esa-sceva/satcom-chunk-collection huggingface dataset:
# Goldmann Tonometry and Corneal Biomechanics
## 2 Materials and Methods
### Inflation Test  
With the purpose of measuring the corneal stiffness and correlate it to any possible difference between the IOP measured by the two instruments, inflation tests were then performed on the same eyes by cutting the cornea from the eye. A ring of sclera around the cornea was left to allow clamping of the cornea into a custom-made device previously described in [53]. During the inflation tests, each specimen was subjected to three loading-unloading pressure cycles with the pressure ranging from 1.8 to 30 mmHg serving as precondition cycles. Then corneal specimens were subjected to a posterior pressure, P (from 1.8 to 30 mmHg in steps of 2.5 mmHg), induced by a column of NaCl solution to simulate the effect of a growing intraocular pressure. In order to obtain quasi-steady response, we waited three minutes before recording images and applying the subsequent pressure step. Images including the entire profiles of the anterior surface of the cornea were acquired at regular intervals by means of a digital camera (Nikon DS5M, Nital, Moncalieri (TO), Italy), mounted on a stereomicroscope (Nikon SMZ800, Nital, Moncalieri (TO), Italy) and then analyzed by image analysis software (Nikon NIS-Elements D2.20, Nital, Moncalieri (TO), Italy) to track the corneal apex displacement as a function of the applied pressure. The linearized shell theory was applied to data in order to convert apex displacement and pressure in stress and strain, and to calculate the secant modulus, \(E\), for each level of applied pressure, as already described in [53]. The geometrical parameters needed to apply the shell theory, besides the apex displacement, are the average in-plane  
Figure 1: Scheme of the experimental setup used to measure the intraocular pressure, IOP. G: Goldmann applanation tonometer, T: pressure transducer, V: power supply, M: multimeter.  
diameter, \(S\), and the radius of curvature, \(R\), also evaluated by image analysis, and the corneal thickness at the apex, measured at the end of inflation tests on rectangular corneal strips cut from the tested corneas. The Poisson's coefficient was set to 0.5, consistent with the hypothesis of isotropic material at the basis of the shell theory [54; 55]. The experimental setup for the execution of the inflation tests is shown in Figure 2.
## 3 Results and Discussion  
The southeastern coastal regions of China have been chronically affected by severe typhoon disasters. This study focuses on Guangzhou (latitude 23.13\({}^{\circ}\) N, longitude 113.28\({}^{\circ}\) E), a representative metropolis in the region, as the primary case study. Situated at the geographic nexus of the Pearl River Delta, Guangzhou exemplifies the vulnerability of coastal urban centers to typhoon-induced hazards.
# Convolutional Neural Network (CNN)-Based Frame Synchronization Method
## 5 Conclusions  
In this study, we proposed a CNN-based frame synchronization method for the synchronized networks. As a conventional correlation-based synchronization method, the proposed CNN-based synchronization method can find the packet arrival time only from the correlator output. Instead of finding the correlation peak, the proposed technique find the time offset directly by the five-layer CNN classifier. The designed CNN consists of three convolutional layers and two fully connected layers. The computer simulation verified that the proposed CNN-based method significantly outperforms the conventional correlation-based method regardless of the preamble size. Those results indicate that the proposed method enable power saving of the transmitter by reducing the transmitted power or longer range transmission due to the enhancement at low SNRs. The proposed technique for the synchronized networks can be applied to the carrier sense multiple access type networks if a valid packet arrival can be identified before the proposed frame synchronizer. In future studies, it is worth (i) verifying through a testbed that the proposed method performs under various channel conditions in practice, and (ii) developing a novel CNN structure to further improve the synchronization performance and/or reduce the training complexity.  
Conceptualization, E.-R.J.; writing--original draft preparation, E.-R.J. and H.O.; simulation, E.-R.J. and E.-S.L.; writing--review and editing, J.J.; supervision, E.-R.J., H.O., and J.J.; funding acquisition, H.O. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development (UD190035 ED). The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript.  
Adaptive Moment Estimation  
Additive White Gaussian Noise  
Binary Phase-Shift Keying  
Convolutional Neural Network  
Carrier Sense Multiple Access  
Compute Unified Device Architecture  
False Detection Probability  
Graphic Process Unit  
Internet-of-Things  
Figure 9: A case when proposed CNN method is correct but the conventional method is wrong.
## 1 Introduction
Gas porosity is the creation of spherical pores that occur due to gas entrapped in the raw metal powder particles or trapped environmental inert gas during the melting process [22]. Gas porosity is most commonly caused by the powder quality of the part or the presence of excessive gas density, making it quite difficult to reduce [32]. Applying ML to AM requires the association of data from different stages of the production process, e.g., data from the part design model, process settings, in-process sensor readings, and post-production CT scanning. Voxelisation is one technique that has been widely used. Voxelisation is the process of discretisation used to divide up data into hexahedral block elements called voxels. It is characterized as converting a three-dimensional model described initially by the surface as a mesh or by volume information in the form of a solid object into 3D elements called voxels that are used to fit the shape of the model [33]. [PERSON] et al. conducted an in-depth study into the application of voxelisation in AM, finding that the voxel-based approach for storing localised model data was an efficient and discrete method [34]. Each voxel then represents a data point that can be used to train an ML model. Many studies have used X-ray computed tomography (CT) scanning of finished parts to identify and characterise pores [35]. CT scanning allows a detailed 3D model of the part to be created, including internal defects. This allows one to have a complete picture of the porosity in the part, with information on the pore size, shape, volume, and distribution in the part readily available [29]. This non-destructive technique allows one to view the pores in three dimensions and is able to uncover potential contamination in powder feedstock and in the finished part, thus revealing local defects that can hardly be detected by conventional means [26]. CT scanning can only be applied after the part is finished so it cannot be used for in-process monitoring; however, it is excellent for providing the analysis of pores required to train ML models [17]. Figure 1: Three-dimensional renderings of (**a**) lack of fusion and (**b**) keyhole pores reproduced from [31]. The existing literature shows the clear potential for ML applied to AM quality improvements and also that there is much research still to be carried out. This paper presents an ML tool to identify porosity in metal laser-based AM, specifically L-PBF, taking input data from the part design, process parameters, toolpath, and in-process photodiode sensors. The discretisation of these photodiode signals using the voxelisation method has not previously been reported to our knowledge. CT scanning of manufactured parts was used to identify and classify porosity, to train and test the ML models. Two-step classification was applied based on the presence of pores and whether they were LOF or KH pores. Three different classifiers were evaluated and compared along with two approaches to voxelisation and two approaches to class-balancing.
# Generative LLMs in Organic Chemistry: Transforming
## 1 Introduction  
Esterification is a fundamental chemical reaction that is used in the synthesis of esters from acids and alcohols [1; 2]. This reaction is important in producing not only various industrial intermediates but is also widely applied in fragrances and flavours [3]. The outcome of an esterification reaction is often dictated by the procedural steps that are followed. Precise procedures, encompassing parameters like the choice of solvents, temperature, and duration, are important to achieve the desired product with a high yield and purity [4; 5]. Such procedures serve as a plan for chemists, guiding them through the process of organic synthesis, ensuring reproducibility, and minimizing the chances of unwanted side products. However, predicting the optimal procedure for a given set of reactants remains a challenge, often requiring iterative experimentation with various chemical reagents (acid-catalyzed Fisher esterification, Steglich esterification, etc.). Recent advancements in deep learning (DL) have demonstrated its potential in modeling chemical properties and reactions [6; 7; 8; 9]. In addition, a shift in computational research methodologies towards viewing chemistry as a text-to-text task signifies a new perspective in the domain [10]. By treating chemical reactions as sequences, like sentences in natural language processing, researchers can utilize machine learning models, originally designed for language translation, to predict chemical outcomes. This novel perspective allows for advanced predictions, optimizations, and innovations in the chemical domain using linguistic models for scientific advancement. Leveraging large language models (LLMs) that have been pre-trained on vast datasets inherently grants the capacity to understand and generate chemical text to a certain degree. For generative text-to-text tasks, such models can be adapted to chemical contexts, bridgingthe gap between language processing and chemistry [11]. Building upon this, our research aims to develop an in silico methodology to predict accurate procedures for esterification reactions. We utilize a dataset of esterification reactions to fine-tune LLMs and then test the performance of the models. Using the methodology presented in this paper, chemists may reconsider their synthesis strategies, ultimately optimizing their reaction conditions before initiating the actual reaction. This predictive approach promises tangible benefits, such as increased efficiency and substantial savings in terms of time and resources.
## 3 Experimental Settings
### Parameters and Metrics  
The experiments conducted in this study were carried out using TensorFlow 2.6.0. The Adam optimizer was employed, with a batch size set to 4 and a learning rate of 0.001. The training process was conducted over 100 epochs. To prevent overfitting of the model, techniques such as dropout and early stopping were utilized.  
To validate our predictions, we employed root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and Nash-Sutcliffe efficiency (NSE) as evaluation metrics. RMSE and MAE are common metrics in regression tasks used to measure the absolute differences between predicted and actual values, with smaller values indicating better predictive ability. NSE and PCC are used to describe the correlation between predicted and actual values, with values closer to 1 indicating better correlation of model predictions. The definitions are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{N}\left(\frac{True_{i}-Predict _{i}}{True_{i}}\right)^{2}} \tag{5}\]  
\[\text{MAE}=\frac{1}{N}\sum\limits_{i=1}^{N}\mid\text{{True}}_{i}-Predict_{i}\mid \tag{6}\]  
\[\text{PCC}=\frac{\sum\limits_{i=1}^{N}\left(True_{i}-\overline{True}\right) \left(Predict_{i}-\overline{Predict}\right)}{\sqrt{\sum\limits_{i=1}^{N}\left( True_{i}-\overline{True}\right)^{2}\sum\limits_{t=1}^{N}\left(Predict_{i}- \overline{Predict}\right)^{2}}} \tag{7}\]  
\[\text{NSE}=1-\frac{\sum_{i=1}^{N}\left(True_{i}-Predict_{i}\right)^{2}}{\sum \limits_{i=1}^{N}\left(True_{i}-\overline{Predict_{i}}\right)^{2}} \tag{8}\]  
where the observed data are represented by True and the predicted values are represented by Predict.  
Figure 3: Distribution map of seas in study area.
## 2 Control of the ARSI  
Figure 1 depicts the single-phase ARSI topology analyzed in this paper, which consists of a standard H-bridge inverter, an auxiliary circuit, and an LC filter. The proper operation of the auxiliary switches S\({}_{\text{r1}}\) and S\({}_{\text{r2}}\) can create zero-voltage-switching (ZVS) conditions for the main switches S\({}_{\text{1}}\)-S\({}_{\text{4}}\). Meanwhile, the auxiliary switches can realize zero-current switching (ZCS).  
Figure 1: Circuit of the single-phase auxiliary resonant snubber inverter (ARSI) with LC filter.  
Figure 2 shows a control diagram of the ARSI. The ARSI controller consists of an output current controller and an auxiliary current controller. In the output current controller, the driving signals of the main switches are generated according to the output current. In order to dampen the resonance caused by the LC filter, a filter capacitor current feedback is introduced. The auxiliary current controller aims to control the auxiliary current to achieve ZVS of the main switches. Due to the large amount of calculations and the need for logic sequential control in the auxiliary current controller, a digital controller is required.  
The auxiliary current control involves two methods, fixed-timing control [19] and variable-timing control [20]. Owing to the advantages of lower conduction losses and achieving soft-switching for a wide load range, variable-time control methods are more widely used. By using the variable-timing control, the auxiliary current follows the magnitude of the load current adaptively. The auxiliary current controller in Figure 2 is realized based on the variable-timing control. The auxiliary controller is influenced by the output current controller, whereas the output current controller is independent of the auxiliary current controller.
## 4 Construction of Morphological Cognitive Intent Prediction Network Model
### Input and Output Layer Configuration  
Using MATLAB 2023a software, a feedforward neural network (_FNN_) model is constructed. The model takes 35 samples' \(S_{1}\) to \(S_{35}\) shape characteristic factors as inputs. Each sample is encoded into 6 groups, with a total of 25 bits. For instance, if sample \(S_{1}\) contains feature factors 2, 1, 1, 4, 1, 1 within morphological categories \(q_{1}\) to \(q_{6}\), then the sample's encoding would be 01000 100 00010 1000 10000. Each feature factor maps to a corresponding value. Similarly, encoding is performed for other samples. These encoded values, categorized into six feature groups, are fed into the input layer of the _FNN_ neural network, forming a 35 \(\times\) 6 matrix with 6 input nodes. The hidden layer of the _FNN_ neural network is set to 10 layers, while the output layer contains 1 node representing the comprehensive intention score \(Y_{1}\)-\(Y_{35}\) of the 35 samples. The network model structure and schematic are illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Inputs**} & \multicolumn{3}{c}{**Outputs**} \\ \cline{2-9}
**Samples** & \multicolumn{6}{c}{**Morphological Feature Categories \(Q\)**} & \multicolumn{3}{c}{**Comprehensive Intent Score**} \\ \cline{2-9}  & \(q_{1}\) & \(q_{2}\) & \(q_{3}\) & \(q_{4}\) & \(q_{5}\) & \(q_{6}\) & \(Y\) & **Converted Percentage Value \(Y\)** \\ \hline \(S_{19}\) & 1.0859 & 1.1001 & 0.
# Decision System Based on Markov Chains for Sizing the Rebalancing Fleet of Bike Sharing Stations
## 3 Methodology
### Subsystem Identification  
The region to be selected has two dimensions: geographical and temporal. The geographical dimension specifies what stations belong to the subsystem while the temporal dimension determines what time intervals (\(\Delta t\)) to use for model identification.  
The predictability of a subsystem is also dependent on the predictability of the underlying bike stations. From the point of view of predictability, similar stations are better suited for participation inside a subsystem. The similarity score from [53] is then used for geographical selection of the bike stations.  
Figure 5: The general state transition diagram for a subsystem.  
In the remainder of this section, the temporal-selection methodology is elaborated.  
As presented in Equation (6), the criticality value over time is the indicator for each bike station used to derive a specific behavior.  
According to historical traffic data collected from Citi Bike, each subsystem exhibits variation in its behavior, intermittently following different patterns. In some situations, the subsystem seems to conform to a certain stable pattern, while at other times it adopts a completely different model [53]. This fact is expressed in Equation (12):  
\[\begin{array}{c}\Psi_{1}\Longrightarrow\\ \Psi_{2}\Longrightarrow\\ \vdots\\ \Psi_{g}\Longrightarrow\end{array}\  
In addition to a reference time interval associated with the model identified for a subsystem (\(\Delta t_{\text{REF}}\mapsto\Psi_{j}\)), a set of probabilistic distribution functions are associated with the state transitions (\(\{F\}\mapsto\Psi_{j}\)). Each function from \(\{F\}\) is identified via model fit (LSQ minimization) over the accumulated histogram of state transition events, visually represented in Figure 8:
## 2 Materials and Methods
### Statistical Probability Distribution Models
#### 2.2.3 Two-Parameter Gamma (GAM) Distribution  
The PDF \(f_{\text{GAM}}(x)\) and the CDF \(F_{\text{GAM}}(x)\) of the GAM distribution are given by Equation (3) [27,30].  
\[f_{\text{GAM}}(x)=\frac{x^{\alpha-1}}{\beta^{\alpha}\Gamma(\alpha)}e^{(-\frac{ x}{\beta})},\ F_{\text{GAM}}(x)=\frac{\Gamma_{x/\beta}(\alpha)}{\Gamma(\alpha)} \tag{3}\]  
for \(x>0,\alpha>0,\beta>0\), where \(\Gamma(\alpha)=\int_{0}^{\infty}t^{\alpha-1}e^{-t}dt\ (\alpha>0)\) is the Gamma function, and \(\Gamma_{x}(\alpha)=\int_{0}^{x}t^{\alpha-1}e^{-t}dt\ (\alpha>0)\) is the incomplete Gamma function. \(\alpha\) is the shape parameter, while \(\beta\) is the scale parameter. Table 3 outlines the SPs of the GAM distribution.  
\begin{table}
\begin{tabular}{l l} \hline
**SP** & **Expression** \\ \hline \(\overline{v}_{dist}\) & \(\beta\Gamma\left(1+\alpha^{-1}\right)\) \\ \(\sigma^{2}_{dist}\) & \(\beta^{2}\left[\Gamma\left(1+2\alpha^{-1}\right)-\Gamma^{2}\left(1+\alpha^{- 1}\right)\right]\) \\ \(\mu_{3}\) & \(\beta^{3}\Gamma\left(1+3\alpha^{-1}\right)\) \\ \(S_{dist}\) & \(\frac{\Gamma\left(1+3\alpha^{-1}\right)-3\Gamma\left(1+2\alpha^{-1}\right) \Gamma\left(1+\alpha^{-1}\right)+2\Gamma^{3}\left(1+\alpha^{-1}\right)}{\left[ \Gamma\left(1+2\alpha^{-1}\right)-\Gamma^{2}\left(1+\alpha^{-1}\right)\right]^ {3/2}}\) \\ \(K_{dist}\) & \(\frac{\Gamma\left(1+\frac{x}{\sigma}\right)-4\Gamma\left(1+\frac{x}{\sigma} \right)\Gamma\left(1+\frac{x}{\sigma}\right)+6\Gamma\left(1+\frac{x}{\sigma} \right)\Gamma^{2}\left(1+\frac{x}{\sigma}\right)-3\Gamma^{4}\left(1+\frac{x}{ \sigma}\right)}{\left[\Gamma\left(1+\alpha^{-1}\right)-\Gamma^{2}\left(1+ \alpha^{-1}\right)\right]^{2}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Statistical properties of the WEI distribution [20,43].  
\begin{table}
\begin{tabular}{l l} \hline
**SP** & **Expression** \\ \hline \(\overline{v}_{dist}\) & \(\sigma\sqrt{\frac{x}{2}}\) \\ \(\sigma^{2}_{dist}\) & \(\left(\frac{4-\pi}{2}\right)\sigma^{2}\) \\ \(\mu_{3}\) & \(3.76\sigma^{3}\) \\ \(S_{dist}\) & \(\frac{\left(-3+\pi\right)\sqrt{\frac{x}{2}}}{\left(2-\frac{x}{2}\right)^{3/2}}\) \\ \(K_{dist}\) & \(\frac{32-3\pi^{2}}{(4-\pi)^{2}}\) \\ \hline \end{tabular}
\end{table}
Table 2: Statistical properties of the RAY distribution [20].
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 6 Evaluation
### Experimental Setup  
To test the utility and ranking quality of the output list of our ontology recommendation system, we conducted a user-based evaluation to compare each output list with those produced by the expert test users. During each recommendation cycle, the user was asked to check the recommendation list and to provide feedback on the ranking of ontologies by re-ranking the ontologies according to his opinion.  
To make the evaluation tasks of the test users comparable, we chose one input \"core\" ontology with familiar, abstract concepts to begin the process. We chose the Core Ontology for Biology and Biomedicine (COB) as it is a small-sized, upper-level ontology that brings together main key terms that can be reused in both biological and biomedical domains, and thus promises to give a large number of reuse opportunities that are tied with existing biomedical ontologies. Thereby, the evaluator's task was to extend the upper-level ontology \"COB\" to develop a new ontology for any selected domain (e.g., botany, human health, chemicals, anatomy, etc.).  
We expected that the best responses would come from test users who are experts in ontologies with a biomedical/bioinformatics background. However, it was hard to reach a large number of such expert test users, so we tried to balance between test users who are familiar with ontologies (i.e., understand the ontology reuse concept) and those who are familiar with bio-terminologies and bio-domains but with a minor ontological background. We obtained responses from 16 candidates who wanted to help with the evaluation task. We asked them to test the system by choosing a domain they know and to extend some classes from the input ontology with classes from ontologies related to the chosen domain.  
The evaluators were free to choose when to stop the iterations and the extensions of the input ontology classes whenever they were satisfied with the resulting output ontology. The number of iterations that each user went through ranged from two to five iterations (46 iterations in total). We also asked them to send us their feedback and notes about the strengths and weaknesses of our platform.