esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using R 4.4.2. As the data did not meet the assumption of normality, as assessed by the Shapiro-Wilk test (\(p<0.05\)), non-parametric methods were applied. For subjective comfort rating, the independent variables were driver seat type and time, and the dependent variable was the subjective comfort rating; for ICM, the independent variables were driver seat type, movement type, and time, and the dependent variable was ICM. Specifically, an Aligned Rank Transform (ART) ANOVA was used to analyze the data; this approach not only handles non-normal distributions but also accommodates mixed-effects models. Pairwise comparisons were then conducted to probe specific contrasts, providing a comprehensive understanding of the observed effects. All statistical tests were conducted with a significance level set at 0.05. This framework provided a robust assessment of the impact of the AFMF system on driver comfort and ICM patterns.
## 4 Numerical Results and Discussion
### Kf Parameter  
Figure 4 shows the results of two plannings with vehicle A and target A. The blue line represents the one performed by setting the \(K_{f}\) parameter to 0, while the green line represents a planning with the \(K_{f}\) parameter set to 1. A further planning with the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Target A** & **Target B** & **Target C** & **Target D** \\ \hline \multirow{2}{*}{Vehicle A} & Baseline & 5430 & 6002 & 4001 & 3097 \\ \cline{2-6}  & \(K_{f}=1\) & 4003 & 7389 & 4769 & 3712 \\ \hline \multirow{2}{*}{Vehicle B} & Baseline & 4654 & 5132 & 3399 & 2604 \\ \cline{2-6}  & \(K_{f}=1\) & 5584 & 6055 & 3990 & 2922 \\ \hline \multirow{2}{*}{Vehicle C} & Baseline & 4490 & 5002 & 3288 & 2506 \\ \cline{2-6}  & \(K_{f}=1\) & 5239 & 5563 & 3941 & 3057 \\ \hline \multirow{2}{*}{Vehicle D} & Baseline & 5012 & 5540 & 3688 & 2799 \\ \cline{2-6}  & \(K_{f}=1\) & 5919 & 6795 & 4724 & 3507 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of LMI calls varying the vehicle and the target.  
parameter set to 0.5 was performed, but it resulted in the same trajectory obtained while considering the \(K_{f}=1\). The following results were obtained:  
* \(K_{f}=0\) required the expansion of 181 nodes, with 5430 evaluations of LMIs;
* \(K_{f}=0.5\) required the expansion of 174 nodes, with 5394 evaluations of LMIs;
* \(K_{f}=1\) required the expansion of 174 nodes, with 4003 evaluations of LMIs.  
The computational effort related to the classification process was roughly a tenth of the overall computational time, and the workstation used was capable, during tests of running 140,000 classifications per second. Training was carried out in 7 min. The relevant confusion matrix for the SVM used is shown in Figure 5.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 1 Introduction
This case demonstrates the robustness of the hybrid approach in accurately decomposing signals with dynamic, non-periodic behaviors, highlighting its potential for addressing noise and nonlinearity more effectively than conventional techniques. Overall, these case studies validate the ability of the hybrid EMD-VMD method to overcome the challenges associated with complex signals, such as nonlinearity and non-stationarity. By integrating the strengths of EMD and VMD, this strategy offers results highlighted in the advanced analysis of nonlinear and nonstationary signals, as well as opening up possibilities for the development of future applications in fields such as engineering, biomedical sciences, and structural monitoring. Its accuracy, efficiency, and robustness against noise confirm its relevance as an essential tool for solving real-world problems in dynamic and noisy systems. Future research will aim to address identified limitations, such as conflicts with extremely close frequencies and high noise levels, as well as develop computational efficiency confirmation indices to further validate the method. This document contributes to the field by presenting an hybrid methodology and demonstrating its effectiveness compared to conventional techniques, laying the groundwork for solutions in the analysis of complex signals.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 4 Simulation Results
### Comparison with the State-of-the-Art Methods  
#### 4.2.1 Scenario 1  
In this scenario, we examine the localization performance of different methods by varying the mean standard deviation, \(\mu_{m}\), of the azimuth measurement errors. Figure 4 shows the comparison results when \(N=5\), \(T=5\), \(\mu_{o}=10\,\mathrm{deg}\), and \(\mu_{n}=6\,\mathrm{dB}\). It is evident that the proposed method exhibits an absolute advantage among the compared methods, owing to its analysis of the measurement errors of each node and the calculation of reasonable weights for weighted least squares. Therefore, it performs well in this scenario.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 5 Conclusions
Specifically, work should be commenced to adjust the algorithm such that it does not prefer filter solutions with over-inflated uncertainty metrics. Furthermore, we need to further push this algorithm toward its autonomous use-case by defining an automated method for triggering an anomaly detection by the gating network, developing a fully-defined decision tree to characterize the anomaly using the filter weightings as a guide, and assessing the computational load of this algorithm using a flight-like code framework. Finally, developing simulations that more realistically represent a broader set of expected anomalies in a navigation solutions will better demonstrate the capabilities of this algorithm. Conceptualization, D.P.L. and T.A.E. ; Methodology, D.P.L. and T.A.E. ; Software, D.P.L. and T.A.E. ; Validation, D.P.L. and T.A.E. ; Formal Analysis, D.P.L. ; Investigation, D.P.L. ; Resources, T.A.E. ; Data Curation, D.P.L. and T.A.E. ; Writing--Original Draft Preparation, D.P.L. ; Writing--Review & Editing, D.P.L. and T.A.E. ; Visualization, D.P.L. and T.A.E. ; Supervision, T.A.E. ; Project Administration, T.A.E. ; Funding Acquisition, T.A.E. All authors have read and agreed to the published version of the manuscript. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80 NM0018D0004). The authors would also like to thank [PERSON] for his input on this work. The authors declare no conflict of interest.
## 6 Illustrative Example
### Illustration of Specification Violation and Semantics Change  
Figure 3 Part 1 presents another system configuration that can be reached by a PFC after starting in an arbitrary system configuration. Notice that in this system configuration, there exists a normal broadcast tree rooted at root Process 0 consisting of Processes 0, 1, 4, and 5 and an abnormal broadcast tree rooted at processes containing Processes 2 and 3. It is easy to see that Process 5 in the normal tree may discover Process 3 to be closer to the root since Process 3's \(L\)-variable is 1, whereas its parent's (Process 4) \(L\)-variable is 2. As in a typical PFC algorithm that constructs a BFS spanning tree starting in an \"arbitrary initial system configuration\", Process 5 may change its parent to Process 3 using only local knowledge. Observe that now Process 5 is contained in an abnormal broadcast tree which will eventually be eliminated, and subsequently Process 5 is included in the normal broadcast tree. Now, notice that Process 5 has received the same message by joining the normal broadcast tree leading to the at least once semantics instead of the exactly once  
Figure 2: Illustration of premature feedback. Normal and abnormal broadcast trees are shown in green and red, respectively.  
semantics. Note that this is caused by an action of Process 5 based on local knowledge. It is easy to see that a mechanism that checks the normality of both trees prior to the action of Process 5 prevents the actions that lead to the semantics change.
## 3 Results and Discussion
### Amino Acid Profile
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Amino Acid** & _D. viridis_ TAV01** & _D. tertioleta_\({}^{1}\)** & _D. salina_\({}^{2}\)** & **Spirulina**\({}^{3}\)** & _Chlorella_ sp. \({}^{4}\)** & **Soy Protein**\({}^{4}\) \\ \hline Phenylalanine \({}^{*}\) & 7.44 \(\pm\) 0.02 & 5.00 \(\pm\) 0.04 & 11.84 & 4.83 & 5.00 & 5.00 \\ Proline & 8.34 \(\pm\) 0.11 & 4.35 \(\pm\) 0.01 & nr & 3.99 & 4.80 & 5.30 \\ Serine & 5.23 \(\pm\) 0.04 & 4.90 \(\pm\) 0.04 & nr & 5.15 & 4.10 & 5.80 \\ Threonine \({}^{*}\) & 5.70 \(\pm\) 0.00 & 4.94 \(\pm\) 0.07 & 2.69 & 5.29 & 4.80 & 4.00 \\ Tryptophan \({}^{*}\) & \(\mathrm{nd}\) & 1.64 \(\pm\) 0.05 & 0.00 & 1.74 & 2.10 & 1.40 \\ Tyrosine & 6.08 \(\pm\) 0.01 & 3.08 \(\pm\) 0.03 & nr & 4.72 & 3.40 & 3.70 \\ Valine \({}^{*}\) & 6.44 \(\pm\) 0.12 & 5.02 \(\pm\) 0.04 & 3.20 & 6.39 & 5.50 & 5.30 \\ Taurine & 0.54 \(\pm\) 0.02 & nr & nr & nr & Nr & Nr \\ \hline EAA & 44.88 & 34.78 & 33.05 & 42.51 & 42.60 & 39.00 \\ \hline EAAI & 1.80 & 1.33 & 0.99 & 1.67 & 1.63 & 1.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 4: Radial chart of the essential amino acids. might be a consequence of changes in the proteome to cope with the imposed stress [68,69]. In addition, other limiting conditions, such as low concentrations of phosphorus and sulphur, can also increase stress-related compounds such as proline, lysine, and tyrosine in _D. salina_[69]. Besides nutrient-limited cultivation conditions, accumulation of proline has also been reported with cultivation under NaCl stress, the most extensively accumulated molecule, which might serve as a stress indicator [70]. Interestingly, proline was one of the most produced AA in _D. viridis_TAV01, which might have been caused by the NaCl stress imposed by the growth conditions used in this study.
# A Distributed RF Threat Sensing Architecture
## 6 Conclusions  
In this research paper, we have proposed a collaborative low-cost RF jamming detector, interconnected with a SOC, for centralized long-term monitoring and alerting. Each system sensor can be customized accordingly to monitor any band within the range of 1 MHz to 8 GHz, providing the capability for simultaneous monitoring of multiple bands and heterogeneous technologies. The developed sensors' algorithm allows the self-calibration and time-depended environment RF profiling in order to minimize false-positive alerts and optimizes performance for each specific environment. It supports TCP/IP interconnection of multiple sensors, which can be deployed in multiple areas of interest, offering holistic early warning and localization possibility of RF jamming sources. Future work includes:  
* The deployment and testing of several collaborative sensors utilizing multiple direction antennas for jamming source localization with AoA/DoA and triangulation techniques.
* Implementation of an RF down-converting receiver chain and integration of sharper RF filters to improve the system sensitivity for the purpose of supporting simple drone activity detection at longer distances.
* Implementation of AI/ML techniques at the SOC level for optimized detection of RF spectrum anomalies from several distributed sensors.  
All authors contributed equally. All authors have read and agreed to the published version of the manuscript.  
This work was supported by RESTART 2016-2020 Programme and co-funded by the European Union and the Research Innovation Foundation of the Republic of Cyprus, under MULISENSE project (Multi-domain wireless threat detection sensors for security operations centres) with contract number ENTERPRISES/0521/0203.  
Not Applicable.  
Data Availability Statement: Data is contained within the article.  
This work is gratefully acknowledged with the support of the RESTART 2016-2020 program, co-funded by the European Union and the Research and Innovation Foundation of the Republic of Cyprus, under the MULISENSE project (Multi-domain Wireless Threat Detection Sensors for Security Operations Centers), with contract No. ENTERPRISES/0521/0203.  
Conflicts of Interest: Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Sight Solutions Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 6 Conclusions  
This study aims to develop Fourier Transform acceleration algorithms that are well-suited to NPU hardware architecture. This enhancement of the data preprocessing capabilities in NPU-based backpack computers facilitates faster execution of conventional engineering algorithms, supporting various computational service scenarios. Specifically, two NPU algorithms, namely MV-2 DFT and MV-2 FFT, based on the DFT and the divide-and-conquer approach, were designed. MV-2 FFT was implemented in two variants, namely,MV-2 FFTm and MV-2 FFTv. In terms of the acceleration effect, the MM-2 DFT algorithm shows superior performance for small-scale inputs due to the powerful computational ability of cube units. However, its advantage diminishes as the input size increases, compared with FFT_CL and MV-2 FFT. As the input size reaches 2048 \(\times\) 2048, FFT_CL and MV_2 FFT outperform MM-2 DFT, primarily attributed to their lower computational complexity. Regarding energy efficiency, it is illustrated that the cube units have the highest energy efficiency among the NPU internal units. However, utilizing CPU or vector units may be advantageous in optimizing speed and reducing energy consumption in specific scenarios, such as handling small-scale inputs and performing sparse matrix computations.  
In future work, there are plans to explore methods for optimal energy-efficient scheduling strategies that automatically select different processors and algorithms and determine the hyperparameters of MV-2 FFT for Fourier Transform processing on NPU platforms. Meanwhile, NPU acceleration methods for some other preprocessing algorithms, like the discrete wavelet transform, will also be investigated. Addressing the identified issue in this paper, the efficiency of NPUs for sparse matrix multiplication will be optimized by proposing a more general sparse matrix multiplication optimization method. This aims to enhance the energy efficiency of NPUs in sparse scenarios based on the energy-efficient underlying arithmetic.  
Conceptualization, D.W.; methodology, Q.L., D.Z. and Y.F.; software, Q.L.; formal analysis, D.Z.; investigation, Y.F.; resources, D.W.; data curation, Y.F.; writing--original draft preparation, Q.L. and D.Z.; writing--review and editing, Y.F.; supervision, D.W.; project administration, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62171155).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We would like to express our gratitude to the members and staff of the Fault Tolerance and Mobile Computing Research Center at Harbin Institute of Technology.  
The authors declare no conflicts of interest.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 3 Results
To allow direct comparison, the inset region in Figure 10 is shown in Figure 12 (Appendix B) prior to aggregation. Figure 9: ROC curves for three different SAR coherence-based classifiers, plotted at a range of resolutions from individual 20 m \(\times\) 22 m pixels up to 300 m \(\times\) 330 m aggregate pixels. The dotted lines show the performance of a random classifier (AUC = 0.5). Masks shown in Figure 7 were applied to the data before calculation. The Bx-S method outperforms the other classifiers for aggregated pixels. Several factors exerted a relatively strong influence on classification ability for the different classifiers: one related to spatial scales, a second to the time window of SAR acquisition, and a third to the definition of 'landslide' pixels. First, increasing the size of the boxcar window worsens performance  
Figure 10: Classifier surfaces for 200 m \(\times\) 220 m aggregate pixels. (**a**) the percentage of each aggregate pixel made up of landslide pixels. (**b–d**) the mean value of the classification surface for each method within each aggregate pixel. These surfaces are normalized to a range between 0 and 1 with 1 being most likely to be a landslide. Colour bars are non-linear, but linear between the white lines and labelled with the percentage of pixels across the whole area that lie in this linear range. for all three classifiers. We have presented all results in this study using a 3 \(\times\) 3-pixel window but we also tested 5 \(\times\) 5 and 20 \(\times\) 20 windows. In most cases increasing the size of the boxcar window reduced ROC AUC, although not for the Bx-S classification surface (Table 1). We carried out the comparison on aggregates of 10 \(\times\) 10 individual pixels. This was in order to lessen the effect of coarsening resolution on classification ability discussed above. Second, for all three classifiers, classification ability worsened when the time window between SAR image acquisition was increased. This was expected as a longer time window will have increased temporal decorrelation unrelated to landsliding, particularly in vegetated areas. Third, results were affected by how aggregate 'landslide' pixels were defined. For the purposes of ROC analysis, an aggregate landslide pixel was defined as one comprising at least 50% individual landslide pixels. In varying this threshold, we found that ROC AUC was higher when landslide pixels were more strictly defined by a higher threshold (Table 2). The classifiers are therefore better able to identify a region that has experienced more severe landsliding, which may affect how they can be applied. We also tested the effect of altering the size of the RapidSAR search window from 41 \(\times\) 41, which is used throughout this study, to 21 \(\times\) 21, 61 \(\times\) 61 and 81 \(\times\) 81. This had little effect on ROC AUC on both individual and aggregated pixel surfaces (No more than 0.01 difference) but computation time was noticeably different. The time taken for an 81 \(\times\) 81 window was around double that of the 21 \(\times\) 21 window.
## 3 Results and Discussion
### Method Validation  
Qualitative analysis was performed using Supelco 37 Component FAME Mix\" standard, the components identified by retention times and profile of chromatogram in the certificate of standards mix (Table 1). Figure 1: Effect of temperature (**A**) on derivatization reaction. Effect of time (**B**) on derivatization reaction; all reactions were performed at 70 \({}^{\circ}\)C. Results are expressed as means \(\pm\) standard error, (\(n=6\)). Values within columns followed by the same lowercase letter (a–d) differed statistically at \(p<\) 0.05 (Tukey’s test). System suitability was evaluated by replicate (\(n=6\)) injection of the same standard solution containing FAMEs. The method validation for fatty acids was performed by analyzing the repeatability and checking the precision of %RSD of retention times and peak areas. After all, the repeatability for standards calculating %RSD for retention times was not greater than 0.5% and for peak areas not greater than 1.0%, calculating the same conditions for the precision of retention times was not bigger than 0.5%, for peak areas are not greater than 1.5%. Quantitative analysis of fatty acids was performed by evaluating the values of analyzed fatty acids and using calibration curves. The concentration on the standards in the mixture of fatty acids was between 3.09 and 599.40 \(\upmu\)g/mL; the calibration curves consisted of seven concentrations. The correlation coefficient was no less than \(\mathrm{r}^{2}=0.999\), thus confirming the linearity of the method (Table 1). The LOD was determined as three times the signal-to-noise ratio, while the LOQ was ten times the signal-to-noise ratio. The LOD and the LOQ values for different FAMEs are reported in Table 1. Accuracy and precision of the method were determined by replicate analysis (\(n=6\)) of FAME reagent, which is reported in Table 1. The LOD values varied between 0.21 \(\upmu\)g/mL to 0.54 \(\upmu\)g/mL, while the LOQ between 0.64 \(\upmu\)g/mL to 1.63 \(\upmu\)g/mL (Table 1), which indicates that the method is sensitive. According to the described data above, it can be concluded that this method is a reliable tool for the identification and quantification of fatty acids in bee products, conforming to the ICH guidelines [11].
###### Abstract  
Accurately partitioning evapotranspiration (ET) of cropland into productive plant transpiration (T) and non-productive soil evaporation (E) is important for improving crop water use efficiency. Many methods, including machine learning methods, have been developed for ET partitioning. However, the applicability of machine learning models in cropland ET partitioning with diverse crop rotations is not clear. In this study, machine learning models are used to predict E, and T is obtained by calculating the difference between ET and E, leading to the derivation of the ratio of transpiration to evapotranspiration (T/ET). We evaluated six machine learning models (i.e., artificial neural networks (ANN), extremely randomized trees (ExtraTrees), gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost)) on partitioning ET at 16 cropland flux sites during the period from 2000 to 2020. The evaluation results showed that the XGBoost model had the best performance (R = 0.88, RMSE = 6.87 W/m\({}^{2}\), NSE = 0.77, and MAE = 3.41 W/m\({}^{2}\)) when considering the meteorological data, ecosystem sensible heat flux, ecosystem respiration, soil water content, and remote sensing vegetation indices as input variables. Due to the unavailability of observed E or T data at the 16 cropland sites, we used three other widely used ET partitioning methods to indirectly validate the accuracy of our ET partitioning results based on XGBoost. The results showed that our T estimation results were highly consistent with their T estimation results (R = 0.83-0.91). Moreover, based on the XGBoost model and the three other ET partitioning methods, we estimated the ratio of transpiration to evapotranspiration (T/ET) for different crops. On average, maize had the highest T/ET of 0.619 \(\pm\) 0.119, followed by soybean (0.618 \(\pm\) 0.085), winter wheat (0.614 \(\pm\) 0.08), and sugar beet (0.611 \(\pm\) 0.065). Lower T/ET was found for paddy rice (0.505 \(\pm\) 0.055), winter barley (0.590 \(\pm\) 0.058), potato (0.540 \(\pm\) 0.088), and rapeseed (0.522 \(\pm\) 0.107). These results suggest the machine learning models are easy and applicable for cropland T/ET estimation with different crop rotations and reveal obvious differences in water use among different crops, which is crucial for the sustainability of water resources and improvements in cropland water use efficiency.  
soil evaporation; XGBoost; typical crops; the ratio of transpiration to evapotranspiration