esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Contours in the Complex Domain for Salient Object Detection
###### Abstract  
The combination of active contour models (ACMs) for both contour and salient object detection is an attractive approach for researchers in image segmentation. Existing active contour models fail when improper initialization is performed. We propose a novel active contour model with salience detection in the complex domain to address this issue. First, the input image is converted to the complex domain. The complex transformation gives salience cue. In addition, it is well suited for cyclic objects and it speeds up the iteration of the active contour. During the process, we utilize a low-pass filter that lets the low spatial frequencies pass, while attenuating, or completely blocking, the high spatial frequencies to reduce the random noise connected with favorable or higher frequencies. Furthermore, the model introduces a force function in the complex domain that dynamically shrinks a contour when it is outside of the object of interest and expands it when the contour is inside the object. Comprehensive tests on both synthetic images and natural images show that our proposed algorithm produces accurate salience results that are close to the ground truth. At the same time, it eliminates re-initialization and, thus, reduces the execution time.  
active contour; complex transformation; low-pass filter; designing complex-force function +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Determination of Static and Rolling Friction Coefficients of Fresh Goji Berry-Fresh Goji Berry  
The ANOVA of the regression model for the central composite design experiment is shown in Table 8. The overall fitting degree of the regression model was \(p<0.0001\), the lack of fit term was \(p=0.0684\), and the coefficient of determination (R\({}^{2}\)) was 0.9845. The regression model was significant, the lack of fit term was not significant, and the coefficient of determination was close to 1, indicating a good fit of the regression model. All factors that have a significant effect on the index have been taken into account, and we obtained well-fitted and analytically meaningful regression: Equation (17).  
\[\mathrm{R_{1}}=2.23+1.79\mathrm{A}-1.58\mathrm{B}+0.25\mathrm{AB}+0.60\mathrm{ A}^{2}+1.12\mathrm{B}^{2} \tag{17}\]  
The AoR value obtained from the physical experiment was used as the target value (31.27\({}^{\circ}\)), and the optimization module of Design-Expert software was used to make the simulation results closest to the target value. The regression model was optimally solved with the constrained objectives (Equation (18)).  
\[\left\{\begin{array}{l}\mathrm{minR_{1}(A,B)}\\ s.t.\left\{\begin{array}{l}0.45\leq A\leq 0.55\\ 0.03\leq B\leq 0.04\end{array}\right.\end{array}\right. \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Error Source** & **Sum of Squares** & **Freedom** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 55.89 & 5 & 11.18 & 88.72 & \(<\)0.0001 ** \\ A-A & 25.50 & 1 & 25.50 & 202.41 & \(<\)0.0001 ** \\ B-B & 19.98 & 1 & 19.98 & 158.59 & \(<\)0.0001 ** \\ AB & 0.2450 & 1 & 0.2450 & 1.94 & 0.2058 \\ A\({}^{2}\) & 2.54 & 1 & 2.54 & 20.16 & 0.0028 ** \\ B\({}^{2}\) & 8.68 & 1 & 8.68 & 68.86 & \(<\)0.0001 ** \\ Residual & 0.8819 & 7 & 0.1260 & & \\ Lack of fit & 0.7074 & 3 & 0.2358 & 5.40 & 0.0684 \\ Pure error & 0.1745 & 4 & 0.0436 & & \\ \hline \end{tabular} Note: ** indicates a highly significant effect (\(p<0.01\)).  
\end{table}
Table 8: Analysis of variance for regression model.  
Figure 22: Results of the simulation tests. (**a**) Second-order polynomial fitting curve of the rolling friction coefficient of fresh goji berry–silicone rubber material; (**b**) Rolling distance–time curve of the particle motion.  
Finally, we obtained the fresh goji berry-fresh goji berry static friction and rolling friction coefficients as 0.454 and 0.037.
## 4 Experiment and Analysis
### Experimental Results
#### 4.3.3 Detection Results of Different Scenes under Low Contrast  
During the experiment, the model superparameters were set to a batch size of 64, 8 subdivisions 8, max batches of 20,000, and a momentum and weight decay of 0.9 and 0.0005, respectively. The adaptive matrix estimation algorithm (Adam) was used to optimize the model, and the initial learning rate was set to 0.0025. When the maximum iteration was 80%, the learning rate was reduced by 10 times; When the maximum iteration was 90%, the learning rate was reduced by 10 times every time, and the weight was saved once every 10,000 training iterations of the model. The information on the experimental platform is shown in Table 5. The improved model proposed in this paper was applied to the detection of nine categories in different environments, as shown in Figure 7. From Figure 7, it can be seen that all categories were successfully detected and had a high recognition rate.
# CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions
## 4 Experiments
### Parameter Settings  
Parallel convolutional layer: 3 \(\times\) 3 convolutional kernel has been proven to be the most effective kernel size for natural images [76]. We used 3 \(\times\) 3 convolutional kernels and 1-padding with stride 1 to guarantee that the size of the outputs equals that of the inputs. In the dilated convolution channel, the dilated rate is 2. The outputs of the two channels are fused by the concatenation method. BN leads to considerable improvements in convergence while eliminating the need for other forms of regularization; every convolution operation is followed by a BN operation.  
Pooling layer: We used max-pooling with a 2 \(\times\) 2 pixel window, and stride is 2 in each pooling operation.  
TC layer: For CIFAR-10 and SVHN, the input size is 32 \(\times\) 32, and the output size is divided by 2 after each max-pool. In the TC layer, the input size is multiplied by 2, 4, 8, and 16 for pool5 to recover the size corresponding to pooling layers, and by 2, 4, and 8 for pool4, and so on.  
Feature fusion layer: Based on the output size of TC and pooling layers, we used the concatenation (CDT_C) and addition (CDT_A) method to fuse the features.  
FC layer: Because the FC layers in VGGs have a large number of redundant parameters, according to the ablation experiments of [33], we added two FC layers in our model; the neuron numbers are 1024 and 10.  
The last FC layer is connected with a 10-class layer with cross-entropy loss. Softmax was selected to obtain the category probability, formulated as Formula (5):  
\[p(y|x)=\frac{exp\big{(}w_{y}\cdot x+b\big{)}}{\sum_{c=1}^{C}exp(w_{c}\cdot x+ b)} \tag{5}\]  
where \(C\) is the number of channels, \(w\in R^{C\times N}\), \(N\) is the number of classes, and \(p(y|x)\in R^{N}\) is the scaled classification score.  
On the CIFAR-10 dataset, we used Nesterov momentum with a momentum weight of 0.9, and a weight decay of 0.0003. All models were trained with an initial learning rate of 0.1, divided by a factor of 10 after 80 and 120 epochs, and the batch size was 250. On the SVHN dataset, the models were trained for 50 epochs, and the batch size was 96. On the FMNIST dataset, all models were trained for 50 epochs with a batch size of 100. We used the Adam optimization method and set the learning rate as 0.001 for SVHN and FMNIST datasets.  
We adopted data augmentation methods such as translation [37] and horizontal flipping [38] for CIFAR-10 and SVHN datasets, and used L2-regularization [34] techniques for limiting network complexity.
## 1 Introduction
### Data Augmentation and Code-Switching  
To address the scarcity of data for low-resource languages, a significant portion of the available datasets are created via translation. Nonetheless, prior research conducted by [PERSON] et al. [3] regarding the influence of translation on multilingual models reveals that when the quality of translation reaches a certain threshold, it begins to have a detrimental impact on the models' performance. This research underscores the potential utility of translation in improving the capabilities of multilingual models but also highlights the importance of recognizing associated risks. As demonstrated by [PERSON] et al. in 2018 [4] and [PERSON] et al. [5], translating phrases can introduce alterations to the superficial patterns of the data, ultimately diminishing the generalizability of preexisting models. Additionally, code-switching, involving the blending of words, phrases, and grammatical elements from multiple languages, commonly occurs in informal contexts such as conversations and social media interactions. Recent studies have highlighted the vulnerability of contextualized embeddings to polyglot adversarial attacks. Therefore, it becomes imperative to evaluate the resilience of neural networks against polyglot adversaries in multilingual settings.  
Both data augmentation and code-switching techniques are still evolving, yet they have already displayed promising outcomes. For instance, a recent investigation by [PERSON] and colleagues [6] introduced a data augmentation strategy that generates new sentence pairs featuring uncommon words in novel, artificially constructed contexts. The findings of this study indicate that this approach results in the generation of a greater number of rare words during translation, consequently leading to enhanced translation quality.
## 3 The Results
### Results of the Literature Review
Future research should develop a more precise market segmentation based on these factors to create targeted communication and marketing strategies. It is also crucial to explore educational approaches that increase awareness and confidence in V2G, tailored to different demographic needs. Additionally, to promote greater adoption, it is important to develop specific incentive programs for less inclined demographic groups, such as the elderly or less educated individuals. An inclusive and personalized approach could help overcome sociodemographic and psychological barriers, accelerating the large-scale adoption of V2G. Environmental conditionsThis topic examines how climatic and environmental conditions influence the effectiveness and functionality of V2G technology. Environmental factors such as temperature, humidity, and exposure to extreme weather events can affect the charging process, the lifetime of EV batteries, and the stability of the power grid. Several studies, carried out primarily in countries with significant climatic variations, such as the United States of America, Japan, Canada, and Germany, have investigated how environmental conditions impact the implementation of V2G. These studies have revealed that extreme temperatures, both hot and cold, can reduce battery efficiency and increase charging times, negatively affecting the vehicle's ability to deliver energy back to the grid. Furthermore, high humidity and weather events, such as storms or floods, can damage the charging infrastructure, reducing the availability and reliability of V2G in certain regions [33]. To ensure the effectiveness of V2G under various environmental conditions, future research should focus on developing more resilient and adaptable charging technologies and batteries for extreme climates. Strategies to protect the charging infrastructure from extreme weather events, such as the implementation of protected charging stations and advanced energy management systems, should also be explored. Furthermore, integrating climate models into energy demand and supply forecasts could improve V2G management, adapting it to variable environmental conditions. Adopting these approaches will contribute to making V2G more robust and sustainable, facilitating its implementation in a wide range of environmental contexts.
## 4 Optimization and Training
### Optimization Algorithm  
The parameters of the model are optimized by using stochastic gradient descent (SGD) or one of its variants, like Adam, which is often preferred due to its adaptive learning rate capabilities:  
\[\theta_{t+1}=\theta_{t}-\eta_{t}\cdot\
abla_{\theta}\mathcal{L}(\theta_{t}), \tag{17}\]where \(\theta_{t}\) is the parameter vector at iteration \(t\), \(\eta_{t}\) is the learning rate at iteration \(t\), and \(\
abla_{\theta}\mathcal{L}(\theta_{t})\) is the gradient of the loss function with respect to the parameters at iteration \(t\). In Algorithm 4, the general structure for the optimization algorithm is presented.  
```
0: Input parameters and initial values
0: Optimized parameters
1: Initialize parameters \(\theta\)
2: Set learning rate \(\alpha\)
3: Set convergence threshold \(\epsilon\)
4:repeat
5: Calculate gradient \(\
abla J(\theta)\)
6: Update parameters \(\theta\leftarrow\theta-\alpha\
abla J(\theta)\)
7: Compute cost \(J(\theta)\)
8:until\(|\
abla J(\theta)|<\epsilon\)
9:return Optimized parameters \(\theta\)
```  
**Algorithm 4** Optimization algorithm  
To fine-tune the model parameters and prevent overfitting, we employ techniques such as early stopping based on validation set performance. Additionally, parameters such as the number of attention layers, the size of the embedding dimensions, and the balance parameter (\(\lambda\)) in the sentence selection scoring are optimized through a grid search approach on the validation dataset:  
* **Early stopping:** Monitor the ROUGE score on the validation set after each epoch, and stop training when the score does not improve for a predefined number of consecutive epochs.
* **Grid search:** Systematically vary parameters within specified ranges, and select the configuration that achieves the highest ROUGE score on the validation set.
## 2 A Synopsis of High Frame Rate Ultrasound Technology
From an application standpoint, the key diagnostic value offered by HiFRUS is the full-field, high-resolution spatiotemporal imaging that can yield functional insight into physiological events taking place inside the human body. For instance, by integrating HiFRUS with Doppler estimation principles, it is possible to achieve time-resolved visualization of complex flow dynamics through the rendering of flow speckles [9] and the derivation of flow vectors at different pixel positions [44] as illustrated in the sample images in Figure 1. Not only is this useful in examining carotid hemodynamics [8; 40], it is also applicable to the evaluation of arterial strain [45; 46; 47], the visualization of pulse wave propagation through the artery wall [48], and the tracking of shear waves propagating in tissues [49; 35]. HiFRUS may also be used in urology applications to gain time-resolved insight into turbulent urinary flow behavior [50]. This technology may be used in cardiac applications to study myocardial contraction [51; 52] and intraventricular flow patterns [53; 54], although these techniques need further refinement. Emerging developments in HiFRUS methodologies, including the incorporation of contrast agents [55] and state-of-the-art 4D imaging [56], will undoubtedly lead to further physiological discoveries to enhance application prospects in this field.
## 3 Results and Discussion
### Iron  
Fe concentrations in game meat ranged between 13 and 267 mg/kg and 15 and 180 mg/kg for red deer and wild boar respectively, with medium values of 53 mg/kg for both species. In red deer, similar values were shown by [PERSON] et al. [11] at 53.6 mg/kg while other authors recently showed Fe concentrations around 30 mg/kg [18; 20]. Recently, in fallow deer other authors found Fe level averages that ranged between 43.2 and 38.3 mg/kg [29]. On the other hand, wild boar displayed varying Fe content levels, with some higher and lower values compared to those obtained in this study. [PERSON] et al. [27] and [PERSON] et al. [32] reported concentrations of 36.4 and 41.9 mg/kg, respectively, while [PERSON] and [PERSON] [44] and [PERSON] et al. [26] indicated Fe concentrations exceeding 80 mg/kg.  
In developed countries, red meat is the main source of Fe in the diet, with pork and beef being the most consumed species of this type of meat [45]. Previous studies have shown Fe contents in pork meat of 8.1 mg/kg [46] or 23.6 mg/kg [44]. In relation to beef, Fe contents ranged between 42 and 65 mg/kg [40], or 14 and 29 mg/kg [45; 47] have also been reported. According to these data, Fe contents in the game meat analyzed in this study are similar to or even higher than those reported in previous works for red meat from animal husbandry. The high content of iron, in contrast to that of domestic animals, could be attributed to the irrigation of the muscle tissue with the blood from the wound itself or internal bleeding caused by the impact or impacts of bullets on the animal. Some health agencies in the United States consider that wounds caused by bullets would inflict enough damage to the animal, ensuring sufficient bleeding [48].  
This highlights game meat as an excellent dietary source of Fe. Furthermore, between 40 and 90% of Fe present in red meat is in the form of heme Fe [49; 50], a chemical form of this trace element with high bioavailability [51].  
The results obtained from the simulation of the probabilistic model with red deer and wild boar indicated intake values for Fe of 11.5 and 12.3 mg, respectively, for the 95 th percentile for hunter-consumers. This intake (corresponding to a serving size) fulfills Fe DRI for men and complies with 64-68% Fe DRI for women. Even at a less favorable percentile (50 th percentile), these intake values of 2.7 mg for red deer and 1.5 mg for wild boar represent more than one-tenth of the Fe DRI and surpass the levels reported in a previous study for pork and meat products (1.2 mg) [40].
## 3 Results
### Daylight Autonomy (DA)
#### 3.2.2 Influence of Window Position (Upper/Centered)  
The position of the window (upper versus centered) significantly impacts daylight distribution and overall performance metrics. Centered windows consistently outperform upper windows in terms of \(DA_{300}\) and \(DA_{500}\) values, providing more uniform light distribution throughout the space and minimizing areas with low daylight exposure. For example, in north-facing rooms with 25% to 45% WWR and bright coatings, centered windows start with \(DA_{500}\) values between 70% and 90% in Seville and 65% and 88% in Barcelona, while upper configurations begin with slightly lower values (79% to 87%), but these decline less steeply, leading to higher average \(DA_{500}\) values at the room's end.
## 2 Data and Methodology
### Data and Preprocessing  
#### 2.2.1 GF-2 Data and Preprocessing  
In this investigation, 16 GF-2 images are collected, which are acquired in four seasons, ranging from 28 January 2017 to 22 December 2021 (see Table 2). Each GF-2 image in the L1A redistributed level consists of two types of data products: the panchromatic (1 m resolution) and the multispectral (4 m resolution) product, respectively. The high-resolution CCD sensor, mounted on a GF-2 satellite platform, works in four spectral bands (B1/blue: 0.45-0.52 um, B2/green: 0.52-0.59 um, B3/red: 0.63-0.69 um and B4/NIR: 0.77-0.89 um), plus a panchromatic band (PAN: 0.45-0.90 um), which is the same as that of the GF-1 satellite.  
The preprocessing of GF-2 data includes radiation calibration, atmospheric correction, geometrical correction and image fusion. First, the radiation calibration procedure was implemented by using ENVI 5.3 software [23] by Harris Corporation in Melbourne, Florida, USA. In this phase, the pixel digital number (DN) in every band (B1-B4) of the GF-2 data was converted to the value of radiance to minimize the uncertainty produced by the CCD sensor. Next, the atmospheric correction was conducted by using the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction toolbox in ENVI 5.3, such that the pixels in the four spectral bands were represented in the value of spectral reflectance. Thereafter, the RPC model-based geo-correction procedure was implemented by analyzing the rational polynomial coefficient (RPC) file, which is associated with the image files in the data package of the GF-2 data. Finally, the NNDiffuse Pan Sharpening toolbox in ENVI 5.3 software was utilized to improve the spatial resolution of a multispectral image from 4 m to 1 m.  
Figure 2: The onsite photos of the typical roads and buildings in the study area.
## 5 Results and Discussion  
### Ablation Study  
Diagnostic performance was compared through an ablation study to find out the effect of improving the performance of data generalization (DG) and data augmentation (DA). The experiment was conducted in the same environment except for DG and DA. The amount of data used for the test was 208, which did not belong to the training or validation dataset.  
Figure 9 shows the diagnostic results of each algorithm on the CNN. The test accuracy was 82% when the audio signal was directly converted into a spectrogram without DG and DA. However, the sensitivity was 43%, which means the accuracy of diseased heartbeats is low. More than half of the abnormalities were classified as normal, and it can be seen that the network could not accurately distinguish between sick and nonsick heartbeats. When augmented data were added, the test accuracy was 76%, which was lower than without the augmented data. This shows that increasing the amount of data is not always good, and the quality of the data is more important for improving performance. Although specificity was high, sensitivity was very low, and most of the cardiac abnormalities could not be detected. When data generalization was added, the accuracy was 83%. Additionally, the sensitivity improved compared to the previous cases. However, the accuracy of detecting pathologic heart beats is about 51%, which still cannot be considered as distinguishing pathologic heart sounds. When the data were created using the DG and DA proposed in the paper, the accuracy increased to 90%. Additionally, the specificity was 96% and the sensitivity was 73%, thus yielding higher diagnostic accuracy and sensitivity than other methods.