esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design
### Network Architectures
#### 4.2.2 Residual Networks (ResNet)  
Residual networks (ResNets) were introduced to tackle the performance degradation in the training of deep CNNs [81] by adding skip connections to allow information to flow through identity mappings, thereby eliminating extra parameters and computational complexity.  
We evaluated ASEM-CAD on a 34-layer residual network (ResNet-34). The ResNet-34 model has 34 layers of convolutional and identity blocks [81], with 3 convolution layers in each of the blocks. The ResNet34 architecture has an input layer. The 3D volume data are passed through the input layer. Our architecture uses the following five stages: Stage one includes the convolution layer, batch normalization, and activation, followed by the max pooling layer. Stage two includes the convolution block and two identity blocks (Res block \(\times\) 3). Stage 3 includes the convolution block and three identity blocks (Res block \(\times\) 4). Stage 4 includes the convolution block and five identity blocks (Res block \(\times\) 6). Stage 5 includes the convolution block and two identity blocks (Res block \(\times\) 3). Stage 5 is followed by the average pooling layer and dense 512 and two classes.  
For supervised learning experiments, the ASEM-CAD model was trained on 80% of the input data, and 20% of the data was used for testing. The initial model was trained with 50% of labeled data using category cross-entropy loss until convergence. The training phase employed an RMSprop optimizer with a learning rate of 0.0001. The initial model was fully trained over 500 epochs using a batch size of 32 samples, and each metaiteration  
Figure 4: Three-dimensional CNN architecture.  
(EM iteration) was trained over 10 epochs. Once fully trained, the initial model was saved. Subsequent EM iterations loaded the initial model parameters and started the active EM training phase. The active learning component selected 10 samples and requested the oracle to label them during an ASEM-CAD iteration. Up to 10 active EM iterations were performed.
# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 2 Related Works
They obtained EEG data from DEAP dataset in their experiment where the data was reduced to improve processing and analysis time to 10 EEG channels. They obtained an average accuracy of 88.93% and 95.21% for both GC and TE with HOG, respectively. Authors in [27] proposed a novel multimodal framework for extracting human emotions using the DEAP dataset. Initially, the research team used three approaches to identify human emotions. The first one was the emotion recognition approach with EEG signals, and the second one involved motion recognition via face data. The last way to recognize emotions involved a fusion-based approach. In these approaches 3D-CNN (convolutional neural network), Mask-RCNN, and SVM methods were employed. The best accuracy was achieved from the 3D-CNN classifier with 96.13% and 96.79% for the equivalence and excitation categories. Authors in [28] proposed a new system to identify the negative emotions that may result from negative news on social media, using EEG signals. For the experiment, 10 participants were used in the study. Then, data were collected with an eight-channel EEG device. To obtain negative emotions, subjects examined 30 articles from different sources. After applying feature extracting methods, researchers applied SVM, and MLP to classify the negative emotions. At the end of the study, researchers observed that the best accuracy was obtained with MLP. In the study of [29], the authors proposed a method for feature extraction by using DTCWT (double tree complex wavelet transform). In the study, 16 subjects and visual stimuli were used. In the classification part, the SVM algorithm was applied, and six different emotions were classified. The F3, FP2, C5, C6, P3, and P4 channels were selected in the analysis and normalization of the data. Finally, they got an accuracy of 90.61%. In line with the studies examined, it has been seen that artificial intelligence-based approaches are effective in the analysis of emotion. Due to these achievements, deep learning, which is one of the artificial intelligence methods, was used in this study and the classification of emotions was carried out.
## 2 CFD Simulations  
Data from Ref. [18] are adopted for comparison analysis, where the Mach number \(M\) is 10, the \(R_{e}\) is 10 \(\times\) 10\({}^{6}\) and the AOA is 0\({}^{\circ}\). Ref. [18] provides the reference data of ratios of local pressure \(P\) and initial pressure \(P_{t}\), local temperature \(T\) and initial temperature \(T_{t}\), and local velocity \(U\) and initial velocity \(U_{t}\), while it does not explicitly give the values of \(P_{t}\) and \(T_{t}\) and the value of wall temperature \(T_{W}\). As we know, the possible achieved \(P\) and \(T\) are directly related to the initial values and the effect of possible deviation of initial values could be eliminated to some extent through the expression of ratios. Therefore, we need to select reasonable initial values. According to Refs. [19; 20], for the hypersonic experiment based on Mach number five, values of \(P_{t}\), \(T_{t}\), and \(T_{W}\) are 850.5 Pa, 79.17 K, and 290 K. For the hypersonic experiment based on Mach number seven, values of those are 295 Pa, 50 K, and 290 K. For the hypersonic experiment based on Mach number ten, values of those are 576 Pa, 81.2 K, and 311 K. In this paper, we perform simulations based on Mach number 10, therefore, we adopt the initial values \(T_{t}\) is 81.2 K, \(P_{t}\) is 576 pa and \(T_{W}\) is 311 K in Ref [19], which are also applied in the validation tests at the hypersonic speed of another paper by the author [21]. The computer configurations are as follows:  
* CPU: AMD Ryzen 7 5800X 8-core.
* RAM: Kingston DDR4 64G.
* SSD: Intel 760p 512G + Kingston A2000 1T.
* OS: WIN10 64 professional version.
* CFD simulation tool: Ansys fluent, double-precision, and 8-core parallel processing calculation.
# Thermography as a Method to Detect Dental Anxiety in Oral Surgery
## 4 Discussion  
The aim of the current study has been to assess if thermography is a valid method to detect dental anxiety, using the MDAS questionnaire as a control. Although none of the anxiety questionnaires currently in use has been individuated as a gold standard, the MDAS is a brief, widely used, and well-validated instrument. Moreover, patients commonly report masking their anxiety, and therefore, evaluation with a brief anxiety questionnaire is considered beneficial and recommended, as it not only discloses the degree of anxiety but also appears to reduce it [20]. The findings of this study show that for nose and ear region there was a statistical correlation between differences in temperature and MDAS scores. Although the only significance correlation was found in these regions, other ROIs should be further investigated because probably in a larger sample they would be of interest and provide relevant information. The aetiology of dental anxiety involves a variety of genetic, behavioural, and cognitive factors [21]. For instance, previous studies have found associations between high levels of dental anxiety and traumatic dental events (e.g., invasive treatments or excessive pain) [22; 23; 24]. However, another line of research has stressed the role that cognitive factors (e.g., vulnerability schema, patient's personal perceptions of the dentist and dental treatment, perceived lack of control) may have in the development of dental anxiety, independently from the past traumatic events [1; 25; 26; 27; 28]. Thus, these studies have demonstrated that dental anxiety, like other forms of anxiety, is a response to a danger or threat that activates the \"fight or flight response\", stimulating the organism to prepare for immediate action or self-protection [29]. When some sort of danger is perceived or anticipated, there is an activation of the autonomic nervous system and a relaxation of chemical hormones like adrenaline, noradrenaline and cortisol from the adrenal glands in the kidneys, which, in turn, provoke cardiovascular effects (increase in the heart rate and blood flow), respiratory effects (increase in the speed and depth of breathing), muscular effects and mental/behavioral effects [30; 31; 32]. Our study seems to demonstrate that changes in the blood flow and variations of the cutaneous temperature can be detected by thermal infrared imaging, that can record the intensity of radiation in the infrared part of the electromagnetic spectrum, converting it into a visible image, with the result that every pixel in the image is a temperature measurement [33]. The last five decades have witnessed a steady increase in the use of thermal imaging cameras to obtain correlations between the thermal physiology and skin temperature.
# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 6 Discussion
### Data Availability  
* The effect of decreasing data availability mainly depends on the distribution of data gaps in the whole dataset. While keeping the data availability constant, continuous data gaps cause much higher deviations than short and randomly distributed gaps. This emphasizes the need to use a full year of measurements if representative results are to be achieved. If data gaps are entirely random, the deviations can be almost neglected, and low data availabilities provide valid results.
* All discussed KPIs are affected by decreasing data availability. KPIs with a cubic equation, like the capacity factor or wind power density, show a higher sensitivity than the mean wind speed, which has a linear dependence. Site-specific simulations are necessary to quantify uncertainties.
* Short and random data gaps are of low importance when calculating the power curve. In contrast, long and continuous data gaps cause significant and systematic deviations and uncertainties, which can only be partially corrected.
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 3 Method
### Numerical Integration  
Given the configuration velocity solution \(\
u(t_{k})\), it is possible to compute the state configuration \(q(t_{k})\) by defining an initial configuration \(q(t_{0})\) and integrating over time. Base position \({}^{\mathcal{I}}p_{\mathcal{B}}\) and joints configuration \(s\) lie in vector space over \(\mathbb{R}\) for which most of the numerical integrations methods proposed in literature can be used [39]. The integration of the base angular velocity \({}^{\mathcal{I}}\omega_{\mathcal{B}}(t_{k})\) is not trivial [40], and numerical integration errors can lead to the violation of the orthonormality condition [41] for the base orientation \({}^{\mathcal{I}}R_{\mathcal{B}}\). There are different schemes that successfully solve discrete angular velocity integration making use of quaternion representation [40]. Concerning rotation matrix representation, the orthonormality condition can be directly enforced using the Baumgarte stabilization [41], and avoiding change of representation. The convergence of \({}^{\mathcal{I}}R_{\mathcal{B}}(t_{k})\) over \(SO(3)\), in fact, is ensured computing the base orientation matrix dynamics \({}^{\mathcal{I}}\hat{R}_{\mathcal{B}}(t_{k})\in\mathbb{R}^{3\times 3}\) as follows:  
\[A(t_{k-1})=\frac{\rho}{2}((^{\mathcal{I}}R_{\mathcal{B}}(t_{k-1}) ^{\mathcal{I}}{}^{\mathcal{I}}R_{\mathcal{B}}(t_{k-1}))^{-1}-I_{3\times 3}), \tag{14a}\] \[{}^{\mathcal{I}}\hat{R}_{\mathcal{B}}(t_{k})={}^{\mathcal{I}}R_{ \mathcal{B}}(t_{k-1})(S(^{\mathcal{I}}\omega_{\mathcal{B}}(t_{k}))+A(t_{k-1})), \tag{14b}\]  
where \(\rho\in\mathbb{R}^{+}\) is the gain regulating the convergence towards the orthonormality condition, and \(\Delta t_{k}=t_{k}-t_{k-1}\) is the integration time step. The advantage of obtaining the configuration \(q(t_{k})\) through integration of velocity \(\
u(t_{k})\) is that the two estimated quantities are directly related, and continuity of the state configuration is ensured.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Nuclear Medicine Tomographic Imaging
#### 2.1.2 Single-Photon Emission Computed Tomography (SPECT)  
SPECT is another nuclear tomography neuroimaging technique that can be used to observe neurotransmitter release in the human brain [97; 98]. To perform a SPECT, a radioactive product that emits gamma radiation is injected into the patient bloodstream. This radiotracer is chosen according to its chemical properties to selectively bind to some tissues and thus highlight certain biological processes. Then, a gamma camera is rotated around the patient to create an image of the radiopharmaceutical distribution. The camera consists essentially of a collimator that allows the angular selection of photons and gamma-ray detectors, and a tomographic reconstruction algorithm estimates the three-dimensional mapping of the radioactive activity, hence giving the distribution of the radiotracer in the body.  
Like PET, SPECT is based on the in vivo competition between the endogenous neurotransmitter and the radiotracer to bind to a receptor. For example, the stimulation of dopamine release by amphetamine reduces the binding of \({}^{123}\)I-iodobenzamide (\({}^{123}\)I-IBZM), which is a dopamine antagonist. However, SPECT differs from PET in other aspects: (1) the single gamma photon emitted in SPECT comes directly from the radioisotope atom, as opposed to the two photons created by annihilationin PET [99], hence allowing PET to offer higher spatial resolution (as it uses two photons to localize each point of interest); (2) More massive isotopes, such as \({}^{123}\)I-IBZM, can be used to label other molecules. For instance, \({}^{123}\)I-IBZM is a used in SPECT to study dopamine neurotransmission in the human brain [97; 98]; (3) SPECT exhibits a much lower sensitivity (approximately two to three orders of magnitude) than PET, but it is also less costly.  
While SPECT and PET are successful techniques for the spatial visualization of molecules such as neurotransmitters, they are limited by the complexity involved in label development, due to the signal overlapping between the labeled compound and the target metabolite [2]. The matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging was developed to provide a high resolution multiplexed spectra at near-cell spatial resolution [3]. However, applying MALDI-MS requires the modification of most neurotransmitters due poor ionization efficiency. [PERSON] et al. used pyrylium salts to ionize several neurotransmitters and, in combination multiplexed data acquisition, several neurotransmitters including GABA, glutamate, serotonin and dopamine, among others, could be accurately detected [2].
# Remediation Opportunities for Arsenic-Contaminated Gold Mine Waste
## 2 Arsenic Speciation and Toxicity  
### Speciation  
Arsenic is the 20 th most abundant element found in the Earth's crust, at concentrations of up to 2.5 mg/kg [1]. Elements in the soil are divided into two groups, essential and non-essential elements [34]. Essential elements include manganese (Mn) and zinc (Zn) and are known as micro-nutrients [34]. Non-essential elements such as As and lead (Pb) are toxic to plants, animals, and humans at high concentrations (>100 mg/kg As) and are referred to as toxic elements [5; 34]. Arsenic commonly occurs in four oxidation states, As\({}^{5+}\), As\({}^{3+}\), As\({}^{9}\), and As\({}^{-3}\). The two most common oxidation states are the pentavalent form As\({}^{5+}\) (arsenate, H\({}_{3}\)AsO\({}_{4}\) in solution) and trivalent form As\({}^{3+}\) (arsenite, i.e., arsenic trioxide as a solid), with arsenite being the most toxic to humans [4; 35; 36]. The microbial community influences As speciation through reduction, oxidation, and methylation processes (Figure 2) [37]. Inorganic arsenate is microbially reduced to arsenite through phosphate transporters, and arsenate-respiring microbes (anaerobic respiration) release arsenite from arsenate-rich sediments, thus increasing As toxicity [35]. Microbes can also methylate arsenic to methylarsonic acid (MMA) and a variety of dimethylated and trimethylated arsenic forms (DMA, TMA) (Figure 2) [35]. Heterootrophic and chemoautotrophic microorganisms such as _Alcaligenes faecalis_ and _Hydrogenophag_ sp. are known to oxidise arsenite to a less toxic state, i.e., arsenate [35]. Microbial activity releases As atmospheric emissions, such as arsenites (AsH\({}_{3}\)), which are then oxidised to a non-volatile species allowing for As-rich sediment deposition [38]. Microbial reduction, oxidation, and methylation influence toxicity of (in)organic arsenicals. For example, arsenate reduction increases toxicity, DMA\({}^{5+}\) is approximately 100-fold less toxic than As\({}^{3+}\), and oxidation of arsenite decreases toxicity [35].  
Speciation has gained attention because the oxidation state of arsenic can influence its behaviour in the environment, such as solubility, reactivity, and bioavailability (i.e.,  
Figure 2: Microbial reduction, oxidation, and methylation of arsenic species. Methylarsonic acid (MMA), dimethylarsinic acid (DMA), trimethylarsine acid (TMA) [35].  
toxicity) [22; 39; 40; 41]. As stated earlier, arsenic trioxide is highly bioavailable and the most toxic; it is a fine dust that can be easily inhaled or accidentally ingested [21]. Soil characteristics such as pH also effects As bioavailability; for example, increasing soil pH to alkaline levels (pH \(>\) 7) induces As mobility and pore water leaching [13; 36; 42; 43]. Identifying soil characteristics, soil microbes, and As species is crucial for developing appropriate remediation techniques [22; 39; 40; 41].
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 3 Energy Awareness and Prediction Model for IWSNs  
### Energy Prediction: Conceptual Model  
This paper defines a top-down approach conceptual model to provide the framework for implementing an energy awareness solution for IWSNs. The conceptual model specifies the components of an IWSN energy-aware solution and the method to interconnect them coherently as illustrated in Figure 1.  
Firstly, the industrial environment and the corresponding use cases should determine the energy requirements for an IWSN. These requirements serve as input and design constraints for the consumption modelling, energy profiling, standard integration and hardware integration components. The consumption model calculates the energy expenditure associated with the wireless communication by factoring in the wireless modules characteristics such as the operation modes, energy consumption rates and the transmitted and received power. The energy profiling component models the IWSN network in terms of energy profiles and clusters. The standard integration component defines the mechanisms and methods to preserve the IWSN standardization and compatibility. To assure ensure the deployment feasibility, the hardware integration component contextualizes the energy aware solution with respect to the processing power, memory resources and coexistence with other IWSN modules.  
The PEAR solution represents a specific implementation of the energy aware conceptual model adapted for environments such as renewable energy plants and oil and gas, respecting the ISA100.11a IWSN standard and using the VR950 IIoT Gateway hardware platform. The PEAR implementation overview is given in Figure 2.  
Figure 1: The top-down approach conceptual model of an IWSN energy-aware solution.  
Figure 2: The PEAR implementation of the energy aware conceptual model respecting the ISA100.11a IWSN standard.  
In what follows, the PEAR implementation will be detailed in the view of the conceptual model interconnection method and components.
## 2 Numerical Methods
### Calculation Scheme  
The calculation domain (Figure 2a) included the rotation domain of the sweeping rods and the static domain of the integrated AITD. The inlet was located 1.5 H in front of the rod and was set as FSTI = 3% and Re = 71,600 (based on the channel height and velocity at inlet). The exit plane was at 2.5C\({}_{x}\) downstream of the integrated LPT-GV and was specified with the ambient pressure. All wall boundaries were prescribed with no-heat-transfer and no-slip conditions. The minimum physical time step was \(\Delta t=0.000065\) s [29], and the total duration of each unsteady simulation was 0.242 s-0.794 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Integrated AITD** & \multicolumn{3}{c}{**LPT-GV**} \\ \hline Inlet channel height, H (mm) & 85 & Radius of leading edge (mm) & 3.5 \\ Mean rise angle, \(\theta\) (\({}^{\circ}\)) & 28.08 & Radius of trailing edge (mm) & 2.5 \\ Inlet aspect ratio, \(R_{\mathrm{hub}}/R_{\mathrm{tip}}\) & 0.656 & Inlet metal angle (\({}^{\circ}\)) & \(-6\) \\ Dimensionless length, L/H & 1.97 & Outlet metal angle (\({}^{\circ}\)) & 62 \\ Outlet-to-inlet area ratio, AR & 1.34 & Stagger angle (\({}^{\circ}\)) & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical geometric information of the LPT-GV and the integrated AITD.  
Figure 1: The integrated AITD [29].  
Using the Numeca/AutoGrid, an HOH-type structured grid (Figure 2a) was adopted to mesh the calculation domain [30,31]. Figure 2b shows the change of the total pressure loss (Y) in the integrated AITD with the number of grid nodes in the case of \(f=1.07\). When the number of grid nodes was greater than 5.06 million, the value of Y basically remained constant. Considering the calculation accuracy and time, the grid with 6.3 million nodes was adopted for the following simulations. For this selected grid, the node numbers were 552, 133, and 73 in the streamwise, spanwise, and circumferential directions, respectively; the node numbers of the O-type grid encompassing the rod and LPT-GV were 369 and 81, respectively. The Y+ value was less than 1 for all boundary layers, with the first layer having a thickness of 0.02 mm and a growth rate of 1.15.
## 5 System Modeling and Simulation
### System Simulation
#### 5.3.2 Traction Simulation  
The simulation test was carried out according to the structure in Figure 18. And the ANFIS, fuzzy, and PID controllers were used for the test.  
The traction speed curve was set as a sinusoidal curve with a maximum of 0.04 m/s and minimum of 0.02 m/s and a period of 2 s according to the use requirements [6]. And the load curves of the claw were set as a sawtooth curve with a maximum of 12 kN and 35 kN, according to the calculation results in Section 4.2, as shown in Figure 21. The load force period was set as half of the speed period to verify the anti-interference ability of the controller more effectively.  
The longitudinal and lateral velocity tracking curves obtained by the simulations are shown in Figures 22 and 23.  
As Figure 22 shows, EMPU can adjust the speed according to the system requirements under the maximum load of 12 kN. Moreover, only the ANFIS controller can achieve a better control effect, and the maximum error is only about 0.25-2%, only half of the fuzzy controller.  
As Figure 23 shows, ERSD can also adjust the speed under the maximum load of 35 kN. ANFIS also achieved a good control effect with an error of about 1-2%, while both the fuzzy and PID controllers had significant errors.  
Compared with Figure 22, the velocity curve in Figure 23 always has a minor fluctuation. This phenomenon is due to the chain drive in ERSD, while EMPU uses the cable drive. Tables 2 and 3 show that the chain has a more significant elastic coefficient than the steel cable. The phenomenon also indicates that the established dynamic model can accurately reflect the dynamic characteristics of the system.  
Figure 21: The load curves of the claw. (**a**) EMPU; (**b**) ERSD.  
Figure 23: The traction velocity curve of the claw. (Lateral velocity curve).  
Figure 22: The traction velocity curve of the claw. (Longitudinal velocity curve).