esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Modal Analysis
### Sensors Setup  
The prosthetic leg has been tested adopting free-free conditions by suspending the prosthetic with an elastic band. In Figure 1, the reference system considered in the experimental campaign is shown. The origin lies on the prosthetic plane of symmetry, at the midpoint of the width (attachment to the socket). The Y-axis is directed along the prosthetic width, while vertical Z-axis points downwards, towards the prosthetic toe.  
The structure is excited using an impact hammer (PCB, sensitivity: 2.2 mV/N) with a medium hardness plastic tip. The selected node configuration for hammering consists of one central row aligned with the prosthetic plane of symmetry and two lateral rows, each containing 19 nodes spaced along the prosthetic shape, for a total number of 57 nodes. Their location has been previously chosen balancing geometry accuracy and expected modal behaviour. The nodes are hammered along all the possible directions allowed by the prosthetic shape, for a total number of 96 possible strokes. Indeed, the hammered directions must be orthogonal to the corresponding node surface. During the roving hammer test, all the nodes at the prosthetic side are excited both along the width (Y direction) and along the component shape.  
Nodes nomenclature is based on the following considerations:  
* middle line of prosthetic is numbered with 1 XX;
* left side (Y+ surface) is numbered with 2 XX;
* right side (Y- surface) is numbered with 3 XX.  
Five of the fifty-seven nodes identified to be hammered are even used as accelerometer locations. One mono-axial (PCB, sensitivity equal to 100 mV/g) and four tri-axial (PCB, sensitivity equal to 100 mV/g) accelerometers are used to evaluate system response, obtaining a total amount of 1248 Frequency Response Functions (FRFs). Details of their location are reported in Figure 2, while the reference geometry of the EMA test is displayed in Figure 3, in which the accelerometer positions are marked with coloured dots.  
Figure 1: Experimental setup scenario.  
The excitation and response signals are acquired using Siemens LMS SCADAS Mobile acquisition device. To do so, LMS Test.Lab 17 software is used: time histories of 4 s (frequency resolution of 0.25 Hz) are acquired with a sampling frequency of 8192 Hz; force-exponential window is applied for the hammer input signal over 0.6% of samples, while exponential window with a decay of 30% is applied to accelerometer outputs; \(H_{1}\) estimation of the system inertance FRFs is linearly averaged on 3 repetitions [41]. On Test.Lab software, the geometry definition requires the orientation each node (Euler angles). These angles are exploited to properly rotate the local reference frame of the hammered nodes with respect to the global reference frame of the component in order to obtain the correct hammering directions (orthogonal to the prosthetic surfaces).
## 5 Experiments
### Experiment of the I-NMF Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Datasets** & **Models** & **HR05** & **HR010** & **HR015** & **NDCG@5** & **NDCG@10** & **NDCG@15** & **MAP@5** & **MAP@10** & **MAP@15** \\ \hline \multirow{6}{*}{**ML-100K**} & **1-NMF** & **0.437091** & **0.368832** & **0.324451** & **0.463643** & **0.406837** & **0.368565** & **0.359102** & **0.271408** & **0.224247** \\  & **CDAE** & 0.421306 & 0.355360 & 0.314747 & 0.447365 & 0.392567 & 0.356070 & 0.339666 & 0.255331 & 0.201602 \\  & **CDAE** & 0.421306 & 0.355360 & 0.314974 & 0.303355 & 0.427511 & 0.375740 & 0.342528 & 0.342452 & 0.244377 & 0.202115 \\  & **C-N**-NF**-CDAE** & 0.037467 & 0.037911 & 0.030830 & 0.036388 & 0.036349 & 0.032663 & 0.057221 & 0.062963 & 0.064696 \\  & **N-NMF**-SD & 0.005365 & 0.002196 & 0.002866 & 0.005829 & 0.003351 & 0.003575 & 0.004329 & 0.001878 & 0.001698 \\ \hline \multirow{6}{*}{**ML-1M00K**} & **1-NMF** & **0.422925** & **0.463630** & **0.328502** & **0.442975** & **0.396000** & **0.364304** & **0.343087** & **0.267112** & **0.225634** \\  & **CDAE** & 0.420267 & 0.34528 & 0.309183 & 0.42340 & 0.359763 & 0.345131 & 0.322861 & 0.247092 & 0.206846 \\  & **C-N**-NF**-p & 0.401836 & 0.349859 & 0.315786 & 0.420715 & 0.377941 & 0.348670 & 0.321740 & 0.250935 & 0.212394 \\  & **N-NF**-p** & 0.052483 & 0.044140 & 0.040269 & 0.052909 & 0.047784 & 0.044839 & 0.066348 & 0.064468 & 0.062334 \\  & **N-NF**-SD & 0.003211 & 0.003311 & 0.002990 & 0.003393 & 0.003400 & 0.003020 & 0.003694 & 0.003469 & 0.002841 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of I-NMF, CIDAE, and O-NeuMF-p based on HR@N, NDCG@N, and MAP@N. (N = 5, 10, 15). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Datasets** & **Models** & **HR05** & **HR010** & **HR015** & **NDCG@5** & **NDCG@10** & **NDCG@15** & **MAP@10** & **MAP@15** \\ \hline \multirow{6}{*}{**ML-100K**} & **1-NMF** & **0.437091** & **0.368832** & **0.324451** & **0.463643** & **0.406837** & **0.368565** & **0.359102** & **0.271408** & **0.224247** \\  & ItemPop & 0.213072 & 0.189456 & 0.168726 & 0.22282
## 1 Introduction
### Isomorphic Layout Properties
To address this, the experiment presented in this paper explores how two independent spatial transformations of isomorphic layouts--shear and adjacency--impact on learning in a set of melody retention and transfer tasks. The shear is used to manipulate the angles of the pitch axis and major second axis, while keeping the octave axis constant; the adjacency manipulation determines whether or not major seconds are spatially adjacent. These two transformations enable us to test our hypotheses that adjacent major seconds and a vertical pitch axis facilitate the learning and playing of melodies. The four layouts that result from these transformations are illustrated in Figure 1a-d. Each figure shows how pitches are positioned, and the orientation of three axes that we hypothesize will impacton the layout's usability. Each label indicates whether the layout has adjacent major seconds or not (_A_ and _A_\({}^{\prime}\), respectively) and whether it is sheared or not (_S_ and _S_\({}^{\prime}\), respectively). The three axes are the _pitch axis_, the _octave axis_, and the _major second axis_, as now defined (the implications of these three axes, and why they may be important, are detailed in Section 1.1.2). * The _pitch axis_ is any axis onto which the orthogonal (perpendicular) projections of all button centres are proportional to their pitch; for any given isomorphic layout, all such axes are parallel [16] (see the caption for Figure 1 for a practical demonstration of how this works). * The _octave axis_ is here defined as any axis that passes through the closest button centres that are an octave apart. * The _major second axis_ (_M2 axis_, for short) is here defined as any axis that passes through the closest button centres that are a major second apart. When considering tunings different to 12-TET (e.g., meantone or Pythagorean), alternative--but more complex--definitions for the octave and M2 axes become useful.
## 5 Results and Discussion
### The DOE Analysis
#### 5.2.2 The Effect of Defect Width and the Type of Material on the Response  
Figure 5 shows the effect of the defect width and type of material on the signal output at a defect depth of 2.5 mm. The material type and defect width show significant effect on output signal, and the interaction of both shows an insignificant effect.
## 4 Case Study
### Impact of Risk Management  
To analyze the impact of the risk aversion, take cases S2, S4, and S6 as examples: the expected revenue of the market participants and the CVaR for different risk-averse parameters of \(\beta\) are calculated and presented in Figure 8. It can be seen that the three curves show the same trend. When \(\beta\) increases, the CVaR increases, while the revenue of market participants decreases. For example, in Case S2, when \(\beta\) increases from 0 to 0.5, the value of CVaR is increased by 6.7% while the market revenue is decreased from USD 55,534.5 to USD 54,029. For Case S4, the CVaR increased by 11.4% while the market revenue decreased by 3.47%. In addition, the results show that with a slight decrease in market revenue, the CVaR is highly reduced. This indicates that the variation in the CVaR is bigger than that of the market revenue. Furthermore, comparing Figure 9a-c, with increasing risk-averse parameter \(\beta\), the rate of decrease in market profit in the cooperative case is lower than that of individual cases. From the result, the decision maker can make an appropriate bidding strategy based on the tradeoff between expected profit and CVaR, because CVaR not only effectively measures the potential downside risk but also provides a clear risk-return trade-off benchmark in various market conditions, enabling decision makers to better balance profit-seeking and risk-controlling.  
Figure 8: Daily profit over 1 week.
## 3 Results and Discussion
### Total Phenolic Content (TPC)  
According to epidemiological research, the consumption of polyphenols has been linked with health improvements [36]. In addition, the consumption of yogurts enriched with polyphenols provokes potential advantages in human health by reducing low-density lipoprotein (LDL), overweight, high blood pressure, cancer, cardiovascular diseases, etc. [37]. In the current study, the total phenolic content in yogurts with extracts of lyophilized sea buckthorn was measured and the results are presented in Figure 3.  
Although polyphenols are commonly found in fruits and vegetables, plain yogurt (0% \(w/w\) extract of LSB) has a TPC 9.6 \(\pm\) 0.17 mg GAE/100 g probably because TPC is naturally present in cows' milk from the feed of cows and the amino acid catabolism [38]. Yogurt with 3% \(w/w\) extract of LSB has the highest TPC (30.1 \(\pm\) 0.21 mg GAE/100 g) and the sample with 0.5% \(w/w\) extract of LSB had the lowest concentration (18.7 \(\pm\) 0.15 mg  
Figure 3: Total phenolic content (mg GAE/100 g) of set-type yogurts containing 0, 0.5, 1, 2, and 3% \(w/w\) extracts of lyophilized sea buckthorn. Different letters within the columns indicate significant differences (\(p<0.05\)) among the different treatments. All measurements were conducted in triplicate.  
Figure 2: Water holding capacity and syneresis of set-type yogurts containing 0, 0.5, 1, 2, and 3% \(w/w\) extracts of lyophilized sea buckthorn. Black columns indicate water holding capacity values (%); white columns indicate syneresis values (%). Different letters within the columns indicate significant differences (\(p<0.05\)) among the different treatments. All measurements were conducted in triplicate.  
GAE/100 g) compared to the rest of the fortified yogurts (\(p\) < 0.05). It should be mentioned that the total phenolic content is increasing according to the increased concentration of extract of LSB. The sample with 3% \(w\)/\(w\) extract of LSB has a level of TPC three times higher compared to plain yogurt. So, the addition of LSB extracts increases the yogurt's nutritional value. Similar results were found in other research studies, such as in yogurts fortified with peanuts [39] and moringa extracts [40].
## 4 Discussion  
The profiles here show strong correlation between the Tikhonov regularized HTS profiles and the associated scintillometer and anemometer data. The pseudo-inverse profiles still show a significant amount of measurement noise, which causes large fluctuations and drop-outs in the profiles. The Tikhonov regularization is successfully able to reduce this noise and correct the profiles. Over the two days of data collection, values of \(C_{n}^{2}\) between \(9\times 10^{-15}\) to \(6\times 10^{-13}\) m\({}^{-2/3}\) were measured by the BLS. Periods of higher turbulence on the order of \(10^{-13}\) m\({}^{-2/3}\) produced less accurate profiles with greater noise. This may be due to the limitations of the centroid tracking method. The system used for this study tends to produce the best profiles during periods of moderate to weak turbulence around \(10^{-14}\) m\({}^{-2/3}\). The profiles accurately captured the predicted and anemometer measured rise in \(C_{n}^{2}\) over the concrete portion of the path. In cases where the anemometers measured a drop in turbulence over the second half of the path, such as in Figure 10 past 300 m, the Tikhonov profiles accurately captured the dip. Given that the profiles always break down past 350 m, the anemometer at 400 m was not reliable for the sake of comparison, but was useful for observing the general turbulence trend along the path.  
There are several challenges with this technique that affect its ability to generate profiles. Many data sets were unusable due to subimage misalignment, which made it impossible to track the centroid motion. Periods of strong turbulence also caused issues due to the subimages shifting outside of their respective tracking windows, thereby affecting the measurements of neighboring subimages. In these cases, the current auto-focusing algorithm was not able to properly track the centroid motions and could therefore not salvage the tilt variance information. These issues could be addressed in future work by a more advanced auto-focusing algorithm or a different optical design. Future work may also consider varied sensing paths. Elevating either the laser beacons or the HTS could provide \(C_{n}^{2}\) profiles that give insight into the turbulence distribution as a function of height. The geometry of the sensing paths between the lasers and HTS subapertures cause the turbulence near the source end of the path to have very little effect on the tilt measurements. The impacts of this have not yet been explored, but could be addressed in future work by switching the placement of the lasers and HTS such that \(z=0\) is over grass and \(z=L\) is over concrete. Profile resolution near the apertures could be improved by decreasing the subaperture separations or increasing the separation of the source beacons. Varying these parameters to observe their effects on the profile is another topic for future work.  
Figure 10: 25 July 22:47 UTC turbulence profile with \(\alpha=90\). At this time, the BLS measured a \(C_{n}^{2}\) of \(5.4\times 10^{-14}\) m\({}^{-2/3}\).
# Propolis as a Potential Novel Histological Tissue Fixative: A Preliminary Analysis
## 3 Results
### Comparison between Different Groups at Different Time Points
#### 3.2.4 Seventy-Two Hours  
Group P tissue samples had the highest mean score (mean = 3.96), followed by Group P + F tissue samples (mean = 3.74) (Table 3).  
Group P tissue samples scored statistically significantly higher mean scores than Group S and Group F tissue samples (\(p\) = 0.0006 and \(p\) = 0.0059, respectively). Similarly, Group P + F tissue samples had a statistically significant higher mean score than Group S tissue samples (\(p\) = 0.0096) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & \multicolumn{2}{c}{**12 h**} & \multicolumn{2}{c}{**24 h**} & \multicolumn{2}{c}{**48 h**} & \multicolumn{2}{c}{**72 h**} \\ \cline{2-9}  & **Mean** & **SD §** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
0.9\% Saline & 3.11 & 0.65 & 3.07 & 0.66 & 2.96 & 0.59 & 3.00 & 0.60 \\ \hline
10\% Formalin & 2.67 & 0.60 & 3.04 & 0.63 & 3.70 & 0.20 & 3.19 & 0.50 \\ \hline
6.6\% Propolis & 3.74 & 0.15 & 3.89 & 0.24 & 4.00 & 0.00 & 3.96 & 0.11 \\ \hline
6.6\% Propolis + 10\% Formalin & 3.48 & 0.38 & 3.70 & 0.26 & 3.74 & 0.28 & 3.74 & 0.36 \\ \hline \multicolumn{9}{c}{Pairwise comparisons by [PERSON]’s multiple post hoc procedures} \\ \hline
6.6\% Saline vs. 10\% Formalin & \(p\) = 0.3702 & \(p\) = 0.9999 & \(p\) = 0.0006 * & \(p\) = 0.9037 \\ \hline
0.9\% Saline vs. 6.6\% Propolis & \(p\) = 0.0905 & \(p\) = 0.0129 * & \(p\) = 0.0001 * & \(p\) = 0.0006 * \\ \hline
0.9\% Saline vs. 6.6\% Propolis + 10\% Formalin & \(p\) = 0.5518 & \(p\) = 0.0852 & \(p\) = 0.0004 * & \(p\) = 0.0096 * \\ \hline
10\% Formalin vs. 6.6\% Propolis & \(p\) = 0.0008 * & \(p\) = 0.0086 * & \(p\) = 0.3814 & \(p\) = 0.0059 * \\ \hline
10\% Formalin vs. 6.6\% Propolis + 10\% Formalin & \(p\) = 0.0142 * & \(p\) = 0.0602 & \(p\) = 0.9994 & \(p\) = 0.0837 \\ \hline
6.6\% Propolis vs. 6.6\% Propolis + 10\% Formalin & \(p\) = 0.8213 & \(p\) = 0.9374 & \(p\) = 0.5153 & \(p\) = 0.8293 \\ \hline \end{tabular} * Indicates statistical significance. § SD: Standard deviation.  
\end{table}
Table 3: Comparison of the fixative ability of different solutions at different treatment times.  
Figure 6 provides a graph showing a comparison between groups. Group P tissue samples demonstrated the highest score in the study, followed by Group P + F tissue samples.
## 3 Case Study Countries  
In this paper, we selected two countries at different stages in the demographic transition, Mexico and Ethiopia, as case studies. Mexico, located in the southern portion of North America, is the eleventh most populous country in the world and the second most populous country in Latin America (after Brazil), covering almost two million square kilometers. It has an estimated population of over 129 million, with 71.9% of the population living in urban areas. Ethiopia is in the horn of Africa. With over 104 million people, 20.4% of whom live in urban areas, Ethiopia is the second most populous nation on the African continent (after Nigeria); it occupies a total area of 1.1 million square kilometers [26].  
Figure 1 presents the urban, rural, and total populations from 1990 to 2017. The data source is from the world bank open data ([[https://data.worldbank.org/](https://data.worldbank.org/)]([https://data.worldbank.org/](https://data.worldbank.org/))). In 1990, the population of Mexico was 85.4 million, and that of Ethiopia was 48.1 million. The population of Ethiopia was only approximately half of that of Mexico in 1990. By 2017, the populations of Mexico and Ethiopia were 129.2 million and 105 million, respectively. The population numbers of the two countries are more similar currently. Over a period of nearly three decades, from 1990 to 2017, the annual population growth rates of Mexico and Ethiopia were 1.6% and 3.05%, respectively. Currently, Ethiopia has a rapidly growing population, whereas Mexico's population has a low growth rate. The proportion of Ethiopia's population living in urban areas increased from 12.6% in 1990 to 20.4% in 2017, whereas Mexico's urban population increased from 52.1% in 1990 to 71.9% in 2017. The rural population of Ethiopia in 1990 and 2017 was 42 million and 80 million, respectively; thus, the rural population nearly doubled in three decades. However, in Mexico, the rural population in 1990 and 2017 was 40 million and 36.9 million, respectively, revealing a slight decline in the rural population. Given that the total population is growing, the shift from a farming population to non-farming is evident in Mexico. Much of the migration (\(\sim\)65%) in Mexico from 2005 to 2010 was to urban destinations [57]. Mexico is a highly urbanized country with a rapidly rising urban population, whereas Ethiopia remains a predominantly agricultural country with a slowly increasing urban population.  
Figure 2 shows the population distribution of Mexico and Ethiopia in 2000. Mexico's population distribution is highly concentrated, with high densities in the capital and surrounding areas and with the population in the northern, eastern, and coastal areas being highly concentrated in urban areas. While having high population density in the capital, Ethiopia has low population density overall and low densities in remote areas far from the capital.  
Figure 1: Urban, rural, and total population of Mexico (**left**) and Ethiopia (**right**).
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Traffic Priority Effects on the HSN
#### 5.3.2 Results and Discussion  
As shown in Table 4, in addition to the phenomenon where the total bandwidth of the hybrid network is higher than that of the pure FC network, it can also be observed that different priority levels of transmissions occupy varying proportions of bandwidth. Interestingly, the ratio of the bandwidth is closely aligned with the priority levels of 1:2:3:4.  
The simulation results provide evidence that the multi-priority strategy achieved the expected outcomes of the design (Table 4). Within the hybrid network, a scheduling and allocation mechanism based on transmission types was implemented. This mechanism ensured that different priority levels of transmissions had varying probabilities of selection, resulting in higher-priority transmissions being allocated a higher proportion of the available bandwidth.
## 1 Introduction
After the antenna experiences a loss of the satellite, it first performs preliminary signal recognition, then divides the array antenna into multiple beams for measurement, obtaining multiple points in the directional diagram and the energy level of each beam's main lobe. Combining the directional diagram, the optimal or sub-optimal beam direction can be quickly calculated to improve the speed of capturing satellites for array antennas. Our research improved communication quality, shortened the time required for the SOTM antenna to reacquire the satellite, and enhanced the reliability of the SOTM systems, offering a practical solution to maintain communication effectiveness in a high maneuvering state. The remainder of this paper is organized as follows. Section 2 introduces SOTM and its servo control system, as well as the directivity of array antenna beams. Section 3 presents a design method for multi-beam fast satellite seeking and acquisition. Section 4 verifies the effectiveness of the designed method through experiments. Section 5 is the final section of this paper, which provides the summary and conclusion.
## 4 Results  
### Surrogate Models  
The accuracies of the investigated surrogate models are shown in Table 2, in terms of the three metrics R-squared, RMSE, and MAPE. Specifically, the three metrics are evaluated  
Figure 4: Cascade procedure: optimal design followed by optimal control problems are solved.  
for the test set. In Table 2, the training time is shown. All three algorithms have been trained on a computer equipped with an Intel(R) Core (TM) i7-10700 KF CPU @ 3.80 GHz, 128 GB of RAM, and an NVIDIA Quadro RTX-4000 graphics card.  
Figure 5 shows an example of the temperature profile prediction using surrogate models compared to the actual profile obtained using Finite Element simulation.  
Based on the results, the Gaussian process regression (GPR) model was selected as the surrogate field model to use in the subsequent approaches. The computational time for one call to GPR was 7 s.