esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Composition of Olive Cakes
The main disparity lies in a higher moisture content and lower oil content in the two-phase extraction system, reflecting more efficient and ecologically sustainable centrifugation than the three-phase system, resulting in process efficiency and lower environmental impact [52]. The fibre fraction in olive cakes consistently constitutes the highest proportion (44-58%) compared to other components [41]. Specifically, NDF contents were highest in COC 2 and TPOC (dehydrated), with values ranging from 57 to 58% (mean of 76.85 \(\pm\) 0.61; \(p<0.001\)), while TPOC pitted exhibited the lowest content at 57.43 \(\pm\) 0.03. This notable fibre abundance aligns with findings from various studies investigating olive cake composition [53; 54; 55; 50; 55]. The lignin content remained consistent across all the cakes, with COC 2 exhibiting the highest content (26.53 \(\pm\) 0.04) and TPOC (pitted) the lowest (23.32 \(\pm\) 0.97). Regarding cellulose, values were statistically similar across all cakes (mean of 26.96 \(\pm\) 0.61, \(p<\) 0.03), except for COC 2, which had the highest content (31.15 \(\pm\) 0.17). These findings are in line with previous studies exploring similar variations of this by-product [56]. Olive cakes typically contain approximately 10% hemicellulose, 14-26% cellulose and 27% lignin, characteristics that classify it as a highly lignified by-product [56; 57]. The crude protein content remained statistically similar across the samples (mean of 6.64 \(\pm\) 0.28), with EOC 2 exhibiting the highest content (7.26 \(\pm\) 0.36) and COC 1 the lowest (5.38 \(\pm\) 0.02), subject to variation based on the extraction method, with some samples recording levels as high as 11.3% [51]. Previous studies have reported similar values for EOC (7.2%) and higher ones for TPOC (pitted; 8.8%) and COC (7.6%) [56].
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 2 Methodologies
### Experimental Tests
#### 2.1.2 Measurement of Sound and Flow Fields  
The magnitude of the acoustic pressure in the far field (\(x/L=6.75\), \(y/L=21.5\)) was measured with a non-directional \(\frac{1}{2}\)-inch microphone (UC-53A, RION, Tokyo, Japan) and a precision sound-level meter (NL-52, RION, Tokyo, Japan) for a freestream flow velocity \(U_{0}=10\)-45 m/s. The measurement time required for data acquisition was 30 s, and the sampling frequency was 40 kHz.  
The flow induced by the plasma actuators was also visualized by using a fog generator (Safex Fog Generator 2010, Dantec Dynamics A/S, Skovlunde, Denmark), with tracer particles (D/SPEZIAL, Safex, Tangstedt, Germany), in the absence of a freestream flow. The generated fog was illuminated by a green laser sheet, and photographs of the visualized flow were taken with a digital camera (Nikon D5200, Nikon, Tokyo, Japan).
## 4 Experimental Evaluation
### BlogCatalog Dataset  
Figure 6 illustrates the accuracy of all embedding methods and similarity measures with the BlogCatalog dataset. In this figure, we do not represent the precision measure due to the same reason as in Figure 3; instead, we show the precision values for all methods in Table 4. In addition, for those embedding methods and similarity measures that show comparable accuracies, we write down the values of their corresponding MAP, PRES, recall, and F-score in the figure to have better comparison.  
As observed in the figure, with the BlogCatalog dataset, NetMF shows the _highest_ accuracy among all the embedding methods in terms of MAP, precision, recall, PRES, and F-score; however, its accuracy is _close_ to that of DeepWalk, while BoostNE, graphGAN, and NERD show the _worst_ accuracies. SimRank* shows _better_ accuracy than other similarity measures, while SimRank shows the _worst_ accuracy in terms of MAP, precision, recall, PRES, and F-score. Now, by comparing the accuracy of NetMF with that of SimRank*, it is observed that NetMF _outperforms_ SimRank* by 73.44%, 35.68%, 42.89%, 50.00%, and 47.10% in terms of MAP, precision, recall, PRES, and F-score, respectively; Table 5 shows the _percentage_ of improvements in accuracy obtained by NetMF over all other methods with the BlogCatalog dataset.
# A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum
## 3 Simulation and Analysis
### Payload Experiment
#### 4.3.2 Motor High-Velocity Experiment  
The desired position trajectory was 24 \(\uppi\)sin (2\(\uppi\)t) in motor high-velocity experiments with payload, and its first derivative was the desired velocity. The observed friction torque (red curve) is shown in Figure 15a. It can be found that the changes of observed friction torque are stable and there are also no obvious accumulative errors with the increase of time. The RMSE of motor trajectory tracking static errors are listed in Table 6 and shown in Figure 15b,c. It can be found that the velocity tracking performance after friction compensation was reduced compared with no friction compensation in high-velocity conditions. This is because more model uncertainties caused by the link payload and more measurement noise make the performance of the observer bad in high-velocity conditions. Therefore, the estimated and compensated friction torque is inaccurate, which causes the velocity tracking performance to decrease.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Errors** & **RMSE\_SPELv[rad]** & **RMSE\_SVeLv [rad/s]** & **RMSE\_SPEHv [rad]** & **RMSE\_SVeHv [rad/s]** \\ \hline No compensation & 1.156 & 1.562 & 3.776 & 5.981 \\ \(K_{\mu}\) = 50 & 1.154 & 1.409 & 3.774 & 6.161 \\ Performance improvement & 0.2\% & 9.8\% & 0.1\% & -3.0\% \\ Coulomb and viscous & 1.154 & 1.446 & 3.760 & 6.447 \\ Performance improvement & 0.2\% & 7.4\% & 0.4\% & -7.8\% \\ \hline \end{tabular}  
* RMSE\_SPELv and RMSE\_SPEHv denote the root mean squared static position error at low velocities and high velocities from 1 s to the end, respectively. RMSE\_SVeLv and RMSE\_SVeHv denote the root mean squared static velocity error at low velocities and high velocities from 1 s to the end, respectively.  
\end{table}
Table 6: Position and velocity tracking error comparison with payload 3.  
Figure 15: Motor high-velocity with payload experiment results. (**a**) Observed total friction torque (red curve) obtained by the proposed method mapped to the motor-side and estimated motor-side friction torque by the Coulomb and viscous friction model at high velocities. (**b**) Motor position tracking errors at high velocities. (**c**) Motor velocity tracking errors at high velocities.
## 3 Methodology  
To assess the performance of our proposed solution, the whole experimental setup provided by [7] was reproduced using a real traffic trace. The purpose was to further validate the data set already publicly available [23]. The traffic represented by features, was processed by different VNFs, and the averaged CPU consumption was monitored in 20 s batches [7]. A set of network, transport, and application layer attributes (total 86 features) were used to describe the traffic. The data set was made by using these traffic features as an input, while the average central processing unit was used as an output.  
Once the data set was generated, it was further used to train supervised ML models (i.e., MLR, SVR, and FNN). Then, the accuracy of these frameworks were evaluated using the cross-validation technique. The data set was further split into two sets: the training set contained 70% of the total samples, while the remaining 30% of the samples were assigned to the test set.  
The training set as used to train the ML model during the training phase, while the test set was used to further validate the model. Finally, the CPU consumption estimated by the ML technique was compared with the actual measurements.
# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 3 Methods
### Feed Forward Network Introduction  
The feed-forward network (FFN) consists of a feed-forward module (FFM) and four \(1\times 1\) convolutional layers; the structure is shown in Figure 7.  
Figure 6: The structure of feature fusion cross attention block.  
Figure 7: The structure of feed-forward network.  
We use the feed-forward module (FFM) to enhance the nonlinear expression of FFCA. The computational procedure is shown in Equation (11):  
\[O_{CA}=O_{FF}+Conv(GELU(DConv(Conv(LN(O_{FF}))))) \tag{11}\]  
where GELU belongs to the class of activation functions, and \(O_{CA}\in R^{H\times W\times C}\) denotes the result obtained by \(O_{FF}\) after feed-forward module (FFM) processing. Finally, the output after denoising is calculated by superimposing multiple \(1\times 1\) convolutions.  
After the above steps of the cross fusion of different path information, the high-frequency information is retained as much as possible while guaranteeing the denoising effect, which enables our method to achieve better performance results in mural image denoising. The overall CA-BSN architecture is shown in Figure 8.  
The original noise-containing mural image is sampled into multiple small images after PD, and the sampled image is passed through a \(1\times 1\) convolutional layer used for linear transformations. First, the feature information is passed through a masked convolution module (MCM) containing three parallel center mask convolutions, and the local input features and global input features obtained after mask convolution are processed using the densely dilated residual block (DDR) and non-local attention mechanism (NLA) in feature extraction network (FEN), respectively. Then, the local and global features obtained after feature extraction are fused by LGCA, and the feature output from LGCA and the previous feature obtained after the \(5\times 5\) mask convolution are fused by FFCA. Finally, the feature information is passed through a feed-forward network (FFN) consisting of a feed-forward module (FFM) and multiple \(1\times 1\) convolutions, which is used to upsample multiple processed small-size maps into a single denoised mural image.  
We train the CA-BSN using the loss \(L_{1}\). The computational procedure is shown in Equations (12) and (13):  
\[L=\left\|I_{out}-I_{in}\right\|_{1} \tag{12}\]  
\[I_{out}=PD^{-1}(CA(PD(I_{in}))) \tag{13}\]  
We first use \(PD\) to break the spatial connection between the noises of neighboring pixels, decompose the noisy image \(I_{in}\) into smaller images, then use the whole network \(CA\) for denoising, and finally combine the outputs through \(PD^{-1}\) to get the denoising result \(I_{out}\) with the same size as the original image.  
Figure 8: The architecture of mural image denoising method based on Cross Attention and Blind-Spot Network.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.2 Evaluation of the Mall Scenario
Again, an increase in the overhead and in the average latency was obtained as the result of adding a node with skills to the network; whilst a reduction of more than four points in the average buffer time was achieved. Similarly to Table 4, the impact of increasing the number of nodes over the number of hops a message must traverse remained negligible (\(B=-0.047\)). However, the main difference with the previous analysis was that the \(d_{\text{prob}}\) variable was statistically significant. Therefore, we can extract from the results that the impact of adding a node with skills to the network is associated with an improvement of 1.521% in the delivery probability. To sum up the OLS regression outcomes, the higher the number of nodes in the network, the higher the overhead ratio and the average latency imposed by the application of SACAR-OCVN. However, a remarkable point is that nodes' resources, such as their buffer to store messages temporarily, were efficiently used by our proposed solution. Finally, the delivery probability was increased in the case of adding nodes with skills to the network.
## 4 Discussion
### Gap Analysis  
There seems to be a diversity of metrics used in different studies for evaluating both the techno-economic and environmental aspects of e-fuels production. Examples follow.  
Studies range from using metrics like \"minimum fuel selling price (MFSP)\" to \"levelised cost of methanol (LCoM)\" to assess economic viability, making direct comparisons challenging. This variance suggests a need for standardised metrics to provide clearer benchmarks for comparisons [51; 52; 53; 54; 70; 71].  
The studies primarily revolve around common feedstocks like CO\({}_{2}\), H\({}_{2}\), and biomass. However, the source of these feedstocks (e.g., direct air capture vs. point-source capture for CO\({}_{2}\), or different types of biomass) can lead to significant differences in economic and environmental outcomes. Research that focuses on comparing feedstock sources more directly is relatively limited [55; 57; 59; 68].  
Some studies highlight the benefits of integrated systems, such as combining Fischer-Tropsch synthesis with corn ethanol production or integrating power-to-methanol with MSW gasification. However, the comparative advantages of these integrated systems versus standalone processes need more comprehensive research [51; 55; 68].  
The studies hail from different geographical regions [62; 63; 64; 65; 66; 72], each with unique energy infrastructures, policies, and natural resources. While some studies provide specific insights about their respective regions, a comprehensive comparative analysis on how geographical factors impact e-fuel production's economic and environmental outcomes is missing.  
Some studies [58; 59; 60; 61; 62; 63; 64] hint at the importance of technological advancements, but in-depth analyses of how emerging technologies (like advancements in catalysts, electrolys, or carbon capture methods) might shift the paradigms are relatively sparse.  
While many studies [59; 60; 61; 62; 63; 64; 65; 66] employ life cycle assessments (LCAs) and primarily focus on greenhouse gas emissions, other environmental impacts, such as water usage, land use, or impacts on biodiversity, are less frequently addressed. This lack of comprehensive environmental assessments might miss some indirect negative impacts of certain e-fuel production methods.  
The study by [PERSON] [28] hints at the importance of CCU- and H\({}_{2}\)-based e-fuel considering societal acceptance, regulatory factors, and safety. However, these socio-political factors are less frequently addressed in other studies, suggesting a potential research gap.  
In terms of economic feasibility, some studies [51; 71] highlight the high costs associated with uneven solar energy distribution, while others, such as the study in the UK, indicate relatively lower costs and positive outcomes. These conflicting outcomes indicate that variables like local conditions, technology choices, and feedstock sources can greatly influence results, emphasising the need for more standardised assumptions and scenarios.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Code Execution Performance
#### 4.5.2 Transforming Noise Figure Datasets into Models  
This section assesses the time required to train the polynomial model. The tested program performs the following steps:  
1. Combine all datasets containing the noise figure calculations for each EDFA device across the various gain ranges.
2. Construct the training data by calculating the additional dataset columns, as outlined in the GLS regression section.
3. Run the GLS algorithm to determine the polynomial coefficients.
4. Save the coefficients in _.parquet_ files.  
The execution times for the program across the six different models are presented in Table 2. The computation time remains dependent on the size of the training datasets. The program requires approximately 0.18 ms per dataset row to calculate the model coefficients. When combined with the previous step, it takes around 7.8 s to generate a model from a single.mat dataset containing \(10^{4}\) combinations of total input power, target gain, tilt, and active channel central frequency. This time increases to 1 min 18 s for \(10^{5}\) combinations, and 13 min for \(10^{6}\) sets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device** & **Gain** & **Dataset No.** & **Time [s]** & **Row Count** & **Time per Row [ms]** \\ \hline EDFA-17 & LOW & 1 & 13.22 & 21,318 & 0.62 \\ \hline EDFA-35 & HIGH & 1 & 32.95 & 56,848 & 0.58 \\ \hline EDFA-35 & HIGH & 2 & 31.99 & 56,848 & 0.56 \\ \hline EDFA-35 & LOW & 1 & 25.79 & 45,144 & 0.57 \\ \hline EDFA-35 & LOW & 2 & 27.77 & 46,816 & 0.59 \\ \hline EDFA-35 & LOW & 3 & 25.77 & 45,144 & 0.57 \\ \hline EDFA-35 & LOW & 4 & 34.54 & 58,608 & 0.59 \\ \hline EDFA-ILA-L & LOW & 1 & 12.08 & 21,360 & 0.57 \\ \hline EDFA-ILA-L & LOW & 2 & 12.52 & 21,360 & 0.59 \\ \hline EDFA-ILA-L & LOW & 3 & 9.03 & 14,976 & 0.60 \\ \hline EDFA-ILA-L & LOW & 4 & 9.16 & 14,976 & 0.61 \\ \hline EDFA-ILA-L & LOW & 5 & 8.83 & 14,976 & 0.59 \\ \hline EDFA-ILA-L & LOW & 6 & 8.66 & 14,976 & 0.58 \\ \hline JUNIPER & HIGH & 1 & 10.79 & 18,816 & 0.57 \\ \hline JUNIPER & HIGH & 2 & 11.44 & 18,816 & 0.61 \\ \hline JUNIPER & HIGH & 3 & 12.15 & 18,816 & 0.65 \\ \hline JUNIPER & HIGH & 4 & 11.06 & 18,480 & 0.60 \\ \hline JUNIPER & LOW & 1 & 16.15 & 29,232 & 0.55 \\ \hline JUNIPER & LOW & 2 & 16.70 & 29,232 & 0.57 \\ \hline JUNIPER & LOW & 3 & 17.23 & 29,232 & 0.59 \\ \hline JUNIPER & LOW & 4 & 16.90 & 29,232 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation time to compute the noise figure for each EDFA dataset.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 2 Materials and Methods  
### Smartphones and Geoscience  
The use of GNSS signal to describe geophysical phenomena is well known: many studies have already been carried out on GNSS seismology, GNSS meteorology, and induced ionospheric perturbations [9; 10; 11]. The studies carried out so far have mainly been focused on the use of geodetic class or low-cost GNSS receivers. However, the technological development that is revolutionizing the mass-market GNSS world--described in Section 1--allows us to name Android smartphones as the new tool to be used within geoscience applications.  
The description of a generic geophysical phenomenon and its effects requires dense information. In the case of GNSS receivers, which can be considered as sensors providing isolated measurements, a densification is required. However, in the case of geodetic class or low-cost receivers, a densification of the actual network is prevented by the associated costs. Android smartphones represent everyday tools and, thanks to the Android Raw GNSS Measurements API, they are becoming the leading GNSS products within the mass-market world. Therefore, following the trend in the GNSS community, Android smartphones seem to be the perfect solution to fulfill the requirements for GNSS data usage in geophysical applications. Moreover, the use of GNSS signal from different constellations--especially from Galileo and BeiDou--and frequencies allows the accuracy of the overall GNSS solutions to be increased. This is necessary when smartphones are used in geophysics because multipath and interference with other signal sources are projected into the reconstruction of the phenomenon of interest, reducing the reliability of the final solution. However, the new generation of Broadcom chipset offers greater resistance to these problems, and further improvements are expected with the next generations of chipset that manufacturers will provide.  
Finally, it is important to consider that smartphones are multi-sensor devices where GNSS and IMUs (inertial measurement units) are able to provide independent measurements with a proper time scale. For example, the availability of GNSS and accelerometer measurements allows the motion parameters of the smartphone in high-frequency kinematic scenarios to be improved and validated. Different research projects are now focused on the possibility of using sensor data from smartphones to describe the effects of geophysical phenomena on the device: MyShake [12] represents an example of a seismic system intended to detect and characterize earthquakes based on accelerometer data from smartphones. In this sense, the integration with raw Android GNSS measurements may be beneficial in ensuring higher accuracy in smartphone positioning and increased reliability in the detection of the motion.
## 6 AOG Downtime Reduction and Implementation Results
### Descriptive Statistics  
The analysis highlights key performance trends based on 1000 entries and eight operational variables. A mean part failure rate of 2.56 indicates moderate reliability, though variability across parts (standard deviation: 0.1-5.4) points to the need for targeted quality control. Low AOG incident variability (standard deviation: 0.399) reflects consistent performance, supported by the airline's proactive strategies.  
However, higher variability in part failure rate (1.424) and maintenance compliance (1423.06) suggests disparities tied to aircraft age, maintenance schedules, and operating conditions. Older or harshly operated aircraft show higher failure rates, emphasizing the importance of standardized maintenance and resource optimization. Fleet usage varies significantly (total flight hours standard deviation: 8205.95), influenced by route demands and aircraft roles. Moderate variability in downtime (11.76) and failures (6.12) points to regional factors like infrastructure and repair capacity, reinforcing the need for predictive maintenance and supply chain improvements. Consistent delay management (delay time standard deviation: 2.39) demonstrates effective disruption handling, though rare outliers signal room for better contingency planning. Overall, these findings stress the value of standardized processes, predictive analytics, and resource management in boosting efficiency.
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 3 Result and Analysis
### Illustrative Examples  
This platform has already been successfully applied in over thirty organizations in China, such as surveying institutes and universities across more than ten provinces and cities, and has received positive user feedback. We illustrate the potential of this platform by using the example of a regional agricultural industry structure survey in Guizhou, China. Guizhou Province features rugged terrain with scattered farmland, with a total area of 1.76 \(\times\) 10\({}^{5}\) km\({}^{2}\), making regional agricultural industry structure surveyschallenging [48]. According to the Koppen climate classification, Guizhou is categorized into humid subtropical climate (cwa, cfb) and subtropical highland climate (cwb) [49]. The persistent cloudiness and humidity in some areas make it impossible to conduct agricultural surveys that rely solely on remote sensing technologies like satellite imagery or aerial photography [50]. Due to these geographic and climatic challenges, on-site data collection in Guizhou is essential. By using the field geographic information collection platform, researchers can navigate the rugged terrain, visit scattered areas of farmland, and gather valuable on-site observational data. This platform collects high-resolution data that can supplement and validate remote sensing information, offering a more comprehensive understanding of Guizhou's agricultural industry structure.  
In a broad sense, agriculture encompasses crop cultivation, livestock production, fisheries, forestry, and the processing of agricultural by-products [51]. This demonstration takes crop cultivation and livestock production as examples.