esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Final Remarks  
This study makes progress in assessing mild to extreme drought stresses in Mediterranean evergreen species with the PRI and reflectance ratio R690/R630. Both PRI and R690/R630 were not only sensitive to progressive drought, but also to plant recovery, significantly tracking the photosynthetic response to enhanced drought levels, and also detecting the photosynthetic recovery after re-watering. Caroenoid/chlorophyll pigment ratios probably control the correlation between PRI and photosynthesis, and ChlF at 690 nm probably plays a role in the relationship of R690/R630 with photosynthesis. Both PRI and R690/R630 can be used for remotely monitoring the effect of drought on the carbon uptake and productivity of Mediterranean species.  
Our work also promotes the possibility of parameterizing LUE models based on the PRI and the ratio R690/R630 for evergreen species. This is because of links between PRI is linked with short-term changes in xanthophyll pigments proportions and long-term shifts in pigment pools, and R690/R630 with chlorophyll content and fluorescence [69]. For other non-evergreen vegetation such as annual or deciduous plants, the potential uses of PRI and R690/R630 in detecting the progressive drought and recovery effects on photosynthetic activity should be further tested given their different physiological traits, but these results open a promising window for them too. The increasing use of hyperspectral spectroradiometers carried on unmanned aerial vehicles (UAVs) [70] and satellites (such as Metop and Sentinel series) provides the possibility of testing the LUE model using PRI and red-edge reflectance indices for multiple vegetation types and at different spatiotemporal scales.  
This work was supported by the European Research Council Synergy grant SyG-2013-610028 IMBALANCE-P, the Spanish Government project CGL2016-79835-P, and the Catalan Government project SGR 2014-274. [PERSON] gratefully acknowledges the support from the Chinese Scholarship Council.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data and wrote the initial draft and figures. All of the authors contributed to the discussion of the results and to the writing of the manuscript.  
The authors declare no conflict of interest.
## 4 Methodology  
### Data Collections  
The data collations concerning climate in the central region of Mozambique (Chimoio, Beira and Tete) were collected from Ara-Centro and provided by The Deutsche Gesellschaft fur Internationale Zusammenarbeit (GIZ) in Manica by February 2017. The data represent the mean average monthly data of temperature and precipitation. GIZ has worked with rural electrification in Mozambique with the objective to provide electricity access to rural households and promote a better quality of life for the rural poor people in the country.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 7 Discussion
### Dual Polarization NFDM  
For the sake of simplicity, this manuscript considers only single-polarization NFDM systems. However--taking advantage of the integrability of the Manakov equation [38]--dual-polarization systems employing NIS or b-modulations have been considered in many works, reporting similar performance with respect to their single-polarization counterpart, but doubling the information rate [10; 15; 16; 39; 40]. In fact, we expect that the power-tilt strategy and the improved detection-strategies described in Section 4 can be applied to dual-polarization NFDM with little difference (but a higher complexity), nearly doubling the information rate and obtaining an overall behavior similar to that shown in Figure 8.
## 2 Method for Estimating Dynamic Mechanical Behaviors of RC Columns
### Realization of the Method  
The step-by-step description of the methodology of the random forest machine learning method is illustrated as follows, and the schematic plot of the procedure to develop the _DMC_ predictive models is as shown in Figure 1.  
1. Due to the lack of dynamic loading test data, a numerical model database was developed for training data. The numerical model needed to accurately simulate the dynamic mechanical behaviors of RC columns. Moreover, the critical design parameters of RC columns were considered and columns with the same parameters were designed to be under different strain rate levels.
2. Based on the simulated results of mechanical behavior parameters, the dynamic modified coefficient (_DMC_) values in terms of the yielding bearing capacity, the ultimate bearing capacity and the ductility coefficient were obtained.
3. Before the model was trained, the obtained data were divided into 80% for the training set (for training data) and 20% for the test set (for verification). Moreover, the parameters of the random forest algorithm were set, including the number of decision trees (\(N=200\)) and the maximum depth of the forest (depth = 10), etc.
4. When employing the random forest algorithm, the _DMC_ values of the yielding bearing capacity, the ultimate bearing capacity and the ductility coefficient were treated as outputs, and 5 structural parameters and the strain rate index introduced in Section 2.1 were selected as input variables.
5. During the training process, the criterion of minimum mean square error (_MSE_) [29] was adopted for selecting features in construction of binary trees. The mean square error (i.e., _MSE_) was obtained by \[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}\right)^{2}\] (5) where \(n\) denotes the number of sample data, while \(y_{i}\) and \(\hat{y}\) represent the value of sample data and the mean value. The _MSE_ of randomly selected features and possible splitting point were calculated. The features and splitting point that minimized the weighted sum of the mean square error were selected for node splitting, and multiple decision trees were constructed.
6. Finally, after the model was trained, by taking the average results of decision trees, the value of _DMC_ predicted by the random forest model was acquired. The feature importance of the node was estimated to evaluate the contribution of each feature in influencing the results of the predictive models.
# Nonlinear Random Forest Classification, a Copula-Based Approach
## 4 Numerical Results
### Simulation Study  
In order to carry out a simulation study, we generated data from normal distribution with copula dependence. Our considered copulas were Gaussian, \(t\) and Gumbel copulas; see, e.g., [PERSON] (2006) [32]. Using the copula library, we first generated \(n\) = 10,000 random samples \(x_{1}\),..., \(x_{10}\) from a 10-variate Gaussian copula where all off-diagonal elements of their correlation matrix equal to \(\rho\) = 0.85 and their marginals follow the standard normal distribution. In a similar fashion, again, we generated another 10 variates \(x_{11}\),..., \(x_{20}\) independent from the first 10 variables. Then, for simulating from \(t\)-copula, we generated 10-variates \(x_{21}\),..., \(x_{30}\) from t-copula with all correlation values equal to \(\rho\) = 0.85, \(df\) = 19 [43] and their marginals follow the standard normal distribution. Finally, a bivariate Gumbel copula with \(\theta\) = 5 [44] and normal marginals were generated and inserted into \(x_{31}\), \(x_{32}\). A schematic heatmap plot of these 32 features is shown in Figure 2. Using a linear combination, we added values of these feathers and made the outcome variable. In order to obtain a class-valued variable, we recoded the negative values of the outcome variable to \"0\" and other values to \"1\".  
Using Algorithm 1, we started with \(n=2\) features. The most important features to classify \(y\) were \(x_{26}\) and \(x_{32}\) with sensitivity = 0.869, specificity = 0.867 and accuracy = 0.875. Continuing the selection of the most relevant features has led us to \(x_{26}\), \(x_{32}\) and \(x_{31}\) as the first three relevant features. In order to obtain unbiased results, we performed a 10-fold cross validation, and in each fold, we left out 1000 cases as a test group and the remainder for the train set. Averages of sensitivity, specificity and accuracy were calculated to assess the algorithm. Table 1 shows the most relevant and least redundant features with their evaluation scales sensitivity, specificity and accuracy. This table helps us to assess our algorithm by monitoring its running time as well as its comparison with other algorithms. Since, after selecting the features, we use the traditional random forest approach, it is reasonable that we compare our results with the results of the traditional random forest approach. Comparing the last two rows of Table 1, we deduce that the results of our algorithm and the traditional random forest algorithm are the same.  
of relationship between attributes play important roles in such a classification procedure. So, from the point of view of the practical implications, these results enable researchers to specify the number of attributes based on the desired levels of sensitivity, specificity and accuracy, and if the relationship between attributes is not complicated, one can choose a greater number of attributes and achieve more accuracy, and vice versa.
## 4 Experimental Results
### Analysis of Residual Network Model Results  
After the one-dimensional MFL signals of 18 types of internal and external broken wires in steel wire ropes with diameters of 20 mm, 22 mm, and 24 mm are converted into two-dimensional time-frequency maps of size 224 \(\times\) 224 through continuous wavelet transform (CWT), a total of 1800 image sample data are obtained; 75% of the sample data is used for network training, and the remaining 25% is used for testing the network. The network structure of ResNet18 consists of an input layer, four residual modules, a fully connected layer, and an output layer, effectively addressing gradient vanishing and exploding issues through the use of residual modules. The network is initialized with a learning rate of 0.0015, and each iteration utilizes 30 training subsets. The training progress is visualized by plotting the training progress, and the recognition accuracy results are shown in Figure 10. The quantitative recognition method for broken wires in steel wire ropes based on ResNet18 can accurately identify the internal and external broken wire faults of steel wire ropes. The overall recognition accuracy reaches 95.33%, which is 6.66% higher than that of convolutional neural networks, providing a reliable basis for identifying damage to steel wire ropes.  
Figure 10: The confusion matrix based on ResNet18.  
Figure 9: The framework of the experimental method.
## 3 Results
### Formaldehyde (CH\({}_{2}\)O) and Acetaldehyde (CH\({}_{3}\)CHO)  
The accepted techniques for measuring formaldehyde and acetaldehyde are DNPH + HPLC, FTIR, and PTR + MS; all from the dilution tunnel. The most common and cheaper method is using HPLC (high-performance liquid chromatography) to separate and quantify the carbonyls after extraction of 2,4-DNPH (dinitrophenylhydrazine) cartridges [173, 174], while PTR-MS (proton transfer reaction--mass spectrometry) [168] is the least common. The DNPH-HPLC method also has its uncertainties, as the quantification by HPLC is not always straightforward due to the chromatogram peak resolution. NO\({}_{2}\), NO, and CO can have an impact on the quantification of formaldehyde and acetaldehyde due to the consumption of the DNPH during sampling [175].  
Table 2 summarizes the studies that assessed the FTIR measuring formaldehyde. Sampling from the dilution tunnel showed in general good agreement with the DNPH + HPLC methodology (slopes close to 1), with one exception where the FTIR was on average 20-25% higher. From the tailpipe, the differences were larger (\(\pm\)30%).  
Table 3 summarizes the studies that assessed the FTIR measuring acetaldehyde. The scatter of the results is high. One study found more than double concentrations of acetaldehyde with the FTIR than with the DNPH + HPLC method. It seems that the reaction of acetaldehyde took place in the exhaust pipe [176]. Another study with a flexi-fuel vehicle found 30% higher concentrations with the FTIR (compared to DNPH-HPLC), but at the same study, the FTIR was 30% lower than the PTR-MS method [168]. The emission levels were around 0.4 mg/km. Other studies found differences within 10%. A study that checked the FTIR formaldehyde concentrations according to analyte spiking EPA method 301 on a coal-fired burner found differences on the order of 10% [177]. The same study found 5% differences for acetaldehyde.
###### Abstract  
In this paper, we propose and prove an innovative radar antenna concept suitable for collision avoidance (CA) systems installed onboard small, unmanned aircraft (UA). The proposed architecture provides 360\({}^{\circ}\) monopulse coverage around the host platform, enabling the detection and accurate position estimation of airborne, non-cooperative intruders using lightweight, low-profile antennas. These antennas can be manufactured using low-cost 3D printing techniques and are easily integrated into the UA airframe without compromising airworthiness. We present a Detect and Avoid (DAA) concept of operations (ConOps) aligned with the SESAR U-space ConOps, Edition 4. In this ConOps, the Remain Well Clear (RWC) and CA functions are treated separately: RWC is the responsibility of ground-based U-space services, while CA is implemented as an airborne safety net using onboard equipment. Based on this framework, we derive operation-centric design requirements and propose an antenna architecture based on a fixed circular array of sector waveguides. This solution overcomes key limitations of existing radar antennas for UAS CA systems by providing a wider field of view, higher power handling, and reduced mechanical complexity and cost. We prove the proposed concept through a combination of simulations and measurements conducted in an anechoic chamber using a 24 GHz prototype.  
collision avoidance; monopulse antenna; sectorial antenna; cylindrical array; 3D-printed antenna +
Footnote †: journal: Applied Sciences  
**Key words:** collision avoidance; monopulse antenna; sectorial antenna; cylindrical array; 3D-printed antenna
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 6 Conclusions and Future Work  
This paper proposes improved methods for addressing challenges such as significant changes in the shape of the target, motion blur, and occlusions, which frequently occur during aerial refueling processes. By using a clustering-based target feature responder, overly feature-rich samples of the same type are adaptively divided into multiple classes with moderate richness, replacing the single-target classifier logic with multiple-target classifiers to handle large variations in single-target features. This has notably enhanced the success rate and accuracy in detecting refueling probes with significant shape changes. With a state judgment module, the appropriate classifier within multi-target classifiers can be switched timely when motion blur and trailing occur in the target. Moreover, a stereovision-based positioning mechanism that uses three-dimensional information to guide two-dimensional positioning effectively addresses the issue of missed and false detections caused by excessive feature information loss during target occlusion. The model improved through these three approaches, effectively copes, with the challenges of autonomous navigation technology during aerial refueling.  
Reflecting on the work completed in the paper, combined with recent knowledge reserves and practical testing results, it is believed that further research and enhancement are needed in the following areas:  
1. The current optimized algorithm is not universal and primarily focuses on the field of aerial refueling; it has not been tested in other domains, and further expansion and optimization of application domains are necessary;
2. Some parameters involved in the current algorithm still require manual intervention and cannot be adaptively selected, prompting future improvements toward automation.  
Conceptualization, B.S.; methodology, B.S.; software, B.S.; validation, B.S., H.Y. and H.W.; formal analysis, B.S.; investigation, B.S.; resources, B.S.; data curation, H.Y.; writing--original draft preparation, B.S.; writing--review and editing, B.S.; visualization, B.S.; supervision, B.S.; project administration, B.S.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
Funding: The APC was funded by the Aerospace Information Research Institute, the Chinese Academy of Sciences (AIR-CAS), and the Key Laboratory of Target Cognition and Application Technology (TCAT) with E2 HN0808002. Data Availability Statement: Restrictions apply to the availability of these data. Data were obtained from the Chinese Academy of Sciences and are available from the authors with the permission of the Chinese Academy of Sciences. Conflicts of Interest: The authors declare no conflicts of interest.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform  
GeoHyTE (current version 1.3) is a new software for the automatic evaluation of the shear moduli from the output signal of bender element experiments. The user is expected to perform the bender element test, load the resulting output signal into GeoHyTE, and build a finite element model of the experimental setup. Also, the user must select a first tentative shear modulus based on the visual interpretation of the output signal. Next, GeoHyTE launches a series of model updating iterations. At each step, the shear modulus is updated to maximise the correlation between the output signals obtained experimentally and using the finite element model. A report containing key information at each step is automatically constructed. When a fixed point is obtained for the shear modulus, meaning that it remains constant (within a certain tolerance) between two successive iterations, the process stops and the final shear modulus is delivered to the user, together with the correlation coefficient, which reflects the degree of similarity between the experimental and numerical signals.  
The displacement in the solid phase and the fluid seepage, and the total stresses in the solid phase and the pore pressure in the fluid phase are stored in a certain number of time points defined by the user, at each iteration. They can be visualised using external software like TecPlot or Paraview (tested on versions 2011 and 5.12, respectively).
## 4 Demonstration of Tunable Shielding Effectiveness in THz Bands  
In the THz band, the conductivity of graphene is still greatly affected by its chemical potential as shown in Figure 1, thus making it possible to realize the tuning of shielding effectiveness. Meanwhile, it is worth mentioning that the generation, modulation and detection methods of the THz-electromagnetic wave are different from those in the microwave band, where the terahertz spectroscopy technique is needed [10; 13; 22; 42], whose schematic and the test scenario of samples are as follows:  
As shown in Figure 10(a), the THz time-domain spectroscopy (THz-TDS) system is a THz generation and detection system based on coherent detection technology [42]. After obtaining the amplitude and phase information of THz pulse, it can directly obtain the absorption coefficient, refractive index and transmittance of the sample by Fourier  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\tau_{fall}\) & \(\tau_{rise}\) \\ \hline MIM structure & 0.578 s & 1.006 s \\ BIB structure & 0.654 s & 0.859 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time constant of two samples.  
Figure 10: Definition of time constant in (a) the falling and (b) rising process.  
transform of time waveform. In this paper, the TPS spectra 1000 was used to test the transmission coefficient of the samples. As shown in Figure 11b, the sample is placed between two parabolic mirrors, and the calculated SE curves of the two samples under different voltages are depicted in Figure 12.  
The results show that with the voltage increasing from 0 to 5 V, the SE regulation range of the GIG structure could still achieve close to 10 dB in the low THz frequency band. For the BIB structure, it revealed a much higher shielding effectiveness than that of the MIM structure. In addition, it can also be observed that as the frequency increased, the SE values of the two samples increased consistently. The main reason is that for the uniform graphene film-type shielding material, it had a better shielding ability for high-frequency electromagnetic waves, similar to the skin effect of metal materials, thus having a higher SE value in the high frequency band.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 2 Related Work
### Deep Learning with Edge Computing  
Deep learning has been recognized as a very powerful technique in many applications. However, it is non-trivial to fit deep learning algorithms in the edge computing paradigm, mainly because of the limited computational resources at the network edge [35; 36]. In order to enable deep learning at the edge, researchers explore various approaches [37]: (1) reducing the latency of executing deep learning on resource constrained devices by customized model design [38; 39; 40], model compression [41; 42; 43], and hardware-based accelerators [44; 45; 46]; (2) off-loading computing intensive tasks to edge servers or cloud [47; 48]; (3) optimizing edge resource management [49; 50]; or (4) partitioning and distributing computing tasks among peer edge devices [51; 52].  
For instance, a framework is proposed that ties front end devices with back end \"helpers\" like a home server or cloud [53]. Based on various metrics like bandwidth, this framework will accordingly determine task size to be offloaded to back end \"helpers\". Besides offloading strategies among the edge-cloud structure, distributed deep neural networks are proposed over distributed computing hierarchies, consisting of the cloud, the edge/fog and end devices [19]. Models are trained with early exits to reduce computational costs at the edge and only shallow portions of networks are deployed on the edge layer. In addition to vertical collaboration between edge and cloud, researchers also attempt to adapt DNNs on edge-cloud with horizontal collaboration [18], where a scalable fused tile partitioning approach is proposed to partition convolutional networks into slices for parallel processing at the edge layer and effectively minimize the memory footprint for edge nodes.  
In this paper, the SurMon architecture explores the application of deep learning in the area of smart transportation leveraging the edge-fog-cloud computing paradigm. It shows that edge AI is promising to handle the large-scale transportation in modern smart cities.