esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simulation Methods of Non-Stationary Stochastic Processes
### Classical Spectral Representation  
An arbitrary zero mean stationary process \(f_{0}(t)\) can be expressed in the frequency domain as follows [6]:  
\[f_{0}(t)=\int_{-\infty}^{+\infty}A(\omega)\cdot e^{i\ \omega\ t}\mathrm{d}Z_{0}(\omega) \tag{1}\]  
in which \(A(\omega)\) is a deterministic function that depends only on the frequency and \(Z_{0}(\omega)\) is a random spectral process with orthogonal increments and satisfies the following:  
\[\begin{array}{ll}E&[dZ_{0}(\omega)]=0\\ E&[dZ_{0}(\omega)dZ_{0}^{*}(\omega)]=S_{f_{0}f_{0}}(\omega)\mathrm{d}\omega\\ E&[dZ_{0}(\omega)dZ_{0}^{*}(\omega)]=0\end{array}\forall\omega\\ \omega\
eq\omega\prime\end{array} \tag{2}\]  
where \(E\) [\(\cdot\)] denotes the expectation operator, the asterisk indicates the complex conjugate, and \(S_{f_{0}f_{0}}(\omega)\) represents the two-sided PSD of \(f_{0}\) (t). Based on the stationary stochastic process [38], the non-stationary stochastic process can be expressed as an extension of Equation (1). Considering the evolutionary spectra theory proposed by [PERSON] [17], it is reasonable to use a time-frequency modulation function \(A(\omega,t)\) to substitute the amplitude \(A(\omega)\) in Equation (1). Therefore, a non-stationary process \(f\) (t) can be expressed as follows [18]:  
\[f(t)=\int_{-\infty}^{+\infty}A(\omega,t)\cdot e^{i\ \omega\ t}\mathrm{d}Z(\omega) \tag{3}\]  
in which the corresponding spectral process \(Z(\omega)\) satisfies the following:  
\[\begin{array}{ll}E&[dZ(\omega)]=0\\ E&[dZ(\omega)dZ^{*}(\omega)]=S_{ff}(\omega,t)d\omega\\ E&[dZ(\omega)dZ^{*}(\omega\prime)]=0\end{array}\forall\omega\\ \omega\
eq\omega\prime\end{array} \tag{4}\]  
In the above equation, \(S_{ff}(\omega,t)\) is the two-sided EPSD of \(f(t)\) and is expressed as follows:  
\[S_{ff}(\omega,t)=|A(\omega,t)|^{2}\cdot S_{f_{0}f_{0}}(\omega) \tag{5}\]  
Integrating [PERSON]'s SRM [4; 6] and the evolutionary theory proposed by [PERSON] [17], [PERSON], [PERSON], and [PERSON] [19] expressed the spectral representation of a one-dimensional, univariate, evolutionary stochastic process as follows:  
\[f(t)=\sqrt{2}\sum_{h=0}^{N-1}\left[2S_{ff}(\omega_{h},t)\cdot\Delta\omega \right]^{\frac{1}{2}}\cdot\cos(\omega_{h}t+\phi_{h}) \tag{6}\]where \(S_{\mathit{ff}}\left(\omega_{h},t\right)\) is the EPSD and reflects the variation of energy distribution with time and frequency; \(\triangle\omega\) is the frequency interval; and \(\omega_{h}=h\cdot\triangle\omega\), \(h=0\), \(1\), \(2\), \(\cdots\), \(N-1\), and \(\phi_{h}\) are independent random phase angles distributed uniformly over \([0,2\pi]\).
# Sirtuin 1 and Hormonal Regulations in Aging
## 4 Natural Activators of SIRT1 and Their Use in the Prevention of Aging
### Curcumin as a SIRT1-Activating Anti-Aging Compound  
Curcumin, a hydrophobic polyphenic compound derived from the rhizome of _Curcuma longa_ (turmeric), has been extensively studied for its anti-inflammatory, antioxidant, and anticancer effects [79; 80; 81; 82; 83]. Curcumin, through its pleiotropic effects, has been found to be an activator of SIRT1, thereby affecting the aging process and alleviating age-related diseases [6; 79; 82]. For example, in vitro studies have demonstrated that curcumin enhances SIRT1 expression in human endothelial cells, protecting them from oxidative damage and inflammation [84]. Additionally, in a randomized controlled trial (RCT), curcumin supplementation improved cognitive performance and reduced markers of oxidative stress in elderly individuals with mild cognitive impairment [85]. Curcumin also promotes SIRT1-induced autophagy, a process that is essential for clearing damaged cellular compo nents and maintaining cellular homeostasis, which declines with age [79; 86]. Through this mechanism, curcumin contributes to reducing the accumulation of damaged proteins and organelles, potentially preventing neurodegenerative diseases such as Alzheimer's and Parkinson's [86].  
Despite its promising effects, the clinical application of curcumin is often limited by its low bioavailability. Clinical studies have shown that conventional curcumin formulations result in limited plasma concentrations, necessitating the development of novel delivery systems. Recent advancements, such as the use of lipid-based nanoparticles and curcumin-phospholipid complexes, have significantly improved its bioavailability [87]. For instance, a study on nanocurcinin demonstrated enhanced absorption and efficacy in reducing systemic inflammation in patients with osteoarthritis [88]. Innovative delivery methods, such as nanocarriers and liposomes, are being developed to improve its absorption and efficacy as an activator of SIRT1 in anti-aging strategies [5; 89].
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 3 The Proposed Method
### Architecture and Parameters of the Proposed Method  
The architecture of the proposed method is illustrated in Figure 2, which is established in three steps:  
Figure 1: Linear and SELU activation functions: (**a**) Linear activation functions; (**b**) SELU activation functions.  
Data preprocessing: the time-sequential signal is intercepted according to the sample length, and the samples of \(m\times(1\times 1)\) is constructed after preprocessing; m is the number of samples.
2. Feature extraction and data dimensionality reduction: four alternating convolution and pooling layers are established layer by layer. Through multi-layer feature extraction, the more abstract and deeper signal features hidden in the data can be obtained. Through multi-layer pooling operation, the high-dimensional data can be transformed into low-dimensional data for subsequent data classification. Moreover, after the convolution layer, batch normalization is introduced to adjust the data distribution so as to accelerate the convergence process as well as improve the generalization ability of the model. The ReLU in traditional CNN is improved to Linear and SELU to avoid a large amount of data being suppressed before feature extraction, which will affect its recognition accuracy.
3. Fault classification: the cross-entropy loss is replaced by the L2-SVM loss, and two new parameters, namely, the dynamic adjustment parameter and the modulation factor, are introduced to increase the recognition accuracy of the minority class samples.  
The main model parameters that need to be designed are the size and number of convolution kernels (pooling kernels). As to the size of the convolution kernel, the first layer adopts 16 \(\times\) 1 for the purpose of better suppression of the input of high-frequency noise [39]. The following convolution layers are 3 \(\times\) 1, which is convenient for network deepening; this improves the feature learning ability of the model. After each convolutional layer, a 2 \(\times\) 1 maximum pooling operation is performed to lessen the number of network parameters, decrease the time of convolutional operations, and effectively avoid overfitting. In addition, a padding operation is added to each convolutional layer in order not to discard the feature information of the input samples and to keep the output dimension of the convolutional layer consistent with the input dimension.  
The effect of model training is also affected by the training parameters. A too-small batch size will cause serious vibration to the loss, and the convergence within the maximum iteration may be difficult; meanwhile, the opposite situation could affect the generalization capability of the model. The batch size is finally set to 128, and the maximum iteration number is 20. The specific parameters of the model are listed in Table 1.  
Figure 2: Architecture of the proposed 1 DCNN-L2 SVM.
## 1 Introduction
### Numerical Analysis of Characteristics  
In this paper, we focus on characteristics of the LS-secondary DLIM, including power factor and thrust. With the aid of [PERSON]'s theorem, the overall equivalent impedance can be written as:  
\[Z_{\rm LS}=R_{1}+j\omega L_{1}+\frac{(R_{\rm m}+j\omega L_{\rm m})(R_{21}/s+j \omega L_{21})}{(R_{\rm m}+R_{21}/s)+j\omega(L_{\rm m}+L_{21})}. \tag{9}\]  
Then the numerical expressions of the characteristics are:  
\[\cos\varphi=\frac{\text{Re}\{Z_{\rm LS}\}}{|Z_{\rm LS}|},F_{\rm e}=\frac{m_{1} U_{1}{}^{2}R_{21}/s}{V_{s}|Z_{\rm LS}|^{2}}\cdot\frac{R_{\rm m}^{2}+(\omega L _{\rm m})^{2}}{\left(R_{\rm m}+R_{21}/s\right)^{2}+\left(\omega L_{\rm m}+ \omega L_{21}\right)^{2}} \tag{10}\]  
By substituting Equation (1) in the equivalent circuit parameters, characteristics in Equation (10) can be regarded as functions of the geometrical dimensions and secondary speed. The model's properties, including cogging ratio, power factor, and secondary speed, have been discussed in detail in previous works [16]. However, in this paper, we are mostly concerned with the characteristic of the thrust and its related stresses. For the purpose of simplification, the thrust can be considered as evenly distributed on each ladder, and average EM shear stress on the secondary can be written as:  
\[T_{\rm e}=\frac{F_{\rm e}}{2[L_{1}l_{\delta 2}-(N-1)(b_{1}-b_{2})l_{\delta 1}]}, \ T_{\rm e,max}=\frac{F_{\rm e,max}}{2[L_{1}l_{\delta 2}-(N-1)(b_{1}-b_{2})l_{ \delta 1}]}, \tag{11}\]  
where \(T_{\rm e,max}\) represents the maximum value of the average EM shear stress, and can be utilized to determine whether the LS-secondary meets the requirement on EM shear stress [17].  
Then, the maximum normal stress \(\sigma_{\rm max}\), maximum shear stress \(\tau_{\rm max}\), and maximum deflection \(k_{\rm max}\) on a single ladder, which are defined in structural analysis [18], can be expressed as:  
\[\sigma_{\rm max}=\frac{T_{\rm e,max}l_{\delta 1}^{2}}{2d_{2}^{2}},\ \tau_{\rm max}=\frac{3T_{\rm e,max}l_{ \delta 1}}{4d_{2}},\ k_{\rm max}=\frac{T_{\rm e,max}l_{\delta 1}^{4}}{16E_{\rm str}d_{2}^{ 3}} \tag{12}\]  
where \(E_{\rm str}\) represents the elastic modulus.  
Although it may appear difficult to derive the analytical variation trend simply from the equivalent circuit, the aforementioned equations not only provide a numerical analysis method for the design of a single DLIM, they also serve as the theoretical basis from which to analyze the kinetics of VPP EM propulsion.
###### Abstract  
The electroencephalogram (EEG) signal in the brain-computer interface (BCI) has suffered great cross-subject variability. The BCI system needs to be retrained before each time it is used, which is a waste of resources and time. Thus, it is difficult to generalize a fixed classification method for all subjects. Therefore, the transfer learning method proposed in this article, which combines XDAWN spatial filter and Riemannian Geometry classifier (RGC), can achieve offline cross-subject transfer learning in the P300-speller paradigm. The XDAWN spatial filter is used to enhanced the P300 components in the raw signal as well as reduce its dimensions. Then, the Riemannian Geometry Mean (RGM) is used as the reference matrix to perform the affine transformation of the symmetric positive definite (SPD) covariance matrix calculated from the filtered signal, which makes the data from different subjects comparable. Finally, the RGC is used to obtain the result of transfer learning experiments. The proposed algorithm was evaluated on two datasets (Dataset I from real patients and Dataset II from the laboratory). By comparing with two state-of-the-art and classic algorithms in the current BCI field, Ensemble of Support Vector Machine (E-SVM) and Stepwise Linear Discriminant Analysis (SWLDA), the maximum averaged area under the receiver operating characteristic curve (AUC) score of our algorithm reached 0.836, proving the potential of our proposed algorithm.  
Keywords:brain computer interface (BCI); XDAWN; Riemannian Geometry; P300; transfer learning +
Footnote †: journal: Applied Sciences
## 3 Results
### Effects of SFA-CHCl3 Extract on HepG2 Cell Morphology  
The morphology of the HepG2 cells in the control group and the SFA-CHCl3 extract-treated groups were evaluated by observation under a microscope. The treatment with 3.125 \(\upmu\)g/mL, 6.25 \(\upmu\)g/mL, and 12.5 \(\upmu\)g/mL SFA-CHCl3 extract did not result in morphological changes in HepG2 cells (Figure 2A-C). An amount of 25 \(\upmu\)g/mL SFA-CHCl3 extract treatment induced the morphological alteration in HepG2 cells (Figure 2D). The treatment with 50 \(\upmu\)g/mL and 100 \(\upmu\)g/mL SFA-CHCl3 extract caused marked morphological alterations of HepG2 cells, demonstrated by the shrinkage and detachment of cells (Figure 2E,F). These results showed that the SFA-CHCl3 extract treatment induced morphological changes in HepG2 cells.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Solvent** & **Matrine (mg/g)** & **Oxymatrine (mg/g)** \\ \hline Ethanol extract & 28.1 & 1.79 \\ Chloroform extract & 70.1 & 4.2 \\ Ethyl acetate extract & 1.1 & 0.09 \\ Aqueous extract & ND & ND \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matrine and oxymatrine contents.  
Figure 2: Morphological alterations of HepG2 cells under SFA-CHCl3 extract treatment. HepG2 cells from the treatment of 3.125 \(\upmu\)g/mL (**A**), 6.25 \(\upmu\)g/mL (**B**), and 12.5 \(\upmu\)g/mL (**C**) SFA-CHCl3 extract  
showed normal morphology. HepG2 cells showed morphological changes in 25 ug/mL (**D**), 50 ug/mL (**E**), and 100 ug/mL (**F**) SFA-CHCl3 extract groups. (**G**) Cell density was analyzed by WST-1 assay. Original magnification 100\(\times\).
## 2 Materials and Methods
### Material Characterization
#### 2.5.2 Optical Evaluation  
Optical properties of the films were obtained according the Kubelka-Munk theory of multiple scattering to determine the film reflection spectra (\(R\)) using the black (\(R_{0}\)) and white (\(R_{g}\)) backgrounds. The internal transmittance (\(T_{i}\)) of the films ranging from 400 to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(\mathbf{X_{s}}\) & \(\mathbf{X_{Gly}}\) & \(\mathbf{X_{CMF}}\) \\ \hline TPS & 0.770 & 0.230 & - \\ TPS1 & 0.763 & 0.229 & 0.008 \\ TPS3 & 0.753 & 0.225 & 0.022 \\ TPS5 & 0.741 & 0.222 & 0.037 \\ TPSDH & 0.770 & 0.230 & - \\ TPSDH1 & 0.763 & 0.229 & 0.008 \\ TPSDH3 & 0.753 & 0.225 & 0.022 \\ TPSDH5 & 0.741 & 0.222 & 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of compositions according to the mass fraction (g/g) of thermoplastic corn starch (TPS), thermoplastic dry heated corn starch (TPSDH), glycerol (Gly), and cellulose microfiber (CMF).  
Figure 1: Image acquisition procedure to obtain the lengths of the cellulose microfhers (CMFs).  
700 nm, the film color coordinates \(L^{*}\) (lightness), \(a^{*}\) (redness-greenness), and \(b^{*}\) (yellowness-blueness) were determined using Equations (1)-(3), respectively. The colorimetric and transparency properties of the films were also evaluated in terms of hue angle (\(l_{ab}\)*) (Equation (4)), chroma (\(C_{ab}\)*) (Equation (5)), opacity (\(O\)) (Equation (6)), and total color difference (\(\Delta E\)*) (Equation (7)). The measurements were performed in triplicate.  
\[T_{i}=\sqrt{\left(a+R_{0}\right)^{2}-b^{2}} \tag{1}\]  
\[a=\frac{1}{2}\left[R+\left(\frac{R_{0}-R+R_{g}}{R_{0}\times R_{g}}\right)\right] \tag{2}\]  
\[b=\sqrt{a^{2}-1} \tag{3}\]  
\[{h_{ab}}^{*}=arctg\left(\frac{b^{*}}{a^{*}}\right) \tag{4}\]  
\[{C_{ab}}^{*}=\sqrt{a^{*2}+{b^{*2}}} \tag{5}\]  
\[O=A_{500}x\,l \tag{6}\]  
In which \(A_{500}\) and \(l\) are the absorbance at 500 nm and the film thickness, respectively.  
\[\Delta E^{*}=\sqrt{\left(\Delta L^{*}\right)^{2}+\left(\Delta a^{*}\right)^{2 }+\left(\Delta b^{*}\right)^{2}} \tag{7}\]  
where \(\Delta L^{*}=(L^{*}-L_{0}^{*})\); \(\Delta a^{*}=(a^{*}-a_{0}^{*})\); \(\Delta b^{*}=(b^{*}-b_{0}^{*})\); and \(L_{0}^{*}\), \(a_{0}^{*}\), and \(b_{0}^{*}\) are the color coordinates of the control TPS film without fibers. The color differences were evaluated according to the following criteria [37]: \(\Delta E^{*}\times 1\) indicate unnoticeable color change; \(1\leq\Delta E^{*}\leq 2\) suggests that only an experienced observer can notice the difference; \(2\leq\Delta E^{*}\leq 3.5\) means that an inexperienced observer notices the difference; \(3.5\leq\Delta E^{*}\leq 5\) indicate clear noticeable difference; and \(\Delta E^{*}\geq 5\) suggests that the observer notices different colors.
## 1 Introduction
### Literature Overview
For instance, lower degrees of variation in age-adjusted HRV indicate chronic stress, pathology, or insufficient functioning in regulatory systems in the neuroaxis, and are associated with all-cause mortality while higher degree of variation indicate resiliency and capacity to self-regulate and adapt to changing demands [26; 27]. Processes within the magnetosphere and the solar wind excite a broad range of magnetic waves that are typically present globally, although there are significant local differences. The most well known are SR. The frequencies of SR are approximately 7.83 Hz, 14, 20, 26, 33, 39, and 45 Hz, which closely correspond with human brainwaves (alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-100 Hz) [28]. [PERSON] et al. has found that the power within EEG spectral profiles has repeated periods of coherence with the first three SR resonance frequencies (7-8 Hz, 13-14 Hz, and 19-20 Hz) in real-time and suggests that fluctuations in the SRs can affect brain activity, including modulations in cognition [29]. Substantially less research has examined the effects of ultra-low frequency (ULF) waves on physiological functions. The primary source of ultra-low frequency waves in the magnetosphere are field-line resonances, which have the largest amplitudes of the all the magnetic waves [30]. The ionosphere, which surrounds the Earth, is a highly ionized plasma layer threaded by magnetic fields. As first described by [PERSON], charged high-energy particles spiraling around the magnetic field lines explain how ultra-low-frequency waves (AW) are created [31]. Magnetic field lines, which have lengths several times the Earth's radius, are excited and vibrate at their resonant frequency, similar to plucking a guitar string. The length of the field lines, field strength, and the speed and density of the solar wind affect their frequency [32]. The longer the field line the lower the resonant frequency. Changes in solar wind speed and polarity of the interplanetary magnetic field can have large effects on the waves, as measured on the surface of the Earth [33]. Magnetic oscillations with frequencies below 1 hertz that have sinusoidal waveforms are classified as pulsations continuous (Pc) and irregular waveforms are classified as pulsations irregular (Pi). They are further subdivided into frequency ranges, which are associated with different mechanisms. Magnetic standing waves with frequencies up to 5 hertz are typically excited by geomagnetic sub-storms are classified as Pc1 and 2, and field-line resonances in the range between 1 mHz and 100 mHz (periods of 1000 to 10 s) are classified as Pc3 to Pc5 waves [32].
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 3 Proposed Method
### Encoding of Complex Blocks  
Blocks with \(b_{i}-a_{i}\geq\tau_{1}\) are classified as complex blocks. Let \(\{l_{c}\}_{c=0}^{N_{c}-1}\) be the set of \(N_{c}\) complex blocks in \(I\). For a given complex block \(l_{c}=\left\{I_{c,j}\right\}_{j=0}^{n\times n-1}\), the proposed method uses the \(k\)-means clustering algorithm to obtain three most representative quantized values \(\left\{q_{c}^{0},q_{c}^{1},q_{c}^{2}\right\}\) and a ternary map \(T_{c}=\left\{T_{c,j}\right\}_{c=0}^{n\times n-1}\), where \(T_{c,j}\) is a ternary digit ranging from 0 to 2 used to indicate which quantized value should be used to reconstruct the \(j-\)th pixel of \(l_{c}\). Because the value of \(T_{c,j}\) is equally distributed over 0 to 2, we can simply encode the ternary digits 0\({}_{3}\), 1\({}_{3}\), and 2\({}_{3}\) by 0\({}_{2}\), 10\({}_{2}\), and 11\({}_{2}\), respectively. We assume the encoded result of \(T_{c}\) is \(T^{\prime}{}_{c}\) of \(L\)-bit. Once the decoder has \(\left\{T^{\prime}{}_{c}\right\}_{c=0}^{N_{c}-1}\) and \(\left\{q_{c}^{0},q_{c}^{1},q_{c}^{2}\right\}_{c=0}^{N_{c}-1}\), blocks \(\left\{I_{c}\right\}_{c=0}^{N_{c}-1}\) can be reconstructed.  
When encoding a \(4\times 4\) ternary map, the average number of bits required in the proposed method is:  
\[\frac{1\times 16}{3}+\frac{2\times 2\times 16}{3}=26.67\;\text{bits}.\]  
Theoretically, recording 16 ternary digits requires \(\left\lceil 16\times\log_{2}3\right\rceil=26\) bits, which is almost the same as in the proposed method. Therefore, the encoding of the ternary map used in the proposed method is effective.
## 4 Expedition Cruising in Svalbard and the Application of ICT
### ICT Tools Developed by AECO to Perform Collective Action
#### 4.1.2 AECO's Vessel Tracking System  
AECO developed its own vessel tracking system. The system is based on vessel tracking technologies AIS and VTS, and it involves satellite-based surveillance. Based on the information received through AIS or VTS, depending on which of the technologies an operator uses, cruise operators can access information about other operators in the area, including the operators' names, positions, courses and speeds. The vessel tracking adds to the safety management system, but also ensures real-time monitoring of operations.  
Additionally, AECO vessel tracking supports communication between the operators at sea. For example, in case of any planned changes to the sailing plan, AECO members are obliged to contact nearby expedition vessels to discuss changes of their initial plans if necessary. Following these procedures helps to reduce uncertainty and the surprise factor related to external changes while supporting the sustainable management of tourism sites. The value of AECO's vessel tracking system has also been highlighted by research participant 1:  
_\"By knowing within 15 min where all the other ships are, we can work towards better planning, greater safety, and the avoidance of eventual conflicts at landing sites.\"_
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 13 Delay-Sensitive Routing in Underwater WSNs  
### System Model and Design Objectives  
Underwater acoustic WSNs (UWA-SNs) were considered in [8]. The propagation delay in UWA-SNs is five times larger than in RF networks which has a non-negligible impact on the performance of UWA-SNs especially since they cover much larger areas (square kilometers) unlike the RF WSNs. In [8] the propagation delay sensitivity was accounted for in optimizing energy for routing purposes. According to the authors of [8], this was not considered before their work due to the added complexity it brings in.  
The sensor nodes are immobile and have relaying capabilities that enables the WSN to use multi-hop communication to convey a packet from any sensor to a sink node. Slotted synchronous time division multiple access (TDMA) was the type of medium access control (MAC) scheme considered. The objective was to design an offline routing scheme to pre-calculate the number of slots per link and the amount of data to be transmitted on each link while factoring in the large propagation delays of water acoustic signals. The routing scheme proposed in [8] was named Delay-constrained Energy-constrained Routing (DER).
## 3 Results
### Inference Energy Efficiency

Table 9: Measurement of operating speed (T), power consumption (P), and inference energy efficiency (E). increase in inference times, which amounted to 605.62 ms for YOLO v4 and 247.75 ms for EfficientNet B0, as shown in Table 4. During the tests, the AMD card ran at 235.14 W for YOLO v4 and 98.42 W for EfficientNet B0. The result was the lowest power efficiency compared to other cards. AMD was 12.77 times less energy efficient on YOLO v4 and more than 183 times less efficient on EfficientNet B0 compared to Nvidia. Intel achieved the highest power efficiency in the YOLO v4 network at 112.81 images per kilojoule, which is approximately 22% better than Nvidia's result (92.22 images per kilojoule). With EfficientNet B0, Nvidia achieved the highest power efficiency of 677.51 images per kilojoule.