esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Baseline Performance Analysis and Bottleneck Identification (Pre-Optimization)
#### 4.2.2 Process Discovery and Workflow Visualization  
The process discovery analysis using Inductive Miner revealed the actual manufacturing workflow across nine active stations (201-209). The automatically generated process model (Figure 7), a direct result of applying process discovery algorithms (S3), identifies five critical bottleneck stations (Stations 202, 203, 204, and 205), marked red in the visualization. This analysis of the model allows for the identification of inefficiencies (S4) and constraints (S5). The process flow shows distinct transition patterns:  
* High-frequency transitions (>4000 events): Station 202 \(\rightarrow\) 203 (5592), Station 203 \(\rightarrow\) 204 (4474), Station 204 \(\rightarrow\) 205 (4646)
* Medium-frequency transitions (2000-4000 events): Station 205 \(\rightarrow\) 206 (3408), Station 206 \(\rightarrow\) 207 (3276), Station 207 \(\rightarrow\) Process End (6379)
* Critical bottleneck transitions: Process Start \(\rightarrow\) 202 (4755), indicating initial process constraints
###### Abstract  
Generally, the key issues of 2D LiDAR-based simultaneous localization and mapping (SLAM) for indoor application include data association (DA) and closed-loop detection. Particularly, a low-texture environment, which refers to no obvious changes between two consecutive scanning outputs, with moving objects existing in the environment will bring great challenges on DA and the closed-loop detection, and the accuracy and consistency of SLAM may be badly affected. There is not much literature that addresses this issue. In this paper, a mapping strategy is firstly exploited to improve the performance of the 2D SLAM in dynamic environments. Secondly, a fusion method which combines the IMU sensor with a 2D LiDAR, based on framework of extended Kalman Filter (EKF), is proposed to enhance the performance under low-texture environments. In the front-end of the proposed SLAM method, initial motion estimation is obtained from the output of EKF, and it can be taken as the initial pose for the scan matching problem. Then the scan matching problem can be optimized by the Levenberg-Marquardt (LM) algorithm. For the back-end optimization, a sparse pose adjustment (SPA) method is employed. To improve the accuracy, the grid map is updated with the bicubic interpolation method for derivative computing. With the improvements both in the DA process and the back-end optimization stage, the accuracy and consistency of SLAM results in low-texture environments is enhanced. Qualitative and quantitative experiments with open-loop and closed-loop cases have been conducted and the results are analyzed, confirming that the proposed method is effective in low-texture and dynamic indoor environments.  
dynamic environment; closed-loop detection; sparse pose adjustment (SPA); inertial measurement unit (IMU); simultaneous localization and mapping (SLAM) 2018 8 2534 10.3390/app8122534
## 4 Discussion
### Generating Long Time Series and Spatio-Temporally Continuous GPP Data  
Atmospheric CO\({}_{2}\) concentration and meteorological environmental factors are sensitive parameters to GPP estimation. However, CO\({}_{2}\) and meteorological data from the same source are incomplete in long time scales, which can lead to significant bias in GPP estimation. Hence, it is essential to process data from diverse sources to ensure coherence and minimize the potential impact on GPP estimation.  
The atmospheric CO\({}_{2}\) concentration data from SCIAMACHY and OCO-2 were used before and after 2009. The scaling factors for the two products ranged from 0.92 to 1.15, and it can be inferred that the change in the value of the atmospheric CO\({}_{2}\) fertilization effect factor derived from the different CO\({}_{2}\) data sources was approximately 0.1 from Equation (5). Therefore, the integration of the atmospheric CO\({}_{2}\) fertilization effect into GPP estimates was found to be minimally impacted by the use of CO\({}_{2}\) data from different sources. The meteorological data used in this study comprised two sources. CMFD was employed for the period before 2018, and supplemented with the land monthly averaged data from 1950 to the present in ERA5 product for the period after 2018. As the distinct meteorological sources resulted in variations in environmental stress factors, such as temperature stress and water stress, data correction was necessary for both datasets.  
In this study, the ERA5 product was calibrated using the cumulative distribution function (CDF) method, which adjusted the ERA5 to match the CMFD distribution by pairing and establishing a relationship between data with the same cumulative distribution frequency from two different source datasets [37]. The results demonstrated that the CDF method performed well on the ERA5 and was able to conform to the CMFD distribution overall. Therefore, the meteorological data for ERA5 in 2019 and 2020 were calibrated using the established CDF matching relationship for the production of long time series GPP data. Although matching two types of data using the CDF method can compensate for the limitations caused by incomplete time series, the matched data are still deficient in terms of consistency compared to the data from a single source. Therefore, environmental data with greater continuity and accuracy will be used to improve the model developed in future work, and more detailed estimates of GPP will be produced.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 4 Gamow
### Instrument Design  
The lobster-eye X-ray telescope (LEXT) with a wide FoV of \(\sim\)1350 sq deg detects GRBs and locates them with arcminute precision. The LEXT utilizes an array of slumped micro-pore optics (MPOs) with 40 \(\upmu\)m square pores and a focal length of 30 cm [36]. The focal plane uses heritage MIT-Lincoln Labs CCDs. The required performance is to detect at least 20 z \(>\) 6-long GRBs over a 2.5-year prime mission. A rapidly slewing spacecraft autonomously points the photo-z infra-red telescope (PIRT) within 100 s to identify high-redshift (z \(>\) 6) GRBs. The photo-z technique takes advantage of the Hydrogen Lyman-\(\sigma\) absorption, which creates a sharp blue-ward drop out. A 30 cm aluminum RC telescope passively cooled to 200 K feeds a dichroic prism beam splitter to place five images onto a single H2 RG detector covering the 0.6 to 2.5 \(\upmu\)m band [37]. The FoV is 10 arcmin square. This design provides the required 15 \(\upmu\)Jy (21 mag AB) 5-\(\sigma\) sensitivity in a single 500 s exposure (see [38]). The high redshift science objectives require rapid follow-up observations to provide NIR medium-resolution spectroscopy (R\(\sim\)3000) to measure the profile of the Ly-\(\sigma\) absorption line and metal absorption lines from the host galaxy. To obtain a sufficient signal-to-noise ratio in a reasonable observing time requires 6 m class or greater telescopes. Ground-based telescopes begin observations within an hour and JWST and the ELT within 2-3 days ([PERSON] et al. 2024, to be submitted). An L2 orbit provides \(>\)95% observing efficiency with pointing optimized for these follow-up observatories. A low-bandwidth continuous S-band connection provides real-time alerts to the ground.
## 5 Algorithm Descriptions
### Feature Extraction
#### 5.1.1. \(\mathrm{FFT}+\mathrm{PCA}\) (Fast Fourier Transform and Principal Component Analysis)  
The sample signals obtain the amplitude spectrum through the Fast Fourier Transform. High spectrum dimension can reduce classifier performance. The principal component analysis (PCA) method is adopted to reduce the dimension of spectrum data.  
\(X\) is the \(N\) observations of \(p\) variable. \(X\)= \([x_{1},\,x_{2},\,x_{3},\,\ldots,\,x_{n}]^{\mathrm{T}},\,x_{1}\)\(n\) is a row vector of p dimension which means the spectrum characteristics of each sample, where \(p\) is the number of the spectrum points. PCA algorithm are briefly described as below [27]:  
* Standardize the observation matrix \(X\) to obtain matrix \(Y\);
* Calculate \(Z\) which is the covariance of matrix \(Y\);  
Calculate the covariance matrix eigenvalue and eigenvector of \(Z\), and order them from large to small order as \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}\geq\ldots\geq\lambda_{p}\). The corresponding eigenvectors are \(U_{1}\), \(U_{2}\), \(U_{3}\), \(\ldots\), \(U_{p}\), and covariance matrix \(Z\) can be expressed as:  
\[Z=U\Sigma U^{T} \tag{1}\]  
\(\Lambda\) is a diagonal matrix, the elements on the diagonal are eigenvalues from large to small. \(U\) is a feature vector according to the orthogonal array composed of columns.  
Figure 8: Algorithms Chart.
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 2 Proposed Methodology
### Localization Phase
#### 2.2.1 Localization of DR Regions  
The FRCNN takes the images and the region proposals as an input. Our method uses max-pooling and convolutional (\(C_{\textit{conv}}\)) layers to processes the whole image and produces the feature map of \(C_{\textit{conv}}\). From the \(C_{\textit{conv}}\) feature map, fixed-size feature vectors are extracted using the ROI pooling layer. And then supplied into a series of fully connected (_fc_) layers before branching into separate output layers, the first layer computes Softmax-probability estimates over \(k=5\) region classes and the other layer produces four real-valued numbers for five region classes which represent a position of the bbox for one of the five classes [43]. We have trained the model for multi-class DR object detection using FRCNN, which can accurately localize the multiclass objects from an image. For DR localization, there are \(4+1\) classes in total i.e., hemorrhages, microaneuryss, soft-, and hard- exudates, plus one background class.  
The RoI pooling layer resizes the features into a fixed-size of hyper-parameters \(h\times w\) (i.e., \(7\times 7\)) by using the max-pooling process. In our proposed method, the RoI consists of a four-sided (_d,e,f,g_)) quadrilateral window, where (_f,g_) represent the height and width and (_d,e_) represent the top left corner. The max-pooling layer divides \(f\times g\), the window of the RoI, into \(F\times G\), a sub-division grid of size \(f/F\times G/G\), and then max-pooling applied independently in the sub-division values into corresponding output cell. The RoI pooling layer is an extraordinary instance of spatial pyramid layer utilized in SPPnets [44], in which there is just one pyramid pooling level. We utilized the calculation of pooling sub-window given in [44].
New experimental data about the physical characteristics of zirconia-ceria ceramics is very important in the development of new structural and functional materials [15]. For example, there is an urgent demand for inert ceramic matrices for nuclear fuels based on zirconia [16]. Because of the above-mentioned properties of advanced ceramics, zirconia-ceria is suitable for holding the plutonium oxide fuel and resisting thermal and radiation impacts inside a nuclear reactor [17]. Another way of using zirconia-ceria ceramics is as a functional material for solid oxide fuel cells. A solid oxide fuel cell is the central object of devices that produce electricity from electrochemical reactions where fuel (natural gas, hydrogen) reacts with oxygen. The operating temperature range of such devices is 500-1000 \({}^{\circ}\)C, so ceramic materials with high ionic and oxygen conductivity are required [18]. Finally, zirconia-ceria solid solutions (Ce\({}_{x}\)Zr\({}_{1-x}\)O\({}_{2}\)) can be used in heterogeneous catalysis because of their textural and redox properties [19]. It is very important to investigate the phase composition, microstructural features, and mechanical and electrical properties of oxide ceramics to evaluate their effectiveness in the above-mentioned applications. To achieve this goal, in this work, partially stabilized CeO\({}_{2}\) zirconia polycrystalline ceramics were successfully synthesized by the conventional ceramic method with different weight concentrations x of ceria in the range of 0 to 1.0 with a step of 0.05. X-ray diffraction (XRD) and Raman spectroscopy were implemented to provide a precise evaluation of the phase composition and the defects in the crystal structure. Scanning electron microscopy (SEM) was used to study the microstructural characteristics, while the Vickers Hardness Test was chosen to study the mechanical properties of the obtained ceramics. Finally, dielectric spectroscopy was used for dielectric measurements.
###### Abstract  
Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA \(\times\) TOF), time-of-flight energy (TOF \(\times\) E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm's FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.  
space environment particle identification technology; convolutional neural network; onboard FPGA platform +
Footnote †: journal: Medical Imaging
## 2 Result and Discussion
### Metabolic Engineering of Aurantiochytrium sp. SK4 for Carotenoid Production
In contrast, the co-expression of the _GPS_, _IDI_ and _VHb_ greatly enhanced the contents of both total carotenoids and fatty acids. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Sk4**} & **AT26** \\ \hline \multirow{8}{*}{**Carotenoid content \(\mathrm{\SIUnitSymbolMicro g}\times\mathrm{g}^{-1}\) DCW**} & Astaxanthin 1  & 28.2 \(\pm\) 2.9 (2.9\%) & 141.1 \(\pm\) 6.5 (7.9\%) \\  & Adonirubin 2  & 65.3 \(\pm\) 4.1 (6.7\%) & 133.5 \(\pm\) 0.1 (7.4\%) \\  & Canthaxanthin 3  & 72.6 \(\pm\) 5.5 (7.5\%) & 156.1 \(\pm\) 12.1 (8.7\%) \\  & 3-OH-Echinenone 4  & 20.0 \(\pm\) 0.6 (2.1\%) & 39.9 \(\pm\) 1.1 (2.2\%) \\  & Echinenone 5  & 70.4 \(\pm\) 5.0 (7.3\%) & 122.4 \(\pm\) 7.0 (6.8\%) \\  & \(\beta\)-carotene 6  & 712.7 \(\pm\) 51.0 (73.5\%) & 1200.5 \(\pm\) 7.31 (6.9\%) \\  & Total & 969.2 \(\pm\) 67.5 & 1793.5 \(\pm\) 86.8 \\ \hline \multirow{8}{*}{**Fatty acid content \(\mathrm{\SIUnitSymbolMicro g}\times\mathrm{g}^{-1}\) DCW**} & C14:0  & 16.4 \(\pm\) 1.2 (9.1\%) & 25.1 \(\pm\) 3.6 (5.8\%) \\  & C15:0  & 8.4 \(\pm\) 0.3 (4.6\%) & 16.0 \(\pm\) 1.8 (3.7\%) \\  & C16:0  & 47.4 \(\pm\) 2.8 (26.2\%) & 91.4 \(\pm\) 8.2 (21.0\%) \\  & C16:1 & 0.0 \(\pm\) 0.0 (0.0\%) & 6.7 \(\pm\) 0.5(1.5\%) \\  & C17:0  & 0.0 \(\pm\) 0.0 (0.0\%) & 6.8 \(\pm\) 0.3 (1.6\%) \\  & C18:0  & 14.3 \(\pm\) 0.7 (7.9\%) & 20.4 \(\pm\) 0.3 (4.7\%) \\  & C18:1 & 0.0 \(\pm\) 0.0 (0.0\%) & 6.9 \(\pm\) 0.1 (1.6\%) \\
**Fatty acid content \(\mathrm{\SIUnitSymbolMicro g}\times\mathrm{g}^{-1}\) DCW** & C20:4 (AA) & 0.0 \(\pm\) 0.0 (0.0\%) & 5.2 \(\pm\) 0.1 (1.2\%) \\
**(\% composition) 8** & C20:5 (EPA) & 5.9 \(\pm\) 0.1 (3.3\%) & 6.4 \(\pm\) 0.1 (1.5\%) \\  & C22:5 (DPA) 9  & 21.8 \(\pm\) 1.1 (12.0\%) & 69.2 \(\pm\) 3.1 (15.9\%) \\  & C22:6 (DHA) 6  & 64.2 \(\pm\) 3.5 (35.4\%) & 175.8 \(\pm\) 7.2 (40.5\%) \\  & others & 2.7 \(\pm\) 3.8 (1.5\%) & 5.0 \(\pm\) 0.1 (1.2\%) \\  & SFA & 86.6 \(\pm\) 5.0 (47.8\%) & 159.8 \(\pm\) 14.2 (36.8\%) \\  & MUFA & 0.0 \(\pm\) 0.0 (0.0\%) & 13.1 \(\pm\) 0.5 (3.0\%) \\  & PUFA & 91.8 \(\pm\) 4.6 (50.7\%) & 256.5 \(\pm\) 10.5 (59.0\%) \\  & TEA & 181.1 \(\pm\) 4.1 & 43.4 \(\pm\) 4.1 \\ \hline \hline \end{tabular}
\begin{tabular}{l l} DCW, dry cell weight; AA, arachidonic acid; EPA, eicosapentaenoic acid; DHA, docosabexaenoic acid. SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acid; TFA, total fatty acids. \({}^{\mathrm{A}}\) Carotenoid content after 120 hours of shaker culture. \({}^{\mathrm{B}}\) Fatty acid content after 72 hours of shaker culture. \({}^{\mathrm{I}}\) Fatty acid content after 72 hours of shaker culture. \({}^{\mathrm{I}}\) 1–6 and \({}^{\mathrm{A}}\)\({}^{\mathrm{B}}\)\({}^{\mathrm{A}}\) were the peak assigned in Figure 4. Data are shown as mean \(\pm\) SD, n = 3. \\ \end{tabular}
\end{table} 
Table 1: Contents and compositions of carotenoids and fatty acid in wild type SK4 and its transformant AT26.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 6 Proof of Concept
### Real Network  
In order to evaluate the optimization algorithm in a real environment, some tests using the UMAHetNet network were carried out. In this case, only the optimization based on the adjustment of the RLC layer in AM mode was performed. As in the optimization of the QoE for the file transfer service in the simulated environment, the RLC AM parameters that were selected to carry out the optimization tasks were t-StatusProhibit and t-PollRetransmit.
## 3 Proposed Methodology
### Linear Regression  
The linear regression model is a basic and simple regression technique [33; 34]. Finding a linear relationship between one or more variables is done by using linear regression. There are two types of linear regression, simple and multiple linear regression. This model is used to estimate the relationship between the input variable (\(X\)) and output variable (\(Y\)) which work under the principle of statistical and machine learning concepts. The LR model predicts and calculates changes in output variable (\(Y\)) when the input variable changes (\(X\)). The input and output variables can be exchanged by correlation coefficient. The LR model predicts the target value (\(Y\)) based on the input variable (\(X\)). The simple linear regression is given in Equation (1),  
\[y=mX+c=>Y=s_{0}X+s_{1}, \tag{1}\]  
where from (1), \(y\) is the target (dependent) variable, \(X\) is the input (independent) variable, \(s_{0}\) is the slope, and \(s_{1}\) is the regression coefficient. The complexities of the regression model are used to evaluate the number of coefficients used in this model. The model coefficient or parameters are obtained together as \(m\) and \(c\); after utilizing the training data to get the model coefficient estimates of \(\hat{s_{0}}\) and \(\hat{s_{1}}\). We have  
\[\hat{y}=\hat{s_{0}}X+\hat{s_{1}}, \tag{2}\]  
where from (2), \(\hat{y}\) indicates the prediction. We also have  
\[e_{j}=y_{j}-\hat{y_{j}}, \tag{3}\]  
where from (3), \(e_{j}\) is the error difference between the actual and predicted value of \(j^{th}\) representation. The sum of square error is defined as  
\[SSE=e_{1}^{2}+e_{2}^{2}....e_{n}^{2}. \tag{4}\]  
In regression, there is a concept of best-fit line, which is the line that best matches the provided data (Algorithm 2). SSE is called loss function or cost function, and minimizing the error would result in good fit or accuracy. This strategy is called the least squares method. By using some calculus, the least-squares method chooses 0 and 1 to minimize the SSE. Then, a new set of coefficients is generated, and it needs some metrics to verify that the estimated coefficients are accurate.  
```
1:Start
2:Load data
3:Split the data into training and testing
4:Set random number for learning parameters
5:Repeat the process to optimal value
6:Fit the LR model
7:Find error (mse) value
8:Continue the same until no more changes in the error value
9:Find the best value for regression
10:Predict the value for regression model
11:End
```  
**Algorithm 2** An algorithm for linear regression
## 4 Results and Discussion
### Effect of the Distance Between the Electrodes  
As the detected ionic current is induced by ion migration, there should be an attenuation of the signals with the increase in the distance between the two electrodes. The effect of the distance (within 60 m) between the working electrode and the sensing electrode on the ionic current signals was investigated in the water pool with tap water. It can be seen from Figure 6 that the peak values of the ionic current signals decrease with the increase in the distance between the working electrode and the sensing electrode (experimental data can be seen in Table A5 of Appendix A). The magnitude of the signals decreases in almost one order for each electrode distance increase of 10 m. As a result, the detected ionic current signal decreases from 233 \(\upmu\)A to 1.5 \(\upmu\)A when the electrode distance increases from 0 m to 60 m. The results can be explained with the electric resistance of water, which increases with the increase in the electrode distance. Meanwhile, the EDL capacitor is unchanged. Therefore, a longer electrode distance leads to a smaller ionic current.
## 4 Discussion  
### Evaluation Criteria  
_TP_, _TN_, _FP_, and _FN_ represent true positive and true negative and false positive and false negative, respectively.  
The calculation of precision in Equation (11) is denoted by the precision for one of images among one of several categories. The calculation of recall is shown in Equation (12).  
\[\text{Precision }=\frac{\textit{TP}}{\textit{TP}+\textit{FP}} \tag{11}\]  
\[\text{Recall}=\frac{\textit{TP}}{\textit{TP}+\textit{FN}} \tag{12}\]  
The confidence score is obtained based on the probability that a classifier predicts that an object is included in the corresponding bonding box. The IoU for one of images of the one of the classes is the overlap between the prediction box and the original label box (so-called ground truth). The ideal situation is completed overlap, that is, the ratio is 1. Most often, the IoU value is between [0, 1]. Both confidence score and IoU jointly determine the detection result and output whether it is _TP_ or _FP_.  
The calculation of the average precision (AP) is basically equivalent to calculating the area under the precision-recall curve, which is a two-dimensional curve using accuracy and recall as the vertical and horizontal axes. The performance of the model is evaluated by classification precision, recall, and accuracy. Each category corresponds to an AP. The formula for AP is defined as in Equation (13). As an example, for one head of a male dog from the 16 categories in testing samples, the AP is as follows.  
\[\text{Average Precision}_{\text{C}}=\frac{\sum_{i=1}^{image\ N}\text{Precision} _{n}\ (\text{based on the head of male dog})}{\text{Total number of the test sample images}} \tag{13}\]  
The mean average precision (mAP), the index of measuring detection accuracy in target detection, is the average value of AP values of each category of the multi-category verification dataset, and the corresponding formula is Equation (14).  
\[\text{mAP}=\frac{\sum_{c=1}^{c-16}\text{Average Precision}_{c}}{\text{Total Category Numbers }(16)} \tag{14}\]