esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A The Photonic Gain of the 2Q Protocol  
We represent the gain of single detected events, such as \(Q_{(\pm,\mp)}\), and the gain of double-detected events, such as \(Q_{(+,+)}\). From [8], we know that for a photonic source with mean \(\mu\), the photonic gain of non-empty pulses, \(Q_{(+)}\), is \(Y_{0}+1-e^{-\mu\eta_{BT}}\), where \(\eta_{BT}\) is the overall efficiency at [PERSON]'s side and \(Y_{0}\) is the background noise. Now, let us assume that the gain of two consecutive non-empty pulses is independent of each other. Then, we can compute \(Q_{(+,+)}\) by multiplying the individual photonic gains \((Q_{0}Q_{0}+Q_{0}Q_{1}+...+Q_{0}Q_{i}+...)+(Q_{1}Q_{0}+Q_{1}Q_{1}+...Q_{1}Q_{i }+...)+...\), with \(i=0,1,2,...\), which can be rewritten as \(Q_{0}(Q_{0}+Q_{1}+...+Q_{i}+...)+Q_{1}(Q_{0}+Q_{1}+...+Q_{i}+...)+...+Q_{j}(Q_ {0}+Q_{1}+...+Q_{i}+...)+...\), with \(j=0,1,2,...\). Thus, factorizing, we get \(\sum_{i=0}^{\infty}Q_{i}\sum_{j=0}^{\infty}Q_{j}\), but replacing \(\sum_{k=0}^{\infty}Q_{k}\) by \(Y_{0}+1-e^{-\mu\eta_{BT}}\), the photonic gain of two consecutive non-empty pulses can be written as \(Q_{(+,+)}\) is \((Y_{0}+1-e^{-\mu\eta_{BT}})^{2}\).  
Similarly, we can compute the corresponding photonic gain for single detection events, \(Q_{(\pm,\mp)}\). Such detection events consist in detecting one pulse, while the other measurement gives no click or _vice versa_. Thus, the symbol, \((+,-)\), means that the first state produces a click, while the second state is lost (no-click). The other symbol, \((-,+)\), is used to represent the event where the order of the detection is inverted. Since the gain of empty and non-empty pulses are \(e^{-\mu\eta_{BT}}-Y_{0}\) and \(Y_{0}+1-e^{-\mu\eta_{BT}}\), respectively,we have that the gain of single-detected events \(Q_{(\pm,\mp)}\) can be written as the product \(2\cdot(e^{-\mu\eta_{BT}}-Y_{0})(Y_{0}+1-e^{-\mu\eta_{BT}})\). In such a manner, the photonic gains of the 3Q protocol can be derived.
## 4 Discussion
### Uncertainty Analysis  
Eddy covariance offers a direct technique for assessing trace gas and energy fluxes between the surface and the atmosphere at an ecosystem level, usually within a 1 km radius of the measurement site [36]. This makes it difficult to directly compare it with other methods. [PERSON] et al. assessed eddy covariance (EC) data by comparing them with other methods, such as inventory and chamber techniques, and found that EC biases are not evident across sites, which indicates the effectiveness of the standard post-processing procedures and enhances confidence in the reliability of EC data [88]. [PERSON] and the eddy covariance site teams further compared and technically validated their measurements based on their in-depth knowledge of the respective sites [36]. While measurement and processing uncertainties are inevitable and can be significant for certain sites and ecosystem conditions, the flux values in this dataset generally align with expectations. Overall, eddy covariance continues to be one of the most dependable methods for evaluating land-atmosphere exchanges at the ecosystem scale. The widespread use of eddy covariance data in many scientific studies indirectly confirms its reliability and value.  
On the other hand, CO\({}_{2}\) flux (net ecosystem exchange, NEE) is primarily partitioned into GPP and ER using two methods: the nighttime flux method and the daytime flux method [36]. The nighttime flux method estimates ER by constructing a respiration-temperature model using nighttime data and calculates GPP based on NEE. The parameterization process accounts for dynamic factors such as water availability, substrate, and phenology [89]. The daytime flux method, which combines both daytime and nighttime data, estimates GPP through a light-response curve and vapor pressure deficit, while ER is estimated using a respiration-temperature relationship [90]. Compared to the nighttime flux method, the daytime flux method is more widely used in research, which is consistent with this study.
## 3 The Software-Radio Approach for Ionospheric Monitoring  
Ionosphere monitoring has traditionally been a prerogative of professional and commercial hardware GNSS receivers. Devices specifically designed for the analysis of ionospheric events are denoted Ionospheric Scintillation Monitoring Receivers (ISMRs). Nevertheless, recent trends in GNSS receivers implementation envisage the use of Software Defined Radio (SDR) technology, as a valuable alternative to commercial GNSS receivers. In its general view, SDR refers to the ensemble of hardware and software technologies and design choices enabling reconfigurable radio communication architectures [19]. According to this paradigm, all of the signal processing stages of the receiver chain are implemented as software routines. Contrarily to hardware GNSS receivers, where only post-processed data are available, software-defined receivers allow users to access any intermediate and low-level signal processing stage, and, in turn, a wider, potentially unlimited, set of observables [8].  
Figure 1: The distribution of the signal samples varies according to scintillation intensity; (**a**) values of the amplitude scintillation index \(S_{4}\) over time; (**b**) histogram of the Signal Intensity (SI) for three different values of amplitude scintillation taken at three different time instants.  
In addition, the software implementation grants a complete control of the receiver architecture, in terms of configuration and of implementation of novel algorithms, including ionospheric monitoring [20]. GNSS SDR receiver architecture is commonly divided into two blocks, as reported in Figure 2.  
* The **data grabber**, composed by an antenna and an RF front-end; the GNSS signal is pre-conditioned, down-converted at Intermediate Frequency (IF), sampled and digitized by an Analog-to-Digital Converter (ADC); then, raw IF samples are stored on memories.
* The **signal processing** stage, fully implemented in software, where the stored samples are processed, either in real-time or in post-processing.  
Examples of SDR implementations for scintillation monitoring are given for instance in [9; 10; 11; 21]. The flexibility granted by this approach eases the implementation of monitoring stations in harsh environments [22; 23], and also the design of new signal processing methods for GNSS signal monitoring [15; 24; 25].
## 2 System Description
### Modeling of the Radial Inflow Turbine
#### 2.3.1. Rotor Modeling  
The rotor is simulated using two common non-dimensional parameters, namely, loading coefficient \(\Psi\) and flow coefficient \(\varphi\). These parameters are shown in Equations (7) and (8) [24].  
\[\varphi=\frac{C_{m5}}{U_{4}} \tag{7}\]  
\[\Psi=\frac{\Delta h_{act}}{U_{4}^{2}} \tag{8}\]  
Knowing that loading and flow coefficients are given, the blade speed U4 and the meridional velocity \(C_{m5}\) can be obtained as per Equations (7) and (8). The rotor radius \(r_{4}\) and blade height \(b_{4}\) can be obtained utilizing Equations (9) and (10), respectively. After that, the blockage factor \(K_{B4}\) and blade thickness \(t_{b4}\) are calculated using Equations (11)  
Figure 4: Flow chart of design process of the radial inflow turbine.  
Figure 5: An enthalpy–entropy diagram of the turbine stage.  
and (12), respectively. The rotor number of blades \(Z_{\tau}\) can be calculated using the Glassman correlation [25], expressed in Equation (13).  
\[r_{4}=\frac{U_{4}}{\omega} \tag{9}\]  
\[m_{calc}=2(1-K_{B4})\pi r_{4}\rho_{4}b_{4}C_{m4} \tag{10}\]  
\[K_{B4}=1-\frac{Z_{r}t_{b4}}{2\pi r_{4}sin\beta_{4}} \tag{11}\]  
\[t_{b4}=0.04\;r_{4} \tag{12}\]  
\[Z_{r}=\frac{\pi}{30}(110-\alpha_{4})\tan\alpha_{4} \tag{13}\]  
The thermodynamic properties of the working fluid can be calculated as per Equations (14)-(17), completing the inlet geometry of the rotor. Refprop, version 9 [26] software was utilized to obtain the thermodynamic properties of the fluid.  
\[h_{4}=h_{04}+\frac{1}{2}C_{4}^{2} \tag{14}\]  
\[P_{04}=P_{01}-\frac{\rho_{01}\Delta h_{ls}(1-n_{ls})}{4} \tag{15}\]  
\[\{h_{04},P_{04}\}=EoS(\rho_{04},\;T_{04},\;a_{04},\;s_{05},\;fluid) \tag{16}\]  
\[\{h_{4},S_{4}\}=EoS(\rho_{4},\;T_{4},\;a_{4},\;P_{4},fluid) \tag{17}\]  
Turbine power can be obtained using the Euler equation, Equation (18). As proposed by [PERSON] [27], the meridional speed \(C_{m5}\) can be calculated using Equation (19).  
\[W_{output}=m^{{}^{\prime}}(U_{4}C_{\theta 4}-U_{5}C_{\theta 5}) \tag{18}\]  
\[C_{m5}=C_{m4}\left(1+5\left(\frac{b_{4}}{r_{4}}\right)^{2}\right) \tag{19}\]  
The smallest distance between two adjacent blades is the rotor throat \(\mathrm{o}_{5}\). It can be obtained considering the behavior of the exit Mach number \(\mathrm{M}_{5\mathrm{rel}}\), as expressed in Equation (20)  
\[\begin{array}{ccc}o_{5}=\frac{s_{5}C_{m5}}{\mathrm{W}_{5}}&for&M_{5 rel}<1\\ o_{5}=\frac{s_{5}C_{m5}\mathrm{o}_{5}}{\rho_{*}W_{*}}&for&M_{5 rel}\geq 1\end{array} \tag{20}\]
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 3 Results
### Quantitative Analysis
#### 3.2.1 Electron Temperature  
A popular method of drawing a Boltzmann plot was utilized for estimation of the plasma electron temperature. Following expression was used to draw the plot [22]:  
\[\ln\left(\frac{l\lambda}{hcAg(k)}\right)=\ln\left(\frac{N(t)}{U(t)}\right)- \frac{E_{k}}{KT_{e}} \tag{1}\]  
Figure 3: Optical emission spectra of chalcopyrite plasma in the range of (**a**) 425–445 nm and (**b**) 510–525 nm containing the signature lines of Fe and Cu respectively.  
In this equation, \(I\), \(\lambda\), \(A\), \(g\), \(N(t)\), \(U(t)\), \(E_{k}\), \(K\), and \(T_{e}\) represent integrated line intensity of the transition, wavelength of spectral line, probability of transition, statistical weight of upper level (\(k\)), total number density, partition function, energy of upper level, Boltzmann constant and electron temperature, respectively. Electron temperature of the plasma can be extracted from the slope of the straight line, \(-1/kT_{e}\), obtained by plotting \(\ln\left(\frac{I\lambda}{kCAS}\right)\) against the upper level energy \(E_{k}\). Uncertainty of about \(\pm 5\%\), caused by errors in the measurement of transition probabilities and integrated line intensities, arose in the estimated value of temperature. A Boltzmann plot was drawn by using the spectroscopic data of atomic emission lines of Cu-I at 249.38 nm, 427.38 nm, 515.30 nm, 521.80 nm, and 578.19 nm, as shown in Figure 4. The value of extracted electron temperature (\(T_{e}\)) was found to be 11,314 \(\pm\) 566 K and this value was subsequently used in further calculations.
## 3 Proposed Method
### Causal Network Learning
#### 3.2.1 Global Causal Network Learning  
Let \(V=\{t,x_{1},x_{2},\cdots,x_{m}\}\) be an \(m+1\) dimensional set of variables in a given system \(S\) where \(t\) is the selected target variable and \(\{x_{1},x_{2},\cdots,x_{m}\}\) are the cause variables associated with \(t\). Let \(Q=\left\{q_{1},q_{2},\cdots,q_{p}\right\}\) be the \(p\) group observation datasets of \(V\), and now it is necessary to discover the causal relationship between variables in \(\{t,x_{1},x_{2},\cdots,x_{m}\}\) based on the observation datasets. We use the FCI algorithm to learn the initial causal network among variables, combined with prior knowledge to supplement the orientation. The FCI algorithm is a classical method in the field of causal network learning that is suitable for high-dimensional and sparse causal network learning [39]. Combined with prior knowledge, it can further improve the efficiency of causal network learning. Finally, we obtain the global causal network. The basic steps are as follows:Step 1: Use Algorithm 4.1 in [40] to learn the causal skeleton [41] (\(\mathcal{C}\)) between variables of the researched system and obtain the separate set [41] (\(\mathcal{S}\)) and the unmasked triplet [41] (\(\mathfrak{M}\)).  
Step 2: Use Algorithm 4.2 in [40] to determine the orientation of the V-structure [41] in \(\mathcal{C}\) and update it.  
Step 3: Use Algorithm 4.3 in [40] to obtain the final causal skeleton, update it, and update the separate set (\(\mathcal{S}\)).  
Step 4: Use Algorithm 4.2 in [40] to determine the orientation of the V-structure in \(\mathcal{C}\) and update it again.  
Step 5: Apply rules (R1)-(R10) in [42] to determine the causal orientation of the skeleton (\(\mathcal{C}\)) as much as possible and then update it.  
Step 6: Use prior knowledge to conduct supplementary orientation for \(\mathcal{C}\) and obtain the global causal network \(\mathcal{G}\).  
In the above causal discovery process, the hypothesis to be satisfied includes the following:  
**Causal Sufficiency Hypothesis.**_The variable set \(V\) is causally sufficient when the direct cause variables of any two variables of \(V\) are also included in \(V\)._  
**Causal Markov Hypothesis.**_For a set of variables that satisfies the causal sufficiency hypothesis, the set of variables satisfies the causal Markov hypothesis if every variable is mutually independent of its non-descendant nodes in the condition when its causal parent nodes are given._  
**Causal Loyalty Hypothesis.**_If variables \(x_{i}\) and \(x_{j}\) are independent or conditionally independent under the premise of a given variable set \(V\), then in the causal network \(\mathcal{C}\) composed of variables and their causal dependency relationships, all pathways between \(x_{i}\) and \(x_{j}\) are \(d\)-separated by the appropriate variable in \(V\). Then, the joint distribution \(P\) of all random variables in \(V\) is said to be causal loyalty to the network \(\mathcal{C}\)._
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 5 Tests on New Methods for Gamma/Hadron Separation
### Results for Gamma/Hadron Separation  
To identify the most effective approach for gamma/hadron segregation, we assessed several supervised Machine Learning methods, including the Random Forest method, Extra Trees method, and Extreme Gradient Boosting (XGB), and compared them to the A-SciSoft benchmark.  
To optimize the performance of each method in the ensemble, we conducted extensive experiments involving multiple trials and cross-validation tests. Based on these tests, we identified the most effective models for our purposes.  
Our best-performing models are:  
* GH SET 1-GH ENS 0: a pure Random Forest model on set of parameters \"GH SET 1\";
* GH SET 3-GH ENS 1: a Stacking Ensemble model on set of parameters \"GH SET 3\" made of 0.65 XGB Regressor, 0.21 Extra Trees Regressor and 0.14 Random Forest;
* GH SET 3-GH ENS 2: a Stacking Ensemble model on set of parameters \"GH SET 3\" made of 0.40 Random Forest and 0.60 Extra Trees;
* GH SET 3-GH ENS 3: a Stacking Ensemble model on set of parameters \"GH SET 3\" made of 0.28 Random Forest, 0.42 Extra Trees and 0.30 XGB  
Results for the true identification of \(\gamma\), hadron rejection, and quality factors are shown in the following Table 3 and in Figures 5-8. Our best threshold for gammaness is 0.8. This threshold was determined through extensive testing and optimization to provide the optimal balance between gamma identification and hadron rejection.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**GH Model** & **\% True Id \(\gamma\)** & **\% h Rejection** & **Quality Factor** & **AUC** \\
**(thres = 0.8)** & & & & \\ \hline A-SciSoft & 67.63 & 98.70 & 5.943 & 0.83 \\ SET 1-ENS 0 & 65.01 & 98.98 & 6.431 & 0.82 \\ SET 3-ENS 1 & 40.51 & 99.54 & 5.998 & 0.75 \\ SET 3-ENS 2 & 65.38 & 99.03 & 6.639 & 0.75 \\ SET 3-ENS 3 & 64.46 & 99.08 & 6.736 & 0.83 \\ \hline \end{tabular}
\end{table}
Table 3: True identification of gammas, hadron rejection and quality factors. Our best threshold for gammaness is 0.8, which was used to classify events as gamma-like or hadron-like. This threshold was determined through extensive testing and optimization to provide the optimal balance between gamma identification and hadron rejection. AUC is the Area Under the Curve, a commonly used metric in classification tasks. AUC represents the overall performance of a classification model by measuring the area under the receiver operating characteristic (ROC) curve (see Section 5.6).  
Figure 5: Classified \(\gamma\) (based on the gammaness classification parameter) out of the total number of \(\gamma\) events for different models and sets of parameters.  
Figure 6: Classified hadron events (based on the gammaness classification parameter) out of the total number of hadron events for different models and sets of parameters.  
Figure 7: Quality factor for different gammaness threshold and for different models and sets of parameters.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Statistical Analyses  
We used Tibco Statistica v13 (TIBCO Software Inc., Palo Alto, CA, USA) to perform statistical analyses of our data. To analyze changes in physiological responses (i.e., SCR amplitudes and number of blinks in 20 s) to glare, we used linear mixed-effect models. We constructed two such models, one for each physiological variable. Each model predicted the respective physiological variable by the following fixed effects: (1) the type of light shining towards the participant (_type_opposite_), (2) the type of light shining away from the participant (_type_own_), (3) the interaction of the previous two effects (_type_opposite_*_type_own_), (4) whether the participant preferred the current type of light shining towards them (_preference_opposite_), (5) whether the participant preferred the current type of light shining away from them (_preference_own_), and (6) the interaction of the previous two effects (_preference_opposite_*_preference_own_). We used [PERSON]'s post-hoc test to further explore potential significant results. Besides the fixed effects, each model also included a random effect of participants to account for repeated measures. Additionally, we included two covariates in each model: the number of each trial in its respective session (_trial_number_, intended to account for habituation effects expected to diminish physiological responses over time) and the reported value of the de Boer scale (_deBoer_). Before constructing the models, we checked the normal distribution of residual and homoskedasticity by visually inspecting the histogram of residuals and the plot of predicted values by residual values, respectively. We found no violation of linear regression assumptions.  
Further, to analyze the subjects' self-reported data, we constructed four linear mixed effect models to analyze variability in the de Boer scale, visual acuity (_Snellen, Tumbling E_), and contrast sensitivity (_Pelli-Robson_) following blocks of glare. All four models included the random effect of participants and the following fixed factors: _type_opposite_, _type_own_, and the interaction _type_opposite_*_type_own_. In addition, the model predicting the reports on the de Boer scale included _preference_opposite_, _preference_own_, and _preference_opposite*preference_own. Because these analyses were performed on the level of session blocks (each block consisted of four trials; i.e., there were eight blocks per subject), we also included a covariate _block_ to account for a possible habituation effect. Again, we visually checked residual normality and homoskedasticity for each model with no assumption violation detected. Due to the relatively small number of participants and the random effect of participants being included in the analyses to account for repeated measures, no other participant-specific characteristics (such as gender, age, etc.) were included in the models separately.  
Finally, we estimated the relationship between the psychophysiological variables and the de Boer ratings by using Pearson correlations.
# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 3 Algorithm Design
### ISS Feature Points Detection Algorithm  
The intrinsic shape signatures (ISS) feature points detection algorithm is based on eigenvalue decomposition of covariance matrix, which has rich geometric feature information. The main steps of extracting ISS feature points of 3D point clouds data \(P\) are as follows:  
(1) Set a search radius \(r_{ISS}\) for each point \(P_{i}\) in \(P\), and calculate the weight \(w_{ij}\) of all points in the area with \(P_{i}\) as the center and \(r_{ISS}\) as the radius, as shown in Formula (1):  
\[w_{ij}=\frac{1}{\left|p_{i}-p_{j}\right|},\left|p_{i}-p_{j}\right|<r_{ISS} \tag{1}\]  
where \(P_{j}\) is any point within the area with \(P_{i}\) as the center and \(r_{ISS}\) as the radius.  
(2) Calculate the covariance matrix \(cov(P_{i})\) of each point \(P_{i}\), as shown in Formula (2):  
\[\text{cov}(p_{i})=\frac{\sum\limits_{\left|p_{i}-p_{j}\right|<r_{ISS}}w_{ij} \big{(}p_{i}-p_{j}\big{)}\big{(}p_{i}-p_{j}\big{)}^{T}}{\sum\limits_{\left|p_{i }-p_{j}\right|<r_{ISS}}w_{ij}} \tag{2}\]  
(3) Calculate the eigenvalues \(\{\lambda_{i}^{1},\lambda_{i}^{2},\lambda_{i}^{3}\}\) of the covariance matrix \(cov(P_{i})\), and arrange \(\lambda_{i}^{1},\lambda_{i}^{2},\lambda_{i}^{3}\) in decreasing order of size, and regard the feature points satisfying the Formula (3) as candidate feature points of intrinsic shape signatures:  
\[\frac{\lambda_{i}^{2}}{\lambda_{i}^{1}}\leq\varepsilon_{1},\frac{\lambda_{i}^ {3}}{\lambda_{i}^{2}}\leq\varepsilon_{2} \tag{3}\]  
where \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the set thresholds, 0 \(<\)\(\varepsilon_{1}\), \(\varepsilon_{2}\) \(<\) 1;  
(4) For the candidate feature points of intrinsic shape signatures, the non-maximum suppression strategy is used to filter out the final feature points.