esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
We also generated 3D models of the scanned objects by using CTVox software without an alginate coating (Figure S2D) and with a 6% alginate coating, helping the visualization by microCT of the hydrogel around the model (Figure S2E). This was consistent with the observation that the brittle porous models could be handled without fracture by containing the model within the crosslinked hydrogel. We used microCT scanning to further analyze the distribution of alginate within the pores of the scaffold. The porous models were initially soaked within 6% wt/vol alginate (Figure S2A); however, the microCT analysis revealed that the gel did not completely penetrate the pores of the scaffold within the time aliquoted, perhaps due to the density of the alginate solution. For this reason, we also exposed the incus models to 4% wt/vol alginate and then crosslinked it with CaCl\({}_{2}\). The porous incus top-half model was then scanned via the Skyscan1176 (Figure 6A) and reconstructed via the NRecon program. Using Dataviewer software, color filters were applied to virtual cross sections of the model to visualize the hydrogel and the CPC structure (Figure 6B). To determine if the alginate was distributed through the pores, a transaxial cross section was taken in the body of the incus (Figure 6C). A different color filter was applied to the cross section to visualize the hydrogel and the printed construct, revealing alginate within the pores of the constructs, not only on the surface (Figure 6D). The two larger ossicle models were also printed in the Carbopol support bath either with alginate alone (Figure 7A) or with an MSC-containing alginate-nanocellulose bioink (Figure 7B). MSC were used as they have been shown capable to undergo in vitro chondrogenic [21] and osteogenic differentiation [22]. In both cases, we obtained an excellent rendering of the ossicle's shapes, as encoded by their CAD (Figure 1). Figure 6: **MicroCT scanning for the determination of the penetration of the alginate hydrogel into the ceramic pores**. (**A**) Top–down view of the crosslinked porous incus top-half model in the Skyscan1176. (**B**) Reconstructed coronal cross section of the crosslinked porous incus top half. A color filter was applied to show the crosslinked gel (green) and the porous CPC print (orange). (**C**) Visualization of the cross section used for the examination of gel penetration in the pores: Meshmixer model, demonstrating the cross section (**left**) and a coronal cross section with a red line indicating the cross section position used in (**D**) (**right**). (**D**) Transaxial cross section of the crosslinked porous incus model, showing the CPC structure as blue and the crosslinked gel penetrating the pores as purple. Bars: (**B**) 3 mm; (**D**) 1 mm.
## 4 Results
### Wheat Yield in Turkey  
This data set is about wheat yields in Turkey. The yield refers to the dry grain of the harvested crop, and it is often expressed in terms of either kilograms per decare or tons per hectare. The yield data are in an annual series, covering the period from 1938 to 2013. Turkey is a major wheat producer and a leading trader of cereals, including wheat. Because of the strategic importance of the crop, speculations regarding wheat yield are a great concern for many parties, particularly those involved in commodities trading. Although wheat is considered a strategic agricultural commodity in Turkey, the output has been flat for years. The decline in land dedicated to wheat agriculture has been partially compensated by an increase in yields, which kept the output at a level of 20 million tons per year [40]. For agricultural commodities such as wheat, yield is defined in terms of kilograms of seeds harvested per decare (1000 m\({}^{2}\)). As a first step in model development, the available data set was split into development and test sections, with 80% and 20% ratios. Yield data from 1938 to 1998 were used for model development, and the remaining portion, data from 1999 to 2013, was used for testing.  
Historical change in yields from 1938 to 2013 is given in Figure 9. As the graph shows, the wheat yield in Turkey has been rising in recent years, but it is still well below the major wheat producers of Europe, such as Germany or France [41; 42]. The yield data have an upward trend with random fluctuations. It does not exhibit any seasonal or periodic variations. For basic and secondary models, ARIMA (0,1,0) time series and an ANN architecture of 7 \(\times\) 7 \(\times\) 1 are selected as final models after testing various combinations.  
The graph of fitted values versus actuals for the test data (from 1999 to 2013) shows that the model captures the recent upward trend with reasonable accuracy (Figure 10). Performance statistics (MSE, MAD, and MAPE) for the hybrid model are given in Table 5. This table includes statistics for two more models. Two sub-components of the hybrid approach, the ARIMA model in the basic model and the ANN model in the secondary models, are run separately on the yield data. The performance of each model is recorded, and relevant error metrics are included in Table 5.
# Cross-Platform Gait Analysis and Fall Detection Wearable Device
## 2 Related Work  
In Refs. [1; 2], it described the importance of fall detection for elders due to deterioration of foot function. To address this issue, a wearable device composed of three-axis accelerometer, gyroscope, and GPS was designed. Hence, the date time and location of fall could be detected by this wearable device.  
A multi-condition adaptive step detection algorithm was proposed to improve the pedestrian dead reckoning system for increasing the measurement precision of the stride, step, and heading better than the existed traditional algorithms [3].  
In Ref. [4], a linear mixed model was used to determine differences between spatial gait and non-motor symptoms, such as freezing in the levodopa-medicated-state (ON-state) called [PERSON]'s disease. The levodopa-medicated-state was divided into non-freezers, freezing with only OFF-levodopa, and freezing with both ON- and OFF-levodopa. The experimental results proved that the variability for intra-patient in spatial gait features in ONOFF-FOG was much higher than those in others.  
In Ref. [5], the rehabilitation of balance and gait between sensory retraining (ESR) and implicit repeated exposure (IRE) was evaluated, such as balance, mobility, assessed sensation, and participation. It showed that both of ESR and IRE are all prone to implement for outpatient clinic.  
In Ref. [13], it compared the traditional approaches with inertial sensors for the measurement of hip and knee osteoarthritis in remote health care. It showed that the inertial sensors are more suitable for remote health care with extremity osteoarthritis.  
The generalizability of deep learning models for predicting outdoor irregular walking surfaces was proposed to show that the results of laboratory-based gait analysis could not be used in real situation. Although the inertial measurement units may be used for real situation, the gait analysis may be still inaccurate since the behavior of walking was more complex in real world. Hence, this paper evaluated the surface classification performance with different data splitting, sensor location, and count by different machine learning models [14].  
In the above motion, it showed that the inertial measurement was often used in real situation for gait analysis. It also showed the importance of gait analysis and fall detection. However, none of them could measure the step count, step length, and angle of knee joint measurements with fall detection. Moreover, the wearable with cross-platform APP was not addressed since the cross-platform APP was required recently, such as Android and iOS. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed to address these issues in this paper.
## 6 RSS Difference-Aware Graph-Based Semi-Supervised Learning RSS Smoothing Method
### Experimental Results
We can readily see the performance gain of the RG-SSL method in the cumulative distribution function (CDF) of the localization error for the RG-SSL method and the other methods, as shown in Figure 13 and Table 1. It is clear that the proposed localization method outperforms the other methods. As discussed above, the RSS values smoothed by RG-SSL are more accurate than those smoothed by the other algorithms, and the location accuracy is increased by 3.5% relative to G-SSL and SPORT, 9.8% compared to SCTW method and 20.6% relative to original data. The average localization error has been reduced from 2.89 m to 2.07 m, and notably, the maximum localization error has been reduced from 10 m to 4 m.  
Figure 12: Comparison of localization results. (**a**) Original radio map (**b**) RG-SSL method (**c**) G-SSL method (**d**) SCTW method (**e**) SPORT method. Figure 13: Cumulative distribution function of the localization error.
## 3 Results
### Solubility and Gel Fraction  
The changes in starch/polyvinyl alcohol/citric acid/glycerol film solubility before (SPGC) and after adding the nZnO particles into the solution (S1-S4) are shown in Figure 6. As validated, at the crosslinking temperature of 85 \({}^{\circ}\)C, the SPGC solubility fell from 58.1% to 45.3%, 45.6%, 45.4%, and 45.1% for S1 to S4 samples, respectively, after adding the nZnO to the SPGC solution. The water barrier properties of the wound dressing are improved due to the following reasons.  
Figure 4: UV–Vis transmittance percentage of biodegradable starch/polyvinyl alcohol wound dressings before (SPGC) and after adding four different amounts of nano-sized ZnO particles to the material (S1–S4).  
To begin with, this decreased solubility in the new composites (S1-S4) is feasibly caused by the reactions between nZnO particles and starch in the film structure. Many studies [37; 38; 39] have shown that the more hydrogen bonds between nanoparticles and other compounds in the composite matrix, the less free water molecules can interact with such films embedded with nZnO particles.  
Furthermore, the intermixed crosslinking and plasticizing influences led to a decrease in the solubility percentage. In fact, the crosslinking effect reduces the solvent transportation into the polymer structure, reducing starch-based solubility. The other studies [35] reported the same observation and results, and it occurs due to the closing of pore channels and the  
Figure 5: The outcomes of the nZnO particles on the starch-based films’ (S1–S4) swelling index compared to the SPGC sample without nZnO.  
Figure 6: The solubility percentage of the starch-based films (S1–S4) was affected by adding the nZnO particles.  
increase in the polymer matrix diffusion path length that were both initiated by adding the nZnO particles to the blend.  
Figure 7 illustrates starch-based films' gel fraction percentage reached 70.3% from 65.2%, adding the nano-ZnO particles to the starch/PVA/CA/glycerol film (SPGC) solution at the steady 85 \({}^{\circ}\)C crosslinking temperature. This feature demonstrates how polymer molecule chains were created and gel fraction correlated with the films' mechanical strength. To be more specific regarding this correlation, the polymer crosslinking ability will be enhanced as the gel fraction increases, and thus the mechanical strength will increase. Even though adding CA within the formulation increases the free residual, it has plasticizing and crosslinking impacts on the final material polymer networks.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 3 Long-Haul Optical Link MIMO Channel Model
It is important to note that, by considering that all the modes propagating through the fiber experience the same attenuation, both matrices are unitary, i.e.,  
\[\underline{\underline{V}}^{(k)}\cdot(\underline{\underline{V}}^{(k)})^{H}= \underline{\underline{I}}=\underline{\underline{U}}^{(k)}\cdot(\underline{ \underline{U}}^{(k)})^{H}. \tag{9}\]  
Alternatively, \(\underline{\underline{H}}(\omega)\) can also be written by applying a singular value decomposition (SVD), as the product of two unitary matrices \(\underline{\underline{U}}^{(tot)}(\omega)\) and \(\underline{\underline{V}}^{(tot)}(\omega)\), and a diagonal matrix \(\underline{\underline{\Delta}}^{(tot)}(\omega)\) as [35]:  
\[\underline{\underline{H}}(\omega)=\underline{\underline{V}}^{(tot)}(\omega) \underline{\underline{\Delta}}^{(tot)}(\omega)\underline{\underline{U}}^{( tot)^{H}}(\omega) \tag{10}\]  
where now, the diagonal matrix \(\underline{\underline{\Delta}}^{(tot)}(\omega)\) is given by:  
\[\underline{\underline{\Delta}}^{(tot)}(\omega)=\text{diag}\bigg{(}\bigg{[}e ^{\big{(}\frac{1}{2}g_{1}^{(tot)}-j\omega\tau_{1}^{(tot)}\big{)}},\ldots,e^{ \big{(}\frac{1}{2}g_{D}^{(tot)}-j\omega\tau_{D}^{(tot)}\big{)}}\bigg{]}\bigg{)} \tag{11}\]  
where \(\underline{g}^{(tot)}=[g_{1}^{(tot)},g_{2}^{(tot)},\ldots,g_{D}^{(tot)}]\) are the coupled modal gains of the overall channel and \(\underline{\underline{\tau}}^{(tot)}=[\tau_{1}^{(tot)},\tau_{2}^{(tot)},\ldots, \tau_{D}^{(tot)}]\) denote the coupled modal group delays. Note that in (10), both \(\underline{\underline{U}}^{(tot)}(\omega)\) and \(\underline{\underline{V}}^{(tot)}(\omega)\) unitary matrices have in general frequency dependence, in contrast to \(\underline{\underline{U}}^{(k)}\) and \(\underline{\underline{V}}^{(k)}\) in (7) that have not [27; 33].
# On the Vaneless Space Vortex Structures in a Kaplan Turbine Model Operating at Speed No Load
## 3 Results
### Dynamic Behavior of the 2V and 3V Structures  
The following sections are only focused on the 2V and 3V structures because, as seen in Figure 4, they present higher intensities than the rest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **2V** & **3V** & **4V** & **6V\({}_{\rm{GV}}\)** & **6V\({}_{\rm{B}}\)** \\ \hline \(k\) & 1 & 1 & 2 & 1 & 1 \\ \(j\) & 3 & 2 & 3 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(k\) and \(j\) combinations for each VSV structure giving a \(N=0\).  
Figure 5: Reduced precessing frequencies corresponding to each VSV structure at all SNL conditions.  
Figure 6a,b present a zoom around 2V and 3V in the PVSA spectra as well as the amplitudes of their induced pressure fluctuations, respectively. At small guide vanes angles from 2.6\({}^{\circ}\) to 4.97\({}^{\circ}\), the 2V peaks appear at \(f_{0}^{*}\) from 1.09 to 1.32 and the 3V peaks from 1.53 to 1.79, which increase in amplitude and frequency linearly. The fact that the 3V presents higher and sharper frequency peaks than the 2V, which are lower and wider, suggests that 3V could be the dominant structure at low discharges. At 6.8\({}^{\circ}\), the amplitude of 2V drops drastically and 3V keeps its dominance showing a sharp amplitude increase. At 7.35\({}^{\circ}\), whereas no pressure fluctuations induced by 2V are detected, the 3V peak shows a \(f_{0}^{*}\) of 1.95 and the highest amplitude equal to 0.0185 bar. Suddenly, the dominance switches and only the pressure fluctuations induced by 2V are measured at 7.86\({}^{\circ}\) and 8.13\({}^{\circ}\) with \(f_{0}^{*}\) values of 1.46 and 1.47 and maximum amplitudes around 0.021 bar.  
The present findings agree with the observations of other authors who reported sudden decreases and increases in amplitude of different VSV structures when changing the SNL condition [18; 33]. The novelty of the present work is the coexistence of VSV structures with different dynamic behaviors, amplitudes and locations. The simultaneity and the various similarities observed in the frequency evolutions and induced rotor vibrations between different VSV structures supports the hypothesis that they are not independent  
Figure 6: (**a**) Spectra and (**b**) amplitudes of the frequency peaks of 2V and 3V, on PVSA at all SNL conditions.  
as considered by previous studies but related to each other. Depending on the discharge factor as well as the guide vane and runner blade angles: (i) both 2V and 3V coexist, (ii) 2V dominates over 3V or (iii) 3V dominates over 2V.
# Weakly Coupled Distributed Calculation of Lyapunov Exponents for Non-Linear Dynamical Systems
## 2 Lyapunov Exponents in Non-Linear Dynamical Systems
### Coupled Oscillations Model  
The set of ODEs to solve in this work is given by the following six first-order equations with variable coefficients:  
\[\dot{x}=v_{x}\equiv V_{x}(v_{x}) \tag{1}\]  
\[\dot{\theta}=v_{\theta}\equiv V_{\theta}(v_{\theta}) \tag{2}\]  
\[\dot{\phi}=v_{\phi}\equiv V_{\phi}(v_{\phi}) \tag{3}\]  
\[\dot{v_{x}}=X\left(x,\theta,\phi,v_{x},v_{\theta},v_{\phi},t\right) \tag{4}\]  
\[\dot{v_{\theta}}=\Theta\left(x,\theta,\phi,v_{x},v_{\theta},v_{\phi},t\right) \tag{5}\]  
\[\dot{v_{\phi}}=\Phi\left(x,\theta,\phi,v_{x},v_{\theta},v_{\phi},t\right) \tag{6}\]  
where \(x,\theta,\phi,v_{x},v_{\theta}\), and \(v_{\phi}\) are the dynamic variables (\(x\) is linear and \(\theta\) and \(\phi\) are angular variables), \(t\) is the curve parameter (time), the dot represents \(t\)-derivative, and the functions \(X,\Theta\) and \(\Phi\) are given by  
\[X=\frac{Ak\cos\omega t+\frac{g}{2}(m_{\theta}\sin 2\theta+m_{\phi}\sin 2\phi)+m _{\theta}l_{\theta}\dot{\theta}^{2}\sin\theta+m_{\phi}l_{\phi}\dot{\phi}^{2} \sin\phi-kx-\gamma\dot{x}}{M-m_{\theta}\cos^{2}\theta-m_{\phi}\cos^{2}\phi} \tag{7}\]  
\[\Theta=-\frac{g}{l_{\theta}}\sin\theta-X\frac{\cos\theta}{l_{\theta}} \tag{8}\]  
\[\Phi=-\frac{g}{l_{\phi}}\sin\phi-X\frac{\cos\phi}{l_{\phi}} \tag{9}\]  
where \(A,k,\omega,g\), \(m_{\theta},m_{\phi},l_{\theta},l_{\phi}\), \(\gamma\) and \(M=m_{g}+m_{\theta}+m_{\phi}\) are physical parameters. Equations (1)-(6) represent three highly-coupled (one harmonic and two pendular) oscillators, and were physically derived by [PERSON] et al. (2017). Thus, in order to obtain the whole spectra of Lyapunov exponents of the system, six of them ought to be calculated.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 5 Performance Evaluation
### Performance Comparison  
#### 5.2.1 User's Download Rate  
Figure 5(a) shows the variation in the download rate over time for a user in Experiment Scenario 1. We can see that the TCTE algorithm has the highest user download rate most of the time, while the IDRS and NR algorithms only have the highest user download rate very few times. In addition, the TCTE algorithm has a more stable download rate than the other two algorithms, and the other two algorithms have large fluctuations in the user download rate during the experimental time. Figure 5(b) shows the variation in a user's download rate over time in Experimental scenario 2. We can see that the TCTE algorithm has the highest download rate most of the time, while IDRS and NR perform poorly. Moreover, the download rate of the TCTE algorithm still has the most stable download rate. Therefore, the TCTE algorithm can enable users to obtain a higher and more stable download rate. In addition, in order to illustrate the reliability of the experimental results, we repeated the experiment 10 times, and took the average download rate of each experiment for analysis. Figure 7 shows the mean and confidence interval (95% confidence level) for the download rate.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 2 Intensity Image KCF Target Tracking Method Based on the Muti-Feature Fusion
### KCF Tracking Algorithm Based on Muti-Feature Fusion  
#### 2.2.1 HOG Feature  
HOG is a statistical method to calculate the local features of an image, which can effectively describe the local gradient information and edge distribution of the target [18]. The main calculation process is as follows:  
Step 1: The tracking region image captured as input is converted into a binary image;  
Step 2: The Gamma correction method was used to normalize the input image, as shown in Equation (11):  
\[I\big{(}h_{x},h_{y}\big{)}=I\big{(}h_{x},h_{y}\big{)}^{Gamma} \tag{11}\]  
where \(I\big{(}h_{x},h_{y}\big{)}\) is the image matrix, gamma = 5.0, generally.  
Step 3: Calculate the oriental gradient information of pixels, as shown in Equations (12) and (13):  
\[G_{x}\big{(}h_{x},h_{y}\big{)} =I\big{(}h_{x+1},h_{y}\big{)}-I\big{(}h_{x-1},h_{y}\big{)} \tag{12}\] \[G_{y}\big{(}h_{x},h_{y}\big{)} =I\big{(}h_{x},h_{y+1}\big{)}-I\big{(}h_{x},h_{y-1}\big{)} \tag{13}\]where \(G_{x}\big{(}h_{x},h_{y}\big{)}\) represents the horizonal gradient value; \(G_{y}\big{(}h_{x},h_{y}\big{)}\) represents the vertical gradient value. So, the gradient amplitude and direction of the image at \(\big{(}h_{x},h_{y}\big{)}\) can be expressed as:  
\[G\big{(}h_{x},h_{y}\big{)}=\sqrt{G_{x}\big{(}h_{x},h_{y}\big{)}^{2}+G_{y}\big{(} h_{x},h_{y}\big{)}^{2}} \tag{14}\]  
\[\theta\big{(}h_{x},h_{y}\big{)}=\tan^{-1}\Bigg{(}\frac{G_{y}\big{(}h_{x},h_{y} \big{)}}{G_{x}\big{(}h_{x},h_{y}\big{)}}\Bigg{)} \tag{15}\]  
Step 4: The image is subdivided into many small cells and the gradient data for each cell is calculated. For each cell, its gradient orientation is divided into nine bins, and each bin is bounded by \(20^{\circ}\). According to the gradient orientation, the gradient information of the cell is mapped to the corresponding bin.  
Step 5: The gradient information in the block composed of cells is collected, and the HOGs in all blocks are connected in series to obtain the overall HOG feature.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 4 Results
### Mapping and Comparison of GPP Estimation Results  
Figure 8 shows the GPP estimation results for the continental United States in 2020 using the SA-LGBM model, as well as the spatial distribution of GPP from the MOD17 and GOSIF products. It can be seen that these three products exhibit similar spatial distribution characteristics, with high GPP values primarily concentrated in densely forested areas. The highest GPP values are found in the Great Lakes region, the Appalachian Mountains, and along the Pacific coast, with values ranging from approximately 2000 to 3000 gCm\({}^{-2}\)yr\({}^{-1}\). The agricultural areas in the Midwest exhibit moderate GPP values. The arid regions in the Southwest, including Nevada, Arizona, and New Mexico, have relatively low GPP values due to limited rainfall, sparse vegetation, and restricted photosynthesis. Compared to GOSIF, MOD17 and SA-LGBM show less variability.  
Figure 7: Assessment of GPP accuracies over a variety of vegetation types: (**a**) R\({}^{2}\) and (**b**) RMSE.  
Figure 9 shows the residual distribution of three GPP products in 2020. Although SA-LGBM, MOD17, and GOSIF are generally consistent in GPP estimation, there are significant differences in certain regions. In many areas of the eastern United States, GOSIF estimates higher GPP values than SA-LGBM and SA-LGBM estimates higher values than MOD17. In contrast, the opposite pattern is observed in the western regions. GOSIF uses SIF data, which are directly associated with plant photosynthesis and directly reflect the activity of photosynthesis. Particularly in the densely vegetated eastern regions, GOSIF may capture higher GPP. MOD17 tends to underestimate GPP in areas with high GPP.  
Figure 8: Distribution of three GPP products (MOD17, GOSIF, and SA-LGBM) in the conterminous United States.  
Figure 9: The distribution of residuals between each pair of the three GPP products.