esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Smart Conservation of Cultural Heritage
### Reasoning in a Smart Cultural Heritage Space
#### 5.2.2 Machine Learning Module  
In order to identify abnormal observations of environmental parameters, ML algorithms were exploited in an autonomous module that is executed in parallel. Specifically, outliers detection [83], an unsupervised learning method, was implemented, which detects irregular observations of temperature, relative humidity and illuminance. The sklearn (available online: [[https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html)) (accessed on 10 June 2021)) isolation forest method was used with the following parameters: estimators = 100, max_features = 2.0, contamination = 0.05 and max_samples = 256. This technique of identifying abnormal observations was used in addition to traditional _if-then_ logic rules, enhancing the procedure with automated detection that does not require manual definition of the rules but exploits the ability of the model to identify patterns and isolate irregular numbers. The resulting output is stored in the knowledge base (KB) and used by the rules module when needed.
## 2 Antenna Configuration and Performance Analysis  
### Antenna Configuration  
The configurations of the proposed wide-beam antenna are presented in Figure 1, which comprises a basic ALTSA and a pair of subwavelength elements. For this basic ALTSA, as shown in Figure 1a, two symmetrical metal tapers with slots are printed on the top and bottom side of the dielectric plate (relative permittivity \(\varepsilon_{\mathrm{r}}=2.94\) and thickness h = 1.5 mm) as radiators. The microstrip line serves as a feed for both tapers, and the substrate-integrated waveguide (SIW) structure is used to optimize the impedance matching between them. The slots in the metal tapers are employed to reduce the antenna size and optimize the antenna pattern, where their width, depth, and spacing are indicated.  
A pencil beam toward the boresight is radiated by the basic ALTSA. To widen the beamwidth, two oblique rows of subwavelength metal units, as shown in Figure 1b, are loaded on the dielectric substrate of the basic ALTSA. The length of the subwavelength metal unit is about \(0.3\lambda_{0}\), where \(\lambda_{0}\) is the free-space wavelength at the central frequency of 15 GHz. Each row has four units, and the subwavelength structures are designed as simple  
Figure 1: Configurations of the proposed wide-beam antenna. (**a**) Basic ALTSA. (**b**) Subwavelength units. (**c**) Proposed wide-beam antenna. (**d**) Exploded view. (**e**) Local geometries.  
I-shapes. Since there are only eight subwavelength elements and they are etched on the surfaces of the dielectric substrate, the overall size and structural complexity of the antenna are hardly increased.  
After the combination of the basic ALTSA and the subwavelength metal units, the proposed antenna in our article is formed, as shown in Figure 1c, and the wide\(-\)beam effect can be realized. Figure 1d shows the exploded view. From these two Figures, it can be seen that the subwavelength elements are attached to the blank area between the two tapers near the end of the antenna. It is notable that the two groups of subwavelength units are not integrated into the same plane, but are printed separately on the top and bottom sides of the dielectric substrate together with one of the tapers. Additionally, the closer to the end of the antenna, the closer the subwavelength units are to the metal tapers, but they do not touch. Figure 1e shows the local geometries of the proposed wide\(-\)beam antenna, and the optimized parameter values are shown in Table 1.
## 3 Research Methodology
### The Metrics for Performance Evaluation
#### 3.3.4 F-Score  
The F-score is a performance measure that considers precision and recall scores. This is the best overall measure when balancing precision and recall is important. The harmonic mean, which is a robust average, between the recall and precision gives an F-score. In text classification models, the F-score provides information on how well a model predicts the low scoring target class. For the default value of alpha, the F-score is equivalent to the balanced accuracy for binary target classes. Hence, it is calculated by Equation (4), as follows:  
\[F-score=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{4}\]
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
###### Abstract  
This article presents elemental analysis of an economically important mineral (chalcopyrite) of local origin. Calibration-free laser-induced breakdown spectroscopy (CF-LIBS) methodology based on the assumption of optically thin plasma and local thermodynamic equilibrium was employed for quantitative analysis. Plasma on the surface of the chalcopyrite target was generated by an Nd:YAG laser beam of wavelength 532 nm, pulse width 5 ns, and operated at repetition rate of 10 Hz. A LIBS2000+ detection system, comprised of five spectrometers, covering the spectral range from 200-720 nm, was used to record the signal of the optical emission from the chalcopyrite plasma. Recorded optical spectrum revealed the presence of Cu and Fe as the major elements while Ca and Na were recognized as the minor elements in the target sample. Quantitative analysis has shown that the relative concentrations of Cu, Fe, and Ca in the sample under study were 58.9%, 40.2%, and 0.9% by weight respectively. However, Na was not quantified due to the unavailability of suitable spectral lines, required for CF-LIBS analysis. Results obtained by CF-LIBS were validated by X-ray fluorescence (XRF) analysis, which showed the presence of five compositional elements viz. Cu, Fe, Si, Se and Ag with weight percentages of 58.1%, 35.4%, 5.7%, 0.7%, and 0.1% respectively. These results endorse the effectiveness of the CF-LIBS technique for quantitative analysis of major elements, however, its usefulness in case of minor and trace elements needs further improvement.  
chalcopyrite; CF-LIBS; XRF; copper; minerals +
Footnote †: journal: Applied Sci. 2020, 10, 6848; doi:10.3390/app10196848
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 3 The Maximum Power Point
### Maximum Power Point Current and Power  
In order to obtain expressions for \(i_{\rm mpp}\) and \(P_{\rm mpp}\), Equation (15) has to be inserted into Equation (7). This results in a composite of \({\rm W}\) functions that cannot be simplified. A simpler approximate expression for \(i_{\rm mpp}\) can be obtained by noting that at the maximum power point, the \({\rm W}\) function in Equation (7) is evaluated at \(z(V)=z_{\rm mpp}\). Since \(z_{\rm mpp}\) needs to be small for Equation (15) to be accurate, \({\rm W}(z_{\rm mpp})\) can be approximated by \(z_{\rm mpp}\) here as well. From Equation (7), this yields:  
\[i_{\rm mpp}=i_{\rm G}-\frac{V_{\rm t}}{r}{\rm W}(z_{\rm mpp}) \approx i_{\rm G}-\frac{V_{\rm t}}{r}z_{\rm mpp} \tag{16}\] \[= i_{\rm G}-i_{0}\exp\left[\frac{V_{\rm mpp}}{V_{\rm t}}+\frac{V_ {\rm G}}{V_{\rm t}}\right].\]  
An analytical expression for \(i_{\rm mpp}\) can now be obtained by inserting Equation (15) into (16), and an analytical expression for \(P_{\rm mpp}\) can be found by evaluating \(V_{\rm mpp}\cdot i_{\rm mpp}\). To shorten the notation, let \(\alpha(r)\) denote the argument of the \({\rm W}\) function in Equation (15). The approximate analytical expressions for \(i_{\rm mpp}\) and \(P_{\rm mpp}\) then become:  
\[i_{\rm mpp}=i_{\rm G}\left(1-\frac{1}{{\rm W}[\alpha(r)]}\right), \tag{17}\] \[P_{\rm mpp}=i_{\rm G}^{2}r\left(1-\frac{1}{{\rm W}[\alpha(r)]} \right)+i_{\rm G}V_{\rm t}\left({\rm W}[\alpha(r)]-2+\frac{1}{{\rm W}[\alpha( r)]}\right). \tag{18}\]  
Note that in the limit \(r\to 0\), Equations (4) and (5) are recovered.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 5 Results and Discussion
### Risk Assessment  
After data were picked up from the historical data of the rock-fall accidents and the traffic flow dataset, we used a Python environment to simulate the risk of falling rocks. The configurations and the setups that were used for simulation are shown in Table 1:  
The rock-fall risk values, \(P_{(Risk)}\), were tested for three different vehicle speeds of 70 km/h, 80 km/h, and 90 km/h, the results ranging from (6360 \(\times\) 10\({}^{-5}\)) to (6386 \(\times\) 10\({}^{-3}\)), as seen in Figure 9. The obtained results were compared with the safety-critical regulation and management as low as reasonably practicable (ALARP) thresholds triangle to classify  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Average vehicle lengths & 5.4 m \\ Average number of vehicles driving on the road every day (N\({}_{\text{V}}\)) & 6245 vehicles \\ Average vehicle speed range (V\({}_{\text{V}}\)) & 70 to 90 Km/h \\ Vulnerability of the vehicle regarding rock-fall incidents V(D:T) & 1 \\ Rock-fall frequency (f\({}_{\text{h}}\)) & 0.07 Per day \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation setups.  
the risk into levels. ALARP criteria were used to define three levels of a rock-fall risk, and Figure 10 shows the ALARP thresholds triangle [54].  
By comparing the rock-fall risk values with the three levels of the ALARP thresholds triangle, we found that 86% took values more significant than (\(1\times 10^{-3}\)) and thus were within an unacceptable level. The remaining 14% of risk values fell less than (\(1\times 10^{-3}\)) and thus were spread between tolerable and acceptable levels.
# Intelligent TCP Congestion Control Policy Optimization
## 4 Experiment
### Packet Loss Sensitivity  
The packet loss rate is an important indicator to measure the reliability of network transmission protocols, and Figure 7 shows the packet loss sensitivity comparison, setting a random loss rate of up to 8%. Other configurations of links in Mininet are a bandwidth of 700 Mbps, a latency of 40 ms, and queues of 1000 packets. When the random loss rate increases from zero, the latency of both congestion control strategies decreases rapidly, while the delay at the beginning of TCP-PPO2 is significantly lower than that of traditional congestion control strategies.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 2 Analysis of the Adhesive Bond  
Figure 1 shows the five typical load cases of bonded joints [29]. In particular, the two load cases of splitting (d) and peeling (e) are critical in terms of component strength and should be avoided as far as possible by suitable positioning of the material interface in the design. These two load cases involve inhomogeneous tensile stresses, which also have very high amounts at the opening end [29]. It should also be noted that these two load cases occur exclusively under tension.  
The developed failure criterion of [21] used for modeling the failure of material interfaces (see [20; 22]) also makes use of this fact. This uses stress limits for the tensile and shear stresses, but not for the compression stresses. The reason for this is that many of the adhesives used in industrial applications have a significantly higher compressive strength compared to the tensile and shear strength (see [20]).  
The above-mentioned facts motivate the two requirements listed below, which are used in this article to develop an objective function for 3D multi-material topology optimization, which should favor a bondable position of the interface:  
1. Minimization of the material interface area subject to tensile stress.  
Figure 1: Five different load cases of adhesive bonds, whereby a bond is made up of the two bonding partners (gray and blue) and the adhesive (orange). (**a**) Compressive loading. (**b**) Tensile loading. (**c**) Shear loading. (**d**) Peel loading. (**e**) Split loading.  
Minimization of the material interface area subject to shear stress.
## 3 Results
### Biomarkers Analysis
#### 3.3.4 HDL-Cholesterol Levels  
The estimated marginal means of high-density lipoprotein cholesterol (HDL-C) at baseline (t1), 1 h (t2), 2 h (t3), and 4 h (t4) are presented in Table 7 and Figure 5. Baseline HDL-C levels were comparable across groups: CTRL (67.875 \(\pm\) 3.464 mg/dL), OLT (66.208 \(\pm\) 3.436 mg/dL), and OLB (66.792 \(\pm\) 2.725 mg/dL).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **TC** \\ **(Mean \(\pm\) SE)** \\ **at Baseline (t1)** \\ \end{tabular} & \begin{tabular}{c} **TC** \\ **(Mean \(\pm\) SE)** \\ **at 1 h (t2)** \\ \end{tabular} & \begin{tabular}{c} **TC** \\ **(Mean \(\pm\) SE)** \\ **at 2 h (t3)** \\ \end{tabular} & \begin{tabular}{c} **TC** \\ **(Mean \(\pm\) SE)** \\ **at 4 h (t4)** \\ \end{tabular} & \
\begin{tabular}{c} **95\% Confidence Interval** \\ **Time** \\ **(Mean \(\pm\) SE)** \\ **found** \\ \end{tabular} &
\begin{tabular}{c} **\(p\) Value** \\ **(Group \(\times\) Time)** \\ **(Group \(\times\) Time)** \\ \end{tabular} \\ \hline CTRL & \(169.208\pm 6.623\) & \(176.375\pm 8.189\) & \(172.167\pm 7.679\) & \(178.667\pm 7.829\) & t1 & \(155.51\) & \(182.91\) & \\  & & & & t2 & \(159.44\) & \(193.32\) & \\  & & & & t3 & \(156.28\) & \(188.05\) & \\  & & & & t4 & \(162.47\) & \(194.86\) & \\ \hline OLT & \(173.333\pm 8.247\) & \(182.458\pm 8.801\) & \(178.125\pm 9.249\) & \(180.333\pm 8.579\) & t1 & \(156.27\) & \(190.39\) & \\  & & & & t2 & \(164.25\) & \(200.67\) & \\  & & & & t3 & \(158.99\) & \(197.26\) & \\  & & & & t4 & \(162.59\) & \(198.08\) & \\ \hline OLB & \(167.042\pm 7.377\) & \(171.375\pm 6.167\) & \(171.083\pm 6.895\) & \(172.458\pm 6.201\) & t1 & \(151.78\) & \(182.30\) & \\  & & & & t2 & \(158.61\) & \(184.13\) & \\  & & & & t3 & \(156.82\) & \(185.35\) & \\  & & & & t4 & \(159.63\) & \(185.29\) & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Postprandial changes in serum total cholesterol (TC) levels.  
Figure 4: Postprandial total cholesterol concentrations (Mean \(\pm\) SE) over time (baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups.  
Throughout the postprandial period, HDL-C concentrations remained relatively stable across all groups. Minor fluctuations were observed, with values ranging between approximately 64 and 68 mg/dL across time points and groups. Specifically, no clear trends were evident.  
Statistical analysis revealed no significant group-by-time interaction (\(p\) = 0.637), indicating that the temporal profile of HDL-C did not differ significantly between the groups. Overlapping 95% confidence intervals across all measurements further support this finding (e.g., t4: CTRL [61.162, 75.588], OLT [59.632, 73.451], OLB [59.770, 71.730]).  
In summary, HDL-C levels remained stable over the postprandial period, with no significant changes attributable to treatment or time.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 6 Conclusions  
This paper presents a background model, including star motion and infrared brightness, based on the MSX catalog. We have achieved autonomous and controllable deep-space background creation from external catalog data. The visual simulation system can obtain infrared information in the deep-space background of space-based scenes and generate image sequences of point target groups. Compared with the default EOIR process, our approach supports 20 sensors simultaneously observing the target groups and achieved simulation of 30 FPS on 1 K \(\times\) 1 K high-resolution sensors. It therefore supports the rapid construction of complex space-based scenarios and provides efficient high-quality data support for target identification and tracking algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resolution/Pixel** & **Original FPS** & **Proposed** &
\begin{tabular}{c} **Number of Real-Time** \\ **Simulation Sensors** \\ \end{tabular} \\ \hline
128 \(\times\) 128 & 0.30 & 607 & 20 \\
256 \(\times\) 256 & 0.09 & 305 & 10 \\
512 \(\times\) 512 & 0.02 & 88 & 2 \\
1024 \(\times\) 1024 & 0.004 & 32 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: System simulation performance at different resolutions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Issues** & **Current System** & **Proposed** \\ \hline Simulation performance & 0.1 FPS in 256 pixels & 305 FPS in 256 pixels \\ \hline Background generation & IRAS, MSX catalog without space gargets & MSX-based Lookup table and accurate infrared targets \\ \hline Target features & Spin and fixed temperature & Complex micromotion and dynamic temperature \\ \hline Development mode & STK internal, MATLAB/VC++ with Connect & C sharp with COM \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overview of the improvement of the proposed methods.  
**Author Contributions:** All the authors contributed to this study. Conceptualization, C.G. and P.R.; Investigation, C.G. and Y.L.; Methodology, P.R. and Y.L.; Software, C.G.; Data curation, C.G.; Funding acquisition, P.R.; Project administration, P.R. and Y.L.; Supervision, Y.L.; writing--original draft preparation, C.G.; writing--review and editing, P.R. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Innovation Project of Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, grant number CX-375.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Negation Detection on Mexican Spanish Tweets:
## 3 Corpus of Negations in Mexican Spanish: The T-MexNeg
### Annotation Protocol
#### 3.1.1 Negation Cues  
The negation cue tags are used to identify the negations that appear in a tweet. The difference between Simple Negation and Related Negation lies in the syntactic distribution of the cues because there are several negative cues that are coordinated. The difference between Simple Negation and False negation has a semantic and pragmatic nature. Some negative cues simply do not negate anything.  
**Simple Negation (Neg_exp)**:  
It refers to the negation cues that are not linked to other negation cues (1). Thus, the Scope and the Event are only directly related to this negation. To identify this, we created the tag neg_exp.  
**No** tango ganas de ir al bano ahora.  
_I do not feel like going to the bathroom right now_  
**Related Negation (Neg_rel)**:  
This label is used for negation cues that are linked to other negation cues in the sentence (2) and are dependent on them. The related negation does not have an event or scope and it is part of the scope of the main negation. An special case is the use of _ni...ni_ (neither, nor) without a new event (3).  
[PERSON] et al. [47] proposed in their work the complex negation tag that is composed of negation reinforcement, the negation with modifiers, comparative negations, and negation phrases. We decided to simplify these tags by identifying the relationship between the principal negation cue and the related cue. We privilege the fact that the second negation in a sentence is related to the first one, regardless  
Figure 1: Example of tagged tweets with cue, event, and scope.  
of the meaning. In the next two examples, we boldfaced only the Related Negation (Neg_rel) tag.  
No me ha gustado **nada** la pelicula.  
_I didn't like the movie at all._  
No quiero **ni** pollo **ni** pan.  
_I want neither chicken nor bread._  
**False negation (No_neg)**  
This tag is used with negation cues that do not negate anything at a semantic level, as well as with some abbreviations such as _no_/_no_--which are used to represent the word _number_ in Spanish. Quite frequent idiomatic phrases that are discursive markers, rather than negations, belong to this category. Some examples are: _no names_ (no way) (5), _de nada_ (you are welcome), and _nada mds_ (just/only) (4). [PERSON] et al. [47] mentioned that there exist both complex and simple structures with negation cues that do not express semantic negations. In our annotation protocol, both structures are tagged in the same way.  
Nada mas quiero darte un beso.  
_I just want to kiss you._  
NO MEMESSS, no hay papel.  
_No way, there is no toilet paper_
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Element**} & \multirow{2}{*}{**Tissue**} & \multirow{2}{*}{**n**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**GM**} & \multirow{2}{*}{**Median**} & \multirow{2}{*}{**Range**} & \multirow{2}{*}{**SEM**} & \multicolumn{2}{c}{**Kruskal–Wallis Test**} & \multirow{2}{*}{\(p\)-Value**} \\ \cline{6-9} \cline{8-9} \multicolumn{2}{c}{} & & & & & & & & \\ \hline \multirow{3}{*}{Zn} & enamel & 9 & 19.9 ab & 17.2 & 15.2 & 9.1–59.8 & 5.069 & \\ \cline{2-9}  & dentin & 10 & 14.7 a & 13.7 & 14.7 & 5.6–24.9 & 1.657 & \\ \cline{1-1} \cline{2-9}  & cementum & 9 & 21.4 b & 20.3 & 22.2 & 8.5–28.8 & 1.988 & \\ \hline \hline \end{tabular}  
* n: number of samples; Mean: arithmetic means, GM: geometric mean, SEM: standard error of measurement; Range: minimal–maximal values; a, b—different letters indicate differences significant at \(p\leq 0.05\).  
\end{table} 
Table 4: _Cont._Figure 2: Average content of selected elements in the enamel of young and adult roe deer and red deer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pair of Variables**} & \multicolumn{2}{c}{**Enamel**} & \multicolumn{2}{c}{**Dentin**} & \multicolumn{2}{c}{**Cementum**} \\ \cline{2-7}  & **n** & **Rs** & **N** & **Rs** & **n** & **Rs** \\ \hline \multicolumn{7}{c}{Young roe deer} \\ \hline Pb and Zn & 41 & ns & 15 & 0.75 *** & 28 & 0.77 *** \\ \hline \multicolumn{7}{c}{Mature roe deer} \\ \hline Pb and Zn & 26 & ns & 24 & ns & 26 & 0.55 ** \\ \hline Ba and Sr & 27 & ns & 24 & \(-\)0.42 * & 28 & ns \\ \hline Mg and Sr & 27 & 0.50 ** & 24 & 0.73 *** & 28 & ns \\ \hline \multicolumn{7}{c}{Mature red deer} \\ \hline Pb and Zn & 9 & ns & 10 & 0.84 ** & 9 & 0.67 * \\ \hline Ba and Zn & 9 & ns & 10 & 0.85 ** & 9 & ns \\ \hline Al and Sr & 9 & ns & 10 & 0.90 *** & 9 & 0.85 ** \\ \hline Mg and Sr & 9 & 0.92 *** & 10 & 0.78 ** & 9 & 0.88 ** \\ \hline Pb and Sr & 9 & ns & 10 & 0.82 ** & 9 & 0.68 * \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlations between the selected elements in dental tissues of roe deer and red deer. Pb and Sr, Cd and Sr, and Cd and Pb (\(p\leq 0.05\)). Moreover, in cementum, there were strong positive correlations between Pb and Zn in mature roe deer. In the dental tissues of mature red deer, there were strong positive correlations in cementum between Al and Sr, Mg and Sr, and between Pb and Sr (\(p\leq 0.05\)). With respect to enamel, a strong correlation was identified only between Mg and Sr. In cementum, there was a weak positive correlation between Pb and Zn (\(p\leq 0.05\)).
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques
### Gradient Boosting  
Gradient Boosting is an ensemble method that combines several weak learners, typically decision trees, to create a strong learner. The method works by iteratively adding new trees to the model, where each new tree is trained to correct the errors made by the previous tree. During the training process, the algorithm minimizes a loss function through gradient descent, using the negative gradient of the loss function to adjust the weights of the trees.