esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 2 Related Work
### Ensemble Methods  
Ensemble methods achieve higher performance than individual algorithms by leveraging multiple algorithms to process data, generating predictions based on extracted features, and integrating the outcomes through a unified mechanism [39; 40]. [PERSON] et al. [41] proposed an ensemble-based CNN approach for hand gesture recognition, where three custom CNN architectures--GoogLeNet-like, VGGNet-like, and AlexNet-like--were trained in parallel and integrated through score-level averaging to improve classification performance. The system was validated on three datasets, including two public infrared gesture datasets and a self-constructed binary image dataset, achieving recognition accuracies of 99.80%, 96.50%, and 99.76%, respectively. [PERSON] et al. [42] proposed a hybrid model (VGG16-RF) that combines the feature extraction capabilities of a lightweight VGG16 model with the classification performance of a Random Forest, in order to overcome the limitations of manual feature extraction in vision-based hand gesture recognition. On the ASL Alphabet, ASL Numbers, and NUS Hand Posture datasets, the proposed model achieved accuracies of 99.98%, 100%, and 100%, respectively, outperforming traditional deep learning and machine learning models. [PERSON] et al. [43] proposed a model for Bengali sign language gesture recognition by combining Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN), and applied a probability-based soft voting technique for ensemble learning. Using a dataset of 190,000 images, the ensemble model achieved an accuracy of 99.50%, outperforming the individual models (CNN: 99.44%, SVM: 96.39%, RF: 95.56%). [PERSON] [44] developed an ensemble model for network attack detection based on three classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), and Gradient Boosting (GB), applying both soft voting and hard voting methods. The individual models achieved accuracies of 86.52% (KNN), 99.95% (DT), and 99.82% (GB), while the ensemble model attained 99.967% accuracy with soft voting and 100% accuracy with hard voting. The study concluded that combining models can enhance overall performance by leveraging the strengths and compensating for the weaknesses of individual classifiers.
## 6 Results and Discussions
### Effect of the Added Mass  
In order to discuss the effect of the added mass, a comparative analysis on the dynamic response of the 3D elastic wedge has been carried out. The impact velocity is constantly set as 6 m/s and the structural damping is not considered. Through Equation (4a), the starting time of the flow separation with the impact velocity 6 m/s is about 0.19 s. Figure 9 shows that the overall trends of stress responses  
Figure 8: Comparison between results of the present model and the commercial software. (**a**) S1; (**b**) S3; (**c**) S5; (**d**) S7. S1–S7 represent the locations on the stiffened plate for the analysis of structural stress responses.  
before the flow separation are the similar. The peak value of the method with the added mass is higher than that without the added mass. The vibration response of the two methods shows significant discrepancies after the flow separation. The vibration frequency of the method with the added mass is lower than that without the added mass. For the position S7, the stress response after the flow separation is even higher than that before the flow separation, due to the effect of the added mass.  
Figure 10 shows the fast Fourier transform (FFT) results of the stress responses without the added mass. The FFT is not conducted on the stress responses with the added mass, which is due to that the oscillating cycles included in the stress responses with the added mass are scarce. It can be seen that the FFT results of the stress responses without the added mass have a peak value near the first dry modal frequency, which indicates that the first order mode plays an important role in the vibration response after the flow separation.  
Table 2 shows the comparison of peak values of absolute stresses. For the position S7, the peak stress of the method with the added mass is about 45.76% higher than that without the added mass, due to that the peak stress of the method with the added mass appears in the stage after the flow separation.
###### Abstract  
When recording the vegetation distribution with a camera, shadows can form due to factors like camera angle and direct sunlight. These shadows result in the loss of pixel information and texture details, significantly reducing the accuracy of fractional vegetation coverage (FVC) extraction. To address this issue, this study proposes an efficient double-exposure algorithm. The method reconstructs the pixel information in shadow areas by fusing normal-exposure and overexposed images. This approach overcomes the limitations of the camera's dynamic range in capturing pixel information in shadowed regions. The study evaluates images with five levels of overexposure combined with five vegetation extraction indices. The aim is to determine the best-performing double-exposure combination under shadow conditions and the most suitable vegetation index. Experimental results reveal that the R\({}^{2}\) value between the best vegetation index and the FVC calculated from the fused double-exposure images and the ground truth FVC increases from 0.750 to 0.969. The root mean square error (RMSE) reduces from 0.146 to 0.046, and the intersection over union (IOU) increases from 0.856 to 0.943. These results demonstrate the excellent vegetation extraction capability of the double-exposure algorithm under shadow conditions, offering a straightforward and effective solution to low accuracy of FVC in shadowed areas.  
double-exposure algorithm; shadow vegetation; FVC; vegetation index +
Footnote †: journal: Applied  
doi: 10.3390/
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 2 Computer Vision
### Hyperspectral and Multispectral Cameras  
Hyperspectral sensors acquire detailed spectral information of targets through continuous narrow bands, forming nearly continuous spectral curves. Multispectral sensors capture spectral information of targets through multiple discrete wide bands (typically 5-10), with wide band intervals (such as visible light, near-infrared, short-wave infrared, etc.). Hyperspectral sensors have been successfully applied in multiple fields, enabling the simultaneous acquisition of spatial and spectral information. Compared to other visual sensors, hyperspectral sensors have more spectral bands, allowing for a more comprehensive capture of spectral information. Hyperspectral imaging modes can be categorized into reflection, transmission, and interaction modes [40]. When there is a need to increase feature quantity and improve processing speed, a multispectral system can be considered. It can acquire data from multiple information wavelengths, capturing their spectral characteristics [41].  
Hyperspectral and multispectral sensors have extensive application value in agriculture [42]. By performing multi-scale decomposition on hyperspectral data and extracting deep spectral features through an encoding-decoding structure, efficient and lossless detection of composite heavy metals in lettuce can be achieved. In [43], researchers selected five wavelength bands using a multispectral camera and combined multispectral images with a deep learning model. This approach was used to predict wheat biomass and yield, guiding precise operations of combine harvesters. In [44], hyperspectral imaging technology was employed to acquire hyperspectral data cubes via line scanning, extracting average spectra from regions of interest to construct linear and nonlinear classification models. This enabled rapid, low-cost detection of orange rot based on hyperspectral imaging. It can be seen that recent research has focused on combining deep learning with hyperspectral and multispectral sensors. This enables ultra-high-precision analysis and early detection in agriculture, multidimensional data fusion, and intelligent decision-making, driving rapid development in precision agriculture [45; 46; 47; 48; 49].
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 5 Simulations and Results  
To evaluate the performance of the proposed KLMS-RFN algorithm, extensive simulations based on chaotic time series prediction and nonlinear channel equalization are conducted. The commonly used Lorenz chaotic time series are generated as the training data to be predicted. Moreover, the nonlinear channel equalization with a time-varying channel and an abrupt change channel are used to demonstrate the advantages of the proposed algorithm. In this simulation, we focus mainly on the nonstationary situations to show the advantages of the proposed algorithm in online applications.  
\begin{table}
\begin{tabular}{l} \hline \hline
**The Number of Calculations of the KLMS-RFN Algorithm**. \\ \hline (1) Calculating random Fourier feature vectors \(\varphi(\mathbf{x}(n))\) \\ Number of multiplications \(=(N+2)M\); \\ Number of additions \(=(N+1)M\); \\ Number of \(sin(\cdot)/cos(\cdot)\) calculation \(=2M\) \\ (2) Calculating the output of the filter \\ Number of multiplications \(=2M\); \\ Number of additions \(=2M-1\); \\ (3) Updating the weight vector \\ Number of multiplications \(=2M+1\); \\ Number of additions \(=2M\); \\ (4) Total calculations \\ Total number of multiplications \(=(N+6)M+1\); \\ Total number of additions \(=(N+5)M-1\); \\ Total number of \(cos(\cdot)/sin(\cdot)=2M\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The complexity of the KLMS-RFN algorithm.
## 3 Results
### ABTS Radical scavenging Activity in SF and SSF Extracts in Different Solvents  
The IC\({}_{50}\) of ABTS radical scavenging activity ranged 13-19 \(\upmu\)L for SF and 6-57 \(\upmu\)L for SSF in ethanol, methanol, and water extracts (Table 2). Although the activity did not significantly vary between SF ethanol and methanol extracts (IC\({}_{50}\) = 13.9 \(\upmu\)L and 14.1 \(\upmu\)g/\(\upmu\)L, respectively), the highest activity was detected in ethanol extract. For SSF, the highest activity was detected in methanol extract (IC\({}_{50}\) = 6.7 \(\upmu\)g/\(\upmu\)L) and the lowest in water extract (IC\({}_{50}\) = 56.5 \(\upmu\)g/\(\upmu\)L), with a similar trend as that for DPPH radical scavenging activity. The ethanol extracts of SF and SSF showed similar levels of ABTS radical scavenging activity, with IC\({}_{50}\) values of 13.9 \(\upmu\)g/\(\upmu\)L and 11.3 \(\upmu\)g/\(\upmu\)L, respectively. However, the SSF methanol extract showed 2.1-fold higher activity than that of SF methanol extract, whereas SSF water extract showed the lowest activity that was 3-fold lower than that of SF water extract. [PERSON] et al. [44] measured the ABTS radical scavenging activity in different SF extracts and found that the activity was lower in SF water extract, obtained by high-pressure extraction, (IC\({}_{50}\) = 1.64 mg/mL) than that of SF ethanol and methanol extracts (IC\({}_{50}\) = 0.90 mg/mL and 0.96 mg/mL, respectively); thus, they reported that the radical scavenging activity of SF was dependent on the extraction solvent. [PERSON] et al. [58] extracted _Padina tetrastromatica_ and _Gracilaria tenuistipitata_ using different extraction solvents (methanol, ethanol, and water) and showed that the ABTS radical scavenging activity was the highest in methanol extracts, followed by ethanol and water extracts; moreover, they observed a positive correlation between the ABTS radical scavenging activity and the total phenolic content, suggesting that the total phenolic content is a critical determinant of the ABTS radical scavenging activity. Similar to these reports, SF and SSF water extracts showed lower DPPH and ABTS radical scavenging activities than those in ethanol and methanol extracts in the present study. The increase in total polyphenol content in SSF ethanol and methanol extracts due to steaming may have had an effect on the ABTS radical scavenging activity, resulting in the higher activity in SSF compared to SF extracts. [PERSON] et al. [59] measured the ABTS radical scavenging activity of seaweeds (_Treptacantha abies-marina, Cystoseira humilis, Asparagopsis armata_, and _Asparagopsis taxiformis_) under different drying conditions; shade-dried extracts demonstrated 20-50% lower activity compared to sun-dried extracts. As the ABTS radical scavenging activity may vary according to the drying method, it is necessary that future studies compare the activity under drying conditions and methods other than those used in our study (65\({}^{\circ}\) C for 48 h).
# Steering Mirror System with Closed-Loop Feedback for Free-Space Optical Communication Terminals
## 6 Thermal Tests  
While the PIA13 VF actuators are rated for vacuum conditions, the AEDR8300 spec sheet does not provide information on vacuum operating conditions. Additionally, neither of the AEDR or PIA13 VF is rated for operation below \(-20\,\mathrm{\SIUnitSymbolCelsius}\). A test rig consisting of an actuator, an encoder and datum switches was created, depicted in Figure 9.  
The chamber was pumped down to a vacuum of \(3\times 10^{-6}\) mbar, and the actuator was cooled to a temperature of 234 K (\(-39\,\mathrm{\SIUnitSymbolCelsius}\)). While the actuator moved between the two end-limit switches, the encoder pulses were recorded by an oscilloscope. The pulse train appears identical to the measurements made under room temperature and pressure. These results indicate that the system functions correctly under these extreme conditions.
###### Abstract  
In this paper, by evaluating the extreme value of the \(q\)th-power current, a torque sharing function (TSF) family for reducing the torque ripples in the switched reluctance motor (SRM) is proposed. The optimization criteria of the TSF has two secondary objectives, including the maximization of the torque-speed range and the minimization of copper loss. The evaluation indices in terms of the peak phase current, the rms (root mean square) phase current, and the torque ripple factor are compared between the proposed TSF family and four conventional TSFs including linear, sinusoidal, exponential, and cubic TSFs. An optimization objective function that combines the maximum absolute value of the rate-of-change of the flux linkage (MAV-RCFL) and the \(q\)th-power of current is proposed and a weighting factor is used to balance the influence of the two optimization objectives. An optimal TSF can be easily obtained by solving the optimization problem from the TSF family. The proposed TSF is validated by using simulations and experiments with a three-phase 6/4 SRM with 7.5 kW, 3000 rpm, and 270 V DC-link voltage. The dynamic simulation model is implemented in Matlab/Simulink. The results demonstrate the validity and superiority of the proposed control method; the optimal TSF provides better torque-speed performance, and a better reduction in copper loss and torque ripples at high speed, as compared to conventional TSFs.  
switched reluctance motor (SRM); torque-speed performance; drive efficiency; torque sharing function (TSF) +
Footnote †: journal: Applied Sciences
## 2 The Study Area Identification  
The study examined the off-grid rural electrification procedure using an off-grid remote rural area located more than 50 km away. Power supply to this area via grid connection is not possible due to the area's hilly terrain and its location in the middle of a dense forest. Four villages are included in the study area, which are located in the Chandrapur block of the Rayagada district of Odisha state in India. Figure 1 depicts the study area's location on a map of India. The study area's latitude and longitude are 19.5927\({}^{\circ}\) N and 83.8712\({}^{\circ}\) E, respectively. The whole population of the villages is accommodated in 153 houses. People in this area continue to rely on candles, kerosene lanterns and solar lamps for illumination.  
Figure 1: The position of the research region on the Indian map.
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 6 Conclusions  
This paper presented an anchor-based method for inter-domain mobility management in SDN architectures. The proposed method addresses the challenge of reducing data transfer delay during mobile node movement across administrative domains.  
The key contributions involve modeling the problem as an efficient route selection task, proposing algorithms for anchor-domain selection based on proximity to the data destination, and selecting routes that traverse the chosen anchor while considering bandwidth and the number of traversed domains.  
However, it is essential to acknowledge certain limitations of our study. An evaluation of the traffic load between controllers will be included in future work. Approaches to limiting this load, such as the use of hierarchical or decentralized controllers, could be explored to guarantee the scalability of RI-SDN in large networks. Additionally, incorporating security considerations into the anchor and route selection and route processes is crucial. Future work could explore incorporating real-time network conditions and security issues.  
Conceptualization, A.A.J.R.K., A.F.A. and A.D.K.; methodology, A.A.J.R.K., A.F.A. and M.B.; implementation, A.A.J.R.K. and Z.F.O.T.; writing--original draft preparation, A.A.J.R.K. and A.F.A.; writing--review and editing, Z.F.O.T. and A.F.A.; supervision, M.B. and A.D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
We would like to thank the anonymous reviewers for their insightful comments and suggestions that significantly improved the quality of this manuscript. The feedback helped us to strengthen the clarity, focus, and overall impact of our work.  
The authors declare no conflicts of interest.
# Application of Workflow Technology for Big Data Analysis Service
## 2 Big Data Analysis Oriented Cloud Workflow System
### Workflow Execution Engines
#### 2.2.3 Lambda Architecture  
Combining offline batch (MapReduce) and online real-time analyses (Storm) into the same cluster is important. [PERSON] [16] proposed three fusion methods according to coupling degree. They are as follows:  
1. Using the same data analysis language to develop analysis projects and execute the projects in the two models which run in the same clusters.
2. Using different data analysis languages and running the two models in the same cluster (sharing the same resource management, such as physical resources and data).
3. Loose coupling that executes the two models in different logistic clusters using virtualization technologies (resource allocation between the two models is isolated and can share data).  
From the second fusion idea, we employ storm-yarn (yet another resource negotiator) to integrate the two analysis models into the same cluster; this is also known as \"Lambda architecture\" (as shown in Figure 11). A zookeeper is employed to manage resources and sharing data through a storage medium such as HDFS or HBase. The lightweight integration not only puts the two models into the same cluster harmoniously, but also keeps the two model frameworks independently.
## 3 Results and Discussion
### Volatile Organic Compounds Composition of Cheeses  
VOC profiles generated by GPP and cheese samples are reported in Table 2.  
The compounds identified belonged to alkanes, aldehydes, monoterpenes, esters, acids, ketones, alcohols and diols. GPP VOC profile was characterized by 20 main compounds and the most abundant belonged to the classes of alcohols, diols and esters groups. The cheeses processed with and without GPP addition emitted 16 and 14 VOCs, respectively with acids, ketones, alcohols and aldehydes being the most represented groups. The higher differences imputable to GPP addition regarded octanoic acid-ethyl ester and 2-phenylethanol. The main acids identified in cheese samples were acetic, hexanoic, butyric and 2-hydroxy4-methyl-pentanoic acids, generally recognized in ewe's milk cheees [41; 47; 48]. Acetic acid may be produced by carbohydrate catabolism by LAB, 2-hydroxy-4-methyl pentanoic acid is formed enzymatically from the corresponding amino acid (L-leucine) [48]. Hexanoic and butyric acids derive mainly from the action of the lamb renet used for curdling, responsible for the high amounts of short-chain free fatty acids [49]. Free fatty acids (FFA) are responsible for cheese flavor both directly and indirectly. FFA are precursors of odor-active compounds such as methyl ketones, aldehydes, esters and lactones [50]. Even though FAA were present in all cheese samples, esters were poorly detected. No ester compound was identified in control cheeses probably because the ripening period is particularly short [48; 51; 52]. Two aldehydes (hexenal and heptanal) were detected in cheese with and without GPP addition. In general, cheese VOC profile derives from the hydrolysis or metabolism of carbohydrates, proteins and fats due to the activity of LAB [53]. 2,3 butanediol and 3 hydroxy 2 butanone were found in all cheeses independently on the presence of GPP. Both compounds are generated from the metabolism of carbohydrates (lactose and citrate) by LAB [54]. As a matter of fact, GPP addition contributed to the presence of octanoic acid, ethyl ester and 2-phenylethanol.
## 5 Conclusions  
In this paper, the capture effect under Rayleigh, Rician and Nakagami-\(m\) fading channels has been analyzed and the closed-form expressions of capture probabilities are derived respectively. Furthermore, we give the optimal frame length which maximizes the channel utilization in the FSA-based networks. With the existence of capture effect, the maximum channel utilization of the FSA-based networks increases significantly, especially when capture ratio \(z\) is small. In particular, we give a special case that the capture probability is the same in every collided slot and derive the optimal frame length. In addition, we also give the optimal frame length when the capture probability is changed by different number of collided packets in the slot. The numeral results show that the capture effect impacts on the channel utilization in the FSA-based networks under different fading channels. As a result, if the capture effect is efficiently utilized, the maximum channel utilization can be achieved by setting the optimal frame length.  
The authors acknowledge the support from the Natural Science Foundation of China (Grant No. 61601388).  
[PERSON] researched the literatures, derived the formulas, carried out the simulations, and took charge of the entire manuscripts. [PERSON] revised the manuscript and verified the formulas. [PERSON] analyzed the simulation results and assisted with the integrity of the entire study.  
The authors declare no conflict of interest.