esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Neural Network-Based Estimation
[96] proposed the basic ANN that was described at the beginning of this section with the addition of a feedback line with the value of the VSA estimated by the network (only in the testing phase, with no feedback line during the training phase indeed). The VSA was introduced with an 8\(\upalpha\upalpha\) time delay. In fact, they assert that the presence of the correct target among input variables would have assigned to the output a value of 1-VSA and 0 to all of the other signals, driving the ANN to a VSA estimation without considering the complete vehicle dynamics becoming unreliable if applied to a manoeuvre that was not present in the training dataset. Thus, the introduction of the time delay was aimed at reducing the relative weight of the VSA in the ANN. To verify the reliability of their method, they performed many tests at various speeds using a real-world vehicle on different adherence friction conditions. They found that the ANN was able to predict the VSA accurately in transient conditions, while the results were not acceptable for steady-state or near-steady state conditions due to the estimation drift caused by the feedback line. The same ANN without the feedback line can only give good a VSA approximation in driving conditions that are far from the vehicle limit of adherence. In another promising work, [PERSON] et al. [104] trained an ANN with a set of manoeuvres in order to take into account a shift in vehicle weight, a change in road surface, and a radical change in tyre characteristics. However, the training procedure is highly time demanding, and they only tested the network on two scenarios. Also, [PERSON] et al., [PERSON] et al., [PERSON] et al. and [PERSON] et al in [107; 108; 109; 110] used a hybrid estimator composed by a neural network and an observer (typically an EKF). The first one has only the task of fitting tyre data, while the second estimates vehicle dynamics states. In these works, the ANN structure is the simplest possible in order to avoid an undesired increase of the computational burden. The main issues of the ANN-based estimation remain the inability of the network to deal with a change in the vehicle after the training procedure, and the possibility of dealing with a road banking angle, which must be estimated with an external algorithm [129], and then, as proposed in [98; 102], filtering out the lateral acceleration component due to gravity.
## 3 Results and Discussion
### The Formation of Biosynthesis LAGNPs Including AA.AgNPs and SD.AgNPs
The  
Figure 2: The UV-vis spectra of AA.ext (dash line) and AA.AgNPs at various reaction times (solid line) (**a**); the UV-vis spectra of SD.ext (dash line) and SD.AgNPs (solid line) (**b**); the FTIR spectra of AA.AgNPs (i) and AA.ext (ii) (**c**); the FTIR spectra of SD.AgNPs (i) and SD.ext (ii) (**d**); the EDXA spectra of AA.AgNPs (**e**) and SD.AgNPs (**f**). medium troughs in the region of 840-790 cm\({}^{-1}\) were assigned to C=C bending vibration. In the FTIR spectrum of AA.ext (Figure 2c(ii)), all of the troughs of O-H, C=O and C-O groups similarly appeared. Figure 2d(i) and 2d(ii) describing the FTIR spectra of SD.AgNPs and SD.ext were also observed in the presence of O-H, C=O and C-O groups. Through FTIR results, two biosynthesized silver nanoparticles of AA.AgNPs and SD.AgNPs were proven to be modified by the similar functional groups. However, they were not completely the same because those FTIR spectra having a different fingerprint region (1500-500 cm\({}^{-1}\)). The EDXA spectra of AA.AgNPs and SD.AgNPs (Figure 2e,f) showed the signal of 3 keV indicating the silver presence [39]. The signals of 0.28 keV and 0.52 keV were assigned to the carbon and oxygen elements. Besides, the Cu peaks were observed strongly, which were a strange element in this system. The Cu contamination came from the sample preparation grid. As a result, AA.AgNPs and SD.AgNPs were demonstrated as the silver nanoparticles covered with organic components composed of carbon and oxygen. These elemental compositions were consisted to FTIR results showing C=O, C-H and O-H bonding vibrations. According to these obtained results in the relation with reported phytoconstituents of _A. Aspera_ and _S. dulcis_, it was suggested that the silver of AA.AgNPs and SD.AgNPs interacted with the oxygen of phytoconstituents to form the organic shells for stabilizing silver nanoparticles (Figure 3). To confirm this opinion, the SD.AgNP and AA.AgNP solution were stored at 4 \({}^{\circ}\)C in a brown bottle for 15 days. Their UV-vis spectra were measured every 3 days (Figure S1). The 433-nm peaks of AA.AgNPs and SD.AgNPs were not changed about the position, shape and height after a 15-day storage. These results implied that the AA.AgNPs and SD.AgNPs had similar properties of shape and size with their initial form.
## 3 Results
### LOC Fractions
#### 3.2.4 Changes in MBC  
The effects of different land use patterns on the content of MBC across different soil layers are illustrated in Figure 7. These land use patterns exhibit distinct responses in relation to soil MBC. In terms of vertical distribution, the MBC content in each treatment decreases with increasing soil depth, with the most significant reduction observed in the FB treatment at 70.53%, and the smallest reduction in the SC treatment at 21.16%. Notable differences exist between the different treatments within the same soil layer. Specifically, the MBC content in the 0-20 cm depth for each treatment is ranked as FB > GB > MC > MSR > SC, with the FB treatment demonstrating a significantly higher MBC content compared to the other land use patterns. In comparison to GB, the FB treatment shows an increase of 20.25%. Conversely, the SC, MC, and MSR treatments lead to reductions in soil MBC content, with declines of 64.32%, 45.66%, and 48.49%, respectively. There are no significant differences between MC and MSR, both of which are significantly higher than the SC treatment, which has the lowest MBC content among all treatments and within the three farming treatments. However, the MBC content for each treatment in the 20-40 cm and 40-60 cm soil layers differs slightly from that in the 0-20 cm layer, following the trend FB > GB > SC > MC > MSR.
## 5 Analysis and Discussion
### Analysis of Temperature Variation Influence Range near Ventilation Shafts  
A CFD analysis was conducted to determine the temperature variation range around tunnel ventilation shafts that affects CWR fractures. Wind speed was found to have a minimal effect on the temperature variation range in tunnels. The temperature variation influence on track structures was observed within \(\pm\)10 m (20 m) from the ventilation shaft center. The maximum influence range extended up to \(\pm\)20 m (40 m), aligning with field investigation results. The temperature variation influence range around ventilation shafts is illustrated in Figure 15.
## 3 Methodology
### Receiver
#### 3.2.1 Channel Equalization  
Here, the received signal \(\bar{r}\) underwent a channel equalization process using the \(R^{2}LMS\) algorithm to compensate for any distortion that occurred in the communication channel. The conventional Least Mean Square (LMS) equalizers had the ability for speedy adaption to fluctuations in the channel. On the other hand, proper tuning of the step size was crucial for the achievement of fast and reliable convergence. However, locating the optimal value was a challenging task. The algorithm had the likelihood of getting stuck in local minima rather than finding the global minimum, especially in complex channel scenarios. Ridge Regularization was used in LMS to overcome this issue. This helped in finding a step size that balanced the convergence speed and stability. Primarily, the weights \(\omega\) were updated using Ridge Regularization \(\beta\) and expressed as  
\[\beta=\lambda_{loss}+x\sum_{\vec{k}=1}^{sp}\|(\omega)^{2}\| \tag{22}\]  
In this context, \(\lambda\) represents the loss function for \(\bar{r}\) (received signal), \(\chi\) indicates the regularization parameter that governs the weights, \((\vec{k})\) specifies the index value, and \((sp)\) refers to the quantity of weight matrices. Subsequently, the LMS algorithm \(\alpha^{\prime}\) was utilized in the utilization of the channel as follows:  
\[\alpha^{\prime}=w_{e}+(\widetilde{v}*\bar{r}*e_{rr}) \tag{23}\]  
Here, \(e_{rr}\) and \(\widetilde{v}\) signify the error rate and step size, respectively, and \(w_{e}\) represents the weight (i.e., \(w_{e}\in\beta\)). Based on \(\alpha^{\prime}\), the channel was equalized. Therefore, the equalized channel is denoted as \(C^{\alpha}\).
###### Abstract  
This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained on either impedance measurements or scattering parameters acquired from the transmitter side, this NN effectively predicts the inter-coil distance and identifies optimal capacitance values for maximizing power transfer. Validation using both simulated and experimental data demonstrates consistently low prediction error rates. Second, a separate NN is employed to predict the optimal transmission frequency for minimizing the phase angle between voltage and current, thereby maximizing the power factor. This NN, validated on experimental data spanning various load conditions and inter-coil distances, achieves performance comparable to traditional PI control, but with significantly faster prediction speeds. This speed advantage is crucial for real-time applications and directly contributes to improved power efficiency. The results presented in this study, including the high accuracy of distance and capacitance prediction and the rapid determination of optimal frequencies for power factor maximization, showcase the significant potential of NNs for optimizing WPT systems. These findings open the way for more efficient, adaptable, and intelligent wireless energy transfer solutions, with potential applications ranging from dynamic charging of electric vehicles to real-time optimization of implantable medical devices.  
N  
e _applied sciences_ Article
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Plant Disease/Pest/Weed Detection System Basic Principles
#### 2.1.1 Image Processing
This conversion stage into a gray image may be omitted in applications in which color features are of relevance; otherwise, this step is crucial because it is much simpler and faster to process an image in a gray color format [17]. The second stage involves the denoising of a specimen image because in most cases, images are not without interference with the noise signal, which affects the visibility of the features in the specimen images [23]. The third step then includes image segmentation, which will be explained more broadly in the coming Section. The last step involves the forming of an outline image, which can be achieved by masking the leafstalk as well as holes while keeping the outer connected region [15; 23]. [PERSON] [24] proposed a similar procedure to that illustrated in Figure 7 for plant-leaf feature identification applications under real-life varying lighting conditions. This procedure involves the conversion of a specimen image into grayscale, noise suppression as well as smoothing, and formation of the image outline through edge filtering. In a comparative study conducted by [PERSON] [25], a histogram equalization method was proven to be the most effective form of image enhancement of the gray images that were originally color images. Conversely, [PERSON] [26] found that red-green-blue (RGB) camera images offer more valuable image enhancement compared to those converted to grayscale in the context of identifying diseases on the plant leaves. Therefore, we could not conclude which image pre-processing technique is better than the other, rather the application in which the image is used, and thing kind of image involved in that application shall be considered in the selection of an appropriate pre-processing technique. Figure 6: Impact of TDI incorporation in CMOS and CCD sensors [5]. Image segmentation is a pivotal part of image-based plant feature identification and phenotyping systems [23]. Segmentation of an image involves the separation between the foreground and the background [15]; that is, the isolation of the feature of interest and masking of the irrelevant part of the image [24; 25; 26]. The features of interest are normally identified by comparing adjacent pixels for similarity by looking at the three main parameters, viz. the texture, color, and shape [15; 17]. Table 2 shows a list of free data libraries available to the public for use in the image segmentation process.
## 4 Discussion
### Shortcomings and Suggestions  
In this study, some of the high-temperature and high-pressure triaxial compression data used were obtained from rocks subjected to high-temperature treatment prior to testing. This may affect the accuracy of parameter fitting and strength prediction of the proposed criterion. Due to the stringent technical requirements of real-time high-temperature and high-pressure triaxial testing--where both elevated temperature and confining pressure must be maintained simultaneously--many researchers have instead adopted post-heating triaxial compression tests to investigate the effects of temperature and confining pressure on rock mechanical behavior. However, rocks subjected to high-temperature treatment may have undergone irreversible structural changes, such as mineral phase transitions and the development of microcracks. These alterations can result in mechanical behavior under subsequent testing conditions that deviates from the actual in situ response of rocks in deep geological environments.  
In addition, this study assumes that the influence of temperature on the mechanical properties of rocks is independent from confining pressure and thus establishes a linear relationship model between temperature and uniaxial compressive strength to characterize the strength degradation induced by temperature. However, some researchers have suggested that the effects of temperature and confining pressure on rock properties are not simply additive. For example, [PERSON] et al. found that, under high-temperature conditions, the plasticity of deep shale is significantly enhanced, which intensifies the compression of pores under high confining pressure and induces thermal stresses between mineral grains in the rock skeleton [13]. As a result, pre-existing bedding planes in shale are more likely to reopen or generate new fractures along these weak planes. This mechanism weakens the reduction in permeability typically caused by the increasing temperature and confining pressure. [PERSON] et al. observed that high temperatures can induce thermal cracking in granite due to differences in thermal expansion among constituent minerals, which significantly reduces the peak strength of the rock. However, this thermal degradation effect can be effectively suppressed under the application of lateral confining pressure [46]. Therefore, future research will continue to investigate the coupled effects of temperature and confining pressure on the mechanical behavior of rocks, with the aim of further improving the failure criterion for rocks under high-temperature and high-pressure conditions.
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 3 Proposed Method
### Motivation  
On the one hand, according to a common empirical result from researchers of adversarial defense [16; 23], a larger model capacity is needed for adversarial examples. Therefore, in the case of insufficient model capacity, it is unwise to treat all examples equally. Training more important examples is helpful to make better use of the model capacity. On the otherhand, intuitively, the examples close to the classification boundary are more vulnerable to adversarial attacks and are more important for the classification boundary. This widely accepted assumption can be proved empirically. Figure 1 shows the distribution of the feature map before the last layer of the two randomly selected classes in the CIFAR-10 dataset. It can be seen that the features of the misclassified examples are concentrated near the boundary no matter whether it is adversarial examples using targeted or untargeted attack.  
According to the idea of SVM [36], to obtain a linear binary classifier that maximizes the margin between two classes, only the examples closest to the decision boundary, i.e., the support vectors, are needed. For a CNN network, although it is not a linear classifier, the last layer is always a fully connected (FC) layer that is linear. So the examples can be transformed into linearly separable high-order feature space after passing the front layers. According to these high-order features, we can find out which examples are support vectors that are close to the boundary. Training models on these examples can improve the efficiency of adversarial training.  
For linear classification tasks, SVM can learn the classifier with the largest margin. We extend the SVM and add an SVM auxiliary layer into the CNNs when training so that the features extracted by the CNNs have a larger margin, hence improving the adversarial robustness of the CNN.
# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 2 Methods
### Validation  
An important step in the development of solutions with artificial intelligence tools is validation, which involves assessing the model's performance on data that were not used during training to ensure that it can generalize well to new unseen examples. Validation is crucial for identifying overfitting, where a model performs well on the training data but fails to generalize to other datasets. For this work, validation is conducted at frame and video level.  
At the frame level, a frame-by-frame check is performed to observe how well the prediction align with the ground truth. Several metrics such as Dice coefficient, IoU, recall, precision, and F1 score have been calculated, and the average value for each artifact (pleura, consolidations, A-lines, and B-lines) has been determined.  
Additionally, to achieve a more thorough validation of the model's behaviour, video-level validation is performed. This involves comparing the presence of artifacts detected by the algorithm with the opinion of an expert clinician, ensuring a holistic evaluation of its diagnostic accuracy. Besides highlighting the artifacts in each frame with the segmentation model, a global indication for the findings on the whole video would also be valuable for the physicians, in order, for example, to calculate a score of the global condition of the patient. In this sense, two different validations were conducted. First, the reliability of the method was tested with all the acquisitions from the database. Second, the software's performance is verified on videos of patients not included in the training set, as explained in Section 2.3. The results of the various validation tests are presented in Section 3.
## 4 Datasets
### Handwritten Dataset Augmentation  
Data augmentation is the process of increasing the amount of data available by making valid data and improving the diversity of data samples. This helps to alleviate the overfitting of the network and improves the generalization ability of the network.  
Using basic data enhancement methods can have a great impact on the model. Therefore, in this paper, several methods in the geometric transformation class as well as the color transformation class were selected for data enhancement in the handwritten text recognition task, as shown in Figure 14a, which is the original picture.  
1. Stochastic affine transformation: The stochastic affine transformation implements translation, rotation, scaling, and shearing transformations on the image, and is more random and realistic compared to single data enhancement methods. Performing random affine transformation on handwritten Uyghur data can simulate the effect of text deformation that occurs in real writing, as shown in Figure 14b.
2. Gaussian noise: In image processing, Gaussian filters can be used to suppress high-frequency noise features in an image to improve image quality. The background of handwritten Uyghur text images is cluttered with a lot of noise and interference points, and Gaussian noise can mitigate such effects, as shown in Figure 14c.
3. Elastic transformation: Random perturbation of pixel points is realized by applying a small random translation to the position of each pixel point in the image. The distortion of characters in text recognition can simulate the pictures produced in the context of handwritten text due to muscle shaking, lighting changes, etc. In the handwritten digit recognition experiments, the recognition effect of handwritten digits was significantly improved after the original picture was augmented. Therefore, for the recognition of handwritten Uyghur language in this paper, this method was also chosen for data augmentation of the dataset, and the effect is shown in Figure 14d.  
In this paper, the pre-processed data were augmented with a mixture of techniques using several methods mentioned above. Finally, the handwritten dataset was merged with the printed dataset for text recognition experiments. To ensure a fast input-output performance and improve the training speed of the network, the Lightning Memory-Mapped Database (LMDB) was used to store the dataset. It is encoded in the 'utf-8' encoding format for storage. When reading the dataset, the images are transformed into a fixed size of width 100 and height 32 and then randomly scrambled into the network for training. When using the CTC loss function, the dictionary is constructed by adding one character [CTCblank], and the final dictionary length is 33. When using the attention mechanism, the dictionary is constructed by adding two characters [GO] and [s], and the final dictionary length is 34.