esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Rheological Properties  
Figure 5 shows the mini-slump flow test results of each mixture. As expected, Mix 1, which has the smallest amount of WRA, showed the minimum flow value. On the other hand, other mixtures showed flowabilty higher than 200 mm and the amount of WRA had little influence on the flowability. For all the mixtures, segregation was not observed. The tendency in the flow test results was found to be very similar in the rheological properties measured from the rheology test. Figure 6 shows the measured yield stress and plastic viscosity of each mixture. The yield stress of Mix 1 was 185 Pa and its plastic viscosity was 166 Pa s, whereas the yield stress values of Mixes 2-4 were in a range of 0-18.5 Pa and their plastic viscosity values were between 51 Pa s and 55 Pa s. Comparing the measured rheological properties of all the mixes, it can be clearly found that both the yield stress and plastic viscosity of Mix 1 were much higher than those of the other mixes, and Mixes 2-4 showed no noticeable difference in the rheological properties. These results indicate that the positive effect of increasing the amount of WRA on the flowability. In addition, a higher dosage of WRA reduces the rheological properties of fresh UHPFRC and the effect is limited at a certain dosage of WRA. A higher amount of  
Figure 4: Schematic diagram of fiber rotational movement in a simple shear flow.  
WRA exceeding the certain dosage, threshold, will contribute little to improving the flowability and the rheological properties.
## 4 Results and Discussion
### Overall Discussion  
Overall, the FIS detected 17 out of 18 stall events present in the data, resulting in a 94% accuracy, which is considered very satisfactory. As this system is not a controller and is not directly connected to any actuator in the process, the figure of the human operator will always be present, and the FIS will act similarly to a second person watching the data at all times, and monitoring the job together with the operator.  
A total of 68,458 acquisition points were run through the system, and each point was recorded at every 1 s during the job. The Python implementation of this FIS could process around 130 points per second, which translates to 8 millisecond of processing time for every 1 s of data, showing the algorithm is lightweight and can probably be ported to a compact and low-cost device such as a FPGA, PIC, or SBC.  
Figure 12: Plot for Dataset 3 datapoints and FIS outputs.  
The different datasets used come from different wells, with different trajectories and different depth. This was deliberately organized in this way in order to expose the FIS and test it in different scenarios. The PDM, however, with a 2\({}^{7/8}\)-inch OD and 5/6 lobes arrangement, is the same in all datasets and the MFs values were adjusted to fit the differential pressure x torque relation applicable to that specific motor. In a situation with a different PDM, these functions must be adjusted for a system to have meaningful results. This specific PDM model is the most widely used for CT operations. Different sizes or lobe setups are considered niche. The use of fluids with different rheology than water (e.g., drilling mud, gelled fluids) may also require this same adjustment.
## 4 Experimental Study
### Performance Comparison of Different Optimization Algorithms  
To verify the performance of the proposed algorithm, the DPSO algorithm was compared with two classic heuristic algorithms, namely the simulated annealing algorithm (SA) and the genetic algorithm (GA) [7]. GA is a typical population-based heuristic algorithm, while SA belongs to another type of heuristic algorithm based on a single individual. We chose these two algorithms for comparison because they are the prototypes of many evolutionary algorithms of the same type, and they have shown excellent performance in many workshop scheduling problems. In addition, we also added the original PSO algorithm for comparison. The GA, SA, and PSO algorithms used the same interval-based decoding scheme as that of DPSO, and the algorithm parameters were also optimized by the ANOVA. To make a fair comparison, all algorithms used the interval numbers of uncertainty parameters under the 80% confidence interval as input, and the cut-off time of the algorithms was set to 120 s. The above three algorithms were applied on each instance with 10 independent runs. In order to comprehensively compare the stability and superiority of different algorithms, we recorded and reported the average value (denoted as Avg) and the best value (denoted as Best) of the makespan on each instance. In addition,  
Figure 7: Objective values of different confidence levels. the Avg/Best makespan of each algorithm was compared with the Avg/Best makespan of all comparison algorithms (denoted as \(Avg^{*}/Best^{*}\)) from the relative percentage error (RPE). The RPE can be denoted as follows:  
\[\text{RPE}=\frac{Best^{*}-Best}{Best^{*}}\times 100\% \tag{19}\]  
The computational results in Table 5 show that the proposed DPSO algorithm is significantly better than the SA and GA. The pair of numbers under each case denote the scale of the case. For example, (92 \(\times\) 5) means that this case contains 92 processes that need to be processed on 5 machines. For Cases 1 and 2, due to the small scale of these problems, the above three algorithms can stably obtain the same optimal solution. However, as the scale of the problem continues to increase, GA and DPSO are superior to SA in terms of optimization capability and algorithm stability. Specifically, for Cases 3 and 5, GA and DPSO can obtain the same optimal solution and perform better than SA on the same CPU time. However, even for these two cases, the average values of DPSO are 3.9% and 1.3% smaller than those of GA, which show that the stability of the proposed algorithm is better. For more complex cases (Cases 6-10), compared with the SA and GA, DPSO obtains all the optimal solutions among the three algorithms. In terms of optimization capability, the best values of optimal solutions obtained by DPSO are 3.8% and 6.3% better than GA
## 2 Materials and Methods
### Testing Environment  
The three main elements that build the solution are data, software and the computing device, and a cloud solution could be recommended. We used one data collection with individual DRG patient information from the Kaggle website [24], the Inpatients Charges Data collection, with 163,066 rows and 12 columns. Most of these data are associated with the corresponding hospital and its location. We focused on Diagnosis-Related Group, Average Covered Charges (ACC), Average Total Payments (ATP) and Average Medicare Payments (AMP). We chose to predict Average Total Payments from the other columns. For this, we trained the neural networks with part of the data collection, selecting 60% of each set of DRG cases. We used another 20% of the DRG sets to test the results. Finally, we tested the predictions of the trained Neural Network with the rest of the 20% of various DRG data sets, and we present these results in the next section.  
This data collection is limited for an exhaustive demonstration, but it is sufficient to show that our proposal is feasible. In addition, any hospital has access to this kind of information in their IT systems.  
With respect to the software environment, we used Tensorflow, a Google Artificial Intelligence Product, which is an open-source, end-to-end machine learning platform [25]. Tensorflow incorporates several tools, such as Keras [26], as a facilitator layer, and Pandas [27], as a data management tool. We used Python [28] as the programming language, for its versatility and efficiency. As a computing device, we used an Intel computer with satisfactory results. In future work, cloud computing should be tested.  
To complete the verification of the methodology, it seemed necessary to establish some type of comparison with some other technique. Choosing the appropriate technique for a comparison is not trivial, as each one has its peculiarities, and often, a data adaptation process is necessary. Therefore, to complement the analysis of the test environment, it is convenient to apply an additional technique to make sure which is the best method to use. Consulting the literature, we compared the Neural Network with the Decision Tree [29].
## 2 Detection Model and Mechanism
### Detection System Excited by the PRBC-PAM Signal
 \tag{8}\]  
where, \(\varphi(t)=\varphi_{0}+\varphi_{\rm i}\), and \(\left.\begin{array}{cc}1\,\Delta\omega\,\right|=\left.\begin{array}{cc}1 \,\omega-\omega_{0}\,\right|.\) Next, we analyze the relationship between equivalent amplitude \(F_{\rm e}(t)\) and critical threshold \(F_{\rm c}\), so as to explore the impact of excitation on the system under different parameter conditions. According to Equation (8), taking \(\Delta\omega=0\) and \(\varphi_{0}=0\) as the simplest example, due to the influence of \(u(t)\) and \(\varphi_{\rm i}\), the relationship between the equivalent amplitude \(F_{\rm e}(t)\) and the amplitude of the internal driving signal \(F_{\rm c}\) is constantly changing, as shown in Figure 6. When \(u(t)=0\), \(F_{\rm e}(t)=F_{c}\), the detection system, i.e., Equation (6), will be in a critical chaotic state; when \(u(t)=1\), if \(\varphi_{\rm i}=0\), \(F_{\rm e}(t)=F_{\rm c}+A\), it will be in a large-scale periodic state; if \(\varphi_{\rm i}=\pi\), then \(F_{\rm e}(t)=\left.\begin{array}{cc}1\,F_{\rm c}-A\,\right|,\) it will be in a chaotic state. When \(\Delta\omega\
eq 0\), \(\varphi_{0}\
eq 0\), in the case of \(u(t)=1\), although \(F_{\rm e}(t)\) is affected by both \(\Delta\omega\) and \(\varphi(t)\), the cosine value is still within the range of \([-1,\,1]\). Then, \(F_{\rm e}(t)\) also varies within the range of \([(\left.\begin{array}{cc}1\,F_{\rm c}-A\,\right|),\,(F_{\rm c}+A)]\), allowing the detection system to switch between chaotic state and large-scale periodic state. Therefore, the phase change pattern of the new weak signal detection system driven by a PRBC-PAM signal is different from that driven by a simple sine signal. Regardless of whether \(\Delta\omega\) equals zero or not, the detection system's output will always be in the intermittent chaotic state, which includes the critical chaotic state in addition to the chaotic state and the large-scale periodic state. The Duffing oscillator changes with the values of \(u(t)\) and \(\varphi(t)\) in the chaotic state, critical chaotic state and large-scale periodic state, and the PRBC-PAM signal parameters can be estimated according to the variation rules of these three states. Figure 6: The magnitude relationship between the equivalent driving force amplitude and the critical threshold of the duffing oscillator system: (**a**) \(F_{\rm e}(t)>F_{c}\); (**b**) \(F_{\rm e}(t)<F_{\rm c}\).
## 2 Methodology  
The goal of the proposed methodology is to obtain the optimal configuration of P2H systems for industrial steam supply which is selected from the superstructure shown in Figure 1. This not only includes the optimal storage capacity and the required heat loads but also optimal storage operation. The generalized methodology present in this work can be summed up as follows:  
* Boundary conditions: heat demand, profiles for electricity costs, upper limit for steam supply temperature (steam generation) and lower limit for steam consumption (steam demand) temperature, maximum capacity and heat loads for cost functions generation (narrow limits increase accuracy of cost functions, but restrict solution space) are specified.
* Cost functions: for each TES technology, a cost function in terms of storage capacity and maximum heat load is obtained using cost data from a database of from the literature considering the most important cost drivers.
* Optimization model: the optimal combination of TES and steam-generation technologies, and their optimal operation is identified using a MILP/MIQP (mixed integer quadratic programming) model which is described in detail in Section 3.
* Recovery of storage details: after the optimal solution is calculated, TES specifications such as vessel size (volume, wall thickness), tube length, valves, etc. are recovered using technology-specific cost-function algorithms.  
Figure 1: Schematic of the electricity-driven steam supply system considered within this work, showing the nodes and connectors considered in the model.  
The storages in the optimization model are described with respect to capacity and heat load. From this, the detailed storage configuration is recovered with the algorithm used to obtain the storage cost-functions. The TES technologies considered in this work include:  
* Ruths steam accumulators, which are the current state-of-the-art technology for steam storage [15]. Steam accumulators offer high charging/discharging rates, but the technology is limited by its relatively low energy density compared to e.g., PCM storage.
* Latent heat thermal energy storage (LHTS) using PCMs. LHTS offers high energy densities, and a temperature range that can be tailored to the application through optimal PCM selection [15]. However, the technology is still at a low TRL level and may suffer low heat-transfer rates.
* Sensible thermal energy storage in concrete, which offers a cost-efficient, safe and easy-to-use alternative for steam storage [16]. Limitations are low charging/discharging rates.
* Molten salt storages, which are widely applied in concentrated solar power [17]. Molten salts offer high thermal storage capacity and are also used as the heat transfer fluid (HTF). Limitations are corrosivity and high melting point temperature.  
This selection of technologies covers a broad range of applications with regards to desired temperature level and charging/discharging rates and includes both state-of-the-art and emerging technologies. For steam generation, depending on the required steam quality, both electric boilers and HTHPs are considered.
## 2 Model and Methodology
### Model Structure
#### 2.2.2 I Step: Input Pre-Processing  
Some of the inputs described in the previous section have direct use in the model, while others need to be further processed. More specifically, the forecasts on BEV and PHEV fleets sizes through the analyzed timeframe were collected as inputs; the model will evaluate separately the number of BEV and PHEV charging using publicly accessible infrastructure from the ones that will use private residential infrastructure, so the following new variables have to be defined:  
\[\left\{\begin{array}{l}\left(BEV_{P}\right)_{y}=\left(\%BEV_{P}\right)_{y} \ast\left(BEV\right)_{y}\\ \left(BEV_{R}\right)_{y}=\left[1-\left(\%BEV_{P}\right)_{y}\right]\ast\left( BEV\right)_{y}\\ \left(PHEV_{P}\right)_{y}=\left(\%PHEV_{P}\right)_{y}\ast\left(PHEV\right)_{y} \\ \left(PHEV_{R}\right)_{y}=\left[1-\left(\%PHEV_{P}\right)_{y}\right]\ast \left(PHEV\right)_{y}\end{array}\right., \tag{4}\]  
where \(\left(BEV_{P}\right)_{y}\) and \(\left(PHEV_{P}\right)_{y}\) refers to the shares charging on public infrastructure and \(\left(BEV_{R}\right)_{y}\) and \(\left(PHEV_{R}\right)_{y}\) refers to the shares charging on private residential infrastructure, both over the y-th year of the time period.  
Finally, in order to define xEV range as the maximum allowable period of time (measured in days) between two successive charging events, it has to be defined the new variable \(\left[\left(avr_{V}\right)_{A}\right]_{y}\) as:  
\[\left[\left(avr_{V}\right)_{A}\right]_{y}=\frac{\left(batt_{V}\right)_{y}}{ \left(avc_{V}\right)_{y}\ast av_{A}}, \tag{5}\]  
where subscript \(A\) refers to the geographical area, \(V\) to the type of vehicle (BEV or PHEV) and \(y\) to the y-th year of the timeframe. This general notation will be used in the following sections of this document.  
Figure 3: Scenarios for charging events probability distribution.
## 4 Results and Discussion
### Performance Analysis of BILSTM-LFSOA
The analysis of prediction results using BILSTM with/without the LFSOA is shown in Table 7. From Table 7, it can be clearly seen that BILSTM with LFSOA has achieved superior results in terms of accuracy (98.963%), precision (99.026%), recall (99.155%), and f1-score (95.758%). Meanwhile, BILSTM without using LFSOA has accuracy, precision, recall, and f1-score of 96.064%, 95.928%, 95.911%, and 95.758%, respectively. Utilizing the LFSOA grants the coconut yield prediction model greater accuracy, faster convergence, and increased resistance to noise, which results in more accurate and dependable predictions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline GWO & 90.570 & 90.595 & 90.600 & 91.754 \\ WOA & 93.623 & 93.881 & 93.022 & 93.260 \\ SOA & 96.046 & 95.402 & 96.817 & 95.384 \\ LFSOA & 98.963 & 99.026 & 99.155 & 98.837 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of prediction measures for different optimization approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline RNN & 0.326 & 0.408 & 0.468 & 0.697 \\ RFC & 0.230 & 0.363 & 0.399 & 0.768 \\ LSTM & 0.202 & 0.260 & 0.348 & 0.812 \\ BILSTM & 0.094 & 0.011 & 0.105 & 0.954 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of statistical measures for classifiers with optimized hyperparameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline BILSTM & 96.064 & 95.928 & 95.911 & 95.758 \\ BILSTM with & 98.963 & 99.026 & 99.155 & 98.837 \\ LFSOA & \multicolumn{1}{c}{} & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of prediction results using BILSTM with/without LFSOA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline RNN & 93.431 & 93.716 & 93.358 & 93.842 \\ RFC & 96.390 & 96.176 & 96.941 & 96.872 \\ LSTM & 97.137 & 98.231 & 97.205 & 97.061 \\ BILSTM & 98.963 & 99.026 & 99.155 & 98.837 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of prediction measures for classifiers with optimized hyperparameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline GWO & 0.327 & 0.219 & 0.639 & 0.693 \\ WOA & 0.252 & 0.159 & 0.602 & 0.716 \\ SOA & 0.246 & 0.121 & 0.510 & 0.770 \\ LFSOA & 0.094 & 0.011 & 0.105 & 0.954 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of statistical measures for different optimization approaches.
## 2 Materials and Methods
### Materials
#### 2.1.1 Chip Design--Cadence Environment  
The prototype chip was designed in the CMOS 130 nm technology, in the professional Cadence environment. Virtuoso tool was used for layout design. Spectre simulator as well as the LVS (layout-vs.-schematics) module were used for the chip verification. Before the fabrication, the chip was thoroughly verified by means of corner analysis, in which simulations were carried out for different values of the process, voltage and temperature conditions. The temperature varied between \(-40\) and \(+140\)\({}^{\circ}\)C, and the supply voltages between 0.8 and 1.2 V. The simulations were carried out for standard, slow and fast transistor models. In Figure 3, we present layout of one of the most important block--a programmable 10-phase clock generator, used in different parts of the sensor, and at different states of the learning process of the ANN.  
The realized chip is a reconfigurable/programmable device. This means that the parameters of particular components as well as the connection scheme in the overall chip may be easily reconfigured. The problem we faced was a limited number of external pins, thus particular digital inputs and outputs had to be multiplexed in a proper way. This required a development a programmable I/O block, composed of configuration switches, an address decoder and the memory block. The overall chip area including the external pads equals 1.4 mm\({}^{2}\), with the areas of particular developed components not exceeding 0.06 mm\({}^{2}\).
# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 3 Materials and Methods
### Development of the Application  
OpenCV 3.4.3 and Python version 2.7.14 were used to develop the application. We applied Haar cascade Classifier to the live videos of the participants. The developed application works in a sequence of the following steps (c.f. Figure 1).  
Step 1: Each participant's interaction with the interviewer was captured using an RGB camera in the form of a video.  
Step 2: Each frame from the video was input into a Haar cascade classifier to detect a face in the frame.  
Step 3: By using the feature extraction, gaze was detected.  
Step 4: The gaze pupil was marked as a bounding box, as shown in Figure 2.  
Step 5: A Calibration file was created in Python to set the reference values for each participant's gaze interaction. The calibration feature was used as described [42]. For example, corresponding eX and eY positions were stored as the horizontal and the vertical coordinates, and eW and eH were captured as the height and width of the bounding box. Reference values were unique for each participant and were figured out according to the participant's gaze bounding box coordinates.  
Figure 1: Process of gaze detection with the developed application.  
Step 6: Selected reference values were compared with the participant's gaze values in real time.  
1. If the real-time video values of a participant were within the limits of selected reference values of gaze interaction, then it was considered gaze interaction.
2. If the real-time video values of a participant were out of the limits of the selected reference value of gaze interaction, then gaze avoidance was considered.  
Step 7: The gaze avoidance was further divided into four subclasses (gazing above, gazing below, gazing right, gazing left) by detecting coordinate values of the bounding box during gaze avoidance (c.f. Figure 3). The decision to collect these data was taken to enrich the dataset, as well as to test any possible links between these gaze behaviors with SAD.  
Figure 3: Gaze avoidance. (**a**) Looking left, (**b**) looking above, (**c**) looking right, (**d**) looking below.  
Figure 2: Gaze interaction and bounding box formation.
## 4 Experimental Results
### Mixed Data Model and Transfer Learning Model Results  
#### 4.3.1 Mixed Data Model  
The mixed data model was obtained by retraining the GoogLeNet-GAP model from scratch. The training set was composed of the augmented versions of partial real data and partial synthetic training data created previously (see Sections 3.1 and 3.2). We randomly selected 66 of the 294 characters from the real data. Then, rotation and resizing augmentation are applied to the original 235 images of these 66 characters and then expanded 20% of the number of training samples of each character. For instance, if the number of training samples per character was 600, then 120 samples were obtained from real data augmentation and 480 samples were taken from the synthetic dataset of the corresponding character. Finally, the model was re-trained. Table 7 shows that the accuracy increased significantly, not only for the effect of the original 66 characters but also for the new characters. Although the results obtained by this method were good, there were still certain drawbacks. Once new real data are available, the model must be re-trained after the new data are mixed with the original data. This causes a substantial increase in training time, and thus, this function cannot be used for real-time recognition services.