esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient DNN Model for Word Lip-Reading
## 6 Conclusions  
We conducted a study on word lip-reading using deep-learning models. Our goal was to find an effective model for this task. We explored different combinations of models such as ResNet, WideResNet, EfficientNet, Transformer, and ViT, referring to the SOTA model. While many papers use one or two datasets, recognition experiments were conducted using four public datasets, namely LRW, OuluVS, CUAVE, and SSSD, with different sizes and languages. As a result, we found that 3D-Conv + ResNet18 is a good model for feature extraction, and MS-TCN is a good model for inference. Although we did not propose a model that surpasses SOTA, our study confirmed the effectiveness of these models.  
This paper investigates an effective word lip-reading model on four public datasets. There are other lip-reading datasets not used in this paper. In the future, we will work on experiments including other datasets. Since it has been clarified that the model structure is effective for lip-reading, we will also verify the training method of the model in the future. The recognition target of this paper is words, but sentence lip-reading has also been actively researched in recent years. Sentence lip-reading is also a target task for the future.  
Conceptualization, T.A. and T.S.; methodology, T.A. and T.S.; software, T.A.; validation, T.A.; formal analysis, T.A. and T.S.; investigation, T.A. and T.S.; resources, T.S.; data curation, T.A.; writing--original draft preparation, T.S.; writing--review and editing, T.S.; visualization, T.S.; supervision, T.S.; project administration, T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the JSPS KAKENHI Grant No. 19 KT0029.  
Not applicable.  
The data used in this article come from [4; 13; 14; 15].  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Model** & **Top-1 Acc. (\%)** \\ \hline LipNet & 90.66 \\ \hline
3D-Conv + ResNet18 + MS-TCN (ours) & 93.08 \\
3D-Conv + ResNet18 + MS-TCN + RA (ours) & 93.68 \\
3D-Conv + ResNet18 + MS-TCN + FT (ours) & 95.14 \\
3D-Conv + ResNet18 + MS-TCN + RA + FT (ours) & 94.86 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recognition results (SSSD).
# Non-Linear Dynamic Analysis of Drill String System with Fluid-Structure Interaction
## 3 Results and Discussion
### The Effect of Forcing Frequency \(\omega\) on Dynamic Characteristics
Figure 5(e\({}_{1}\)-e\({}_{3}\)) presents the periodic-8 motion, which is due to the presence of eight points in the Poincare map at \(\omega\) = 46.35. When the increased excitation frequency is \(\omega\) = 47.5, as shown in Figure 5(f\({}_{1}\)-f\({}_{3}\)), the system motion state is periodic-4 motion, and four points exhibit in the Poincare map. With the increase in \(\omega\), the system shows chaotic intermittency motion at [57.75, 60.75] in Figure 3. Figure 5(g\({}_{1}\)-g\({}_{3}\)) shows the dynamic response of the system when \(\omega\) = 59.5, the observation shows that the system is in chaotic motion. Further increasing the forcing frequency \(\omega\), the system displays strong non-linear characteristics, the vibration amplitude significantly increases and periodic motion, quasi-periodic motion and chaotic motion appear at [66.25, 120]. The system again displays the phenomenon of period-doubling reverse bifurcation in the range of [70.55, 79.75]. In the range [89.5, 91.25], a narrow quasi-periodic motion is observed, as shown in Figure 5(h\({}_{3}\)), and two closed circles are exhibited in the Poincare map. The dynamic characteristics of the system at \(\omega\) = 110 are displayed in Figure 5(i\({}_{1}\)-i\({}_{3}\)). It can be found that the system has obvious chaotic characteristics. Figure 5(j\({}_{1}\)-j\({}_{3}\)) presents the periodic-2 motion at \(\omega\) = 119 via waveform, phase diagram and Poincare map. Figure 4b presents the bifurcation diagram of the forcing frequency \(\omega\) at \(k_{\rm a}\) = 2.8 \(\times\) 10\({}^{5}\) and \(k_{\rm b}\) = 2 \(\times\) 10\({}^{3}\) in the range [40, 60]. In the interval [40, 41.22], the dynamic response of the system mostly comprises quasi-periodic motion. With the increase in the forcing frequency from 41.22 to 54.68, the system changes from chaotic motion at [41.22, 49.26], through to a series of inverse period-doubling bifurcations at 49.26 \(<\omega\) < 51.16, to return to chaotic motion at [51.16, 54.68]. As the forcing frequency continues to increase, the system exhibits periodic motion, quasi-periodic motion at 51.16 \(<\omega\) < 60 and obvious jump discontinuity. It is observed that the system has abundant dynamic behaviors--periodic, quasi-periodic and chaotic behaviors all alternating with each other with different support stiffness--and the system shows complicated non-linear phenomena, and the exchange frequency between the motion is enhanced.
## 4 Pressure Loss Correction Model for Bend Section
### Verification of Pressure Loss in Bend Section
The results indicate that the pressure loss values for the bends, calculated using the combination of Morinage's empirical formula and the converted horizontal length method, are consistently lower than the experimental test values. The relative error compared to the experimental values exceeds 30%, with the error reaching 43.95% at a flow velocity of 0.63 m/s. The pressure loss values calculated using the combination of the one-dimensional flow model and the converted horizontal length method are consistently higher than the experimental test values, approximately twice the magnitude of the experimental results. The pressure loss value calculated using the correction formula in this paper is closer to the experimental test values, with the relative error controlled within 20%. Notably, the relative error is as low as 11.16% when the pumping speed is 0.77 m/s. There are two main reasons for this result. Firstly, the Morinage empirical formula significantly underestimates the pressure loss in the straight pipe section compared to the actual measurements. According to [10], the Morinage formula also shows a discrepancy in typical straight pipes, with a relative error exceeding 50% when compared to the experimental results. Secondly, the horizontal length conversion method only accounts for the bend's tension angle and radius of curvature. The impact of horizontal inclination and pumping speed on pressure loss is neglected. This omission affects the accuracy of the calculations. This paper's particle flow numerical simulations reveal that the pressure loss in the bend section is influenced not only by the radius of curvature but also by pumping speed, horizontal inclination, and other factors. This paper builds upon the straight pipe section pressure loss calculated using a one-dimensional flow model. By analyzing the influence of pumping speed, radius of curvature, and horizontal inclination on the pressure loss in the bend section, the formula for pressure loss in the bend section has been revised. The results indicate that the amended formula meets the engineering accuracy requirements, thereby validating the effectiveness of the method.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation
### Objective Function  
Traditionally, the UC problem considered the duration within one hour as a single value, and it does not consider the effect between each time period. In this paper, the economic cost is the sum of costs of units within 24 h as shown in Formula (1) below.  
\[\mathit{TPC}_{Tn}=\min\sum_{t=1}^{T}\sum_{j=1}^{n}\left[F_{j}(P_{j,t})u_{j,t}+ SUI_{j,t}(1-u_{j,t-1})u_{j,t}\right] \tag{1}\]  
In Formula (1), the total economic cost \(\mathit{TPC}_{Tn}\) is composed of two parts, namely fuel cost and start-up cost respectively. The start-up cost is generated at the moment of the unit start. \(F_{j}(P_{j,t})\) is the fuel cost function of the \(j\)th unit, and generation output is denoted as \(P_{j,t}\) during the \(t\) hour. \(SU_{j,t}\) is the star-up cost of the \(j\)th unit during the \(t\) time, and \(u_{j,t}\) is the status of \(j\)th unit at the \(t\) hour described by 0 or 1.
## 6 Data Collection and Analysis
### Subpayload 2  
Preliminary processing of mission data shows that there were far fewer objects (both stars and RSOs) in each frame than was expected. Initial hardware investigation indicated that a degradation in hardware may have occurred, specifically the camera. Despite this reduced performance, at least five unique RSOs were observed visually in the images. The algorithm detected all these RSOs as they passed through the imager's FOV, with few dropped detections. Furthermore, these detections were found to have been made in less than 500 ms, which included the attitude determination algorithm processing time, 100 ms image exposure time, and sensor data saving time, among other processing steps. This indicates that for these detections, the linear motion model assumed by the algorithm for RSOs performed well. Figure 8 shows an illustration of the RSO detection algorithm's outputs.  
Further processing needs to be performed to calculate metrics such as True Positives (TP), False Positives (FP), and False Negatives (FN), along with precision and recall. The extended analysis will also verify the presence of dimmer RSOs that were not visually observed during the initial analysis of the data. A better calculation of the processing time and a thorough investigation of the camera's performance will be conducted as well.  
Preliminary results for the attitude determination LIS algorithm had shown an average cross-boresight accuracy of 32 arc minutes and around boresight of 75 arc minutes. The tracking mode had shown an average cross-boresight accuracy of 2.5 degrees and around boresight of 3 degrees. Further analysis is required to examine calculations with incorrect star identification, primarily caused by the sensitivity of the barrel distortion correction. Investigation is also required for calculations with bright RSOs in the image incorrectly identified as stars, leading to incorrect attitude estimation. Removal of these RSOs during star detection and the stars for RSO detection can lead to improved results but is left for future work.  
Figure 8: An illustration of the RSO detection algorithm’s processing steps, taking the lit pixels corresponding to RSOs in 3 different images and associating them together into a detection. The red, green, and blue pixels represent the centroids of the RSO from the first, second, and third image, respectively.  
Figure 7: Sample downlinked image enhanced with the Zscale algorithm.
## 4 Discussion
### Manufacturing Process
#### 4.3.4 Production Process Cost  
[PERSON] et al. [12] estimated the cost of 3D printing to be relatively low, ranging from approximately USD 50 to USD 150, depending on the volume and the amount of resin used. In contrast, [PERSON] et al. [33] reported that a single specimen in their study was quoted at USD 600, suggesting that a definitive full-scale maxillofacial prosthesis would likely be even more expensive. The overall cost is highly dependent on the 3D printing procedure, the materials involved, and local market quotations.
## 2 Simulation Methodology, Material, and the Experiments
### Field Emission Theory  
FE refers to the quantum-mechanical phenomenon of electron tunneling from a conductor into the vacuum through a surface potential barrier whose width is significantly reduced by a high local electric field [21; 22; 23; 24; 25]. The tunneling probability depends very strongly on the width of the potential barrier and decreases exponentially with increasing barrier width. The local electric field can be orders  
Figure 1: X-ray tube divided into finite elements for electric field calculations. Red and blue parts are anode and cathode, respectively.  
of magnitude greater than the applied macroscopic field, owing to the enhancement effect at sharp tips or edges.  
The FE current (\(I\)) can be calculated from the quantum-mechanical tunneling probability of electrons through a triangular potential barrier, and the result is the well-known Fowler-Nordheim (F-N) equation:  
\[I=\alpha\mathcal{A}(\beta^{2}E^{2}\,/\,\phi)\exp(-b\phi^{3/2}\,/\beta E) \tag{1}\]  
where \(\alpha\) and \(b\) are constants; \(A\) is the effective FE area; \(E\), \(\phi\), and \(\beta\) are the applied macroscopic electric field (without the local microscopic geometrical effect that causes enhancement), the material work function, and the field enhancement factor, respectively. The value of \(\beta\) depends on the microscopic geometry of the emitters and is the most important factor that affects the FE performance. Nanomaterials with enormously high aspect ratios, such as CNCs and CNTs, are known to have very high \(\beta\), and have been demonstrated to be excellent field emitters with low turn-on \(E\) fields [21, 22, 23, 24, 25]. The value of \(\beta\) can be extracted from the experimentally obtained slope of the F-N plot.  
To obtain the macroscopic electric field \(E\), which determines the FE current density, [PERSON]'s equation was firstly solved numerically for the potential distribution \(V(x,\,y,\,z)\) using the FEM. The electric field distribution is then calculated by differentiation:  
\[\widetilde{E}=-\widetilde{\
abla}V \tag{2}\]  
In this work, three different kinds of software packages were used in the simulation; they were (1) SolidWorks(Dassault Systemes SolidWorks Corp., Waltham, MA, USA), which was used to define a precise device geometry; (2) Comsol Multiphysics (Comsol Inc., Stockholm, Sweden), which was used to calculate the distribution of the electric field on the designed cathode surface; and (3) Simion 8.0 (Scientific Instrument Services, Inc., Ringoes, NJ, USA), which simulated the trajectory of the emitted electrons that were accelerated by the electric field between the cathode and the anode.
## 3 Determination Methods of Shear Modulus
### Hysteresis Loop Method  
Assuming the visco-elastic of soil, the dynamic stress, \(\sigma_{\rm d}\), and the corresponding dynamic strain, \(\varepsilon_{\rm d}\), can be written as follows [28]:  
\[\left\{\begin{aligned} \sigma_{\rm d}&=\sigma_{\rm a }sinot\\ \varepsilon_{\rm d}&=\varepsilon_{\rm a}sin(\omega t- \delta)\end{aligned}\right. \tag{1}\]  
where \(\sigma_{\rm a}\), and \(\varepsilon_{\rm a}\) are, respectively, the amplitudes of dynamic stress and dynamic strain, \(\omega\) is the frequency of cyclic loading; \(\delta\) is the phase lag between strain and stress. As shown in Figure 4a, the hysteresis loop is symmetric at a given strain level. Therefore, the shear modulus is usually expressed as the secant modulus determined by the extreme points on the hysteresis loop. This method is widely utilized in current practice [23], using the following equation:  
\[E_{\rm sec}=\frac{\sigma_{\rm d}}{\varepsilon_{\rm d}}=\frac{\sigma_{\rm d, max}-\sigma_{\rm d,min}}{\varepsilon_{\rm max}-\varepsilon_{\rm min}} \tag{2}\]  
Figure 3: Cyclic triaxial test system.  
\[G=E_{\text{sec}}/2(1+\mu) \tag{3}\] \[\gamma=(1+\mu)\varepsilon \tag{4}\]  
where \(\mu\) is the Poisson's ratio. For the saturated specimen, a value of 0.5 is utilized.
## 3 Literature Review
The work in [11, 12] demonstrates the potential of deep learning for MIMO detection, but its focus lies on optimizing existing DNN architectures. In contrast, this work proposes a novel DNN-based detector architecture tailored for the complexities of MIMO systems, aiming to potentially surpass the performance limitations of conventional techniques, particularly inscenarios with high modulation orders or dense antenna configurations. By evaluating the effectiveness of the proposed DNN detector and comparing its performance to traditional methods, this research seeks to contribute to the advancement of data detection techniques for future wireless communication systems.
###### Abstract  
In this study, a novel configuration for luminescent solar concentrator photovoltaic (LSC PV) devices is presented, with vertically placed bifacial PV solar cells made of mono-crystalline silicon (mono c-Si). This LSC PV device comprises multiple rectangular cuboid lightguides, made of poly (methyl methacrylate) (PMMA), containing Lumogen dyes, in particular, either Lumogen red 305 or orange 240. The bifacial solar cells are located in between these lightguide cubes and can, therefore, receive irradiance at both of their surfaces. The main aim of this study is to theoretically determine the power conversion efficiency (PCE) of five differently configured LSC PV devices. For this purpose, Monte Carlo ray tracing simulations were executed to analyze the irradiance at receiving PV cell surfaces, as well as the optical performance of these LSC PV devices. Five different LSC PV devices, with different geometries and varying dye concentrations, were modeled. To maximize the device efficiency, the bifacial cells were also attached to the back side of the lightguides. The ray tracing simulations resulted in a maximum efficiency of 16.9% under standard test conditions (STC) for a \(15\times 15\) cm\({}^{2}\) LSC PV device, consisting of nine rectangular cuboid \(5\times 5\times 1\) cm\({}^{3}\) PMMA lightguides with 5 ppm orange 240 dye, with 12 vertically positioned \(5\times 1\) cm\({}^{2}\) bifacial cells in between the lightguides and nine \(5\times 5\) cm\({}^{2}\) PV cells attached to the back of the device. If the cells are not applied to the back of this LSC PV device configuration, the maximum PCE will be 2.9% (under STC), where the LSC PV device consists of 25 cubical \(1\times 1\times 1\) cm\({}^{3}\) PMMA lightguides with 110 ppm red 305 dye and 40 vertically oriented bifacial PV cells of \(1\times 1\) cm\({}^{2}\) in between the lightguides. These results show the vast future potential for LSC PV technologies, with a higher performance and efficiency than the common threshold PCE for LSC PV devices of 10%.  
luminescent solar concentrator photovoltaic (LSC PV); ray tracing; simulation; power conversion efficiency (PCE); bifacial solar cells +
Footnote †: journal: Applied Soft. **2020**, _10_, 871; doi:10.3390/app10030871
## 2 Materials and Methods
### Dental Training Models
#### 2.2.2 3D-Printed Teeth with Multicolored Layers  
(Figure 1): The analog models of regular plastic teeth described above were digitally scanned using an Einscan-SP 3D Scanner (Shining 3D(r), Hangzhou, China), the files of which were uploaded into FUSION 360 CAD/CAM 2017 software 3D-Builder programs (AUTODESK, San Rafael, CA, USA) and MySolidWorks 2017-2018 (SOLIDWORKS, Waltham, MA, USA), intended for planning models for 3D printing. After attaining the Standard Tessellation Language (STL) files, a master model of each tooth--containing multicolored layers analogous to the traffic light array (i.e., green, yellow, and red) as internal layers--was designed for each tooth. The precise outer/enveloping layers, as well as the cavity preparation of each model (both in white color), were planned according to specific wall forms, depths, and marginal forms, owing to the principles of ideal cavity preparation for amalgam restoration.  
The design for the tooth model was planned for Class I cavity preparation, according to [PERSON]'s classification [19], as if for an amalgam restoration including central fossa and pits and fissures on the occlusal surface; it included a 0.5-mm-thick green enveloping layer underlying the white margin, under which there was a 0.5-mm-thick yellow layer, with the deepest being an inner red layer. The concept of the layered, 3D-color-printed tooth design was to designate the cavity preparation with colored limit zones; as such, preparation of the cavity within the green zone was to be considered good preparation, preparation of the cavity within the yellow zone incomplete but not a failure, and preparation that exposes the red layer--representing the pulp chamber and pulp horns--as a clinical failure. These demarcations do not exist in conventional analog plastic teeth.  
The teeth were printed using an OBJET J750 printer (Stratasys Ltd., Eden Prairie, MN, USA) utilizing PolyjetTM jetting technology, which prints models in multicolored layers with a resolution of 17 microns. The models were printed with Vero photopolymer material, creating custom-made RGB using: Black (OBJ-03286), Cyan VIVID (OBJ-03296), Magenta VIVID (OBJ-03299), Yellow VIVID (OBJ-03302), Pure White (OBJ-03327), and Support 706 (OBJ-03326) (Stratasys Ltd., Eden Prairie, MN, USA). These materials offer the following mechanical properties: modulus of elasticity = 2000-3000 MPa; flexural strength = 75-110 MPa; and tensile strength = 60-70 MPa.  
Figure 1: Upper row: Class I preparations in 3D-printed teeth with multicolored layers. Lower row: Class I preparation in conventional plastic teeth. Left column: preparations that are too wide and deep. Right column: ideal preparations.
## 4 Discussion
### Limitations and Uncertainties  
Firstly, we only took into account a single drought and/or warming event in the simulation. However, these extremes will occur more frequently in the future [15; 27]. Further research could consider impacts with various frequencies on the productivity. Secondly, we assumed that seasonal extremes would occur after 5 years because we mainly focused on the impacts on the seasonal variation of NPP without considering the interannual effects. However, the year of disturbance in the future was often uncertain. We also assumed that these extremes would occur in July, August, and September according to the history records in the study area [80; 101]; however, they may occur earlier (June) or later (October) [33]. Thirdly, in future work, the Predictive Ecosystem Analyzer (PEcAn) toolbox could be used to constrain PFT-dependent parameters for reducing the ED-2.2 uncertainties through Bayesian model calibration and emulation with the aid of more field measurements [84; 102; 103; 104]. PECsAn can perform ensemble analysis for producing a confidence interval on the model output. Fourthly, we combined monthly verification and annual validation for NPP in the study; however, the fast timescale NPP-related variables (e.g., monthly GPP) can be used to quantificationally evaluate the model performance once the eddy flux tower is available in the future [48].