esa-sceva/satcom-chunk-collection huggingface dataset:
# Using an Uptake Enhancer to Mitigate Nitrogen Leaching While Enhancing Uptake Efficiency
## 3 Results  
### Effect of Uptake Enhancer Application on N Distribution  
#### 3.1.1 Effect of Uptake Enhancer Application on N Distribution through a Soil Profile  
The effect of uptake enhancer application on N distribution through the soil profile at all four growth stages is presented in Table 2. Significant differences in N content between the treatments were found in all the respective soil layers for sampling time 1. The CFA treatment had the most N in the upper soil layers compared to the other treatments, followed by treatment FA. Except for the CFA treatment, all the treatments showed slightly increased N content with soil depth, especially below the 10-20 cm soil layer. As expected, the lowest N content was found in the control treatment (CNTRL), which also showed no significant differences in N content across the soil depths. For sampling time 2 at 8 weeks, the only soil layers not showing significant differences between the treatments were the bottom soil layers (30-40 cm and 40-50 cm). Treatment CFA showed the most stable N content at the 0-20 cm soil layer. All other treatments showed an increase in N content with soil depth, except for treatments FA and CNTRL, which demonstrated a straight-line trend, which shows that the soil could not retain the N.  
Almost all treatments exhibited significant differences within their soil depths for sampling time 3 at 12 weeks, except for the CNTRL treatment, which did not show any significant differences throughout the soil profile. For the two upper soil layers (0-10 cm and 10-20 cm), the CFA treatment, as well as treatment FA, had significantly higher N values than the other treatments without the uptake enhancer. In fact, the reference treatments (CNTRL and C) showed a free flow of N through the soil profile.
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 3 AI-Based Modulation Format Recognition
### Format Recognition System Based on Inception-ResNet-v2 Convolutional Neural Network Modeling
The Inception-ResNet-A module utilizes symmetric convolutional kernels, whereas the Inception-ResNet-B and Inception-ResNet-C modules employ asymmetric convolutional kernels such as \(1\times 7\), \(7\times 1\), \(1\times 3\), and \(3\times 1\) to reduce the computation time for parameter operations and increase the depth and nonlinearity of the network. In this study, the Batch Normalization (BN) algorithm is used for normalization processing to prevent the activation function from entering the nonlinear saturation region, thereby avoiding the problem of gradient dispersion, and to enhance the network's generalization capability. By introducing learnable reconstruction parameters \(\gamma\) and \(\beta\), the network can learn to restore the original feature distribution of the network, as shown in Equation (5) [22]. \[y^{(k)}=\gamma^{(k)}\tilde{x}^{(k)}+\beta^{(k)} \tag{5}\]  
Figure 8: Model of the MFI method based on the Inception-ResNet-v2. When the condition in Equation (6) is met, the network can recover the original features learned by a specific layer [22]. \(E\Big{[}x^{(k)}\Big{]}\) refers to the mean value of the neurons in a batch of data, and \(Var\Big{(}x^{(k)}\Big{)}\) refers to the standard deviation of the input values of the neurons in a batch of training data. \[\beta^{(k)}=E\Big{[}x^{(k)}\Big{]},\gamma^{(k)}=\sqrt{Var\big{(}x^{(k)}\big{)}} \tag{6}\]  
The Reduction block is inserted between two Inception-ResNet modules, which is primarily used to adjust the width and depth of the network, serving as a pooling function. This module also employs multi-scale convolutional kernel and pooling with a parallel structure to mitigate model overfitting. Next, a down-sampling operation is performed on the overall feature information at the Average Pooling layer to reduce the parameter dimensions and allow more complete information to be passed on. To prevent overfitting of the model, some neurons are removed randomly during each training iteration through the Dropout layer, and the outputs of these neurons will not be updated in this training. This reduces the overall complexity and number of parameters of the neural network, which enhances the generalization ability of the model. In this way, the Dropout layer helps the model learn more robust features and decreases the interdependencies among neurons, enabling the model to perform better on different data distributions. Finally, the fully connected layer and the SoftMax function are used to output the recognition results. The model collected a total of 4752 eye diagrams of the 100 Gbps Apol-CRZ-FSK, CRZ-FSK, and DQPSK modulation formats at different OSNR levels and distances, respectively. The entire image dataset was randomly divided into a training set (80%) and a test set (20%), which means 3802 images were used for training and 950 images for testing, thereby facilitating the research on modulation format recognition.
## 2 Background Theory and Method  
### Collinear Deflection Method  
The photothermal effect used in this study is a phenomenon in which heat is generated as the energy level of a material increases due to the photons colliding with the surface of the material (15,35). The collinear deflection method uses the temperature gradient inside the material, which is induced from the photothermal effect by the modulated laser (pump beam). This temperature gradient causes changes in the density of the transparent material, which leads to changes in the refractive index of the material. As shown in Figure 1b, when the probe beam is irradiated on the area where temperature and refractive index gradients occurred so it is perpendicular to the specimen and parallel to the pump beam, deflection occurs. In this instance, the deflection angle of the probe beam (\(\psi\)) can be expressed by Equation (1) [PERSON] _et al._ (2013).  
\[\psi=\int_{path}\frac{1}{n}\bigg{(}\frac{\partial n}{\partial T}\bigg{)}\
abla _{n}T(x,y,z,t)ds \tag{1}\]  
where \(n\) is the refractive index, \(\partial n/\partial T\) is the temperature coefficient of the refractive index, and \(\
abla_{n}T\) is the temperature gradient perpendicular to the probe beam path.  
As a pump beam modulated with a constant period is used, the temperature gradient inside the material also repeats rising (heating) and falling (cooling) according to the heating period of the pump beam. In this case, a time difference occurs between the temperature gradient and pump beam periodsdepending on the thermal properties of the target material. As a result, when the deflection of the probe beam passing through the temperature gradient is compared with the period of the pump beam, a time difference occurs, as shown in Figure 2, and this is referred to as the phase delay. The phase delay increases as the relative position between the pump beam and probe beam increases, and it varies depending on the thermal properties of the material. Therefore, it is possible to derive the thermal conductivity by analyzing the phase delay according to the relative position.
## 1 Introduction
### IEC's Principles  
Intelligence Edge Computing (IEC) enables the use of computing servers closest to the user instead of centralized devices far from the user. Therefore, it is characterized by fast data transfer and a significant reduction in response time for 5G networks [13]. This network technology is used in advanced digital systems such as (IoT) technologies, Virtual Reality VR video games, autonomous vehicles, cloud computing, and data protection. IEC includes some main principles such as portability, connection, interaction, and character [14] which are described as follows:  
_Portability:_ A mobile computing system is connected through devices/nodes that facilitate mobility. Such devices/nodes even have some limitations in capabilities and power supplies, but they have effective processing capacity and physical portability to work in a mobile environment [15].  
_Connection:_ The quality of service (QoS) of the network connection is defined on a mobile computing system. This ensures a high level of service availability with minimal delay and prevents certain hurdles without affecting the operation of connected nodes [5].  
Figure 2: IEC Architecture [9].  
_Interaction:_ Mobile edge computing system nodes (IECs) are interconnected through active data transactions for communication and cooperation.  
_Character:_ The operation of the mobile device or mobile node connected to the mobile network is summarized by the reference to a specific element; therefore, the mobile computing system is able to adopt specific technology to meet individual needs and also to obtain contextual information for each node [16].
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 4 Production Costs of Wood Biomass
### Woody Biomass Production Costs  
As mentioned previously, woody biomass primarily consists of unused wood, which is often left in large quantities in forests. In this context, we considered a scenario in which woody biomass is harvested independently as the main product and a scenario in which both timber and woody biomass are harvested simultaneously as by-products. In the case of the main products, woody biomass is rarely harvested only at the final filling stage, and for by-products, the costs of final felling and thinning are almost identical; therefore, thinning costs were used for analysis. Of note, the available data on fuel conversion costs, forest road construction costs, and reforestation costs are limited. Furthermore, because forest road construction and reforestation are typically financed by public government funds, these three costs were excluded. The distribution of production costs for woody biomass is depicted in Figure 5; the production costs for the main product ranged from 3.5 to 29.9 thousand JPY/m\({}^{3}\), while those of by-products ranged from 1.3 to 8.6 thousand JPY/m\({}^{3}\). This phenomenon can be attributed to two factors. First, the production cost structures considered in each study differed. Second, the estimation results exhibited regional characteristics owing to varying factors of production in different regions, such as forestry operation systems and transportation distances. In the case of the main product, the mean and median production costs are 9.9 and 8.7 thousand JPY/m\({}^{3}\), respectively, which exceed the average price of 7 thousand JPY/m\({}^{3}\) for material for chips in Japan in 2022, rendering it less economically viable. On the other hand, for by-products, the mean and median production costs are 5.5 and 5.3 thousand JPY/m\({}^{3}\), respectively, falling below the average price and demonstrating a certain level of economic viability. The distributions of the direct, indirect, and transportation costs of the main products and by-products are shown in Figures 6 and 7, respectively. Regardless of whether it is for the main product or by-products, the main operation costs constituted a substantial proportion of the total costs. For the main products, the proportion of indirect costs was significant and may even surpass that of the main operation costs. However, for by-products, in addition to the main operation costs, transportation costs also constitute a substantial portion. The higher transportation costs can be attributed to the larger volume of unused wood, such as branches and leaves, compared with logs, making transportation less efficient. In some studies that estimated the production cost of by-products, indirect
# Nonlinear Nonsingular Fast Terminal Sliding Mode Control Using Deep Deterministic Policy Gradient
## 5 Conclusions  
In this paper, a DDPG-NNFTSMC control strategy was proposed to solve the problem of chattering and chattering caused by traditional sliding mode control, which is successfully applied to manipulator systems with uncertain dynamic characteristics. Based on NNFTSMC, the sliding surface was proposed, and the error function can converge on the sliding surface quickly. Then the Lyapunov stability condition was used to prove that the NNFTSMC has good stability and finite time convergence. Compared with the traditional TSMC, NTSMC, and RBF-SMC, the DDPG-NNFTSMC has the following advantages: (1) DDPG network is used to train and update the control parameters in real time to estimate the model uncertainties (including unknown disturbance, dynamic model uncertainty, and approximation error), which effectively eliminates the chattering phenomenon of the system and ensures the robustness of the system under various uncertain disturbances; (2) the chattering elimination greatly improves the tracking accuracy, reduces the tracking average error, and enhances the ability of antidisturbance and uncertainty of the system. Therefore, it can be concluded that DDPG-NNFTSMC proposed in this paper has excellent control performance, and theoretically has good application prospects for industrial manipulators with uncertain dynamic models and general second-order nonlinear systems. Next, based on the research of scholars [12; 31], we will try to apply DDPG-NNFTSMC to other different scenarios and combine it with deep reinforcement learning to further improve the control performance [32; 36; 37].  
**Author Contributions:** Conceptualization, Z.X. and W.H.; methodology, Z.X. and W.H.; software, Z.X. and Z.L.; validation, Z.X. and L.H.; W.H.; results analysis, Z.X. and P.L.; investigation, Z.X. and L.H. and Z.L.; data curation, Z.X. and P.L.; writing--original draft preparation, Z.X. and W.H.; writing--review and editing, Z.X. and W.H.; visualization, W.H.; supervision, Z.X. and W.H.; project administration, Z.X. and W.H.; funding acquisition, W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangzhou Science and Technology Planning Project, grant number 202002030279.  
This work was supported by the Basic Science Research Program through the Guangzhou Science and Technology Planning Project (202002030279).  
All authors announce that they have no conflict of interest in relation to the publication of this article.
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 1 Introduction
The proposed method's meta-parameters of the U-Net network are optimized using the grasshopper optimization algorithm (GOA). These parameters are the initial learning rate, the learning rate drop rate, the learning rate of cuts with the drop rate, and the mini-batch size. U-Net integrates local information through the downsampling and upsampling of the path, thereby extracting texture information (patterns). Because the kernel used in U-networks is independent of the input image size, this model does not require a dense layer and works well with input images of any size.
###### Abstract
Other researchers have highlighted that sterilisation also affects antioxidant compounds and vitamins by causing their oxidative degradation [6]. According to Royal Degree 679/2016 [7] on the quality standard for table olives, olives can be classified according to their physical defects in different categories: extra, select and standard. For this classification, it is necessary to carry out a sensory analysis to determine whether the defects are due to strange smells, fermentation or lack of flavour. This must be carried out by well-trained and validated tasters. The International Olive Council (IOC) recently published a recommendation related to sensory evaluation of table olives by panellists [8]. Sensory analysis is carried out by a tasting panel made up of experts; however, setting up a panel is usually costly and time-consuming and requires specialists who know how to evaluate the sensory qualities of food [4]. Alternatively, chromatographic analysis may be used to identify volatile organic compounds related to positive and negative aromas in olives, making it possible to characterize the product according to its commercial category [9; 10]. There are electronic devices that can help the tasting panel evaluate olives based on their quality. This is the case for the E-nose electronic sensory device, which is used to discriminate between the aroma profiles of samples for different foods. Other authors have successfully used the E-nose to control the decrease in taste and quality for coffee beans [11], tomatoes [12] and even virgin olive oil [13]. In addition, the E-nose has been used to classify Spanish-style table olives into the extra, first and second categories [9] and even for the classification of sparkling wines after subjecting them to different thermal treatments and using different yeasts during the elaboration process [14]. This instrument is a low-cost method that allows rapid measurements to be obtained compared to the traditional methods, such as the chromatography instruments described above. E-noses could be used as a support for tasting panels. Therefore, stuffed Californian-style black olives that provide a pleasant characteristic aroma would be interesting as an innovative product to be placed on the market. Thus, the aim of this work was to classify Californian-style black olives that underwent different sterilisation treatments in the extra, select and standard categories. The classification was based on a tasting panel, phenolic composition, an E-nose and volatile organic compounds. In addition, we intended to establish relationships between the aromatic and phenolic compounds obtained and the electronic device in order to develop predictions that would allow us to quantify certain parameters of the final product.
## 3 Experiments and Results
### Real Images  
We have also tested our proposed method using real images captured by two cameras with 1280 \(\times\) 800 resolution. These cameras are assumed to have zero-distortion, zero-skew and unit aspect ratio. We place some control points in these two camera fields of view as shown in Figure 8.  
Because the ground truth of the intrinsic and extrinsic camera parameters in real scenarios is unknown, we cannot directly compare measuring precision of the intrinsic and extrinsic parameter estimation from real images. Therefore, we compare the precision of our proposed method, [PERSON]'s method and the GP4 Pf indirectly--some control points are placed in the two camera fields of view, and the positions of these points and cameras are measured as the ground truth by a total station (NTS-330R, measuring precision is better than 0.5 cm). We use 3 or 4 control points for the intrinsic and extrinsic parameter estimation and the positions of the other points are measured by binocular vision. Then, the average error of relative position between the measured value and the ground truth is used to verify the accuracy of the intrinsic and extrinsic parameter estimation. The measuring result of relative position error is 0.43% with our proposed method, 0.47% with [PERSON]'s method and 1.37% with the GP4 Pf. This shows our proposed method works well in real scenarios.  
In addition, because the intrinsic and extrinsic parameter estimation only needs feature point extraction from real images, synthetic data and real images of simple scenes can be used to perform a thorough comparison with other PnP methods [13; 21; 23; 25].
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 7 Countermeasures for Threats in Cloud Computing
### Protecting against Data Breaches and System Vulnerability
#### 7.1.1 Multifactor Authentication (MFA)  
Implementing MFA significantly enhances security. It introduces multiple layers of authentication beyond traditional passwords, raising the bar for potential attackers. MFA fortifies cloud environments against unauthorized access.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion
### Antenna Bending Test Setup and Test
#### 4.3.2 Radiation Direction Map Measurement Results  
As shown in Figure 15, the actual measured radiation patterns of the proposed antenna under bending conditions are essentially consistent with those measured under planar conditions. The maximum radiation direction and null position of the antenna can also be maintained under deformation conditions. Additionally, the xoz and yoz plane patterns indicate that the antenna also has good omnidirectional radiation characteristics under bending conditions. Therefore, the analysis of the experimental results shows that the designed antenna meets the design requirements for implantable antennas, and the antenna can maintain stable radiation characteristics regardless of the direction of structural deformation.
###### Abstract  
For a non-contact, non-destructive quality evaluation, laser ultrasonic testing (LUT) has received increasing attention in complex manufacturing processes, such as additive manufacturing (AM). This work assessed the LUT method for the inspection of internal hole defects in additive manufactured Ti-6 Al-4V part. A Q-switched pulsed laser was utilized to generate ultrasound waves on the top surface of a Ti-6 Al-4V alloy part, and a laser Doppler vibrometer (LDV) was utilized to detect the ultrasound waves. Sub-millimeter (0.8 mm diameter) internal hole defect was successfully detected by using the established LUT system in pulse-echo mode. The method achieved a relatively high resolution, suggesting significant application prospects in the non-destructive evaluation of AM part. The relationship between the diameter of the hole defects and the amplitude of the laser-generated Rayleigh waves was studied. X-ray computed tomography (XCT) was conducted to validate the results obtained from the LUT system.  
additive manufacturing; non-destructive evaluation; laser ultrasonic testing; X-ray computed tomography 2020 10, 365; doi:10.3390/app10010365
## 3 Problem Statement and Model Construction
### Notations  
The notations for the relevant variables and parameters in the model constructed in this paper are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Symbols** & **Definition** \\ \hline \multirow{4}{*}{_Set_} & \(S\) & Resource sites, \(S=\{S_{1}\ldots S_{M}\}\) \\  & \(A\) & Disaster sites, \(A=\{A_{1}\ldots A_{N}\}\) \\  & \(G\) & Types of EVs, \(G=\{G_{1}\ldots G_{K}\}\) \\  & \(P_{i}\) & Scheduling priority for disaster site \(i\), \(P_{i}=\{0,1\}\) \\ \hline \multirow{4}{*}{_Variable_} & \multirow{4}{*}{\(w_{ijk}\)} & Scheduling scheme for a single resource site \\  & & \(w_{i}=\begin{bmatrix}w_{11}&\cdots&w_{1k}\\ \vdots&\ddots&\vdots\\ w_{n1}&\cdots&w_{nk}\end{bmatrix}_{N\times K}\) \\ \cline{1-1}  & & Total scheduling scheme, the number of category \(k\) EVs dispatched from resource site \(i\) to disaster site \(j\), \(\left\{w_{ijk}\right\}_{M\times N\times K}=\left\{w_{1}\ldots w_{M}\right\}\) \\ \cline{1-1}  & \(Z_{1}\sim Z_{3}\) & Optimization objective \\ \cline{1-1}  & \(\overline{t_{i}}\) & Average time for disaster site \(i\) to receive all EVs \\ \cline{1-1}  & \(x_{ij}\) & Number of EVs dispatched from resource site \(i\) to disaster site \(j\) \\ \cline{1-1}  & \(\theta(x)\) & Penalty term \\ \hline \end{tabular}
\end{table}
Table 2: Related variables and parameters.  
Figure 2: Concurrent emergency response scheduling diagram.