esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 3 Experimental Setup and Results
### Noise Reduction Experiment Based on Range-Gated Technology  
The experimental setup is depicted in Figure 2. FPGA controls the laser to emit a pulse and simultaneously generates a synchronous signal input to the HydraHarp 400 to trigger the START signal to start timing. After each fixed number of pulses produced by the laser (1024 pulses in our experiment), FPGA controls the scanning mirror to rotate once. Then SPD records the corresponding photon signal. Since the pulse width (30 ps) of the laser is smaller than the dead time of SPD (80 ns), the SPD can only respond to one photon at most for each pulse. So the probability that SPD detects the signal light is as shown in Equation (5). After the entire object is scanned by the scanning mirror, the computer can get the image of the object by rearranging the photon signals of HydraHarp 400 according to the preset scanning mode of scanning mirror.  
The experimental results are shown in Figure 3. During the experiment, the number of noise photons in the environment is kept at around 10,000 counts per second (cps), and the number of signal photons is decreased from 3000 cps to 500 cps in turn. The experimental results are compared between the imaging results with and without the range-gated technology. As shown in Figure 3, the experimental image quality with gating is obviously better than that without gating. When the number of signal photons decreased to 500 cps, that is SNR is \(-13\) dB, the experimental results with gating can still distinguish the object while the image without gating cannot. Besides, the SSIM, defined as Equation (6), is used to evaluate the qualities of the reconstructed images.  
\[\text{SSIM}(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_ {2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)}, \tag{6}\]  
where \(\mu_{x},\mu_{y},\sigma_{x},\sigma_{y}\) and \(\sigma_{xy}\) are the local means, standard deviations, and cross-covariance for images \(x,y\). Furthermore, \(C_{1}=(0.01\times L)^{2}\), \(C_{2}=(0.03\times L)^{2}\), where \(L\) is the specified dynamic range value (10 bits in this experiment). As shown in Figure 4, the SSIM of reconstructed images with gating is 10 times higher than that without gating.  
Figure 2: The schematic of the experimental setup. FPGA controls the laser emission and the scanning mirror rotation, and sends gating signal to SPD. Scanning mirror scans the object according to the scanning mode preset by FPGA. The computer can get the image of the object by rearranging the photon signals of HydraHarp 400 according to the preset scanning mode of scanning mirror.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 3 Design Framework
### Guidelines for System Design  
The _Global UTM Association_ (GUTMA) is a non-profit consortium of worldwide UTM stakeholders, and it has promoted a discussion about which key properties should be present in future UTM systems [13]. After reviewing their technical report, we came up with a summary of these key features for UTM systems. We believe that the following aspects should be taken into account during the design phase of any UTM architecture:  
* **Digital.** The process of system digitization consists of making the communication between the different actors and components digital, and introducing automated decision-making procedures. This is a key aspect in UTM to reduce the operators' workload in an efficient and secure manner. Moreover, it enables the real-time exchange of data between the relevant parties for situation awareness and an easier integration of the UTM services.
* **Flexible and modular.** A UTM architecture should be flexible and adaptable to incorporate new actors (e.g., stakeholders) and functionalities (e.g., services), as they appear. Besides, the system should be modular, i.e., made of composable and reusable modules, in order to ease the process of creating more complex functionalities.
* **Scalable.** A scalable architecture is needed to grow with new actors and services. In order to achieve that, not only is the aforementioned modularity desirable, but also a paradigm with distributed responsibilities, rather than the obsolete scheme with a centralized ATC.
* **Safe and secure.** These two features are top priorities in any UTM ecosystem. In this sense, the system should know who is flying each unmanned aircraft, where they are flying (or intend to fly) to, and whether they are conforming (or not) to mandatory operating requirements.
* **Automated.** A UTM system providing automated services to assist the UAS operators will be more efficient and secure. Therefore, the system should provide support through automated functionalities for flight planning, monitoring, and real-time deconfliction, in order to ensure safe operations for both manned and unmanned aircraft.  
* **Open-source**. The use of open-source technologies is preferable, as they offer a global approach towards creating and evolving the necessary services and protocols for scalable operations. Moreover, open-source components can speed up the development and the deployment of UTM services.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 3 Reeb Graphs on Triangulated Surfaces  
### Morse Functions in the Piece-Wise Linear Setting  
In this paper, we will be working with triangulated surfaces. We will denote the set of vertices, edges, and faces of a triangulated mesh \(M\) by \(V(M)\), \(E(M)\), and \(F(M)\), respectively. [PERSON] provided the extension of Morse theory to triangulated manifolds [70]. Let \(M\) be a triangulated 2-manifold and let \(f:M\longrightarrow[a,b]\subset\mathbb{R}\) be a piece-wise linear function on \(M\). We will use the abbreviation PL for \"piece-wise linear\". The star of \(v\), denoted by \(star(v)\), is the set of simplices that intersect with the vertex \(v\). The closure \(\overline{star(v)}\) of \(star(v)\) is the smallest simplicial subcomplex of \(M\) that contains \(star(v)\). The link \(Lk(v)\) consists of the subcomplex of \(M\) of simplices belonging to \(\overline{star(v)}\), but not to \(star(v)\). The upper link of \(v\) is defined to be the set:  
\[Lk^{+}(v)=\{u\in Lk(v):f(u)>f(v)\}\cup\{[u,v]\in\overline{star(v)}:f(u)>f(v)\},\]  
the lower link is defined similarly by:  
\[Lk^{-}(v)=\{u\in Lk(v):f(u)<f(v)\}\cup\{[u,v]\in\overline{star(v)}:f(u)<f(v)\},\]  
and mixed link  
\[Lk^{\pm}(v)=\{[u_{1},u_{2}]\in Lk(v):f(u_{1})<f(v)<f(u_{2})\}.\]  
Using the link definitions, we classify vertices of \(M\), as follows. A vertex \(v\) in \(M\) is PL regular if the cardinality \(|Lk^{\pm}(v)|\) of \(Lk^{\pm}(v)\) is equal to 2. If \(|Lk^{+}(v)|=0\), then \(v\) is a PL maximum vertex with index 1, and if \(|Lk^{-}(v)|=0\), then \(v\) is a PL minimum with index 0. If \(|Lk^{\pm}(v)|=2+2m\), then \(v\) is a PL saddle with index 1 and multiplicity \(m\geq 1\). See Figure 3. A PL function \(f:V\longrightarrow[a,b]\subset\mathbb{R}\) is a PL Morse function if each vertex is either PL regular or PL simple, and the function values of the vertices are distinct. Similar to the smooth case, when \(\partial M\
eq\emptyset\), we further assume that \(f\) satisfies: (1) \(f^{-1}(\partial[a,b])=\partial M\) and (2) there are no critical points on \(\partial M\).  
Figure 3: The types of vertices on a triangulated mesh. From left to right: minimum, maximum, regular vertex, and saddle.  
Figure 2: An example of a Reeb graph (right) of a scalar function (left) defined on a surface with a boundary.
## 2 Related Work
### Approaches Based on Grayscale Symmetry  
[PERSON] [19] introduces a sub-pixel detector using a round template under image physical coordinates. The round template is employed to pass through a dilated image, and corners are ultimately determined by calculating the centroid of redundant points based on the symmetry of chessboard patterns. [PERSON] [20] proposes a method based on the property that the symmetry of a square region is more significant when the central pixel of it is closer to a corner. Symmetric factors of all pixels in a selected area are calculated for obtaining a weighted sub-pixel corner position. These approaches can achieve better results when detecting blurred or overexposed images. However, they need a bigger region of interest (ROI), and come at a higher computation cost due to the determination of symmetric factors using a sliding template or window and, therefore, they are subject to certain constraints of lens distortions and expensive for real-time use.
## 6 Conclusions and Future Works  
From the analysis presented, it can be determined that: when evaluating the set of preselected CNN models with two data sets, one of them is previously processed by applying segmentation by color-space; an average decrease of 39.45% is obtained in the function of loss categorical_crossentropy, which is an increase of 14.83% on average in the accuracy coefficient, and a decrease of 11.18% on average in the regression coefficient.  
On basis of the analysis presented, when evaluating the CNN with a previously processed dataset by applying color-space segmentation, a decrease of 36% in the loss value is obtained, increasing the value of hits generated by the accuracy metric by 17%, given that the distance between the value of the prediction and the expected value decreases. By implementing color-space segmentation to a set of test data, a positive contribution was made to the identification and extraction of patterns or characteristics that are necessary for the classification of images. Since the data has a coefficient of 0.8221 for the Jaccard method and a coefficient of 0.8767 for the Sorensen-Dice method, and since its probabilities are greater than 0.70, we can conclude that the segmentation of images by color-space contributes to image processing and subsequent prediction, given that through these values, it can be inferred that when applying segmentation by color space, the original and segmented images retain a large percentage of similarity, emphasizing the definition of the characteristics of the images.  
When the area under the ROC curve is taken as a reference, an average of 0.975, corresponding to the CNN performance, is presented under the processed dataset, increasing by a value of 0.085 with respect to the average AUC generated from the raw dataset.  
From the implementation of these tools, it is evident that the combination of areas, such as multimedia and artificial intelligence, provides a great field of action to continue researching and making proposals. The development of the technologies worked provides opportunities for making proposals in sectors such as health and education. In these sectors, proposals can be made for motor skill development, the development of basic knowledge in early childhood, reactions to certain situations, and psychology, among others.  
In future work, it is proposed that an immersive environment is implemented by using augmented reality tools and devices to support motor therapy in children, continuously monitoring the child's emotional behavior through brain-computer interfaces. On the other hand, an expansion of the scope of the developed CNN is planned, in order to achieve the identification of other geometric figures, forms, and symbols, in order to apply it to the abovementioned fields of action.  
[PERSON] and [PERSON] worked on the implementation, [PERSON] and [PERSON] worked on the methodological part, R.G.C. worked on the global and methodological review of the paper. This research received no external funding. The authors declare no conflict of interest.
## 1 Introduction
A XGBoost classifier using only radiomic and semantic features from the nodule obtained an AUC of 0.58 and 0.65, respectively [4]. Previous studies have suggested that combining radiomic and semantic data allows the development of integrated predictors that exhibit improved performance compared to learning models that use only one type of data. However, there is an urgent need to create learning models that use only radiomic features that are automatically extracted from images, as these features are more objective than semantic data and allow to reduce thedependency on data annotation by experts in assessing mutational status while reducing human error. Furthermore, the automatic detection of radiomic features from CT images is an important tool to help radiologists with the extensive and exhaustive work of CT annotation and overcome the lack of mass annotated datasets. Generally, the nodule is the main focus for lung cancer malignancy assessment and follow-up based on the well-established Fleischner Society and Lung-RADs Guidelines [17; 18], and previous studies have analyzed only this important cluster of tumor cells for _EGFR_ mutation status prediction [10; 14; 16]. Exploratory studies have recently shown that there is a correlation of _EGFR_ mutation status with other lung diseases, such as emphysema and fibrosis, which seems to indicate that cancer development is related to multiple physiological changes not restricted to the nodule region [19; 20], and that the inclusion of extratumor features allows a significant increase in _EGFR_ mutation predictive performance [4; 5; 21]. Therefore, the current work applies a comprehensive approach for predicting _EGFR_ mutation status, using only objective radiomic features directly extracted from a region of interest (ROI) containing the entire lung where the nodule is located. The hypothesis that it might be possible to develop predictive models with enhanced performance by combining nodule-related features with features from other lung structures is the main motivation for this work. This study includes combinations of six ML classification models--Logistic Regression, Elastic Net, Support Vector Machine (SVM) with linear and radial basis function (RBF) kernels, Random Forest, and Extreme Gradient Boosting (XGBoost)--and nine feature selection methods--Pairwise correlation filter, Principal component analysis (PCA) with feature set variance ranging from 65% to 95%, and QR decomposition, along with a baseline where no feature selection is used.
Moreover, the most significant findings of this work include that while the standard PID controller requires a drive-train damper to remove oscillation due to the drive-train mode, the optimal controllers, particularly MPC and \(H_{\infty}\), do not require one, thereby significantly simplifying the controller design process. The remainder of this paper is organized as follows. The linear models, which are obtained using Bladed software, and wind speed model required to run the models are reported in Section 2. In Section 3, a brief description of control objectives and optimal controllers (MPC, \(H_{\infty}\), and LQG) are presented. In Section 4, the simulation results are demonstrated, followed by conclusions in Section 5.
## 3 Results
### Multiple Linear Regression Model Results  
#### 3.2.1 MLR Established by Temperature and Humidity Variables  
A Multiple Linear Regression model is developed, utilizing the full set of temperature and humidity variables as the independent input variables. The results of this model are presented in Tables 4 and 5.  
The model's adjusted R\({}^{2}\) is 0.910, which is not much different from the R\({}^{2}\) of 0.919. The RMSE of the model is 0.881%, indicating a good fit of the model to the data, albeit with a larger error. Meanwhile, the _VIF_ values in Table 4 can only reflect the problem of covariance between the model variables. Specifically, there are 3 variables with _VIF_ values less than 10 and 11 variables with _VIF_ values greater than 10. This suggests a significant issue of multicollinearity among the underlying variables in the model. As a result, the coefficients corresponding to most of the variables are not statistically significant, which may affect the interpretation and stability of the model. Although the presence of multicollinearity did not compromise the prediction results, and the joint test of the model remains significant, there is still a pressing need for further improvement in this model.
###### Abstract  
Raman and mid-infrared (MIR) spectroscopy are useful tools for the specific detection of molecules, since both methods are based on the excitation of fundamental vibration modes. In this study, Raman and MIR spectroscopy were applied simultaneously during aerobic yeast fermentations of _Saccharomyces cerevisiae_. Based on the recorded Raman intensities and MIR absorption spectra, respectively, temporal concentration courses of glucose, ethanol, and biomass were determined. The chemometric methods used to evaluate the analyte concentrations were partial least squares (PLS) regression and multiple linear regression (MLR). In view of potential photometric sensors, MLR models based on two (2D) and four (4D) analyte-specific optical channels were developed. All chemometric models were tested to predict glucose concentrations between 0 and 30 g L\({}^{-1}\), ethanol concentrations between 0 and 10 g L\({}^{-1}\), and biomass concentrations up to 15 g L\({}^{-1}\) in real time during diauxic growth. Root-mean-squared errors of prediction (RMSEP) of 0.68 g L\({}^{-1}\), 0.48 g L\({}^{-1}\), and 0.37 g L\({}^{-1}\) for glucose, ethanol, and biomass were achieved using the MIR setup combined with a PLS model. In the case of Raman spectroscopy, the corresponding RMSEP values were 0.92 g L\({}^{-1}\), 0.39 g L\({}^{-1}\), and 0.29 g L\({}^{-1}\). Nevertheless, the simple 4D MLR models could reach the performance of the more complex PLS evaluation. Consequently, the replacement of spectrometer setups by four-channel sensors were discussed. Moreover, the advantages and disadvantages of Raman and MIR setups are demonstrated with regard to process implementation.  
Raman spectroscopy; mid-infrared spectroscopy; fermentation of _Saccharomyces cerevisiae_, real-time monitoring; multi-channel photometric sensors; multiple linear regression; partial least squares regression; monitoring of glucose; ethanol; biomass +
Footnote †: journal: Applied Soft. 2019, 9, 2472; doi:10.3390/app9122472
## 6 Conclusions  
The rapid and reliable detection of VT and VF is fundamental in patient monitoring, but also in Automated External Defibrillation (AED) or Implantable Cardioverter Defibrillation (ICD) therapies. Any incorrect interpretation of a ventricular arrhythmia, or even the confusion between VF and VT, can be dangerous for the life of the patient.  
In this paper, we propose a feature extraction method based on Topological Data Analysis (TDA) that provides near 99% accuracy in the discrimination of ventricular arrhythmias, normal and other rhythms (98.68% to VF; 99.05% to VT; 99.09% to Other; and 98.76% to Normal episodes). It also provides very high accuracy of 99.5% when discriminating between shockable (VT/VF) and non-shockable rhythms.  
The novelty of this work is the incorporation of geometric features proceeding from Topological Data Analysis to the detection and classification of ventricular arrhythmias. Note also that these powerful results were obtained without preselection of episodes. Taking into consideration the obtained results, we can conclude that TDA, and the geometric features derived from it, can be successfully used both in the detection and classification of ventricular arrhythmias and in the classification of shockable rhythms. Moreover, it proves that the geometric features derived from Topological Data Analysis (TDA) provide valuable features easing the task of the classifier. Finally, we can conclude that TDA features can be beneficial in other classification tasks.  
Individual contributions: conceptualization, A.M., and A.R.-M.; methodology, A.M., A.R.-M., and M.B.-M.; software, A.M.; formal analysis, A.M., A.R.-M., and J.V.F.-V.; writing--original draft preparation, A.M., and J.V.F.-V.; writing--review and editing, A.M., J.V.F.-V., A.R.-M., and M.B.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Patient consent was waived due to the use of publicly available Physionet databse.  
Data Availability StatementData can be publicly obtained from MIT and AHA databases, described in the references section.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TDA & Topological Data Analysis \\ PH & Persistent Homology \\ VF & Ventricular Fibrillation \\ VT & Ventricular tachycardia \\ AED & Automated External Defibrillator \\ ICD & Implantable Cardioverter Defibrillator \\ ECG & Electrocardiogram \\ RF & Random Forest \\ kNN & k-Nearest Neighbor \\ ICA & Independent Component Analysis \\ DT & Decision Tree \\ PC & Point Cloud \\ RPC & Representation of Point Cloud \\ PD & Persistence Diagram \\ PDI & Persistence Diagram Image \\ KDE & Kernel Density Estimation \\ PI & Persistence Images \\ WRM & Window Reference Mark \\ TR & Time Representation \\ PDR & Persistence Diagram Representation \\ SFS & Sequential Forward Selection \\ \end{tabular}
## 2 Optimization Methodology
### Methodology of Optimization Process in Trajectory Planning and Contour Tracking
An important point regarding the methodology adopted lies in the fact that from the techniques used, the obstacle presented here has the shape of a circle hence reducing the generation of local minimums while creating the path as indicated in [30], although the system is capable of analyzing obstacles of other various shapes like triangular, rectangular and trapezoidal but with the generation of more local minimums in path planning.
## 4 Discussion
On average, the least amount of significant correlations was seen using the NIRv-GPP product. Possibly, it has a weaker connection to surrounding climate variables than the other two products, as this data set relies more heavily on spectral radiation measurements. Further, outward disturbances on GPP, such as forest fires, anthropogenic land-cover change, or insect attacks, are not accounted for. Ideally, these factors should give rise to low significant correlations and thus be excluded from the analysis. However, the extent to which this has occurred is unclear. The modeling used to fill in the gaps for GOSIF-GPP and FluxSat-GPP could also, to some degree, explain the differences seen in dominating drivers between the three products. Climate variables such as soil moisture and temperature were generally considered more important for GOSIF-GPP and FluxSat-GPP, possibly an echo from utilizing climate variables as input to fill the gaps. Still, for the northern high latitudes, temperature increase has been shown to be the main factor facilitating increased plant productivity in summer [37; 41]. However, our results also indicate a relatively cohesive north-south gradient trend of climate vegetation controls during summer, meaning ambiguity between different data-sets and types of vegetation indices can be bridged. Further, the changes in the dominant climate drivers, particularly during the autumn season, could be the result of a relatively marginal difference between a cluster of controls, where the legacy effects from previous seasons also play an important part [42]. This ambiguity between controls of the late season is to some degree also reflected in previous research, e.g., work by [43] indicates that light limitation is also an important factor for autumn productivity in northern ecosystems. To include this driver in our analysis might therefore lead to a more cohesive result between products. In addition, [35; 44] find that there is a complex coupling between soil moisture and temperature, both of which seem to be important controls towards the end of the growing season (a proxy for autumn productivity in this context). In an increasingly warmer world, the limitations imposed by temperature during autumn may shift to an expansion of mainly water-limited ecosystems, where the legacy effects from previous seasons may be amplified even further.