on the initial evaluation, it was observed that the SVR base model with RBF kernel performed better than other models. Therefore, it was set as the kernel for SVR in further experimental analyses. Further, using the machine learning regression approaches discussed in Section 2.3, ten regression models were developed using the default hyperpa  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} \\ \cline{2-7}
**Kernel** & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** \\ \hline linear & 0.811 & 0.790 & 3.281 & 3.290 & 2.153 & 10.538 \\ poly & 0.793 & 0.744 & 3.437 & 3.723 & 2.659 & 13.174 \\ rbf & 0.855 & 0.828 & 2.873 & 3.029 & 1.970 & 10.586 \\ sigmoid & 0.708 & 0.694 & 4.082 & 4.078 & 3.094 & 14.300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the kernel experiment.  
rameters and a preliminary evaluation was carried out. The evaluation metrics of the base models for the training and testing phases have been summarized in Table 5.
## 2 Materials and Methods
### Modelling Support
#### 2.3.2 Modelling Deformation and Stress Distribution in the Notch  
It is important to understand the stress triaxiality as a result of the notch since sharp notches with high triaxialities have been shown to exacerbate HE in steels. A deformation model using 8-node quadratic axisymmetric elements (CAX8) was created that models the extension of the gauge length during the test to monitor the stress development. True stress and strain data were derived from preliminary test results on smooth BM specimens and used as input for an elastoplastic isotropic hardening deformation model, making it accurate up to the onset of necking. An internal pressure of 10 MPa was exerted on the inside surface to model the hoop stress caused by the internal gas pressure. The SSRT test was simulated by displacing the edge of the model, which represent the end of the specimen's gauge length, by small incremental steps. The model output matches well with experimental data from notched BM specimens. Mechanical properties for the WM were not found in literature nor obtained in this work, so it was therefore not modelled in the deformation model. The stress triaxiality, which is a dimensionless indicator of the relative severity of hydrostatic stress defined as \(\eta=\sigma_{\mathrm{H}}/\sigma_{\mathrm{Von\,Mises}}\), was monitored at the onset of necking in the model, where \(\sigma_{\mathrm{H}}\) and \(\sigma_{\mathrm{Von\,Mises}}\) are the hydrostatic and Von Mises stresses, respectively. Stress triaxiality is only dependent on the notch geometry, and not on material properties, and will therefore not differ between the BM and WM, except for differences in neck shape, which are expected to be minimal. The stress triaxiality distribution in the notch around the onset of necking is shown in Figure 6. It reaches 0.75, which is typical for a blunt notch. At this level of triaxiality notch effects are not expected to significantly influence the HE behaviour of both metals. However, the location of maximum triaxiality differs from the location of maximum hydrogen concentration without an applied load. The absorbed H is therefore expected to diffuse further into the specimen, and in higher amounts, when the specimen starts deforming during the test.  
Figure 5: The evolution of the hydrogen concentration profile on the notch root plane with time. Equilibrium is reached after approximately 6 h.
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 2 Results
### Function Diversity of TPSs in Rosaceae  
For the six representative plants shown in Figure 3, we predicted their function based on blast searching against Uniprot and KEGG pathway databases; their subcellular localization was also predicted using TargetP [23] and pLoc-mPlant (www.jci-bioinfo.cn/pLoc-mPlant/, accessed on 24 August 2021). Detailed information is listed in Tables 3 and S2. Subcellular localization analysis indicated that TPSs were substantially localized to cytoplasm and chloroplasts, and only a few TPSs were localized to mitochondria. TPSs from the TPS-a clade were mostly located in cytoplasm and chloroplasts, and demonstrated varied function diversity. Most of the characterized TPSs (-95%) in the TPS-a clade are involved in sesquiterpenoid and triterpen biosynthesis, and monoterpene biosynthesis pathway, using geranyl diphosphate GPP and farnesyl diphosphate (FPP) as substrate. Whereas for _M. domestica_ and _P. betulifolia_, several TPSs from TPS-a clade are involved in diterpenoid biosynthesis that used geranylgeranyl diphosphate (GGPP) as substrate. The finding suggested both the cytosolic mevalonic acid (MVA) pathway and the plastid methylerythnitol phosphate (MEP) pathway coexist in the TPS-a clade. All characterized TPSs in TPS-b clade are involved in either a sesquiterpenoid or monoterpene biosynthesis pathway. The members from the TPS-g clade mainly function in producing acyclic mono- and sesquiterpenoid products. TPS-c and TPS-e clades mainly participate in diterpen biosynthesis. Surprisingly, despite the close relationship between TPS-f and TPS-e/c clades, we found that TPS-f members are involved in sesquiterpenoid and triterpen biosynthesis, and monoterpene biosynthesis pathway. Furthermore, we observed that TPSs from the same clade participated in different pathways, such as, for TPS-e clade, TPSs were all predicted as ent-kaur-16-ene synthases with exceptions in the _M. domestica_ that involved in other pathways. For the Prunoideae subfamily, the function of TPSs among different clades was relatively conserved in three plants, and more TPS copies from the TPS-a clade involved in the monoterpene biosynthesis pathway were detected in _P. persica_. However, the functional diversity of TPSs is more obvious for Maloideae and Rosoideae species, such as the TPS-a clade in _P. betulifolia_. The above findings indicated that the TPS family in Rosaceae species possesses remarkable flexibility to evolve enzymes substrate specificity, and different clades expand in different lineages by gene duplication and divergence. It can be expected that proteins with altered subcellular localization and new substrate specificities would have evolved.
# In Vivo Evaluation of a Novel Radiofrequency Ablation Electrode in Pig Layers
###### Abstract  
(1) Background: Endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) is an alternative treatment for pancreatic tumors. Currently, EUS-RFA has been trialled to treat hepatic tumors. However, little has been reported about optimal settings for EUS-RFA in patients with hepatic tumors. We evaluated the ablation effect after in vivo RFA using a new EUS-RFA electrode in a pig model. (2) Methods Four pigs were used for the in vivo test. The in vivo testing was divided into two tests based on the length of the RFA electrode (0.5, 0.7, 1 or 1.5 cm), the ablation power (30 W or 50 W), and the ablation time (10 or 15 s). In test one, ablation effect was evaluated based on the electrode length and power. In test two, ablation effect was assessed based on power and time. (3) Results: In test one, the ablation width and depth correlated with the length of the electrode and power (0.5 cm, 10 W, 10 s: width 0.46 cm, depth 0.65 cm vs. 1.5 cm, 75 W, 10 s: width 0.77 cm, depth 1.80 cm). In test two, ablation width and depth were similar when RFA was set at 1.5 cm, 50 W, and 10 s or 1 cm, 30 W, and 15 s (0.65 cm, 1.14 cm vs. 0.65 cm, 1.26 cm). (4) Conclusions: The relationship between electrode length, ablation power, and ablation time, and the resulting ablation effect in pig livers suggest that EUS-RFA produces effective ablation while minimizing thermal injury.  
endoscopic ultrasound; radiofrequency ablation; liver; animals; swine +
Footnote †: journal: Medical Imaging
## 4 Results  
### Model Validation  
The R\({}^{2}\), NSE, and RMSE are calculated to evaluate the model performance as shown in Table 1. The results show that GPP could be captured quite well with the R\({}^{2}\), NSE, and RMSE which are 0.97, 0.97, and 11.03 gCm\({}^{-2}\)mon\({}^{-1}\). It can also be seen from 11 catchments that the R\({}^{2}\) is higher than 0.9 in most catchments, except for that of the Poyang Lake, lower trunk stream, and Taihu Lake within the values about 0.85. The NSE values in most of the subbasins are higher than 0.9, except for the Dongting Lake, Poyang Lake, lower trunk stream, and Taihu Lake within the values around 0.8. The RMSE of most basins are lower than 20 gCm\({}^{-2}\)mon\({}^{-1}\), except for that of the Wujiang River, Poyang Lake, lower trunk stream, and Taihu Lake which are lower than 25 gCm\({}^{-2}\)mon\({}^{-1}\). Figure 3 shows the spatial patterns of GPP in different seasons between the model simulated GPP and LUE GPP. The results show that the simulated GPP could reflect the spatial distribution pattern of GPP in different seasons well. In addition, it can be seen that the LUE-RE model could capture the interannual and seasonal variations quite well with good agreement between the simulated and LUE-based GPP from Figure 4. Overall, the model results are acceptable for GPP simulations in the Yangtze River.  
Figure 3: Spatial comparison between simulated GPP and LUE GPP.  
Figure 4: Comparison of monthly GPP changes between model simulation values and LUE GPP in the Yangtze River and 11 sub basins.
# Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments
## 5 Potentials Scenarios and Validation
### Performance Comparison and Discussion  
To test the feasibility of our ASSACR approach, we have to test the performance of our centralized/distributed semantic event detection algorithm and service semantic prediction algorithm on a laptop running Windows 7 with 6 GB of RAM and a i7-2630 QM quadruple core-processor (2 GHz).  
We have considered almost 80 users with different constraints (different number of conditions). Each constraint is translated as an event-condition-action rule. We have measured the computation time of the distributed/centralized semantic event detection mechanism. As shown in Figure 21, the centralized event detection gets alarmingly slow when the ontology grows in user profile instances and in the number of constraints. The difference between the centralized event detection and our centralized/distributed semantic-based dynamic event detection is the larger time amount being consuming in the centralized event detection. While checking the event, we have noticed that the more the users there are, the greater the time that is consumed; we improved the execution time by applying our algorithm, which performs each event detection separately; thus, we reduce size of the rules, thereby reducing the time it takes.  
To evaluate the matching accuracy, we compare the probability of the high quality service matching precision with/without the prediction strategy. The repository of services increases from 10 to 100. The system is modeled as a Poisson-process, and each mobile device's CPU speed is initialized with a random value in the range of [100, 800] and reduced automatically by a random value in the range of [0, 5]. We gathered results consisting of services in the Kalimucho server and the number of services selected from the Kalimucho server and deployed locally and/or in nearby devices. As we can see in Figure 22, in the majority of the cases, the selection frequency of the predicted services was higher than the probability of their selection without the prediction strategy. For example, with a repository size of 50, the probability of its selection was about 95% using our prediction strategy. Any modifications to those complex user contexts may require less checking time, thus increasing the flexibility of the user applications.  
Figure 21: The comparison of the computation time between our distributed event detection and the centralized/distributed event detection in terms of the number of constraints.