esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimization Model for Process Parameters of Zinc Smelting Slag Melting Point
### Selection of Key Process Parameters
In summary, based on relevant studies and the actual production parameters requirements of the smelting plant, this study selects the key process parameters that have a significant impact on slag melting point, namely, the iron-silicon ratio, calcium-silicon ratio, coal quantity, oxygen flow, and river sand usage.
# On the Use of PEDOT as a Catalytic Counter Electrode Material in Dye-Sensitized Solar Cells
###### Abstract  
Dye-sensitized solar cells (DSSCs) emerged in the early 1990s as a promising alternative to the classic silicon-based solar cell due to their unique combination of low cost, ease of fabrication, color palette for building integration, and high efficiency in indoor applications. This review article describes the fabrication and the properties of poly (3,4-ethylenedioxythiophene) (PEDOT)-based catalytic counter electrodes (CEs) for DSSCs. In particular, the electrochemical reactivity PEDOT CEs used in conjunction with alternative redox mediators for DSSCs is outlined. Among alternative redox shuttles, cobalt and copper complexes, as well as totally organic thiolate/disulfide, have been considered. Finally, PEDOT can also be used as a hole conductor material in _electrolyte-free_ solid-state dye-sensitized solar cells. This review clearly shows that the progress in DSSCs development is strongly linked to the introduction of PEDOT as a new counter electrode material.  
dye-sensitized solar cells; PEDOT; alternative redox mediators; cobalt complexes; copper complexes +
Footnote †: journal: Applied Sciences
## Appendix A  
To explain the \(p\)-value calculation process, the following example is considered. Consider a positional quality control for which a sample of \(n=25\) control elements has been taken. Consider two metrics tolerances \(T_{1}\) and \(T_{2}\). In this sample, the number of errors is the following:  
* \(n_{1}\ =\ 15\); number of errors less than or equal to \(T_{1}\),
* \(n_{2}\ =\ 7\); number of errors greater than \(T_{1}\) and less than or equal to \(T_{2}\),
* \(n_{3}\ =\ 3\); number of errors greater than \(T_{2}\),  
Therefore, \(\
u^{*}=(n_{1}=15,\ n_{2}=7,\ n_{3}=3)\). The next step is to look for cases that are worse than \(\
u^{*}\). Consider those worse cases \(m=(m_{1},m_{2},m_{3})\) that verify:  
\[[m_{1}<n_{1}]\ \text{OR}\ [[m_{1}=n_{1}]\ \text{AND}\ [m_{2}<n_{2}]]\]  
Note that the above conditions establish an order of worseness. Table A1 shows some of these cases in the set order. For all of them, their probabilities according to the multinomial law established by \(\mathbb{H}_{0}\) is calculated. For instance, using R [42], the function \"dmultinom\" is used as follows: dmultinom (\"Worse case\", size = \(n\), \"proportions under \(\mathbb{H}_{0}\)\") (e.g., dmultinom (c(15, 7, 3), size = 25, c(0.50,0.40,0.10)) to obtain each single probability. The \(p\)-value is the sum of the probabilities of all the worse cases. In this example, where the \(p\)-value equals 0.81918, if \(\alpha=5\%\), the null hypothesis (\(\mathbb{H}_{0}\)) is not rejected.
## 2 Materials and Methods
### Analysis and Processing of the Gingiva Area  
The image sequence after the initial Le (m, n, i) filtration is further processed using mathematical morphology, conditional opening and closing methods allowing the separation of the lower and upper gingiva in the Le (m, n, i) image sequence. The threshold value necessary for erosion and conditional dilatation was adopted at the level of pr = 0.5. The image after erosion and conditional expansion of joints Ledw (m, n) was further subjected to GLCM analysis to determine Contrast Energy Homogeneity. These features formed the basis for segmentation of gingiva objects. The separated objects and their features (Contrast, Energy, Homogeneity) were compared with the standard. The standard was a uniform temperature distribution with the average value calculated for the first frames of the Lw images (m, n). Based on this isolated ROI, sensitivity (TPR), specificity (SPC), and efficacy (ACC) values were calculated based on results (false positive--FP, false negative--FN, true positive--TP, true negative--TN). Four different classifiers were selected for testing: binary decision trees, naive Bayes classifier, discriminant analysis, and support vector machine (SVM). The results obtained are shown in Table 1. Block diagrams of the image analysis and processing algorithm are depicted in the block diagram in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **TP** & **TN** & **FP** & **FN** & **TPR** & **SPC** & **ACC** \\ \hline Classifier type/parameter & 94 & 98 & 22 & 26 & 0.78 & 0.81 & 0.8 \\ \hline Decision trees & 102 & 104 & 16 & 18 & 0.85 & 0.86 & 0.85 \\ \hline Naive Bayes classifier & 111 & 117 & 3 & 9 & 0.92 & 0.97 & 0.95 \\ \hline Discriminant Analysis & 84 & 93 & 27 & 36 & 0.7 & 0.77 & 0.73 \\ \hline \end{tabular}
\end{table}
Table 1: Results obtained for 120 healthy patients and 120 treated patients. The measurements of the diagnostic value of the classifiers used. TP—true positive; TN—true negative; FP—false positive; FN—false negative; TPR—true positive rate; SPC—specificity; ACC—calculating accuracy.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Risk-Based Access Control Implementation
#### 4.3.5 Risk Estimation  
The final risk value associated with each access request is calculated based on the values of resource sensitivity risk, contextual risk, user risk history and operational risk using Equation/Formula (1). The final calculated risk value is then categorized into a specific risk level from the five levels, Negligible, Low, Medium, High and Extreme, as shown in Table 6.
## 5 Conclusions  
This study modeled the permanent deformation of HMAC pavement in sloped and horizontally curved road using experimentation-based viscoelastic mechanics. Some primary conclusions are as follows.  
1. The maximum rutting depth and bump height of HMAC pavement in curved and sloped road were greater than those in straightaway. The downward pavement areas of curved and sloped road developed evident upheaval due to longitudinal friction force. In addition, the outside pavement of horizontal curve presented larger upheaval than the inner side due to sideway force.
2. Analysis shows the upper and middle asphaltic courses were more crucial to pavement anti-rutting performance in curved and sloped road since inclusion of longitudinal and transverse shear force changed pavement deformation characteristic and the potential rutting area had a tendency to move up.
3. A preliminary model to predict rutting in HMAC pavement with sloped and horizontally curved alignment was presented based on material and structural features of asphalt pavement in China today.  
It should be noted that in this study there are still some issues that need to be further addressed. First, this research simply considered the deformation at 50 \({}^{\circ}\)C. Permanent deformations in a wider temperature range should be investigated for a better understanding of HMAC pavement behavior. Second, the dynamic loading effect as well as the intermittent loading was not considered in the simulation for simplicity, which could possibly lead to a larger deformation. The last point is that only six factors were chosen as the variable in the prediction model, and other factors such as modulus of the base, subbase and subgrade did not appear in the equation but were taken as fixed values. However, though limited to some extent, the aforementioned model could be used for engineering precision especially when confronted with common practice in China.  
Finally, from the viewpoint of life cycle cost [33], technical measures such as putting strict traffic control on special road, using effective anti-rutting asphalt mixtures were strongly suggested to be taken into account in the construction of special road to cut the life cycle pavement cost.  
This research was supported by the Fundamental Research Funds for the Central Universities in China(No. 310821163502), the 2016 Outstanding Doctoral Dissertation project of Fundamental Research Funds for the Central Universities in China (No. 310821165008), the Transportation Department of Shandong Province (No. 2008Y007) and the National Natural Science Foundation of China (No. 51008033).  
**Author Contributions:** [PERSON] was in charge of the whole research plan and paper submission matters. The first author extends gratitude to [PERSON], [PERSON] and [PERSON] for performing the experiment and analyzing test data, to [PERSON] and [PERSON] for offering help in doing the experiment.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Traffic Monitoring Applied Results  
The video acquired by the drone sensor is transmitted by the 4G network thorough the VPN and received in the EXO Data server endpoint. There, the video is stored in a docker, namely the video log repository. Tacking advantage of the higher computing capacities of the data server (compared with GCS and with the on-boards RPi), the video can be processed with the computer vision algorithms to detect traffic-flow events obtaining near-real time information.  
In this work we have used the state-of-the-art algorithm for object detection, Faster R-CNN [21], trained on the Vishrone dataset ([[http://aiskyeye.com/](http://aiskyeye.com/)]([http://aiskyeye.com/](http://aiskyeye.com/))) for the detection of six previously defined vehicle classes: Pedestrian, bicycle, car, van, bus, and truck. The algorithm processes an input video frame by frame. The output of the algorithm is a bounding box with the coordinates of each detected vehicle and the probability that the detected vehicle belongs to the corresponding vehicle class. The threshold for probability is set to 70%, meaning that only the vehicles that are detected with the probability higher than 70% are considered. After detecting the vehicles, we have applied the IOU-tracker algorithm [22] ([[https://github.com/bochinski/iou-tracker](https://github.com/bochinski/iou-tracker)]([https://github.com/bochinski/iou-tracker](https://github.com/bochinski/iou-tracker))), assigning a unique ID to each detected vehicle. Finally, the algorithm implemented for speed estimation is based on [PERSON] et al. proposal [23]. The imagery analyzed was taken by a static drone where the surface of the road was flat and the camera was mounted with a zenithal orientation, so the distortion given by perspective is minimal.  
The three algorithms streamline the process of manually annotating data by a traffic analyst, allowing, among others, the automatic computation of a roundabout matrix. The main applications of these CV algorithm are (Figure 8):  
1. Generation of in-out matrices in roundabouts per vehicle type.
2. Estimation of vehicle velocities and trajectories.
3. Heatmap generation per each vehicle class.
## 2 Applications of Nanotechnology in the Petroleum Industry
### Production and Stimulation
#### 2.3.2 Stimulation Process Improvement  
NPs have been studied for improving hydraulic fracturing and acidizing jobs [72]. [PERSON] et al. [73] introduced nanoparticle-coated proppant which has the ability to maintain viscosity at high temperature, control fluid loss, and reduce fines migration. NPs can pass through the pore throat and flow back easily with the producing fluid, which can preserve reservoir permeability. [PERSON] et al. [34] reported the use of pyroelectric NPs combined with a viscoelastic surfactant (VES) to increase the fracturing efficiency and improve production rates. [PERSON] et al. [74] applied polyelectrolyte complex NPs, which worked to clean up fracturing fluids by delaying the release of enzymes that cause premature gel breaking and improve fracturing efficiency. [PERSON] and [PERSON] [36] found that ZnO NPs could raise the low shear rate viscosity of threadlike micellar fluids that exhibit similar behavior as crosslinked polymer systems. [PERSON] et al. [37] conducted experimental studies on the effect of metal oxide NPs on fracturing fluids and found that adding NPs to the fracturing fluid improved the viscosity by 23-116% at an elevated temperature and reduced polymer loading.  
These studies suggest that NPs have great potential for stimulation and for solving production problems. Several patents based on these studies have been granted for nanotechnology related to hydraulic fracturing, including nano-proppant for fracture conductivity by [PERSON] [75] and high-temperature fracturing fluids using nano crosslinkers by [PERSON] [76].
## 3 Processing Method  
The complete process, covering radargram preprocessing, discontinuity detection and validation in addition to subsequent analysis is shown in Figure 3. The flowchart can be summarized as follows:  
1. The SHARAD tracks for the target regions are identified by inspecting track locations/coverage.
2. The processed and raw radargrams are obtained using the CO-SHARPS processing boutique.
3. A bilateral filtering method is applied to the radargram to reduce the backscatter noise.
4. The clutter-gram is co-registered with raw radargram using a Fourier-based phase correlation process.
5. The labelled data is prepared by manually detecting discontinuities in the radargram.
6. New features such as Euclidean norm, entropy, etc. are prepared for training the CNN model.
7. The model is validated using various quality metrics. The discontinuity points are accumulated and processed into a subsurface discontinuity plane reconstruction algorithm.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Region** & **Extend** & **Track No.** \\ \hline \multirow{6}{*}{Euripus Mons} & & 352701, 579501, 600601, 615101, \\  & 44.22\({}^{\circ}\) S-45.44\({}^{\circ}\) S & 626301, 650701, 671801, 683001, \\  & 106.20\({}^{\circ}\) E-104.11\({}^{\circ}\) E & 686301, 707401, 718601, 721901, \\  & & 783201, 797701, 905801, 1333701, \\  & & 1790601, 1949501 \\ Phoenix landing site & 67.0–69.5\({}^{\circ}\)N, 229.5–238.5\({}^{\circ}\) E & 177501, 183401, 190001, 203201, \\  & & 693701, 233501 \\ Martian polar deposits (Planum Australe) & Below 70\({}^{\circ}\) S & 718601, 721901 \\ Anonymous LDA & 39.83\({}^{\circ}\) S-42,23\({}^{\circ}\) S & 600601, 686301, 718601, 721901 \\  & 103.59\({}^{\circ}\) E-106.80\({}^{\circ}\) E & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The extent of regions and the SHARAD tracks from respective areas considered for the discontinuity detection using our model for training and testing.  
A detailed description of the methodology is provided in following sub-sections.
## 3 Results
### Analysis and Selection
#### 3.4.1 Stress Analysis  
To ensure safe grasping of the objects, any components directly involved in the grasping process must be able to tolerate the maximum stress. This gripper is composed of various components, but only the moving parts are essential for motion and stress analysis. Two components are analyzed in this study: the flexible element, made of TPU, and the handle, made of NYLON Carbon 12, both of which experience the maximum force during the grasping process.  
The flexible element is connected to the handle, and therefore, the entire grasping force will directly transfer to the handle. The most vulnerable part of the handle is the end of the connection part to the gripper hard shell, which experiences maximum torque. Through a SolidWorks stress analysis, and based on the current material (NYLON Carbon 12) and the thickness of the parts, it is found that the handle can withstand a maximum force of 220 N. This force is also equivalent to the maximum force that can be applied by the motor. The result is presented in Figure 8.  
The minimum force required for the complete deformation of the flexible element during object grasping is crucial for successful gripping. However, this force is influenced by factors such as the thickness, shape, size, and orientation of the object, making it difficult to directly measure. Therefore, a simulation study was conducted to estimate the maximum allowable thickness by subjecting square or rectangular objects of various sizes to the  
Figure 8: Stress analysis of the handle—the maximum stress at the connection corner.  
flexible element with different thicknesses. It was found that a thickness of 0.3 cm required a force of 186 N for full deformation, which falls within the safe maximum stress limit of the hard shell when considering a safety factor of 1.183. Simulation studies were also performed to analyze displacement forces, calculate von Mises stress, and determine the factor of safety for the flexible element. The deflections of the flexible element with a thickness of 0.3 cm during grasping of square- and rectangle-shaped objects are shown in Figures 9 and 10.
## 4 Fault Current Limiting Devices
### Non-Superconducting FCL
#### 4.1.5. Resistive Type FCL: Variable Resistive Type FCL  
In [29], a variable resistive type FCL (VR-FCL) has been placed in the PCC to improve the transient stability of a hybrid power system, including the DFIG, a Photovoltaic (PV) plant, and a synchronous generator. The configuration of the VR-FCL is similar to [30] and is shown in Figure 8a. However, to make better transient stability improvement, the pre-fault conditions, including the pre-fault active power of the hybrid generation units and fault location, have been taken into account. Regarding the pre-fault conditions, to control the VR, three nonlinear controller schemes, including fuzzy logic controller (FLC), static nonlinear controller (SNC), and an adaptive-network-based fuzzy inference system (ANFIS) have been discussed. The operation of the ANFIS-based VR-FCL is better than the FLC, and the FLC is better than the SNC. The factors to evaluate the operation of each controller are the rotor speed deviation of the DFIG, the DC-link voltage deviation of the PV generator, and the load angle deviation of the synchronous generator. The concept of the pre-fault conditions to achieve the maximum transient stability has been previously proposed in [30]. However, it should be noted that the dynamic behaviour of the DFIG is completely different than that of the other generation units [13]. As soon as the fault happens, especially the three-phase fault, the voltage drop on the FCL cannot be enough due to the exponential current variation in the stator of the DFIG, particularly after damping the stator current in the first cycles of the power system frequency.  
Figure 8. (**a**) The variable resistive type fault current limiter (VR-FCL) [29]; and (**b**) the parallel resonance type fault current limiter [31].
## 4 Method
### Network Architecture
#### 4.2.2 Feature Sampling  
Our primary contribution in this study is the development of a feature sampling block for the person detection stage, which enables spatial sampling of features from the supporting frames without requiring additional supervision. Moreover, it facilitates seamless integration with other object detection architectures.  
Firstly, we sample the reference frame \(\mathrm{I_{t}}\) and the supporting frames \(\mathrm{I_{t-s_{1}}}\) and \(\mathrm{I_{t-s_{2}}}\) from the given video, and then pass them through the same feature extraction network mentioned in Section 4.2. Let \(\mathrm{c}\) denote the number of channels, \(\mathrm{h}\) denote the height, and \(\mathrm{w}\) denote the width of the feature map. This yields three groups of feature maps: \(\mathrm{F_{t}}\), \(\mathrm{F_{t-s_{1}}}\), and \(\mathrm{F_{t-s_{2}}}\) all possessing the same dimensions. Subsequently, we concatenate these three groups into a new feature map denoted as \(\mathrm{F_{t}}\), \(\mathrm{F_{t-s_{1}}}\), and \(\mathrm{F_{t-s_{2}}}\in\mathbb{R}^{3\times\mathrm{h}\times\mathrm{w}}\). As a result, the feature map \(\mathrm{F_{t}}\), \(\mathrm{F_{t-s_{1}}}\), and \(\mathrm{F_{t-s_{2}}}\) now contains not only the information from the reference frame but also from the supporting frames.  
Secondly, we utilize the deformable convolutional layer [34] to facilitate the sampling of relevant information from the supporting feature maps \(\mathrm{F_{t}}\), \(\mathrm{F_{t-s_{1}}}\), and \(\mathrm{F_{t-s_{2}}}\). For the sake of clarity, we describe our feature sampling mechanism using a single group of supporting feature maps \(\mathrm{F_{t-s_{1}}}\). The input to our deformable convolutional layer consists of two components: the supporting feature map and the predicted location offsets \(\mathrm{F_{t-s_{1}}}(\mathrm{x},\mathrm{y})\). The number of predicted location offsets \((\mathrm{x},\mathrm{y})\) depends on the number of supporting frames. The output of our deformable convolutional layer is a newly sampled feature map \(\mathrm{U_{t,t-s_{1}}}\), which is then passed to the feature aggregation block. A detailed illustration of our feature sampling mechanism can be found in Figure 3.  
Figure 3: Our spatiotemporal sampling mechanism, as outlined in Section 4.2, can be summarized in four steps. First, we utilize a feature extraction network to compute features for each video frame. Next, spatiotemporal sampling blocks are implemented to selectively sample pertinent features from neighboring frames. Subsequently, the sampled features are temporally aggregated, integrating the temporal information across frames. Finally, the aggregated features are fed as input to the detection network for further processing.
## 4 Implementation Scenario and Conceptual Framework
### Training Latency and Predicting Latency of NTPA Using Different DL Models  
Figure 11 shows the comparison of training latency for the LSTM, GRU, CNN, MLP models. Training latency is represented as the amount of time taken in training an NTPA. The training latency was measured over different dataset size. According to the figure, we see that CNN has the lowest training latency compared to other models. However, GRU has the highest training latency. The average training latency values for 1 year long dataset over 20 training trials for the CNN, MLP, LSTM, and GRU models are 74.036 s, 120.873 s, 269.647 s, and 291.274 s, respectively. In addition to the training latency measure, we assessed the predicting latency. Predicting latency is defined as the amount of time taken to predict the traffic at a previously unknown time slot. As shown in Figure 12, the predicting latency was evaluated over a simulation period of one hour. On average, the LSTM, MLP, CNN, and GRU NTPA designs have predicting latency values of 0.769 us, 0.834 us, 1.025 us, and 1.523 us, respectively.  
The result shows that NTPA takes longer to be trained; however, it takes a shorter amount of time to predict, compared to NTCA. This confirms the advantage of the DL methods over ML methods, where a DL model is much quicker in testing, while the ML model's testing time will increase as the dataset size grows as mentioned in Section 2.3.  
Figure 10: MAPE for different dataset size.  
Figure 9: RMSE for different dataset size.