esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 4 Systems Description and Configuration
### Baseline ASR Architectures  
#### 4.2.1 DNN-HMM Based System  
This first baseline system that we consider in this work corresponds to the primary system [7] presented in the Albayzin-RTVE 2020 Speech to Text Transcription Challenge. This ASR engine was built through the _mnet3_ DNN setup of the Kaldi recognition system, and using the so-called _chain_ acoustic model based on Convolutional Neural Network (CNN) layers and a factorised time-delay neural network (TDNN-F) [39], which reduces the number of parameters of the network by factorising the weight matrix of each TDNN layer into the product of two low-rank matrices.  
The acoustic model integrated a CNN-TDNN-F based network, with six CNN layers followed by 12 TDNN-F layers. The internal cell-dimension of the TDNN-F layers was of 1536, with a bottleneck-dimension of 160 and a dropout schedule of '0,[EMAIL_ADDRESS],[EMAIL_ADDRESS],0'. The number of training epochs was set to 4, with a learning rate of \(1.5\times 10^{-4}\) and a mini-batch size of 64. The input vector corresponded to a concatenation of 40 dimensional high-resolution MFCC coefficients, augmented through speed (using factors of 0.9, 1.0, and 1.1) [29] and volume (with a random factor between 0.125 and 2) [30] perturbation techniques, and the appended 100-dimensional iVectors.  
This system included a 3-gram language model for decoding and a 4-gram pruned RNNLM model for lattice-rescoring following the work presented in [31]. The 3-gram LM was trained with texts coming from the _Transcriptions_, _RTVE2018_ and _A la Carta_ corpora presented in Table 2, and the 4-gram pruned RNNLM model was estimated adding the _Wikipedia_ text corpus as well.
## 2 Materials and Methods
### Chemo-Physical Characterization  
A D8 Advance diffractometer (Bruker, Billerica, MA, USA) equipped with a copper K\(\alpha\) X-ray source (\(\lambda\) = 1.5406 A) was employed to collect X-ray diffraction (XRD) data on the sample. The XRD pattern was recorded over a wide range of angles, spanning from 20 = 2\({}^{\circ}\) to 50\({}^{\circ}\). The instrument operated at 40 kV and 40 mA, acquiring data points at a step size of 0.01\({}^{\circ}\). This proposed data collection ensures high-resolution, detailed information about the sample's crystalline structure.  
The specific surface area and pore characteristics of the specimens were further examined using N\({}_{2}\) sorption/desorption isotherms at 77 K (liquid nitrogen temperature). This analysis was conducted using a NOVA 1200e analyzer (Quantachrome Instruments, Boynton Beach, FL, USA) specifically designed for such measurements. The surface areas and pores volumes were measured based on the standard nitrogen adsorption/desorption isotherms, after degassing samples at 150 \({}^{\circ}\)C for 12 h. The specific surface area was then  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **Ratio Fe(III):(H3 BTC):Water** & **Synthesis Time** \\ \hline MF-S0 & 1:0.1:56 & 12 h \\ MF-S1 & 1:0.67:56 & 12 h \\ MF-S2 & 1:0.67:56 & 16 h \\ MF-S3 & 1:0.67:112 & 12 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hydrothermal synthesis at different conditions.  
calculated according to the Brunauer-Emmett-Teller (BET) method and indicated as S\({}_{\text{BET}}\). The Barrett-Joyner-Halenda (BJH) approach was employed for pore volume measurement.  
With the aim of assessing the chemical structure of individual molecules and the composition of compounds, a PerkinElmer spectrometer (Waltham, MA, USA) and the attenuated total reflectance (ATR) method were used to collect a Fourier-transform infrared spectroscopy (FTIR) spectrum within the 500-4000 cm\({}^{-1}\) wavelength range with a resolution of 2 cm\({}^{-1}\) and under open-air conditions. Background measurements were obtained under the same conditions.  
The density of all samples was determined using an Ultrapyc 5000 gas pycnometer ([PERSON], Graz, Austria) with helium as the purge gas. Both the reference and sample chambers were calibrated beforehand. A 1.8 cm\({}^{3}\) microcell was chosen for sample density analysis.  
The optimal MOF batch underwent morphological and compositional analysis using an environmental scanning electron microscope (ESEM), specifically a FEI Quanta 450 (FEI Inc., Hillsboro, OR, USA) operating at an accelerating voltage of 8 kV. Prior to imaging, the material was dehydrated in an oven at 80 \({}^{\circ}\)C for 12 h and then sputter-coated with chromium to enhance conductivity.
## Finite Element Models
### Finite Element Type and Mesh  
#### 2.3.1 Finite Element Type  
The L-shaped CFST column frames mainly consisted of L-shaped concrete-filled steel tubular columns, steel beams and composite slab. In finite element analysis which was based on ABAQUS, four-node doubly curved shell and complete integral format shell elements (S4) (which was proved to meet the yield criterion of [PERSON] by [PERSON] [66]) were applied to steel tubes and the steel beam. Meanwhile, eight-node linear brick and reduced integral solid element (C3D8R) (which was proved to meet the yield criterion of Drucker-Prager by [PERSON] [67]) was used for core concrete, composite slab, L-shaped inner diaphragm, horizontal loading plate, and rigid plates at the end of the CFST columns.  
A linear element was adopted in the element model, and the mesh sensitivity experiment was carried out to analyze the convergence of the grid and the accuracy of the calculation computational results. Under the circumstances above, the specific approach was given, as follows: First, a more reasonable initial analysis of grid division was performed in order to guarantee the accuracy. Second, three-dimensional measurement of the grid should be similar, if not, doubled measurement of grid and reanalysis until the difference between two results was less than 1%. Otherwise, the refinement of grid measurement should be continued until the calculated computation results of the partitioning were approximately equal.
# Integrated IR Modulator with a Quantum Cascade Laser
## 4 Tests of the Laser Switching Module  
The switching module tests were performed to analyze the influence of the load resistance on the shapes of the generated current pulses from the switching module using a special test board. The electrical signals were registered using the same scope Tektronix MSO 6 with the current probe CT-1 and the active differential voltage TDP-1500 probe. Figure 4 presents both the current and voltage signals for the two load resistances. These resistances were measured using a Keithley 236 source-measure unit. The module was operating in the current source region, where the current depends only on the 'load' of the biasing voltage. In this region, its output resistance is lower than 300 m\(\Omega\).  
Figure 4: Normalized electrical driving signals: current (**a**) and voltage (**b**).  
Figure 3: Simulation results of the modulator for different lasing structure electrical parameters: capacitance (**a**), inductance (**b**), and resistance (**c**).  
We observed both voltage and current oscillations at the falling slope for low load resistance. Impedance mismatching caused the transmission and reflection effects in the signal line. An increase in load resistance allowed for the minimizing of these effects with the limited bandwidth.  
The load resistance influenced both edges of the voltage signals, but for the current, that was only noticed for the falling one. These differences can modulate the lasing conditions of the QC structure as determined by both current and voltage signals. In practice, these conditions are difficult to predict because they are related to the change in the QC laser impedance during its operation. Therefore, both the signal interface and QC laser parameters form the shapes of the light pulses.
# Understanding Olive Oil Stability Using Filtration and High Hydrostatic Pressure
## 3 Discussion
The oxidative degradation of secoiridoids may follow both an enzymatic and a non-enzymatic degradation pathway. In the former pathway, polyphenol oxidases (PPO) and peroxididases (POD) catalyse the oxidation of phenolic compounds to corresponding quinones [40]. In the latter pathway, which is connected to termination reactions of radical oxidation of triacylglycerglycerols to peroxides and derivatives, the release of hydrogen atoms by phenolic compounds can inhibit the formation of hydroperoxide radicals [41]. Our experimental data on the phenolic compound content (Table 4) showed that the secoiridoids in the olive oil samples underwent a clear hydrolytic transformation during storage. This effect can be related to the water content and water activity of the oil samples, without the involvement of microbial activity. Indeed, the 3,4-DHPEA and p-HPEA contents increased during storage and the high level of water activity caused the greatest increase in the veiled oil samples. A hydrolytic increase in 3,4-DHPEA and p-HPEA contents has also been reported in the literature [16; 18; 42; 43]. The R-Index behaviour consistently showed no variations in the hydrolytic status of secoiridoids for the FIL and F-HHP oil samples only (Figure 2). Our experimental data showed that the secoiridoids also underwent oxidative degradation during storage, but this behaviour was primarily influenced by the absence of the significant radical oxidation of triacylglycerols in all of the olive oil samples. There was no decrease in the total phenolic compound content, or the content of oleupreein, ligstroside and their derivatives (Table 4), conversely to some literature data [16; 20] which have shown a decrease in phenolic compound contents due to their antioxidant role. Instead, after 15 days of storage a difference quickly occurred between the contents of oleupreein and its derivatives and 3,4-DHPEA-EDA of the filtered and veiled oil samples (Table 4). The oxidative enzymatic degradation of secoiridoids by endogenous PPO and POD may be involved in the decrease of the above phenolic compounds in the veiled oil samples [44]; this phenomenon may be consistent with both the effect of the water content/water activity and the panellists' different perceptions of bitterness and pungency between the filtered and veiled oil samples. Figure 2: R-index as a function of storage time for the four treatments (CON = veiled and not HHP-treated oil samples; HHP = veiled and HHP-treated oil samples; FIL = filtered and not HHP-treated oil samples; F-HHP = filtered and HHP-treated oil samples). Error bars represent the standard errors.
## Materials and Methods  
### Training and Testing Dataset  
Diabetic retinopathy is generally divided into five groups: Normal, Mild DR, Moderate DR, Severe DR, and Proliferative DR (Table 1). Diseases start with small changes in blood vessels, which are designated as Mild DR. At this stage, a complete recovery is possible. If proper care is not taken, in a few years, it will progress to Moderate DR, where leakage in blood vessels may begin. Then, the diseases progress further to Severe and Proliferative DR and may lead to complete vision loss.  
To predict DR with a higher accuracy using a machine learning algorithm, a large amount of training data are needed. The data need to come from reliable sources with accurate labels. We used the Kaggle dataset, which was provided by EyePacs [22]. EyePacs screened more than 750,000 patients and collected 5 million retina images [22]. The Kaggle dataset contains 35,126 images for training and another 53,594 for testing. The size of the images ranged from 360 KB to 2 MB. However, only a few images were below 500 KB. The Kaggle dataset is one of the largest datasets of diabetic retinopathy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Frame in** & **0** & **3-5** & **5-10** & **10-15** & **>15** \\ \hline \multirow{3}{*}{Stages of DR} & \multirow{3}{*}{Normal} & Mild DR: & Moderate DR: & Severe DR: Stage-3 & Proliferate DR: \\  & & Stage-1 & Stage-2 & non-proliferate & & Stage-4 proliferate \\ \hline \multirow{3}{*}{Changes in Retina} & \multirow{3}{*}{No retinopathy} & A few small & A few small bulges in the blood vessels. & Larger spots of blood & \multirow{3}{*}{Reading in veins. Growth of new blood} \\  & & A few small bulges in the bulges in the blood vessels. & Spots of blood & & & \\ \cline{1-1}  & & & leakage. & Growth of new blood & \\ \cline{1-1}  & & & Deposts of & vessels at the optic & & \\ \cline{1-1}  & & & cholesterol. & disc. Blockage of blood vessels. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Various stages of diabetic retinopathy [21].  
images currently available. Table 2 below shows the number of images in each DR category among the training and testing datasets. Figure 1 shows samples images in different classes representing different stages of DR. The Kaggle database provided images of all DR categories in one folder and a comma separate value (CSV) file with a description of each image category. For training and testing, the images need to be separated and placed in separate folders. A script was written to separate the images based on CSV labels, which is shown below. Next, the images were cropped using the Otsu method to isolate the main features [23, 24]. Furthermore, images were normalized and contrast adjusted using a filtering algorithm. Data augmentation was also performed to improve the diversity of data. Different operations of padding, cropping, and flipping were also performed.
## 5 First Medal Prediction Model (FMPM)
### Results of Performance Evaluation  
The comprehensive performance and confusion matrix of the model on the independent test set (a total of 688 records) are shown in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0 (No Award) & 0.85 & 0.90 & 0.88 & 352 \\
1 (Awarded) & 0.86 & 0.80 & 0.83 & 236 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model evaluation results.  
Table 5 shows the classification performance indicators of the model on the test set, and the confusion matrix is presented in Table 6. For the non-medal-winning category (0), the model has a precision of 0.85, a recall of 0.90, and an F1-score of 0.88, indicating that the model can effectively identify non-medal-winning samples with a low false-positive rate (40/432). For the medal-winning category (1), the precision is 0.86, the recall is 0.80, and the F1-score is 0.83, suggesting that the model is a bit conservative in predicting medal-winning samples and has a relatively high false-negative rate (60/296).  
The confusion matrix further reveals that the model correctly predicted 352 non-medal-winning samples and 236 medal-winning samples, but there were some misclassifications: 40 non-medal-winning samples were misjudged as medal-winning, and 60 medal-winning samples were misjudged as non-medal-winning. It is possible that due to the class imbalance in the dataset, the model performs better on the non-medal-winning category than on the medal-winning category. However, overall, the model is accurate and can be used to predict which countries will win medals for the first time in the 2028 Olympics.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 3 Collaboration between Seaports and Dry Ports
### Indicator Selection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**First Level** & **Second Level** & \multicolumn{4}{c}{**Third Level**} \\ \cline{3-6}  & **Suzhou** & **Ningbo** & **Zhengzhou** & **Xi’an** \\ \hline \multirow{6}{*}{Customer satisfaction degree} & Added value of tertiary industry (CNY billion) & \multirow{2}{*}{73.26} & \multirow{2}{*}{103.53} & \multirow{2}{*}{50.07} & \multirow{2}{*}{39.56} \\  & C1 & & & & \\ \cline{2-6}  & Growth rate of tertiary industry (\%) & \multirow{2}{*}{10.1} & \multirow{2}{*}{17.7} & \multirow{2}{*}{14.1} & \multirow{2}{*}{11.7} \\  & C2 & & & & \\ \cline{2-6}  & Market share (\%) & \multirow{2}{*}{38} & \multirow{2}{*}{45} & \multirow{2}{*}{60} & \multirow{2}{*}{40} \\  & C3 & & & & \\ \cline{2-6}  & Generalized transportation cost (CNY/TEU) & \multirow{2}{*}{1184.7} & \multirow{2}{*}{1472.3} & \multirow{2}{*}{4431.1} & \multirow{2}{*}{4510.8} \\  & C4 & & & & \\ \cline{2-6}  & Customs clearance time (h) & \multirow{2}{*}{2} & \multirow{2}{*}{0.8} & \multirow{2}{*}{1} & \multirow{2}{*}{4.5} \\  & C5 & & & & \\ \hline \multirow{6}{*}{Financial indicator} & Growth amount of import and export & \multirow{2}{*}{39.17} & \multirow{2}{*}{59.65} & \multirow{2}{*}{8.57} & \multirow{2}{*}{6.67} \\  & (USD billion) & \multirow{2}{*}{14.3} & \multirow{2}{*}{13.3} & \multirow{2}{*}{10.9} & \multirow{2}{*}{36.4} \\  & C6 & & & & \\ \cline{2-6}  & Growth rate of import and export (\%) & \multirow{2}{*}{14.3} & \multirow{2}{*}{13.3} & \multirow{2}{*}{10.9} & \multirow{2}{*}{36.4} \\  & C7 & & & & \\ \hline \multirow{6}{*}{Cooperative relationship of both parties} & \multirow{2}{*}{Information share degree} & \multirow{2}{*}{8} & \multirow{2}{*}{7} & \multirow{2}{*}{7} & \multirow{2}{*}{8} \\  & C9 & & & & \\ \cline{2-6}  & Number of connected railways & \multirow{2}{*}{3} & \multirow{2}{*}{1} & \multirow{2}{*}{4} & \multirow{2}{*}{2} \\  & C10 & & & & \\ \cline{2-6}  & Cooperation degree of both parties & \multirow{2}{*}{9} & \multirow{2}{*}{8} & \multirow{2}{*}{6} \\  & C11 & & & & \\ \hline \multirow{6}{*}{Non-market tools} & Regional transport scale (million tons) & \multirow{2}{*}{262.57} & \multirow{2}{*}{301.70} & \multirow{2}{*}{251.30} & \multirow{2}{*}{238.88} \\  & C12 & & & & \\ \cline{2-6}  & Relevant promotion policies (article) & \multirow{2}{*}{2} & \multirow{2}{*}{4} & \multirow{2}{*}{3} & \multirow{2}{*}{3} \\  & C13 & & & & \\ \cline{2-6}  & Fixed asset investment (CNY billion) & \multirow{2}{*}{562.96} & \multirow{2}{*}{585.44} & \multirow{2}{*}{707.57} & \multirow{2}{*}{519.14} \\  & C14 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cooperation performance evaluation system. Establishment of the Performance Evaluation Model for the Cooperation between Dry Ports and Seaports
###### Abstract  
The main goal of this work is to combine the usage of the numerical homogenization technique for determining the effective properties of representative volume elements with artificial neural networks. The effective properties are defined according to the classical laminate theory. The purpose is to create and train a rapid surrogate model for the quick calculation of the mechanical properties of hollow concrete slabs. First, the homogenization algorithm was implemented, which determines membrane, bending and transverse shearing properties of a given parametrized hollow-core precast slab reinforced with steel bars. The algorithm uses the finite element mesh but does not require a formal solution of the finite element method problem. Second, the learning and training artificial intelligence framework was created and fed with a dataset obtained by optimal Latin hypercube sampling. In the study, a multilayer perceptron type of artificial neural network was used. This allows for obtaining rapid calculations of the effective properties of a particular hollow-core precast slab by using a surrogate model. In the paper, it has been proven that such a model, obtained via complex numerical calculations, gives a very accurate estimation of the properties and can be used in many practical tasks, such as optimization problems or computer-aided design decisions. Above all, the efficient setup of the artificial neural network has been sought and presented.  
Keywords:hollow-core slabs; numerical homogenization; laminate theory; artificial neural network; multi-layer perceptron regressor +
Footnote †: journal: Applied Mechanics and Physics Letters
## 2 Results and Discussion
### Fingerprinting of Cinnamon NMR Dataset
#### 2.4.3 Quantification of Major Metabolites via \({}^{1}\)H-NMR
Such differences may be related to the extraction and analytical difference between GC/MS and NMR, where sample analysis by SPME/GC-MS undergoes a pre-concentration step followed by a chromatographic process before analysis by the MS detector. However, NMR analysis is a direct method conducted on the solvent crude extract. Additionally, NMR results could be explained by the fact that the three commercial products belong to the same origin, namely _C. cassia_, in addition to _C. burmannii_ in CP-2. This could be further verified from the ratio of coumarin to (\(E\))-cinnamaldehyde which is approximately (1:2) in CA together with CP-1, CP-2, and CP-4. The significant difference (\(p\leq 0.05\)) in metabolites concentration (Table 2) between these three products and authenticated _C. cassia_ is attributed to the presence of other excipients, i.e., cellulose and gelatin, in these commercial products, in which cinnamon powder represent less than 25% of the total powder as labeled on some products (Table 1). Furthermore, three vitamins were detected in the commercial product CP-3 \({}^{1}\)H-NMR analysis, i.e., Niacin (vitamin B3), ascorbic acid (vitamin C), \(\alpha\)-tocopherol (vitamin E) quantified at 14.2, 44.5, and 5.4 \(\mathrm{\SIUnitSymbolMicro\text{mg}}\)/mg, respectively. These findings were consistent with the labeled product composition, Table 1.
## 3 Data Analysis
### Analysis of SART Score Differences
The remote tower may depend more on technological gadgets, which may vary in dependability and reaction time compared to the conventional tower. Consequently, controllers in the remote tower setting often obtain a bigger volume and caliber of information, along with enhanced situational awareness (t = 6.355, \(p=0.001\)). Furthermore, the disparities observed in the two situations are also influenced by psychological variables. Controllers may encounter diverse psychological conditions, such as stress and exhaustion, in various work environments, which might influence their situational awareness. The aforementioned elements may operate independently or synergistically, resulting in variations in the SART situational awareness ratings across controllers in remote tower and conventional tower settings (t = 2.719, \(p=0.035\)). As shown in Figure 2. Controllers demonstrate considerable variations across the two control contexts due to variances in the control environment, equipment, and psychological states. A lot of progress has been made in recent years in studying how people use classic tower controllers. However, subjective data representation can still show differences in how people perceive situations in two settings, but it is still open to biases and interference. Consequently, a comprehensive investigation of situational awareness among remote tower controllers is essential, highlighting the need for a thorough examination of diverse signs that might signify situational awareness inside the remote tower context.