esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 3 Results and Discussion
### Characteristic Wavelength Selection
#### 3.4.1 Discriminant Models Based on the Full-Band Spectra  
After image segmentation and spectral preprocessing, taking the full-band spectrum as the input, PLS-DA, KNN, BN, RF, and SVM classification models are established, respectively. The discriminant analysis results are shown in Figure 6 and Table 3. The red mark indicates the actual category of corn kernels, and the blue mark indicates the predicted category of corn kernels. The abscissa represents the sample number, and the ordinate represents the type label.  
As can be seen from Figure 6 and Table 3, the classification models of maize seeds based on full-spectrum have achieved good results. However, there are certain differences in the results of different recognition algorithms. The accuracy of KNN, RF, SVM-Linear, and SVM-RBF all reach more than 96%. However, due to the small data set, when the proportion of KNN's modeling set and test set changes, the classification results differ greatly, and the model stability is poor. SVM based on linear kernel has a high calculation speed, but the recognition process is greatly affected by the reflected light intensity. After preprocessing, better recognition results can be obtained. The accuracy of PLS-DA and SVM-Sigmaid is relatively low. The recognition accuracy of individual varieties is even lower than 60%, mainly because the training accuracy is affected by the size of data sets, so when the number of modeling sets is small, the recognition effect is bad. The classification accuracy of NB and SVM based on the polynomial kernel function has reached 100%, and they can correctly identify different maize seeds.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
###### Abstract  
This study introduces an electronically tunable resistorless mixed-mode universal filter and dual-mode quadrature oscillator configuration utilizing merely two voltage differencing gain amplifiers and two grounded capacitors. The suggested filter can perform all generic biquadratic filter functions in all four modes: voltage mode, trans-admittance mode, current mode, and trans-impedance mode, while utilizing the same design. The pole frequency and the quality factor can be tuned electronically and orthogonally by means of the transconductances of the voltage differencing gain amplifier. The dual-mode quadrature oscillator featuring both voltage and current outputs can also be obtained from the proposed filter core. It additionally provides separate electronic control of the oscillation condition and frequency. Several PSPICE simulations with the TSMC 0.18 \(\upmu\)m CMOS model confirm the feasibility of the proposed configurations. Both proposed circuits were experimentally evaluated using commercially available integrated circuit LM13600s. Both simulation and experimental results have validated the performance of the design.  
voltage differencing gain amplifier (VDGA); mixed mode; dual mode; universal filter; quadrature oscillator Article
## 5 Results
### Robustness Analysis
#### 5.2.2 Noise Analysis  
Figure 13 illustrates the VSP shot gathers for both far and near offsets after noise addition, with the left panel showing the noise-free shot gather and the right panel displaying the shot gather with a \(2\sigma_{0}\) noise level. In the experimental implementation, the FAFWI method was initialized using the constant-velocity model (Figure 8a), and the inversion was performed using noise-contaminated observational data.  
Figure 14 shows the inversion results of the FAFWI method for the Marmousi model under noisy conditions. Specifically, (a) presents the inversion result under noise-free conditions, (b) illustrates the inversion result under the \(2\sigma_{0}\) noise level. The FAFWI method maintained a high inversion accuracy even under \(2\sigma_{0}\) noise conditions through successfully reconstructing the velocity structures in both shallow and middle layers. However, the coverage area in the inverted-triangular region showed a reduction, which may be attributed to the loss of high-frequency information owing to noise interference. Nevertheless, the resolution and accuracy of the inversion results were sufficient to meet the imaging requirements. Overall, the FAFWI method demonstrated considerable noise robustness and maintained a high inversion accuracy despite noise interference.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **BP TTI** \\ \hline R\({}^{2}\) Score & 0.9118 \\ SSIM & 0.8413 \\ Correlation Coefficient & 0.9566 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of inversion results using the FAFWI method for the BP TTI model.  
Figure 12: Velocity profiles from FAFWI method for the BP TTI model. **(a)** Velocity profile at 75 m left of the wellbore. **(b)** Velocity profile at 100 m right of the wellbore.
## 2 Theoretical Foundation
### Radar System  
For the detection and location of objects, according to [8], a radar operates as an electromagnetic system.  
Radar systems can be classified according to a series of basic aspects such as the number of antennas, working frequency, and waveform.  
Also, [9] gave details showing that this type of radar requires the aircraft to carry an on-board receiver-transmitter device called a transponder.  
A Secondary Surveillance Radar (SSR) transmits pulses from an antenna, just like a primary radar.  
When the transponder on-board the aircraft receives the radar pulse, it emits a response at a different frequency; this allows for differentiation of the echoes from Clutter.  
Generally, the response from the transponder includes the height of the aircraft, in addition to its identification [5].  
Indra's MSSR-S mode represents the next generation of radar systems in the secondary surveillance domain.  
It is a fully digital version with a powerful and advanced set of functionalities, which ensure that any functional need will be covered at all times with a performance that is well above the defined requirements.
## 5 STCDRR Protocol  
The proposed STCDRR protocol enhances the reduction of data redundancy in WSNs. It operates in the source level and the aggregator level separately. First, it reduces the temporal data redundancy at the source level for a time T consists of small timestamps. Then it reduces the spatial data redundancy at the aggregator level using the Pearson Correlation Coefficient technique. The In-Network or Data aggregation method employs some aggregation functions which are SUM, COUNT, MAX, MIN, AVG to get a relevant aggregated result.
## 3 Materials and Methods  
This method begins by utilizing the octree data structure to perform supervoxel over-segmentation, and then reorganizes the original vehicle-mounted laser point cloud, and transforms the processing from point-based to supervoxel-based. Next, within the supervoxel segmented regions, the MLS trajectory data are used to extract the required seed supervoxel for the region growing algorithm. The region growing algorithm then utilizes constraints based on residuals, slopes, and curvatures to extract road surface point clouds. Finally, the Alpha Shape algorithm was employed for the boundary extraction and optimization of the extracted road point cloud.  
This method can be summarized in the following three main steps: (1) building a supervoxel model for point cloud data, (2) the extraction of road surfaces using an improved region growing segmentation algorithm, and (3) optimizing road boundaries using an Alpha Shape algorithm [38]. The overall technical process is illustrated in Figure 1.  
Figure 1: Process based on improved region growth segmentation algorithm.
# GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction
## 3 Chemometric Methods
### Fingerprint Template Determination  
When using NIST mass spectral library, we found that a chromatographic peak could be represented by several compounds with high probability and the selection might be influenced by human factors. To preserve the integrity of analysis information, these compounds should also be considered during prediction. The suitable number of preserved compounds could be five to nine. For each key odor compound in peanut oil, we picked up another six compounds which might be odor compounds or might influence the olfactory result though they could not be perceived by nose [17] to make a comprehensive consideration of oil differences and avoid human error. Then the number of compounds for the regression was expanded to 375 (a \(55\times 7\) matrix). The determined odor compounds matrix was named as fingerprint template for peanut oil because the compounds represented the odor characteristics. A piece of fingerprint template of peanut oil is displayed in Figure 2, where all compounds were colored by MF index (the height of the green column represented the MF value) and key compounds were placed in the central column.  
Figure 2: A piece of the fingerprint compounds template (\(55\times 7\)) colored by modified frequency (MF).  
A total of seven columns were extracted as shown in the fingerprint template (Figure 2). Column 4 in the middle contained the key odor compounds. Columns 3 and 5 collected carboxylic acids, esters and other compounds that might be a major influence on human sniffing. Columns 2 and 6 selected ketones, aldehydes, alcohols, and other compounds which might make a subordinate contribution to the olfactometry results. Columns 1 and 7 included cyclic compounds, which were released in a low frequency and might prevent correct perceptions the key compounds [18].  
For the convenience of programming and explanation of the model, we named the fingerprint compounds template as 'Matrix C'. Each element C(i,j) represented a specific compound in the template, where i \(\in\) (1, 2,..., 55) and j \(\in\) (1, 2,..., 7). As shown in Figure 2, C(1,4) was Hexanal and C(3,2) was 2-Heptanone.
# Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
## 2 Methods
### Type of Outcome Measures  
The AR outcomes in the upper limb rehabilitation were categorized using the two levels of functioning classified by the ICF-WHO [23]: (1) body structure/function, which is physiological functions of body systems (e.g., Fugl Meyer Assessment); (2) body activities and participation level, which alludes to the execution of assignments, and participation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Databases** & **Search Syntax** & **No. of Articles** \\ \hline \multirow{4}{*}{PubMed} & (Augmented Reality OR AR) AND (stroke OR & \\  & Hemiplegia) AND (rehabilitation) AND (Upper & \\  & Limb OR Upper Extremity OR Lower Limb OR & \\  & Lower Extremity OR Gait and Balance) & \\ \hline \multirow{4}{*}{Embase} & (Augmented Reality OR AR) AND (stroke OR & \\  & Hemiplegia) AND (rehabilitation) AND (Upper & \\  & Limb OR Upper Extremity OR Lower Limb OR & \\  & Lower Extremity OR Gait and Balance) & \\ \hline \multirow{4}{*}{SAGE Publication} & (Augmented Reality OR AR) AND (stroke OR & \\  & Hemiplegia) AND (rehabilitation) AND (Upper & \\  & Limb OR Upper Extremity OR Lower Limb OR & \\  & Lower Extremity OR Gait and Balance) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._in the life situation (e.g., Wolf Motor Function Test, the Box and Blocks Test, the subscales of quality of movement, and amount of use of the Motor Activity Log) (Figure 1) [24]. In the lower limb rehabilitation, we extracted data of berg balance scale, time up and go test, gait velocity, muscle strength, and 10 m walk test. From every investigation, we took absolute scores (mean and standard deviation (SD)) of the baseline, control, and AR treatment groups.
## 3 Net Impact and Capturing Risks
### Dynamic Risks
#### 3.3.3 Solar Array Dynamics Risks  
The solar array dynamics can be found in Figure 11. With a maximum solar array deviation of \(-8\) deg, the lateral positional deviation of the array inside the net is 2.23 m. With this type of significant deflection, the thin solar array could either break due to increased periodical stress or/and the net itself could tear resulting in a freely oscillating array. In the case of net tearing, the free solar array vibrations could result in breaking at the primary hinge location resulting in the creation of a large, significant piece of secondary space debris.  
Figure 10: Two-body debris body-frame self-rotation velocity \(\boldsymbol{\dot{\theta}}_{\mathrm{d}}\) due to the net contact dynamics.  
Figure 9: Debris body-frame self-rotation velocity \(\boldsymbol{\dot{\theta}}_{\mathrm{d}}\) due to the net contact dynamics.  
The severity of the dynamic results is accentuated by the loads encountered by the primary solar array hinge, as depicted in Figure 12. Here, \(M_{S_{li_{i}}}\) denotes the elements of \(\mathbf{M}_{S}\) in the local frame of the hinge.  
By omitting the initial impact loads, which do not have sufficient time to propagate in the material, structural moments of \(\pm 4\) kNm (\(M_{S_{li_{1}}}\)) and \(\pm 5\) kNm (\(M_{S_{li_{3}}}\)) are consistently experienced from \(t\approx 0.25\) s onwards. With an allowable limit, for the hinge-panel interface (location 1, see Figure 5), ranging from 200-1000  Nm [18], the results show a high likelihood of breaking and loss of structural integrity. Therefore, the frontal net impact results in the generation of a large secondary space debris object of \(5\times 14.3\) m-size.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 5 Experimental Results
### Spatial Stream
#### 5.3.2 Comparison with State of the Art  
Finally, we compare the overall results of our approach with the state-of-the-art methodologies by comparing the mean accuracies over three splits of UCF101. For that, the temporal stream was trained on dense optical flow images which were extracted beforehand, with a stack of L = 5 frames. The spatial stream on the other hand used pretrained MobileNet. Further improvements were achieved by fine tuning the spatial stream on the augmented data. Both streams were fused in the end by averaging their softmax scores to produce the results. We first compared the results of both streams with other state-of-the-art methods. Table 7 shows the comparisons as well as the models used by other methodologies in the motion and appearance stream. We can see that our spatial stream performed much better than the original spatial stream in [13], i.e., an increase of 6% in accuracy. For the temporal stream, we used the same model as [13].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Input Configuration** & **Accuracy (\%)** \\ \hline Temporal Stream & 80.20 \\ Enhanced Spatial Stream & 76.70 \\ Fusion by Averaging & 89.63 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fusion results of two-stream network on UCF101 (split 1).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Appearance**} & \multicolumn{2}{c}{**Motion**} \\ \cline{2-4}  & **Model** & **Acc (\%)** & **Model** & **Acc (\%)** \\ \hline [PERSON] [13] & AlexNet & 73.00 & CNN & 83.70 \\ [PERSON] [23] & VGG-16 & 82.60 & VGG-16 & 86.25 \\ [PERSON] [22] & ResNet & 82.29 & ResNets & 87.0 \\ [PERSON] [27] & ResNeXt & 85.20 & ResNeXt & 87.00 \\ \hline Proposed & MobileNet & 79.00 & CNN & 82.60 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of appearance and motion path with other SOA models.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Input Configuration** & **Accuracy (\%)** \\ \hline Training from scratch & 41.60 \\ MobileNet (fine-tuning on UCF101) & 75.23 \\ MobileNet (fine-tuning on augmented UCF101) & 76.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spatial stream performance on UCF101 (split 1).  
The results in Table 8 show the overall comparison with state-of-the-art methods, and we observe that our results performed well compared to almost all of them, with an accuracy of 91.20%.  
In this research, our goal was to recreate the existing two-stream network for HAR by enhancing its spatial stream and, therefore, we only compared our method with some corresponding methods, shown in Tables 7 and 8. Our spatial stream result outperformed the original two-stream model [13]. In [22,23,29] researchers used very deep networks which require a lot of computation power and time to train them. Keeping in view the edge they have over us in terms of computational power, our model still outperformed most of them when comparing the overall accuracy over three splits, as shown in Table 8.
# Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
## 6 Comparison with Current Dynamic Thermal Rating Applications in Sicily  
This section presents a comparison between the Dynamic Thermal Rating simulations carried out and the current supplied by some DTR devices already installed in Sicily.  
Specifically, the operational DTR devices have been installed on the following lines:  
* 220 kV electrical transmission line;
* 150 kV electrical transmission line.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**REZ’s HOT SPOT** & \(\Delta\)**P\_DTR [MW]** & **REC [EUR/h]** \\ \hline A & 116.80 & 10,511.69 \\ B & 116.28 & 10,464.97 \\ C & 101.74 & 9156.26 \\ D & 101.74 & 9156.26 \\ E & 91.77 & 8259.51 \\ F & 88.69 & 8259.51 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Brief economic considerations.  
Figure 5: Reduction of economic costs.  
The validation of our algorithm aligns with the results obtained by Terna under certain considerations:  
* The implemented IEEE algorithm extracts data from a database with a resolution of 1 km\({}^{2}\);
* Terna's DTR devices, on the other hand, perform point measurements at the installation sites and compare them with data provided by Aeronautica Militare [21].  
Considering these premises, the daily average current provided by our algorithm was compared with the daily average current provided by the DTR devices on 19 April 2024 (Table 8).  
Table 8 shows that the values are very similar to those provided by Terna's DTR devices. This allows us to conclude that the methodology used for the simulations provides reliable results.
###### Abstract  
This research provides a method for a reliable fatigue life estimation at high testing frequencies. The investigations are based on the lifetime prediction method StressLifeHCF considering test frequencies of 80 and 260 Hz for normalized SAE 1045 (C45E, 1.1191) steel. Therefore, load increase tests and constant amplitude tests were carried out using a resonant testing rig. To ensure a mechanism-oriented lifetime prediction, the material response to dynamic loading is monitored via temperature and electrical resistance measurements. Due to the higher energy input per time unit, when the test frequency is increased, the heat dissipation also increases. For this reason, a precise differentiation between frequency- and temperature-related effects for adequate fatigue assessment is challenging. To evaluate the temperature's influence on electrical resistance, an electrical resistance-temperature hysteresis is measured, and the frequency influence is analyzed by considering cyclic deformation curves. In addition to an extension of the fatigue life due to an increased test frequency, the lifetime prediction method was validated for high frequencies. The generated S-N curves show a reliable agreement with the data points from conventional constant amplitude tests. In this context, the temperature correction of the electrical resistance proved to be an important input variable for a reliable lifetime prediction.  
StressLife; fatigue life; lifetime prediction methods; material response; resistivity; frequency; electrical resistance-temperature hysteresis; resonant testing; S-N curves; NDT
# Study on Dynamic Loading Characteristics of Rock Containing Holes
## 1 Introduction
The insights gained from the referenced studies stem from a combination of indoor experiments and numerical simulations, each with its own set of advantages and drawbacks. The variability in the composition of rock materials might influence the results of indoor experiments, and utilizing high-speed cameras to analyze the dynamic failure process of rocks may not fully reveal the underlying mechanisms. The Discrete Element Method (DEM) model [39; 40] addresses these challenges by enabling the continuous monitoring of cracking patterns throughout the loading process. Researchers have widely adopted this method to study rock fracture mechanisms, demonstrating its efficacy in precisely depicting the dynamic fracture behaviors of rocks [41; 42]. Despite considerable research on the mechanical behaviors and crack development in rocks and similar brittle materials with cavities, the specifics of rock loading under diverse conditions are not fully comprehended, largely because of a concentration on static loading in the literature. This study employs Split Hopkinson Pressure Bar (SHPB) testing for rock specimens with cavities. After laboratory experiments, a numerical SHPB system, simulated via DEM coding, is created, with its micro-level parameters carefully adjusted to match empirical observations. This research investigates the effects of strain rates and cavity parameters on the rock specimens' dynamic mechanical attributes and breakdown mechanisms. Ultimately, it seeks to deepen our understanding of the dynamic loading phenomena in rock materials with cavities, thereby offering detailed insights into their structural behavior under different stress scenarios.