esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Materials Characterization
Taking into account the mass loss in the composites at temperatures higher than 400 \({}^{\circ}\)C (indicated by \(\Delta\)mass in Figure 3 for sample U) and the corresponding weight loss in the pure Pal and TiO\({}_{2}\), the Pal content was calculated equal to 7 wt.% in the PU and PUT samples, and the TiO\({}_{2}\) content was calculated equal to 39 wt.% in the PUT composite. Diffuse reflectance UV/Vis spectroscopy was applied to study the electronic properties related with light absorption. Figure 4a presents the UV/Vis absorption spectra of the materials. The corresponding Tauc plots [38; 39; 53] used to estimate the bandgap energies (\(E_{\text{g}}\)) are given in Figure 4b. Materials containing TiO\({}_{2}\) were treated as direct, while the remaining materials were treated as indirect semiconductors [17]. Pure UiO-66 absorbed light at wavelengths lower than 310 nm in accordance with the literature [24]. The same absorption onset was observed for the PU sample. This corresponded to \(E_{\text{g}}\) values of ca. 4 eV. The absorption edge of TiO\({}_{2}\) was observed at ca. 390 nm, while Pal absorbed light in the whole visible region [38; 39]. Light absorption of the composites was a combination of the absorption of the individual counter parts. The absorption onset of the PUT material was observed at ca. 390 nm, as for the pure TiO\({}_{2}\). This corresponded to an \(E_{\text{g}}\) value of 3.18 eV, typical for pure anatase TiO\({}_{2}\)[16]. A broad absorption was observed in visible region (400-800 nm) for both PU and PUT samples. This was strictly attributed to the presence of Pal. Overall, it was verified through DR UV/Vis spectroscopy that all parts in the composite contributed to the light absorption properties of the material. Figure 3: TGA profiles of the pure Pal and TiO\({}_{2}\) samples and the prepared U, PU, and PUT materials.
## 2 Methodology
### Multiscale Dispersion Entropy Based on RMS (MDE_rms_)
Then, the relative probability of the potential patterns \({{\pi_{{{\tau_{0}}}{{v_{1}}}\cdots{{v_{m-1}}}}}}\) is calculated by the following form:  
\[p({{\pi_{{{v_{0}}}\ldots{{v_{m-1}}}}}})=\frac{Number\left\{{i\left|{i\leq N-(m- 1)d},{{z_{i}}^{m,c}}has\ \ type}\ \ \ \ {{\pi_{{{\tau_{0}}}\ldots{{v_{m-1}}}}}}\ \right\}}{N-(m-1)d} \tag{5}\]  
(5) Finally, the entropy value for \(\tau\) scale factor is achieved based on the theory of Shannon entropy as follows:  
\[e(x,m,c,d)=-\sum\limits_{\pi=1}^{c^{m}}{p({{\pi_{{{\tau_{0}}}\ldots{{v_{m-1}} }}}})\cdot\ln(p({{\pi_{{{\tau_{0}}}\ldots{{v_{m-1}}}}}}))} \tag{6}\]  
Then the final MDE\(rms\) of \(\tau\) scales is expressed as  
\[MDE_{RMS}=E(x,\tau,m,c,d)=[e_{1},e_{2}\ldots,e_{\tau}]^{T} \tag{7}\]  
Reasonable selection of the key parameters in the method including embedding dimension \(m\), time delay \(d\), and mapping classes \(c\) can improve the performance of MDE\(rms\) effectively. On the one hand, the larger \(m\) or \(c\) is, the more reliable MDE\(rms\) will perform, and the more the corresponding computational cost will be. Besides, since the signal is mapped to \(c\) classes, \(c\) should be bigger than one. When \(c\) is too small, the MDE_rms_ may have a poor resolution to classify the dataset in signal. When \(c\) is too big, the MDE_rms_ with high resolution may be sensitive to noise and reduce the reliability. Based on the practical application and test, the appropriate parameter range of \(c\) is from 4 to 8. On the other hand, according to the analysis of parameter selection in [20], \(d\) is suggested to 1 in order to avoiding the frequency alias, and the recommended parameter of \(m\) is from 2 to 5.
# Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
## 2 Materials and Methods
### Simulation Experiments and Validation  
In this study, we used the FORMIND model to explore the forest dynamics of the investigated Malagasy forests. For the analysis of a mature forest, we simulated 50 hectares of forest succession over 1000 years starting with bare ground conditions. As this study did not focus on climate change, climate and soil conditions were held constant throughout all simulations. All calculated output values are mean values over the whole simulation area (50 ha) and over the last 500 years of the forest succession simulation, based on the assumption that the model in that period is in the equilibrium/climax state. In detail, we analyzed total stem numbers, aboveground biomass, basal area, and stem size distribution. We also analyzed gross primary production (GPP), respiration, biomass mortality, net primary production (NPP), and the stand carbon balance called net ecosystem exchange (NEE).  
Though there are several definitions of NEE and NEP in recent literature, for our study, we defined NEE as the following (see: [38]):  
\[\text{NEE}=\text{GPP}-R-R_{dand}-R_{soil}\]  
where GPP is the sum of the gross primary productivity of all the trees in the forest, \(R\) is the total respiration of all of the living trees, \(R_{dand}\) is the amount of carbon released from the deadwood pool to the atmosphere, and \(R_{soil}\) is the amount of carbon released from the soil carbon pool to the atmosphere. Thus, a positive NEE value indicates that the forest takes up carbon.  
The simulation results were compared first between model output and the Betampona (BTP) forest verification dataset. For this comparison, only trees with DBH above 10 cm were included. To validate the FORMIND model for lowland rainforest in Madagascar, several simulated forest attributes were compared with the Zahamena (ZNP) dataset. Again, only trees greater than 10 cm DBH were included. The ZNP dataset was compared to both the model output and the BTP 2008 dataset, and the deviations of the two comparisons were analyzed. Further details and the results of this analysis can be found in Supplementary A.
## 1 Introduction
### Role of Autonomy  
This paper introduced the concept of the autonomy to be used for the constellations for the problem defined in the Section 1.1. It is crucial to know how the satellites choose the optimal solution for their orbit travel period autonomously under operation.  
This research focused on implementing the autonomous behavior of the small satellite constellations using the optimization techniques to solve the major issues in Low-Earth Orbits respectively. Each satellite obtains enough strength and robust capabilities through autonomous optimizing of the trajectories in such a way that the operator intervention is minimized and the data transmission is significantly paced. Not only in the LEOs, the deep space missions can also utilize these enhanced capabilities from autonomous control for the smooth operations to communicate among the satellites and to optimize the orbits in order to precisely and quickly communicate with the astronauts on the ground (Support for Artemis missions). Hence, with this as a primary purpose, this research progressed and simulations were designed thoroughly.
# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
## 2 Related Work
### Bitcoin Price  
In the existing literature, researchers have extensively studied the price prediction of Bitcoin. Many previous studies treat the price prediction of Bitcoin as a time series prediction. The studies [19; 20; 21] used traditional machine learning including Random Forest, XGBoost, Support Vector Machine to predict the Bitcoin price. However, traditional machine learning methods cannot capture the time dependency of time series. Recently, deep learning methods [22; 23; 24] such as Recurrent Neural Networks (RNN) [25] can handle the issue of the time dependency. However, RNN is struggling to learn long-term dependencies due to the vanishing gradient. LSTM and Gated Recurrent Unit (GRU) that are the most commonly used variants of RNN can solve the vanishing gradient problem. For instance, the study [26] used the LSTM network to predict the price direction of Bitcoin. [PERSON] et al. [27] used two various LSTM models including the conventional LSTM model and LSTM with AR model to predict Bitcoin price. Meanwhile, the work [28] used GRU to predict Bitcoin price, which performs better than RNN and LSTM models. However, all these studies have only simply applied common machine learning methods used in stock price prediction for Bitcoins while failing to capture the unique characteristics of Bitcoin. Moreover, some studies use specific characteristics of Bitcoin that are different from stocks to make a prediction. In particular [29], used Bitcoin transaction graph to predict Bitcoin price. All Bitcoin transactions are available on a public ledger, authors used the transaction ID, sender, recipient, value and a timestamp included in a transaction to construct features to predict Bitcoin price. The work [30] designed a complex method that uses the transaction network's most frequent edges to predict the future price of Bitcoin. The study [31] used various variables including Blockchain data (e.g., transactions per block, median confirmation time, hash rate, difficulty) and macroeconomic variables (e.g., S&P500, gold) to predict the evolution of Bitcoin price.  
Our work mainly leverages the large price fluctuation Bitcoin. To the best of our knowledge, the presented problem in this paper is firstly investigated in both Bitcoin and stocks. Our work can also enrich the research in the field of Bitcoin and provide investors with more effective tools for investment analyses.
# Status of the SXFEL Facility
## 2 The SXFEL Test Facility  
### Layout and Main Parameters  
The SXFEL-TF consists of an 840 MeV electron linac and a two-stage cascaded seeding scheme-based undulator system, as shown in Figure 1. The initial proposal of the SXFEL-TF project in 2016 was to test the cascaded HGHG scheme [25]. In the following years, it was gradually optimized and more contents of the EEHG were added to the project when the construction started in 2014. A new cascaded EEHG-HGHG operation scheme was incorporated into the SXFEL-TF to further improve the ultra-high harmonic up-conversion efficiency.  
The main parameters of the SXFEL-TF are shown in Table 1. The wavelength of the seeding laser used in the first stage is 265 nm. The output radiation wavelength from the first stage is about 44 nm. In the second stage, an 8.8 nm soft X-ray FEL radiation pulse will be eventually produced based on the HGHG scheme. The total harmonic up-conversion number of the two stages is 30.  
\begin{table}
\begin{tabular}{c c} \hline
**Linac** & **Values** \\ \hline Electron energy & 840 MeV \\ Energy spread (rms) & \(\leq\)0.1\% \\ Normalized emittance (rms) & \(\leq\)1.5 mm-mrad \\ Bunch length (FWHM) & \(\leq\)1.0 ps \\ Bunch charge & 0.5 pC \\ Peak current at undulator & \(\geq\)500 A \\ Pulse repetition rate & 10 Hz \\ \hline Undulator \\ \hline Stage 1 \\ \hline Seed laser wavelength & 265 nm \\ FEL output wavelength & 44 nm \\ Modulator undulator period & 80 mm \\ Modulator modulator K value & 5.81 \\ Radiator undulator period & 40 mm \\ Radiator undulator K value & 2.22 \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the SXFEL-TF. (Reprint from reference [26]).  
Figure 1: Schematic layout of the Shanghai soft X-ray Free-Electron Laser Test Facility (SXFEL-TF), including a photo-cathode injector, a main linac, and an undulator system (BC: bunch compressor, M: modulator, DS: dispersion section, R: radiator, FB: fresh bunch).
## Appendix A
### Data Storage  
The final step required for integrating data into the unified system is to extract the decoded payloads and store them in a chosen database. In this final portion of the data flow (Figure 10), the timestamps, sensor parameters, SANs, measured values, and units are arranged into the appropriate InfluxDB timestamp, tags, and field-value pairs, and are stored in the database.  
The concept of tags and field-value pairs are unique and constructed to the InfluxDB data model, which has the following features. Firstly, each record in an InfluxDB database (referred to as a \"bucket\") is characterized by a measurement name, which in our implementation will always be \"sensor_data\", to denote the sensor data recorded at a specific site. We further organize recorded data into separate buckets for each site or deployment so as to isolate each collection of site data.  
Secondly, within each bucket and measurement, each data record is stored with a timestamp as its index, a tag metadata, and one or more field-value pairs to store the actual record data. The difference between tags and field-value pairs is that tags are indexed and hence can be quickly queried by the database engine, whereas field-value pairs are not. This difference is useful for storing metadata about records that does not change or will have a finite and relatively limited range of possible values. For each record, our implementation stores device ID (SAN) and sensor parameter type (e.g., temperature, humidity, CO\({}_{2}\), etc.) as tags and a \"value\" field with the recorded sensor measurement.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 2 Materials and Methods
### Data Processing Methodology
#### 2.3.4 Crop Calendar  
For each type of crop studied, a cultivation characteristics file was created, specifying dates from day 1 after souring to adjust the growth stages to specific crops and properly calibrate the model to prevailing conditions in western Poland.
## 5 Results
### Performance Metrics (RQ4)
\(\bullet\)**Set coverage metric (C-metric)**  
If there are two optimal sets of non-dominated solutions, then the set coverage is the comparison metric between these two sets, which represents the ratio of the non-dominated solutions in set\({}_{2}\), dominated by non-dominated solutions in set\({}_{1}\) and divided by the number of solutions in the set\({}_{2}\). This means, if C(set\({}_{1}\),set\({}_{2}\)) \(<\) C(set\({}_{2}\),set\({}_{1}\)), then set\({}_{2}\) has better a solution than set\({}_{1}\)[99]. \(\bullet\)**Diversity metric (\(\Delta\))**  
The diversity metric (\(\Delta\)) determines the range of spread accomplished through the obtained solutions. \(\bullet\)**Hypervolume metric (HV-metric or S-metric)**The hypervolume metric (HV) (also defined as the S-metric) presents the joint information about closeness and diversity in the acquired non-dominated set of solutions (Ps) [42]. \(\bullet\) **Generalal distance metric (GD-metric)**  
The generational distance metric (GD-metric) determines how far the acquired Ps set of solutions is from the true set PT. \(\bullet\) **Computation time or complexity**  
The computation time of the optimization algorithm is required to measure its efficiency. An efficient algorithm should provide optimal solutions within an acceptable time. So, this metric is important for comparison between the algorithms [43; 46]. The performance metrics of each conducted study in this survey are presented in Table 1.
# From Iris Image to Embedded Code: System of Methods
## 2 Eye Segmentation Framework
It can be considered the most general geometric property of the iris, and the first step of eye detection should be locating this eye center. Note that only the position of the center is to be found, rather than the size of any contour. Excluding size and allowing approximate detection involves both concentric borders in the process. This is especially significant for eyes with poorly visible inner boundaries, where pupil location alone fails frequently. A modification of Hough method [24] is used. It is very likely that after center detection pupil size should be estimated. To the best of our knowledge, this is carried out in all works where iris segmentation starts from eye center location, as in [19]. However, this method is not stable and universal for a wide range of imaging conditions. Detecting the radius may easily mistake the outer border for the inner, especially for images with poor inner border contrast [25]. Here we decide to employ both correlated contours around the detected center, and detect sizes of them simultaneously. Hereinafter this detection is referred to as _base radii_ detection, meaning that it finds approximate (base) radii of inner and outer circles around a given center. The method relies on circular projections of gradient [26]. Base radii detection produces approximate center coordinates and radii of pupil and iris circles, which satisfy some reasonable limitations. Furthermore, the quality of detection is calculated. The quality should be high enough to pass the image to further processing. Then both boundaries are re-estimated with better precision (refined). Pupil refinement is carried out by a specially developed version of the shortest path method [27]. Iris is refined by the same method as that of base radius. The difference is that the position of the pupil is now fixed and only the iris center and radius are being searched. Iris segmentation here results in detecting two nearly concentric circles, which are approximating the inner and outer borders of the iris ring. Occlusion detection [28] is carried out to ensure the quality of iris data, i.e., to reject strongly occluded irises from further processing, but apart from this the occlusion mask is not used. Summing up, the segmentation stage of the system employs five steps: center detection, base radii detection, pupil refinement, iris refinement and occlusion detection, see Figure 2.  
At each stage of segmentation, quality value is estimated and the process is terminated if the quality is below acceptable.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 5 Broadcasting in Path-Connected Cliques  
In this section, we study the broadcasting problem in path-connected cliques, a graph defined on \(n\) vertices and containing \(k\) cliques of arbitrary sizes \(l_{1},l_{2},\ldots,l_{k}\), with neighboring cliques being connected by an edge. Path-connected cliques can be classified as block graphs since the edge connecting two neighboring cliques is also a clique \(K_{2}\).  
Consider a graph \(G_{k}\) consisting of a path \(P\) with vertices \(u_{1},u_{2},\ldots,u_{k}\) (in the path order), and \(k\) pairwise vertex disjoint arbitrarily-sized cliques \(C_{1},C_{2},\ldots,C_{k}\) which are attached to \(P\) with the following properties:  
* \(v_{i}\in V(C_{i})\) for all \(1\leq i\leq k\);
* For all \(1\leq i\leq k\), \(1\leq j\leq k\), \(i=j\), an arbitrary vertex \(v\in C_{i}\), and an arbitrary vertex \(w\in C_{j}\), \((v,w)\
otin E(G_{k})\).  
We may denote such a graph as \(G_{k}=(P,C_{1},C_{2},\ldots,C_{k})\). For all \(1\leq i\leq k-1\) We define \(t_{i}=\lceil\log l_{i}\rceil\) as the time units required for clique \(C_{i}\) to complete broadcasting without needing to inform \(C_{i+1}\). We also define \(a_{i}\) to be the last possible time unit at which \(u_{i}\in V(C_{i})\) informs \(u_{i+1}\in V(C_{i+1})\) such that the overall broadcasting is completed in minimum time. Figure 2 illustrates the general structure of path-connected cliques.  
Given a graph \(G=(P,G_{1},G_{2},\ldots,G_{k})\), where \(G_{1},G_{2},\ldots,G_{k}\) can be graphs with any structure and \(P=\{u_{1},u_{2},\ldots,u_{k}\}\) is a path, a fixed-deadline message dissemination algorithm for general path-connected graphs has been presented [31]. In this work, we present an \(O(1)\) method to calculate the broadcast time for an arbitrary path-connected cliques graph.
# Morphological Evaluation of Cranium Facial Asymmetry in Class III Malocclusion Patients
###### Abstract  
The maxillofacial region of patients with facial asymmetry is deformed not only in the mandible but also in the maxilla, suggesting that the head region may also be deformed. Therefore, in this study, skeletally originated mandibular prognathism with facial asymmetry was evaluated in relation to cranial morphology. The cranial morphology of patients who visited the Chiba Dental Center of Tokyo Dental College and were diagnosed with skeletal mandibular prognathism with facial asymmetry (asymmetry group: ANB 0\({}^{\circ}\) or less; Menton deviation 4 nm or more; 30 subjects) and without facial asymmetry (symmetry group: ANB less than 0\({}^{\circ}\); Menton deviation less than 3 mm) was measured and evaluated. As a method, the length and area of the cranium were measured using axial cephalometric radiographs. In the asymmetry group, there was a significant difference in the left-right difference in the long diameter of the posterior part of the cranium compared to the symmetry group (\(p\) = 0.009). The asymmetry group also had significant differences in the central and occipital areas of the cranium on the left and right sides compared to the symmetry group (\(p\) < 0.001). In the asymmetry group, the direction of Menton deviation and the direction of head region deviation coincided in about 70% of the cases. There was also a positive correlation between head deviation and the amount of Menton deviation. The results of this study suggested that patients with facial asymmetry had greater head deformity than patients without facial asymmetry.  
cranial vaults; facial asymmetry; mandibular protrusion Article
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 6 Simulation Examples
### Results of the STA Modification Proposal
The improvement compared to the performance of the LS estimator was due to the dynamic update of the channel estimation, which is performed for each symbol, instead of maintaining the initial estimate for all the data frame. In addition to the BER curves in Figure 11, Table 8 shows the required Eb/No levels to attain 10%, 1%, and 0.1% BER in low mobility conditions over the three described propagation scenarios by considering frame lengths ranging from 16 to 128 OFDM symbols. From Table 8, we can observe that the performance of the modified STA technique decreased as the frame length increased. However, significant performance improvement was observed compared to that obtained by employing the original STA technique (cf. Table 6). Such improvement allowed yielding BER levels of 1% when the Eb/No regimes were greater than 19.05 dB for a frame length of 64, and greater than 22.46 dB for a frame length of 128. For these frame lengths, the original STA technique did not attain such BER performance. Furthermore, this modification also allowed reaching 0.1% BER levels for high Eb/No regimes, which is an improvement with respect to the performance of the LS estimator in the same simulation conditions. This improvement is because the modified STA technique performes a dynamical estimation that tracks the channel variations from symbol to symbol. This action is not performed by the LS technique, whose initial estimate is used to equalize all the symbols in the frame. Although requiring higher Eb/No levels, the proposed modification enabled attaining BER performances that were not reachable when employing the LS estimator. In addition, the modified STA scheme overcame the limitations of its original version in non-WSSUS channels by removing the spectral averaging operations. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Scenario I**} & \multicolumn{3}{c}{**Scenario II**} & \multicolumn{3}{c}{**Scenario III**} \\ \hline Frame length & & & & & & & & & & & & \\ \(F\) & & & & & & & & & & & & \\ \hline Estimator & BER & & & & & & & & & & & \\ \hline Mod. STA (a = 2) & \(10^{-1}\) & 4.85 & 6.01 & 7.18 & 8.75 & 4.85 & 6.05 & 7.29 & 8.68 & 4.81 & 6.11 & 7.35 & 8.7 \\ \hline Mod. STA (a = 2) & \(10^{-2}\) & 15.88 & 17.21 & 18.67 & 22.46 & 15.93 & 17.15 & 19.05 & 21.83 & 15.76 & 17.36 & 18.97 & 21.99 \\ \hline Mod. STA (a = 2) & \(10^{-3}\) & 29.96 & 31.04 & 38.9 & NA & 27.06 & 30.68 & 38.66 & NA & 27.1 & 30.96 & 39.2 & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Required Eb/No levels to attain 10%, 1%, and 0.1% BER over Scenarios I, II, and III by employing the STA modification proposal for different frame lengths with initial speeds of \(\
u_{T}=\
u_{R}=40\) km/h and acceleration components of \(a_{T}=a_{R}=20\) m/s\({}^{2}\) (NA—not achieved).