esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 3 A PLL-Less Load Current Sensorless Improved PR Control
### PLL-Less Load Current Sensorless Strategy  
Figure 2 illustrates the reference current generation process based on the instantaneous power theory [16]. In the proposed method, the grid-side current \(\mathbf{i}_{gabc}\) and grid-side voltage  
\(\mathbf{e}_{gabc}\) are used as inputs to calculate the instantaneous active power \(p\) and reactive power \(q\) of the system. The active power \(p\) is decomposed into the fundamental active component \(p_{1}\) and the higher-order active component \(p_{n}\) for \(n\geq 2\). To extract the component requiring compensation, a low-pass filter (LPF) is employed to isolate \(p_{1}\) from \(p\). Subsequently, \(p_{1}\) is subtracted from the total active power \(p\), and the output \(p_{d}\) from the DC bus voltage controller is added to form the desired active compensation command: \(p^{*}=p-p_{1}+p_{d}\). Considering that this study aims to minimize reactive power, the reactive compensation command is set to \(q=0\). Finally, the compensation reference current \(i_{resf}\) is calculated using the inverse formula of instantaneous power theory, based on the desired power commands \(p\) and \(q\), along with the \(\alpha\beta\) components of the grid voltage \(e_{gabc}\).  
\[i_{resf}=[p+jq]/\mathbf{e}_{gabc}^{*} \tag{9}\]  
This reference current will be used as a tracking target in the subsequent PR controller to achieve real-time compensation of harmonics and reactive current.  
The enhanced harmonic suppression capability of the proposed method is theoretically supported by the application of instantaneous power theory. Instead of relying on load current measurements, the method utilizes only grid-side voltage and current to compute the total instantaneous power. A low-pass filter is then applied to extract the fundamental active power component, which is used to reconstruct the ideal grid current that should be delivered. The difference between this ideal reference and the actual grid current inherently contains harmonic and reactive components, which are subsequently compensated by the PR controller through closed-loop tracking.
## 4 Results
### Statistical Significance of Model Comparisons  
We performed formal statistical significance testing to assess whether BETO's observed performance improvements over the baseline models are statistically reliable. Specifically, we applied [PERSON]'s exact test (two-tailed) to paired model predictions on the development sets for both racism and sexism tasks, under both single-turn (ST) and contextual (CTX) evaluation settings. Table 9 reports F1 scores and \(p\)-values for each model pair and configuration.  
Overall, BETO achieved the highest F1 scores in every setting, most notably in single-turn racism (F1 = 0.814) and single-turn sexism (F1 = 0.667), outperforming both the TextCatBOW (BOW) and logistic regression (LogReg) baselines. However, [PERSON]'s test indicated that none of the pairwise differences between models reached statistical significance at the conventional \(\alpha=0.05\) level. The smallest \(p\)-values were found for the single-turn racism setting, where BETO's advantage over BOW (\(p=0.0522\)) and over LogReg (\(p=0.0614\)) was nearly significant, suggesting a strong but not statistically confirmed benefit.  
In contextual settings, and for sexism detection overall, \(p\)-values were higher (all \(>0.20\)), indicating no significant differences in error patterns between BETO and the baselines, despite higher F1 for BETO. This is likely attributable to moderate dataset size, as well as the difficulty of the tasks.  
In summary, BETO consistently outperforms baseline models in absolute F1 score across all configurations; however, the improvements did not reach statistical significance in pairwise [PERSON]'s tests. These results highlight BETO's practical utility for toxic language detection, while also suggesting that larger datasets may be needed to robustly establish significance for future model comparisons.
## 5 Synthetic Data Example
### Data Generation
\[x_{2}=\sin(0.1:0.1:100)^{T}+\cos(0.1:0.1:100)^{T}\]3 (**a**) KPCA-KDE-based SSGLR charts; (**b**) monitoring fault using KPCA-KDE-based Q chart; (**c**) monitoring fault using KPCA-KDE-based T\({}^{2}\) chart. The KPCA-KDE-based SSGLR chart proposed to identify changes in the mean presented in Figure 5(a) displays that the SSGLR chart considered mainly to spot changes in the mean is capable of detecting the whole fault, with minimum missed detections. It can be perceived that the KDE-based control bounds are underneath the Gaussian distribution-based control bounds for T\({}^{2}\) and Q charts, while it is above the Gaussian limit as in the case of SSGLR statistics. In other words, the tracking indicators surpass the KDE-based threshold values to a larger extent than the Gaussian distribution-based thresholds. This suggests that KDE-based control levels for the KPCA methodology result in better tracking efficiency relative to Gaussian distribution-based constraints. Assessing the overview of the findings of the failure identification, Table 1 shows that the KPCA-KDE-SSGLR chart constructed to evaluate changes in the mean offered the most considerable false detection rate (FDR) and gives a minimal ARL\({}_{1}\) value relative to the T\({}^{2}\) statistic and the Q statistic. From the outcomes displayed in Table 1, it can be seen that the KPCA-KDE-SSGLR graph explicitly developed to discover shifts in the mean exceeds almost all statistics in context of the false detection level, whereas it offers nearly equivalent ARL\({}_{1}\) values to other methods. The KPCA-KDE-SSGLR shows excellent performance, as can be seen from Table 1 its false detection rate is as high as 99.50%, whereas KPCA-based T\({}^{2}\) has only an 11.58% false detection rate. This shows that KPCA can perform much better by integrating it with the SSGLR statistic. The SSGLR statistics already proved to have much better performance in terms of ARL [4,8]. Moreover, the findings demonstrate that KDE control bounds editions obtain a substantially higher error detection accuracy relative to the consequent Gaussian distribution-based models. In addition, Table 1 shows that the missed prevalence estimates of KDE-based models are mainly equivalent to or lesser than non-KDE-based methodologies. This assumes that KDE-based initiatives to UCLs identified flaws relatively earlier than their Gaussian distribution-based counterparts. Affiliating the KDE-based threshold values with the KPCA failure detection method gives excellent tracking, especially compared to using the Gaussian accusation critical bounds.
## 5 Conclusions  
This paper combines the NSCT with the Otsu-PCNN for detecting AD in mammograms, this is a new method to detect AD. Firstly, the top-bottom hat transformation and the Gamma transformation are combined to enhance the mammograms. This method not only enhances AD information, but also suppresses surrounding tissue and background information. Secondly, NSCT is employed to decompose the mammograms. For high frequency coefficient, we enhance the coefficient whose amplitude is large than the threshold, and set the coefficient whose amplitude is small than the threshold to zero, through this process to enhance the edge detail information of AD and filter out the noise. For the low frequency coefficient, pulling the low-pass sub-bands coefficient to expand the overall contrast of the mammograms. Finally, the AD is obtained by the improved PCNN model. The optimal threshold calculated by Otsu is set as the initial threshold of the PCNN model, which reduces the impact of manual setting parameters and the complexity of the method, and then removes isolated points by marking the maximum connected region to obtain the final detection result. According to the principle of the proposed model, our method can be applied to other data sets to achieve image detection or segmentation, but the parameters need to be adjusted.  
In this work, 30 mammography images of different density rating in the DDSM database are selected for experimental comparison. We divide the data set into three parts based on the image density rating and find out the adaptability of our proposed method to different images, which is the highlights of this work. The subjective evaluation result is that only two images are detection errors for the scores are lower than 8. The objective evaluation results are that the specificity of the proposed method is 98.73%, the accuracy is 93.16%, the F1-score is 79.80%, and the AUC is 0.93. These results fully demonstrate the reliability of the proposed method, and it can effectively help doctors to perform computer-aided detection of AD. These experiments are based on a limited data set. In the future, we are ready to work with local hospitals to increase experimental data sets and make the method more universal.  
Conceptualization, G.D., M.D., Y.S. and S.L.; methodology, G.D., M.D., Y.S., S.L. and X.M.; software, G.D., M.D., S.L., H.W., L.M. and B.L.; validation, G.D., M.D. and S.L.; formal analysis, G.D., M.D., Y.S. and X.M.; investigation, G.D., M.D., S.L., H.W., L.M. and B.L.; data collection, G.D., M.D. and S.L.; writing--original draft preparation, G.D., S.L., H.W., L.M. and B.L.; writing--review and editing, G.D., M.D., Y.S., S.L. and X.M.; project administration, G.D. and M.D.; funding acquisition, G.D., M.D. and X.M. This research is funded by \"The Key Research Projects of Henan Higher Education Institutions, grant number 18A510017\" and \"The Henan Postdoctoral Research Project, grant number 001701004\".  
The experimental data in this article is supported by DDSM database.  
The authors declare no conflict of interest.
## 1 Introduction
### Related Work
#### 1.2.1 Methods Based on 2D Images
Additionally, these algorithms are computationally intensive and heavily reliant on large, high-quality datasets, limiting their practical application. Notably, even the most advanced models currently detect defects no smaller than 0.5 mm, which falls short of the stringent requirements for certain high-precision products.
## 2 3 GPP Vehicular Communication Standards
### LTE and NR Use Cases
* Remote driving: use cases that allow remote operation of a vehicle either from a human operator or a V2X server. Table 2 shows the main requirements defined for the NR V2X use cases. Such requirements depend on the specific use case, for example, for Remote Driving the maximum latency that the network must offer must be less than 5 ms. Other services for Extended Sensors may require higher bandwidth to transmit video information. In all cases, the reliability of the network must be higher than 90%. For Vehicle Platooning and Advanced Driving, the exchange of up to 50 V2X messages per second is required. This requirement is particularly important since, as shown below through simulation results, an increase in packet transmission frequency results in a higher rate of packets lost due to congestion and interference. This obviously decreases the reliability of the network for a given coverage distance. Some use cases shown in Tables 1 and 2 can be associated with green transport solutions. For example, use cases defined for LTE V2X such as Automated Parking System and V2N Traffic Flow Optimization reduce the time a vehicle is circulating on a road and, therefore, decrease greenhouse gas emissions, reduce energy consumption in the case of battery-powered vehicles, reduce tire wear and contribute to the reduction of traffic jams. In addition, the exchange of vehicle information with road traffic management platforms can be useful to avoid traffic jams and recommend alternative routes that reduce travel time. For this, it is necessary that this information is constantly updated and from this information it is possible to generate traffic prediction algorithms to anticipate events. With respect to use cases defined for 5G V2X, Advanced Driving services enable vehicles, user devices and infrastructures to manage communications for the smooth movement of vehicles on the roads. For example, at an intersection, two vehicles can manage a traffic priority, thus avoiding the need for one or the other vehicle to stop and restart. For Vehicle Platooning (autonomous or semi-autonomous), it has been demonstrated that through proper coordination in the separation and speed of the vehicles, fuel consumption and gas emissions can be reduced. For this purpose, it is necessary that the vehicles belonging to the platoon manage communications among themselves and with other surrounding vehicles. Such communications must be carried out in unicast, groupcast or broadcast modes, as shown in Figure 2.
# Singular Phenomenon Analysis of Wind-Driven Circulation System Based on Galerkin Low-Order Model
## 1 Introduction
Through the stability analysis of nonlinear dynamics, it was determined that the system can possess both stable and unstable equilibria under identical parameters, where the circulation system in an unstable state will oscillate until it reaches a stable equilibrium [40]. [PERSON]'s work demonstrated that the overall system maintains antisymmetry as long as the zonal wind stress is symmetric. However, as the wind stress intensifies and more energy is injected into the system, the symmetry of the circulation system can break, leading to the emergence of multiple equilibria [41]. [PERSON] studied the Rossby resonance modes under single and double vortex wind stress, finding that the motion of vortices within the circulation system exhibits periodic variation characteristics following the system's bifurcation, with vortices changing periodically as wind stress further increases [42]. [PERSON] and [PERSON] also posited that asymmetric oscillation modes are inherent in the circulation system, and a Hopf bifurcation occurs as wind stress increases [41; 43]. Interestingly, [PERSON] and [PERSON] discovered a homoclinic bifurcation by increasing lateral friction in the original antisymmetric model and by adding two higher-order meridional modes to the low-order simplified model. They identified the homoclinic bifurcation as a primary cause of irregular changes within the circulation system [41]. To gain a deeper understanding of the influence of ocean circulation on climate anomalies, it is crucial to examine the bifurcation and catastrophic behaviors of low-order circulation models as wind stress intensity changes. In subsequent studies, the quasi-geostrophic gyre equation, coupled with a normalized gravity model, will be employed to describe wind-driven ocean circulation. The Galerkin method will be introduced to approximate the solution. The nonlinear dynamics of ocean circulation, from stability to instability under varying wind stress, will be analyzed in detail. The findings of this research will hold significant implications for the prediction and prevention of extreme climate events.
## 2 Modified Hammerstein-Like (MHL) Model and Its Identification Method
### MHL Model  
According to basic control theory, it is evident that the final value of the correction term \(\Psi(t,s)\) of \(H_{D}(v(t))\) approaches \(k_{1}/k_{2}\) as the input frequency tends to infinity, which is not consistent with the actual situation (as the output of the system should be 0 under infinite input frequency excitation). Therefore, the rate-dependent model in [23] is not adequate for describing the high-frequency characteristics of the hysteresis system.  
Since the Hammerstein model includes a dynamic term \(G(s)\), it can accurately describe the dynamic characteristics of the system in the high-frequency region. Inspired by the Hammerstein model, we can attempt to add \(G(s)\) after the rate-dependent model \(H_{D}(v(t))\) to enhance its modeling accuracy in the high-frequency range. Furthermore, by introducing the time delay term from the Hammerstein-like model [26; 27; 28; 29], a novel hysteresis model is proposed as shown in Figure 4, where \(v(t)\) and \(w(t)\) are unmeasurable. The introduction of a time delay can reduce the order of the linear system and alleviate the issue of inaccurate high-frequency descriptions. This model consists of an input time delay, a rate-dependent  
Figure 3: Schematic diagram of rate-dependent hysteresis model.  
Figure 2: Schematic diagram of description method for hysteresis curve. (**a**) Ascending curve; (**b**) Descending curve.  
term, and a dynamic term cascaded in series, and it is named the modified Hammerstein-like (MHL) model. The output of the model can be expressed as  
\[y(t,s,\tau)=u(t)\cdot\left(e^{-\tau s}\odot H_{D}(v(t))\odot G(s)\right) \tag{5}\]  
In [26; 27; 28; 29], the time delay term appears at the end of the model and is considered to be generated by the sensor. The FSM employs a resistance strain gauge sensor, which has a very high bandwidth and is not the main cause of the time delay. We believe that this time delay is caused by the relaxation effect inside the piezoelectric ceramic and therefore place the time delay term at the front of the MHL model.
## 2 Methodology  
### Materials  
The SSEA binder used for the APJs includes two components. Component A is bisphenol A diglycidyl ether epoxy resin (E-51) and component B is a mixture of asphalt and a curing agent (Anhydrides). In this study, we used a selection of common AH70 base asphalt, the SBR modifier for the production of Tianjin Mingjintai Rubber and Plastic Products, and the SBS modifier for the production of Sinopec YH792 (SBS 1401). The properties of E-51 epoxy resin, curing agents, base asphalt, SBR, and SBS are presented in Table 1. The chemical structure models of SBR and SBS are shown in Figure 1.  
The research [33] has shown that using 4.75-19 mm gap-graded aggregates results in asphalt mixtures with excellent high- and low-temperature performance. Therefore, aggregate gradation was used in this study, as shown in Table 3. These aggregates include basalt with an apparent density of 2.933 g/cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Materials** & **Indicators** & **Value** \\ \hline \multirow{3}{*}{E-51 Epoxy Resin} & Epoxy equivalent g/mol & 187 \\  & Viscosity (25 \({}^{\circ}\)C)/mPa-s & 12,700 \\  & Density/g/cm\({}^{3}\) & 1.18 \\  & Viscosity (25 \({}^{\circ}\)C)/Pa-s & 0.44 \\ \multirow{3}{*}{Curing agent} & Density/g/cm\({}^{3}\) & 0.92 \\  & Dispersion & Light yellow \\  & Penetration (25 \({}^{\circ}\)C)/0.1 mm & 70 \\ \multirow{3}{*}{Base asphalt} & Softening point (\({}^{\circ}\)C) & 46.7 \\  & Ductility (15 \({}^{\circ}\)C)/cm & \textgreater{}100 \\  & Elastic recovery (25 \({}^{\circ}\)C)/\% & 17.5 \\ \multirow{6}{*}{SBR} & Proportion & 1.37 \\  & Durometer & 78 \\  & 100\% tensile strength/MPa & 6 \\  & 200\% tensile strength/MPa & 12 \\  & Tearing strength/MPa & 15.2 \\ \multirow{6}{*}{SBS} & Tearing elongation (\%) & 310 \\  & Tear elongation retention after & 76.1 \\  & aging at 100 \({}^{\circ}\)C \(\times\) 72 h (\%) & \\ \multirow{6}{*}{SBR} & Structure & Line \\  & Molecular weight (million) & 10 \\ \multirow{3}{*}{SBS} & Block ratio & 20/80 \\  & Volatile (\%), \(\leq\) & 0.7 \\ \multirow{3}{*}{SBS} & Ash (\%), \(\leq\) & 0.2 \\  & Melt flow rate (g/10 min) & 0.1–5.0 \\ \multirow{3}{*}{Tensile strength /MPa} & 24 \\  & Tearing elongation (\%), \(\geq\) & 730 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of E-51 epoxy resin, the curing agent, base asphalt, SBR, and SBS.  
Figure 1: Chemical structure modelling, (**a**) SBR, (**b**) SBS.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 4 Crowd Related Research Approaches
### Crowd Management  
Crowd management has made enormous progress over the last few years. Within the literature, various models have been proposed. Like in [37], the authors have proposed a Finite State Machine (FSM) model to simulate the movement of crowd during Tawaf (to move around the Kaaba seven times as part of the hajj in Mecca). The model can be used to monitor and manage crowd in Mataf (place of Tawaf) in real time. Similarly in [38], the authors have proposed a framework weighted round robin to overcome the congestion and overcrowded during hajj (pilgrimage). The framework was designed to be proactive in accurately predicting potential problems through the use of smart monitoring of each path of rituals locations. A model has planned to count the number of people by using non-participatory (non-intrusive) technique supported by statistical approach by Design of Experiments (DOE) for crowd safety and management [39]. In [40], an information management module and decision support system was used to monitor and manage the crowd. The proposed framework provides an automated approach for detecting and evaluating the video scene and classifying crowds and traffic management [55].
## 2 Materials and Methods
### Experimental Method
#### 2.3.7 Human Bone Marrow-Derived Mesenchymal Stem Cells (hBM-MSCs) Culture Test  
In the cell culture test and physicochemical analysis using HF cells, the ability of H3 to improve cell culture efficiency was the highest, so hBM-MSCs were tested only with H3 GO substrates. The experimental method using hBM-MSCs is described below.  
Cell Viability and Proliferation Analysis  
The cell viability and proliferation rate were evaluated using the trypan blue staining method. The same numbers of hBM-MSCs were inoculated on both polystyrene (PS) and graphene-oxide (GO) culture substrates. Following 72 h of incubation, they were detached and stained with trypan blue dye. Then, the live cells (unstained) and dead cells (stained) were counted. The population doubling level (PDL) and population doubling time (PDT) were calculated with the following formula: PDL = log\({}_{2}\)(N\({}_{i}\)/N\({}_{i}\)). PDT = CT/PDL. N\({}_{f}\) and N\({}_{i}\) indicate the final and initial cell numbers, respectively. Additionally, CT refers to culture time (h).  
Flow Cytometry  
hBM-MSCs cultured on PS or GO substrates for 7 days were harvested and washed with cold DPBS. Then, they stained with eight antibodies (CD29-PE. CD34-PE, CD44-PE, CD45-PE, CD73-PE, CD90-PE, CD105-PE, and HLA-DR-PE) for 30 min at 4 \({}^{\circ}\)C in the dark. The cells were analyzed with BD FACSCanto II and FACSDivaTM software. All the used antibodies were obtained from BD Biosciences.  
Enzyme-Linked Immunosorbent Assay (ELISA)  
hBM-MSCs were incubated on PS or GO culture substrates for 7 days, and the cell supernatants were obtained. The concentration of vascular endothelial growth factor (VEGF) was measured using Human VEGF Quantikine ELISA Kit (DVE00, R&D systems) according to the manufacturer's instruction.