esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### Visual Results  
This study performed visual testing to compare the original YOLOv8s model with the proposed CIID-YOLO model in detail. Random samples from the HCIE dataset were used, focusing on images in low-light conditions and with smaller detection targets, as depicted in Figures 12 and 13. The test sets in these figures include original real images, detection outcomes of YOLOv8s, and detection outcomes of CIID-YOLO.  
In Figure 12, the original YOLOv8s model struggles to accurately detect human bodies in low-light indoor settings, often resulting in false positives. In such conditions, color features and complex backgrounds make human recognition challenging for the model. Due to unclear color features, similar-shaped objects are sometimes misidentified as targets. In contrast, CIIHD-YOLO excels in precisely localizing human bodies, even in low-light scenarios. While YOLOv8s relies heavily on shape features when color features are vague, CIIHD-YOLO's enhanced neck network effectively recognizes features, even when colors are indistinct, leading to precise bounding box localization. The improved detection accuracy and visual results in Figure 12 highlight CIID-YOLO's superiority in object detection within poorly illuminated indoor environments.  
In Figure 13, it is evident that the original model struggles to distinguish small-scale true positive targets effectively. Small objects occupying minimal pixels provide limited information for accurate feature extraction, posing a challenge for the model. The original YOLOv8s model's receptive field may not cover the entire small target, impeding its ability to capture comprehensive global and contextual information. On the contrary, CIIHD-YOLO's pyramid pooling incorporates partial self-attention, facilitating the capture of long-range dependencies and establishing global feature correlations, which is especially beneficial for small objects. This approach enables effective fusion and attention to multi-scale features, enhancing detection even in crowded scenes. CIIHD-YOLO excels in detecting small-scale human bodies in indoor environments, showcasing improved effectiveness compared to the original model.  
In order to investigate how camera angle affects detection accuracy, some test set images were rotated by 30\({}^{\circ}\) for further detection, as shown in Figure 14. The results indicate that the rotation of the angle has a significant impact on the test results. The original YOLOv8s model experienced serious missed detections and may even be unable to detect a target from the rotated image. Although CIIHD-YOLO also experienced some missed detections, the situation is far better than the original model.  
The integration of the three enhanced networks proposed in this study notably boosts the model's performance. The CIID-YOLO algorithm proves highly effective across diverse complex indoor environments, enhancing both human detection accuracy and precise bounding box localization significantly.  
Figure 13: Test image set of small-scale human body.
## 3 Special Model: Exponentiated Exponential-Pareto Distribution (EEP)
### Mode  
Since both exponential distribution and Pareto distribution are associated with monotonically decreasing pdf, the pdf of the original EP distribution is also monotonically decreasing. Thus, as an composite distribution, EP still does not have the ability to model the data with hump-shaped frequency distributions. One of the advantages of the EEP distribution against the original EP distribution is that the EEP can have a pdf with a hump shape, or equivalently, the EEP distribution has a mode under certain conditions. We explore the conditions for such hump-shaped distributions in the following part:  
**Proposition 1**.: _Suppose that \(T\) is an EEP composite random variable. Assume that \(f_{T}(t;\theta,\eta)\) is the pdf associated with \(T\). Then, the mode of \(T\) exists if and only if \(\eta>1\). When \(\eta>1\), the mode of \(T\) is \([\frac{\theta(\eta-1)}{\eta(\pm 1)}]^{\frac{1}{\eta}}\)._  
Proof of Proposition 1.: First, suppose \(t\geq\theta^{\frac{1}{\eta}}\); then, \(f_{T}(t;\theta,\eta)=c\alpha\frac{\theta^{\frac{1}{\eta}}}{(t^{\eta})^{\eta}+1 }\eta t^{\eta-1}\).  
Correspondingly, \(\frac{d_{f_{T}}(t;\theta,\eta)}{dt}=(-\alpha\eta-1)c\alpha\theta^{\alpha}\eta t ^{-\alpha\eta-2}<0\) for all \(t\geq\theta^{\frac{1}{\eta}}\).  
Therefore, when \(t\geq\theta^{\frac{1}{\eta}}\), \(f_{T}(t;\theta,\eta)\) is monotonically decreasing.  
Since \(f_{T}(t;\theta,\eta)\) is continuous, this implies that the maximum of \(f_{T}(t;\theta,\eta)\) must occur when \(t<\theta^{\frac{1}{\eta}}\) if the maximum exists.  
Assume \(t<\theta^{\frac{1}{\eta}}\), then \(f_{T}(t;\theta,\eta)=c(\frac{\theta+1}{\theta})e^{-\frac{(\theta+1)t^{\eta}} {\theta}}\eta t^{\eta-1}\).  
When \(f_{T}(t;\theta,\eta)\) is a positive real-valued function, maximizing \(f_{T}(t;\theta,\eta)\) is equivalent to maximizing \(\ln f_{T}(t;\theta,\eta)\). Thus, we use \(\ln f_{T}(t;\theta,\eta)\) for the rest of the derivations.  
Then, \(\ln f_{T}(t;\theta,\eta)=\ln(c)+\ln(\frac{\theta+1}{\theta})-\frac{(\theta+1) t^{\eta}}{\theta}+\ln(\eta)+(\eta-1)\ln(t)\).  
Figure 1: The pdf of an EEP distribution with different parameters.  
Correspondingly, \(\frac{d\ln f_{T}(t;\theta,\eta)}{dt}=-\frac{(\alpha+1)\eta t^{\eta-1}}{\theta}+\frac{ \eta-1}{t}\)  
Let \(\frac{d\ln f_{T}(t;\theta,\eta)}{dt}=0\). Then, we have the equation of \(t\): \(\eta-1=\eta(\frac{\alpha+1}{\theta})t^{\eta}\).  
Notice that \(t>0\). Hence, the above equation has a solution only when \(\eta>1\). Therefore, the mode of \(T\) exists if and only if \(\eta>1\).  
When \(\eta\geq 1\), the equation has a unique solution: \(t=[\frac{\theta(\eta-1)}{\eta(\alpha+1)}]^{\frac{1}{\eta}}\). It can be shown that \(\frac{d^{2}f_{T}(t;\theta,\eta)}{dt^{2}}<0\) when \(t=[\frac{\theta(\eta-1)}{\eta(\alpha+1)}]^{\frac{1}{\eta}}\). Thus, \(t=[\frac{\theta(\eta-1)}{\eta(\alpha+1)}]^{\frac{1}{\eta}}\) is the mode of \(T\).  
Therefore, the addition of the exponent parameter \(\eta\) in the EEP model definitely improves the flexibility of the original EP model since it can model the data with a hump-shaped distribution.
## 4 Strain Measurement System Development and Results
### Strain on the Solid-Body Gear  
The radial strain results for the solid over time are presented in Figure 8. Strains on three parts--namely, the tooth root, ring, and hub of the solid gear--are plotted in three different curves. The measurements are shown from 0.74 s to 0.95 s. The results show that the strain on the tooth root is significantly high compared to the ring and hub portions. On the other hand, the strain on the ring is comparatively higher than on the hub. In addition, the radial strain of the solid gear does not remain stable over time.  
The strains in the tangential direction in the solid gear over time are shown in Figure 9. The results show that the strain is not stable over time when the pressure is applied in the tangential direction. However, as shown in Figures 8 and 9, the strain in the tangential direction is higher than the strain in the radial direction, which is due to the phenomenon of the tangential and radial load difference.
## 4 Conclusions  
DoE has been successfully implemented to identify important factors that control the graphene synthesis using a-C as the carbon source and Cu as the catalyst. Full-factorial DoE analysis reveals that the interaction of annealing temperature and a-C layer thickness is statistically significant for the \(I_{\rm D}/I_{\rm G}\) peak intensity ratio and the quality of grown graphene. On the other hand, annealing temperature, a-C layer thickness along with the interaction of the a-C layer thickness and annealing time are statistically significant for the \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio and the number of layers of graphene. The physical reasons for the factors being significant for different aspects of the grown graphene are also explained, showing the consistency of the DoE method and the underlying process physics and chemistry.  
Contour plots suggest that a higher annealing temperature and a thinner a-C layer are favorable for producing graphene with a smaller value of the \(I_{\rm D}/I_{\rm G}\) peak intensity ratio and a larger value of the \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio. However, the annealing time has to be optimized in this case, in order to synthesize mono-layer/few-layer graphene with low defects. On the other hand, higher annealing temperature, thinner a-C layer and longer annealing time will result in multi-layer graphene with low defects. This is verified by our experimental data.  
In short, the application of DoE analysis on this type of graphene synthesis not only helps to determine the key controlling factors, it can also indicate a suitable combination of the levels for different factors for obtaining graphene of different qualities and different numbers of layers.  
The following are available online at www.mdpi.com/2076-3417/6/7/204/s1. Figure S1: Sample preparation using PVD (Physical Vapor Deposition) method; 2 sets of samples each having 36 nm and 12 nm thick a-C (amorphous carbon) thin films are prepared. Figure S2: Thermal Annealing after sample preparation; Annealing is carried out at 2 levels of temperatures that are 820 \({}^{\circ}\)C / 1020 \({}^{\circ}\)C and 2 levels of duration that are 10 min/50 min. Table S1: Thermo-mechanical properties of the materials used for Finite Element Analysis (FEA).  
We acknowledge the support of the Ministry of Science and Technology (MOST), Taiwan. This work is funded by a MOST grant (Grant Number MOST 104-2221-E-182-040). The samples were prepared and annealed at the Semiconductor Laboratory in the Department of Electronic Engineering of Chang Gung University, Taiwan. Raman characteristic data were obtained at the Biosensor Laboratory of Chang Gung University.  
[PERSON] conceived of the experiments. [PERSON] designed and performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
###### Abstract  
This study investigated the effects of a 10-week plyometric training program on sprint performance, reactive power, and biomechanical muscle properties in soccer players. Twenty soccer players were randomly assigned to an experimental group (\(n=10\)) or a control group (\(n=10\)). Both groups maintained their regular weekly training, with the experimental group performing additional pylometric sessions twice weekly. Pre- and post-intervention assessments included 5 m and 30 m sprint times, Reactive Strength Index (RSI), and biomechanical properties (tension, stiffness, elasticity) of the rectus femoris (RF) and vastus lateralis (VL). The experimental group demonstrated significant improvements in 5 m (\(p<0.01\); ES = 1.44) and 30 m (\(p<0.01\); ES = 1.11) sprint times and RSI (\(p<0.05\); ES = 0.87). No significant changes were observed in muscle tension, stiffness, or elasticity at the group level. However, correlations indicated that higher baseline elasticity in the VL was linked to greater 5 m sprint improvements, while changes in RF elasticity were negatively associated with 5 m sprint gains. These findings suggest that pylometric training effectively enhances short-distance sprint performance and reactive power in soccer players. Although group-level biomechanical properties did not change significantly, individual variability in muscle elasticity may modulate training outcomes, supporting the integration of pylometric exercises into soccer training regimens.  
functional performance; physical activity; myotonometry; drop jump; sprint; stiffness +
Footnote †: journal: Applied Sciences
## 2 Complex Natural Resonances Methods  
### [PERSON]'s Method  
In order to resolve a nonlinear problem, [PERSON] proposed a method back in 1795 where a linear prediction equation is formed with an unknown coefficient vector and, before the amplitudes can be retrieved from the sample set, the frequencies are obtained from the roots of the prediction coefficient vector. Several modifications to this method have been proposed to increase its SNR [9; 13], some used for SEM poles extraction and based on linear prediction (LP) for large data and presence of noise [14; 15]. The least square (LS) and total least square (TLS) methods are applied as follows, using singular value decomposition (SVD) to lower the rank of the data matrix [16].  
Starting with the linear prediction equation for a signal composed of complex sinusoids as in (2), [PERSON]'s approach takes the data and their associated noise in the data matrix and the observation vector [17]. The linear prediction (LP) matrix (3) \(Ab\approx g\) for data, the linear predictor, and the observation vector can be treated as if we were searching for the solution vector in the form of minimizing the \(l_{2}\) norm for \(g\).  
\[y_{n}=\sum_{m=1}^{p}c_{m}e^{nS_{m}t}+W_{n}\quad n=0,1,\ldots,N-1 \tag{2}\]  
\[\begin{bmatrix}y_{L-1}&y_{L-2}&\cdots&y_{0}\\ y_{L}&y_{L-1}&\cdots&y_{1}\\ \vdots&\vdots&\ddots&\vdots\\ y_{N-2}&y_{N-3}&\cdots&y_{N-L-1}\end{bmatrix}\begin{bmatrix}b_{0}\\ b_{1}\\ \vdots\\ b_{m-1}\end{bmatrix}=\begin{bmatrix}y_{L}\\ y_{L+1}\\ \vdots\\ y_{N-1}\end{bmatrix} \tag{3}\]  
The parameter \(L\) is an overestimate of the number of complex exponentials to compute from the data, and \(b_{m}\) are the polynomial coefficients from which the poles are calculated with \(b_{0}=1\). \(L\) is to be updated after a rank fix is applied, employing SVD.  
The general description and steps for [PERSON]'s algorithm are described in Figure 1 with the samples for the linear prediction (LP) matrix \(H\), which becomes \(H1\) after a rank fix using SVD.  
Now, as an example, a time series benchmark signal \(y(t)=\sin(8t+\sin(12t))\) is used to observe the behavior of this algorithm in terms of reconstruction accuracy after the poles have been found and the residues extrapolated. In Figure 2, the resulting approximation alongside the input time series is shown, and in Figure 3, the conjugated poles extracted with [PERSON]'s method are presented.  
Figure 1: [PERSON]’s method algorithm in a general step-by-step flow chart.
## 9 Topology Optimization Results
### Cantilever Beam: Compliance Minimization Results  
The objective functions obtained by each of the different optimization algorithms were in good agreement, converging towards an approximately equal value of compliance. However, a different trend can be observed for the \"OC--discrete and decreasing\" case. In this particular case, the initial solution considers a fully solid design that must gradually reduce its mass to meet the volume constraint, causing a consequent reduction of the structural stiffness and an increase of the compliance value.  
Figure 5: Graphic representation of the objective function and volume constraint obtained for the compliance minimization of the Cantilever beam. The geometry displayed represents the final solution obtained by the MMA algorithm.  
This observation is also in line with the volume constraint graphic, which shows a gradual decrease until the value 0.5 is reached. On the other hand, the remaining algorithms maintained the volume constraint within a feasible domain.  
The final Cantilever geometry obtained was similar across all cases considered and illustrated, in Figure 5, with the solution found by the MMA. In this particular case, the elements with intermediate densities may also be the result of using a coarser mesh.
## 3 Results and Analysis
### Lumen Maintenance Characteristics of OLED Panels and Luminaires

Table 3: Maintained lumen output and luminous efficacy of OLED panels and luminaires with operating of thousand hours. Figure 2: Characteristics of maintained lumen output of OLED products. (**a**) Maintained lumen output of OLED panels. (**b**) Maintained lumen output of OLED luminaires. The following Equation (2) was used to project lumen maintenance lifetime. \[Lp(D\ k)= \frac{\ln\Bigl{(}100\times\frac{B}{p}\Bigr{)}}{\alpha} \tag{2}\]  
where:  
\(Lp\) = lumen maintenance life expressed in hours where \(p\) is the percentage of initial luminous flux that is maintained  
\(D\) = total duration time divided by 1000 and rounded to the nearest integer  
Above Equations (1) and (2) were used to evaluate the lifetime of OLED panels and luminaires with six thousand hours data, the results are shown in Table 4. Reported L70(6k) means that a lifetime of OLED product which maintains 70% of its initial luminous flux output, and the reported L70(6k) using extrapolation method in IES TM-21 standard was over ten thousand hours for all type of OLED panels (type A, B, C, and D) and under five thousand hours for OLED luminaires (type E and F). In case of reported L50(6k) means that a lifetime of OLED product which maintains 50% of its initial luminous flux output, and the reported L50(6k) was over twenty thousand hours for all type of OLED panels (type A, B, C, and D) and under six thousand hours for OLED luminaires (type E and F). Alpha parameter and B parameter for all four types of OLED panels have similar values, thus all OLED panels have same degradation mechanism. Actually, these four types of OLED panels were manufactured by same manufacturer and fabrication process, thus their alpha parameter and B parameter are very similar to each other. In case of OLED luminaires, we had same results as shown in Table 4. Remarkably, flexible OLED panel (type B) show equivalent reliability characteristics to that of rigid OLED panel (type C) which has the same dimensions and operating conditions. Maximum error between the measured lumen degradation data and the predictive model at six thousand hours was about 2% for all cases.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### System Planning of IECPS
#### 5.2.3 Multi-Region Interaction on Planning  
In the process of advancing energy integration, users with similar energy consumption characteristics gradually aggregate to form regional integrated energy systems dominated by industries, commerce, and residents. Traditional regional integrated energy system planning focuses only on infrastructure within the region. However, due to similar internal resource endowments and limited adjustable resources, it is difficult to establish a stable and reliable energy defense and control system. Therefore, breaking the spatial limitations of a single region and promoting multi-region energy complementarity and coordination enables the tapping into the adjustment potential of regional integrated energy systems with different energy consumption characteristics, thereby enhancing the system's ability to resist deliberate attacks.  
The key to multi-regional interactive planning technology lies in reasonably determining the site selection and capacity configuration of regional energy stations and optimizing the layout of energy transmission pipelines between regions. Subsequently, through the unified scheduling of the cyber system, it addresses the imbalance of energy supply and demand in both time and space, achieving complementary coordination of energy. Overall, benefiting from the energy usage differences in different regional systems, multi-regional interactive planning enhances the system's ability to respond to uncertainty, improve anti-interference performance, and enables rapid recovery characteristics. This ensures that the system possesses sufficient flexibility to maintain sustainable energy supply in the face of diversified deliberate attacks.
## Materials and Methods
### HPLC Analysis of the Polymethoxyflavones in OPO  
Five microliters of orange peel oil were dissolved in 995 \(\upmu\)L of methanol to generate the HPLC sample, and the injection volume was 2 \(\upmu\)L. An Agilent Poroshell EC-C8 column (i.d. 4.6 \(\times\) 150 mm, 2.7 \(\upmu\)m particle) was used with an Agilent 1260 Infinity chromatography system. The system consisted of a G1311B quaternary pump, a G1329B auto sampler, a G1316A thermostated column compartment and a G1315D diode array detector (DAD). The solvent consisted of 0.05% phosphoric acid/water (A), methanol (B) and 50% THF/water (C), and the flow rate was 1 mL min\({}^{-1}\). The solvent program consisted of the following linear gradients after a 0-5.00 min isocratic period, 52% B and 7.5% C; 5.01-7.50 min: 58-62% B and 0% C; 7.50-10.00 min: 62-100% B and 0% C; and 10.01-35.00 min: 52% B and 7.5% C to re-establish the initial conditions. The unlisted percentages of eluent were solvent A. The column temperature was maintained at 30 \({}^{\circ}\)C. The diode array detector was set to scan from 190-400 nm. The bandwidth and slit were set at 4 nm; the response time was 2.0 s; and the sampling frequency was 2.5 Hz. To identify the constituents, ultraviolet (UV) absorbance data and retention times of the sample peaks were compared with those of standards. The standard calibration equations were employed to quantify the identified PMF compounds.
## 1 Introduction  
Infrared (IR) small-target detection plays a critical role in large amounts of practical projects such as infrared warning and defense alertness, in which not only accuracy is needed but also robustness is required [1]. Various algorithms have been developed in the past few decades [2; 3; 4]. Conventional small target detection methods such as top-hat filter [2], max-mean/max-median filter [3] and high-pass filtersbased on least squares support vector machine (LS-SVM) [4] are widely used to reduce the background clutters. In recent years, a series of simple and fast algorithm based on Fourier transform was proposed, such as spectral residual (SR) [5], phase spectrum of Fourier transform (PFT) [6], hypercomplex Fourier transform (HFT) [7]. With regards to small target detection, frequency domain method is quite different from other methods. It transforms the airspace information to the frequency domain, defines significant target and tests in the frequency domain. While, spectral residual (SR) approach does not rely on the parameters. It calculates the difference between the original signal and a smooth one in the log amplitude spectrum, and then makes up a saliency map by transforming SR to spatial domain. PFT approach detects small targets from the reconstruction that is calculated only by the phase spectrum of the input signal. It omits the computation of SR in the amplitude spectrum, which saves about 1/3 computational cost. HFT approach explains the intrinsic theory of saliency detector in the frequency domain and use spectral filter to suppress repeated patterns.  
Although numerous methods have been proposed, many of them may fail in certain circumstances, e.g., ground-sky background [8], which is common in the helicopter view. In this situation, targets are easily mixed up with the background clutters in size and easily overlapped by vegetation, roads, rivers, bridges [9], resulting a huge false rate in traditional algorithm.  
In order to design an appropriate method, a small target detection method inspired by the human visual system (HVS) has been designed in this paper. HVS is a kind of layered image processing system consisting of optical system, retina and visual pathways, which is nonuniform and nonlinear. The rest of this paper is organized as follows. In Section 2, we describe the framework of the proposed algorithm for small target detection. In Section 3, we present the experimental results. Section 4 is the conclusion of this article.