esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### BER Performance  
After obtaining the statistical channel path loss, we can evaluate the BER performance combined with the Rx configuration and solar radiation level. We considered the case of on-off keying modulation (OOK) due to its simplicity and widespread usage in the real-time UOWC system. The transceivers are assumed to be perfectly aligned. Figures 11 and 12 show the BER performance versus communication range under various system specifications (FOV, aperture diameter). In order to quantify the ambient light noises impact, we place the transceiver at different depths. By using the proposed model mentioned before, the dowelling solar irradiance values at these operation depths are 0.0062, 0.0019, and \(4.65\times 10^{-6}\)\(W\)/(\(m^{-}sr\cdot nm\)), the lines of the same color represent cases at the same depths, with black, blue, and gray corresponding to 10 m, 25 m and 100 m, respectively.  
It is apparent in Figures 11 and 12 that increasing the aperture diameters from 5 cm to 20 cm can certainly improve the system performance at the same depth and this enhancement becomes a little more obvious in deeper water. The reason is, that while a larger aperture can detect more optical signals from Tx, it also receives stronger solar radiation in the shallow underwater environment. In the deep sea, however, the downwelling irradiance from sunlight becomes quite weak such that the raising of an aperture can deliver more performance improvement. It is worth mentioning that we assumed the attenuation coefficient remains the same at different depths for the worst-case analysis, whereas the water turbidity level drops with the increase of depths in reality. Thus, the reliable communication range in deep sea may be longer than the simulation results. Besides, the BER  
Figure 11: BER performance versus communication distance with different apertures and operation depths in coastal water for (**a**) FOV = 180\({}^{\circ}\), (**b**) FOV = 15\({}^{\circ}\).  
Figure 12: BER performance versus communication distance with different apertures and operation depths in turbid harbor water for (**a**) FOV = 180\({}^{\circ}\), (**b**) FOV = 15\({}^{\circ}\).  
performance seems to be more sensitive to the depth with larger FOV using the same Rx aperture. If we consider the target BER of \(10^{-4}\) and the aperture diameter of 11 cm, the viable communication range is extended from 8 m to 18 m with the water depth varying from 10 m to 100 m when FOV = 180\({}^{\circ}\). Nevertheless, for the case of FOV = 15\({}^{\circ}\), the distance increases from 19 m to 20.5 m. Likewise, considering that a narrow FOV has already restricted most of the sunlight in shallow water, it is reasonable that the increase of working depth has little impact on the performance improvement.
## 3 WFBRCSP Methods  
### Process  
Based on the FBCSP framework, this study proposes an EEG-decoding method using a weighted filter bank regularized common spatial pattern (WFBRCSP) for feature extraction. The block diagram is exhibited in Figure 1, and the specific process is as follows.  
1. Weighted Filter Bank  
Considering the individual variability in the active frequency ranges during motor imagery tasks, where some subjects exhibit significant motor-related EEG activity outside the conventional \(\alpha\) (8-12 Hz) and \(\beta\) (13-30 Hz) bands, this study constructs a filter bank by dividing the 4-40 Hz range into 4 Hz sub-bands [19]. Sub-band decomposition is performed on the raw EEG signals of both the target subject and all of the source subjects. Subsequently, the mutual information between the target subject and each source subject is calculated within each sub-band, and the total mutual information across all sub-bands is aggregated. Based on the mutual information maximization criterion, adaptive weights are assigned to each source subject.  
2. RCSP Feature Extraction  
On each weighted sub-band of the source domain subjects, the RCSP algorithm is applied to combine the covariance matrices of the EEG data from the target subject and each source domain subject to extract the spatiotemporal features of the target subject, and then all sub-band features are merged.  
3. Feature Selection and Classification  
The Fisher score is used to evaluate the separability of the feature set, selecting a subset of features with high discriminability. Based on the CSP feature pairs, the corresponding features are selected from the feature subset, and finally, they are input into an SVM to complete the intention classification task.
## 4 Discussion
### Influence of Burial Depth on the Development of Pore Structure
#### 4.2.2 Controlling the Mechanism of Burial Depth and the Development of Pores
New OM pores are formed under these burial conditions because of the shale gas's continuous generation of organic matter. Under the action of the overlying strata, the pore size is relatively small, generally being less than 5 nm (Figure 4h). After the strata formation is lifted to 1000 m, the pressure of the overlying strata further decreases, and the pores continue to expand. The expansion force in pores with a pore size greater than 200 nm exceeds \(\upomega_{\text{bp}}\), leading to fracture, ultimately forming a complex network-like structure with small pores (Figure 4f). After experiencing the maximum burial depth, the greater the degree of uplift of shale reservoirs, the lower the pressure exerted by the overlying strata. After the overpressure state is removed, this will stimulate the formation of more MF or inorganic pores [65]. Therefore, the shale reservoirs in the Zhaotong area, which are currently shallowly buried, have well-developed interP pores and MF (Figure 5a-d). As the burial depth increases, the inorganic pores are continuously compressed, while the OM pores are preserved due to fluid overpressure [65; 69; 70]. Therefore, we can observe that as the burial depth increases, the inorganic pore size gradually decreases. Overall, when the burial depth is less than 2000 m, the OM pore porosity is smaller than that of inorganic pores, and the OM pores exhibit complex network characteristics with pore sizes of less than 50 nm. When the burial depth is at 2000-4000 m, the porosity of the organic and inorganic pores is equivalent, and the heterogeneity of the OM pores is extremely strong, mainly developing into micropores smaller than 5 nm and macropores larger than 200 nm. When the burial depth is greater than 4000 m, the porosity of the organic pores is greater than that of the inorganic pores. OM pores are more isolated and exhibit a more uniform pore size distribution, being concentrated at 50-200 nm. Characteristics and Influencing Factors of Free Gas and Adsorbed Gas in Shale Reservoirs at Different Burial Depths
# A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases
###### Abstract  
In this paper, we proposed a hybrid deep learning approach for detecting and classifying tomato plant leaf diseases early. This hybrid system is a combination of a convolutional neural network (CNN), convolutional attention module (CBAM), and support vector machines (SVM). Initially, the proposed model can detect nine different tomato diseases but is not limited to this. The proposed system is tested using a database containing images of tomato leaves. The obtained results were very encouraging, giving us accuracy up to 97.2%, which can be improved with the improvement of learning processes. The proposed system is very efficient and lightweight, so the farmer can install it on any smart device having a digital camera and processing capabilities. With a bit of training, a farmer can detect any disease immediately, which will help him take timely pre-emptive action.  
plant disease; smart agriculture; deep learning; machine learning; CNN; SVM; attention module 20222020220202202022020220202020220202022020202202022  
model to correctly identify and classify the type of disease at an early stage, we gained the motivation to write this paper.  
This paper proposes an efficient hybrid classification approach for identifying nine diseases in tomato plants. The developed system consists of three different technologies, the convolutional neural network (CNN), support vector machine (SVM), and convolutional block attention module (CBAM), and The ResNet50 model is adopted to implement CNN. Rather than fully connected network layers, SVM is used to connect to the CNN model, which will enhance the classification accuracy. Finally, the CBAM is employed to process the feature extraction function. The proposed model achieves an efficient classification accuracy in comparison to the recently developed approaches. Therefore, the main contribution of this paper lies in the following aspects:  
1. Studying and evaluating the recently developed tomato leaf disease classification and identification approaches.
2. Developing an efficient tomato leaf disease identification and classification system based on a customized CNN model and SVM approach.
3. Validating the efficiency of the proposed hybrid approach using a dataset of tomato leaves.  
The rest of this paper is organized as follows: Section 2 discusses the recently developed disease classification systems for tomato crops. Section 3 discusses the proposed system's design, whereas Section 4 presents an analysis of the results. Section 5 discusses the results and compares the obtained classification accuracy with the existing classification models. Finally, Section 6 concludes the work presented in this paper and draws a future outcome.
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
## 7 Numerical Results
When employing conventional phase angles for covert communication, i.e., \(\theta_{1}=45^{\circ}\) and \(\theta_{2}=135^{\circ}\), the covert transmission rate is 40 bits/Hz/s. After optimization, the maximum covert rate achieves 60 bits/Hz/s, indicating an almost 50% increase in the transmission rate, which is a significant enhancement. Figure 5: Covert transmission performance in different scenarios. (**a**) Covert transmission rate versus covertness constraint in scenario 2. (**b**) Covert transmission rate versus covertness constraint in scenario 3. Figure 6: Covert communication performance in scenario 2. (**a**) Main view of covert transmission rate versus phase angles. (**b**) Top view of covert transmission rate versus phase angles.
###### Abstract
This makes it easier to integrate m-MIMO systems with existing infrastructure and devices. Therefore, it is essential to explore the potential of FDD operation in m-MIMO systems allowing URLLC so they operate with short CCT. In the FDD operation mode, users may need to estimate the DL channels to obtain CSI for DL precoding. This requires the BS to optimize the DL TS and broadcast them to the users, who must provide the quantized channel estimates back to the BS to precoder the signals to different users in the DL [23]. However, this approach is impractical for FDD m-MIMO systems with a large \(N\), as the overhead for the DL CSI estimate scales linearly with the number of antennas, which can be much larger than the number of users [24; 25]. As a result, the channel training would consume a significant amount of the available CCT, leaving little time for broadcasting data to the users [25; 26]. Increasing the number of antennas at the BS makes it very difficult to optimize the DL TS and estimate CSI in FDD systems due to the short CCT. This has led researchers to look for viable solutions for designing a DL TS for CSI estimation in m-MIMO systems with FDD transmission mode.
## 4 Major Components of Network Slicing System
### Intent-Based Networking (IBN) Tool
#### 4.2.5 RAN Policy Configurator  
The RAN policy configurator is an important component of the IBN system because it is responsible for slicing the resources of eNB. This policy configurator can converts the requested slice requirements in terms of bandwidth, spectrum allocation, uprate, and downrate that are provided through IBN GUI in the form of contract by the clients. It converts the contracts into the JSON file because FlexRAN accepts the slice information in the JSON template as a network slice template. Afterward, FlexRAN deploys those configurations to the eNB through the master controller and agents via Northbound API. Finally, the requested network slice can be deployed at the access network. In thisway, dedicated EPC and RAN resources could be assigned to a requested slice using specified S-NSSAI in the slice template.
## 2 Basic Theory  
### Gan  
Generator and discriminator together constitute the basic GAN. The classic generator has a self-encoder as the bone network, whose function is to generate fake images usingGaussian white noise or random noise. The discriminator is usually a classifier whose function is to clarify whether the input is from the training dataset or the generator. Therefore, the output probability of the discriminator complies with binomial distribution.  
Generators and discriminators in GAN networks are trained in turn. In the initial training, the weights of the discriminator are frozen. Gaussian noise \(z\), complying with noise distribution \(P_{z}\), is fed to the generator with real images from the training dataset as the label. The generator is trained to reduce the mean square error between the output of the generator and the real image, so as to make the generator outputs fit the distribution of the real images. Then, the weights of the generator are frozen, and those of the discriminator are activated. With the discriminator trained, the classification performance is increased, and the true-positive rate of identifying the real image is improved. When training the generator again, the discriminator weight is frozen; the mean square error of the discriminator is taken as the loss function, and the generator weights are trained to improve the quality of output images from the generator. The weights of the generator and discriminator are fixed in turn so that the generator and discriminator have optimal generation performance and discrimination performance, respectively. Since this process is similar to the maximum and minimum game between the generator and the discriminator, the objective function of the GAN network is as shown in Equation (1).  
\[\underset{G}{\text{min}}\underset{D}{\text{max}}V(D,G)=E_{x\sim P_{data}}[ \log D(x)]+E_{z\sim P_{z}}[\log(1-D(G(z)))] \tag{1}\]  
where \(E_{x}\) is the mathematical expectation of the discriminator distinguishing the correct image; \(E_{z}\) is the mathematical expectation that the discriminator misjudges the generated image as true; \(z\) is a noise vector (such as Gaussian noise) that obeys the \(P_{z}\) distribution; and \(x\) is a training set image that obeys the real data distribution, \(P_{data}\). By maximizing the discriminator (\(D\)) loss, the discriminant ability of discriminator \(D\) on the generated images is improved. This process implicates that the closer the generated image is to an image in the training set, the smaller the loss is. Therefore, the generator is not able to produce creative images. Such a system can only imitate existing images but cannot create new images. The reason is that there is a lack of motivation within the system to encourage generators to explore creative space, which is also a basic limitation of the process of artistic creation using generators versus networks.
# Green Synthesis Method and Application of NaP
## Results and Discussion
### XRD Result Analysis
#### 3.2.2 Influence of SiO2/Al2O3  
Figure 2b shows the XRD spectrum of the synthesized product at a molar ratio of SiO2/Al2O3 = 1.00-9.00 Although we use the method without adding exogenous silica-alumina to prepare zeolite, the silica-alumina ratio is still an important factor in zeolite synthesis. Therefore, we still investigated the change of the final product under different silicon-aluminum ratios by adding exogenous silicon-aluminum. The initial gel composition is xSiO2:1.0 Al2O3:5.0 Na2O:100H2O. When  
Figure 2: XRD patterns of CGCS and NaP zeolite synthesized under different conditions: (**a**) different SiO2/Na2O molar ratio, (**b**) SiO2/Al2O3 molar ratio, (**c**) H2O/Al2O3 molar ratio, (**d**) aging time, (**e**) crystallization temperature, (**f**) crystallization time.  
the molar ratio of SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 1.00, the crystallinity of the NaP zeolite is insufficient, and the characteristic peak of the boehmite is very prominent, accompanied by a peak of Phillipsite-Na zeolite. As the molar ratio of SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) increases, the boehmite gradually weakens. Until the molar ratio was 5.20, the boehmite disappeared completely and the product tended to be converted only to NaP zeolite. At this time, we did not adjust the silica-alumina molar ratio of CGCS. Since the relative solubility of Al\({}^{3+}\) in alkaline NaOH solution is lower than that of Si\({}^{4+}\), the addition of a small amount of NaOH in a higher SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratio promotes the formation of NaP zeolite [21]. When the molar ratio reached 9.00, the crystallinity of the NaP zeolite began to decrease because the amount of NaOH was too small to accelerate the reaction.
# A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
## 7 Growing User Community  
The \"timing\" in our model is on the basis of contributions. However, if the size of the user community grows (or shrinks), the correspondence between \"contribution clock\" and real time breaks.  
Let us denote by \(M(t)\) the number of active users, assuming that their average activity is not too dissimilar. In a unit of \"real\" time there are a number of contributions proportional to \(M(t)\), so the time for each contribution is in the order of \(1/M(t))\).  
In other words, it is possible to rescale our results given the number of users. Let us explore the hypothesis of an initially exponentially growing user base by means of the Markov approximation.  
Equation (5) becomes  
\[P\bigg{(}y,t+\frac{\alpha}{M(t)}\bigg{)}=P(y,t)\bigg{(}\frac{y-1}{y}\bigg{)}+P (y-1,t)\bigg{(}\frac{1}{y-1}\bigg{)}, \tag{10}\]  
and its continuous approximation is  
\[\frac{\partial P}{\partial t}=-\frac{M(t)}{y}\frac{\partial P}{\partial y}. \tag{11}\]  
If \(M\) grows exponentially, \(\dot{M}=\alpha M\), and we have that \(P\) depends on \(y^{2}-2\alpha M\). Since \(P\), starting from a delta (the initial contributions), always keeps a sharp peaked shape, the average maximum knowledge is  
\[mg(t)=m(t)\simeq\sqrt{\frac{2}{\alpha}M}=\sqrt{\frac{2}{\alpha}}\exp\bigg{(} \frac{\alpha}{2}t\bigg{)}. \tag{12}\]  
It is thus plausible that an exponentially growing user base produces an exponential growing corpus, even in our model.  
Let us now check what happens for a logistic growth of the user base, which can be considered as a rough estimation of the actual base consistency [12]. The corresponding logistic equation is  
\[\dot{M}=\alpha M(1-M). \tag{13}\]  
Figure 11: (**a**) Number of pages edited by users in hundreds of thousands (\(10^{5}\)), excluding redirect pages of some of the biggest Wikipedia editions. Codes: Du = Dutch, En = English, Fr = French, De = German, It = Italian, Ru = Russian, Es = Spanish, Se = Swedish. Data collected from [33]. (**b**) Zoom of the first part of Figure 6, where for \(L=15\) the trend has not stabilized yet.  
We do not have an analytical solution for this case, but the partial differential equation (Equation (11)), which can be discretized as  
\[P(y,t+1))=P(y,t)-\frac{M(t)}{y}\big{(}P(y,t)-P(y-1,t)\big{)}, \tag{14}\]  
can be numerically integrated.  
The growth of the maximum knowledge \(m(t)\) is reported in Figure 12 and again has a shape that resembles that of Wikipedia's growth. The first phase is very near to an exponential, as shown in Figure 13a, but asymptotically, when \(M(t)\) becomes a constant, it approximates the power-law growth with exponent \(1/2\); see Figure 13b.  
It is not easy to get a correct time estimate of the number of active users to be fed into our model, since Wikipedia records all editing per user regardless of the creation of new pages, editing and discussions, and allows anonymous contributions.
_Remote Sens._**2014**, \(6\), 10676-10693; doi:10.3390/rs61110676  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
Sentinel-1 for Monitoring Reservoirs: A Performance Analysis  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 1 and [PERSON] 1,***  
1 Department of Electrical Engineering and Information Technology, University of Napoli Federico II,  
Via Claudio 21, 80125 Napoli, Italy; E-Mails: [EMAIL_ADDRESS] (D.A.);  
[EMAIL_ADDRESS] (G.D.M.); [EMAIL_ADDRESS] (A.I.); [EMAIL_ADDRESS] (D.R.)  
2 Department of Civil Engineering, University of Salerno, Via [PERSON] 132, 84084 Fisciano (SA), Italy; E-Mails: [EMAIL_ADDRESS] (F.M.); [EMAIL_ADDRESS] (M.N.P.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-081-7683512; Fax: +39-081-7683149.  
External Editors: [PERSON] and [PERSON]  
_Received: 16 May 2014; in revised form: 23 October 2014 / Accepted: 24 October 2014 / Published: 4 November 2014_  
**Abstract:** In this paper we explore the performances and the opportunities provided by the European satellite Sentinel-1 for water resource management applications in low-income countries. The analysis is supported by a synthetic aperture radar (SAR) simulator, which allowed the quantification of the expected characteristics of Sentinel-1 products in three applications: interferometric digital elevation models (DEMs) generation, land cover mapping and estimation of water volumes retained by small reservoirs. The obtained results quantitatively show that Sentinel-1 data characteristics are fully suitable for most of the application already explored in the recent SAR literature.  
**Keywords:** Synthetic Aperture Radar; Sentinel-1; water resources; semi-arid regions; reservoirs management