esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Residual Dense Attention Block (RDAB)
The final attention map is computed by summing the channel and spatial attention maps, applying a sigmoid activation, and performing element-wise multiplication with the original input:  
\[\mathrm{M(I_{A})}\mathrm{=\sigma(M_{c}(I_{A})+M_{s}(I_{A}))} \tag{11}\]  
To further interpret the role of attention mechanisms within WPD-Net, we visualize both spatial and channel attention maps with two noisy wrapped-phase images, generated from the first RDAB layer. Figure 2 illustrates the following: Figure 2(a-1,a-2) the input noisy wrapped-phase images; Figure 2(b-1,b-2) the spatial attention maps; Figure 2(c-1,c-2) the up-sampled visualizations of the channel attention vectors; and Figure 2(d-1,d-2) the combined attention maps formed by summing the spatial and the channel attention maps element-wise. The spatial attention maps retain the full spatial resolution (256 \(\times\) 256), highlighting informative regions across the input image that are affected by strong noise or exhibit sharp phase transitions. In contrast, channel attention maps are inherently 1D vectors that assign weights to each feature channel without any spatial structure. We up-sample these 1D channel attention vectors to 2D visual maps of size 256 \(\times\) 256 for better interpretation. This contributes to a deeper understanding of the role of attention mechanisms within RDABs. The final RDAB output is:  
\[\mathrm{I_{d}}=\mathrm{I_{A}}+\mathrm{I_{A}}\ \otimes\ \mathrm{M(I_{A})}. \tag{12}\]  
Here, \(\otimes\) represents element-wise multiplication. Figure 2: Visualization of attention maps from the first RDAB layer in WPD-Net for two representative noisy wrapped-phase images. (**a-1,a-2**) Input noisy images; (**b-1,b-2**) Spatial attention maps showing key regions affected by noise or sharp transitions; (**c-1,c-2**) Up-sampled channel attention vectors; (**d-1,d-2**) Combined attention maps obtained by summing spatial and channel attention maps.
# Design of Zoom Optical System from Visible to NIR-II for Vivo Fluorescence Imaging Device
## 4 Results
### Analysis of Tolerance  
Tolerance analysis is one of the most important aspects of an optical lens before production. Using the tolerance analysis function in ZEMAX, the manufacturing complexity of the lens can be evaluated. When considering the tolerance sensitivity of each optical element of the zoom optical system at the long focal length, medium focal length, and short focal length, the distribution of MTF in the optical system at 100 lp/mm is given according to Monte Carlo analysis.  
The tolerance requirements are a material refractive index tolerance of \(\pm\)0.0005, an Abbe number tolerance of \(\pm\)0.005; a radius of curvature tolerance of \(\pm\)0.02 mm; a thickness eccentricity tolerance of \(\pm\)0.02 mm; a tilt tolerance of \(\pm\)6\({}^{\prime}\); an integration between components and components with lens barrel integration tolerance; and eccentricity tolerance of \(\pm\)0.02 mm and tilt tolerance of \(\pm\)1\({}^{\prime}\). At the probability of the short focal part above 98%, the MTF is greater than 0.2. From the center of the field of view to the 0.7 fields of view at the long focal length, a 98% field of view can meet the MTF of 0.1, and the edge field of view can reach 0.1 with a 90% probability. The above tolerance analysis shows that the existing processing and mounting level can fully meet the design requirements of the system.  
Figure 5: (**a**) Final layout of the zoom optical system; (**b**) MTF curves of the zoom optical system [\(f\) = 25 mm, \(f\) = 100 mm, \(f\) = 150 mm]; (**c**) Spot diagrams; (**d**) Distortion diagrams.
## 4 Experimental Setup
### Data Processing  
The number of data samples corresponding to each wear label is more than 200,000, and the sampling frequency is 50 kHz and contains invalid data during the tool engage and retract steps. Firstly, these invalid data are deleted, and denoising is implemented to eliminate irrelevant components from the original signal via VMD. The original signal is decomposed into 6 intrinsic mode function (IMF) components via VMD. Then, the uncorrelated IMFs were removed by calculating the Pearson correlation coefficient (PCC) between the IMFs and the original signal, for which the PCC is less than 0.3. The other IMF components are retained to reconstruct the denoised signal. Figure 7a,b show the original vibration signal and the corresponding spectral analysis in the \(x\) direction of the C1 dataset. It can be observed that the original signal has more noise information, which shows high-frequency characteristics. Figure 7c,d show the denoised signal and the corresponding spectral analysis. It can be found that the high noise energy in the high-frequency part has been effectively eliminated, retaining the effective information.  
Figure 6: The tool wear value of three datasets: (**a**) C1; (**b**) C4; and (**c**) C6.  
Figure 7: The effect of signal denoising: (**a**) original signal; (**b**) the spectral analysis of (**a**); (**c**) denoised signal; (**d**) the spectral analysis of (**c**).  
Then, the remaining denoised data are divided into \(S\) subsequences using a nonoverlapping sliding window, and the variance, maximum, and minimum of each subsequence are extracted as features, as shown in Figure 8. For example, when the denoised data are divided into 60 subsequences along the time dimension, the processing results of the cutting force signal in the \(x\) direction of the C1 dataset are shown in Figure 9.  
In addition, all sub-datasets are normalized using z-score normalization to remove the influence of scale differences between multi-sensor time-series data, written as:  
Figure 8: The schematic diagram of data processing: **(a)** steady-state cutting segmentation; **(b)** time-domain feature extraction after signal denoising.  
Figure 9: The data processing results of the cutting force signal in the \(x\) direction of the C1 dataset: **(a)** variance; **(b)** maximum; and (c) minimum.  
\[x^{i}_{\text{norm}}=\frac{x^{i}-x^{i}_{\text{mean}}}{x^{i}_{\text{std}}} \tag{14}\]  
where \(x^{i}\) represents the \(i\)-th column data; \(x^{i}_{\text{mean}}\) represents the mean value of the \(i\)-th column data; \(x^{i}_{\text{std}}\) represents the standard deviation of the \(i\)-th column data; and \(x^{i}_{\text{norm}}\) represents the \(i\)-th column normalized data.  
Then the normalized variance, maximum, and minimum are segmented by a sliding time window with a length of \(T\). Three-fold cross-validation is used for three sub-datasets, where two sub-datasets are used as the training set, and the other sub-dataset is used as the testing set, as illustrated in Table 1. Additionally, the training set is divided into training data and validation data according to a ratio of 8:2.
## 2 Materials and Methods
### Sample Preparation and Testing  
The sample preparation and testing was identical for all three sub-investigations. With a part height of 300 mm, the top and bottom 25 mm were cut off to exclude all starting and finishing effects from the mechanical testing. In accordance with DIN EN ISO 527-4 [22], the parts were then cut into 250 mm \(\times\) 25 mm type 2 specimens using a water-cooled abrasive cutter. After manual deburring, the thickness and width of the samples were determined at various locations on the specimen as input for the tensile tests. All samples were labeled and then placed in a climatic chamber where they were conditioned for 10 days at 70 \({}^{\circ}\)C and at 62% relative humidity, as suggested by DIN EN ISO 1110 [23].  
Tensile tests were performed on a universal testing machine of type Z100/SN5A from Zwickoell GmbH & Co. KG, Ulm, Germany. Test parameters were selected according to the standard to a preload of 50 N with 2 mm/min, a free gripping length of 150 mm, and a measuring length of the tactile strain gauge of 50 mm. The tensile modulus was determined between the 0.05% and 0.25% strain at 2 mm/min by secant. All tests were performed at a controlled room temperature of 23 \({}^{\circ}\)C. The fracture pattern and position were recorded for each specimen based on the fractured modes specified in ASTM D3039 [24] to identify potential patterns. Figure 2 presents the test setup in the universal testing machine after fracture as well as fractured specimens for both the x- and z-directions of testing.  
Figure 1: Schematic representation of the three manufacturing setups: (**a**) heating, (**b**) wide cooling with aligned cooling nozzle, and (**c**) narrow cooling with perpendicular cooling nozzle and shorter cooling distance. Infrared heaters are depicted in red, cooling nozzles are depicted in blue.  
The specimen thickness used to calculate the tensile strength and modulus corresponds to the strand width (\(\sim\)3.1 mm), not the contact width between successive strands. Therefore, to determine the nominal tensile strength and tensile modulus of the bulk material, the specimen thickness had to be adjusted to the contact width. To determine the contact width, small samples were taken for all setups and examined under an optical microscope, measuring the specific contact width between 10 layers and using the average value as input for nominal specimen thickness. For both the standard and nominal tensile results, the mean values and standard deviations of five samples per setup were determined.
## 3 Results and Discussion
### HPLC-DAD Method Development for the Analysis of Vitamin D\({}_{2}\) and Ergosterol  
A novel, rapid HPLC-DAD method was developed for the simultaneous analysis of vitamin D\({}_{2}\) and ergosterol, tailored for quality control in mushroom cultivation and the production of mushroom-based food supplements and functional foods. This cost-effective method, described in detail in Section 2, was thoroughly investigated and integrated into routine analyses within mushroom cultivation processes.  
To assess the method's effectiveness, commercially available cultivated mushrooms were analyzed. The developed HPLC method was subsequently applied to UV-irradiated mushroom samples. The analytical procedure involved straightforward sample preparation, including saponification and solid-liquid extraction, followed by HPLC-UV determination.  
Under the conditions applied, the retention times were 5.68 min for vitamin D\({}_{2}\) and 6.66 min for ergosterol (Figures 2 and 3). Quantitation was based on calibration curves generated from triplicate injections of standard solutions, with manual integration of all peaks.  
The method demonstrated high efficiency in analyzing different cultivated mushrooms, yielding results comparable to LC-MS measurements, with differences remaining below 5% (Table 1). The extraction efficiency of vitamin D\({}_{2}\) was inferred by monitoring the recovery of the spiked vitamin D\({}_{3}\) internal standard, based on the assumption that both compounds exhibit similar physicochemical behavior under the applied conditions. The method achieved high recovery rates of 97-99%, which aligns with previous studies reporting similarly efficient extraction and quantitation methods for sterols in food matri  
Figure 3: Calibration curves of vitamin D\({}_{2}\) and ergosterol (vitamin D\({}_{2}\): 0.05, 0.1, 0.5, 1, 5, 10, and 50 mg/L; ergosterol: 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 200, 500, 1000, and 2000 mg/L).  
Figure 2: Chromatogram of vitamin D\({}_{2}\) and ergosterol in a white button mushroom sample.  
ces [21; 22]. The method also exhibited a limit of detection (LOD) of 0.1 mg/kg dry weight and a limit of quantitation (LOQ) of 0.5 mg/kg dry weight, both well within the required analytical range.  
Our findings indicated that the average vitamin D\({}_{2}\) content in untreated white button mushrooms ranged from 1.5 to 4.5 mg/kg dry weight, while shitake mushrooms contained between 7 and 8.5 mg/kg dry weight. The higher vitamin D\({}_{2}\) content in shitake mushrooms can be attributed to differences in cultivation conditions: white button mushrooms are grown in the dark, whereas shitake cultivation involves several hours of artificial illumination.
###### Abstract  
Presently, a major challenge is the search for new compounds that may exhibit an inhibitory effect on tumor progression. Recently, the 4-thiazolidinone (4-TZD) group has gained attention in this research field. The aim of the present study was to evaluate the anticancer effects of two new 4-TZD-based derivatives (Z)-5-[5-(2-hydroxyethylphenyl)- (- Les- 4368) and (Z)-5-[5-(4-dimethylaminophenyl)-3-phenyl-4,5-dihydroxypropyl-1-ylmethylene]-3-(3-acetoxyphenyl)-2-thiazol-4-ones (Les-4370) on peroxisome proliferatorator-activated receptor gamma (PPARy)-dependent cytotoxicity in human colorectal adenocarcinoma cells (CACO-2) and in normal human fibroblasts (BJ) in vitro. Les-4368 and Les-4370 exerted a toxic effect on both tested cell lines in high (micromolar) concentrations (10-100 uM). In addition, Les-4368 and Les-4370 applied in the BJ and CACO-2 cells in the concentration range of 10 uM to 100 uM increased the activity of caspase-3 and the production of reactive oxygen species (ROSs). The mRNA expression of PPARy-related genes (_PPAR\(\gamma\)_, _AhR_, _PXR_, and _NF-k_B) showed certain changes in these parameters, proving the engagement of this receptor in the induction of the biological effects of both tested 4-TZD derivatives. Moreover, the treatment of the BJ and CACO-2 cells with Les-4368, Les-4370, an antagonist (GW9662), or an agonist (rosigltazone) of the PPARy receptor also resulted in changes in the above-mentioned parameters. Unfortunately, the tested substances studied cell line work in a non-selective way at a relatively high concentration, which reduces their potential for clinical application. Our research is the preliminary study with the use of these compounds and requires further studies to elucidate the mechanisms of action of their anticancer potential.  
cancer; 4-thiazolidinones; oxidative stress; anticancer treatment; apoptosis; PPARy
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## 4 AARO Model for Security Constrained Unit Commitment (SCUC)
### AARO Formulation to Solve the SCUC Problem
```
1 repeat
2 Solve Model
3 for\(l\in{1,k}\in K,t\in T\)do
4 Estimate:
5\(\hat{\eta}^{5}_{l,k,t,u,\tau}=\left|\sum_{s}OTDF^{k}_{l,s}\cdot\left(\sum_{i} \gamma^{*}_{i,t,u,\tau}\cdot A^{s}_{i}+A^{s}_{w}\cdot 1^{\tau=t}\right)\right|\quad \forall w\in W,\tau\leq t\)
6\(\hat{\eta}^{5}_{l,k,t,p,\tau}=\left|\sum_{s}OTDF^{k}_{l,s}\cdot\left(\sum_{i} \gamma^{*}_{i,t,p,\tau}\cdot A^{s}_{i}+A^{s}_{p}\cdot 1^{\tau=t}\right)\right|\quad \forall p\in P,\tau\leq t\)
7\(\hat{\eta}^{5}_{l,k,t,s}=\left|\sum_{ss}OTDF^{k}_{l,ss}\cdot\left(\sum_{i} \gamma^{*}_{i,t,t}\cdot A^{ss}_{i}-1^{\tau=t/\,ss=s}\right)\right|\quad\forall s \in S,\tau\leq t\)
8\(\delta_{l,k,l}=\sum_{w\in W}\sum_{\tau\leq t}\theta^{5}_{l,k,t,w,\tau}\cdot \hat{\sigma}^{max}_{w,\tau}+\sum_{p\in P}\sum_{\tau\leq t}\theta^{5}_{l,k,t,p,\tau}\cdot\hat{\sigma}^{max}_{p,\tau}+\) \(\sum_{s\in S}\sum_{\tau\leq t}\theta^{5}_{l,k,t,s}\cdot\hat{\sigma}^{max}_{s,\tau}\)
9 Estimate:\(\hat{F}_{l,k,t}=\left|\sum_{s}OTDF^{k}_{l,s}\cdot Pnet^{s0}_{s,t}\right|+ \delta_{l,k,t}\)
10 Adjust:\(OP_{l,k,t}=0\)
11 if\(\hat{F}_{l,k,t}\geq F^{max}_{l}\)then
12 Compute:\(OP_{l,k,t}=\hat{F}_{l,k,t}-F^{max}_{l}\)
13 Add: Constraints (47)-(51)
14 else
15\(OP_{l,k,t}=0\)
16
17 end
18
19 end
20 Compute:\(TO=\sum_{l,k,t}OP_{l,k,t}\)
21 until\(TO\leq tol\);
```  
**Algorithm 1**Method for Adding N - 1 Security Constraints as user cuts  
Algorithm 1 iteratively defines the feasible region of the SCUC, by adding only the binding \(N-1\) security constraints. In this way, it is possible to compact the model and considerably reduce its computation times. Step 1 is where the iterative process is started. Step 2 solves AAROUC model defined by Equations (22)-(46) (contingencies \(N-1\) are not considered) only at the first iteration; after the first iteration, it also solves the AARO-SCUC model defined by Equations (22)-(51), considering only the binding security constraints added in step 13. Post-optimization values of the wind (\(\gamma^{*}_{i,t,u,\tau}\)), solar (\(\gamma^{*}_{i,t,p,\tau}\)) and demand (\(\gamma^{*}_{i,t,\tau}\)) deviations related to the power dispatched by each generator are obtained in every iteration. Steps 4-8 estimate the absolute values of the wind (\(\hat{\eta}^{5}_{l,k,t,u,\tau}\)), solar (\(\hat{\eta}^{5}_{l,k,t,p,\tau}\)) and demand (\(\hat{\eta}^{5}_{l,k,t,\tau}\)) deviations of the power flow under contingency conditions \(\hat{F}_{l,k,t}\) estimated in step 9. Steps 11-16 establish the rules for selecting and adding (step 13) those \(N-1\) security constraints (47)-(51) that define the feasible region of the model. Finally, the iterative process ends when the total system overload factor \(TO\) is less than the tolerance \(tol\) (in this case, \(tol=1\times 10^{-6}\)). This procedure could be seen as a computational novelty of our approach.
# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 4 Result and Discussion  
### Experimental Steps  
To effectively detect street signs in natural scenes and test the effectiveness of our proposed method, we conducted the following experiments using the NSSS, ICDAR2015, and ICDAR2017-MLT datasets. (1) The images were directly input into the DBNet network for the experiments, and the results of this experiment were used as a reference to test the effectiveness of other methods. (2) The images were input into the DBNet network with a ratio-invariant feature enhancement module (HLFE). (3) The images were input into the DBNet network with a fused feature pyramid enhancement module (FPRE). (4) The images were input into the DBNet network with FPRE and HLFE modules. (5) The data set processed by multi-channel MSER was input into the modified DBNet network for the experiments to test the effectiveness of the MSER method. The overall flowchart of the experiments in this paper is shown in Figure 7, Y means to use this method for subsequent text detection experiments, and N means not to use this method for text detection experiments.  
The evaluation of natural scene text detection has three main metrics: precision, recall, and F-Score, which are defined as follows:  
\[prescision=\frac{TP}{TP+FP} \tag{7}\]  
\[recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F-score=2*\frac{precision*recall}{precision+recall} \tag{9}\]  
where _TP_ represents the number of samples that are positive and predicted to be positive samples, _FP_ represents the number of samples that are negative but predicted to be positive samples, _FN_ represents the number of samples that are positive but predicted to be negative samples, and the _F-Score_ is a comprehensive metric based on the accuracy and recall, which can better characterize the text detection performance of a network.
## 5 Data Analysis and Results
### Relation of CAIN Scores to Risk Level  
The proportion of correct responses for each subject (arcsin transformed) was used as a dependent variable in an ANOVA design with risk level (none, low, and medium) as the factor. As shown in Figure 3, the numerical difference between the medium-risk group and the other two is visible, but the overall main effect did not reach statistical significance (F2,272 = 2.160, \(p\) = 0.117). Nevertheless, the [PERSON] post hoc analysis demonstrated a significant difference between the no-risk and medium-risk levels (\(p\) = 0.029) and a marginal significance between low- and medium-risk levels (\(p\) = 0.081).
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 4 Materials and Methods
### Implementation Details  
This subsection describes the experimental settings used for model training and evaluation, including hardware configuration, software framework, training strategies, and data augmentation techniques. These details ensure reproducibility and provide insight into the computational environment and optimization setup.  
In this study, our approach was implemented using the PyTorch 2.5.1 framework on a computing server running Windows 11. The server is equipped with an Intel Core i7-14700 KF CPU, 32 GB of RAM, and an NVIDIA RTX 4080 SUPER GPU with 16 GB of graphics memory, providing robust computational support for model training. Additionally, SimuNPS was utilized for simulation-based validation and performance analysis, further ensuring the reliability of the experimental results. During the training phase, a total of 60 training cycles were performed, with a batch size set to 8. The optimizer employed Stochastic Gradient Descent (SGD) with an initial learning rate of \(1.0\times 10^{-2}\), a momentum coefficient of 0.937, a weight decay factor of 0.0005, and a cosine annealing learning rate decay strategy. The input image resolution was set to 640 \(\times\) 640 during training, while 640 \(\times\) 512 was used as the input size for testing. To enhance the model's generalization ability, mosaic data augmentation and random flipping were applied for data expansion. The loss function followed the loss calculation methods used in the YOLOv5 and FCOS object detectors. The experimental environment and hyperparameter settings are summarized in Table 4.
## 3 Results
### Analysis of WUE in Anhui Province by Time Scale
#### 3.1.3 Trends in Spatial Changes in WUE in Anhui Province on Different Time Scales
The Huaibei Plain, with its large areas of grain cultivation, is typically characterized by weak vegetation activity and low temperatures in winter, which drastically reduce both photosynthesis and transpiration in plants [59; 60]; however, small fluctuations (e.g., minor precipitation or fluctuating temperatures) during these periods of relatively low activity may have a significant effect on the WUE. As a result, even small fluctuations caused by climate change or environmental changes can appear significant, leading to \"significant\" or even \"highly significant\" winter WUE trends. However, July and August were the peak months for crop growth, with high plant transpiration and sufficient precipitation, resulting in a significant upward or downward trend in the WUE during these two months. Overall, the significant trend in the WUE was notable in the mountainous and plain areas of southern Anhui Province during winter. This seasonal and monthly trend analysis of WUE based on the Sen+MK test clearly shows the spatial and temporal differences in water use efficiency, and provides data support for understanding the water use efficiency in different regions of Anhui Province. Figure 8: Seasonal average WUE trends: (**a**) spring, (**b**) summer, (**c**) fall, and (**d**) winter. Figure 7: (**a**) Spatial distribution of average WUE in Anhui province. (**b**) Interannual trends in average WUE.