esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of MABPA  
### Outline of MABPA  
Figure 1 shows the flow of the updated MABPA. Here, the U-axis is the principal axis of the first modal response, while the V-axis is the axis perpendicular to the U-axis, following previous studies [39; 41]. In this study, the predictions of the peak responses of the first and second modes (steps 2 and 4) were updated as follows:  
* The bidirectional momentary input energy proposed in the previous study was applied as the seismic intensity parameter.
* The peak response of each mode was predicted from the energy balance in a half cycle of the structural response.  
Figure 1: Flow of updated mode-adaptive bi-directional pushover analysis (MABPA).  
It was assumed that the upper bound of the unidirectional maximum momentary input energy considering the various directions of the seismic input is approximated by the bidirectional maximum momentary input energy. For the prediction of the peak equivalent displacements of the first and second modal responses, the bidirectional maximum momentary input energy spectrum (bidirectional \(V_{\Delta E}\) spectrum) was used. This is because the influences of the simultaneous input of two horizontal components are included in the bidirectional \(V_{\Delta E}\) spectrum, as discussed in previous studies [52; 53]. Note that the maximum momentary input energy of each modal response varies depending on the angle of incidence of the horizontal ground motion. The assumption that the maximum momentary input energy of each modal response can be predicted from the bidirectional \(V_{\Delta E}\) spectrum can lead to conservative predictions. The validation of this assumption is discussed in Section 5.
## 2 Mixed-Integer Nonlinear Programming Model
### Objective Function Formulation  
The objective function associated with the optimal placement and dimensioning of D-STATCOMs in electrical distribution grids corresponds to a linear combination of the annual costs of the energy losses and the annual required investments in D-STATCOMs. The objective function is presented below:  
\[\min A_{\text{cost}}=z_{1}+z_{2}, \tag{1}\] \[z_{1}=\text{C}_{\text{kWh}}T\sum_{h\in\mathcal{H}}\sum_{j\in \mathcal{L}}R_{ij}I_{jhh}^{2}\Delta_{h},\] \[z_{2}=T\bigg{(}\frac{c_{1}}{c_{2}}\bigg{)}\sum_{j\in\mathcal{N }}\bigg{(}\alpha\left(q_{j}^{cr}\right)^{2}+\beta q_{j}^{cr}+\gamma\bigg{)}q_ {j}^{cr},\]  
where \(A_{\text{cost}}\) represents the objective function value associated with the annual operational costs of the grid; \(z_{1}\) represents the part of the objective function associated with the costs of the annual energy losses; \(z_{2}\) is the part of the objective function related with the annualized investment costs in D-STATCOMs; \(\text{C}_{\text{kWh}}\) represents the average energy cost for a typical month of operation of the electrical distribution grid; \(T\) represents a constant related with the operational period, i.e., 365 days; \(R_{ij}\) is the resistance value of the conductor that connects nodes \(i\) and \(j\); \(I_{ij,h}\) is the magnitude of current flowing in the line that connects nodes \(i\) and \(j\) in the period of time \(h\); \(\Delta_{h}\) corresponds to the length of the time period associated with the power flow calculation (here, we assumed this time as 1 h); \(c_{1}\) and \(c_{2}\) are positive constants associated with the annualization of the investment costs and the life-time of the D-STATCOM, respectively; \(\alpha\), \(\beta\), and \(\gamma\) are positive constants related with variable costs of installing a D-STATCOM with a reactive power rate of \(q_{j}^{cr}\). Notice that \(\mathcal{H}\), \(\mathcal{L}\), and \(\mathcal{N}\) are the sets that contain all the periods of time, all the branches, and all the nodes of the network, respectively.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### Comparison between the Proposed Method and the Other Methods Based on the Steganography Metrics  
The comparison of different steganography approaches provides valuable insights into their performance in terms of imperceptibility, capacity, robustness, and security. By examining the results presented in the Table 7, we can gain a deeper understanding of the strengths and weaknesses of each approach, leading us to identify the most effective technique for achieving optimal results in steganographic applications.  
Upon careful analysis of the provided table, it becomes evident that the \"Diacritics and Hamzas\" approach, denoted as \"our approach\", excels across all evaluated criteria, namely imperceptibility, capacity, robustness, and security. Among the listed approaches, the hybrid technique demonstrates high imperceptibility and capacity, but specific details are lacking. The Kashida approach appears to achieve high imperceptibility and capacity in several instances, but robustness and security information remain undisclosed or unknown. Similarly, the diacritics approach shows high imperceptibility and capacity, but lacks explicit information about robustness and security. The combination of Kashida and Unicode and the use of shape both have insufficient information regarding their effectiveness. The Unicode approach exhibits high imperceptibility and capacity, while the robustness and security aspects remain unspecified. In contrast, the \"Diacritics + Hamzas\" approach, characterized as our approach, achieves high ratings across all four dimensions, making it the most comprehensive and reliable choice for ensuring superior imperceptibility, capacity, robustness, and security in the context of the given table.
# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
###### Abstract  
Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules--a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model's ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model's discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in _precision_, _recall_, _F1 score_, and _mAP@50_, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value.  
object detection; deep learning; corn leaf disease; pest; attention mechanism; feature enhancement +
Footnote †: journal: Applied sciences
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 1 Introduction
For instance, the machine learning models to predict the velocity of quench front propagation, the minimum film boiling temperature, and the transient boiling curve using fiber temperature measurement data were implemented [27]. Their findings suggest that multi-layer perceptrons or deep neural networks (DNNs) can more accurately predict quenching behavior than support vector machine (SVM) and random forest (RF). Shortly after that, they integrated machine learning with minimum film boiling temperature correlations, resulting in predictions that surpassed those from standalone machine learning models [28]. [PERSON] et al. (2023) also explored predictions for rod cladding temperatures during RBHT reflood tests, utilizing both machine learning and the coolant-boiling in rod arrays-two fluid (COBRA-TF) code [29]. Their analysis indicated that artificial neural networks and RF are adept at capturing the intricate phenomena observed during reflooding, yielding precise predictions. In this study, machine learning models such as linear regression (LR), RF, and DNN to examine the accuracy of the SPACE predictions are implemented. Our work consists of two main tasks, which are (1) to examine the UQ of SPACE for the FLECHT SEASET reflood tests and (2) to implement the machine learning models using the UQ datasets derived from the sampling method for highly nonlinear system uncertainty analysis (STARU) framework. Via these tasks, this study aims to lay the initial foundation for implementing the machine learning models to enhance the efficiency of the UQ analyses for the reflood phenomena. Moreover, these machine learning models are also employed to identify the most influential physical model in simulating the reflood phenomena; this outcome can be utilized to enhance the physical model developments of the system code.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 2 A Posteriori Error Estimation Via the Dual Weighted Residual Method
### Adaptation Scheme  
For a given initial grid, the DWR method is as follows  
1. Solve forward Problem (4) to obtain \(u_{h}\).
2. Construct and solve adjoint Problem (5) to obtain \(z_{h}\).
3. Calculate \(z_{h}^{+}\approx z\) and the error estimate \(\eta(u_{h})\).
4. Check \(\eta(u_{h})\leq\tau\), if not met, refine grids and restart with new initial grid.  
The scheme is very expensive due to the need of solving adjoint problems to obtain the error estimate. While one can use generic schemes to adapt grids, the adjoint problem and the error estimate are problem specific. Construction and solution of the adjoint problem can be automated using software such as \(\mathtt{dolfin-adjoint}\)[22]. An advantage of the method is that the error estimate is global and one can expect the resulting discretizations to be of high quality.  
We use a finer grid to approximate \(z\) by \(z_{h}^{+}\), making this the most expensive step in the computation of \(\eta(u_{h})\). There are cheaper but less accurate ways to compute \(z_{h}^{+}\), see [4]. These, however, increase the complexity of implementation even further.
## 3 ADRC Design and Parameterization
### ESO Design  
In cases where both internal and external disturbances manifest in the physical motor system, ESO enables the simultaneous observation and compensation of these disturbances as a unified \"total disturbance\" to mitigate vibration. Additionally, ESO facilitates the expansion of speed information into a novel state variable for precise speed-tracking observation, thereby establishing conducive conditions for ensuring overall system stability.  
The angular velocity \(\hat{\omega}_{m}\) and its differential \(\hat{\omega}_{m}\) as well as the total perturbation are determined using the nonlinear ESO based on the second-order model in Equations (24)-(27) and the definition of the total perturbation \(a(t)\), where \(fal(e,\alpha,\delta)\) is a nonlinear function, as shown by Equation (30).  
\[fal(e,\alpha,\delta)=\left\{\begin{array}{l}\frac{e}{\delta^{1-\alpha}},|e| \leq\delta\\ sign(e)|e|^{\alpha},|e|>\delta\end{array}\right. \tag{30}\]  
where \(fal(e,\alpha,\delta)\) is an idempotent function; \(\alpha\) is the power; and \(\delta\) is a linear interval. The nonlinear ESO is realized in the form of the following Equation (31).  
\[\left\{\begin{array}{l}e(t)=\hat{\omega}_{m}(t)-\omega_{m}(t)\\ fe(t)=fal(e(t),\alpha_{1},\delta)\\ fe_{1}(t)=fal(e(t),\alpha_{2},\delta)\\ \hat{\omega}_{m}(t+h)=\hat{\omega}_{m}(t)+h(\hat{\omega}_{m}(t)-\beta_{1}e(t)) \\ \hat{\omega}_{m}(t+h)=\hat{\omega}_{m}(t)+h(z(t)-\beta_{2}f(e))+hb_{0}u_{q}(t) \\ z(t+h)=z(t)+h(-\beta_{3}fe_{1}(t))\end{array}\right. \tag{31}\]  
where \(\omega_{m}\) is the feedback's actual angular velocity, \(\hat{\omega}_{m},\hat{\omega}_{m}\) is the predicted value for the feedback's angular velocity and its differentiation, \(z\) is the overall perturbation value estimated in real time, \(\beta_{1},\beta_{2},\beta_{3}\) is the ESO's correction gain, \(b_{0}\) is the compensation factor, and \(u_{q}\) is the voltage on the rotor of a motor. The ESO's settings that need to be adjusted are \(\alpha,\delta,\beta_{1},\beta_{2},\beta_{3}\). The effects of this parameter on the system are analyzed one by one below.
## 4 The Throughput Prediction with a Deep Learning Neural Network
This behavior is repeated for all considered cases, also where the number of machines varies, reducing in intensity when the number of machines decreases and amplifying when it increases. In conclusion, the [PERSON] and Spearman's iterative model has been shown to be very accurate for values of processing time variability close to the assumption of exponential distribution (i.e., in situations where the coefficient of variation is equal to 1) but exhibits a growing systematic error as the processing time distribution deviates from this assumption. Using an approach based on machine learning, the proposed neural network model, on the other hand, demonstrated good accuracy even in situations where processing time distribution varied substantially and to be completely generalized with respect to the average processing times, that is, normalized with respect to the bottleneck rate \(r_{b}\). Figure 9: Performance estimation of the proposed model compared with the [PERSON] and [PERSON] [33]’s iterative model and the data from the simulation tool in a scenario with coefficient of variation of 0.33 and 2.
# Detection of Rock Mass Defects Using Seismic Tomography
## 3 Synthetic Experiment
### Delineation of Empty Anomalous Areas  
We tested more extreme cases in which the abnormal area is empty (velocity = 0 km/s). Our approach included tests of an empty circle, square, and triangle. In the original model,  
Figure 5: The correct rates of the recognized boundary for the 12 tests. The dots, cross-hatching, and vertical hatching indicate the recognition performance for the normal area (\(cr_{nor}\)), abnormal area (\(cr_{abn}\)), and total (\(cr_{tot}\)), respectively.  
Figure 6: Inversion results when we do not know whether or not abnormal regions exist and homogeneous prior models are used. The colored lines show the ray paths. The dots, cross-hatching, and vertical hatching in subplot (**d**) indicate the recognition performance for the normal area, abnormal area, and total, respectively.  
the normal area wave velocity is 4.5 km/s, the abnormal area wave speed is 0 km/s, and the prior model is isotropic and homogeneous, with a wave speed of 4.5 km/s.  
As shown in Figure 7, the inversion accuracy of the velocity field gradually decreases from the circle to the square and triangle. From the ray distribution and intersection points, the identified boundary of the circular void is the clearest. Only an approximate boundary can be recognized when a rectangular anomalous area forms, for which the identified boundary accuracy rate is only 80%. In the case of the triangular anomalous area, there is a large error in the recognized boundary, and the overall accuracy rate is only 70%. That is, the rounder the abnormal area, the easier it is to identify the boundary, and the more angular the shape, the harder it is to describe clearly.
## 3 Methodology
### Proposed Integration of PSO and FCM Algorithms
#### 3.2.1 FCM  
Fuzzy c-means (FCM) is a clustering algorithm that assigns each data point to clusters with varying membership degrees. This approach effectively groups data into fuzzy subsets, which is crucial for applications in pattern recognition or data analysis. Notably, FCM requires a predefined cluster count to perform optimally, which is obtained from the cluster centers determined by the SN-SC algorithm.  
Let \(\mathbf{x}=\{x_{1},\cdots,x_{i},\cdots,x_{N}\}\) represent a clustering dataset of \(N\) objects indexed by \(i\), with each \(x_{i}\) represented by a vector of quantitative variables. \(\mathbf{z}=\{z_{1},\cdots,z_{C}\}\) denotes the set of centers of \(C\) clusters listed by \(j\) and \(U=[u_{ij}]_{N\times C}\) as a fuzzy partition matrix, where \(u_{ij}\) indicates the membership of the \(i\)th object to the \(j\)th cluster. The constraints on \(u_{ij}\) are as follows: \(u_{ij}\in[0,1],\sum_{j=1}^{C}u_{ij}=1\), and \(0<\sum_{i=1}^{N}u_{ij}<N\). The FCM algorithm aims to minimize an objective function by finding the optimal cluster centers and their corresponding membership degrees, as defined in the following equation:  
\[J=\sum_{i=1}^{N}\sum_{j=1}^{C}(u_{ij})^{m}d_{ij}^{2}, \tag{6}\]  
where \(m(m>1)\) is the fuzzy weighting exponent and \(d_{ij}=\|x_{i}-z_{j}\|\) represents the Euclidean distance. The dissimilarity between data \(x_{i}\) and cluster center \(z_{j}\) is indicated. To minimize \(J\), clustering center \(z_{j}\) and the membership degree \(u_{ij}\) are updated according to:  
\[z_{j}=\frac{\sum_{i}^{N}u_{ij}^{m}x_{i}}{\sum_{i=1}^{N}u_{ij}^{m}}\qquad j\in \{1,2,\cdots,C\} \tag{7}\]  
and  
\[u_{ij}=\frac{1}{\sum_{k=1}^{C}(\frac{d_{ij}}{d_{ik}})\frac{2}{n-1}}. \tag{8}\]  
The update stops when the cluster centers from the previous iteration closely approximate those generated in the current iteration.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 2 Materials and Methods
### Solid-Phase Extraction Procedure  
In order to apply our microgel for the preconcentration of bisphenols and calculate the recoveries, we needed to achieve complete sorption of all investigated bisphenols. To optimize the SPE method, the influence of microgel dosages (1, 2.5 and 5 mg) and the effect of the concentration of BPA (0.01-1 mg\(\cdot\)L\({}^{-1}\)) were investigated. After applying the microgel into microcentrifuge tubes containing 1 mL of the standard aqueous solution of bisphenols, the mixtures were shaken continually in a tube rotator at 20 \({}^{\circ}\)C for 30 min. After the microgel was separated by centrifugation (2 min, 14,000 rpm), the concentration of bisphenols in the supernatant was determined by the LC-FLD method.  
The desorption of bisphenols from the microgel matrix was investigated by testing various elution solvents needed for the complete elution of all the analytes from the microgel sorbent. Different compositions of methanol/water mixtures were tested for the evaluation of clean-up efficiency. After each sorption (5 mg of microgel, 50 \(\upmu\)g\(\cdot\)L\({}^{-1}\) of a standard mixture of four bisphenols) the microgel was washed with deionized water to check the retention of bisphenols and the next step was eluting bisphenols with the appropriate organic solvent.
## 2 Affecting Factors
### Daily Changes
#### 2.1.2. Canopy Level  
Fifteen of the publications studied the diurnal response of canopy PRI to the ecophysiological status of the vegetation and to photosynthetic performance. The status of epoxidation of the xanthophyllic cycle accounted for most of the diurnal PRI variability [76; 106], especially in the early morning and afternoon [107], and short-term adaptations to varying levels of solar irradiance increased PRI variability [106]. [PERSON] et al. [108] demonstrated the sensitivity of canopy reflectance and vegetation indices derived from spatial and spectral high-resolution data to varying irradiances, and reported that unknown direct/diffuse irradiance caused by complex interactions of surface irradiance and reflectance anisotropy accounted for up to 32% of the uncertainty of PRI for crops. Another study found that spectra were best obtained ca. 10 a.m. but that deviation from the zenith affected PRI, although the effect of the departure of 10\({}^{\circ}\) from the nadir view precision on PRI accuracy was acceptable [109]. Further, a high relative azimuth angle showed higher PRI values at any view zenith angles by capturing more shaded foliage and thus decreasing the effect of soil background [82; 83]. In addition, PRI tracked RUE poorly when chlorophyllic concentrations during senescence were low [109] and when wheat canopies at the elongation stage were sparse [110]. In contrast, PRI values were lower for dark soil, even with dense vegetation, but estimated RUE and PSII efficiency better [62; 82; 110]. PRI has also been used as an indicator of salinity stress in coastal species but varied with tissue chloroides and not with pigments [111]. Some studies have also reported that soil reflectance [62], species [110], canopy structure [75; 82], tree age and illumination patterns [84] complicated the interpretation of PRI. [PERSON] and [PERSON] [84], however, supported the hypothesis that photosynthesis is coordinately regulated, allowing PRI to be used as an indicator of diurnal photosynthetic activity.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### State-of-the-Art
In our previous work, we showed a 100 Gbps WDM receiver with a single surface grating coupler [34], and a wide field-of-view \"windmill\" array of six surface grating coupler array [35]. In this paper, we combine our previous work. In [34], the focus lies on achieving high-capacity wireless data transmission while collecting light from free space with a surface grating coupler, and in [35], the focus is on increasing the field-of-view of the receiver so that light can be collected from free space from wider range of angles. The platform which we used for the fabrication of the Photonic Integrated Circuits (PIC) is Indium-Phosphide Membrane on Silicon (IMOS) [36]. IMOS wafers consist of InP and Silicon wafers bonded together with BCB which creates benefits over other commonly used platforms for the fabrication of PICs. Because of the high refractive index ratio between the InP and the Silicon, IMOS allows small feature sizes of the devices on-chip and small bend radii of waveguides, hence one can achieve the same level of functionality on a much smaller area compared to a conventional InP platform. Furthermore, unlike silicon photonics, IMOS allows for light generation and amplification on-chip, and enables the fabrication of monolithtically integrated receivers for indoor OWC.