esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Speed** & **Random Forest** & **Decision Tree** & **Random Tree** \\ \hline
1500 & 90.34 & 93.93 & 89.72 \\
1800 & 99.00 & 98.33 & 98.67 \\
2000 & 98.83 & 98.16 & 96.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of the tree family classifiers. Similarly, in the last row, among the 100 data points belonging to HSBT, all are correctly classified as HSBT. Hence, there was no misclassification. In this manner, the confusion matrix was used to identify the number of correctly classified data points and misclassified data points, and to classify the trained data and determine the total number of data points that were used for classification. Among the 600 data points belonging to all fault conditions, 594 data points were correctly classified with a classification accuracy of 99.00%. The above classification accuracy was found using the decision rules. These decision rules were used to design the LabVIEW model based on the conditions defined for selected statistical features. The raw signal data were fed into the LabVIEW model. They identified the condition in which the statistical features belong. Figure 8 shows the LabVIEW program used to develop the online model for analyzing the gear train condition. The advantages of the proposed approach are as follows:  
* While driving, any simulated faults in the ATV can be identified immediately. * This study will help implement online gear health monitoring in ATVs, ensuring the safety of drivers. The model will be tested in real-world systems to make it an online condition monitoring system as the future scope of this study. In the future, other defects such as pitting and scuffing can be considered with dynamic load conditions.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Recent Methods of Mitigating Spectral Leakages in Spectrum
#### 2.3.1 Antileakage and Arbitrary Sampled Fourier Transforms  
The AntiLeakage Fourier Transform (ALFT) [20,21] and Arbitrarily Sampled Fourier Transform (ASFT) [22] are two Fourier-based methods that try to mitigate the spectral leakages in the Fourier spectrum of unequally spaced time series. The ALFT and ASFT estimate the Fourier coefficients of a time series, first by searching for the peak of maximum energy and subtracting its component from the time series and then repeating the procedure again on the residual series until all the significant Fourier coefficients are estimated. This way, the spectral leakages of Fourier coefficients emerging from the non-orthogonality of the global Fourier basis functions will be attenuated. The accuracy of signal frequency in ALFT depends on the preselected set of frequencies. The denser the set is, the better the estimation but the slower the computation. On the other hand, ASFT is a direct cost function minimization approach. Upon finding a frequency \(\omega_{1}\) in the preselected set of frequencies minimizing the cost function, ASFT searches a small neighborhood around \(\omega_{1}\) to find a more accurate frequency. ALFT and ASFT have been applied to regularize unevenly sampled seismic data. Flowcharts of ALFT and ASFT are shown in Figures 12 and 13, respectively.  
Although ALFT and ASFT perform much better than DFT for analyzing unequally spaced time series, they usually cannot find the correct location of a peak of maximum energy from a preselected set of frequencies due to the correlation between the sinusoids, and so these methods do not effectively reduce the leakage [19]. This shortcoming becomes more severe when a time series has more spectral components. Moreover, the constituents of known forms, such as datum shifts and trends, are not explicitly considered in these  
Figure 1: Spectral leakages in DFT and LSSA for unequally spaced time series. Arrows show many of the leakages.  
algorithms. Therefore, if the trends or datum shifts are present in a time series, then ALFT and ASFT implicitly approximate them by sinusoids of different frequencies ([PERSON], 2016). [PERSON] et al. proposed a method that uses an Orthogonal Matching Pursuit (OMP) to improve the ALFT results ([PERSON], 2016). In the OMP approach, the coefficients of all previously selected Fourier components are re-optimized, producing better interpolation results compared to the ALFT results.
# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 3 Deep Learning Network Principles  
### ResNet50 Convolutional Neural Network  
Deep learning is an important research direction in the field of machine learning. Its introduction to machine learning makes it closer to artificial intelligence. The convolutional neural network is a deep neural network model that includes convolution. Its core process is to train and learn from a large amount of sample data through multiple iterations to extract the deep feature expression of the sample data, and finally predict the sample data according to different required tasks [56].  
As the number of layers in the deep learning network increases, features of different layers can be extracted. The more abstract the feature expression, the richer the semantic information. However, as the number of original network layers in deep learning simply increases, it will cause gradients to disappear or become extremely large. The traditional method to solve this problem is generally to use reasonable weight initialization and regularization, but the implementation of the method will bring new problems of network performance degradation [57]. ResNet is a residual learning framework that can improve the performance of the network under the premise of increasing depth. The basic residual unit structure diagram of ResNet is shown in Figure 4[58].  
If the back layer of the deep network is an identity mapping, the model can be degenerated into a shallow network. However, it is more difficult to directly use some layers to fit potential identity mapping functions, such as \(H(x)=x\). Therefore, the network is designed as \(H(x)=F(x)+x\), and the problem is transformed into learning a residual function \(F(x)=H(x)-x\). When \(F(x)=0\), it constitutes an identity mapping \(H(x)=x\), so that it is easier to implement residual fitting.  
In summary, the residual network structure was chosen for this paper to increase the network depth to improve the performance and accuracy of the network, and the residual structure can solve the problem of the disappearance of the gradient caused by the increase in the network depth.  
The common residual network models are ResNet18, ResNet50 and ResNet101. As the number of layers increases, the amount of network calculation also increases [15; 59]. By considering the calculation speed and the fault detection accuracy of the network employed, the ResNet50 structure is selected as the backbone network, and the residual unit adopts a three-layer bottleneck layer design as shown in Figure 5.  
Figure 4: Basic residual unit structure diagram [58].
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 2 Basic Definitions
### Octonion Fourier Transform
: \(L^{2}\)_-norm of any function \(u\in L^{2}(\mathbb{R}^{3};\mathcal{O})\) is equal to the \(L^{2}\)-norm of its octonion Fourier transform, i.e.,_  
\[\|u\|_{L^{2}(\mathbb{R}^{3};\mathcal{O})}=\|U_{\mathrm{OFF}}\|_{L^{2}(\mathbb{ R}^{3};\mathcal{O})}.\]  
On the other hand, we could consider the symmetric real scalar product  
\[\langle f,g\rangle_{L^{2}(\mathbb{R}^{d};\mathcal{O})}=\frac{1}{2}\Big{(}(f,g )_{L^{2}(\mathbb{R}^{d};\mathcal{O})}+(g,f)_{L^{2}(\mathbb{R}^{d};\mathcal{O} )}\Big{)}=\int_{\mathbb{R}^{d}}\mathrm{Re}\left(f(\mathbf{x})\overline{g( \mathbf{x})}\right)\mathrm{d}\mathbf{x}.\]  
Using exactly the same reasoning as in the proof of the Rayleigh theorem for octonion-valued functions (see [46]), we immediately obtain  
\[\langle u,v\rangle_{L^{2}(\mathbb{R}^{3};\mathcal{O})}=\langle U_{\mathrm{OFF} },V_{\mathrm{OFF}}\rangle_{L^{2}(\mathbb{R}^{3};\mathcal{O})}\]  
for all \(u,v\in L^{2}(\mathbb{R}^{3};\mathcal{O})\). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Function 1** & **Octonion Fourier Transform** \\ \hline
1. & \(u(\frac{x_{1}}{d},\frac{x_{2}}{b},\frac{x_{3}}{c})\) & \(|abc|U(af_{1},bf_{2},cf_{3})\) \\ \hline
2. & \(u(\mathbf{x})\cdot\cos(2\pi f_{0}\mathbf{x}_{1})\) & \(\big{(}U(f_{1}+f_{0},f_{2},f_{3})+U(f_{1}  
In the context of the signal theory, the uncertainty principle plays a vital role. Let us recall the theorem concerning its form for the OFT, proved by [PERSON] in [44]. Theorem 4: _Let \(u\in L^{2}(\mathbb{R}^{3};\mathbb{O})\). For \(s,\beta>0\), there exists a constant \(C_{s,\beta}>0\) such that_  
\[\big{\|}|\mathbf{x}|^{s}u(\mathbf{x})\|_{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{ \frac{2\beta}{s+\beta}}\big{\|}\big{\|}|\mathbf{f}|^{\beta}U_{\text{\rm OFT}}( \mathbf{f})\big{\|}_{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{\frac{2\alpha}{s+\beta} }\geq C_{s,\beta}\|u\|_{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}.\]
###### Abstract  
Most object detection models cannot achieve satisfactory performance under nighttime and other insufficient illumination conditions, which may be due to the collection of data sets and typical labeling conventions. Public data sets collected for object detection are usually photographed with sufficient ambient lighting. However, their labeling conventions typically focus on clear objects and ignore blurry and occluded objects. Consequently, the detection performance levels of traditional vehicle detection techniques are limited in nighttime environments without sufficient illumination. When objects occupy a small number of pixels and the existence of crucial features is infrequent, traditional convolutional neural networks (CNNs) may suffer from serious information loss due to the fixed number of convolutional operations. This study presents solutions for data collection and the labeling convention of nighttime data to handle various types of situations, including in-vehicle detection. Moreover, the study proposes a specifically optimized system based on the Faster region-based CNN model. The system has a processing speed of 16 frames per second for \(500\times 375\)-pixel images, and it achieved a mean average precision (mAP) of 0.8497 in our validation segment involving urban nighttime and extremely inadequate lighting conditions. The experimental results demonstrated that our proposed methods can achieve high detection performance in various nighttime environments, such as urban nighttime conditions with insufficient illumination, and extremely dark conditions with nearly no lighting. The proposed system outperforms original methods that have an mAP value of approximately 0.2.  
vehicle detection; deep learning; nighttime surveillance; convolutional neural networks; insufficient lighting; ambient illumination; real-time detection; residual architecture
## 8 Discussion  
Battery life is one of the main challenges in developing an electrically controlled prosthesis. Electrical power consumption directly affects the walking duration of an amputee. The walking comfort of an amputee gets better as the duration of a charge cycle gets longer. The battery life of the above-knee prosthesis presented is computed for three cases, namely minimum prosthesis mass, maximum prosthesis mass and optimized prosthesis mass. The electrical power consumptions and battery life durations obtained in this study are compared to [PERSON] and his colleagues' study [35], which is the only document to give battery results in the current literature. In order to have meaningful results, the same power supply specifications as used in [PERSON] and his colleagues' study are used in the computations. The electrically controlled prosthesis in [PERSON] and his colleagues' study [35] is supplied by a lithium polymer battery with a 29.6 V nominal rating and a 4000 mA\(\cdot\)h capacity. Table 7 illustrates power consumptions, gait cycle life and battery life durations of both prostheses. Table 7 also illustrates the minimum and maximum number of steps that a healthy human can generate in one day [36]. As can be seen from Table 7, the new prosthesis proposed in this study consumed about 51% less power than the prosthesis in [35]. As a consequence, the new prosthesis proposed in this study provides 1.89 h additional battery life compared to the prosthesis in [35]. Considering the step numbers a healthy person will perform in one day [36], the 10,543 steps provided by the new prosthesis illustrate that the new prosthesis presents almost one full day of service with one charge.  
The optimized actuator torques are obtained between \(-\)7.44 Nm and 5.56 Nm along a walking cycle. These values are less than the specified actuator torque values 7.5 Nm given in Table 2. Acceptable joint and Cartesian tracking errors are obtained from the PID control scheme for each actuator.  
The link mass limits of the prosthesis are obtained according to the infill density and infill pattern of PLA material given by Table 3. Although there are several infill patterns for 3D printing, the grid pattern type is chosen in this study. Since the stiffness coefficients of the links (\(k_{\theta_{1}},k_{\theta_{2}},k_{\theta_{3}}\)) are directly affected by the infill density and infill pattern of the PLA material, the stiffness coefficients of links are computed by using the data in Table 3. Simulations have been performed at the optimal infill rate of 60%. According to the simulations, the link masses are optimized as \(m_{1}=2910\) g and \(m_{2}=m_{3}=1080\) g. Thus, the total mass of the prosthesis is obtained as 3.99 kg. Considering the mass (3.99 kg) and the duration of a walking cycle (2.52 s), total consumed power is obtained as 32.07 W.
###### Abstract  
Dermal papilla cells (DPCs) are a rich source of nutrients and secrete multiple growth factors that can affect hair growth. As oxidative stress leads to hair loss in humans, it is considered to be one of the factors that can impair the function of DPCs. Herb-derived phytochemicals exhibit potent antioxidant activities; therefore, this study investigated whether a set of essential oils (lavender, lemongrass, rosemary, and chamomile oils) promote the hair-growth activity of DPCs. Intracellular reactive oxygen species (ROS) increased markedly in ultraviolet B-irradiated DPCs (50 mJ/cm\({}^{2}\)) and were efficiently blocked by essential oils. Essential oils upregulated the mRNA and protein levels of phase II enzymes (detoxifying and antioxidant), including heme oxygenase-1, NAD(P)H quinone oxidoreductase-1, and glutathione S-transferase pi. They also upregulated and activated nuclear factor E2-related factor 2, an essential transcription factor for phase II enzymes. Regarding biomarkers for hair growth, essential oils significantly increased vascular endothelial cell growth factor and insulin-like growth factor-1 mRNA levels. In conclusion, phytochemicals in essential oils enhance hair growth through ROS-scavenging activity in DPCs.  
dramal papilla cells; phytochemical; reactive oxygen species; herb-derived essential oil; hair growth factor; antioxidant; UVB; phase II enzyme; nuclear factor E2-related factor Article
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 3 Plant Extracts and Phytochemicals against Bacteria Causing Foodborne Diseases
### Salmonella
& [PERSON] and _Achillea millefolium_ L. from Japan [81], _Butomus umbellatus_ L., _Polygonum amphibium_ L. and two species of the genus _Sparganium_ (_S. erectum_ L. and _S. emersum_ [PERSON]) from Turkey [82], _Spathodea campanulata_ [PERSON], _Ficus bubu_ [PERSON], _Cara papaya_ L., _Cissus anlioides_ ([PERSON]. ex [PERSON]) [PERSON], _Piptadenistrum africana_ ([PERSON].) [PERSON], _Hilleria latifolia_ ([PERSON].) [PERSON], _Gladiolus gregarious_ [PERSON]. [PERSON] [PERSON] and _Phylanthus muellerianus_ ([PERSON]) [PERSON] from Cameroon [83; 84], persimmon (_Diospyros lotus_ L.), guava (_Psidium guajava_ L.), sweetosp (_Anmona squamosal_ Linn.) and _Cichorium intybus_ L. from China [85; 86], _Anisophyllae laurina_ [PERSON] from Guinea [87], _Sambucus australis_ [PERSON]. [PERSON] from Brazil [88], _Terminalia avicennioides_ [PERSON]. & [PERSON], _Momordia balsamina_, _Combretum paniculatum_ Vent., _Trema guinensis_ ([PERSON] & [PERSON].) [PERSON], _Morinda lucida_ [PERSON] and _Ocimum gratissimum_ L. from Nigeria [89] were reported to have strong antibacterial activity against _Salmonella_ species. It has been observed that EO disrupt and increase the permeability of bacterial cell walls, therefore causing the release of intracellular organelles and proteins. The end result is inactivation and death of the microbes as reviewed by [PERSON] et al. [90]. Most of the spices contain EO and consumption of spices has additional health benefits. The common spice ingredients like black pepper, fennel, coriander, cardamom are rich in EO [91]. Biosynthesized nanoparticles of metals (metals + natural materials) are frequently reported to have enhanced pharmacological activities. A newly emerged concept is nano-antibiotics [92], which use plants mediated biogenic nanoparticles with improved antimicrobial, chemotherapeutic and biologic properties [93; 94]. Using this approach, several metals are reduced using aqueous extracts of medicinal plants [95] and then used as antimicrobial agents with advanced drug delivery and therapeutic outcomes [96]. Silver (Ag) nanoparticles of aqueous leaf extract of _Eupatorium odoratum_ L. were shown to have high anti-_Salmonella_ activities than the aqueous leaf extract of _E. odoratum_ and also AgNO\({}_{3}\)[97]. Silver nanoparticles using the aqueous leaf extracts of _Lippia citriodora_ (Palau) [PERSON] have also been proved to be active against _Salmonella_ species [98]. Other authors have also applied nanoparticles of phytoconstituents against _Salmonella_ and obtained potent antibacterial activity [99; 100; 101].
## 5 The Proposed Protocol
### Communication Overhead of the Proposed Protocol
#### 5.6.2 Optimized Data Fusion in Devices/Links Failures and Recovery
* If the sink node receives health status messages from PDFH, FSDFH, and SSDFH and finds anomalous parameters within these messages. It is essential to note that multipath routing in the proposed protocol is an additional method for not only improving the resilience, efficiency, and reliability of the fusion and transmission of data in the network, but also for opening the door to equal and unequal load balancing. In reality, instead of being dependent on a single path, this technique creates many pathways between SNs and the sink node. This reduces the hazards brought on by congestion, changing network conditions, and connection failures. Furthermore, multipath routing distributes traffic across several routes to balance the network load, extending the life of SNs by avoiding the improper utilization of certain channels. In dynamic and resource-constrained contexts, this redundancy not only increases fault tolerance but also improves the overall quality of service, making it an essential strategy for preserving reliable and efficient communication and boosting the network's resistance to interruptions. In the end, the proposed protocol provides multipath communications by utilizing a novel RDCF that ultimately chooses between PDFR, FSDFR, and SSDFR, meaning primary and secondary routes, respectively. It is critical to spot routing failures (i.e., PDFR, FSDFR, and SSDFR failures). Since they are SNs in the network, their failures are addressed with the same procedures followed in local and global repair that were employed previously.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 7 Open Challenges
### Network and Topology Management  
Most resource management techniques available such as Smart and SDWN have the problem of not addressing overhead in control traffic which is costly for resource constrained WSNs. Therefore, further investigation on how to reduce in-band control traffic between the control and data planes is required. Efficient techniques for in-band communication of control and data traffic can be proposed and implemented.  
Context based clustering can be studied further using other techniques other than HyperFlow to efficiently implement the logical controller concept while making use of its improved performance metrics such as improved scalability and reduced latency. Aspects of network decomposition hierarchies can be looked into to minimize control traffic and improve energy efficiency. Further investigation is also required to determine the number of controllers needed and their placement given an SDN-based WSN topology similar to the analysis done in [141] for WANs.  
SDN-based network monitoring for WSNs is an essential part of network management that still remains largely unexplored. There is need for more quick and efficient network monitoring techniques for WSNs based on SDN. Node localisation is another issue that has the potential to be improved by SDN, conventional WSNs may require nodes to determine their geographical location based on information from other nodes [5] thus expending more energy. This task can be moved to the controller by virtue of its global position reducing the energy consumption on the nodes. In addition, novel localization techniques can be developed harnessing the potential of SDN.