esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
In addition, the lipidomic analysis demonstrated that, in comparison with baseline, the tested products significantly (\(p\) = 0.007) increased the amount of triacylglycerlycerols (TAGs) by 25% (Figure 4) with a fold change of 1.3 \(\pm\) 0.1. Triacylglycerglycerols are the most abundant lipids in the skin. They are found both in the skin's lipid barrier and in sebum. In general, patients with acne-prone skin have lower TAG levels than patients without acne. It is generally believed that the bacterium _C. acnes_, present in greater quantities in the microbiome of acne skin, produces a variety of enzymes that break down TAGs in sebum, producing fatty acids (FA) that induce skin inflammation and abnormal follicular keratinization [25]. During this study, the dermatologists assessed both physical signs (skin erythema, skin oedema, skin dryness, skin desquamation) and functional signs (stinging/itching sensation, burning sensation, tight feeling, other). None of the subjects showed the onset of dermatological physical or functional signs. All subjects tolerated the test product during the study period. Figure 4: Lipidomic analysis of ceramides classes (**a**) and triglycerides classes (**b**) with sebum contents at baseline (T0) and day 42 (T42). Ceramides and triglycerides show a statistical increase at T42 in comparison with baseline (\(p\) < 0.023). Self-assessment questionnaire results demonstrated that 84% of the subjects reported a global clinical improvement. Ninety-five percent reported improvement in skin hyperpigmentation, pores reduction, uniform skin texture, and reduction in skin imperfections. The total number of subjects (100%) confirmed that the treatment spreads easily on the skin, and does not leave a greasy feeling and residue on the skin after application.
## 5 Simulation Results and Analysis  
There are three BS-Sw techniques investigated in this work, namely binomial (optimal), distance-based (heuristic) and random (heuristic) methods. Furthermore, the proposed binomial BS-Sw method was simulated for three different scenarios, i.e., (1) CoMP-enabled harvesting; (2) CoMP-enabled non-harvesting; (3) non-CoMP harvesting scenarios (the legends of the graphs are abbreviated as follows: Binomial BS-Sw technique for CoMP-enabled harvesting system (Bin-CoMP-H), distance-based BS-Sw technique for CoMP-enabled harvesting system (Dist-CoMP-H), random BS-Sw technique for CoMP-enabled harvesting system (Rand-CoMP-H), Binomial BS-Sw technique for Non-CoMP harvesting system (Bin-Non-CoMP-H) and Binomial BS-Sw technique for CoMP-enabled non-harvesting system (Bin-CoMP-NoH)).
## 2 Materials and Methods
### Experimental Setup  
In order to verify the reliability of the simulation results, phantom and bovine liver experiments were designed, and the experimental system was as shown in Figure 2. The liver tissue was cut into 50 \(\times\) 50 \(\times\) 50 mm cubes. The cubes were placed in a pressure chamber with degassing water, and the pressure chamber was sealed. The tissue was degassed for 40 min with a pump with a vacuum degree of \(-\)0.09 MPa.  
The arbitrary signal generator (DG5072, RIGOL, Beijing, China) was used to generate the excitation signal, the parameters of the transducer used in the experimental system were consistent with the simulation, the curvature radius of the transducer was 150 mm, and the aperture diameter was 95 mm. The excitation signal was a sine wave with a center frequency of 1.12 MHz, and HIFU was applied in 200-ms pulses with a 200-ms pulsing interval (i.e., 50% duty cycle), such that the signal passed through the power amplifier (AR800W, Amplifier Research, Souderton, PA, USA) and drove the transducer to irradiate the ultrasonic wave. In the phantom experiment, the camera (30 fps) could accurately monitor the BFR due to the transparency of the phantom. The phantom was placed at the focus of the focused transducer in water, and the signal generated by the signal source was excited by the power amplifier. The camera was used to record the variations in the BFRs of the phantom during HIFU ablation.  
In the liver experiment, since the bovine liver tissue was not transparent, it was not possible to directly observe BFR. It was necessary to observe BFR by sectioning and take photos of the sectioning results with a camera. Rulers were used to measure the size of the BFR, so there was no need for the upper computer to measure the BFR size through image processing.
## 3 Materials and Methods
### Data Analysis  
#### 3.3.1 System Architecture Verification and Analysis  
To validate the system's architecture, simulation experiments were conducted using 50 classroom videos from secondary PE lessons. These videos were pre-processed and analyzed by the CogVLM2-Video model, which achieved 90% accuracy in teaching behavior recognition and 95% consistency with manual annotations (see Table 1), confirming the system's operational effectiveness.  
To ensure the reliability of the manual annotations used as reference, a subset of 20 videos was independently labeled by two trained coders. Inter-rater agreement reached a Cohen's Kappa of 0.87, indicating high consistency. These verified annotations served as the ground truth for evaluating the model's outputs across the full dataset.  
The 50 video samples covered a range of instructional phases--including teacher demonstration, student practice, interaction, and transitions--enhancing the representativeness of the evaluation. Although no formal baseline model was applied, the expert-coded annotations provided a valid benchmark under authentic classroom conditions.  
Figure 2: Implementation principles of the system for evaluating teaching behavior in PE classrooms.
###### Abstract  
This paper presents algorithms to estimate the signal-to-noise ratio (SNR) in the time domain and frequency domain that employ a modified Constant Amplitude Zero Autocorrelation (CAZAC) synchronization preamble, denoted as CAZAC-TD and CAZAC-FD SNR estimators, respectively. These SNR estimators are invoked in a space-time block coding (STBC)-assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. These SNR estimators are compared to the benchmark frequency domain preamble-based SNR estimator referred to as the Milan-FD SNR estimator when used in a non-adaptive \(2\times 2\) STBC-assisted MIMO-OFDM system. The performance of the CAZAC-TD and CAZAC-FD SNR estimators is further investigated in the non-adaptive \(4\times 4\) STBC-assisted MIMO-OFDM system, which shows improved bit error rate (BER) and normalized mean square error (NMSE) performance. It is evident that the non-adaptive \(2\times 2\) and \(4\times 4\) STBC-assisted MIMO-OFDM systems that invoke the CAZAC-TD SNR estimator exhibit superior performance and approach closer to the normalized Cramer-Rao bound (NCRB). Subsequently, the CAZAC-TD SNR estimator is invoked in an adaptive modulation scheme for a \(2\times 2\) STBC-assisted MIMO-OFDM system employing \(M\)-PSK, denoted as the AM-CAZAC-TD-MIMO system. The AM-CAZAC-TD-MIMO system outperformed the non-adaptive STBC-assisted MIMO-OFDM system using 8-PSK by about 2 dB at BER = \(10^{-4}\). Moreover, the AM-CAZAC-TD-MIMO system demonstrated an SNR gain of about 4 dB when compared with an adaptive single-input single-output (SISO)-OFDM system with \(M\)-PSK. Therefore, it was shown that the spatial diversity of the MIMO-OFDM system is key for the AM-CAZAC-TD-MIMO system's improved performance.  
A _Article_  
Preamble-Based Signal-to-Noise Ratio Estimation for Adaptive Modulation in Space-Time Block Coding-Assisted Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing System  
1
## 3 Antenna Sensor Simulation and Parameter Optimization  
The boundary of the antenna sensor is obtained via double Koch curve transformations of a 180 mm line segment, and the radius of its circumcircle is 103.9 mm. Based on Equation (4), the lowest resonant frequency of the antenna is approximately 403 MHz, which satisfies the PD detection need. A 280 mm long and 240 mm wide epoxy resin board with \(\varepsilon_{r}\) equal to 4.4 is used as the dielectric layer. The dielectric layer thickness is initially set as 5 mm. A perfect conductor layer with 280 mm length and 240 mm width is used to simulate the high-voltage switchgear casing, and it is also regarded as the ground plane of the antenna sensor. The feed position and the dielectric layer thickness are simulated in details and optimized by taking the multi-frequency characteristic and impedance matching degree of resonant points into consideration.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 1 Introduction  
The US Census Bureau reports that the number of employees who worked mostly from home increased three folds between 2019 and 2021, from around 9 million to about 27 million. The COVID-19 pandemic is regarded as the main reason for such an increase [1]. The reaction to the pandemic has spurred hybrid work and the cloud-based digitization of corporate operations, both of which present new security issues [2]. It is a fact that various components tightly integrate into the currently used advanced Information Technology (IT)-enabled ecosystem. In a system like this, the constituent components' functionalities are interdependent. Hence, their associated risk, threat, and vulnerability are also interdependent. The need for and the problems relating to the requirements of virtually unlimited IT resources are increasing daily. Cloud computing addresses such a need for vast and high-speed information processing. According to [PERSON] [3], users are vulnerable to various security issues, including access control, while accessing the system in an integrated and cloudified environment. A generic approach cannot address the associated risks, threats, and vulnerabilities in a heterogeneous environment like the cloud. Instead, specialized approaches can ensure safe and secure utilization of the cloud's IT infrastructure and cyber systems. A typical access control mechanism is required to access and use such a system.  
Safe and secure accessing of a system of this nature needs a control mechanism consisting of four definite steps: identification, authentication, authorization, and auditing [4; 5].
# Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
## 3 Dhs-Kfc
### Dhs-Kfc  
In this paper, we use the proposed DHS to search the optimal result of the kernel fuzzy clustering (KFC). When DHS is applied to KFC, we must define the optimization variables. In this paper, the cluster center \(\mathbf{v}=\{v_{1},\cdots,v_{n}\}\) is chosen as the optimization variables. The weight matrix \(\mathbf{w}=[w_{1},\cdots,w_{K}]^{T}\) is given by the experts using a method which determines the relative importance of attributes, such as Fuzzy Analytic Hierarchy Process (FAHP) [22]. The membership matrix \(\mathbf{u}=\{u_{1},\cdots,u_{n}\}\) can be obtained by Equation (10). The objective function of KFC is shown as:  
\[J=\sum_{i=1}^{C}\sum_{j=1}^{N}u_{ij}^{m}\left|\left|\Phi\left(\mathbf{x}_{j}^{ \prime}\right)-\Phi\left(\mathbf{v}_{i}^{\prime}\right)\right|\right|^{2} \tag{16}\]  
The DHS algorithm will find the optimized result which minimizes the objective function. The procedure is described as follows (Figure 1):  
1. initialize the parameters of the harmony search algorithm, initialize the harmony memory.
2. initialize the parameters of the KFC, maximum generation \(N\), and the weight matrix \(\mathbf{w}\), set the initial value of the cluster center matrix \(\mathbf{v}^{0}\) to a randomly-generated matrix. Then the membership matrix \(\mathbf{u}^{0}\) can be obtained from Equation (10).
3. generate a new solution vector based on the harmony search algorithm.
4. obtain the cluster center matrix \(\mathbf{v}^{n}\) from the solution vector, calculate the membership matrix \(\mathbf{u}^{n}\) based on Equation (10), and then calculate \(J^{n}\) based on Equation (16).
5. compare \(J^{n}\) with \(J^{n-1}\). If \(J^{n}\) remains unchanged until 10 iterations, and go to Step 7.
6. set the current iteration \(n=n+1\). If \(n>N\) go to Step 7, otherwise go to Step 3.
7. classify the samples based on their membership.  
Figure 1: Framework of the proposed method.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service  
The PVCS infrastructure consists of PV modules, a stationary storage system, and a connection with the public grid [42], as illustrated in Figure 1.  
In Figure 1, \(p_{PV\_MPPT}\) is the PV power operating in maximum-power point-tracking (MPPT) mode, \(p_{PV}\) is the PV power after shedding (if it is necessary), \(p_{\_G\_I}\) is the power injection into the public grid, \(p_{\_S}\) is the power supply from the public grid, \(p_{\_S}\),\(c\) is the charging power of the stationary storage, \(p_{\_S}\),\(D\) is the discharging power of the stationary storage, \(p_{\_PVCS}\)\(D\) is the total demand power of EVs, and \(p_{\_PVCS}\) is the total charging power of EVs after shedding (if it is necessary). The PVCS is designed such that the public grid can provide power to charge EVs and also absorb power in case of excess energy from PV sources or discharging EVs. Each component of the PVCS is connected to the DC bus usingdedicated converters. The EVs' batteries are considered as controllable loads because they can be charged or discharged at variable or constant power. To ensure a consistent power supply and reduce the difference between power production and load, the public grid connection is formed through a bidirectional AC/DC converter. The stationary storage is charged solely by PV sources and can be discharged to provide power for EVs' load.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 5 Mission Process Control Strategy
### Control Strategy Solution  
#### 5.2.1 Controllable Moment  
Mission process control strategies first address the question of when to control. Because controlling mission process involves changing the plan, too much control increases the cognitive load on the executor, and control must be achieved with as few controls as possible.  
First, we define the delay flag bit of the mission \(m_{i}\) at moment \(t\) as \(l_{m_{i}}(t)\), which reflects whether mission \(m_{i}\) is delayed or not. If mission \(m_{i}\) is delayed at moment \(t\) then \(l_{m_{i}}(t)=1\)if mission \(m_{i}\) is not delayed at moment \(t\) then \(l_{m_{i}}(t)=0\). The discriminant equation for \(l_{m_{i}}(t)\) is shown below:  
\[l_{m_{i}}(t)=\begin{cases}1,&\text{if }t=T_{PF}^{m_{i}}(t)\wedge m_{i}\in\mathcal{ M}_{1}(t)\\ l_{m_{i}}(t-\Delta t),&\text{if }t>T_{PF}^{m_{i}}(t)\\ 0,&\text{otherwise}\end{cases} \tag{47}\]  
The meaning of (47) is that if the time reaches the plan end time of the mission \(m_{i}\), but the mission \(m_{i}\) is in the unending state, then it is proven that the mission \(m_{i}\) is delayed. Furthermore, at the subsequent time, the mission will always be marked as a delayed state. According to (45), the set of delayed missions at time \(t\) is obtained as \(\mathcal{M}_{4}(t)=\{m_{i}|l_{m_{i}}(t)=1\}\).  
We set the controllable moment to be \(t_{c}\), and for \(|\boldsymbol{u}(t_{c})|\
eq 0\), \(t_{c}\) must satisfy the following four conditions  
\[\begin{split}& e(t_{c})>0\\ &\mathcal{M}_{4}(t_{c})\
eq\varnothing\\ &\mathcal{M}_{4}(t_{c})\in\mathcal{M}_{2}(t_{c})\\ &\mathcal{M}_{3}(t_{c})\cap\mathcal{M}_{4}(t_{c})\
eq\varnothing \end{split} \tag{48}\]  
The meaning of the first condition of (48) is that there must be an output error at the current moment, and there is no need for control when the error is zero. The meaning of the second condition is that there must be a delayed mission at the current moment, because all errors must be caused by mission delays. The meaning of the third condition is that all delayed missions must have finished. The meaning of the fourth condition is that there are delayed missions in the set of just-completed missions at the current moment. We define (48) as a control condition, where the control quantity is applied to the system only when the control condition is met. When the control condition is satisfied, it means that all delayed missions have been finished, there is no disturbance at this time, and the system is essentially a deterministic system.
# Exceptional Points in Non-Hermitian Photonic Crystals Incorporated With a Defect
## 1 Introduction  
Non-Hermite, which stems from quantum mechanics [1], has promptly expanded to optics [2; 3; 4; 5], acoustics [6], and electronics [7]. Systems including gain or loss are non-Hermitian [8; 9; 10; 11; 12]. There is an energy exchange between non-Hermitian systems and the external environment. Compared with the quantum structures, gain and loss in optics are relatively easy to realize [13; 14; 15], so non-Hermitian optics has become an intriguing research subject. The eigenvalues of Hamiltonian are real in Hermitian systems, while they are generally complex in non-Hermitian systems. However, as the Hamiltonian in optical systems meets parity-time (PT) symmetry, that is, the refractive indices of materials are satisfying \(n(\mathbf{r})=n^{*}(\mathbf{-r})\)[16; 17], the eigenvalues can be real. The PT-symmetry will spontaneously break if the gain and loss surpass some critical value, which is defined as the exceptional point (EP) [8; 13]. The eigenvalues and eigenvectors of Hamiltonian degenerate in non-Hermitian systems at the EPs, around which many fascinating optical properties may be induced, consist of sharp changes in the complex phase [18], unidirectional invisibility [19; 20], and topological boundary states [21].  
In recent years, complex photonic crystals (PCs) have provided a new opportunity to explore the non-Hermitian optical properties and other nonlinearities theoretically and experimentally [22; 23; 24; 25; 26; 27; 28]. Many significant optical characteristics have been discovered in PCs, such as solitons [29; 30; 31; 32; 33], topological modes [34; 35], and strong absorptions [36; 37; 38; 39]. Furthermore, there are bandgap structures in the transmission spectra as light impinges upon PCs, and light field localization can also be implementedin defective PCs, which could be utilized for enhancing the nonlinearity of materials [40,41] and the lateral shifts of reflected beams [42, 43, 44, 45].  
EPs and coherent perfect absorption-laser points (CPA-LPs) exist in PT-symmetric PCs [46]; however, it is more difficult to make complex dielectrics obey PT symmetry experimentally. Here, we embedded a defect in PCs. The refractive index of the defect is complex and the imaginary part of the refractive index (denoted by a gain-loss coefficient \(\sigma\)) can be tuned. We searched EPs and CPA-LPs in the parameter space composed of the frequency and gain-loss coefficient. We then explored the complex phase changes of reflection and transmission coefficients around EPs and CPA-LPs. Next, we investigated the degeneration of the eigenvalues of Hamiltonian at EPs and gave the distribution of the electric field. Finally, we simulated the Goos-Hanchen (GH) shift of reflected and transmitted beams around EPs and CPA-LPs.
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 3 Simulation Analysis of Static and Dynamic Characteristics of Bus Body Frame
### Performance Comparison Analysis before and after Optimization
During the operation of the bus, it is subjected to various types of excitations. Internal excitations mainly originate from the engine, transmission system, and electric motors, among others, while external excitations primarily come from the road surface. For the hydrogen fuel cell city bus studied in this paper, its typical operating conditions mainly involve city roads. The road surface excitations generated under urban road conditions can be calculated using the following equation [35]:  
\[f=\frac{v}{3.6\times\lambda} \tag{2}\]  
In the equation, \(f\) represents the excitation frequency generated by the road surface, Hz; \(v\) is the vehicle speed, km/h; and \(\lambda\) is the wavelength of the road surface irregularities, m.  
On urban asphalt roads, the road surface irregularity is generally taken as 7, and the chosen passenger bus has a maximum travel speed of 75 km/h. Substituting the corresponding values into the equation, the excitation frequency from the road surface is calculated to be 2.98 Hz. Since the lowest frequency of the bus body frame is 6.44 Hz, it is higher than the excitation frequency from the urban road surface, indicating that the bus will not experience resonance in the bus body while driving in the city. Furthermore, based on the relevant information provided by the company, the excitation frequency range of the internal drive shaft and the drive motor in the bus is between 30 and 40 Hz. Therefore, the vibration generated by internal excitation will also not cause resonance in the bus body frame. Based on Table 7, it can be observed that after optimizing the passenger bus body frame, not only are the strength and stiffness requirements of the body frame design met but also the lightweighting objective is achieved. The optimized body frame exhibits a reduction in mass by 106 kg, which corresponds to a weight reduction of 5.03%. The lightweighting effect is evident, showcasing a significant improvement in the overall performance of the bus body frame.
## 3 Experimental Results and Discussion
### Experimental Results of Evaluation Indicators in Engineering  
Models are constantly being proposed, but industry adoption usually takes several years. In addition to algorithm updates, in the industrial world it is much more important that models can be applied appropriately on accurate data to solve real-world problems. Therefore, in the application scenario of this study, whether the proposed crack detection model provides complete and accurate identification and classification results for cracks is the major concern for road monitoring and maintenance.  
Table 4 shows the missed detection rate and false detection rate of the proposed network and YOLOv5. Both networks do not exhibit false detections, but there are differing degrees of missed detections, and all missed detection rates have lower values in the proposed network. From Table 4, it can be observed that the original YOLOv5 network works poorly on transverse cracks and longitudinal cracks, but better on block and alligator cracks, where the reason is that in our dataset, transverse cracks and longitudinal cracks are widely distributed with complex and changeable backgrounds, while block and alligator cracks are more concentrated and have similar backgrounds. As can be seen, the proposed network has reduced the missed detection rate of the four types of cracks to varying degrees, and the missed detection rate of all cracks is reduced from 8.95% to 2.20%.
## 4 Systems Implementation and Evaluation
### Evaluation Metrics  
The performance of the proposed method was analyzed by considering the evaluation measures, such as precision, recall and F-measure.  
_Precision:_ It is the proportion of true positives to overall positives, and the precision measure is expressed as  
\[\delta=\frac{A}{A+B} \tag{60}\]  
where \(\delta\) specifies the precision, \(A\) denotes the true positives and \(B\) signifies the false positives.  
_Recall:_ Recall is a measure that defines the proportion of true positives to the summing up of false negatives and true positives, and the equation is given as  
\[\omega=\frac{A}{A+E} \tag{61}\]  
where the precision measure is signified as \(\omega\), and \(E\) symbolizes the false negatives.  
_F-measure:_ It is a statistical measure of the accuracy of a test or an individual based on the recall and precision, and is given as  
\[F_{m}=2*\left(\frac{\delta^{*}\omega}{\delta+\omega}\right) \tag{62}\]  
where \(F_{m}\) denotes F-measure.